System

Abstract

A system is provided.SOLUTION: Means for collecting sales information, means for collecting environmental data, means for packetizing the collected sales information and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, and means for displaying real-time data on a management dashboard. The system includes a means for setting an advertisement campaign based on the displayed data and a means for transmitting the set advertisement campaign to the plurality of terminals.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional capsule toy machines are easy to install because they do not require a power source, but they require a lot of effort and cost to collect cash and replenish products. Furthermore, problems include lost sales opportunities due to sold-out items and unnecessary deliveries. The purpose of this invention is to achieve efficient operation of capsule toy machines and optimize business by collecting marketing data, thereby contributing to energy savings and CO2 reduction. [Means for solving the problem]

[0005] The system includes a means for collecting sales information, a means for collecting environmental data, a means for packetizing the collected sales information and environmental data, a means for transmitting the packetized data via a communication line, a means for receiving data transmitted from a terminal, a means for saving the received data in a database, a means for generating a sales forecast model based on the saved data, a means for optimizing a delivery route based on the generated sales forecast model, a means for notifying a delivery company of the optimized delivery route, a means for displaying real-time data on a management dashboard, a means for setting an advertising campaign based on the displayed data, and a means for transmitting the set advertising campaign to multiple terminals. Furthermore, the system includes a means for installing solar panels on the terminals and supplying power using a power storage device, and a means for acquiring SNS trend information and sales data via an API and integrating them into a database, thereby streamlining the operation of the capsule toy machine and realizing energy savings and CO2 reduction.

[0006] "Sales information" is data that indicates the sales status of capsule toys, and specifically includes information such as the number of units sold and inventory status.

[0007] "Environmental data" refers to information relating to the surrounding environment of the installation location, and specifically includes numerical data such as temperature, humidity, and foot traffic.

[0008] "Packetization" is the process of bundling multiple pieces of data together into a transmittable format.

[0009] "Communication lines" refers to network lines for transmitting data to remote servers or other devices, including, for example, 4G and 5G.

[0010] "Database" means an electronic recording device for managing and storing received data, facilitating data retrieval and analysis.

[0011] A "sales forecasting model" is an algorithm or mathematical model that analyzes past sales data and environmental data to predict future sales.

[0012] A "delivery route" is a physical route for efficiently replenishing products, and by optimizing it, it reduces wasted time and costs.

[0013] A "management dashboard" is a user interface that visually displays real-time business data, allowing managers to easily understand and manipulate the information.

[0014] "Advertising Campaign" means an advertising strategy deployed as part of a specific marketing event or promotional activity intended to drive sales.

[0015] A "solar panel" is a device that converts sunlight into electricity and is used to power a device.

[0016] A "power storage device" is a device that stores generated power and supplies it as needed.

[0017] "API" stands for Application Programming Interface, a standard for sharing data and functions between different software programs. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a system that equips capsule toy machines with a power source and IoT devices to operate as standalone machines. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information in real time, utilizing marketing data, and calculating optimal replenishment routes.

[0040] Embodiment of terminal (capsule toy machine)

[0041] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[0042] The terminal is also equipped with a solar panel that uses sunlight to generate electricity and stores it in a storage device, ensuring a sustainable power supply and reducing the burden on the environment.

[0043] Server embodiment

[0044] The server receives the sales information and environmental data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[0045] The server also retrieves social media trend information and related product sales data via API and integrates it into the database, enabling more detailed analysis of marketing data.

[0046] The optimal replenishment route is calculated based on the sales forecast model. The server notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[0047] User (operation manager) implementation form

[0048] Operations managers can check real-time sales information and inventory status using the management dashboard, which also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation.

[0049] Administrators can also set up new event- or campaign-based advertising through the dashboard, which is then distributed to each device via the server, maximizing sales during specific event periods.

[0050] For example, if a campaign for a specific product is to be carried out at a station or event venue where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends and surrounding environmental data, and launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[0051] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and the implementation of campaigns using marketing data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0052] The processing flow will be explained below.

[0053] Processing of terminals (capsule toy machines)

[0054] Sales information collection and transmission process

[0055] Step 1:

[0056] The terminal uses a sales sensor to measure the number of capsule toys sold. For example, each time a capsule toy is dispensed, a counter increases by one.

[0057] Step 2:

[0058] The device uses environmental sensors to capture data on the surrounding environment, such as temperature, humidity, and foot traffic, which then records the local environmental conditions.

[0059] Step 3:

[0060] The terminals then packetize the collected sales and environmental data, and these packets also contain meta-information such as the type of data and a timestamp.

[0061] Step 4:

[0062] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is performed at regular intervals, and the data is updated in real time.

[0063] Powered by solar panels

[0064] Step 1:

[0065] The device converts sunlight into electricity using solar panels, which are designed to efficiently absorb sunlight during the day.

[0066] Step 2:

[0067] The generated power is stored in a power storage device and used to operate sensors and communication devices.

[0068] Server Processing

[0069] Data Receipt and Storage Process

[0070] Step 1:

[0071] The server receives the data packets sent from the terminal, and the reception process is performed according to the communication protocol, checking that the data is not lost or distorted.

[0072] Step 2:

[0073] The received data is analyzed to extract sales information and environmental data, which are then stored in a database.

[0074] Generate a sales forecast model

[0075] Step 1:

[0076] The server retrieves existing sales and environmental data from the database. The retrieved data must be up-to-date.

[0077] Step 2:

[0078] The server applies machine learning algorithms to the acquired data to generate a sales forecasting model, which is used to predict future sales.

[0079] Step 3:

[0080] Evaluate the generated sales forecast model and check its accuracy. If the model accuracy is not above a certain level, retrain it.

[0081] Delivery route optimization

[0082] Step 1:

[0083] The server creates a list of devices that need replenishment based on the sales forecast model, and prevents unnecessary replenishment by predicting when inventory will be low.

[0084] Step 2:

[0085] The server calculates the optimal replenishment route for the delivery company, taking into account the distance to each replenishment point and traffic conditions.

[0086] Step 3:

[0087] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[0088] User (operation administrator) processing

[0089] Dashboard management process

[0090] Step 1:

[0091] Users access a management dashboard to view real-time sales information and inventory status, which is visually displayed in graphs and tables.

[0092] Step 2:

[0093] Users can analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including the content and duration of advertising sent to devices.

[0094] Step 3:

[0095] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[0096] This processing flow enables efficient operation of capsule toy machines and optimization of marketing strategies.

[0097] Example 1

[0098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0099] Conventional capsule toy machines often required manual inventory management and sales data collection, making efficient operation difficult. Furthermore, environmental data was not collected and energy was not used effectively, making sustainable operation difficult. Furthermore, there was no system in place for real-time advertising tailored to customer needs, making it difficult to maximize sales.

[0100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0101] In this invention, the server includes means for collecting sales data, means for collecting environmental data, means for packetizing the collected sales data and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management display device, means for setting advertising activities based on the displayed data, and means for transmitting the set advertising activities to multiple terminals. This enables efficient management and sustainable operation of capsule toy machines. Furthermore, real-time data collection and analysis enables optimal inventory management and advertising activities, thereby maximizing sales.

[0102] "Sales data" is information regarding the number and price of products sold in a capsule toy machine.

[0103] "Environmental data" refers to information regarding the temperature, humidity, foot traffic, etc. at the location where the capsule toy machine is installed.

[0104] "Packetization" is the process of dividing multiple pieces of data into a certain format or unit so that they can be sent over a communication line.

[0105] A "communication line" refers to the network infrastructure for sending and receiving data, and includes, for example, 4G and 5G mobile communication networks.

[0106] A "terminal" is a device for collecting sales data and environmental data, and in the present invention refers to a capsule toy machine.

[0107] A "database" is a system for storing and managing data electronically in an organized form.

[0108] A "sales forecasting model" is a mathematical model for predicting future sales based on past sales data.

[0109] A "delivery route" is the optimal route for replenishing or delivering products.

[0110] The "management display device" is an interface for displaying sales data, inventory status, and the like in real time, and includes, for example, a dashboard.

[0111] "Advertising activities" are marketing activities aimed at promoting the sale of specific products or services.

[0112] A "photovoltaic power generation device" is a device that generates electricity using sunlight.

[0113] A "power storage device" is a device that can store generated electricity and supply it when needed.

[0114] A "social network service" is a platform that allows users to share information and interact with each other via the Internet.

[0115] An "application programming interface" is a set of definitions and protocols that allow software to interact with other software through an interface.

[0116] This invention is a system that equips a capsule toy machine with a power source and IoT devices, allowing it to operate as a standalone machine. Specifically, the capsule toy machine is equipped with sales sensors and environmental sensors, which collect sales data and environmental data and send it to a server via a communication line. The terminal is equipped with a solar power generation device, and the generated electricity is stored in a power storage device.

[0117] Terminal embodiment

[0118] The terminal is equipped with a sales sensor and an environmental sensor, which collect the respective data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, if a capsule toy machine installed at a station sells 100 capsule toys in a day during the summer, and the environmental data at that time is a temperature of 30°C and humidity of 70%, that information will be recorded by the sensors.

[0119] The device periodically converts the collected sales and environmental data into packets and transmits them to a server via 4G or 5G communication lines, where the data is accumulated in real time and used for further analysis.

[0120] Server embodiment

[0121] The server receives the sales data and environmental data sent from the device and stores it in a database. This data is stored and managed using specific software such as Python and MySQL. Based on the stored data, a sales forecasting model is generated using a machine learning algorithm (e.g., SKlearn). This forecasting model is used to forecast sales for the next week and achieve optimal inventory management.

[0122] The server also obtains social media trend information and sales data for related products via APIs (e.g., Twitter API) and integrates this data into the database. This enables multifaceted data analysis and improves the accuracy of the sales forecast model.

[0123] The server then calculates the optimal delivery route based on the sales forecast model. For example, it uses the Google Maps API to calculate the shortest distance and optimal time between points, and notifies the delivery company of this information. This allows for efficient replenishment.

[0124] User's embodiment

[0125] Operations managers can view real-time sales information and inventory status using a management dashboard, which displays sales data, environmental data, and analysis results from sales forecasting models.

[0126] Users can also set up new advertising activities through the dashboard. Once an advertising campaign is set up, it is distributed to each device via the server. For example, a campaign for a specific product during the summer vacation can be set up, and the information will be immediately notified to customers via the display and voice message of each capsule toy machine.

[0127] Prompt Sentence Examples

[0128] "Calculate the next week's sales forecast and optimal replenishment route based on capsule toy machine sales data and environmental data."

[0129] Introducing such a system will enable efficient management and sustainable operation of capsule toy machines, while also improving energy efficiency and reducing operating costs.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] The terminal collects sales data and environmental data. A sales sensor attached to the terminal measures the number of capsule toys sold, and an environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, the sales sensor measures the sales of 100 capsule toys, and the temperature at that time is 30°C and the humidity is 70%. This becomes the input data.

[0133] Step 2:

[0134] The data collected by the device is packetized. Specifically, sales data and environmental data are combined into a single data packet, which can then be sent to the server. For example, the data is packetized in JSON format as follows: {"sales": 100, "temperature": 30, "humidity": 70}.

[0135] Step 3:

[0136] The device transmits the packetized data over a communication line to the server, for example, using a 4G or 5G network. The output of this step is data packets that are received by the server.

[0137] Step 4:

[0138] The server stores the received data in a database. Specifically, it writes the received data to a database management system (e.g., MySQL). For example, it inserts the data into the database using an SQL query. The query executed is "INSERT INTO sales_data (sales, temperature, humidity) VALUES (100, 30, 70)".

[0139] Step 5:

[0140] The server generates a sales forecasting model based on the stored data. Specifically, it uses a machine learning algorithm (e.g., SKlearn) to train a model that predicts future sales from past data. The inputs to the forecasting model are past sales data and environmental data, and the output is future sales forecasts.

[0141] Step 6:

[0142] The server obtains social media trend information and sales data for related products via API and integrates them into a database. For example, use the Twitter API to obtain trend information related to capsule toys and store it in a database. The query "INSERT INTO trend_data (trend_info) VALUES ('Capsule Toy Boom')" is executed.

[0143] Step 7:

[0144] The server calculates the optimal delivery route based on the sales forecast model. Specifically, it uses the Google Maps API to calculate the delivery distance and time and identify the most efficient route. For example, it calculates the "shortest route from point A to point B" and notifies the delivery company of the results.

[0145] Step 8:

[0146] The server notifies the delivery company of the optimal delivery route information, for example, via email or a dedicated application. The output of this step is the route information received by the delivery company.

[0147] Step 9:

[0148] A user uses the management dashboard to check real-time sales information and inventory status. The user opens a web browser and accesses the management dashboard, where the latest sales data and environmental data are displayed as graphs and tables.

[0149] Step 10:

[0150] The user sets up a new advertising activity via the dashboard. For example, the user sets up a "summer vacation campaign" using the dashboard interface. This setting information is sent to the server.

[0151] Step 11:

[0152] The server sends the configured advertising activities to each terminal, for example, specific appealing messages or visual advertisements are instantly displayed on the display of each capsule toy machine.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] Conventional capsule toy machines have limited collection of sales information and environmental data, making it difficult to optimize inventory management and marketing strategies. It has also been difficult for managers to obtain accurate data in real time and effectively set up advertising campaigns. Furthermore, there are issues with the power supply to each terminal, and improvements are needed from a sustainability perspective. The purpose of this invention is to solve these issues and realize efficient and sustainable capsule toy machine operation.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes means for collecting sales information, means for collecting environmental data, means for packetizing the collected sales information and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management dashboard, means for setting up an advertising campaign based on the displayed data, means for transmitting the set advertising campaign to multiple terminals, means for adding a visualization means for displaying data collected on the terminals to facilitate data display, means for providing an input means for an administrator to set up an advertising campaign, means for transmitting event data set based on the advertising campaign to the server and managing it, means for acquiring real-time sales information and environmental data and analyzing and visualizing the data to support administrator decision-making, means for supporting marketing optimization using a generative AI model on the data, and means for automatically setting up a campaign based on the generated prompt sentences.This enables efficient inventory management, marketing campaign optimization, sustainable power supply, and support for administrator decision-making.

