system

Abstract

We provide the system. [Solution] A means for generating prescription information using a mobile device and storing the information in a database, A means for performing biometric authentication of the user based on the said prescription information, A means for providing drug management information to a terminal based on the results of the biometric authentication, and for the terminal to issue instructions for dispensing the drug, A means of predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence, A means to support drug inventory management based on the said forecast, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern medical industry, two contradictory problems of drug depletion and drug waste occur simultaneously. Drug depletion is a problem that drugs are in short supply in the market due to hoarding and reselling associated with pandemics and the prevalence of seasonal diseases. On the other hand, drug waste is a problem that occurs as a result of pharmacies having surplus inventory to meet all prescriptions. These problems are difficult to solve with existing methods because they are caused by a lack of appropriate demand forecasting and inventory management. Furthermore, the cost of authentication terminals and the low accuracy of biometric authentication technology, which are factors hindering the spread of digital prescriptions, are also mentioned. Effective means to solve these are required.

Means for Solving the Problems

[0005] This invention solves the above problems through a comprehensive system utilizing a mobile device, a database, biometric authentication technology, and generative artificial intelligence. Specifically, digital prescription information is generated using a mobile device and stored in a database. Next, biometric authentication of the user is performed using the stored prescription information, and the pharmacy terminal issues instructions for dispensing medication to the authenticated user. Furthermore, generative artificial intelligence is used to predict regional or seasonal demand for medications, and appropriate inventory management is supported based on the results. This prevents drug depletion and reduces the disposal of medications due to excess inventory. Through these means, efficient distribution and management of medications become possible, and the supply of medications in the healthcare industry can be stabilized.

[0006] A "mobile device" refers to a portable electronic device such as a mobile phone or smartphone, and is a means of generating and transmitting information.

[0007] A "database" is a system for efficiently storing and managing information, and it structures and stores various types of data, including prescription information.

[0008] "Biometric authentication" refers to technologies that verify an individual's identity based on their physical characteristics, and includes technologies such as facial recognition.

[0009] "Generative artificial intelligence" refers to a technology equipped with algorithms that analyze large amounts of data to predict future demand and other factors, and its accuracy can be improved through learning.

[0010] A "digital prescription" is electronic data used to create and manage prescription information electronically, and it includes the information necessary to obtain medication.

[0011] "Drug management information" refers to information used in pharmacies to manage drug inventory and dispensing procedures, thereby supporting the efficient distribution of drugs.

[0012] "Regional or seasonal demand for pharmaceuticals" refers to the required amount of pharmaceuticals in a specific geographical area or time period, and is data used to forecast supply and demand. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include a flash memory (SSD (Solid State Drive)), a magnetic disk (e.g., a hard disk), or a magnetic tape, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] One embodiment of the present invention is based on a system combining a mobile device, a database, biometric authentication technology, and generative artificial intelligence to solve the problems of drug depletion and waste in the medical industry.

[0035] First, the user creates a digital prescription using a mobile device. The user opens the mobile app and enters the necessary personal and prescription information. This information generates prescription information as a QR code (registered trademark) and is sent from the mobile device to the server. The server stores the received information in a database and enables information exchange with medical institutions and pharmacies as needed.

[0036] When a user visits the pharmacy, the terminal (pharmacy system) scans a QR code presented on the user's mobile device. Next, the server obtains the user's facial data based on the QR code and verifies their identity using biometric authentication technology. Once verification is complete, the server sends instructions for dispensing medication to the pharmacy system. The pharmacy terminal follows these instructions, prepares the necessary medication, and provides it to the user.

[0037] Furthermore, the generating artificial intelligence analyzes historical prescription data related to each region and season stored on the server to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information and support efficient drug distribution. This system enables the proper supply of drugs, prevents drug depletion, and reduces waste due to excess inventory.

[0038] As a concrete example, a user digitally handles prescriptions to address seasonal allergies, and a pharmacy terminal efficiently dispenses medication using information from a server. In this case, artificial intelligence can predict the demand for allergy medications in the area and notify pharmacies in advance, creating an environment where necessary medications are always supplied appropriately.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches a dedicated app on their mobile device and enters their personal information and prescription details. The app then generates a QR code from this information.

[0042] Step 2:

[0043] The user uses the generated QR code to send prescription information to the server. The server receives this information and securely stores it in its database.

[0044] Step 3:

[0045] When a user visits a pharmacy, the pharmacy's terminal scans a QR code presented on the user's mobile device. The scanned data is then sent to a server.

[0046] Step 4:

[0047] The server retrieves the user's facial data from the database based on the QR code data and initiates the process for biometric authentication.

[0048] Step 5:

[0049] The device scans the user's face with its camera and checks if it matches the facial recognition model provided by the server. The server then determines the authentication result.

[0050] Step 6:

[0051] If authentication is successful, the server sends information for drug provision to the terminal. Based on this information, the terminal prepares the necessary drugs for the user.

[0052] Step 7:

[0053] The generative artificial intelligence analyzes regional and seasonal drug demand from data stored on the server and creates future demand forecasts.

[0054] Step 8:

[0055] The server provides the generated demand forecasts to the pharmacy, helping pharmacy staff implement appropriate inventory management. This information is used to adjust the pharmacy's inventory.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In the modern medical industry, the problems of drug shortages and waste due to excess inventory are serious. In particular, appropriate supply tailored to regional and seasonal needs is difficult, leading to the waste of medical resources. Furthermore, efficient and secure methods for managing information and verifying the identity of prescriptions are required.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for generating prescription data using a mobile device and storing the data in a storage device, means for performing biometric authentication of the user based on the prescription data, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This enables proper supply management of pharmaceuticals and efficient identity verification.

[0061] A "mobile device" is a portable communication device that is an electronic device used by users to input and operate information and to send and receive data over a network.

[0062] "Prescription data" refers to digital information that includes the name of the medication, dosage, administration method, and duration of administration, as specified by a medical professional.

[0063] A "storage device" is a hardware component or medium for holding digital data, enabling the long-term storage of information.

[0064] "Biometric authentication" is a technology that identifies individuals based on their biological characteristics, and generally includes facial recognition, fingerprint authentication, and iris recognition.

[0065] An "information processing device" is an electronic device that inputs, processes, and outputs data, and usually refers to a computer or its peripherals.

[0066] "Generative artificial intelligence" is an artificial intelligence technology that uses algorithms and models to learn from past data and predict future events and demands.

[0067] A "two-dimensional code" is a code that visually encodes information and is in a format that can be scanned by a reading device, such as a QR code or a barcode.

[0068] "Facial recognition technology" is a technology that identifies individuals by analyzing the features of each face and comparing them with existing facial data in a database.

[0069] This invention is a system designed to improve the efficiency of drug supply in healthcare. The system utilizes mobile devices, a database, biometric authentication technology, and generative artificial intelligence.

[0070] Users create prescription data using a mobile device. The mobile device has a dedicated application installed, which users use to input necessary information such as name, address, medication name, and dosage. This input information is generated and displayed as a QR code.

[0071] The terminal receives a QR code from the user's mobile device. The pharmacy terminal scans this QR code to obtain the user's prescription data. The server performs biometric authentication based on the obtained prescription data. Facial recognition technology is implemented in this process, and the user's identity is verified using this authentication.

[0072] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. By analyzing historical data and predicting future demand fluctuations, it aims to optimize pharmaceutical supply. For example, the server can predict the demand for allergy medications during a specific season and use the results to notify pharmacies in advance, thereby reducing the risk of stockouts.

[0073] An example of a prompt would be, "Based on data from the past five years, predict the demand for allergy medication in this region for the next pollen season." Based on this prompt, generative artificial intelligence performs analysis and provides the necessary inventory information for the pharmacy. This enables efficient prescription management and supply of medications.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user launches a dedicated app using a mobile device. The user enters their name, address, and medication information provided by their doctor (drug name, dosage, usage instructions, etc.) into the app. The entered data is converted into a QR code by the application. This QR code visually represents the prescription information in digital format.

[0077] Step 2:

[0078] The user presents the generated QR code to the pharmacy terminal. The terminal scans the QR code with its camera and extracts the prescription data contained within it. In this step, the string data within the QR code is decoded and passed to the terminal as prescription information.

[0079] Step 3:

[0080] The server receives prescription data sent from the terminal. At this time, the server requires the user's facial data for biometric authentication. Based on this input, the server uses facial recognition technology to check if it matches the facial data in the database. If the match is successful, identity verification is complete.

[0081] Step 4:

[0082] After biometric authentication is complete, the server sends instructions for dispensing medication to the terminal. In this process, a list of necessary medications is generated based on prescription data and transferred to the pharmacy terminal. The terminal displays the list, and the pharmacist prepares the medications according to the list.

[0083] Step 5:

[0084] The server uses a generative AI model to analyze historical prescription data. Specifically, it inputs regional and seasonal demand patterns into the model to predict future drug demand. In this process, it takes in a large amount of prescription data and outputs prediction results using an algorithm.

[0085] Step 6:

[0086] Based on predicted demand, the server assists pharmacies with inventory management. Using the forecast results, it provides pharmacies with guidance on optimal inventory levels. This enables pharmacies to ensure efficient supply of medicines while preventing excess inventory and stockouts.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Traditional pharmaceutical supply systems suffered from significant problems such as drug depletion and waste due to excess inventory, highlighting the need for efficient inventory management. Furthermore, users had to visit pharmacies, making the purchasing and payment process inconvenient. There is a need to address these challenges and improve the efficiency of pharmaceutical distribution and user convenience.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for generating prescription information using a mobile device and storing it in a database, means for performing electronic payment based on electronic prescriptions using a smartphone device, and means for automatically charging at an appropriate price based on demand forecasts using a generating AI. This enables efficient inventory management, and allows users to purchase medicines in a convenient manner and make payments immediately.

[0092] A "mobile device" is a portable electronic device used for generating prescription information and transmitting data.

[0093] A "database" is an electronic system that enables the efficient storage and management of information and its retrieval when needed.

[0094] Biometric authentication is a technology that uses a user's physical characteristics to verify their identity, and it is a safe and reliable means of authentication.

[0095] A "terminal" refers to a computer device or interface that a user uses to perform information processing.

[0096] "Generative artificial intelligence" refers to machine learning or AI models that predict future events and demands based on past data.

[0097] "Regional or seasonal demand for pharmaceuticals" refers to an indicator that indicates the need for or demand for pharmaceuticals in a specific region or season.

[0098] A "smartphone" is a mobile phone with internet connectivity and an electronic device capable of running a variety of applications.

[0099] An "electronic prescription" is a system where paper prescriptions are managed as digital data and can be exchanged using QR codes or similar methods.

[0100] "Electronic payment" refers to a method of paying for goods and services online, using a smartphone or similar device.

[0101] "Automatic billing at a fair price" refers to a process of setting the optimal price based on demand forecasts and collecting fees from users accordingly.

[0102] To implement this invention, the user must first launch a dedicated application using a mobile device and input prescription information in digital format. This information is generated as a QR code, and the data is securely stored on a cloud-based database server. Ideally, a database capable of real-time data management, such as Firebase, should be used.

[0103] When a user purchases medication, they use a smartphone application to present a stored QR code to the pharmacy terminal. The terminal scans the QR code to retrieve the relevant prescription information from its database and performs biometric authentication. The biometric authentication utilizes GOOGLE FI® rebase Authentication facial recognition technology. If the authentication is successful, the terminal sends instructions for dispensing the medication to the pharmacy staff.

[0104] Meanwhile, the server uses a generative AI model to forecast drug demand by region or season. This forecast is fed back into the inventory management system, enabling pharmacies to secure the optimal amount of medication when needed. Machine learning frameworks such as TENSORFLOW® are suitable for the generative AI. For example, if the demand for allergy medication is predicted to increase during the spring pollen season, the server will notify pharmacies in advance of the appropriate inventory level.

[0105] An example of a prompt message is, "For spring hay fever, predict and suggest the demand for necessary medications." This text can be input into the AI, which can then suggest appropriate quantities. This allows for efficient inventory management of medications and prevents unnecessary waste.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users launch a dedicated app on their mobile device and enter their prescription information. This information includes the drug name, dosage, instructions for use, and the user's personal information. This data is generated as a QR code and sent to and stored in the Firebase database. This process ensures data centralization and secure storage.

[0109] Step 2:

[0110] When a user arrives at the pharmacy, they present a QR code displayed on their smartphone to the pharmacy's terminal. The terminal scans the QR code and retrieves the corresponding prescription information from the database. The retrieved data is then entered into the user's biometric authentication process. Biometric authentication uses facial recognition data and is performed using Firebase Authentication. Upon successful authentication, the user's identity is verified.

