KR102579810B1 - Ai-based home health product distribution platform system - Google Patents

Abstract

Embodiments provide an artificial intelligence-based home health assistance device distribution platform system. A health assistance device distribution platform system according to an embodiment includes an order analysis module that receives a plurality of device order information from a user terminal and generates consumable order prediction information based on the plurality of device order information; The plurality of device order information includes information corresponding to order time, ordered product, order quantity, and order price, and the consumable order prediction information includes analysis results regarding the consumable order possibility of the user terminal and the consumable order expected time. a database that receives and stores the plurality of device order information and the consumables order prediction information from the order analysis module; a product analysis module that receives device manufacturing information and device sales information and generates device analysis information based on the device manufacturing information and device sales information; The device analysis information includes information on the replacement cycle of consumables for health assistance devices, the price ratio of health assistance devices and consumables, and the sales volume ratio of health assistance devices and consumables, and the device analysis information and the consumables order prediction information. It may include a processor that determines the inventory quantity of consumables for the health assistance device based on and controls the notification module to transmit a consumable purchase notification to the user terminal.

Description

Artificial intelligence-based home health assistance device distribution platform system {AI-BASED HOME HEALTH PRODUCT DISTRIBUTION PLATFORM SYSTEM}

Embodiments of the present invention relate to an artificial intelligence-based home health assistance device distribution platform system. More specifically, artificial intelligence is used to calculate the purchase cycle for consumables that need to be replaced during use of a finished home health assistance device and provide notifications. It is about a platform system that provides user convenience by transmitting data.

Recently, as the scale of the online commercial industry continues to increase, consumers can purchase a variety of products from online shopping malls. In particular, with the growing interest in health, various health aid devices available at home, for example, spray drugs in the form of fine aerosols to be inhaled through a mouthpiece or face mask to treat respiratory diseases such as asthma and bronchiectasis. Nebulizers and massage devices that can be used for diseases can also be easily purchased and used online.

However, these health-helping products require constant replacement of various consumables during product use due to hygiene issues. For these consumables, shopping malls need to secure sufficient inventory compared to finished products, and consumers must continuously purchase consumables according to the recommended replacement cycle. According to these characteristics, there is a need for a technical plan that can manage the appropriate purchase cycle and inventory of consumables by considering various characteristics of health assistance devices.

In addition, as the Fourth Industrial Revolution occurs in countries around the world, the paradigm that dominated one era is completely disappearing, and new paradigms are replacing paradigms that complement and compete with each other. The 4th Industrial Revolution focuses on value creation methods that collect data from the real world (data acquisition), analyze it in the virtual world to extract knowledge (data analysis), and use it again in the real world (apply to reality). Meanwhile, the development of intelligent information technology related to AI, big data, IoT, blockchain, cloud computing, mobile, etc. is underway as a variety of SW fields that go beyond the previous information and communication technology (ICT). In particular, the central indicator of the 4th Industrial Revolution is the convergence of various technologically advanced technologies, with AI based on computer software (SW) taking on the most important position.

The background technology described above is possessed or acquired by the inventor in the process of deriving the disclosure of the present application, and cannot necessarily be said to be known technology disclosed to the general public before this application.

Embodiments provide an artificial intelligence-based home health assistance device distribution platform system.

The technical challenges to be achieved in the embodiments are not limited to the matters mentioned above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. You can.

A platform system according to an embodiment of the present invention includes an order analysis module that receives device order information and one or more consumable order information from a user terminal and generates consumable order prediction information based on the device order information and consumable product order information; The device order information includes at least one of information corresponding to the order time, ordered product, order quantity, and order price, and the consumable order information includes at least one of information corresponding to the order time, order item, and order quantity of the consumable product. The consumable order prediction information includes a possibility of ordering consumable goods at a specific time, and a database configured to receive and store the plurality of device order information and the consumable order prediction information from the order analysis module. a product analysis module that receives device manufacturing information and device sales information from an external server and generates device analysis information based on the device manufacturing information and device sales information; The device analysis information includes information on the replacement cycle of consumables for a specific health auxiliary device, the price ratio of the health auxiliary device and the consumables, the sales unit cost of the health auxiliary device and the consumables, and predicts orders for the consumables. Determines an appropriate inventory quantity of the consumables for the health auxiliary device based on the information and the device analysis information, and controls the notification module to send a notification about the time to purchase the consumables to a user terminal with a history of ordering the health auxiliary device. It may include a processor;

The order analysis module receives first device order information for the health assistance device at a first time from the user terminal, and orders a first consumable product for the consumable product at a second time subsequent to the first time. Receive information, and receive second consumable order information for the consumable at a third time subsequent to the second time, the first device order information, the first consumable order information, and the second consumable order. Based on the information, first consumable order prediction information may be generated.