[0158] "Sales information" is data regarding the quantity and price of products sold in capsule toy machines.

[0159] "Environmental data" refers to data related to the temperature, humidity, foot traffic, etc. of the location where the capsule toy machine is installed.

[0160] "Packetization" means organizing collected sales information and environmental data into a fixed format and breaking the data into smaller pieces for transmission over communication lines.

[0161] "Communication lines" refer to wireless networks such as 4G and 5G, which are the infrastructure for sending and receiving data.

[0162] A "database" is an information management system that effectively stores, searches, and analyzes collected data.

[0163] A "sales forecasting model" is an algorithm or statistical model for predicting future sales based on past sales data and environmental data.

[0164] "Delivery route optimization" is the calculation of the most efficient delivery route for replenishing products.

[0165] A "management dashboard" is a graphical user interface for displaying and analyzing sales information and environmental data in real time.

[0166] An "advertising campaign" is a sales promotion activity for a specific product or event, and is a means of increasing sales by offering special prices or benefits for a specific period of time.

[0167] A "visualization tool" is a method or technique for visually displaying data as graphs or charts.

[0168] "Input means" is an interface through which an administrator can set up advertising campaigns and other settings.

[0169] A "generative AI model" is a model created using machine learning and deep learning algorithms to perform data analysis and predictions.

[0170] A "prompt sentence" is an instruction sentence that prompts a specific operation or input.

[0171] A "solar panel" is a device that converts sunlight into electricity.

[0172] "Electricity storage device" refers to a battery or electrical equipment for storing generated electricity.

[0173] An "API" is an interface for exchanging data between different software.

[0174] This invention is a system for achieving efficient operation and sustainability of capsule toy machines. The system includes the following main components:

[0175] Embodiment of terminal (capsule toy machine)

[0176] The terminals are equipped with sales sensors and environmental sensors. The sales sensors measure the number of capsule toys sold, while the environmental sensors collect data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via communication lines (4G / 5G). The terminals are also equipped with solar panels that use sunlight to generate electricity, which is then stored in a power storage device. This ensures a sustainable power supply and reduces the environmental impact.

[0177] Server embodiment

[0178] The server receives sales information and environmental data sent from the device and stores them in a database. A sales forecasting model is generated based on this stored data. This model uses machine learning algorithms to predict future sales and enable effective inventory management. The server also obtains social media trend information and related product sales data via API and integrates it into the database. This enables more detailed analysis of marketing data. The server also obtains real-time sales information and environmental data, and performs data analysis and visualization to support managerial decision-making.

[0179] The server calculates the optimal replenishment route based on the generated sales forecast model. This replenishment route information is notified to delivery companies, realizing efficient replenishment without waste. It also provides an input method for administrators to set up advertising campaigns.

[0180] User (operation manager) implementation form

[0181] Operations managers can use the management dashboard to check real-time sales information and inventory status. The dashboard also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation. Managers can also use the dashboard to set up advertisements based on new events and campaigns. These set advertising campaigns are distributed to each terminal via the server and notified to customers via display screens and voice messages.

[0182] For example, when setting up a Christmas campaign, the administrator can apply discounts to specific products and send that information to each terminal via the server, thereby attracting interest in the specific products and increasing sales.

[0183] Hardware and software used

[0184] Hardware: smartphones, IoT sensors, solar panels, energy storage devices

[0185] Software: Python, Flask (web framework), Pandas (data analysis library), Matplotlib (data visualization library), Requests (API communication library)

[0186] Examples of prompt statements

[0187] Prompt to retrieve sales data from the server: "Please retrieve capsule toy sales data and visualize it."

[0188] Prompt to set up a campaign: "For product ID 1234, set up a campaign offering 20% ​​off from December 20th to 25th."

[0189] This enables efficient inventory management, optimized marketing campaigns, sustainable power supply, and managerial decision support.

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] The server receives sales information and environmental data from the terminal.

[0193] Input: Sales information and environmental data sent from the terminal

[0194] Output: Raw sales and environmental data

[0195] The server receives data from the device in real time and performs initial processing to store it in a database. Specifically, the server receives data packets sent from the device via 4G or 5G communication lines and analyzes the contents. The received data includes sales volume, temperature, humidity, foot traffic, etc., and identifies and classifies each data point.

[0196] Step 2:

[0197] The server stores the received sales information and environmental data in a database.

[0198] Input: Identified and classified sales information and environmental data

[0199] Output: Sales information and environmental data stored in a database

[0200] The server converts the received data into an appropriate format and stores it in the database. Specifically, it establishes a connection to the database and writes the sales information to the sales table and the environmental data to the environmental table.

[0201] Step 3:

[0202] The server generates a sales forecast model based on the stored data.

[0203] Input: Sales information and environmental data stored in a database

[0204] Output: Generated sales forecast model

[0205] The server analyzes the stored past sales data and environmental data using a machine learning algorithm to generate a sales forecasting model. Specifically, it creates a training dataset using a Python machine learning library (such as scikit-learn) and builds a forecasting model based on that data.

[0206] Step 4:

[0207] The server calculates the optimal delivery route based on the generated sales forecast model.

[0208] Input: Generated sales forecast model

[0209] Output: Optimal delivery route

[0210] The server calculates the predicted stock-out timing for each terminal based on the sales forecast model, and generates the optimal delivery route based on this.Specifically, it uses a Python route optimization library (such as ORTools) to calculate a route that minimizes delivery cost and time.

[0211] Step 5:

[0212] The server notifies the delivery company of the optimized delivery route.

[0213] Input: Optimal delivery route

[0214] Output: Delivery route notified to the delivery company

[0215] The server sends the calculated delivery route information to the specified delivery company API and notifies it. Specifically, it sends the optimized route information via a POST request to the delivery company's specific API endpoint.

[0216] Step 6:

[0217] Users can view real-time sales information and inventory status on an administrative dashboard.

[0218] Input: Real-time data stored in a database

[0219] Output: Sales information and inventory status displayed on a dashboard

[0220] Users can access the dashboard via a web browser and check real-time data. Specifically, sales information and environmental data are displayed in graphs on the dashboard, allowing users to check them.

[0221] Step 7:

[0222] A user sets up an advertising campaign through a dashboard.

[0223] Input: Campaign setting information entered by the administrator

[0224] Output: Configured ad campaigns

[0225] Users use the dashboard's input form to set up advertising campaigns for specific products and time periods. Specifically, they enter campaign information into the form and press the "Set" button, which sends the information to the server.

[0226] Step 8:

[0227] A server transmits a configured advertising campaign to multiple terminals.

[0228] Input: Set advertising campaign information

[0229] Output: Advertising campaign information sent to each device

[0230] The server distributes advertising campaign information set by the user to each terminal. Specifically, the server sends the campaign information to the communication address of each terminal and sets the terminal to display the content on its display or as a voice message.

[0231] Step 9:

[0232] The server provides marketing optimization support using generative AI models.

[0233] Input: Sales information, environmental data, and social media trend information stored in the database

[0234] Output: Optimized marketing strategies

[0235] The server uses the generative AI model to analyze sales data, environmental data, and social media trend information to optimize marketing strategies. Specifically, it analyzes data using Python's machine learning library and recommends optimal marketing actions.

[0236] Step 10:

[0237] The server automatically sets up a campaign based on the generated prompt text.

[0238] Input: A prompt generated by a generative AI model

[0239] Output: Automated ad campaign

[0240] The server automatically sets up advertising campaigns based on the prompts created by the generative AI model. Specifically, it analyzes the generated prompts, creates campaign setting information with appropriate content, and sends it to each device.

[0241] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0242] This invention is a system that combines a capsule toy machine with a power source, IoT devices, and an emotion engine to operate as an autonomous machine. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information and environmental data in real time, analyzing emotion data, utilizing marketing data, and calculating optimal replenishment routes.

[0243] Embodiment of terminal (capsule toy machine)

[0244] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[0245] The device is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. This emotional data is transmitted along with sales information and environmental data. Solar panels are used to convert sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces environmental impact.

[0246] Server embodiment

[0247] The server receives the sales information, environmental data, and emotion data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[0248] The server also retrieves social media trend information and related product sales data via API and integrates it into the database. This allows for more detailed analysis of marketing data. Furthermore, incorporating emotion data obtained from the emotion engine into the analysis improves the accuracy of the sales forecast model.

[0249] The optimal replenishment route is calculated based on a sales forecast model. By taking into account emotional data, the system predicts when customers' purchasing motivation will be highest and sets an optimal replenishment schedule. The server then notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[0250] User (operation manager) implementation form

[0251] Operations managers can check real-time sales information and inventory status using the management dashboard, which displays sales statistics, environmental data, and even user sentiment data, allowing managers to quickly and accurately grasp the situation.

[0252] Administrators can also use the dashboard to set up advertisements based on new events or campaigns. These set up advertising campaigns are then distributed to each device via the server. Furthermore, the content and timing of advertising campaigns can be optimized based on data obtained from the emotion engine. This allows for more precise targeting and maximizes user purchasing intent.

[0253] For example, if a campaign for a specific product is to be carried out at a shopping mall where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends, surrounding environmental data, and even sentiment data, and then launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[0254] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and marketing strategies that utilize user sentiment data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0255] The processing flow will be explained below.

[0256] Processing of terminals (capsule toy machines)

[0257] Sales information collection and transmission process

[0258] Step 1:

[0259] The terminal uses a sales sensor to measure the number of capsule toys sold. Specifically, it counts each time a capsule toy is dispensed.

[0260] Step 2:

[0261] The device uses environmental sensors to capture surrounding environmental data such as temperature, humidity, and foot traffic, allowing it to record the environmental conditions of the location in real time.

[0262] Step 3:

[0263] The device uses an emotion engine to analyze the user's voice and facial expressions to collect emotional data, for example, analyzing voice to determine whether the user is having fun.

[0264] Step 4:

[0265] The device then packets the collected sales data, environmental data, and emotion data, each of which includes the type of data and a timestamp.

[0266] Step 5:

[0267] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is done at regular intervals, and the data is always kept up to date.

[0268] Powered by solar panels

[0269] Step 1:

[0270] The device uses solar panels to convert sunlight into electricity, and is designed to efficiently absorb sunlight during the day.

[0271] Step 2:

[0272] The generated power is stored in a power storage device, and is used to operate sensors and communication devices.

[0273] Server Processing

[0274] Data Receipt and Storage Process

[0275] Step 1:

[0276] The server receives the data packets sent from the device and checks that the data is not lost or distorted according to the communication protocol.

[0277] Step 2:

[0278] The server analyzes the received data and extracts sales information, environmental data, and emotional data, which are then stored in a database.

[0279] Generate a sales forecast model

[0280] Step 1:

[0281] The server retrieves historical sales data, environmental data, and emotional data stored in the database, providing the most up-to-date information.

[0282] Step 2:

[0283] The server uses the acquired data to apply machine learning algorithms to generate a sales forecasting model, which is used to predict future sales.

[0284] Step 3:

[0285] The generated sales forecast model is evaluated to check its accuracy. If the accuracy is insufficient, the model is retrained to improve it.

[0286] Delivery route optimization

[0287] Step 1:

[0288] The server creates a list of devices that need replenishment based on the sales forecast model, predicts when inventory will be low, and prevents unnecessary replenishment.

[0289] Step 2:

[0290] The server calculates the optimal replenishment route, taking into account the distance to each replenishment point and traffic conditions.

[0291] Step 3:

[0292] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[0293] User (operation administrator) processing

[0294] Dashboard management process

[0295] Step 1:

[0296] Users access a management dashboard that provides real-time sales information, inventory status, and sentiment data, which is visually displayed in graphs and tables.

[0297] Step 2:

[0298] Users analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including ad content and duration.

[0299] Step 3:

[0300] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[0301] Step 4:

[0302] Through the dashboard, users can optimize the content and timing of their advertising campaigns based on data obtained from the emotion engine, resulting in highly accurate targeting and maximizing user purchasing intent.

[0303] In this way, this system, combined with the emotion engine, enables efficient operation of capsule toy machines and a highly accurate marketing strategy. Inventory management based on sales forecasts and advertising campaigns utilizing emotion data significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0304] Example 2

[0305] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0306] In the operation of conventional capsule toy machines, it was difficult to collect sales information and environmental data in real time, and marketing strategies utilizing emotional data were limited. As a result, inventory management and replenishment routes were not adequately optimized, leading to reduced operational efficiency, wasted energy, and increased CO2 emissions. Effective targeting of advertising campaigns was also difficult. These issues need to be resolved.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0308] In this invention, the server includes means for receiving sales information, means for receiving environmental data, means for receiving emotion data, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for integrating SNS trend information and sales data of related products, means for calculating an optimal replenishment route based on the sales forecast model, means for notifying a delivery company of the optimized replenishment route, means for displaying real-time data on a management dashboard, and means for setting up and distributing advertising campaigns based on the displayed data. This enables efficient inventory management of capsule toy machines, optimization of operations based on environmental data, and realization of marketing strategies utilizing emotion data.

[0309] "Sales information" refers to data indicating the number of capsule products sold by the capsule toy machine and the sales amount.

[0310] "Environmental data" refers to data related to the operating environment of the capsule toy machine, such as temperature, humidity, and foot traffic.