[0111] Step 3:

[0112] After authentication, the terminal generates medication management information based on prescription information retrieved from the database. This information is displayed to pharmacy staff as instructions, guiding them to retrieve the necessary medications. The process outputs instructions for staff and a list of medications. These instructions enable accurate and prompt medication provision.

[0113] Step 4:

[0114] The server uses a generative AI model to predict regional or seasonal drug demand based on stored historical prescription data. The input data consists of specific regions and historical seasonal trends, and the AI ​​model, using TensorFlow, performs data calculations. The output is a demand forecast, which is then fed back into the inventory management system.

[0115] Step 5:

[0116] Based on predicted demand, the server sends appropriate inventory adjustment instructions to the pharmacy's inventory management system. These instructions include specific drug names and required quantities. The system automatically replenishes or adjusts inventory according to these instructions. This process enables more efficient and less wasteful inventory management.

[0117] Step 6:

[0118] After the user receives their medication, electronic payment is made via a smartphone device. The input uses price information for the medication and a fair price based on demand forecasts. Payment is completed through a payment app on the smartphone. The output is a digital receipt confirming the transaction, sent to both the user and the pharmacy. This process ensures fast and transparent payment.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a system that provides a more personalized medical experience by combining a mobile device, a database, biometric authentication technology, generative AI, and an emotion engine.

[0121] First, the user creates a digital prescription using a mobile device. During this process, an emotion engine within the mobile device analyzes the user's facial expression data and recognizes their emotional state. This emotional state is added to the user's prescription information and sent to the server. The server receives this information, stores it in a database, and shares it with pharmacies and medical institutions as needed.

[0122] Next, when a user visits a pharmacy, the pharmacy's terminal scans the user's QR code. The server performs biometric authentication to verify the user's identity. The emotional state, as determined by the emotion engine, is reflected in the medication provided at the pharmacy, enabling pharmacy staff to respond appropriately to the user. For example, staff can treat a user who is feeling stressed with greater kindness.

[0123] Furthermore, the generative artificial intelligence analyzes emotional and regional data obtained from users to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information.

[0124] For example, if a user creates a prescription to address chronic pain, the system can analyze the user's emotional state from their facial expressions and, for instance, add information about tranquilizers to the prescription if they are experiencing high levels of anxiety. In this way, flexible medication provision tailored to the user's needs is achieved. Based on predictive data provided by the server, pharmacies can maintain inventory according to demand, preventing drug depletion and reducing waste.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user activates the mobile device, opens a dedicated application, and enters personal and prescription information. Simultaneously, the user's facial expressions are scanned by the camera.

[0128] Step 2:

[0129] The emotion engine analyzes the user's facial expression data to understand their emotional state in real time. This emotional information is then added to the prescription information.

[0130] Step 3:

[0131] The user generates prescription information and emotional information as a QR code and sends it to the server. The server stores the received information in a database.

[0132] Step 4:

[0133] When a user visits a pharmacy, the pharmacy's terminal scans the QR code presented by the user and sends the information to the server.

[0134] Step 5:

[0135] The server analyzes the QR code data, retrieves relevant user information and facial data from the database, and initiates the biometric authentication process.

[0136] Step 6:

[0137] The device scans the user's face again with its camera and verifies that it matches the facial data provided by the server. Once identity verification is complete, the server sends the results to the device.

[0138] Step 7:

[0139] If biometric authentication is successful, the terminal will follow instructions from the server, execute a medication dispensing process that takes the user's emotional information into account, and prepare the necessary medications.

[0140] Step 8:

[0141] The generative artificial intelligence analyzes accumulated emotional and regional data to predict future demand for pharmaceuticals. This allows the server to provide pharmacies with appropriate inventory management information.

[0142] Step 9:

[0143] Pharmacies use information from servers to adjust inventory and achieve efficient supply and management of medications.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In modern healthcare services, there is a challenge in providing sufficient personalized care based on the individual needs and emotional states of users. In particular, there is a need to effectively manage drug inventory by analyzing user emotions during prescription creation and by forecasting demand in real time.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for generating prescription information using a mobile device and analyzing the user's emotional state, means for storing the prescription information including the analysis results in a database and sharing it with other institutions as needed, and means for adjusting the response to the user based on the emotional state. This enables personalized medical care tailored to the user's emotional state and highly accurate drug inventory management.

[0149] A "mobile device" is a portable electronic device that serves as a terminal for users to input information or create prescriptions.

[0150] "Prescription information" refers to information about medication prescriptions issued by medical institutions, including the user's symptoms and desired medications.

[0151] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes emotions such as joy and anxiety.

[0152] A "database" is a system for systematically storing and managing information, such as prescription information and emotional data.

[0153] "Biometric authentication" is a technology that uses an individual's biometric information to verify the identity of a user.

[0154] "Generative artificial intelligence" is a type of artificial intelligence that uses machine learning techniques to analyze data and support predictions and decision-making.

[0155] "Inventory management" is the process of understanding the inventory status of medicines and products and managing them to ensure that an appropriate supply is maintained.

[0156] An "identification code" is a code that encodes unique information and is used for data transmission and identity verification.

[0157] This invention comprises a system combining a mobile device, a database, biometric authentication technology, a generative artificial intelligence model, and an emotion engine. This system provides users with a more personalized medical experience.

[0158] The user operates a mobile device and launches a dedicated application. This application creates a digital prescription by allowing the user to input information about their current symptoms and desired medications. An emotion engine built into the mobile device uses a camera to collect and analyze the user's facial expression data to determine their emotional state. This analyzed emotional data is added to the prescription information in real time and transmitted to the server.

[0159] The server receives prescription information and emotional data submitted by users and stores it in a database. This information may be shared with healthcare institutions and pharmacies. This sharing allows staff at pharmacies and healthcare institutions to understand the user's emotional state and suggest more appropriate responses.

[0160] In a pharmacy, a terminal scans an identification code presented by the user. The server uses biometric authentication technology to verify the user's identity. This verification method often utilizes facial recognition technology. This ensures that the pharmacy can reliably verify the user's identity, and based on this, they can provide support using emotional data. For example, if a user is feeling anxious, pharmacy staff will strive to provide kind and reassuring guidance.

[0161] Furthermore, by employing a generative artificial intelligence model, the server analyzes user sentiment data and regional data to predict future demand for pharmaceuticals. This provides pharmacies with accurate information for inventory management. For example, it becomes possible to predict the demand for cold medicines during the winter season, enabling pharmacies to implement efficient and waste-free inventory management.

[0162] As a concrete example, suppose a user suffers from chronic headaches. A prescription is created using a mobile device, and if the user's facial expression indicates that relaxation is needed, information about tranquilizers is added to the prescription. This information is sent to a server, and the pharmacy provides the user with the appropriate medication.

[0163] Examples of prompts include, "Based on local sentiment data, predict the next month's drug demand," and "Analyze the user's emotional state from their facial expression data and provide drug information to suggest." Inputting such prompts into an AI model facilitates data analysis.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user creates a digital prescription using a mobile device. The user launches the application and inputs their symptoms and desired medications. During this process, the mobile device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data. The input consists of the user's symptoms, a list of desired medications, and facial expression data, while the output is prescription information with the emotion analysis results added. The emotion analysis identifies emotional states, such as "anxiety."

[0167] Step 2:

[0168] The mobile device transmits generated prescription information and sentiment data to a server. The input is prescription information integrated with sentiment analysis results, and the output is data transmission to the server. This information reaches the server and is securely registered in the database. The transmitted data is stored for later pharmacy operations and verification by healthcare institutions.

[0169] Step 3:

[0170] When a user visits a pharmacy, a terminal scans the user's QR code. The terminal sends the scan result to a server, which retrieves the corresponding prescription information from a database based on the scanned data. The input is the user's QR code information, and the output is the identified prescription information and sentiment data. The server performs a database check and sends the medication information requested by the user to the terminal.

[0171] Step 4:

[0172] The server performs biometric authentication to verify the user's identity. The input is user information corresponding to a QR code, and the output is the biometric authentication result. Specifically, facial recognition technology is used to check the user's facial image and determine if it matches. If the identity verification is successful, the pharmacy can obtain the user's emotional data and use it to inform the provision of medication.

[0173] Step 5:

[0174] Pharmacy staff provide personalized service to users based on emotional data displayed on a terminal. Input is prescription information including emotional status, and output is specific action plans for the user. For example, staff are recommended to provide particularly empathetic support to users who are experiencing stress.

[0175] Step 6:

[0176] The server uses generative artificial intelligence to analyze user sentiment data and regional drug demand data. The input is historical sentiment data and regional data, and the output is a forecast of future drug demand. Through the generative AI model, it predicts drug demand trends for the following month and provides the results to pharmacies. This enables pharmacies to properly manage inventory and prevent shortages or surpluses.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] Traditional medical services and medication provision lack personalization that takes into account the emotional state of the user, resulting in inadequately alleviating the stress and anxiety experienced by users. Furthermore, pharmacy staff lack sufficient information to select appropriate customer service methods, making it difficult to improve user satisfaction.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for generating prescription data using a mobile information processing device and storing the data on an information recording medium, means for analyzing the user's emotional state and proposing customer service methods based on that emotional state, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This makes it possible to provide a personalized medical experience that responds to the user's emotions, and enables pharmacy staff to improve user satisfaction by providing appropriate customer service.

[0182] A "mobile information processing device" is a portable electronic device that allows users to input, store, and transmit data.

[0183] "Prescription data" refers to digital data containing information about medications and treatment methods prescribed by medical institutions to patients.

[0184] An "information recording medium" is a digital data recording device that can store data and retrieve it as needed.

[0185] "Biometric authentication" is a technology that uses a user's physical characteristics to verify their identity.

[0186] "Pharmaceutical management data" refers to data related to the management of pharmaceuticals, such as inventory status, dispensing instructions, and consumption trends.

[0187] A "terminal device" is an electronic device used by a user to receive or process data.

[0188] "Generative artificial intelligence" refers to computational modeling technology that analyzes information based on given data and generates new data or predictions.

[0189] "Emotional state" refers to the psychological state that can be interpreted from the user's facial expressions and behavior.

[0190] "Customer service methods" refer to the methods and guidelines for how to interact with customers or users.

[0191] To realize this invention, it is necessary to construct a system involving multiple hardware and software components. First, the user generates their prescription data using a mobile information processing device and stores it on an information recording medium. Next, this prescription data is received by a server, which performs biometric authentication of the user to ensure the accuracy of the information. Facial recognition technology is used for authentication.

[0192] Subsequently, the server uses an emotion engine to analyze the user's facial expression data and identify their emotional state. This analysis result is then input into a generative AI model. Based on the emotional state, the generative AI model proposes customer service methods. Based on this, guidelines for customer service methods are displayed on the terminal device, allowing store staff to provide the most appropriate service.

[0193] The server further uses generative artificial intelligence to predict regional or seasonal drug demand. This predicted data is provided to terminal devices to help optimize pharmacy inventory management. For example, the server analyzes infectious disease outbreak data and seasonal changes in each region to suggest the types and quantities of drugs that are expected to be in demand next.

[0194] For example, when a user visits a pharmacy and the emotion engine detects feelings of anxiety, the generating AI model will generate prompts recommending that store staff provide thorough explanations and reassurance. Examples of these prompts include, "Identify the user's emotional state from their facial expression data and suggest appropriate customer service methods," or "Please tell me what to do when the user is in (emotional state)." This allows users to receive more attentive and personalized medical services.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user uses a mobile data processing device to input prescription data. The prescription data is digitized within the mobile device and stored on a data storage medium. The input data includes drug name, dosage, and duration of use. Output is the transmission of the prescription data from the mobile device to the server.

[0198] Step 2:

[0199] The server analyzes the received prescription data and performs biometric authentication. It uses facial recognition technology to verify the user's identity and authenticate that they are the correct user. The input consists of prescription data and the user's facial information, and the output is the user authentication result.

[0200] Step 3:

[0201] The server uses an emotion engine to analyze facial expression data and identify the user's emotional state. Emotional states are classified into categories such as joy, sadness, stress, and anxiety. The input is facial expression data, and the output is the analyzed emotional state.

[0202] Step 4:

[0203] The server inputs the emotional state into a generative AI model, which then derives the optimal customer service method. Based on the previously analyzed emotional state, the generative AI model generates a prompt message and sends it to the terminal device. The input is the emotional state, and the output is a prompt message describing the customer service method.