The order analysis module determines the possibility of ordering consumables at the specific time based on the first consumable order information and the second consumable order information, and the processor sends the user terminal to the user terminal when the specific time has elapsed. The notification module can be controlled to send a notification about when to purchase consumables.

[Equation 1]

The order analysis module determines the possibility of ordering consumables at a specific time using [Equation 1]. In [Equation 1], P(T) is the possibility of ordering consumables at a specific time, and T is The order time, mT may refer to the midpoint between the third and fourth time points, ki may refer to the time correction coefficient, and di may refer to the probability density correction coefficient.

The processor may determine the appropriate inventory quantity based on a sales unit ratio between the health assistance device and the consumables, a reception time of the first consumable order information, and a reception time of the second consumable order information.

[Equation 2]

The processor determines the appropriate inventory quantity of the consumables using [Equation 2]. In [Equation 2], WT is the appropriate inventory quantity at a specific time, Tx is the x order time, and b is The cumulative number of orders for consumables, Da may refer to the average time between order times for multiple consumables, and Rs may refer to the sales unit cost between health assistance devices and consumables.

The order analysis module generates second consumable order prediction information using a correction model, and the correction model includes an input layer, one or more hidden layers, and an output layer, and a plurality of learning methods related to the first consumable order prediction information. Data is input to the input layer of the correction model and passes through the one or more hidden layers and the output layer to output an output vector, and the output vector is input to a loss function layer connected to the output layer, and the loss function layer is A loss value is output using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the correction model can be learned in a direction that reduces the loss value.

According to embodiments, the health assistance device distribution platform system receives a plurality of device order information from a user terminal, analyzes the plurality of device order information, and calculates a forecast period during which future consumable repurchase is expected through the user terminal. . Based on the consumable repurchase prediction data, the server can instruct the shopping mall connected to the health assistance device distribution platform to smoothly prepare the inventory of consumables for the health assistance device. In addition, a purchase notification is sent in advance to a user who has purchased a health assistance device from a shopping mall before the consumables in possession are exhausted or the replacement cycle of the consumables reaches, so that the user terminal can use the health assistance device smoothly. can help you

The effects that can be obtained from the examples are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art based on the detailed description below. It can be.

The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and together with the detailed description describe technical features of the various embodiments.
1 is a diagram showing the configuration of an electronic device according to an embodiment of the present invention.
Figure 2 is a diagram showing the structure of a program according to an embodiment of the present invention.
Figure 3 is a diagram schematically showing a health assistance device distribution platform system according to an embodiment of the present invention.
FIG. 4 is a block diagram for explaining the structure of the server of FIG. 3.
Figure 5 is a flowchart illustrating a method for generating an order notification operation of a platform system according to an embodiment of the present invention.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. Additionally, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may change. Some features or features of one embodiment may be included in other embodiments or may be replaced with corresponding features or features of other embodiments.

In the description of the drawings, procedures or steps that may obscure the gist of the various embodiments are not described, and procedures or steps that can be understood at the level of a person with ordinary knowledge in the relevant technical field are not described. did.

Throughout the specification, when a part is said to “comprise or include” a certain element, this means that it does not exclude other elements but may further include other elements, unless specifically stated to the contrary. do. In addition, terms such as "... unit", "... unit", and "module" used in the specification refer to a unit that processes at least one function or operation, which refers to hardware, software, or a combination of hardware and software. It can be implemented as: Additionally, the terms “a or an,” “one,” “the,” and similar related terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, it may be used in both singular and plural terms.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the attached drawings. The detailed description set forth below in conjunction with the accompanying drawings is intended to illustrate exemplary embodiments of various embodiments and is not intended to represent the only embodiment.

In addition, specific terms used in various embodiments are provided to aid understanding of the various embodiments, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the various embodiments. .

1 is a diagram showing the configuration of an electronic device according to an embodiment of the present invention.

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1, in the network environment 100, the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108. According to one embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197. In some embodiments, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134. According to one embodiment, the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, if the electronic device 101 includes a main processor 121 and a secondary processor 123, the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can. The auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.

The auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled. According to one embodiment, co-processor 123 (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module 180 or communication module 190). there is. According to one embodiment, the auxiliary processor 123 (eg, neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108). Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited. An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto. Memory 130 may include volatile memory 132 or non-volatile memory 134.

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.

The input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user). The input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101. The sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.

The audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).

The sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 can capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 can manage power supplied to the electronic device 101. According to one embodiment, the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (e.g., : LAN (local area network) communication module, or power line communication module) may be included. Among these communication modules, the corresponding communication module is a first network 198 (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (e.g., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN). These various types of communication modules may be integrated into one component (e.g., a single chip) or may be implemented as a plurality of separate components (e.g., multiple chips). The wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 can be confirmed or authenticated.

The wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology). NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported. The wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates. The wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199). According to one embodiment, the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC. Example: Downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive signals or power to or from the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected to the plurality of antennas by, for example, the communication module 190. can be selected Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna. According to some embodiments, in addition to the radiator, other components (eg, radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module 197.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( (e.g. commands or data) can be exchanged with each other.

According to one embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199. Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101. According to one embodiment, all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108. For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 may perform the function or service instead of executing the function or service on its own. Alternatively, or additionally, one or more external electronic devices may be requested to perform at least part of the function or service. One or more external electronic devices that have received the request may execute at least part of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device 101. The electronic device 101 may process the result as is or additionally and provide it as at least part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology can be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of Things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199. The electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide services to the connected electronic device 101. In addition, the server 108 may perform a membership registration process, store and manage various information of users who have registered as members, and provide various purchase and payment functions related to the service. Additionally, the server 108 may share execution data of service applications running on each of the plurality of electronic devices 101 in real time so that services can be shared between users. This server 108 may have the same hardware configuration as a typical web server or WAP server. However, in terms of software, it may be implemented through any language such as C, C++, Java, Visual Basic, and Visual C, and may include program modules that perform various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives work performance requests from clients or other servers and derives and provides work results in response. It refers to a computer system and the computer software (server program) installed for it. In addition, in addition to the server program described above, the server 108 includes a series of application programs running on the server 108 and, in some cases, various databases (DBs) built internally or externally, hereinafter " It should be understood as a broad concept including “DB”). Accordingly, the server 108 classifies membership registration information and various information and data about games, stores them in a DB, and manages this DB, which may be implemented inside or outside the server 108. In addition, the server 108 uses a variety of server programs provided depending on the operating system such as DOS, Windows, Linux, UNIX, and Macintosh on general server hardware. Representative examples include Website, IIS (Internet Information Server) used in a Windows environment, and CERN, NCSA, and APPACH used in a Unix environment. Additionally, the server 108 may be linked with an authentication system and payment system for user authentication of the service or payment for purchases related to the service.

The first network 198 and the second network 199 are a connection structure that allows information exchange between each node, such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. It means (Network). The first network 198 and the second network 199 are the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), and 3G. , 4G, LTE, 5G, Wi-Fi, etc., but are not limited to these. The first network 198 and the second network 199 may be closed, such as a LAN or WAN, but are preferably open, such as the Internet. The Internet uses the TCP/IP protocol and several services existing at its upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), and SNMP ( It refers to a worldwide open computer first network (198) and second network (199) structure that provides Simple Network Management Protocol (NFS), Network File Service (NFS), and Network Information Service (NIS).

A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage format that allows for free search (extraction), deletion, editing, addition, etc. of data. Databases are relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and an XML native database such as Excelon, Tamino, and Sekaiju, and has its own functions. To achieve this, you can have appropriate fields or elements.

Figure 2 is a diagram showing the structure of a program according to an embodiment of the present invention.

Figure 2 is a block diagram 200 illustrating program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. It can be included. Operating system 142 may include, for example, AndroidTM, iOSTM, WindowsTM, SymbianTM, TizenTM, or BadaTM. At least some of the programs 140 are preloaded into the electronic device 101, for example, at the time of manufacture, or are stored in an external electronic device (e.g., the electronic device 102 or 104, or a server) when used by a user. It can be downloaded or updated from 108)). All or part of the program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or retrieval) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively operate on other hardware devices of electronic device 101, such as input module 150, audio output module 155, display module 160, and audio module 170. , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that functions or information provided from one or more resources of the electronic device 101 can be used by the application 146. The middleware 144 includes, for example, an application manager 201, a window manager 203, a multimedia manager 205, a resource manager 207, a power manager 209, a database manager 211, and a package manager 213. ), connectivity manager (215), notification manager (217), location manager (219), graphics manager (221), security manager (223), call manager (225), or voice recognition manager (227). You can.

The application manager 201 may, for example, manage the life cycle of the application 146. The window manager 203 may, for example, manage one or more GUI resources used on the screen. For example, the multimedia manager 205 identifies one or more formats required for playing media files, and encodes or decodes the corresponding media file using a codec suitable for the selected format. It can be done. The resource manager 207 may, for example, manage the source code of the application 146 or the memory space of the memory 130. The power manager 209 manages, for example, the capacity, temperature, or power of the battery 189, and may use this information to determine or provide related information necessary for the operation of the electronic device 101. . According to one embodiment, the power manager 209 may interface with a basic input/output system (BIOS) (not shown) of the electronic device 101.

Database manager 211 may create, search, or change a database to be used by application 146, for example. The package manager 213 may, for example, manage the installation or update of applications distributed in the form of package files. The connectivity manager 215 may manage, for example, a wireless connection or direct connection between the electronic device 101 and an external electronic device. For example, the notification manager 217 may provide a function for notifying the user of the occurrence of a designated event (eg, an incoming call, message, or alarm). The location manager 219 may, for example, manage location information of the electronic device 101. The graphics manager 221 may, for example, manage one or more graphic effects to be provided to the user or a user interface related thereto.