[0311] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[0312] "Packetization" is a process of consolidating multiple pieces of collected data based on a certain standard.

[0313] "Communication lines" refers to communication infrastructure such as the Internet and 4G / 5G networks used to send and receive data.

[0314] A "database" is an information system for efficiently storing, retrieving, and managing structured data.

[0315] A "sales forecasting model" is a statistical model that uses machine learning algorithms to predict future sales based on collected data.

[0316] "Optimization" is the best way to allocate and operate resources to achieve a specific goal.

[0317] A "delivery route" is a route taken by a delivery company set up for replenishing capsule toy machines and managing inventory.

[0318] The "management dashboard" is a web-based interface that allows operations managers to check various data and perform operations in real time.

[0319] An "advertising campaign" is a series of advertising activities to promote a particular product or service.

[0320] A "solar panel" is a device that converts sunlight into electricity.

[0321] A "power storage device" is a device that stores generated electricity and supplies it when needed.

[0322] "SNS trend information" refers to information and content that is trending on social networking services.

[0323] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[0324] The present invention relates to a system for collecting sales information, environmental data, and emotion data from capsule toy machines in real time, and for carrying out efficient inventory management, marketing strategies, and energy management. Specific embodiments of the present invention are described below.

[0325] Server embodiment

[0326] The server operates using the following hardware and software:

[0327] Hardware: Standard server machine (CPU, memory, storage)

[0328] Software: Cloud infrastructure (e.g., Amazon Web Services), machine learning algorithms (e.g., TensorFlow), database management systems (e.g., MySQL, MongoDB)

[0329] The server's main function is to receive sales information, environmental data, and emotion data sent from the device and store it in a database. A sales forecasting model is generated using a machine learning algorithm based on this stored data. It also obtains social media trend information and related product sales data via API and integrates it into the database. This integrated data is used to improve the accuracy of the sales forecasting model.

[0330] The server then calculates the optimal replenishment route based on the sales forecast model. The results are then sent to the delivery company via email, SMS, or a dedicated app, enabling an efficient replenishment schedule.

[0331] An example of a specific prompt is as follows:

[0332] "Please generate a sales forecasting model by integrating capsule toy machine sales information, environmental data, and sentiment data. Please also take into account social media trend information and sales data for related products."

[0333] Embodiment of terminal (capsule toy machine)

[0334] The device is equipped with the following hardware:

[0335] Hardware: Raspberry Pi, sales sensor, environmental sensors (temperature and humidity sensor, human presence sensor), camera, microphone, 4G / 5G communication module, solar panel, power storage device

[0336] The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. Furthermore, an emotion engine is installed to analyze the user's voice and facial expressions to collect emotional data. This data is packetized at regular intervals and sent to a server via a communication line (4G / 5G).

[0337] The solar panel converts sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces the environmental impact. The device always performs optimal power management, ensuring efficient operation.

[0338] An example of a specific prompt is as follows:

[0339] "Please packetize the data obtained from the sales sensor, environmental sensor, and emotion engine and periodically send it to the server. Please use the solar panel to provide energy."

[0340] User (operation manager) implementation form

[0341] The operations manager uses the management dashboard to perform the following operations:

[0342] Real-time confirmation of sales information, inventory status, environmental data, and sentiment data

[0343] Advertisements based on new events and campaigns are set up and delivered to each device via the server.

[0344] Monitor and analyze data to take appropriate action

[0345] The dashboard displays detailed data using interactive charts and graphs, and uses sentiment data to optimize the content and timing of advertising campaigns to maximize user purchase intent.

[0346] An example of a specific prompt is as follows:

[0347] "Check sales information, inventory status, environmental data, and sentiment data from the dashboard and set up new advertising campaigns. The ads you set up are delivered to each device via our server."

[0348] As described above, the embodiment of the present invention collects and analyzes sales information, environmental data, and sentiment data in real time to realize optimal inventory management and marketing strategies, while also saving energy and reducing environmental impact.

[0349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0350] server

[0351] Step 1: Receiving the data

[0352] The server receives packets of sales information, environmental data, and emotion data sent from the device. As input, it receives the data packet containing each data field, analyzes and decomposes the payload, and as output, it extracts the sales information, environmental data, and emotion data as separate datasets.

[0353] Specific operation: The server opens a receiving port and waits for data from each terminal. When the data arrives, it analyzes the payload and extracts each data field.

[0354] Step 2: Save your data

[0355] The server stores the received data in a database. As input, it receives analyzed sales information, environmental data, and emotion data. As output, each piece of information is stored in the corresponding table in the database.

[0356] What happens: The server connects to the database and inserts the data into the appropriate tables using SQL queries or NoSQL document operations.

[0357] Step 3: Generate a sales forecast model

[0358] The server uses machine learning algorithms to generate a sales forecasting model based on the stored data. As input, it takes sales information from the database, environmental data, and sentiment data. The output is a trained sales forecasting model.

[0359] What it does: The server queries the required data from the database, pre-processes the data, and then feeds it to the machine learning algorithm to train the model.

[0360] Step 4: Obtaining trend information

[0361] The server obtains SNS trend information and related product sales data via API. As input, it sends an API request and receives trend information and sales data. The output is to store the obtained data in a database.

[0362] Specific operation: The server sends API requests at specified intervals, converts the received data into an appropriate format, and stores it in the database.

[0363] Step 5: Model integration and analysis

[0364] The server integrates sales data, environmental data, sentiment data, and social media trend information and analyzes the sales forecasting model. It takes all these datasets as input. The output is the integrated dataset and an improved sales forecasting model.

[0365] Specific operation: The server integrates each dataset and uses the integrated data to retrain the model and tune parameters.

[0366] Step 6: Calculate replenishment routes

[0367] The server calculates the optimal replenishment route based on the sales forecast model and real-time emotion data. The inputs are the sales forecast model and real-time emotion data. The output is the optimal replenishment route and time schedule.

[0368] Specific operation: The server analyzes the location information and inventory information of each terminal and calculates the optimal replenishment route using Dijkstra's algorithm or A algorithm.

[0369] Step 7: Send notifications

[0370] The server notifies the calculated replenishment route information to the delivery company. As input, it receives the optimized replenishment route information. The output is a notification message to the delivery company.

[0371] Specific operation: Based on the generated replenishment schedule, the server creates a notification message for the delivery company and sends it via email, SMS, or a dedicated app.

[0372] Terminal (capsule toy machine)

[0373] Step 1: Collect data

[0374] The device collects data using sales sensors, environmental sensors, and an emotion engine. The inputs include sales volume, temperature, humidity, foot traffic, and voice and facial expression data. The output is the collected data.

[0375] Specific operation: The sales sensor increments the count each time it is triggered, the environmental sensor periodically captures data and stores it in a buffer, and the emotion engine analyzes audio and video data at regular intervals to generate emotion data.

[0376] Step 2: Packetize and transmit the data

[0377] The terminal packetizes the collected sales information, environmental data, and emotion data and sends them to the server. The terminal receives the collected data as input. The output is the packetized data.

[0378] Specific operation: The terminal collects all sensor data into one packet, properly formats each data field, and then transmits it through the 4G / 5G communication module.

[0379] Step 3: Managing the power supply

[0380] The device uses a solar panel to convert sunlight into electricity and stores it in a storage device. The input is sunlight. The output is the generated electricity.

[0381] How it works: The device's control unit monitors the power generated by the solar panel and controls charging, supplying power from the storage device as needed.

[0382] User (operation administrator)

[0383] Step 1: Use the dashboard

[0384] Users use the management dashboard to view real-time sales information, inventory status, environmental data, and sentiment data. As input, it receives data sent from the server. The output is detailed data displayed on the dashboard.

[0385] What it does: A user opens a web browser and logs into the dashboard, which displays data in sections and allows users to drill down into details using interactive charts and graphs.

[0386] Step 2: Set up your ad campaign

[0387] Users can set up new event- and campaign-based ads through the dashboard and distribute them to devices via the server. The input is the campaign content and conditions. The output is the configured ad campaign.

[0388] How it works: Users enter campaign details, duration, target audience, etc. into the dashboard and save the settings. The server then sends this information to each device, and the ads are displayed and played through the device's display and speaker.

[0389] Step 3: Monitor and analyze the data

[0390] Users can monitor sales statistics, environmental data, and sentiment data on the dashboard, analyze them, and take appropriate actions. The input is real-time data, and the output is analysis results and actions based on them.

[0391] What happens next? Users use the dashboard's filters and analytical tools to narrow down data for specific periods and conditions, discover trends and anomalies, and take action based on those findings.

[0392] Above we have detailed how the overall system functions specifically at each processing step to turn data from input to output.

[0393] (Application example 2)

[0394] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0395] With conventional capsule toy machines, managing sales information and environmental data was complicated, making optimal inventory management and marketing strategies difficult. Furthermore, the accuracy of sales forecasts using emotion data was not sufficiently improved, and there was a need for real-time data confirmation and more efficient replenishment work. In addition, there was a lack of a way for managers to easily access data and check efficient replenishment routes.

[0396] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0397] In this invention, the server includes a means for collecting sales information, a means for collecting environmental data, and a means for collecting emotional data. This allows for real-time analysis of user emotional data along with sales information and environmental data, improving the accuracy of the sales forecast model. Furthermore, by providing the smart glasses with a means for displaying real-time data and replenishment route information, managers can easily access the data and perform efficient replenishment work. This allows for the optimization of inventory management and marketing strategies, and the efficiency of management work to be achieved.

[0398] "Sales information" refers to data such as the number of units sold and the sales amount from capsule toy machines.

[0399] "Environmental data" refers to information such as temperature, humidity, and foot traffic in the area where the capsule toy machine is installed.

[0400] "Packetization" refers to converting collected data into a certain format so that it can be transmitted over a communication line.

[0401] "Communication lines" refer to the infrastructure for sending and receiving data, specifically wireless communication networks such as 4G and 5G.

[0402] "Database" refers to a computer system for storing and managing various collected data.

[0403] A "sales forecasting model" refers to a statistical and machine learning algorithm that predicts future sales based on past sales information, environmental data, and emotional data.

[0404] "Delivery route" refers to the optimized route for replenishing and delivering capsule toys.

[0405] An "administrative dashboard" is a software interface used by operations managers to monitor real-time data and set up advertising campaigns.

[0406] An "advertising campaign" refers to a marketing activity that distributes information for the purpose of sales promotion at a specific time.

[0407] "Emotion data" refers to information that indicates the emotional state of the user analyzed from their voice and facial expressions.

[0408] "Smart glasses" refer to a wearable device that can be worn by a user and display real-time information in their field of vision.

[0409] "Refill route information" refers to data regarding the optimal refill route for a capsule toy machine.

[0410] Terminal embodiment

[0411] The terminal is equipped with various sensors that collect sales information and environmental data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via a communication line (4G / 5G). The terminal is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. The terminal is powered by a solar panel and operates sustainably using the power stored in a power storage device.

[0412] Server embodiment

[0413] The server receives sales information, environmental data, and emotion data sent from the device and stores them in a database. A sales forecasting model is generated based on the stored data, and this model is created using a machine learning algorithm (specifically, TensorFlow or Scikit-learn). Furthermore, by obtaining social media trend information and related product sales data via API and integrating this into the database, more detailed analysis of marketing data becomes possible. Emotional data is also incorporated into the analysis to improve the accuracy of the sales forecasting model. Furthermore, the optimal replenishment route is calculated based on the generated sales forecasting model, and this information is notified to the delivery company.

[0414] User's embodiment

[0415] Operations managers can check various data in real time using the management dashboard. The dashboard displays sales statistics, environmental data, and user sentiment data, allowing managers to quickly grasp the situation. Managers can also set up advertising campaigns from the dashboard and distribute them to each device. Based on data obtained from the sentiment engine, it is also possible to optimize the content and distribution timing of advertising campaigns. Furthermore, by using smart glasses, managers can visually check necessary data in real time during replenishment work and information on the optimal replenishment route.

[0416] Specific examples

[0417] Imagine a scenario where a shopping mall manager wears smart glasses and stands in front of a capsule toy machine to check "sales data" and "stock status." In this case, the manager can display the data directly in his field of vision and immediately begin replenishing the inventory. Examples of specific prompts include "Show me the latest sales figures for the capsule toy machine" and "Tell me the current stock status."

[0418] This system enables efficient capsule toy management, utilizes marketing data, and enables real-time data collection and analysis. Furthermore, by incorporating user sentiment data, the accuracy of sales forecasts can be improved and optimal replenishment schedules and routes can be generated. Furthermore, managers can visually check the data through smart glasses, improving the efficiency of replenishment work and marketing activities.

[0419] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0420] Step 1: The device collects sales information

[0421] A sales sensor installed in the terminal measures the number of capsule toys sold.

[0422] (Input) Sales Event

[0423] (Data processing) Add the number of items sold to the counter

[0424] (Output) Updated sales information

[0425] (Specific operation) The sensor detects the release of the toy capsule and updates the count data.

[0426] Step 2: Device collects environmental data

[0427] Environmental sensors installed on the device measure temperature, humidity, foot traffic, etc.

[0428] (Input) Surrounding environmental conditions

[0429] (Data processing) Read sensor data and convert it into a format

[0430] (Output) Environmental data

[0431] (Specific operation) Temperature and humidity sensors and human presence sensors periodically collect environmental information.

[0432] Step 3: The device collects emotion data

[0433] The device's emotion engine analyzes the user's voice and facial expressions to collect emotional data.

[0434] (Input) User's voice and facial expression

[0435] (Data processing) Analysis of audio and image data, conversion to emotional data

[0436] (Output) Emotion data

[0437] (Specific operation) The camera and microphone capture the user's facial expressions and voice, and the analysis engine measures their emotional state.