[0204] Step 5:

[0205] The terminal device visualizes and presents the received prompt message to the staff. Based on the visualized instructions, the staff provides the most appropriate customer service to the user. The input is the prompt message, and the output is the actual customer service action taken by the staff.

[0206] Step 6:

[0207] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. The server takes regional infectious disease data and seasonal demand patterns as input and makes predictions based on this data. The output is the type and quantity of pharmaceuticals needed in the future.

[0208] Step 7:

[0209] The server provides drug management data to terminal devices based on predictive data. The terminal devices use this data to manage store inventory. The input is predictive data, and the output is inventory management instructions or improvement suggestions.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] One embodiment of the present invention is based on a system combining a mobile device, a database, biometric authentication technology, and generative artificial intelligence to solve the problems of drug depletion and waste in the medical industry.

[0227] First, the user creates a digital prescription using a mobile device. The user opens the mobile app and enters the necessary personal and prescription information. This information generates prescription information as a QR code and is sent from the mobile device to the server. The server stores the received information in a database and enables information exchange with medical institutions and pharmacies as needed.

[0228] When a user visits the pharmacy, the terminal (pharmacy system) scans a QR code presented on the user's mobile device. Next, the server obtains the user's facial data based on the QR code and verifies their identity using biometric authentication technology. Once verification is complete, the server sends instructions for dispensing medication to the pharmacy system. The pharmacy terminal follows these instructions, prepares the necessary medication, and provides it to the user.

[0229] Furthermore, the generating artificial intelligence analyzes historical prescription data related to each region and season stored on the server to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information and support efficient drug distribution. This system enables the proper supply of drugs, prevents drug depletion, and reduces waste due to excess inventory.

[0230] As a concrete example, a user digitally handles prescriptions to address seasonal allergies, and a pharmacy terminal efficiently dispenses medication using information from a server. In this case, artificial intelligence can predict the demand for allergy medications in the area and notify pharmacies in advance, creating an environment where necessary medications are always supplied appropriately.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user launches a dedicated app on their mobile device and enters their personal information and prescription details. The app then generates a QR code from this information.

[0234] Step 2:

[0235] The user uses the generated QR code to send prescription information to the server. The server receives this information and securely stores it in its database.

[0236] Step 3:

[0237] When a user visits a pharmacy, the pharmacy's terminal scans a QR code presented on the user's mobile device. The scanned data is then sent to a server.

[0238] Step 4:

[0239] The server retrieves the user's facial data from the database based on the QR code data and initiates the process for biometric authentication.

[0240] Step 5:

[0241] The device scans the user's face with its camera and checks if it matches the facial recognition model provided by the server. The server then determines the authentication result.

[0242] Step 6:

[0243] If authentication is successful, the server sends information for drug provision to the terminal. Based on this information, the terminal prepares the necessary drugs for the user.

[0244] Step 7:

[0245] The generative artificial intelligence analyzes regional and seasonal drug demand from data stored on the server and creates future demand forecasts.

[0246] Step 8:

[0247] The server provides the generated demand forecasts to the pharmacy, helping pharmacy staff implement appropriate inventory management. This information is used to adjust the pharmacy's inventory.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0250] In the modern medical industry, the problems of drug shortages and waste due to excess inventory are serious. In particular, appropriate supply tailored to regional and seasonal needs is difficult, leading to the waste of medical resources. Furthermore, efficient and secure methods for managing information and verifying the identity of prescriptions are required.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for generating prescription data using a mobile device and storing the data in a storage device, means for performing biometric authentication of the user based on the prescription data, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This enables proper supply management of pharmaceuticals and efficient identity verification.

[0253] A "mobile device" is a portable communication device that is an electronic device used by users to input and operate information and to send and receive data over a network.

[0254] "Prescription data" refers to digital information that includes the name of the medication, dosage, administration method, and duration of administration, as specified by a medical professional.

[0255] A "storage device" is a hardware component or medium for holding digital data, enabling the long-term storage of information.

[0256] "Biometric authentication" is a technology that identifies individuals based on their biological characteristics, and generally includes facial recognition, fingerprint authentication, and iris recognition.

[0257] An "information processing device" is an electronic device that inputs, processes, and outputs data, and usually refers to a computer or its peripherals.

[0258] "Generative artificial intelligence" is an artificial intelligence technology that uses algorithms and models to learn from past data and predict future events and demands.

[0259] A "two-dimensional code" is a code that visually encodes information and is in a format that can be scanned by a reading device, such as a QR code or a barcode.

[0260] "Facial recognition technology" is a technology that identifies individuals by analyzing the features of each face and comparing them with existing facial data in a database.

[0261] This invention is a system designed to improve the efficiency of drug supply in healthcare. The system utilizes mobile devices, a database, biometric authentication technology, and generative artificial intelligence.

[0262] Users create prescription data using a mobile device. The mobile device has a dedicated application installed, which users use to input necessary information such as name, address, medication name, and dosage. This input information is generated and displayed as a QR code.

[0263] The terminal receives a QR code from the user's mobile device. The pharmacy terminal scans this QR code to obtain the user's prescription data. The server performs biometric authentication based on the obtained prescription data. Facial recognition technology is implemented in this process, and the user's identity is verified using this authentication.

[0264] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. By analyzing historical data and predicting future demand fluctuations, it aims to optimize pharmaceutical supply. For example, the server can predict the demand for allergy medications during a specific season and use the results to notify pharmacies in advance, thereby reducing the risk of stockouts.

[0265] An example of a prompt would be, "Based on data from the past five years, predict the demand for allergy medication in this region for the next pollen season." Based on this prompt, generative artificial intelligence performs analysis and provides the necessary inventory information for the pharmacy. This enables efficient prescription management and supply of medications.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user launches a dedicated app using a mobile device. The user enters their name, address, and medication information provided by their doctor (drug name, dosage, usage instructions, etc.) into the app. The entered data is converted into a QR code by the application. This QR code visually represents the prescription information in digital format.

[0269] Step 2:

[0270] The user presents the generated QR code to the pharmacy terminal. The terminal scans the QR code with its camera and extracts the prescription data contained within it. In this step, the string data within the QR code is decoded and passed to the terminal as prescription information.

[0271] Step 3:

[0272] The server receives prescription data sent from the terminal. At this time, the server requires the user's facial data for biometric authentication. Based on this input, the server uses facial recognition technology to check if it matches the facial data in the database. If the match is successful, identity verification is complete.

[0273] Step 4:

[0274] After biometric authentication is complete, the server sends instructions for dispensing medication to the terminal. In this process, a list of necessary medications is generated based on prescription data and transferred to the pharmacy terminal. The terminal displays the list, and the pharmacist prepares the medications according to the list.

[0275] Step 5:

[0276] The server uses a generative AI model to analyze historical prescription data. Specifically, it inputs regional and seasonal demand patterns into the model to predict future drug demand. In this process, it takes in a large amount of prescription data and outputs prediction results using an algorithm.

[0277] Step 6:

[0278] Based on predicted demand, the server assists pharmacies with inventory management. Using the forecast results, it provides pharmacies with guidance on optimal inventory levels. This enables pharmacies to ensure efficient supply of medicines while preventing excess inventory and stockouts.

[0279] (Application Example 1)

[0280] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0281] Traditional pharmaceutical supply systems suffered from significant problems such as drug depletion and waste due to excess inventory, highlighting the need for efficient inventory management. Furthermore, users had to visit pharmacies, making the purchasing and payment process inconvenient. There is a need to address these challenges and improve the efficiency of pharmaceutical distribution and user convenience.

[0282] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0283] In this invention, the server includes means for generating prescription information using a mobile device and storing it in a database, means for performing electronic payment based on an electronic prescription using a smartphone device, and means for automatically charging at an appropriate price based on demand prediction using a generation AI. This enables efficient inventory management, allowing users to purchase medications in a convenient manner and make immediate payments.

[0284] A "mobile device" is a portable electronic device used for generating prescription information and transmitting data.

[0285] A "database" is an electronic system that enables efficient storage and management of information and allows retrieval as needed.

[0286] "Biometric authentication" is a technology for verifying a user's identity using physical characteristics of the user and is a secure and reliable authentication method.

[0287] A "terminal" refers to a computer device or interface used by a user for information processing.

[0288] A "generation artificial intelligence" is a machine learning or AI model for predicting future events and demands based on past data.

[0289] "Regional or seasonal pharmaceutical demand" is an indicator that refers to the necessity and required quantity of pharmaceuticals in a specific region or season.

[0290] A "smartphone device" is a mobile phone with an internet connection function and is an electronic device capable of running various applications.

[0291] An "electronic prescription" is a digital management of a paper prescription, enabling exchange via a QR code or the like.

[0292] "Electronic payment" is a method of paying for goods or services online, performed using a smartphone or the like.

[0293] "Automatic billing at a fair price" refers to a process of setting the optimal price based on demand forecasts and collecting fees from users accordingly.

[0294] To implement this invention, the user must first launch a dedicated application using a mobile device and input prescription information in digital format. This information is generated as a QR code, and the data is securely stored on a cloud-based database server. Ideally, a database capable of real-time data management, such as Firebase, should be used.

[0295] When a user purchases medication, they use a smartphone application to present a saved QR code to the pharmacy terminal. The terminal scans the QR code to retrieve the relevant prescription information from its database and performs biometric authentication. Google® Firebase Authentication facial recognition technology is used for biometric authentication. If authentication is successful, the terminal sends instructions for dispensing the medication to the pharmacy staff.

[0296] Meanwhile, the server uses a generative AI model to forecast drug demand by region or season. This forecast is fed back into the inventory management system, enabling pharmacies to secure the optimal amount of medication when needed. Machine learning frameworks such as TensorFlow are suitable for the generative AI. For example, if increased demand for allergy medication is predicted during the spring pollen season, the server can notify pharmacies in advance of the appropriate inventory levels.

[0297] An example of a prompt message is, "For spring hay fever, predict and suggest the demand for necessary medications." This text can be input into the AI, which can then suggest appropriate quantities. This allows for efficient inventory management of medications and prevents unnecessary waste.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] Users launch a dedicated app on their mobile device and enter their prescription information. This information includes the drug name, dosage, instructions for use, and the user's personal information. This data is generated as a QR code and sent to and stored in the Firebase database. This process ensures data centralization and secure storage.

[0301] Step 2:

[0302] When a user arrives at the pharmacy, they present a QR code displayed on their smartphone to the pharmacy's terminal. The terminal scans the QR code and retrieves the corresponding prescription information from the database. The retrieved data is then entered into the user's biometric authentication process. Biometric authentication uses facial recognition data and is performed using Firebase Authentication. Upon successful authentication, the user's identity is verified.

[0303] Step 3:

[0304] After authentication, the terminal generates medication management information based on prescription information retrieved from the database. This information is displayed to pharmacy staff as instructions, guiding them to retrieve the necessary medications. The process outputs instructions for staff and a list of medications. These instructions enable accurate and prompt medication provision.

[0305] Step 4:

[0306] The server uses a generative AI model to predict regional or seasonal drug demand based on stored historical prescription data. The input data consists of specific regions and historical seasonal trends, and the AI ​​model, using TensorFlow, performs data calculations. The output is a demand forecast, which is then fed back into the inventory management system.

[0307] Step 5:

[0308] Based on the predicted demand, the server sends appropriate inventory adjustment instructions to the pharmacy's inventory management system. These instructions include the specific drug name and the required quantity. The system automatically replenishes or adjusts the inventory according to these instructions. This process enables more efficient and less lossy inventory management.

[0309] Step 6:

[0310] After the user receives the medicine, electronic payment is made using a smartphone device. As input, the price information of the medicine and the appropriate price based on demand prediction are used, and the payment is completed through the payment app on the smartphone. The output is a digital receipt for transaction confirmation, which is sent to both the user and the pharmacy. This process enables quick and transparent payment.

[0311] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0312] The present invention is a system that provides a more personalized medical experience by combining a mobile device, a database, biometric authentication technology, generative AI, and an emotion engine.

[0313] First, the user creates a digital prescription using a mobile device. At this time, the emotion engine in the mobile device analyzes the user's facial expression data and recognizes the emotional state. This emotional state is added to the user's prescription information and sent to the server. The server receives this information, saves it in the database, and shares it with pharmacies and medical institutions as necessary.

[0314] Next, when a user visits a pharmacy, the pharmacy's terminal scans the user's QR code. The server performs biometric authentication to verify the user's identity. The emotional state, as determined by the emotion engine, is reflected in the medication provided at the pharmacy, enabling pharmacy staff to respond appropriately to the user. For example, staff can treat a user who is feeling stressed with greater kindness.