Security manager 223 may provide, for example, system security or user authentication. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101. For example, the voice recognition manager 227 transmits the user's voice data to the server 108 and provides a command corresponding to a function to be performed in the electronic device 101 based at least in part on the voice data, Alternatively, text data converted based at least in part on the voice data may be received from the server 108. According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least a portion of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142.

The application 146 includes, for example, home 251, dialer 253, SMS/MMS (255), instant message (IM) 257, browser 259, camera 261, and alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Album (275), Watch (277), Health (279) (such as exercise amount or blood sugar) It may include applications that measure biometric information) or environmental information 281 (e.g., measure atmospheric pressure, humidity, or temperature information). According to one embodiment, the application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to deliver designated information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage the external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a specified event (e.g., mail reception) generated in another application (e.g., email application 269) of the electronic device 101 to an external electronic device. You can. Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide it to the user of the electronic device 101.

The device management application, for example, controls the power (e.g., turn-on or turn-off) of an external electronic device or some component thereof (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device 101. ) or functions (such as brightness, resolution, or focus). A device management application may additionally or alternatively support installation, deletion, or update of applications running on external electronic devices.

Figure 3 is a diagram schematically showing the transaction environment of a health assistance device distribution platform according to an embodiment of the present invention.

Referring to Figure 3, the health assistance device distribution platform may include a server 310. That is, the health assistance device distribution platform may be implemented by the server 310 connected to the network 340. The server 310 can exchange data with a plurality of user terminals 320 and an external server 330 through the network 340. Here, the external server 330 is an electronic device controlled by a seller selling a health assistance device, and each of the plurality of user terminals 320 refers to an electronic device of a buyer who purchased the health assistance device from the seller.

The plurality of user terminals 320 may include a first user terminal 321 and a second user terminal 322. Although only two user terminals are shown in FIG. 3, the embodiment of the present invention is not limited thereto, and the plurality of user terminals 320 may include three or more user terminals. That is, when a user accesses the health assistance device distribution platform provided by the server 310 using his or her user terminal and purchases a health assistance device, the server 310 stores the identification information of the user terminal and registers it as a user. can do.

Additionally, the server 310 may generate a plurality of user information based on a plurality of device order information provided from each of the plurality of user terminals 320. The plurality of user information may include first user information, second user information, and third user information. Each user information may include information for identifying the user (orderer) corresponding to the plurality of user terminals 320. The unique identification information of a user terminal may be, for example, either an ID (Identification) or a unique IP (Internet Protocol) address of each user terminal.

The server 310 may obtain various information about the health assistance device from the external server 330. The server 310 may receive product price information, device manufacturing information, and device sales information from the external server 330. The external server 330 may be a web server connected to the Internet environment. In one embodiment, the server 310 may obtain product price information, device manufacturing information, and device sales information from the external server 330 by web crawling. The server 310 may crawl information on a web space about a product, parse the crawled information, and generate a parse tree. The server 310 may extract product price information, device manufacturing information, and device sales information based on crawled information. However, this is an example, and the method by which the server 310 obtains product price information, device manufacturing information, and device sales information from the external server 330 may vary. At a first time, the server 310 may provide product price information to a plurality of user terminals 320.

Here, product price information may mean information about the price of health assistance devices provided for sale by the seller. Product price information may include product identification information and product sales price. For example, if the selling price of product A is 10,000 won, the product price information may include product identification information called 'Product A' and the selling price of the product being '10,000 won.'

Device manufacturing information may refer to information regarding the manufacturing of health assistance devices provided for sale by a seller. Device manufacturing information may include the product manufacturing date, product manufacturer, and product usage information.

Device sales information may refer to information about the sale of health assistance devices provided by the seller for sale. Device sales information may include product sales volume, product sales period, and repurchase rate. The repurchase rate may refer to the ratio of the sales volume of a product sold to buyers with an existing purchase history among the sales volume of the product.

The server 310 may receive device order information from a plurality of user terminals 320. Device order information may include information corresponding to the order time, ordered product, order quantity, and order price of the health assistance device. For example, the first device order information may include information corresponding to the first order time, first order product, first order quantity, and first order price. Likewise, the second device order information may include information corresponding to the second order time, second order product, second order quantity, and second order price. Here, when the first device order information and the second device order information are received by the server 310 at different times, the first order time and the second order time may be different from each other. On the other hand, even in this case, the first order product and the second order product may be the same, the first order quantity and the second order quantity may be the same, and the first order price and the second order price may be the same.