[0438] Step 4: Packetize the data and send it over the communication line to the server

[0439] The sales information, environmental data, and emotional data collected by the device are packaged into packets and sent to a server via 4G / 5G lines.

[0440] (Input) Sales information, environmental data, emotional data

[0441] (Data processing) Data formatting and packetization

[0442] (Output) Outgoing packets

[0443] (Specific operation) The data is formatted into a specific format, and the communication module forms a transmission packet, which is then sent to the server via the line.

[0444] Step 5: The server saves the received data to the database

[0445] The server receives the data sent from the terminal and stores it in a database.

[0446] (Input) Outgoing packets

[0447] (Data processing) Packet analysis and data insertion into database

[0448] (Output) Database records

[0449] (Specific Operation) The receiving module of the server analyzes the packet and inserts the data into the corresponding table.

[0450] Step 6: The server generates the sales forecast model

[0451] A sales forecasting model is generated using machine learning algorithms based on the data stored in the database.

[0452] (Input) Stored sales information, environmental data, and emotional data

[0453] (Data processing) Training machine learning models

[0454] (Output) Sales forecast model

[0455] (Specific operation) Using libraries such as TensorFlow and Scikit-learn, the algorithm analyzes the training dataset and generates a predictive model.

[0456] Step 7: The server calculates the optimal delivery route and notifies the delivery company.

[0457] The server calculates the optimal replenishment route based on the generated sales forecast model and notifies the delivery company.

[0458] (Input) Sales forecast model, geographic information

[0459] (Data processing) Application of optimization algorithms

[0460] (Output) Replenishment route information

[0461] (Specific operation) The server's calculation module optimizes the replenishment route, and the notification system sends instructions to the delivery company.

[0462] Step 8: Users review data via the admin dashboard

[0463] Operations managers view real-time data via an administrative dashboard.

[0464] (Input) Latest sales information, environmental data, emotional data

[0465] (Data processing) Data visualization

[0466] (Output) Dashboard screen

[0467] (Specific operation) The dashboard interface retrieves the latest data and displays it in graph and table format.

[0468] Step 9: User sets up ad campaign and sends it to each device

[0469] The operations manager sets up advertising campaigns from the dashboard and sends them to each device.

[0470] (Input) Advertising campaign setting information

[0471] (Data processing) Formatting and sending campaign data

[0472] (Output) Campaign instructions to each device

[0473] (Specific operation) Campaign information set from the dashboard is saved in a database and sent to each device for distribution.

[0474] Step 10: The server uses the emotional data to optimize the content and timing of the ad campaign.

[0475] Optimize the content and timing of advertising campaigns based on emotional data.

[0476] (Input) Emotion data, campaign setting information

[0477] (Data processing) Data analysis and optimization calculations

[0478] (Output) Optimized campaign timing and content

[0479] (Specific operation) A machine learning algorithm analyzes emotional data and calculates the optimal timing and content.

[0480] Step 11: The administrator checks the replenishment route information using the smart glasses.

[0481] Managers use smart glasses to check replenishment route information in real time.

[0482] (Input) Replenishment route information

[0483] (Data processing) Real-time display of data

[0484] (Output) HUD (Head-Up Display) for smart glasses

[0485] (Specific operation) The smart glasses display the replenishment route information sent from the server on the HUD.

[0486] Example prompt sentence:

[0487] "Show me the latest sales for capsule toy machines."

[0488] "Please let me know the current stock situation."

[0489] "Check the optimal replenishment route"

[0490] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0491] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0492] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0493] [Second embodiment]

[0494] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0495] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0496] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0497] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0498] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0499] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0500] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0501] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0502] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0503] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0504] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0505] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0506] This invention is a system that equips capsule toy machines with a power source and IoT devices to operate as standalone machines. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information in real time, utilizing marketing data, and calculating optimal replenishment routes.

[0507] Embodiment of terminal (capsule toy machine)

[0508] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[0509] The terminal is also equipped with a solar panel that uses sunlight to generate electricity and stores it in a storage device, ensuring a sustainable power supply and reducing the burden on the environment.

[0510] Server embodiment

[0511] The server receives the sales information and environmental data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[0512] The server also retrieves social media trend information and related product sales data via API and integrates it into the database, enabling more detailed analysis of marketing data.

[0513] The optimal replenishment route is calculated based on the sales forecast model. The server notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[0514] User (operation manager) implementation form

[0515] Operations managers can check real-time sales information and inventory status using the management dashboard, which also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation.

[0516] Administrators can also set up new event- or campaign-based advertising through the dashboard, which is then distributed to each device via the server, maximizing sales during specific event periods.

[0517] For example, if a campaign for a specific product is to be carried out at a station or event venue where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends and surrounding environmental data, and launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[0518] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and the implementation of campaigns using marketing data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0519] The processing flow will be explained below.

[0520] Processing of terminals (capsule toy machines)

[0521] Sales information collection and transmission process

[0522] Step 1:

[0523] The terminal uses a sales sensor to measure the number of capsule toys sold. For example, each time a capsule toy is dispensed, a counter increases by one.

[0524] Step 2:

[0525] The device uses environmental sensors to capture data on the surrounding environment, such as temperature, humidity, and foot traffic, which then records the local environmental conditions.

[0526] Step 3:

[0527] The terminals then packetize the collected sales and environmental data, and these packets also contain meta-information such as the type of data and a timestamp.

[0528] Step 4:

[0529] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is performed at regular intervals, and the data is updated in real time.

[0530] Powered by solar panels

[0531] Step 1:

[0532] The device converts sunlight into electricity using solar panels, which are designed to efficiently absorb sunlight during the day.

[0533] Step 2:

[0534] The generated power is stored in a power storage device and used to operate sensors and communication devices.

[0535] Server Processing

[0536] Data Receipt and Storage Process

[0537] Step 1:

[0538] The server receives the data packets sent from the terminal, and the reception process is performed according to the communication protocol, checking that the data is not lost or distorted.

[0539] Step 2:

[0540] The received data is analyzed to extract sales information and environmental data, which are then stored in a database.

[0541] Generate a sales forecast model

[0542] Step 1:

[0543] The server retrieves existing sales and environmental data from the database. The retrieved data must be up-to-date.

[0544] Step 2:

[0545] The server applies machine learning algorithms to the acquired data to generate a sales forecasting model, which is used to predict future sales.

[0546] Step 3:

[0547] Evaluate the generated sales forecast model and check its accuracy. If the model accuracy is not above a certain level, retrain it.

[0548] Delivery route optimization

[0549] Step 1:

[0550] The server creates a list of devices that need replenishment based on the sales forecast model, and prevents unnecessary replenishment by predicting when inventory will be low.

[0551] Step 2:

[0552] The server calculates the optimal replenishment route for the delivery company, taking into account the distance to each replenishment point and traffic conditions.

[0553] Step 3:

[0554] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[0555] User (operation administrator) processing

[0556] Dashboard management process

[0557] Step 1:

[0558] Users access a management dashboard to view real-time sales information and inventory status, which is visually displayed in graphs and tables.

[0559] Step 2:

[0560] Users can analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including the content and duration of advertising sent to devices.

[0561] Step 3:

[0562] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[0563] This processing flow enables efficient operation of capsule toy machines and optimization of marketing strategies.

[0564] Example 1

[0565] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0566] Conventional capsule toy machines often required manual inventory management and sales data collection, making efficient operation difficult. Furthermore, environmental data was not collected and energy was not used effectively, making sustainable operation difficult. Furthermore, there was no system in place for real-time advertising tailored to customer needs, making it difficult to maximize sales.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0568] In this invention, the server includes means for collecting sales data, means for collecting environmental data, means for packetizing the collected sales data and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management display device, means for setting advertising activities based on the displayed data, and means for transmitting the set advertising activities to multiple terminals. This enables efficient management and sustainable operation of capsule toy machines. Furthermore, real-time data collection and analysis enables optimal inventory management and advertising activities, thereby maximizing sales.

[0569] "Sales data" is information regarding the number and price of products sold in a capsule toy machine.

[0570] "Environmental data" refers to information regarding the temperature, humidity, foot traffic, etc. at the location where the capsule toy machine is installed.

[0571] "Packetization" is the process of dividing multiple pieces of data into a certain format or unit so that they can be sent over a communication line.

[0572] A "communication line" refers to the network infrastructure for sending and receiving data, and includes, for example, 4G and 5G mobile communication networks.

[0573] A "terminal" is a device for collecting sales data and environmental data, and in the present invention refers to a capsule toy machine.

[0574] A "database" is a system for storing and managing data electronically in an organized form.

[0575] A "sales forecasting model" is a mathematical model for predicting future sales based on past sales data.

[0576] A "delivery route" is the optimal route for replenishing or delivering products.

[0577] The "management display device" is an interface for displaying sales data, inventory status, and the like in real time, and includes, for example, a dashboard.

[0578] "Advertising activities" are marketing activities aimed at promoting the sale of specific products or services.

[0579] A "photovoltaic power generation device" is a device that generates electricity using sunlight.

[0580] A "power storage device" is a device that can store generated electricity and supply it when needed.

[0581] A "social network service" is a platform that allows users to share information and interact with each other via the Internet.

[0582] An "application programming interface" is a set of definitions and protocols that allow software to interact with other software through an interface.

[0583] This invention is a system that equips a capsule toy machine with a power source and IoT devices, allowing it to operate as a standalone machine. Specifically, the capsule toy machine is equipped with sales sensors and environmental sensors, which collect sales data and environmental data and send it to a server via a communication line. The terminal is equipped with a solar power generation device, and the generated electricity is stored in a power storage device.

[0584] Terminal embodiment

[0585] The terminal is equipped with a sales sensor and an environmental sensor, which collect the respective data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, if a capsule toy machine installed at a station sells 100 capsule toys in a day during the summer, and the environmental data at that time is a temperature of 30°C and humidity of 70%, that information will be recorded by the sensors.

[0586] The device periodically converts the collected sales and environmental data into packets and transmits them to a server via 4G or 5G communication lines, where the data is accumulated in real time and used for further analysis.

[0587] Server embodiment

[0588] The server receives the sales data and environmental data sent from the device and stores it in a database. This data is stored and managed using specific software such as Python and MySQL. Based on the stored data, a sales forecasting model is generated using a machine learning algorithm (e.g., SKlearn). This forecasting model is used to forecast sales for the next week and achieve optimal inventory management.

[0589] The server also obtains social media trend information and sales data for related products via APIs (e.g., Twitter API) and integrates this data into the database. This enables multifaceted data analysis and improves the accuracy of the sales forecast model.

[0590] The server then calculates the optimal delivery route based on the sales forecast model. For example, it uses the Google Maps API to calculate the shortest distance and optimal time between points, and notifies the delivery company of this information. This allows for efficient replenishment.

[0591] User's embodiment

[0592] Operations managers can view real-time sales information and inventory status using a management dashboard, which displays sales data, environmental data, and analysis results from sales forecasting models.

[0593] Users can also set up new advertising activities through the dashboard. Once an advertising campaign is set up, it is distributed to each device via the server. For example, a campaign for a specific product during the summer vacation can be set up, and the information will be immediately notified to customers via the display and voice message of each capsule toy machine.

[0594] Prompt Sentence Examples

[0595] "Calculate the next week's sales forecast and optimal replenishment route based on capsule toy machine sales data and environmental data."

[0596] Introducing such a system will enable efficient management and sustainable operation of capsule toy machines, while also improving energy efficiency and reducing operating costs.

[0597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0598] Step 1:

[0599] The terminal collects sales data and environmental data. A sales sensor attached to the terminal measures the number of capsule toys sold, and an environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, the sales sensor measures the sales of 100 capsule toys, and the temperature at that time is 30°C and the humidity is 70%. This becomes the input data.

[0600] Step 2:

[0601] The data collected by the device is packetized. Specifically, sales data and environmental data are combined into a single data packet, which can then be sent to the server. For example, the data is packetized in JSON format as follows: {"sales": 100, "temperature": 30, "humidity": 70}.

[0602] Step 3:

[0603] The device transmits the packetized data over a communication line to the server, for example, using a 4G or 5G network. The output of this step is data packets that are received by the server.

[0604] Step 4:

[0605] The server stores the received data in a database. Specifically, it writes the received data to a database management system (e.g., MySQL). For example, it inserts the data into the database using an SQL query. The query executed is "INSERT INTO sales_data (sales, temperature, humidity) VALUES (100, 30, 70)".

[0606] Step 5:

[0607] The server generates a sales forecasting model based on the stored data. Specifically, it uses a machine learning algorithm (e.g., SKlearn) to train a model that predicts future sales from past data. The inputs to the forecasting model are past sales data and environmental data, and the output is future sales forecasts.

[0608] Step 6:

[0609] The server obtains social media trend information and sales data for related products via API and integrates them into a database. For example, use the Twitter API to obtain trend information related to capsule toys and store it in a database. The query "INSERT INTO trend_data (trend_info) VALUES ('Capsule Toy Boom')" is executed.

[0610] Step 7:

[0611] The server calculates the optimal delivery route based on the sales forecast model. Specifically, it uses the Google Maps API to calculate the delivery distance and time and identify the most efficient route. For example, it calculates the "shortest route from point A to point B" and notifies the delivery company of the results.

[0612] Step 8:

[0613] The server notifies the delivery company of the optimal delivery route information, for example, via email or a dedicated application. The output of this step is the route information received by the delivery company.

[0614] Step 9:

[0615] A user uses the management dashboard to check real-time sales information and inventory status. The user opens a web browser and accesses the management dashboard, where the latest sales data and environmental data are displayed as graphs and tables.

[0616] Step 10:

[0617] The user sets up a new advertising activity via the dashboard. For example, the user sets up a "summer vacation campaign" using the dashboard interface. This setting information is sent to the server.