[0315] Furthermore, the generative artificial intelligence analyzes emotional and regional data obtained from users to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information.

[0316] For example, if a user creates a prescription to address chronic pain, the system can analyze the user's emotional state from their facial expressions and, for instance, add information about tranquilizers to the prescription if they are experiencing high levels of anxiety. In this way, flexible medication provision tailored to the user's needs is achieved. Based on predictive data provided by the server, pharmacies can maintain inventory according to demand, preventing drug depletion and reducing waste.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The user activates the mobile device, opens a dedicated application, and enters personal and prescription information. Simultaneously, the user's facial expressions are scanned by the camera.

[0320] Step 2:

[0321] The emotion engine analyzes the user's facial expression data to understand their emotional state in real time. This emotional information is then added to the prescription information.

[0322] Step 3:

[0323] The user generates prescription information and emotional information as a QR code and sends it to the server. The server stores the received information in a database.

[0324] Step 4:

[0325] When a user visits a pharmacy, the pharmacy's terminal scans the QR code presented by the user and sends the information to the server.

[0326] Step 5:

[0327] The server analyzes the QR code data, retrieves relevant user information and facial data from the database, and initiates the biometric authentication process.

[0328] Step 6:

[0329] The device scans the user's face again with its camera and verifies that it matches the facial data provided by the server. Once identity verification is complete, the server sends the results to the device.

[0330] Step 7:

[0331] If biometric authentication is successful, the terminal will follow instructions from the server, execute a medication dispensing process that takes the user's emotional information into account, and prepare the necessary medications.

[0332] Step 8:

[0333] The generative artificial intelligence analyzes accumulated emotional and regional data to predict future demand for pharmaceuticals. This allows the server to provide pharmacies with appropriate inventory management information.

[0334] Step 9:

[0335] Pharmacies use information from servers to adjust inventory and achieve efficient supply and management of medications.

[0336] (Example 2)

[0337] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0338] In modern healthcare services, there is a challenge in providing sufficient personalized care based on the individual needs and emotional states of users. In particular, there is a need to effectively manage drug inventory by analyzing user emotions during prescription creation and by forecasting demand in real time.

[0339] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0340] In this invention, the server includes means for generating prescription information using a mobile device and analyzing the user's emotional state, means for storing the prescription information including the analysis results in a database and sharing it with other institutions as needed, and means for adjusting the response to the user based on the emotional state. This enables personalized medical care tailored to the user's emotional state and highly accurate drug inventory management.

[0341] A "mobile device" is a portable electronic device that serves as a terminal for users to input information or create prescriptions.

[0342] "Prescription information" refers to information about medication prescriptions issued by medical institutions, including the user's symptoms and desired medications.

[0343] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes emotions such as joy and anxiety.

[0344] A "database" is a system for systematically storing and managing information, such as prescription information and emotional data.

[0345] "Biometric authentication" is a technology that uses an individual's biometric information to verify the identity of a user.

[0346] "Generative artificial intelligence" is a type of artificial intelligence that uses machine learning techniques to analyze data and support predictions and decision-making.

[0347] "Inventory management" is the process of understanding the inventory status of medicines and products and managing them to ensure that an appropriate supply is maintained.

[0348] An "identification code" is a code that encodes unique information and is used for data transmission and identity verification.

[0349] This invention comprises a system combining a mobile device, a database, biometric authentication technology, a generative artificial intelligence model, and an emotion engine. This system provides users with a more personalized medical experience.

[0350] The user operates a mobile device and launches a dedicated application. This application creates a digital prescription by allowing the user to input information about their current symptoms and desired medications. An emotion engine built into the mobile device uses a camera to collect and analyze the user's facial expression data to determine their emotional state. This analyzed emotional data is added to the prescription information in real time and transmitted to the server.

[0351] The server receives prescription information and emotional data submitted by users and stores it in a database. This information may be shared with healthcare institutions and pharmacies. This sharing allows staff at pharmacies and healthcare institutions to understand the user's emotional state and suggest more appropriate responses.

[0352] In a pharmacy, a terminal scans an identification code presented by the user. The server uses biometric authentication technology to verify the user's identity. This verification method often utilizes facial recognition technology. This ensures that the pharmacy can reliably verify the user's identity, and based on this, they can provide support using emotional data. For example, if a user is feeling anxious, pharmacy staff will strive to provide kind and reassuring guidance.

[0353] Furthermore, by employing a generative artificial intelligence model, the server analyzes user sentiment data and regional data to predict future demand for pharmaceuticals. This provides pharmacies with accurate information for inventory management. For example, it becomes possible to predict the demand for cold medicines during the winter season, enabling pharmacies to implement efficient and waste-free inventory management.

[0354] As a concrete example, suppose a user suffers from chronic headaches. A prescription is created using a mobile device, and if the user's facial expression indicates that relaxation is needed, information about tranquilizers is added to the prescription. This information is sent to a server, and the pharmacy provides the user with the appropriate medication.

[0355] Examples of prompts include, "Based on local sentiment data, predict the next month's drug demand," and "Analyze the user's emotional state from their facial expression data and provide drug information to suggest." Inputting such prompts into an AI model facilitates data analysis.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The user creates a digital prescription using a mobile device. The user launches the application and inputs their symptoms and desired medications. During this process, the mobile device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data. The input consists of the user's symptoms, a list of desired medications, and facial expression data, while the output is prescription information with the emotion analysis results added. The emotion analysis identifies emotional states, such as "anxiety."

[0359] Step 2:

[0360] The mobile device transmits generated prescription information and sentiment data to a server. The input is prescription information integrated with sentiment analysis results, and the output is data transmission to the server. This information reaches the server and is securely registered in the database. The transmitted data is stored for later pharmacy operations and verification by healthcare institutions.

[0361] Step 3:

[0362] When a user visits a pharmacy, a terminal scans the user's QR code. The terminal sends the scan result to a server, which retrieves the corresponding prescription information from a database based on the scanned data. The input is the user's QR code information, and the output is the identified prescription information and sentiment data. The server performs a database check and sends the medication information requested by the user to the terminal.

[0363] Step 4:

[0364] The server performs biometric authentication to verify the user's identity. The input is user information corresponding to a QR code, and the output is the biometric authentication result. Specifically, facial recognition technology is used to check the user's facial image and determine if it matches. If the identity verification is successful, the pharmacy can obtain the user's emotional data and use it to inform the provision of medication.

[0365] Step 5:

[0366] Pharmacy staff provide personalized service to users based on emotional data displayed on a terminal. Input is prescription information including emotional status, and output is specific action plans for the user. For example, staff are recommended to provide particularly empathetic support to users who are experiencing stress.

[0367] Step 6:

[0368] The server uses generative artificial intelligence to analyze user sentiment data and regional drug demand data. The input is historical sentiment data and regional data, and the output is a forecast of future drug demand. Through the generative AI model, it predicts drug demand trends for the following month and provides the results to pharmacies. This enables pharmacies to properly manage inventory and prevent shortages or surpluses.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] Traditional medical services and medication provision lack personalization that takes into account the emotional state of the user, resulting in inadequately alleviating the stress and anxiety experienced by users. Furthermore, pharmacy staff lack sufficient information to select appropriate customer service methods, making it difficult to improve user satisfaction.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes means for generating prescription data using a mobile information processing device and storing the data on an information recording medium, means for analyzing the user's emotional state and proposing customer service methods based on that emotional state, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This makes it possible to provide a personalized medical experience that responds to the user's emotions, and enables pharmacy staff to improve user satisfaction by providing appropriate customer service.

[0374] A "mobile information processing device" is a portable electronic device that allows users to input, store, and transmit data.

[0375] "Prescription data" refers to digital data containing information about medications and treatment methods prescribed by medical institutions to patients.

[0376] An "information recording medium" is a digital data recording device that can store data and retrieve it as needed.

[0377] "Biometric authentication" is a technology that uses a user's physical characteristics to verify their identity.

[0378] "Pharmaceutical management data" refers to data related to the management of pharmaceuticals, such as inventory status, dispensing instructions, and consumption trends.

[0379] A "terminal device" is an electronic device used by a user to receive or process data.

[0380] "Generative artificial intelligence" refers to computational modeling technology that analyzes information based on given data and generates new data or predictions.

[0381] "Emotional state" refers to the psychological state that can be interpreted from the user's facial expressions and behavior.

[0382] "Customer service methods" refer to the methods and guidelines for how to interact with customers or users.

[0383] To realize this invention, it is necessary to construct a system involving multiple hardware and software components. First, the user generates their prescription data using a mobile information processing device and stores it on an information recording medium. Next, this prescription data is received by a server, which performs biometric authentication of the user to ensure the accuracy of the information. Facial recognition technology is used for authentication.

[0384] Subsequently, the server uses an emotion engine to analyze the user's facial expression data and identify their emotional state. This analysis result is then input into a generative AI model. Based on the emotional state, the generative AI model proposes customer service methods. Based on this, guidelines for customer service methods are displayed on the terminal device, allowing store staff to provide the most appropriate service.

[0385] The server further uses generative artificial intelligence to predict regional or seasonal drug demand. This predicted data is provided to terminal devices to help optimize pharmacy inventory management. For example, the server analyzes infectious disease outbreak data and seasonal changes in each region to suggest the types and quantities of drugs that are expected to be in demand next.

[0386] For example, when a user visits a pharmacy and the emotion engine detects feelings of anxiety, the generating AI model will generate prompts recommending that store staff provide thorough explanations and reassurance. Examples of these prompts include, "Identify the user's emotional state from their facial expression data and suggest appropriate customer service methods," or "Please tell me what to do when the user is in (emotional state)." This allows users to receive more attentive and personalized medical services.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The user uses a mobile data processing device to input prescription data. The prescription data is digitized within the mobile device and stored on a data storage medium. The input data includes drug name, dosage, and duration of use. Output is the transmission of the prescription data from the mobile device to the server.

[0390] Step 2:

[0391] The server analyzes the received prescription data and performs biometric authentication. It uses facial recognition technology to verify the user's identity and authenticate that they are the correct user. The input consists of prescription data and the user's facial information, and the output is the user authentication result.

[0392] Step 3:

[0393] The server uses an emotion engine to analyze facial expression data and identify the user's emotional state. Emotional states are classified into categories such as joy, sadness, stress, and anxiety. The input is facial expression data, and the output is the analyzed emotional state.

[0394] Step 4:

[0395] The server inputs the emotional state into a generative AI model, which then derives the optimal customer service method. Based on the previously analyzed emotional state, the generative AI model generates a prompt message and sends it to the terminal device. The input is the emotional state, and the output is a prompt message describing the customer service method.

[0396] Step 5:

[0397] The terminal device visualizes and presents the received prompt message to the staff. Based on the visualized instructions, the staff provides the most appropriate customer service to the user. The input is the prompt message, and the output is the actual customer service action taken by the staff.

[0398] Step 6:

[0399] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. The server takes regional infectious disease data and seasonal demand patterns as input and makes predictions based on this data. The output is the type and quantity of pharmaceuticals needed in the future.

[0400] Step 7:

[0401] The server provides drug management data to terminal devices based on predictive data. The terminal devices use this data to manage store inventory. The input is predictive data, and the output is inventory management instructions or improvement suggestions.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0413] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0414] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] One embodiment of the present invention is based on a system combining a mobile device, a database, biometric authentication technology, and generative artificial intelligence to solve the problems of drug depletion and waste in the medical industry.

[0419] First, the user creates a digital prescription using a mobile device. The user opens the mobile app and enters the necessary personal and prescription information. This information generates prescription information as a QR code and is sent from the mobile device to the server. The server stores the received information in a database and enables information exchange with medical institutions and pharmacies as needed.

[0420] When a user visits the pharmacy, the terminal (pharmacy system) scans a QR code presented on the user's mobile device. Next, the server obtains the user's facial data based on the QR code and verifies their identity using biometric authentication technology. Once verification is complete, the server sends instructions for dispensing medication to the pharmacy system. The pharmacy terminal follows these instructions, prepares the necessary medication, and provides it to the user.

[0421] Furthermore, the generating artificial intelligence analyzes historical prescription data related to each region and season stored on the server to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information and support efficient drug distribution. This system enables the proper supply of drugs, prevents drug depletion, and reduces waste due to excess inventory.

[0422] As a concrete example, a user digitally handles prescriptions to address seasonal allergies, and a pharmacy terminal efficiently dispenses medication using information from a server. In this case, artificial intelligence can predict the demand for allergy medications in the area and notify pharmacies in advance, creating an environment where necessary medications are always supplied appropriately.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The user launches a dedicated app on their mobile device and enters their personal information and prescription details. The app then generates a QR code from this information.