Additionally, the server 310 may receive consumable order information from a plurality of user terminals 320. Here, consumables refer to parts that are required for use of a specific health assistance device and are scheduled to be replaced. The consumable order information may include information corresponding to the order time, ordered item, and order quantity of the consumable product.

The server 310 may generate consumable order prediction information based on device order information provided from the first user terminal 321 and one or more consumable order information. The consumable order prediction information may include the possibility of ordering consumables at a specific time for consumables used in a health assistance device. For example, the server 310 receives device order information from the first user terminal 321 at a first time, and receives first consumable order information at a second time after a first time has elapsed from the first time, If the second consumable order information is received at the third time after the first time has elapsed from the second time, the server 310 receives the order information from the first user terminal 321 at the fourth time after about the first time has elapsed from the third time. You may expect to receive third party ordering information. In addition, information corresponding to the ordered product, order quantity, and order price included in the fourth consumable order information is based on the information corresponding to the ordered product, order quantity, and order price included in the first to third consumable order information. You will be able to decide.

Based on a plurality of consumable order information provided from the first user terminal 321, the server 310 may provide a consumable order notification informing the first user terminal 321 of a consumable order time at a specific time. The server 310 may determine a specific time based on a plurality of consumable order information provided from the first user terminal 321. Specifically, the server 310 may determine a specific time based on a plurality of order times included in the plurality of consumable order information.

The server 310 may include the same hardware module as a typical web server or network server. The server 310 may be implemented in the form of a web server or network server, or may be implemented as a series of application programs that operate on a web server, including various databases built inside a tangible storage device. . Therefore, the server 310 should be understood as a broad concept that is not limited to any one of the above-described forms.

The server 310 may be implemented through a physical server. That is, the server 310 is implemented as a server including a communication device, receives data from the external server 330 and a plurality of user terminals 320, and also receives data from the external server 330 and a plurality of user terminals 320. You can also send data to . The server 310 is a workstation, a data center, an internet data center (IDC), a direct attached storage (DAS) system, a storage area network (SAN) system, a network attached storage (NAS) system, It may be implemented as at least one of a RAID (redundant array of inexpensive disks, or redundant array of independent disks) system and an EDMS (Electronic Document Management) system, but the present embodiment is not limited thereto.

If the server 310 is implemented through a physical server, the server 310 may transmit or receive data through the network 340 connected wirelessly or wired. The network 340 may include networks based on wired Internet technology, wireless Internet technology, and short-range communication technology. Wired Internet technology may include, for example, at least one of a local area network (LAN) and a wide area network (WAN).

Wireless Internet technologies include, for example, Wireless LAN (WLAN), DLNA (Digital Living Network Alliance), Wibro (Wireless Broadband), Wimax (World Interoperability for Microwave Access: Wimax), and HSDPA (High Speed Downlink Packet). Access), HSUPA (High Speed Uplink Packet Access), IEEE 802.16, Long Term Evolution (LTE), LTE-A (Long Term Evolution-Advanced), Wireless Mobile Broadband Service (WMBS) and 5G NR (New Radio) technology. However, this embodiment is not limited to this.

When the server 310 transmits/receives data through wireless communication, the server 310 may comply with technical standards and standard communication methods for mobile communication. For example, standard communication methods include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), CDMA2000 (Code Division Multi Access 2000), and EV-DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only). , at least one of Wideband CDMA (WCDMA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTEA), and 5G New Radio (NR) may include. However, this embodiment is not limited to this.

FIG. 4 is a block diagram for explaining the structure of the server 310 of FIG. 3.

Referring to Figure 4, the server 310 may include a database 311, an order analysis module 312, a product analysis module 313, an artificial intelligence module 314, a processor 315, and a notification module 316. You can.

The database 311 may receive device order information, consumable order information, and consumable order prediction information from the order analysis module 312, and store them. The database 311 may have a general data structure implemented in the storage space of the server 310 using a database management program (DBMS). The database 311 uses a database management program to respond to a control signal from an external device or the processor 315, and stores any one of one or more device order information, consumable order information, and consumable order prediction information stored in the database 311. You can choose. Specifically, the database 311 may select and output one or more device order information, consumable product order information, and consumable product order prediction information stored in the database 311 based on the control of the processor 315.

The order analysis module 312 may receive device order information and one or more consumable order information from a plurality of user terminals 320, and generate one or more consumable order prediction information based on the device order information and one or more consumable order information. there is. Each piece of device order information may include information corresponding to order time, order product, order quantity, and order price.

Additionally, the order analysis module 312 may correct the first consumable order prediction information to generate second consumable order prediction information. The consumable order prediction information generation and correction operations of the order analysis module 312 will be described in detail later.

The order analysis module 312 receives first device order information for a health assistance device at a first time from the user terminal, and receives first consumable order information for a consumable product at a second time subsequent to the first time. And, second consumable order information for consumables at a third time following the second time may be received. The order analysis module 312 may generate first consumable order prediction information based on the first device order information, first consumable order information, and second consumable order information.