[0618] Step 11:

[0619] The server sends the configured advertising activities to each terminal, for example, specific appealing messages or visual advertisements are instantly displayed on the display of each capsule toy machine.

[0620] (Application example 1)

[0621] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0622] Conventional capsule toy machines have limited collection of sales information and environmental data, making it difficult to optimize inventory management and marketing strategies. It has also been difficult for managers to obtain accurate data in real time and effectively set up advertising campaigns. Furthermore, there are issues with the power supply to each terminal, and improvements are needed from a sustainability perspective. The purpose of this invention is to solve these issues and realize efficient and sustainable capsule toy machine operation.

[0623] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0624] In this invention, the server includes means for collecting sales information, means for collecting environmental data, means for packetizing the collected sales information and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management dashboard, means for setting up an advertising campaign based on the displayed data, means for transmitting the set advertising campaign to multiple terminals, means for adding a visualization means for displaying data collected on the terminals to facilitate data display, means for providing an input means for an administrator to set up an advertising campaign, means for transmitting event data set based on the advertising campaign to the server and managing it, means for acquiring real-time sales information and environmental data and analyzing and visualizing the data to support administrator decision-making, means for supporting marketing optimization using a generative AI model on the data, and means for automatically setting up a campaign based on the generated prompt sentences.This enables efficient inventory management, marketing campaign optimization, sustainable power supply, and support for administrator decision-making.

[0625] "Sales information" is data regarding the quantity and price of products sold in capsule toy machines.

[0626] "Environmental data" refers to data related to the temperature, humidity, foot traffic, etc. of the location where the capsule toy machine is installed.

[0627] "Packetization" means organizing collected sales information and environmental data into a fixed format and breaking the data into smaller pieces for transmission over communication lines.

[0628] "Communication lines" refer to wireless networks such as 4G and 5G, which are the infrastructure for sending and receiving data.

[0629] A "database" is an information management system that effectively stores, searches, and analyzes collected data.

[0630] A "sales forecasting model" is an algorithm or statistical model for predicting future sales based on past sales data and environmental data.

[0631] "Delivery route optimization" is the calculation of the most efficient delivery route for replenishing products.

[0632] A "management dashboard" is a graphical user interface for displaying and analyzing sales information and environmental data in real time.

[0633] An "advertising campaign" is a sales promotion activity for a specific product or event, and is a means of increasing sales by offering special prices or benefits for a specific period of time.

[0634] A "visualization tool" is a method or technique for visually displaying data as graphs or charts.

[0635] "Input means" is an interface through which an administrator can set up advertising campaigns and other settings.

[0636] A "generative AI model" is a model created using machine learning and deep learning algorithms to perform data analysis and predictions.

[0637] A "prompt sentence" is an instruction sentence that prompts a specific operation or input.

[0638] A "solar panel" is a device that converts sunlight into electricity.

[0639] "Electricity storage device" refers to a battery or electrical equipment for storing generated electricity.

[0640] An "API" is an interface for exchanging data between different software.

[0641] This invention is a system for achieving efficient operation and sustainability of capsule toy machines. The system includes the following main components:

[0642] Embodiment of terminal (capsule toy machine)

[0643] The terminals are equipped with sales sensors and environmental sensors. The sales sensors measure the number of capsule toys sold, while the environmental sensors collect data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via communication lines (4G / 5G). The terminals are also equipped with solar panels that use sunlight to generate electricity, which is then stored in a power storage device. This ensures a sustainable power supply and reduces the environmental impact.

[0644] Server embodiment

[0645] The server receives sales information and environmental data sent from the device and stores them in a database. A sales forecasting model is generated based on this stored data. This model uses machine learning algorithms to predict future sales and enable effective inventory management. The server also obtains social media trend information and related product sales data via API and integrates it into the database. This enables more detailed analysis of marketing data. The server also obtains real-time sales information and environmental data, and performs data analysis and visualization to support managerial decision-making.

[0646] The server calculates the optimal replenishment route based on the generated sales forecast model. This replenishment route information is notified to delivery companies, realizing efficient replenishment without waste. It also provides an input method for administrators to set up advertising campaigns.

[0647] User (operation manager) implementation form

[0648] Operations managers can use the management dashboard to check real-time sales information and inventory status. The dashboard also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation. Managers can also use the dashboard to set up advertisements based on new events and campaigns. These set advertising campaigns are distributed to each terminal via the server and notified to customers via display screens and voice messages.

[0649] For example, when setting up a Christmas campaign, the administrator can apply discounts to specific products and send that information to each terminal via the server, thereby attracting interest in the specific products and increasing sales.

[0650] Hardware and software used

[0651] Hardware: smartphones, IoT sensors, solar panels, energy storage devices

[0652] Software: Python, Flask (web framework), Pandas (data analysis library), Matplotlib (data visualization library), Requests (API communication library)

[0653] Examples of prompt statements

[0654] Prompt to retrieve sales data from the server: "Please retrieve capsule toy sales data and visualize it."

[0655] Prompt to set up a campaign: "For product ID 1234, set up a campaign offering 20% ​​off from December 20th to 25th."

[0656] This enables efficient inventory management, optimized marketing campaigns, sustainable power supply, and managerial decision support.

[0657] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0658] Step 1:

[0659] The server receives sales information and environmental data from the terminal.

[0660] Input: Sales information and environmental data sent from the terminal

[0661] Output: Raw sales and environmental data

[0662] The server receives data from the device in real time and performs initial processing to store it in a database. Specifically, the server receives data packets sent from the device via 4G or 5G communication lines and analyzes the contents. The received data includes sales volume, temperature, humidity, foot traffic, etc., and identifies and classifies each data point.

[0663] Step 2:

[0664] The server stores the received sales information and environmental data in a database.

[0665] Input: Identified and classified sales information and environmental data

[0666] Output: Sales information and environmental data stored in a database

[0667] The server converts the received data into an appropriate format and stores it in the database. Specifically, it establishes a connection to the database and writes the sales information to the sales table and the environmental data to the environmental table.

[0668] Step 3:

[0669] The server generates a sales forecast model based on the stored data.

[0670] Input: Sales information and environmental data stored in a database

[0671] Output: Generated sales forecast model

[0672] The server analyzes the stored past sales data and environmental data using a machine learning algorithm to generate a sales forecasting model. Specifically, it creates a training dataset using a Python machine learning library (such as scikit-learn) and builds a forecasting model based on that data.

[0673] Step 4:

[0674] The server calculates the optimal delivery route based on the generated sales forecast model.

[0675] Input: Generated sales forecast model

[0676] Output: Optimal delivery route

[0677] The server calculates the predicted stock-out timing for each terminal based on the sales forecast model, and generates the optimal delivery route based on this.Specifically, it uses a Python route optimization library (such as ORTools) to calculate a route that minimizes delivery cost and time.

[0678] Step 5:

[0679] The server notifies the delivery company of the optimized delivery route.

[0680] Input: Optimal delivery route

[0681] Output: Delivery route notified to the delivery company

[0682] The server sends the calculated delivery route information to the specified delivery company API and notifies it. Specifically, it sends the optimized route information via a POST request to the delivery company's specific API endpoint.

[0683] Step 6:

[0684] Users can view real-time sales information and inventory status on an administrative dashboard.

[0685] Input: Real-time data stored in a database

[0686] Output: Sales information and inventory status displayed on a dashboard

[0687] Users can access the dashboard via a web browser and check real-time data. Specifically, sales information and environmental data are displayed in graphs on the dashboard, allowing users to check them.

[0688] Step 7:

[0689] A user sets up an advertising campaign through a dashboard.

[0690] Input: Campaign setting information entered by the administrator

[0691] Output: Configured ad campaigns

[0692] Users use the dashboard's input form to set up advertising campaigns for specific products and time periods. Specifically, they enter campaign information into the form and press the "Set" button, which sends the information to the server.

[0693] Step 8:

[0694] A server transmits a configured advertising campaign to multiple terminals.

[0695] Input: Set advertising campaign information

[0696] Output: Advertising campaign information sent to each device

[0697] The server distributes advertising campaign information set by the user to each terminal. Specifically, the server sends the campaign information to the communication address of each terminal and sets the terminal to display the content on its display or as a voice message.

[0698] Step 9:

[0699] The server provides marketing optimization support using generative AI models.

[0700] Input: Sales information, environmental data, and social media trend information stored in the database

[0701] Output: Optimized marketing strategies

[0702] The server uses the generative AI model to analyze sales data, environmental data, and social media trend information to optimize marketing strategies. Specifically, it analyzes data using Python's machine learning library and recommends optimal marketing actions.

[0703] Step 10:

[0704] The server automatically sets up a campaign based on the generated prompt text.

[0705] Input: A prompt generated by a generative AI model

[0706] Output: Automated ad campaign

[0707] The server automatically sets up advertising campaigns based on the prompts created by the generative AI model. Specifically, it analyzes the generated prompts, creates campaign setting information with appropriate content, and sends it to each device.

[0708] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0709] This invention is a system that combines a capsule toy machine with a power source, IoT devices, and an emotion engine to operate as an autonomous machine. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information and environmental data in real time, analyzing emotion data, utilizing marketing data, and calculating optimal replenishment routes.

[0710] Embodiment of terminal (capsule toy machine)

[0711] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[0712] The device is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. This emotional data is transmitted along with sales information and environmental data. Solar panels are used to convert sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces environmental impact.

[0713] Server embodiment

[0714] The server receives the sales information, environmental data, and emotion data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[0715] The server also retrieves social media trend information and related product sales data via API and integrates it into the database. This allows for more detailed analysis of marketing data. Furthermore, incorporating emotion data obtained from the emotion engine into the analysis improves the accuracy of the sales forecast model.

[0716] The optimal replenishment route is calculated based on a sales forecast model. By taking into account emotional data, the system predicts when customers' purchasing motivation will be highest and sets an optimal replenishment schedule. The server then notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[0717] User (operation manager) implementation form

[0718] Operations managers can check real-time sales information and inventory status using the management dashboard, which displays sales statistics, environmental data, and even user sentiment data, allowing managers to quickly and accurately grasp the situation.

[0719] Administrators can also use the dashboard to set up advertisements based on new events or campaigns. These set up advertising campaigns are then distributed to each device via the server. Furthermore, the content and timing of advertising campaigns can be optimized based on data obtained from the emotion engine. This allows for more precise targeting and maximizes user purchasing intent.

[0720] For example, if a campaign for a specific product is to be carried out at a shopping mall where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends, surrounding environmental data, and even sentiment data, and then launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[0721] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and marketing strategies that utilize user sentiment data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0722] The processing flow will be explained below.

[0723] Processing of terminals (capsule toy machines)

[0724] Sales information collection and transmission process

[0725] Step 1:

[0726] The terminal uses a sales sensor to measure the number of capsule toys sold. Specifically, it counts each time a capsule toy is dispensed.

[0727] Step 2:

[0728] The device uses environmental sensors to capture surrounding environmental data such as temperature, humidity, and foot traffic, allowing it to record the environmental conditions of the location in real time.

[0729] Step 3:

[0730] The device uses an emotion engine to analyze the user's voice and facial expressions to collect emotional data, for example, analyzing voice to determine whether the user is having fun.

[0731] Step 4:

[0732] The device then packets the collected sales data, environmental data, and emotion data, each of which includes the type of data and a timestamp.

[0733] Step 5:

[0734] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is done at regular intervals, and the data is always kept up to date.

[0735] Powered by solar panels

[0736] Step 1:

[0737] The device uses solar panels to convert sunlight into electricity, and is designed to efficiently absorb sunlight during the day.

[0738] Step 2:

[0739] The generated power is stored in a power storage device, and is used to operate sensors and communication devices.

[0740] Server Processing

[0741] Data Receipt and Storage Process

[0742] Step 1:

[0743] The server receives the data packets sent from the device and checks that the data is not lost or distorted according to the communication protocol.

[0744] Step 2:

[0745] The server analyzes the received data and extracts sales information, environmental data, and emotional data, which are then stored in a database.

[0746] Generate a sales forecast model

[0747] Step 1:

[0748] The server retrieves historical sales data, environmental data, and emotional data stored in the database, providing the most up-to-date information.

[0749] Step 2:

[0750] The server uses the acquired data to apply machine learning algorithms to generate a sales forecasting model, which is used to predict future sales.

[0751] Step 3:

[0752] The generated sales forecast model is evaluated to check its accuracy. If the accuracy is insufficient, the model is retrained to improve it.

[0753] Delivery route optimization

[0754] Step 1:

[0755] The server creates a list of devices that need replenishment based on the sales forecast model, predicts when inventory will be low, and prevents unnecessary replenishment.

[0756] Step 2:

[0757] The server calculates the optimal replenishment route, taking into account the distance to each replenishment point and traffic conditions.

[0758] Step 3:

[0759] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[0760] User (operation administrator) processing

[0761] Dashboard management process

[0762] Step 1:

[0763] Users access a management dashboard that provides real-time sales information, inventory status, and sentiment data, which is visually displayed in graphs and tables.

[0764] Step 2:

[0765] Users analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including ad content and duration.

[0766] Step 3:

[0767] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[0768] Step 4:

[0769] Through the dashboard, users can optimize the content and timing of their advertising campaigns based on data obtained from the emotion engine, resulting in highly accurate targeting and maximizing user purchasing intent.

[0770] In this way, this system, combined with the emotion engine, enables efficient operation of capsule toy machines and a highly accurate marketing strategy. Inventory management based on sales forecasts and advertising campaigns utilizing emotion data significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0771] Example 2

[0772] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0773] In the operation of conventional capsule toy machines, it was difficult to collect sales information and environmental data in real time, and marketing strategies utilizing emotional data were limited. As a result, inventory management and replenishment routes were not adequately optimized, leading to reduced operational efficiency, wasted energy, and increased CO2 emissions. Effective targeting of advertising campaigns was also difficult. These issues need to be resolved.