[0426] Step 2:

[0427] The user uses the generated QR code to send prescription information to the server. The server receives this information and securely stores it in its database.

[0428] Step 3:

[0429] When a user visits a pharmacy, the pharmacy's terminal scans a QR code presented on the user's mobile device. The scanned data is then sent to a server.

[0430] Step 4:

[0431] The server retrieves the user's facial data from the database based on the QR code data and initiates the process for biometric authentication.

[0432] Step 5:

[0433] The device scans the user's face with its camera and checks if it matches the facial recognition model provided by the server. The server then determines the authentication result.

[0434] Step 6:

[0435] If authentication is successful, the server sends information for drug provision to the terminal. Based on this information, the terminal prepares the necessary drugs for the user.

[0436] Step 7:

[0437] The generative artificial intelligence analyzes regional and seasonal drug demand from data stored on the server and creates future demand forecasts.

[0438] Step 8:

[0439] The server provides the generated demand forecasts to the pharmacy, helping pharmacy staff implement appropriate inventory management. This information is used to adjust the pharmacy's inventory.

[0440] (Example 1)

[0441] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0442] In the modern medical industry, the problems of drug shortages and waste due to excess inventory are serious. In particular, appropriate supply tailored to regional and seasonal needs is difficult, leading to the waste of medical resources. Furthermore, efficient and secure methods for managing information and verifying the identity of prescriptions are required.

[0443] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0444] In this invention, the server includes means for generating prescription data using a mobile device and storing the data in a storage device, means for performing biometric authentication of the user based on the prescription data, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This enables proper supply management of pharmaceuticals and efficient identity verification.

[0445] A "mobile device" is a portable communication device that is an electronic device used by users to input and operate information and to send and receive data over a network.

[0446] "Prescription data" refers to digital information that includes the name of the medication, dosage, administration method, and duration of administration, as specified by a medical professional.

[0447] A "storage device" is a hardware component or medium for holding digital data, enabling the long-term storage of information.

[0448] "Biometric authentication" is a technology that identifies individuals based on their biological characteristics, and generally includes facial recognition, fingerprint authentication, and iris recognition.

[0449] An "information processing device" is an electronic device that inputs, processes, and outputs data, and usually refers to a computer or its peripherals.

[0450] "Generative artificial intelligence" is an artificial intelligence technology that uses algorithms and models to learn from past data and predict future events and demands.

[0451] A "two-dimensional code" is a code that visually encodes information and is in a format that can be scanned by a reading device, such as a QR code or a barcode.

[0452] "Facial recognition technology" is a technology that identifies individuals by analyzing the features of each face and comparing them with existing facial data in a database.

[0453] This invention is a system designed to improve the efficiency of drug supply in healthcare. The system utilizes mobile devices, a database, biometric authentication technology, and generative artificial intelligence.

[0454] Users create prescription data using a mobile device. The mobile device has a dedicated application installed, which users use to input necessary information such as name, address, medication name, and dosage. This input information is generated and displayed as a QR code.

[0455] The terminal receives a QR code from the user's mobile device. The pharmacy terminal scans this QR code to obtain the user's prescription data. The server performs biometric authentication based on the obtained prescription data. Facial recognition technology is implemented in this process, and the user's identity is verified using this authentication.

[0456] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. By analyzing historical data and predicting future demand fluctuations, it aims to optimize pharmaceutical supply. For example, the server can predict the demand for allergy medications during a specific season and use the results to notify pharmacies in advance, thereby reducing the risk of stockouts.

[0457] An example of a prompt would be, "Based on data from the past five years, predict the demand for allergy medication in this region for the next pollen season." Based on this prompt, generative artificial intelligence performs analysis and provides the necessary inventory information for the pharmacy. This enables efficient prescription management and supply of medications.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The user launches a dedicated app using a mobile device. The user enters their name, address, and medication information provided by their doctor (drug name, dosage, usage instructions, etc.) into the app. The entered data is converted into a QR code by the application. This QR code visually represents the prescription information in digital format.

[0461] Step 2:

[0462] The user presents the generated QR code to the pharmacy terminal. The terminal scans the QR code with its camera and extracts the prescription data contained within it. In this step, the string data within the QR code is decoded and passed to the terminal as prescription information.

[0463] Step 3:

[0464] The server receives prescription data sent from the terminal. At this time, the server requires the user's facial data for biometric authentication. Based on this input, the server uses facial recognition technology to check if it matches the facial data in the database. If the match is successful, identity verification is complete.

[0465] Step 4:

[0466] After biometric authentication is complete, the server sends instructions for dispensing medication to the terminal. In this process, a list of necessary medications is generated based on prescription data and transferred to the pharmacy terminal. The terminal displays the list, and the pharmacist prepares the medications according to the list.

[0467] Step 5:

[0468] The server uses a generative AI model to analyze historical prescription data. Specifically, it inputs regional and seasonal demand patterns into the model to predict future drug demand. In this process, it takes in a large amount of prescription data and outputs prediction results using an algorithm.

[0469] Step 6:

[0470] Based on predicted demand, the server assists pharmacies with inventory management. Using the forecast results, it provides pharmacies with guidance on optimal inventory levels. This enables pharmacies to ensure efficient supply of medicines while preventing excess inventory and stockouts.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0473] Traditional pharmaceutical supply systems suffered from significant problems such as drug depletion and waste due to excess inventory, highlighting the need for efficient inventory management. Furthermore, users had to visit pharmacies, making the purchasing and payment process inconvenient. There is a need to address these challenges and improve the efficiency of pharmaceutical distribution and user convenience.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes means for generating prescription information using a mobile device and storing it in a database, means for performing electronic payment based on electronic prescriptions using a smartphone device, and means for automatically charging at an appropriate price based on demand forecasts using a generating AI. This enables efficient inventory management, and allows users to purchase medicines in a convenient manner and make payments immediately.

[0476] A "mobile device" is a portable electronic device used for generating prescription information and transmitting data.

[0477] A "database" is an electronic system that enables the efficient storage and management of information and its retrieval when needed.

[0478] Biometric authentication is a technology that uses a user's physical characteristics to verify their identity, and it is a safe and reliable means of authentication.

[0479] A "terminal" refers to a computer device or interface that a user uses to perform information processing.

[0480] "Generative artificial intelligence" refers to machine learning or AI models that predict future events and demands based on past data.

[0481] "Regional or seasonal demand for pharmaceuticals" refers to an indicator that indicates the need for or demand for pharmaceuticals in a specific region or season.

[0482] A "smartphone" is a mobile phone with internet connectivity and an electronic device capable of running a variety of applications.

[0483] An "electronic prescription" is a system where paper prescriptions are managed as digital data and can be exchanged using QR codes or similar methods.

[0484] "Electronic payment" refers to a method of paying for goods and services online, using a smartphone or similar device.

[0485] "Automatic billing at a fair price" refers to a process of setting the optimal price based on demand forecasts and collecting fees from users accordingly.

[0486] To implement this invention, the user must first launch a dedicated application using a mobile device and input prescription information in digital format. This information is generated as a QR code, and the data is securely stored on a cloud-based database server. Ideally, a database capable of real-time data management, such as Firebase, should be used.

[0487] When a user purchases medication, they use a smartphone application to present a saved QR code to the pharmacy terminal. The terminal scans the QR code to retrieve the relevant prescription information from its database and performs biometric authentication. Google Firebase Authentication's facial recognition technology is used for biometric authentication. If authentication is successful, the terminal sends instructions to the pharmacy staff to dispense the medication.

[0488] Meanwhile, the server uses a generative AI model to forecast drug demand by region or season. This forecast is fed back into the inventory management system, enabling pharmacies to secure the optimal amount of medication when needed. Machine learning frameworks such as TensorFlow are suitable for the generative AI. For example, if increased demand for allergy medication is predicted during the spring pollen season, the server can notify pharmacies in advance of the appropriate inventory levels.

[0489] An example of a prompt message is, "For spring hay fever, predict and suggest the demand for necessary medications." This text can be input into the AI, which can then suggest appropriate quantities. This allows for efficient inventory management of medications and prevents unnecessary waste.

[0490] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0491] Step 1:

[0492] Users launch a dedicated app on their mobile device and enter their prescription information. This information includes the drug name, dosage, instructions for use, and the user's personal information. This data is generated as a QR code and sent to and stored in the Firebase database. This process ensures data centralization and secure storage.

[0493] Step 2:

[0494] When a user arrives at the pharmacy, they present a QR code displayed on their smartphone to the pharmacy's terminal. The terminal scans the QR code and retrieves the corresponding prescription information from the database. The retrieved data is then entered into the user's biometric authentication process. Biometric authentication uses facial recognition data and is performed using Firebase Authentication. Upon successful authentication, the user's identity is verified.

[0495] Step 3:

[0496] After authentication, the terminal generates medication management information based on prescription information retrieved from the database. This information is displayed to pharmacy staff as instructions, guiding them to retrieve the necessary medications. The process outputs instructions for staff and a list of medications. These instructions enable accurate and prompt medication provision.

[0497] Step 4:

[0498] The server uses a generative AI model to predict regional or seasonal drug demand based on stored historical prescription data. The input data consists of specific regions and historical seasonal trends, and the AI ​​model, using TensorFlow, performs data calculations. The output is a demand forecast, which is then fed back into the inventory management system.

[0499] Step 5:

[0500] Based on predicted demand, the server sends appropriate inventory adjustment instructions to the pharmacy's inventory management system. These instructions include specific drug names and required quantities. The system automatically replenishes or adjusts inventory according to these instructions. This process enables more efficient and less wasteful inventory management.

[0501] Step 6:

[0502] After the user receives their medication, electronic payment is made via a smartphone device. The input uses price information for the medication and a fair price based on demand forecasts. Payment is completed through a payment app on the smartphone. The output is a digital receipt confirming the transaction, sent to both the user and the pharmacy. This process ensures fast and transparent payment.

[0503] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0504] This invention is a system that provides a more personalized medical experience by combining a mobile device, a database, biometric authentication technology, generative AI, and an emotion engine.

[0505] First, the user creates a digital prescription using a mobile device. During this process, an emotion engine within the mobile device analyzes the user's facial expression data and recognizes their emotional state. This emotional state is added to the user's prescription information and sent to the server. The server receives this information, stores it in a database, and shares it with pharmacies and medical institutions as needed.

[0506] Next, when a user visits a pharmacy, the pharmacy's terminal scans the user's QR code. The server performs biometric authentication to verify the user's identity. The emotional state, as determined by the emotion engine, is reflected in the medication provided at the pharmacy, enabling pharmacy staff to respond appropriately to the user. For example, staff can treat a user who is feeling stressed with greater kindness.

[0507] Furthermore, the generative artificial intelligence analyzes emotional and regional data obtained from users to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information.

[0508] For example, if a user creates a prescription to address chronic pain, the system can analyze the user's emotional state from their facial expressions and, for instance, add information about tranquilizers to the prescription if they are experiencing high levels of anxiety. In this way, flexible medication provision tailored to the user's needs is achieved. Based on predictive data provided by the server, pharmacies can maintain inventory according to demand, preventing drug depletion and reducing waste.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user activates the mobile device, opens a dedicated application, and enters personal and prescription information. Simultaneously, the user's facial expressions are scanned by the camera.

[0512] Step 2:

[0513] The emotion engine analyzes the user's facial expression data to understand their emotional state in real time. This emotional information is then added to the prescription information.

[0514] Step 3:

[0515] The user generates prescription information and emotional information as a QR code and sends it to the server. The server stores the received information in a database.

[0516] Step 4:

[0517] When a user visits a pharmacy, the pharmacy's terminal scans the QR code presented by the user and sends the information to the server.

[0518] Step 5:

[0519] The server analyzes the QR code data, retrieves relevant user information and facial data from the database, and initiates the biometric authentication process.

[0520] Step 6:

[0521] The device scans the user's face again with its camera and verifies that it matches the facial data provided by the server. Once identity verification is complete, the server sends the results to the device.

[0522] Step 7:

[0523] If biometric authentication is successful, the terminal will follow instructions from the server, execute a medication dispensing process that takes the user's emotional information into account, and prepare the necessary medications.

[0524] Step 8:

[0525] The generative artificial intelligence analyzes accumulated emotional and regional data to predict future demand for pharmaceuticals. This allows the server to provide pharmacies with appropriate inventory management information.