The order analysis module 312 can determine the possibility of ordering consumables at a specific time using [Equation 1]. In [Equation 1], P(T) is the possibility of ordering consumables at a specific time, T is the order time, mT is the midpoint between the third and fourth time points, ki is the time correction coefficient, and di is the probability density correction coefficient. It can mean.

The product analysis module 313 can generate device analysis information based on various information about health assistance devices. The product analysis module 313 may receive product price information, device manufacturing information, and device sales information, and generate device analysis information based on the product price information, device manufacturing information, and device sales information. Device analysis information may include information about the replacement cycle of consumables for health assistance devices (finished products), the price ratio of finished products and consumables, the sales volume ratio of finished products and consumables, etc.

The artificial intelligence module 314 may include a multi-layer neural network and a learning engine.

The learning engine can pre-train a multi-layer neural network using multiple learning data. A multilayer neural network is a prediction model implemented in software or hardware that imitates the computational ability of a biological system using a large number of artificial neurons (or nodes).

The learning engine can perform supervised learning of a multi-layer neural network using learning data that uses user data as input and preset correction data as output so that the multi-layer neural network can receive data as input and generate appropriate correction data. At this time, supervised learning refers to learning to find an output value according to a given input value by using data with input values and corresponding output values as learning data, and refers to learning that takes place when the correct answer is known. The set of input and output values given in supervised learning is called training data. That is, the object vector and threshold obtained by analyzing the image of the above-described video analysis data are input and output values, respectively, and can be used as learning data for supervised learning of a multilayer neural network.

In one embodiment, the artificial intelligence module 314 may generate second consumable order prediction information based on the first consumable order prediction information using a correction model. To this end, the correction model may include an input layer, one or more hidden layers, and an output layer. The correction model may be trained in advance to accurately generate second consumable order prediction information based on the first consumable order prediction information through a learning process. To this end, in the learning process, a plurality of learning data regarding the first consumable order prediction information may be input to the input layer of the correction model and pass through one or more hidden layers and an output layer to output an output vector. The output vector can be input to the loss function layer connected to the output layer. The loss function layer can output loss values using a loss function that compares the output vector with the correct answer vector for each training data. The parameters of the correction model can be learned in a direction that reduces the loss value.

For example, the loss function can calculate the loss value according to [Equation 2]. In [Equation 2], N is the number of a plurality of learning data, n is a natural number identifying the learning data, k is a natural number identifying the value of the nth learning data, and nk is the kth value of the nth learning data. , t may mean the correct answer data, y may mean the output vector, and E may mean the loss value.

Alternatively, the loss function can calculate the loss value according to [Equation 3]. In [Equation 3], n is the number of training data for each class, y and j are identifiers representing the classes, C is a constant value, M is the number of classes, x_y is the probability that the training data belongs to class y, and x_j is the learning data. The probability value that the data belongs to class j, L may mean the loss value.

To this end, the artificial intelligence module 314 may include an artificial intelligence machine learning model. When the server 310 is implemented as hardware, the artificial intelligence module 314 may include a hardware structure specialized for processing machine learning models. Machine learning models can be created through artificial intelligence machine learning.

Machine learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but examples of the foregoing It is not limited to An artificial intelligence model may include multiple artificial neural network layers. Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above. In addition to hardware structures, artificial intelligence models may additionally or alternatively include software structures.

In one embodiment, the processor 315 receives device order information for a health assistance device from the first user terminal 321, and then receives two consumable order information, the sales unit ratio between the health assistance device and the consumables. Based on this, the appropriate inventory quantity of consumables on the platform can be determined.

In one embodiment, the processor 315 may determine an appropriate inventory quantity of consumables using [Equation 5]. In [Equation 5], WT is the appropriate inventory quantity at a specific time, Tx is the x order time, b is the cumulative order number of consumables, Da is the average time between multiple consumable order times, and Rs are health assistance devices and consumables. It may refer to the cost of sales and repairs.

The processor 315 may control at least one other component (e.g., a hardware or software component) of the server 310 connected to the processor 315 by, for example, executing pre-stored software, and may control various data. Processing or calculations can be performed.

The notification module 316 may provide a consumable order notification informing the plurality of user terminals 320 of the order time of consumables at a specific time based on the plurality of device order information provided from the plurality of user terminals 320. there is. The order analysis module 312 may determine a specific time based on a plurality of device order information provided from a plurality of user terminals 320. Specifically, the order analysis module 312 may determine a specific time based on a plurality of order times included in the plurality of device order information. When a specific time is determined by the order analysis module 312, the notification module 316 may generate a consumable order notification under the control of the processor 315. The generated notification signal may be transmitted to a plurality of user terminals 320 at a specific time.