[0774] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0775] In this invention, the server includes means for receiving sales information, means for receiving environmental data, means for receiving emotion data, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for integrating SNS trend information and sales data of related products, means for calculating an optimal replenishment route based on the sales forecast model, means for notifying a delivery company of the optimized replenishment route, means for displaying real-time data on a management dashboard, and means for setting up and distributing advertising campaigns based on the displayed data. This enables efficient inventory management of capsule toy machines, optimization of operations based on environmental data, and realization of marketing strategies utilizing emotion data.

[0776] "Sales information" refers to data indicating the number of capsule products sold by the capsule toy machine and the sales amount.

[0777] "Environmental data" refers to data related to the operating environment of the capsule toy machine, such as temperature, humidity, and foot traffic.

[0778] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[0779] "Packetization" is a process of consolidating multiple pieces of collected data based on a certain standard.

[0780] "Communication lines" refers to communication infrastructure such as the Internet and 4G / 5G networks used to send and receive data.

[0781] A "database" is an information system for efficiently storing, retrieving, and managing structured data.

[0782] A "sales forecasting model" is a statistical model that uses machine learning algorithms to predict future sales based on collected data.

[0783] "Optimization" is the best way to allocate and operate resources to achieve a specific goal.

[0784] A "delivery route" is a route taken by a delivery company set up for replenishing capsule toy machines and managing inventory.

[0785] The "management dashboard" is a web-based interface that allows operations managers to check various data and perform operations in real time.

[0786] An "advertising campaign" is a series of advertising activities to promote a particular product or service.

[0787] A "solar panel" is a device that converts sunlight into electricity.

[0788] A "power storage device" is a device that stores generated electricity and supplies it when needed.

[0789] "SNS trend information" refers to information and content that is trending on social networking services.

[0790] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[0791] The present invention relates to a system for collecting sales information, environmental data, and emotion data from capsule toy machines in real time, and for carrying out efficient inventory management, marketing strategies, and energy management. Specific embodiments of the present invention are described below.

[0792] Server embodiment

[0793] The server operates using the following hardware and software:

[0794] Hardware: Standard server machine (CPU, memory, storage)

[0795] Software: Cloud infrastructure (e.g., Amazon Web Services), machine learning algorithms (e.g., TensorFlow), database management systems (e.g., MySQL, MongoDB)

[0796] The server's main function is to receive sales information, environmental data, and emotion data sent from the device and store it in a database. A sales forecasting model is generated using a machine learning algorithm based on this stored data. It also obtains social media trend information and related product sales data via API and integrates it into the database. This integrated data is used to improve the accuracy of the sales forecasting model.

[0797] The server then calculates the optimal replenishment route based on the sales forecast model. The results are then sent to the delivery company via email, SMS, or a dedicated app, enabling an efficient replenishment schedule.

[0798] An example of a specific prompt is as follows:

[0799] "Please generate a sales forecasting model by integrating capsule toy machine sales information, environmental data, and sentiment data. Please also take into account social media trend information and sales data for related products."

[0800] Embodiment of terminal (capsule toy machine)

[0801] The device is equipped with the following hardware:

[0802] Hardware: Raspberry Pi, sales sensor, environmental sensors (temperature and humidity sensor, human presence sensor), camera, microphone, 4G / 5G communication module, solar panel, power storage device

[0803] The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. Furthermore, an emotion engine is installed to analyze the user's voice and facial expressions to collect emotional data. This data is packetized at regular intervals and sent to a server via a communication line (4G / 5G).

[0804] The solar panel converts sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces the environmental impact. The device always performs optimal power management, ensuring efficient operation.

[0805] An example of a specific prompt is as follows:

[0806] "Please packetize the data obtained from the sales sensor, environmental sensor, and emotion engine and periodically send it to the server. Please use the solar panel to provide energy."

[0807] User (operation manager) implementation form

[0808] The operations manager uses the management dashboard to perform the following operations:

[0809] Real-time confirmation of sales information, inventory status, environmental data, and sentiment data

[0810] Advertisements based on new events and campaigns are set up and delivered to each device via the server.

[0811] Monitor and analyze data to take appropriate action

[0812] The dashboard displays detailed data using interactive charts and graphs, and uses sentiment data to optimize the content and timing of advertising campaigns to maximize user purchase intent.

[0813] An example of a specific prompt is as follows:

[0814] "Check sales information, inventory status, environmental data, and sentiment data from the dashboard and set up new advertising campaigns. The ads you set up are delivered to each device via our server."

[0815] As described above, the embodiment of the present invention collects and analyzes sales information, environmental data, and sentiment data in real time to realize optimal inventory management and marketing strategies, while also saving energy and reducing environmental impact.

[0816] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0817] server

[0818] Step 1: Receiving the data

[0819] The server receives packets of sales information, environmental data, and emotion data sent from the device. As input, it receives the data packet containing each data field, analyzes and decomposes the payload, and as output, it extracts the sales information, environmental data, and emotion data as separate datasets.

[0820] Specific operation: The server opens a receiving port and waits for data from each terminal. When the data arrives, it analyzes the payload and extracts each data field.

[0821] Step 2: Save your data

[0822] The server stores the received data in a database. As input, it receives analyzed sales information, environmental data, and emotion data. As output, each piece of information is stored in the corresponding table in the database.

[0823] What happens: The server connects to the database and inserts the data into the appropriate tables using SQL queries or NoSQL document operations.

[0824] Step 3: Generate a sales forecast model

[0825] The server uses machine learning algorithms to generate a sales forecasting model based on the stored data. As input, it takes sales information from the database, environmental data, and sentiment data. The output is a trained sales forecasting model.

[0826] What it does: The server queries the required data from the database, pre-processes the data, and then feeds it to the machine learning algorithm to train the model.

[0827] Step 4: Obtaining trend information

[0828] The server obtains SNS trend information and related product sales data via API. As input, it sends an API request and receives trend information and sales data. The output is to store the obtained data in a database.

[0829] Specific operation: The server sends API requests at specified intervals, converts the received data into an appropriate format, and stores it in the database.

[0830] Step 5: Model integration and analysis

[0831] The server integrates sales data, environmental data, sentiment data, and social media trend information and analyzes the sales forecasting model. It takes all these datasets as input. The output is the integrated dataset and an improved sales forecasting model.

[0832] Specific operation: The server integrates each dataset and uses the integrated data to retrain the model and tune parameters.

[0833] Step 6: Calculate replenishment routes

[0834] The server calculates the optimal replenishment route based on the sales forecast model and real-time emotion data. The inputs are the sales forecast model and real-time emotion data. The output is the optimal replenishment route and time schedule.

[0835] Specific operation: The server analyzes the location information and inventory information of each terminal and calculates the optimal replenishment route using Dijkstra's algorithm or A algorithm.

[0836] Step 7: Send notifications

[0837] The server notifies the calculated replenishment route information to the delivery company. As input, it receives the optimized replenishment route information. The output is a notification message to the delivery company.

[0838] Specific operation: Based on the generated replenishment schedule, the server creates a notification message for the delivery company and sends it via email, SMS, or a dedicated app.

[0839] Terminal (capsule toy machine)

[0840] Step 1: Collect data

[0841] The device collects data using sales sensors, environmental sensors, and an emotion engine. The inputs include sales volume, temperature, humidity, foot traffic, and voice and facial expression data. The output is the collected data.

[0842] Specific operation: The sales sensor increments the count each time it is triggered, the environmental sensor periodically captures data and stores it in a buffer, and the emotion engine analyzes audio and video data at regular intervals to generate emotion data.

[0843] Step 2: Packetize and transmit the data

[0844] The terminal packetizes the collected sales information, environmental data, and emotion data and sends them to the server. The terminal receives the collected data as input. The output is the packetized data.

[0845] Specific operation: The terminal collects all sensor data into one packet, properly formats each data field, and then transmits it through the 4G / 5G communication module.

[0846] Step 3: Managing the power supply

[0847] The device uses a solar panel to convert sunlight into electricity and stores it in a storage device. The input is sunlight. The output is the generated electricity.

[0848] How it works: The device's control unit monitors the power generated by the solar panel and controls charging, supplying power from the storage device as needed.

[0849] User (operation administrator)

[0850] Step 1: Use the dashboard

[0851] Users use the management dashboard to view real-time sales information, inventory status, environmental data, and sentiment data. As input, it receives data sent from the server. The output is detailed data displayed on the dashboard.

[0852] What it does: A user opens a web browser and logs into the dashboard, which displays data in sections and allows users to drill down into details using interactive charts and graphs.

[0853] Step 2: Set up your ad campaign

[0854] Users can set up new event- and campaign-based ads through the dashboard and distribute them to devices via the server. The input is the campaign content and conditions. The output is the configured ad campaign.

[0855] How it works: Users enter campaign details, duration, target audience, etc. into the dashboard and save the settings. The server then sends this information to each device, and the ads are displayed and played through the device's display and speaker.

[0856] Step 3: Monitor and analyze the data

[0857] Users can monitor sales statistics, environmental data, and sentiment data on the dashboard, analyze them, and take appropriate actions. The input is real-time data, and the output is analysis results and actions based on them.

[0858] What happens next? Users use the dashboard's filters and analytical tools to narrow down data for specific periods and conditions, discover trends and anomalies, and take action based on those findings.

[0859] Above we have detailed how the overall system functions specifically at each processing step to turn data from input to output.

[0860] (Application example 2)

[0861] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0862] With conventional capsule toy machines, managing sales information and environmental data was complicated, making optimal inventory management and marketing strategies difficult. Furthermore, the accuracy of sales forecasts using emotion data was not sufficiently improved, and there was a need for real-time data confirmation and more efficient replenishment work. In addition, there was a lack of a way for managers to easily access data and check efficient replenishment routes.

[0863] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0864] In this invention, the server includes a means for collecting sales information, a means for collecting environmental data, and a means for collecting emotional data. This allows for real-time analysis of user emotional data along with sales information and environmental data, improving the accuracy of the sales forecast model. Furthermore, by providing the smart glasses with a means for displaying real-time data and replenishment route information, managers can easily access the data and perform efficient replenishment work. This allows for the optimization of inventory management and marketing strategies, and the efficiency of management work to be achieved.

[0865] "Sales information" refers to data such as the number of units sold and the sales amount from capsule toy machines.

[0866] "Environmental data" refers to information such as temperature, humidity, and foot traffic in the area where the capsule toy machine is installed.

[0867] "Packetization" refers to converting collected data into a certain format so that it can be transmitted over a communication line.

[0868] "Communication lines" refer to the infrastructure for sending and receiving data, specifically wireless communication networks such as 4G and 5G.

[0869] "Database" refers to a computer system for storing and managing various collected data.

[0870] A "sales forecasting model" refers to a statistical and machine learning algorithm that predicts future sales based on past sales information, environmental data, and emotional data.

[0871] "Delivery route" refers to the optimized route for replenishing and delivering capsule toys.

[0872] An "administrative dashboard" is a software interface used by operations managers to monitor real-time data and set up advertising campaigns.

[0873] An "advertising campaign" refers to a marketing activity that distributes information for the purpose of sales promotion at a specific time.

[0874] "Emotion data" refers to information that indicates the emotional state of the user analyzed from their voice and facial expressions.

[0875] "Smart glasses" refer to a wearable device that can be worn by a user and display real-time information in their field of vision.

[0876] "Refill route information" refers to data regarding the optimal refill route for a capsule toy machine.

[0877] Terminal embodiment

[0878] The terminal is equipped with various sensors that collect sales information and environmental data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via a communication line (4G / 5G). The terminal is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. The terminal is powered by a solar panel and operates sustainably using the power stored in a power storage device.

[0879] Server embodiment

[0880] The server receives sales information, environmental data, and emotion data sent from the device and stores them in a database. A sales forecasting model is generated based on the stored data, and this model is created using a machine learning algorithm (specifically, TensorFlow or Scikit-learn). Furthermore, by obtaining social media trend information and related product sales data via API and integrating this into the database, more detailed analysis of marketing data becomes possible. Emotional data is also incorporated into the analysis to improve the accuracy of the sales forecasting model. Furthermore, the optimal replenishment route is calculated based on the generated sales forecasting model, and this information is notified to the delivery company.

[0881] User's embodiment

[0882] Operations managers can check various data in real time using the management dashboard. The dashboard displays sales statistics, environmental data, and user sentiment data, allowing managers to quickly grasp the situation. Managers can also set up advertising campaigns from the dashboard and distribute them to each device. Based on data obtained from the sentiment engine, it is also possible to optimize the content and distribution timing of advertising campaigns. Furthermore, by using smart glasses, managers can visually check necessary data in real time during replenishment work and information on the optimal replenishment route.

[0883] Specific examples

[0884] Imagine a scenario where a shopping mall manager wears smart glasses and stands in front of a capsule toy machine to check "sales data" and "stock status." In this case, the manager can display the data directly in his field of vision and immediately begin replenishing the inventory. Examples of specific prompts include "Show me the latest sales figures for the capsule toy machine" and "Tell me the current stock status."

[0885] This system enables efficient capsule toy management, utilizes marketing data, and enables real-time data collection and analysis. Furthermore, by incorporating user sentiment data, the accuracy of sales forecasts can be improved and optimal replenishment schedules and routes can be generated. Furthermore, managers can visually check the data through smart glasses, improving the efficiency of replenishment work and marketing activities.

[0886] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0887] Step 1: The device collects sales information

[0888] A sales sensor installed in the terminal measures the number of capsule toys sold.