[0526] Step 9:

[0527] Pharmacies use information from servers to adjust inventory and achieve efficient supply and management of medications.

[0528] (Example 2)

[0529] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0530] In modern healthcare services, there is a challenge in providing sufficient personalized care based on the individual needs and emotional states of users. In particular, there is a need to effectively manage drug inventory by analyzing user emotions during prescription creation and by forecasting demand in real time.

[0531] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0532] In this invention, the server includes means for generating prescription information using a mobile device and analyzing the user's emotional state, means for storing the prescription information including the analysis results in a database and sharing it with other institutions as needed, and means for adjusting the response to the user based on the emotional state. This enables personalized medical care tailored to the user's emotional state and highly accurate drug inventory management.

[0533] A "mobile device" is a portable electronic device that serves as a terminal for users to input information or create prescriptions.

[0534] "Prescription information" refers to information about medication prescriptions issued by medical institutions, including the user's symptoms and desired medications.

[0535] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes emotions such as joy and anxiety.

[0536] A "database" is a system for systematically storing and managing information, such as prescription information and emotional data.

[0537] "Biometric authentication" is a technology that uses an individual's biometric information to verify the identity of a user.

[0538] "Generative artificial intelligence" is a type of artificial intelligence that uses machine learning techniques to analyze data and support predictions and decision-making.

[0539] "Inventory management" is the process of understanding the inventory status of medicines and products and managing them to ensure that an appropriate supply is maintained.

[0540] An "identification code" is a code that encodes unique information and is used for data transmission and identity verification.

[0541] This invention comprises a system combining a mobile device, a database, biometric authentication technology, a generative artificial intelligence model, and an emotion engine. This system provides users with a more personalized medical experience.

[0542] The user operates a mobile device and launches a dedicated application. This application creates a digital prescription by allowing the user to input information about their current symptoms and desired medications. An emotion engine built into the mobile device uses a camera to collect and analyze the user's facial expression data to determine their emotional state. This analyzed emotional data is added to the prescription information in real time and transmitted to the server.

[0543] The server receives prescription information and emotional data submitted by users and stores it in a database. This information may be shared with healthcare institutions and pharmacies. This sharing allows staff at pharmacies and healthcare institutions to understand the user's emotional state and suggest more appropriate responses.

[0544] In a pharmacy, a terminal scans an identification code presented by the user. The server uses biometric authentication technology to verify the user's identity. This verification method often utilizes facial recognition technology. This ensures that the pharmacy can reliably verify the user's identity, and based on this, they can provide support using emotional data. For example, if a user is feeling anxious, pharmacy staff will strive to provide kind and reassuring guidance.

[0545] Furthermore, by employing a generative artificial intelligence model, the server analyzes user sentiment data and regional data to predict future demand for pharmaceuticals. This provides pharmacies with accurate information for inventory management. For example, it becomes possible to predict the demand for cold medicines during the winter season, enabling pharmacies to implement efficient and waste-free inventory management.

[0546] As a concrete example, suppose a user suffers from chronic headaches. A prescription is created using a mobile device, and if the user's facial expression indicates that relaxation is needed, information about tranquilizers is added to the prescription. This information is sent to a server, and the pharmacy provides the user with the appropriate medication.

[0547] Examples of prompts include, "Based on local sentiment data, predict the next month's drug demand," and "Analyze the user's emotional state from their facial expression data and provide drug information to suggest." Inputting such prompts into an AI model facilitates data analysis.

[0548] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0549] Step 1:

[0550] The user creates a digital prescription using a mobile device. The user launches the application and inputs their symptoms and desired medications. During this process, the mobile device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data. The input consists of the user's symptoms, a list of desired medications, and facial expression data, while the output is prescription information with the emotion analysis results added. The emotion analysis identifies emotional states, such as "anxiety."

[0551] Step 2:

[0552] The mobile device transmits generated prescription information and sentiment data to a server. The input is prescription information integrated with sentiment analysis results, and the output is data transmission to the server. This information reaches the server and is securely registered in the database. The transmitted data is stored for later pharmacy operations and verification by healthcare institutions.

[0553] Step 3:

[0554] When a user visits a pharmacy, a terminal scans the user's QR code. The terminal sends the scan result to a server, which retrieves the corresponding prescription information from a database based on the scanned data. The input is the user's QR code information, and the output is the identified prescription information and sentiment data. The server performs a database check and sends the medication information requested by the user to the terminal.

[0555] Step 4:

[0556] The server performs biometric authentication to verify the user's identity. The input is user information corresponding to a QR code, and the output is the biometric authentication result. Specifically, facial recognition technology is used to check the user's facial image and determine if it matches. If the identity verification is successful, the pharmacy can obtain the user's emotional data and use it to inform the provision of medication.

[0557] Step 5:

[0558] Pharmacy staff provide personalized service to users based on emotional data displayed on a terminal. Input is prescription information including emotional status, and output is specific action plans for the user. For example, staff are recommended to provide particularly empathetic support to users who are experiencing stress.

[0559] Step 6:

[0560] The server uses generative artificial intelligence to analyze user sentiment data and regional drug demand data. The input is historical sentiment data and regional data, and the output is a forecast of future drug demand. Through the generative AI model, it predicts drug demand trends for the following month and provides the results to pharmacies. This enables pharmacies to properly manage inventory and prevent shortages or surpluses.

[0561] (Application Example 2)

[0562] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0563] Traditional medical services and medication provision lack personalization that takes into account the emotional state of the user, resulting in inadequately alleviating the stress and anxiety experienced by users. Furthermore, pharmacy staff lack sufficient information to select appropriate customer service methods, making it difficult to improve user satisfaction.

[0564] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0565] In this invention, the server includes means for generating prescription data using a mobile information processing device and storing the data on an information recording medium, means for analyzing the user's emotional state and proposing customer service methods based on that emotional state, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This makes it possible to provide a personalized medical experience that responds to the user's emotions, and enables pharmacy staff to improve user satisfaction by providing appropriate customer service.

[0566] A "mobile information processing device" is a portable electronic device that allows users to input, store, and transmit data.

[0567] "Prescription data" refers to digital data containing information about medications and treatment methods prescribed by medical institutions to patients.

[0568] An "information recording medium" is a digital data recording device that can store data and retrieve it as needed.

[0569] "Biometric authentication" is a technology that uses a user's physical characteristics to verify their identity.

[0570] "Pharmaceutical management data" refers to data related to the management of pharmaceuticals, such as inventory status, dispensing instructions, and consumption trends.

[0571] A "terminal device" is an electronic device used by a user to receive or process data.

[0572] "Generative artificial intelligence" refers to computational modeling technology that analyzes information based on given data and generates new data or predictions.

[0573] "Emotional state" refers to the psychological state that can be interpreted from the user's facial expressions and behavior.

[0574] "Customer service methods" refer to the methods and guidelines for how to interact with customers or users.

[0575] To realize this invention, it is necessary to construct a system involving multiple hardware and software components. First, the user generates their prescription data using a mobile information processing device and stores it on an information recording medium. Next, this prescription data is received by a server, which performs biometric authentication of the user to ensure the accuracy of the information. Facial recognition technology is used for authentication.

[0576] Subsequently, the server uses an emotion engine to analyze the user's facial expression data and identify their emotional state. This analysis result is then input into a generative AI model. Based on the emotional state, the generative AI model proposes customer service methods. Based on this, guidelines for customer service methods are displayed on the terminal device, allowing store staff to provide the most appropriate service.

[0577] The server further uses generative artificial intelligence to predict regional or seasonal drug demand. This predicted data is provided to terminal devices to help optimize pharmacy inventory management. For example, the server analyzes infectious disease outbreak data and seasonal changes in each region to suggest the types and quantities of drugs that are expected to be in demand next.

[0578] For example, when a user visits a pharmacy and the emotion engine detects feelings of anxiety, the generating AI model will generate prompts recommending that store staff provide thorough explanations and reassurance. Examples of these prompts include, "Identify the user's emotional state from their facial expression data and suggest appropriate customer service methods," or "Please tell me what to do when the user is in (emotional state)." This allows users to receive more attentive and personalized medical services.

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The user uses a mobile data processing device to input prescription data. The prescription data is digitized within the mobile device and stored on a data storage medium. The input data includes drug name, dosage, and duration of use. Output is the transmission of the prescription data from the mobile device to the server.

[0582] Step 2:

[0583] The server analyzes the received prescription data and performs biometric authentication. It uses facial recognition technology to verify the user's identity and authenticate that they are the correct user. The input consists of prescription data and the user's facial information, and the output is the user authentication result.

[0584] Step 3:

[0585] The server uses an emotion engine to analyze facial expression data and identify the user's emotional state. Emotional states are classified into categories such as joy, sadness, stress, and anxiety. The input is facial expression data, and the output is the analyzed emotional state.

[0586] Step 4:

[0587] The server inputs the emotional state into a generative AI model, which then derives the optimal customer service method. Based on the previously analyzed emotional state, the generative AI model generates a prompt message and sends it to the terminal device. The input is the emotional state, and the output is a prompt message describing the customer service method.

[0588] Step 5:

[0589] The terminal device visualizes and presents the received prompt message to the staff. Based on the visualized instructions, the staff provides the most appropriate customer service to the user. The input is the prompt message, and the output is the actual customer service action taken by the staff.

[0590] Step 6:

[0591] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. The server takes regional infectious disease data and seasonal demand patterns as input and makes predictions based on this data. The output is the type and quantity of pharmaceuticals needed in the future.

[0592] Step 7:

[0593] The server provides drug management data to terminal devices based on predictive data. The terminal devices use this data to manage store inventory. The input is predictive data, and the output is inventory management instructions or improvement suggestions.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] One embodiment of the present invention is based on a system combining a mobile device, a database, biometric authentication technology, and generative artificial intelligence to solve the problems of drug depletion and waste in the medical industry.

[0612] First, the user creates a digital prescription using a mobile device. The user opens the mobile app and enters the necessary personal and prescription information. This information generates prescription information as a QR code and is sent from the mobile device to the server. The server stores the received information in a database and enables information exchange with medical institutions and pharmacies as needed.

[0613] When a user visits the pharmacy, the terminal (pharmacy system) scans a QR code presented on the user's mobile device. Next, the server obtains the user's facial data based on the QR code and verifies their identity using biometric authentication technology. Once verification is complete, the server sends instructions for dispensing medication to the pharmacy system. The pharmacy terminal follows these instructions, prepares the necessary medication, and provides it to the user.

[0614] Furthermore, the generating artificial intelligence analyzes historical prescription data related to each region and season stored on the server to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information and support efficient drug distribution. This system enables the proper supply of drugs, prevents drug depletion, and reduces waste due to excess inventory.

[0615] As a concrete example, a user digitally handles prescriptions to address seasonal allergies, and a pharmacy terminal efficiently dispenses medication using information from a server. In this case, artificial intelligence can predict the demand for allergy medications in the area and notify pharmacies in advance, creating an environment where necessary medications are always supplied appropriately.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The user launches a dedicated app on their mobile device and enters their personal information and prescription details. The app then generates a QR code from this information.

[0619] Step 2:

[0620] The user uses the generated QR code to send prescription information to the server. The server receives this information and securely stores it in its database.

[0621] Step 3:

[0622] When a user visits a pharmacy, the pharmacy's terminal scans a QR code presented on the user's mobile device. The scanned data is then sent to a server.

[0623] Step 4:

[0624] The server retrieves the user's facial data from the database based on the QR code data and initiates the process for biometric authentication.

[0625] Step 5:

[0626] The device scans the user's face with its camera and checks if it matches the facial recognition model provided by the server. The server then determines the authentication result.

[0627] Step 6:

[0628] If authentication is successful, the server sends information for drug provision to the terminal. Based on this information, the terminal prepares the necessary drugs for the user.

[0629] Step 7:

[0630] The generative artificial intelligence analyzes regional and seasonal drug demand from data stored on the server and creates future demand forecasts.

[0631] Step 8:

[0632] The server provides the generated demand forecasts to the pharmacy, helping pharmacy staff implement appropriate inventory management. This information is used to adjust the pharmacy's inventory.

[0633] (Example 1)

[0634] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] In the modern medical industry, the problems of drug shortages and waste due to excess inventory are serious. In particular, appropriate supply tailored to regional and seasonal needs is difficult, leading to the waste of medical resources. Furthermore, efficient and secure methods for managing information and verifying the identity of prescriptions are required.

[0636] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0637] In this invention, the server includes means for generating prescription data using a mobile device and storing the data in a storage device, means for performing biometric authentication of the user based on the prescription data, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This enables proper supply management of pharmaceuticals and efficient identity verification.