Each component included in the server 310 is connected to a communication path connecting software modules or hardware modules within the device and can operate organically with each other. These components may communicate using one or more communication buses or signal lines. Each component of the server 310 refers to a unit that processes at least one function or operation, and may be implemented as a software module, a hardware module, or a combination of software and hardware.

The configuration of the server 310 shown in FIG. 4 is not necessarily essential, and some configurations may be omitted or other configurations not shown in FIG. 4 may be added as needed. That is, the configuration shown in FIG. 4 is for convenience of explanation, and the embodiments are not limited thereto. A person skilled in the art of the present invention will be able to modify and practice the embodiments of the present invention without departing from the scope of the present invention.

Figure 5 is a flowchart illustrating a method for generating an order notification operation of a platform system according to an embodiment of the present invention.

Referring to Figure 5, first, the order analysis module 312 receives device order information from a plurality of user terminals 320 (S510). Device order information may include information corresponding to order time, order product, order quantity, and order price. That is, the first device order information may include information corresponding to the first order time, first order product, first order quantity, and first order price. Likewise, the second device order information may include information corresponding to the second order time, second order product, second order quantity, and second order price.

For example, the order analysis module 312 may receive first device order information from a plurality of user terminals 320 at a first time. The first device order information may include order information about a health assistance device (finished product) and consumables needed to use the health assistance device. Subsequently, the order analysis module 312 may receive second device order information from the plurality of user terminals 320 at a second time following the first time. Here, the second device order information may be order information only for consumables excluding health assistance devices (finished products). Likewise, the order analysis module 312 may receive third device order information from the plurality of user terminals 320 at a third time following the second time. Here, the third device order information may be order information only for consumables excluding health assistance devices (finished products).

The order analysis module 312 generates consumable order prediction information based on device order information (S520).

The order analysis module 312 may output first consumable order prediction information based on the first device order information to the third device order information. The consumable order prediction information may include analysis results regarding the possibility of future orders and expected timing of future orders of the user terminal that provided the device order information through the plurality of user terminals 320. When the order analysis module 312 receives the first device order information at a first time and the second device order information at a second time, the order analysis module 312 receives the first device order information included in the first device order information. A specific time at which the third device order information is expected to be received can be predicted based on the time information corresponding to the first time and the time information corresponding to the second time included in the second device order information. Here, the third device order information follows the first device order information and the second device order information and may include consumable device order information input by the orderer through a user terminal connected to the plurality of user terminals 320.

The order analysis module 312 may generate first consumable order prediction information including a first repurchase prediction period during which third device order information is expected to be received from the plurality of user terminals 320 . The first repurchase prediction period may have the third time following the second time as the start time, and the fourth time following the third time as the end time. That is, the order analysis module 312 may predict that third device order information will be received from the plurality of user terminals 320 in the first repurchase prediction period between the third time and the fourth time. The order analysis module 312 may calculate the third time based on the first time and the second time. Additionally, the order analysis module 312 may calculate the fourth time based on the first time, the second time, and the third time. The order analysis module 312 may store the generated first consumable order prediction information in the database 311.

Next, the product analysis module 313 receives device manufacturing information and device sales information from the external server 330 and generates device analysis information (S530). The product analysis module 313 may generate device analysis information based on the received device manufacturing information and device sales information. Specifically, the product analysis module 313 may receive product price information, device manufacturing information, and device sales information, and generate device analysis information based on the product price information, device manufacturing information, and device sales information. Device analysis information may include information about the replacement cycle of consumables for health assistance devices (finished products), the price ratio of finished products and consumables, the sales volume ratio of finished products and consumables, etc.

The processor 315 determines an appropriate inventory quantity for finished products based on the consumable order prediction information and device analysis information, and the notification module 316 generates a consumable order notification under the control of the processor 315 (S540). The processor 315 displays the notification module 316 based on whether third device order information is received from the plurality of user terminals 320 of the order analysis module 312 at the fifth time included in the first repurchase prediction period. You can decide whether to generate a notification signal. If the order analysis module 312 does not receive third device order information from the plurality of user terminals 320 in the first repurchase prediction period, the notification module 316 generates a plurality of devices under the control of the processor 315. A consumable order notification may be transmitted to the user terminal 320. Here, the notification signal may mean a signal notifying the order time to a user terminal connected to a plurality of user terminals 320. In one embodiment, the notification module 316 may transmit a consumable order notification to the plurality of user terminals 320 at a sixth time following the fourth time. The order analysis module 312 may calculate the sixth time based on the first time, second time, third time, and fourth time.

When the order analysis module 312 receives third device order information from a plurality of user terminals 320 in the first repurchase prediction period, the order analysis module 312 receives the first consumable order prediction information and the third device order information. Based on , the first consumable order prediction information may be corrected to generate second consumable order prediction information. Alternatively, when the order analysis module 312 receives third device order information from a plurality of user terminals 320 before the third time, the order analysis module 312 similarly receives the first consumable order prediction information and the third device. The first consumable order prediction information may be corrected based on the order information to generate the second consumable order prediction information.