[0889] (Input) Sales Event

[0890] (Data processing) Add the number of items sold to the counter

[0891] (Output) Updated sales information

[0892] (Specific operation) The sensor detects the release of the toy capsule and updates the count data.

[0893] Step 2: Device collects environmental data

[0894] Environmental sensors installed on the device measure temperature, humidity, foot traffic, etc.

[0895] (Input) Surrounding environmental conditions

[0896] (Data processing) Read sensor data and convert it into a format

[0897] (Output) Environmental data

[0898] (Specific operation) Temperature and humidity sensors and human presence sensors periodically collect environmental information.

[0899] Step 3: The device collects emotion data

[0900] The device's emotion engine analyzes the user's voice and facial expressions to collect emotional data.

[0901] (Input) User's voice and facial expression

[0902] (Data processing) Analysis of audio and image data, conversion to emotional data

[0903] (Output) Emotion data

[0904] (Specific operation) The camera and microphone capture the user's facial expressions and voice, and the analysis engine measures their emotional state.

[0905] Step 4: Packetize the data and send it over the communication line to the server

[0906] The sales information, environmental data, and emotional data collected by the device are packaged into packets and sent to a server via 4G / 5G lines.

[0907] (Input) Sales information, environmental data, emotional data

[0908] (Data processing) Data formatting and packetization

[0909] (Output) Outgoing packets

[0910] (Specific operation) The data is formatted into a specific format, and the communication module forms a transmission packet, which is then sent to the server via the line.

[0911] Step 5: The server saves the received data to the database

[0912] The server receives the data sent from the terminal and stores it in a database.

[0913] (Input) Outgoing packets

[0914] (Data processing) Packet analysis and data insertion into database

[0915] (Output) Database records

[0916] (Specific Operation) The receiving module of the server analyzes the packet and inserts the data into the corresponding table.

[0917] Step 6: The server generates the sales forecast model

[0918] A sales forecasting model is generated using machine learning algorithms based on the data stored in the database.

[0919] (Input) Stored sales information, environmental data, and emotional data

[0920] (Data processing) Training machine learning models

[0921] (Output) Sales forecast model

[0922] (Specific operation) Using libraries such as TensorFlow and Scikit-learn, the algorithm analyzes the training dataset and generates a predictive model.

[0923] Step 7: The server calculates the optimal delivery route and notifies the delivery company.

[0924] The server calculates the optimal replenishment route based on the generated sales forecast model and notifies the delivery company.

[0925] (Input) Sales forecast model, geographic information

[0926] (Data processing) Application of optimization algorithms

[0927] (Output) Replenishment route information

[0928] (Specific operation) The server's calculation module optimizes the replenishment route, and the notification system sends instructions to the delivery company.

[0929] Step 8: Users review data via the admin dashboard

[0930] Operations managers view real-time data via an administrative dashboard.

[0931] (Input) Latest sales information, environmental data, emotional data

[0932] (Data processing) Data visualization

[0933] (Output) Dashboard screen

[0934] (Specific operation) The dashboard interface retrieves the latest data and displays it in graph and table format.

[0935] Step 9: User sets up ad campaign and sends it to each device

[0936] The operations manager sets up advertising campaigns from the dashboard and sends them to each device.

[0937] (Input) Advertising campaign setting information

[0938] (Data processing) Formatting and sending campaign data

[0939] (Output) Campaign instructions to each device

[0940] (Specific operation) Campaign information set from the dashboard is saved in a database and sent to each device for distribution.

[0941] Step 10: The server uses the emotional data to optimize the content and timing of the ad campaign.

[0942] Optimize the content and timing of advertising campaigns based on emotional data.

[0943] (Input) Emotion data, campaign setting information

[0944] (Data processing) Data analysis and optimization calculations

[0945] (Output) Optimized campaign timing and content

[0946] (Specific operation) A machine learning algorithm analyzes emotional data and calculates the optimal timing and content.

[0947] Step 11: The administrator checks the replenishment route information using the smart glasses.

[0948] Managers use smart glasses to check replenishment route information in real time.

[0949] (Input) Replenishment route information

[0950] (Data processing) Real-time display of data

[0951] (Output) HUD (Head-Up Display) for smart glasses

[0952] (Specific operation) The smart glasses display the replenishment route information sent from the server on the HUD.

[0953] Example prompt sentence:

[0954] "Show me the latest sales for capsule toy machines."

[0955] "Please let me know the current stock situation."

[0956] "Check the optimal replenishment route"

[0957] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0958] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0959] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0960] [Third embodiment]

[0961] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0962] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0963] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0964] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0965] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0966] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0967] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0968] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0969] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0970] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0971] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0972] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0973] This invention is a system that equips capsule toy machines with a power source and IoT devices to operate as standalone machines. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information in real time, utilizing marketing data, and calculating optimal replenishment routes.

[0974] Embodiment of terminal (capsule toy machine)

[0975] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[0976] The terminal is also equipped with a solar panel that uses sunlight to generate electricity and stores it in a storage device, ensuring a sustainable power supply and reducing the burden on the environment.

[0977] Server embodiment

[0978] The server receives the sales information and environmental data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[0979] The server also retrieves social media trend information and related product sales data via API and integrates it into the database, enabling more detailed analysis of marketing data.

[0980] The optimal replenishment route is calculated based on the sales forecast model. The server notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[0981] User (operation manager) implementation form

[0982] Operations managers can check real-time sales information and inventory status using the management dashboard, which also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation.

[0983] Administrators can also set up new event- or campaign-based advertising through the dashboard, which is then distributed to each device via the server, maximizing sales during specific event periods.

[0984] For example, if a campaign for a specific product is to be carried out at a station or event venue where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends and surrounding environmental data, and launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[0985] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and the implementation of campaigns using marketing data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[0986] The processing flow will be explained below.

[0987] Processing of terminals (capsule toy machines)

[0988] Sales information collection and transmission process

[0989] Step 1:

[0990] The terminal uses a sales sensor to measure the number of capsule toys sold. For example, each time a capsule toy is dispensed, a counter increases by one.

[0991] Step 2:

[0992] The device uses environmental sensors to capture data on the surrounding environment, such as temperature, humidity, and foot traffic, which then records the local environmental conditions.

[0993] Step 3:

[0994] The terminals then packetize the collected sales and environmental data, and these packets also contain meta-information such as the type of data and a timestamp.

[0995] Step 4:

[0996] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is performed at regular intervals, and the data is updated in real time.

[0997] Powered by solar panels

[0998] Step 1:

[0999] The device converts sunlight into electricity using solar panels, which are designed to efficiently absorb sunlight during the day.

[1000] Step 2:

[1001] The generated power is stored in a power storage device and used to operate sensors and communication devices.

[1002] Server Processing

[1003] Data Receipt and Storage Process

[1004] Step 1:

[1005] The server receives the data packets sent from the terminal, and the reception process is performed according to the communication protocol, checking that the data is not lost or distorted.

[1006] Step 2:

[1007] The received data is analyzed to extract sales information and environmental data, which are then stored in a database.

[1008] Generate a sales forecast model

[1009] Step 1:

[1010] The server retrieves existing sales and environmental data from the database. The retrieved data must be up-to-date.

[1011] Step 2:

[1012] The server applies machine learning algorithms to the acquired data to generate a sales forecasting model, which is used to predict future sales.

[1013] Step 3:

[1014] Evaluate the generated sales forecast model and check its accuracy. If the model accuracy is not above a certain level, retrain it.

[1015] Delivery route optimization

[1016] Step 1:

[1017] The server creates a list of devices that need replenishment based on the sales forecast model, and prevents unnecessary replenishment by predicting when inventory will be low.

[1018] Step 2:

[1019] The server calculates the optimal replenishment route for the delivery company, taking into account the distance to each replenishment point and traffic conditions.

[1020] Step 3:

[1021] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[1022] User (operation administrator) processing

[1023] Dashboard management process

[1024] Step 1:

[1025] Users access a management dashboard to view real-time sales information and inventory status, which is visually displayed in graphs and tables.

[1026] Step 2:

[1027] Users can analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including the content and duration of advertising sent to devices.

[1028] Step 3:

[1029] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[1030] This processing flow enables efficient operation of capsule toy machines and optimization of marketing strategies.

[1031] Example 1

[1032] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1033] Conventional capsule toy machines often required manual inventory management and sales data collection, making efficient operation difficult. Furthermore, environmental data was not collected and energy was not used effectively, making sustainable operation difficult. Furthermore, there was no system in place for real-time advertising tailored to customer needs, making it difficult to maximize sales.

[1034] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1035] In this invention, the server includes means for collecting sales data, means for collecting environmental data, means for packetizing the collected sales data and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management display device, means for setting advertising activities based on the displayed data, and means for transmitting the set advertising activities to multiple terminals. This enables efficient management and sustainable operation of capsule toy machines. Furthermore, real-time data collection and analysis enables optimal inventory management and advertising activities, thereby maximizing sales.

[1036] "Sales data" is information regarding the number and price of products sold in a capsule toy machine.

[1037] "Environmental data" refers to information regarding the temperature, humidity, foot traffic, etc. at the location where the capsule toy machine is installed.

[1038] "Packetization" is the process of dividing multiple pieces of data into a certain format or unit so that they can be sent over a communication line.

[1039] A "communication line" refers to the network infrastructure for sending and receiving data, and includes, for example, 4G and 5G mobile communication networks.

[1040] A "terminal" is a device for collecting sales data and environmental data, and in the present invention refers to a capsule toy machine.

[1041] A "database" is a system for storing and managing data electronically in an organized form.

[1042] A "sales forecasting model" is a mathematical model for predicting future sales based on past sales data.

[1043] A "delivery route" is the optimal route for replenishing or delivering products.

[1044] The "management display device" is an interface for displaying sales data, inventory status, and the like in real time, and includes, for example, a dashboard.

[1045] "Advertising activities" are marketing activities aimed at promoting the sale of specific products or services.

[1046] A "photovoltaic power generation device" is a device that generates electricity using sunlight.

[1047] A "power storage device" is a device that can store generated electricity and supply it when needed.

[1048] A "social network service" is a platform that allows users to share information and interact with each other via the Internet.

[1049] An "application programming interface" is a set of definitions and protocols that allow software to interact with other software through an interface.

[1050] This invention is a system that equips a capsule toy machine with a power source and IoT devices, allowing it to operate as a standalone machine. Specifically, the capsule toy machine is equipped with sales sensors and environmental sensors, which collect sales data and environmental data and send it to a server via a communication line. The terminal is equipped with a solar power generation device, and the generated electricity is stored in a power storage device.

[1051] Terminal embodiment

[1052] The terminal is equipped with a sales sensor and an environmental sensor, which collect the respective data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, if a capsule toy machine installed at a station sells 100 capsule toys in a day during the summer, and the environmental data at that time is a temperature of 30°C and humidity of 70%, that information will be recorded by the sensors.

[1053] The device periodically converts the collected sales and environmental data into packets and transmits them to a server via 4G or 5G communication lines, where the data is accumulated in real time and used for further analysis.

[1054] Server embodiment

[1055] The server receives the sales data and environmental data sent from the device and stores it in a database. This data is stored and managed using specific software such as Python and MySQL. Based on the stored data, a sales forecasting model is generated using a machine learning algorithm (e.g., SKlearn). This forecasting model is used to forecast sales for the next week and achieve optimal inventory management.

[1056] The server also obtains social media trend information and sales data for related products via APIs (e.g., Twitter API) and integrates this data into the database. This enables multifaceted data analysis and improves the accuracy of the sales forecast model.

[1057] The server then calculates the optimal delivery route based on the sales forecast model. For example, it uses the Google Maps API to calculate the shortest distance and optimal time between points, and notifies the delivery company of this information. This allows for efficient replenishment.

[1058] User's embodiment

[1059] Operations managers can view real-time sales information and inventory status using a management dashboard, which displays sales data, environmental data, and analysis results from sales forecasting models.

[1060] Users can also set up new advertising activities through the dashboard. Once an advertising campaign is set up, it is distributed to each device via the server. For example, a campaign for a specific product during the summer vacation can be set up, and the information will be immediately notified to customers via the display and voice message of each capsule toy machine.

[1061] Prompt Sentence Examples

[1062] "Calculate the next week's sales forecast and optimal replenishment route based on capsule toy machine sales data and environmental data."

[1063] Introducing such a system will enable efficient management and sustainable operation of capsule toy machines, while also improving energy efficiency and reducing operating costs.

[1064] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1065] Step 1:

[1066] The terminal collects sales data and environmental data. A sales sensor attached to the terminal measures the number of capsule toys sold, and an environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, the sales sensor measures the sales of 100 capsule toys, and the temperature at that time is 30°C and the humidity is 70%. This becomes the input data.

[1067] Step 2:

[1068] The data collected by the device is packetized. Specifically, sales data and environmental data are combined into a single data packet, which can then be sent to the server. For example, the data is packetized in JSON format as follows: {"sales": 100, "temperature": 30, "humidity": 70}.

[1069] Step 3:

[1070] The device transmits the packetized data over a communication line to the server, for example, using a 4G or 5G network. The output of this step is data packets that are received by the server.

[1071] Step 4:

[1072] The server stores the received data in a database. Specifically, it writes the received data to a database management system (e.g., MySQL). For example, it inserts the data into the database using an SQL query. The query executed is "INSERT INTO sales_data (sales, temperature, humidity) VALUES (100, 30, 70)".

[1073] Step 5:

[1074] The server generates a sales forecasting model based on the stored data. Specifically, it uses a machine learning algorithm (e.g., SKlearn) to train a model that predicts future sales from past data. The inputs to the forecasting model are past sales data and environmental data, and the output is future sales forecasts.