[0638] A "mobile device" is a portable communication device that is an electronic device used by users to input and operate information and to send and receive data over a network.

[0639] "Prescription data" refers to digital information that includes the name of the medication, dosage, administration method, and duration of administration, as specified by a medical professional.

[0640] A "storage device" is a hardware component or medium for holding digital data, enabling the long-term storage of information.

[0641] "Biometric authentication" is a technology that identifies individuals based on their biological characteristics, and generally includes facial recognition, fingerprint authentication, and iris recognition.

[0642] An "information processing device" is an electronic device that inputs, processes, and outputs data, and usually refers to a computer or its peripherals.

[0643] "Generative artificial intelligence" is an artificial intelligence technology that uses algorithms and models to learn from past data and predict future events and demands.

[0644] A "two-dimensional code" is a code that visually encodes information and is in a format that can be scanned by a reading device, such as a QR code or a barcode.

[0645] "Facial recognition technology" is a technology that identifies individuals by analyzing the features of each face and comparing them with existing facial data in a database.

[0646] This invention is a system designed to improve the efficiency of drug supply in healthcare. The system utilizes mobile devices, a database, biometric authentication technology, and generative artificial intelligence.

[0647] Users create prescription data using a mobile device. The mobile device has a dedicated application installed, which users use to input necessary information such as name, address, medication name, and dosage. This input information is generated and displayed as a QR code.

[0648] The terminal receives a QR code from the user's mobile device. The pharmacy terminal scans this QR code to obtain the user's prescription data. The server performs biometric authentication based on the obtained prescription data. Facial recognition technology is implemented in this process, and the user's identity is verified using this authentication.

[0649] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. By analyzing historical data and predicting future demand fluctuations, it aims to optimize pharmaceutical supply. For example, the server can predict the demand for allergy medications during a specific season and use the results to notify pharmacies in advance, thereby reducing the risk of stockouts.

[0650] An example of a prompt would be, "Based on data from the past five years, predict the demand for allergy medication in this region for the next pollen season." Based on this prompt, generative artificial intelligence performs analysis and provides the necessary inventory information for the pharmacy. This enables efficient prescription management and supply of medications.

[0651] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0652] Step 1:

[0653] The user launches a dedicated app using a mobile device. The user enters their name, address, and medication information provided by their doctor (drug name, dosage, usage instructions, etc.) into the app. The entered data is converted into a QR code by the application. This QR code visually represents the prescription information in digital format.

[0654] Step 2:

[0655] The user presents the generated QR code to the pharmacy terminal. The terminal scans the QR code with its camera and extracts the prescription data contained within it. In this step, the string data within the QR code is decoded and passed to the terminal as prescription information.

[0656] Step 3:

[0657] The server receives prescription data sent from the terminal. At this time, the server requires the user's facial data for biometric authentication. Based on this input, the server uses facial recognition technology to check if it matches the facial data in the database. If the match is successful, identity verification is complete.

[0658] Step 4:

[0659] After biometric authentication is complete, the server sends instructions for dispensing medication to the terminal. In this process, a list of necessary medications is generated based on prescription data and transferred to the pharmacy terminal. The terminal displays the list, and the pharmacist prepares the medications according to the list.

[0660] Step 5:

[0661] The server uses a generative AI model to analyze historical prescription data. Specifically, it inputs regional and seasonal demand patterns into the model to predict future drug demand. In this process, it takes in a large amount of prescription data and outputs prediction results using an algorithm.

[0662] Step 6:

[0663] Based on predicted demand, the server assists pharmacies with inventory management. Using the forecast results, it provides pharmacies with guidance on optimal inventory levels. This enables pharmacies to ensure efficient supply of medicines while preventing excess inventory and stockouts.

[0664] (Application Example 1)

[0665] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] Traditional pharmaceutical supply systems suffered from significant problems such as drug depletion and waste due to excess inventory, highlighting the need for efficient inventory management. Furthermore, users had to visit pharmacies, making the purchasing and payment process inconvenient. There is a need to address these challenges and improve the efficiency of pharmaceutical distribution and user convenience.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0668] In this invention, the server includes means for generating prescription information using a mobile device and storing it in a database, means for performing electronic payment based on electronic prescriptions using a smartphone device, and means for automatically charging at an appropriate price based on demand forecasts using a generating AI. This enables efficient inventory management, and allows users to purchase medicines in a convenient manner and make payments immediately.

[0669] A "mobile device" is a portable electronic device used for generating prescription information and transmitting data.

[0670] A "database" is an electronic system that enables the efficient storage and management of information and its retrieval when needed.

[0671] Biometric authentication is a technology that uses a user's physical characteristics to verify their identity, and it is a safe and reliable means of authentication.

[0672] A "terminal" refers to a computer device or interface that a user uses to perform information processing.

[0673] "Generative artificial intelligence" refers to machine learning or AI models that predict future events and demands based on past data.

[0674] "Regional or seasonal demand for pharmaceuticals" refers to an indicator that indicates the need for or demand for pharmaceuticals in a specific region or season.

[0675] A "smartphone" is a mobile phone with internet connectivity and an electronic device capable of running a variety of applications.

[0676] An "electronic prescription" is a system where paper prescriptions are managed as digital data and can be exchanged using QR codes or similar methods.

[0677] "Electronic payment" refers to a method of paying for goods and services online, using a smartphone or similar device.

[0678] "Automatic billing at a fair price" refers to a process of setting the optimal price based on demand forecasts and collecting fees from users accordingly.

[0679] To implement this invention, the user must first launch a dedicated application using a mobile device and input prescription information in digital format. This information is generated as a QR code, and the data is securely stored on a cloud-based database server. Ideally, a database capable of real-time data management, such as Firebase, should be used.

[0680] When a user purchases medication, they use a smartphone application to present a saved QR code to the pharmacy terminal. The terminal scans the QR code to retrieve the relevant prescription information from its database and performs biometric authentication. Google Firebase Authentication's facial recognition technology is used for biometric authentication. If authentication is successful, the terminal sends instructions to the pharmacy staff to dispense the medication.

[0681] Meanwhile, the server uses a generative AI model to forecast drug demand by region or season. This forecast is fed back into the inventory management system, enabling pharmacies to secure the optimal amount of medication when needed. Machine learning frameworks such as TensorFlow are suitable for the generative AI. For example, if increased demand for allergy medication is predicted during the spring pollen season, the server can notify pharmacies in advance of the appropriate inventory levels.

[0682] An example of a prompt message is, "For spring hay fever, predict and suggest the demand for necessary medications." This text can be input into the AI, which can then suggest appropriate quantities. This allows for efficient inventory management of medications and prevents unnecessary waste.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] Users launch a dedicated app on their mobile device and enter their prescription information. This information includes the drug name, dosage, instructions for use, and the user's personal information. This data is generated as a QR code and sent to and stored in the Firebase database. This process ensures data centralization and secure storage.

[0686] Step 2:

[0687] When a user arrives at the pharmacy, they present a QR code displayed on their smartphone to the pharmacy's terminal. The terminal scans the QR code and retrieves the corresponding prescription information from the database. The retrieved data is then entered into the user's biometric authentication process. Biometric authentication uses facial recognition data and is performed using Firebase Authentication. Upon successful authentication, the user's identity is verified.

[0688] Step 3:

[0689] After authentication, the terminal generates medication management information based on prescription information retrieved from the database. This information is displayed to pharmacy staff as instructions, guiding them to retrieve the necessary medications. The process outputs instructions for staff and a list of medications. These instructions enable accurate and prompt medication provision.

[0690] Step 4:

[0691] The server uses a generative AI model to predict regional or seasonal drug demand based on stored historical prescription data. The input data consists of specific regions and historical seasonal trends, and the AI ​​model, using TensorFlow, performs data calculations. The output is a demand forecast, which is then fed back into the inventory management system.

[0692] Step 5:

[0693] Based on predicted demand, the server sends appropriate inventory adjustment instructions to the pharmacy's inventory management system. These instructions include specific drug names and required quantities. The system automatically replenishes or adjusts inventory according to these instructions. This process enables more efficient and less wasteful inventory management.

[0694] Step 6:

[0695] After the user receives their medication, electronic payment is made via a smartphone device. The input uses price information for the medication and a fair price based on demand forecasts. Payment is completed through a payment app on the smartphone. The output is a digital receipt confirming the transaction, sent to both the user and the pharmacy. This process ensures fast and transparent payment.

[0696] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0697] This invention is a system that provides a more personalized medical experience by combining a mobile device, a database, biometric authentication technology, generative AI, and an emotion engine.

[0698] First, the user creates a digital prescription using a mobile device. During this process, an emotion engine within the mobile device analyzes the user's facial expression data and recognizes their emotional state. This emotional state is added to the user's prescription information and sent to the server. The server receives this information, stores it in a database, and shares it with pharmacies and medical institutions as needed.

[0699] Next, when a user visits a pharmacy, the pharmacy's terminal scans the user's QR code. The server performs biometric authentication to verify the user's identity. The emotional state, as determined by the emotion engine, is reflected in the medication provided at the pharmacy, enabling pharmacy staff to respond appropriately to the user. For example, staff can treat a user who is feeling stressed with greater kindness.

[0700] Furthermore, the generative artificial intelligence analyzes emotional and regional data obtained from users to predict future drug demand. This allows the server to provide pharmacies with appropriate inventory management information.

[0701] For example, if a user creates a prescription to address chronic pain, the system can analyze the user's emotional state from their facial expressions and, for instance, add information about tranquilizers to the prescription if they are experiencing high levels of anxiety. In this way, flexible medication provision tailored to the user's needs is achieved. Based on predictive data provided by the server, pharmacies can maintain inventory according to demand, preventing drug depletion and reducing waste.

[0702] The following describes the processing flow.

[0703] Step 1:

[0704] The user activates the mobile device, opens a dedicated application, and enters personal and prescription information. Simultaneously, the user's facial expressions are scanned by the camera.

[0705] Step 2:

[0706] The emotion engine analyzes the user's facial expression data to understand their emotional state in real time. This emotional information is then added to the prescription information.

[0707] Step 3:

[0708] The user generates prescription information and emotional information as a QR code and sends it to the server. The server stores the received information in a database.

[0709] Step 4:

[0710] When a user visits a pharmacy, the pharmacy's terminal scans the QR code presented by the user and sends the information to the server.

[0711] Step 5:

[0712] The server analyzes the QR code data, retrieves relevant user information and facial data from the database, and initiates the biometric authentication process.

[0713] Step 6:

[0714] The device scans the user's face again with its camera and verifies that it matches the facial data provided by the server. Once identity verification is complete, the server sends the results to the device.

[0715] Step 7:

[0716] If biometric authentication is successful, the terminal will follow instructions from the server, execute a medication dispensing process that takes the user's emotional information into account, and prepare the necessary medications.

[0717] Step 8:

[0718] The generative artificial intelligence analyzes accumulated emotional and regional data to predict future demand for pharmaceuticals. This allows the server to provide pharmacies with appropriate inventory management information.

[0719] Step 9:

[0720] Pharmacies use information from servers to adjust inventory and achieve efficient supply and management of medications.

[0721] (Example 2)

[0722] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] In modern healthcare services, there is a challenge in providing sufficient personalized care based on the individual needs and emotional states of users. In particular, there is a need to effectively manage drug inventory by analyzing user emotions during prescription creation and by forecasting demand in real time.

[0724] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0725] In this invention, the server includes means for generating prescription information using a mobile device and analyzing the user's emotional state, means for storing the prescription information including the analysis results in a database and sharing it with other institutions as needed, and means for adjusting the response to the user based on the emotional state. This enables personalized medical care tailored to the user's emotional state and highly accurate drug inventory management.

[0726] A "mobile device" is a portable electronic device that serves as a terminal for users to input information or create prescriptions.

[0727] "Prescription information" refers to information about medication prescriptions issued by medical institutions, including the user's symptoms and desired medications.

[0728] "Emotional state" refers to the psychological state analyzed from the user's facial expressions and voice, and includes emotions such as joy and anxiety.

[0729] A "database" is a system for systematically storing and managing information, such as prescription information and emotional data.

[0730] "Biometric authentication" is a technology that uses an individual's biometric information to verify the identity of a user.

[0731] "Generative artificial intelligence" is a type of artificial intelligence that uses machine learning techniques to analyze data and support predictions and decision-making.

[0732] "Inventory management" is the process of understanding the inventory status of medicines and products and managing them to ensure that an appropriate supply is maintained.

[0733] An "identification code" is a code that encodes unique information and is used for data transmission and identity verification.