Next, how the artificial intelligence module 314 corrects the consumable order prediction information will be described.

Correcting the first consumable order prediction information may include changing the first repurchase prediction period included in the first consumable order prediction information. That is, the second consumable order prediction information may include a second repurchase prediction period in which the first repurchase prediction period included in the first consumable order prediction information is changed. The change in the first repurchase prediction period may mean that the third time, which is the start time of the first repurchase prediction period, and the fourth time, which is the end time of the first repurchase prediction period, are changed. That is, the second consumable order prediction information may include a second repurchase prediction period with the 7th time when the 3rd time changed as the start time and the 8th time when the 4th time changed as the end time.

The artificial intelligence module 314 may generate a second repurchase prediction period based on the interval between the third time and the fifth time and the interval between the fifth time and the fourth time. The interval between the third time and the fifth time may mean the time elapsed from the third time to the fifth time. Likewise, the interval between the fifth time and the fourth time may mean the time elapsed from the fifth time to the fourth time.

The artificial intelligence module 314 can calculate the second repurchase prediction period using [Equation 6]. In [Equation 6], k is a unique correction coefficient, t3 is the third time, t4 is the fourth time, t5 is the fifth time, and t'3 is the seventh time at which the third time is corrected (t '3), t'4 may mean the 8th time (t'4) at which the fourth time is corrected, and t may mean the elapsed time.

The artificial intelligence module 314 may store the generated second consumable order prediction information in the database 311.

As described above, the health assistance device distribution platform system according to an embodiment of the present invention receives a plurality of device order information from a plurality of user terminals 320, analyzes the plurality of device order information, and distributes the plurality of user terminals. Through (320), the forecast period during which future repurchase of consumables is expected can be calculated. Based on the consumable repurchase prediction data, the server 310 may instruct the shopping mall connected to the health assistance device distribution platform to smoothly prepare an inventory of consumables for the health assistance device. In addition, a purchase notification is sent in advance to a user who has purchased a health assistance device from a shopping mall before the consumables in possession are exhausted or the replacement cycle of the consumables reaches, so that the user terminal can use the health assistance device smoothly. can help you

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., singly or in combination. Program instructions recorded on the medium may be specially designed and configured for the embodiment or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -Includes optical media (magneto-optical media) and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine language code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with limited drawings as described above, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or other components are used. Alternatively, appropriate results may be achieved even if substituted or substituted by an equivalent.

Therefore, other implementations, other embodiments, and equivalents of the claims also fall within the scope of the claims described below.

Claims (5)

an order analysis module that receives device order information and one or more consumable order information from a user terminal, and generates consumable order prediction information based on the device order information and consumable product order information;
The device order information includes at least one of information corresponding to the order time, ordered product, order quantity, and order price, and the consumable order information includes at least one of information corresponding to the order time, order item, and order quantity of the consumable product. Includes, the consumables order prediction information includes the possibility of ordering consumables at a specific time,
a database that receives and stores the plurality of device order information and the consumables order prediction information from the order analysis module;
a product analysis module that receives device manufacturing information and device sales information from an external server and generates device analysis information based on the device manufacturing information and device sales information;
The device analysis information includes information on the replacement cycle of consumables for a specific health auxiliary device, the price ratio of the health auxiliary device and the consumables, and the sales unit cost of the health auxiliary device and the consumables,
To determine an appropriate inventory quantity of the consumables for the health auxiliary device based on the consumable order prediction information and the device analysis information, and to send a notification about the time to purchase the consumables to a user terminal with an order history of the health auxiliary device. Includes a processor that controls the notification module,
The order analysis module is,
Receive first device order information for the health assistance device at a first time from the user terminal, receive first consumable order information for the consumables at a second time subsequent to the first time, and receive second consumable ordering information for the consumables at a third time subsequent to the second time;
Generating first consumable order prediction information based on the first device order information, the first consumable order information, and the second consumable order information,
The order analysis module determines the possibility of ordering consumables at the specific time based on the first consumable order information and the second consumable order information,
The processor controls the notification module to send a notification about the time to purchase the consumables to the user terminal when the specific time has elapsed,
The processor determines the appropriate inventory quantity based on the sales unit ratio between the health assistance device and the consumables, the time of receiving the first consumable order information, and the time of receiving the second consumable order information,
[Equation]

The processor determines the appropriate inventory quantity using the [mathematical formula],
In the above [mathematical formula], WT is the appropriate inventory quantity at a specific time, Tx is the x order time, b is the cumulative order number of consumables, Da is the average time between order times for multiple consumables, and Rs are health assistance devices and consumables. refers to the cost of sales per unit,
Health assistance device distribution platform system.
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