[0734] This invention comprises a system combining a mobile device, a database, biometric authentication technology, a generative artificial intelligence model, and an emotion engine. This system provides users with a more personalized medical experience.

[0735] The user operates a mobile device and launches a dedicated application. This application creates a digital prescription by allowing the user to input information about their current symptoms and desired medications. An emotion engine built into the mobile device uses a camera to collect and analyze the user's facial expression data to determine their emotional state. This analyzed emotional data is added to the prescription information in real time and transmitted to the server.

[0736] The server receives prescription information and emotional data submitted by users and stores it in a database. This information may be shared with healthcare institutions and pharmacies. This sharing allows staff at pharmacies and healthcare institutions to understand the user's emotional state and suggest more appropriate responses.

[0737] In a pharmacy, a terminal scans an identification code presented by the user. The server uses biometric authentication technology to verify the user's identity. This verification method often utilizes facial recognition technology. This ensures that the pharmacy can reliably verify the user's identity, and based on this, they can provide support using emotional data. For example, if a user is feeling anxious, pharmacy staff will strive to provide kind and reassuring guidance.

[0738] Furthermore, by employing a generative artificial intelligence model, the server analyzes user sentiment data and regional data to predict future demand for pharmaceuticals. This provides pharmacies with accurate information for inventory management. For example, it becomes possible to predict the demand for cold medicines during the winter season, enabling pharmacies to implement efficient and waste-free inventory management.

[0739] As a concrete example, suppose a user suffers from chronic headaches. A prescription is created using a mobile device, and if the user's facial expression indicates that relaxation is needed, information about tranquilizers is added to the prescription. This information is sent to a server, and the pharmacy provides the user with the appropriate medication.

[0740] Examples of prompts include, "Based on local sentiment data, predict the next month's drug demand," and "Analyze the user's emotional state from their facial expression data and provide drug information to suggest." Inputting such prompts into an AI model facilitates data analysis.

[0741] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0742] Step 1:

[0743] The user creates a digital prescription using a mobile device. The user launches the application and inputs their symptoms and desired medications. During this process, the mobile device uses its camera to capture the user's facial expressions, and an emotion engine analyzes this data. The input consists of the user's symptoms, a list of desired medications, and facial expression data, while the output is prescription information with the emotion analysis results added. The emotion analysis identifies emotional states, such as "anxiety."

[0744] Step 2:

[0745] The mobile device transmits generated prescription information and sentiment data to a server. The input is prescription information integrated with sentiment analysis results, and the output is data transmission to the server. This information reaches the server and is securely registered in the database. The transmitted data is stored for later pharmacy operations and verification by healthcare institutions.

[0746] Step 3:

[0747] When a user visits a pharmacy, a terminal scans the user's QR code. The terminal sends the scan result to a server, which retrieves the corresponding prescription information from a database based on the scanned data. The input is the user's QR code information, and the output is the identified prescription information and sentiment data. The server performs a database check and sends the medication information requested by the user to the terminal.

[0748] Step 4:

[0749] The server performs biometric authentication to verify the user's identity. The input is user information corresponding to a QR code, and the output is the biometric authentication result. Specifically, facial recognition technology is used to check the user's facial image and determine if it matches. If the identity verification is successful, the pharmacy can obtain the user's emotional data and use it to inform the provision of medication.

[0750] Step 5:

[0751] Pharmacy staff provide personalized service to users based on emotional data displayed on a terminal. Input is prescription information including emotional status, and output is specific action plans for the user. For example, staff are recommended to provide particularly empathetic support to users who are experiencing stress.

[0752] Step 6:

[0753] The server uses generative artificial intelligence to analyze user sentiment data and regional drug demand data. The input is historical sentiment data and regional data, and the output is a forecast of future drug demand. Through the generative AI model, it predicts drug demand trends for the following month and provides the results to pharmacies. This enables pharmacies to properly manage inventory and prevent shortages or surpluses.

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0756] Traditional medical services and medication provision lack personalization that takes into account the emotional state of the user, resulting in inadequately alleviating the stress and anxiety experienced by users. Furthermore, pharmacy staff lack sufficient information to select appropriate customer service methods, making it difficult to improve user satisfaction.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes means for generating prescription data using a mobile information processing device and storing the data on an information recording medium, means for analyzing the user's emotional state and proposing customer service methods based on that emotional state, and means for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence. This makes it possible to provide a personalized medical experience that responds to the user's emotions, and enables pharmacy staff to improve user satisfaction by providing appropriate customer service.

[0759] A "mobile information processing device" is a portable electronic device that allows users to input, store, and transmit data.

[0760] "Prescription data" refers to digital data containing information about medications and treatment methods prescribed by medical institutions to patients.

[0761] An "information recording medium" is a digital data recording device that can store data and retrieve it as needed.

[0762] "Biometric authentication" is a technology that uses a user's physical characteristics to verify their identity.

[0763] "Pharmaceutical management data" refers to data related to the management of pharmaceuticals, such as inventory status, dispensing instructions, and consumption trends.

[0764] A "terminal device" is an electronic device used by a user to receive or process data.

[0765] "Generative artificial intelligence" refers to computational modeling technology that analyzes information based on given data and generates new data or predictions.

[0766] "Emotional state" refers to the psychological state that can be interpreted from the user's facial expressions and behavior.

[0767] "Customer service methods" refer to the methods and guidelines for how to interact with customers or users.

[0768] To realize this invention, it is necessary to construct a system involving multiple hardware and software components. First, the user generates their prescription data using a mobile information processing device and stores it on an information recording medium. Next, this prescription data is received by a server, which performs biometric authentication of the user to ensure the accuracy of the information. Facial recognition technology is used for authentication.

[0769] Subsequently, the server uses an emotion engine to analyze the user's facial expression data and identify their emotional state. This analysis result is then input into a generative AI model. Based on the emotional state, the generative AI model proposes customer service methods. Based on this, guidelines for customer service methods are displayed on the terminal device, allowing store staff to provide the most appropriate service.

[0770] The server further uses generative artificial intelligence to predict regional or seasonal drug demand. This predicted data is provided to terminal devices to help optimize pharmacy inventory management. For example, the server analyzes infectious disease outbreak data and seasonal changes in each region to suggest the types and quantities of drugs that are expected to be in demand next.

[0771] For example, when a user visits a pharmacy and the emotion engine detects feelings of anxiety, the generating AI model will generate prompts recommending that store staff provide thorough explanations and reassurance. Examples of these prompts include, "Identify the user's emotional state from their facial expression data and suggest appropriate customer service methods," or "Please tell me what to do when the user is in (emotional state)." This allows users to receive more attentive and personalized medical services.

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The user uses a mobile data processing device to input prescription data. The prescription data is digitized within the mobile device and stored on a data storage medium. The input data includes drug name, dosage, and duration of use. Output is the transmission of the prescription data from the mobile device to the server.

[0775] Step 2:

[0776] The server analyzes the received prescription data and performs biometric authentication. It uses facial recognition technology to verify the user's identity and authenticate that they are the correct user. The input consists of prescription data and the user's facial information, and the output is the user authentication result.

[0777] Step 3:

[0778] The server uses an emotion engine to analyze facial expression data and identify the user's emotional state. Emotional states are classified into categories such as joy, sadness, stress, and anxiety. The input is facial expression data, and the output is the analyzed emotional state.

[0779] Step 4:

[0780] The server inputs the emotional state into a generative AI model, which then derives the optimal customer service method. Based on the previously analyzed emotional state, the generative AI model generates a prompt message and sends it to the terminal device. The input is the emotional state, and the output is a prompt message describing the customer service method.

[0781] Step 5:

[0782] The terminal device visualizes and presents the received prompt message to the staff. Based on the visualized instructions, the staff provides the most appropriate customer service to the user. The input is the prompt message, and the output is the actual customer service action taken by the staff.

[0783] Step 6:

[0784] The server uses generative artificial intelligence to predict regional or seasonal demand for pharmaceuticals. The server takes regional infectious disease data and seasonal demand patterns as input and makes predictions based on this data. The output is the type and quantity of pharmaceuticals needed in the future.

[0785] Step 7:

[0786] The server provides drug management data to terminal devices based on predictive data. The terminal devices use this data to manage store inventory. The input is predictive data, and the output is inventory management instructions or improvement suggestions.

[0787] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0791] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0792] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0793] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0794] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0795] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0796] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0800] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0801] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0802] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0803] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0804] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0805] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0806] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0807] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means for generating prescription information using a mobile device and storing the information in a database,

[0811] A means for performing biometric authentication of the user based on the said prescription information,

[0812] A means for providing drug management information to a terminal based on the results of the biometric authentication, and for the terminal to issue instructions for dispensing the drug,

[0813] A means of predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence,

[0814] A means to support drug inventory management based on the said forecast,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, wherein the mobile device comprises means for generating a two-dimensional code and transmitting information using the two-dimensional code.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the biometric authentication means includes means for verifying the identity of the user using facial recognition technology.

[0820] "Example 1"

[0821] (Claim 1)

[0822] A means for generating prescription data using a mobile device and storing said data in a storage device,

[0823] A means for performing biometric authentication of the user based on the prescription data,

[0824] A means for providing drug control information to an information processing device based on the results of the biometric authentication, and for the information processing device to issue instructions for supplying the drug,

[0825] A method for predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence,

[0826] A means to support drug inventory control based on the said prediction,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the mobile device comprises means for generating a two-dimensional code and transmitting data using the two-dimensional code.

[0830] (Claim 3)

[0831] The system according to claim 1, characterized in that the biometric authentication means includes means for verifying the identity of a user using facial recognition technology.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] A means for generating prescription information using a mobile device and storing the information in a database,

[0835] A means for performing biometric authentication of the user based on the said prescription information,

[0836] A means for providing drug management information to a terminal based on the results of the biometric authentication, and for the terminal to issue instructions for dispensing the drug,

[0837] A means of predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence,

[0838] A means to support drug inventory management based on the said forecast,

[0839] A means of making electronic payments based on electronic prescriptions using a smartphone device,

[0840] A method for automatically charging based on demand forecasts using generation AI,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the mobile device comprises means for generating a two-dimensional code and transmitting information using the two-dimensional code.

[0844] (Claim 3)

[0845] The system according to claim 1, characterized in that the biometric authentication means includes means for verifying the identity of the user using facial recognition technology.

[0846] "Example 2 of combining an emotion engine"

[0847] (Claim 1)

[0848] A means for generating prescription information using a mobile device and analyzing the user's emotional state,

[0849] A means of storing prescription information, including the analysis results, in a database and sharing it with other institutions as needed,

[0850] A means of adjusting responses to users based on their emotional state,

[0851] A means of verifying the user's identity by performing biometric authentication,

[0852] A means of using artificial intelligence to predict regional or seasonal demand for pharmaceuticals and supporting pharmaceutical inventory management based on said predictions,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the mobile device comprises means for generating an identification code and transmitting data using the identification code.

[0856] (Claim 3)

[0857] The system according to claim 1, characterized in that the biometric authentication means includes means for verifying the identity of the user using authentication technology.

[0858] "Application example 2 when combining with an emotional engine"

[0859] (Claim 1)

[0860] A means for generating prescription data using a mobile information processing device and storing said data on an information recording medium,

[0861] A means for performing biometric authentication of the user based on the said prescription data,

[0862] A means for providing drug management data to a terminal device based on the results of the biometric authentication, and for the terminal device to issue instructions for dispensing the drug,

[0863] A means of predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence,

[0864] A means to support drug inventory management based on the said forecast,

[0865] A means for analyzing the emotional state of a user and proposing customer service methods based on that emotional state,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the mobile information processing device comprises means for generating a two-dimensional code and transmitting information using the two-dimensional code.

[0869] (Claim 3)

[0870] The system according to claim 1, characterized in that the biometric authentication means includes means for verifying the identity of a user using facial recognition technology. [Explanation of Symbols]

[0871] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating prescription information using a mobile device and storing the information in a database, A means for performing biometric authentication of the user based on the said prescription information, A means for providing drug management information to a terminal based on the results of the biometric authentication, and for the terminal to issue instructions for dispensing the drug, A means of predicting regional or seasonal demand for pharmaceuticals using generative artificial intelligence, A means to support drug inventory management based on the said forecast, A system that includes this.

2. The system according to claim 1, wherein the mobile device comprises means for generating a two-dimensional code and transmitting information using the two-dimensional code.

3. The system according to claim 1, characterized in that the biometric authentication means includes means for verifying the identity of the user using facial recognition technology.

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