[1075] Step 6:

[1076] The server obtains social media trend information and sales data for related products via API and integrates them into a database. For example, use the Twitter API to obtain trend information related to capsule toys and store it in a database. The query "INSERT INTO trend_data (trend_info) VALUES ('Capsule Toy Boom')" is executed.

[1077] Step 7:

[1078] The server calculates the optimal delivery route based on the sales forecast model. Specifically, it uses the Google Maps API to calculate the delivery distance and time and identify the most efficient route. For example, it calculates the "shortest route from point A to point B" and notifies the delivery company of the results.

[1079] Step 8:

[1080] The server notifies the delivery company of the optimal delivery route information, for example, via email or a dedicated application. The output of this step is the route information received by the delivery company.

[1081] Step 9:

[1082] A user uses the management dashboard to check real-time sales information and inventory status. The user opens a web browser and accesses the management dashboard, where the latest sales data and environmental data are displayed as graphs and tables.

[1083] Step 10:

[1084] The user sets up a new advertising activity via the dashboard. For example, the user sets up a "summer vacation campaign" using the dashboard interface. This setting information is sent to the server.

[1085] Step 11:

[1086] The server sends the configured advertising activities to each terminal, for example, specific appealing messages or visual advertisements are instantly displayed on the display of each capsule toy machine.

[1087] (Application example 1)

[1088] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1089] Conventional capsule toy machines have limited collection of sales information and environmental data, making it difficult to optimize inventory management and marketing strategies. It has also been difficult for managers to obtain accurate data in real time and effectively set up advertising campaigns. Furthermore, there are issues with the power supply to each terminal, and improvements are needed from a sustainability perspective. The purpose of this invention is to solve these issues and realize efficient and sustainable capsule toy machine operation.

[1090] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1091] In this invention, the server includes means for collecting sales information, means for collecting environmental data, means for packetizing the collected sales information and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management dashboard, means for setting up an advertising campaign based on the displayed data, means for transmitting the set advertising campaign to multiple terminals, means for adding a visualization means for displaying data collected on the terminals to facilitate data display, means for providing an input means for an administrator to set up an advertising campaign, means for transmitting event data set based on the advertising campaign to the server and managing it, means for acquiring real-time sales information and environmental data and analyzing and visualizing the data to support administrator decision-making, means for supporting marketing optimization using a generative AI model on the data, and means for automatically setting up a campaign based on the generated prompt sentences.This enables efficient inventory management, marketing campaign optimization, sustainable power supply, and support for administrator decision-making.

[1092] "Sales information" is data regarding the quantity and price of products sold in capsule toy machines.

[1093] "Environmental data" refers to data related to the temperature, humidity, foot traffic, etc. of the location where the capsule toy machine is installed.

[1094] "Packetization" means organizing collected sales information and environmental data into a fixed format and breaking the data into smaller pieces for transmission over communication lines.

[1095] "Communication lines" refer to wireless networks such as 4G and 5G, which are the infrastructure for sending and receiving data.

[1096] A "database" is an information management system that effectively stores, searches, and analyzes collected data.

[1097] A "sales forecasting model" is an algorithm or statistical model for predicting future sales based on past sales data and environmental data.

[1098] "Delivery route optimization" is the calculation of the most efficient delivery route for replenishing products.

[1099] A "management dashboard" is a graphical user interface for displaying and analyzing sales information and environmental data in real time.

[1100] An "advertising campaign" is a sales promotion activity for a specific product or event, and is a means of increasing sales by offering special prices or benefits for a specific period of time.

[1101] A "visualization tool" is a method or technique for visually displaying data as graphs or charts.

[1102] "Input means" is an interface through which an administrator can set up advertising campaigns and other settings.

[1103] A "generative AI model" is a model created using machine learning and deep learning algorithms to perform data analysis and predictions.

[1104] A "prompt sentence" is an instruction sentence that prompts a specific operation or input.

[1105] A "solar panel" is a device that converts sunlight into electricity.

[1106] "Electricity storage device" refers to a battery or electrical equipment for storing generated electricity.

[1107] An "API" is an interface for exchanging data between different software.

[1108] This invention is a system for achieving efficient operation and sustainability of capsule toy machines. The system includes the following main components:

[1109] Embodiment of terminal (capsule toy machine)

[1110] The terminals are equipped with sales sensors and environmental sensors. The sales sensors measure the number of capsule toys sold, while the environmental sensors collect data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via communication lines (4G / 5G). The terminals are also equipped with solar panels that use sunlight to generate electricity, which is then stored in a power storage device. This ensures a sustainable power supply and reduces the environmental impact.

[1111] Server embodiment

[1112] The server receives sales information and environmental data sent from the device and stores them in a database. A sales forecasting model is generated based on this stored data. This model uses machine learning algorithms to predict future sales and enable effective inventory management. The server also obtains social media trend information and related product sales data via API and integrates it into the database. This enables more detailed analysis of marketing data. The server also obtains real-time sales information and environmental data, and performs data analysis and visualization to support managerial decision-making.

[1113] The server calculates the optimal replenishment route based on the generated sales forecast model. This replenishment route information is notified to delivery companies, realizing efficient replenishment without waste. It also provides an input method for administrators to set up advertising campaigns.

[1114] User (operation manager) implementation form

[1115] Operations managers can use the management dashboard to check real-time sales information and inventory status. The dashboard also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation. Managers can also use the dashboard to set up advertisements based on new events and campaigns. These set advertising campaigns are distributed to each terminal via the server and notified to customers via display screens and voice messages.

[1116] For example, when setting up a Christmas campaign, the administrator can apply discounts to specific products and send that information to each terminal via the server, thereby attracting interest in the specific products and increasing sales.

[1117] Hardware and software used

[1118] Hardware: smartphones, IoT sensors, solar panels, energy storage devices

[1119] Software: Python, Flask (web framework), Pandas (data analysis library), Matplotlib (data visualization library), Requests (API communication library)

[1120] Examples of prompt statements

[1121] Prompt to retrieve sales data from the server: "Please retrieve capsule toy sales data and visualize it."

[1122] Prompt to set up a campaign: "For product ID 1234, set up a campaign offering 20% ​​off from December 20th to 25th."

[1123] This enables efficient inventory management, optimized marketing campaigns, sustainable power supply, and managerial decision support.

[1124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1125] Step 1:

[1126] The server receives sales information and environmental data from the terminal.

[1127] Input: Sales information and environmental data sent from the terminal

[1128] Output: Raw sales and environmental data

[1129] The server receives data from the device in real time and performs initial processing to store it in a database. Specifically, the server receives data packets sent from the device via 4G or 5G communication lines and analyzes the contents. The received data includes sales volume, temperature, humidity, foot traffic, etc., and identifies and classifies each data point.

[1130] Step 2:

[1131] The server stores the received sales information and environmental data in a database.

[1132] Input: Identified and classified sales information and environmental data

[1133] Output: Sales information and environmental data stored in a database

[1134] The server converts the received data into an appropriate format and stores it in the database. Specifically, it establishes a connection to the database and writes the sales information to the sales table and the environmental data to the environmental table.

[1135] Step 3:

[1136] The server generates a sales forecast model based on the stored data.

[1137] Input: Sales information and environmental data stored in a database

[1138] Output: Generated sales forecast model

[1139] The server analyzes the stored past sales data and environmental data using a machine learning algorithm to generate a sales forecasting model. Specifically, it creates a training dataset using a Python machine learning library (such as scikit-learn) and builds a forecasting model based on that data.

[1140] Step 4:

[1141] The server calculates the optimal delivery route based on the generated sales forecast model.

[1142] Input: Generated sales forecast model

[1143] Output: Optimal delivery route

[1144] The server calculates the predicted stock-out timing for each terminal based on the sales forecast model, and generates the optimal delivery route based on this.Specifically, it uses a Python route optimization library (such as ORTools) to calculate a route that minimizes delivery cost and time.

[1145] Step 5:

[1146] The server notifies the delivery company of the optimized delivery route.

[1147] Input: Optimal delivery route

[1148] Output: Delivery route notified to the delivery company

[1149] The server sends the calculated delivery route information to the specified delivery company API and notifies it. Specifically, it sends the optimized route information via a POST request to the delivery company's specific API endpoint.

[1150] Step 6:

[1151] Users can view real-time sales information and inventory status on an administrative dashboard.

[1152] Input: Real-time data stored in a database

[1153] Output: Sales information and inventory status displayed on a dashboard

[1154] Users can access the dashboard via a web browser and check real-time data. Specifically, sales information and environmental data are displayed in graphs on the dashboard, allowing users to check them.

[1155] Step 7:

[1156] A user sets up an advertising campaign through a dashboard.

[1157] Input: Campaign setting information entered by the administrator

[1158] Output: Configured ad campaigns

[1159] Users use the dashboard's input form to set up advertising campaigns for specific products and time periods. Specifically, they enter campaign information into the form and press the "Set" button, which sends the information to the server.

[1160] Step 8:

[1161] A server transmits a configured advertising campaign to multiple terminals.

[1162] Input: Set advertising campaign information

[1163] Output: Advertising campaign information sent to each device

[1164] The server distributes advertising campaign information set by the user to each terminal. Specifically, the server sends the campaign information to the communication address of each terminal and sets the terminal to display the content on its display or as a voice message.

[1165] Step 9:

[1166] The server provides marketing optimization support using generative AI models.

[1167] Input: Sales information, environmental data, and social media trend information stored in the database

[1168] Output: Optimized marketing strategies

[1169] The server uses the generative AI model to analyze sales data, environmental data, and social media trend information to optimize marketing strategies. Specifically, it analyzes data using Python's machine learning library and recommends optimal marketing actions.

[1170] Step 10:

[1171] The server automatically sets up a campaign based on the generated prompt text.

[1172] Input: A prompt generated by a generative AI model

[1173] Output: Automated ad campaign

[1174] The server automatically sets up advertising campaigns based on the prompts created by the generative AI model. Specifically, it analyzes the generated prompts, creates campaign setting information with appropriate content, and sends it to each device.

[1175] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1176] This invention is a system that combines a capsule toy machine with a power source, IoT devices, and an emotion engine to operate as an autonomous machine. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information and environmental data in real time, analyzing emotion data, utilizing marketing data, and calculating optimal replenishment routes.

[1177] Embodiment of terminal (capsule toy machine)

[1178] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[1179] The device is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. This emotional data is transmitted along with sales information and environmental data. Solar panels are used to convert sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces environmental impact.

[1180] Server embodiment

[1181] The server receives the sales information, environmental data, and emotion data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[1182] The server also retrieves social media trend information and related product sales data via API and integrates it into the database. This allows for more detailed analysis of marketing data. Furthermore, incorporating emotion data obtained from the emotion engine into the analysis improves the accuracy of the sales forecast model.

[1183] The optimal replenishment route is calculated based on a sales forecast model. By taking into account emotional data, the system predicts when customers' purchasing motivation will be highest and sets an optimal replenishment schedule. The server then notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[1184] User (operation manager) implementation form

[1185] Operations managers can check real-time sales information and inventory status using the management dashboard, which displays sales statistics, environmental data, and even user sentiment data, allowing managers to quickly and accurately grasp the situation.

[1186] Administrators can also use the dashboard to set up advertisements based on new events or campaigns. These set up advertising campaigns are then distributed to each device via the server. Furthermore, the content and timing of advertising campaigns can be optimized based on data obtained from the emotion engine. This allows for more precise targeting and maximizes user purchasing intent.

[1187] For example, if a campaign for a specific product is to be carried out at a shopping mall where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends, surrounding environmental data, and even sentiment data, and then launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[1188] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and marketing strategies that utilize user sentiment data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[1189] The processing flow will be explained below.

[1190] Processing of terminals (capsule toy machines)

[1191] Sales information collection and transmission process

[1192] Step 1:

[1193] The terminal uses a sales sensor to measure the number of capsule toys sold. Specifically, it counts each time a capsule toy is dispensed.

[1194] Step 2:

[1195] The device uses environmental sensors to capture surrounding environmental data such as temperature, humidity, and foot traffic, allowing it to record the environmental conditions of the location in real time.

[1196] Step 3:

[1197] The device uses an emotion engine to analyze the user's voice and facial expressions to collect emotional data, for example, analyzing voice to determine whether the user is having fun.

[1198] Step 4:

[1199] The device then packets the collected sales data, environmental data, and emotion data, each of which includes the type of data and a timestamp.

[1200] Step 5:

[1201] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is done at regular intervals, and the data is always kept up to date.

[1202] Powered by solar panels

[1203] Step 1:

[1204] The device uses solar panels to convert sunlight into electricity, and is designed to efficiently absorb sunlight during the day.

[1205] Step 2:

[1206] The generated power is stored in a power storage device, and is used to operate sensors and communication devices.

[1207] Server Processing

[1208] Data Receipt and Storage Process

[1209] Step 1:

[1210] The server receives the data packets sent from the device and checks that the data is not lost or distorted according to the communication protocol.

[1211] Step 2:

[1212] The server analyzes the received data and extracts sales information, environmental data, and emotional data, which are then stored in a database.

[1213] Generate a sales forecast model

[1214] Step 1:

[1215] The server retrieves historical sales data, environmental data, and emotional data stored in the database, providing the most up-to-date information.

[1216] Step 2:

[1217] The server uses the acquired data to apply machine learning algorithms to generate a sales forecasting model, which is used to predict future sales.

[1218] Step 3:

[1219] The generated sales forecast model is evaluated to check its accuracy. If the accuracy is insufficient, the model is retrained to improve it.

[1220] Delivery route optimization

[1221] Step 1:

[1222] The server creates a list of devices that need replenishment based on the sales forecast model, predicts when inventory will be low, and prevents unnecessary replenishment.

[1223] Step 2:

[1224] The server calculates the optimal replenishment route, taking into account the distance to each replenishment point and traffic conditions.

[1225] Step 3:

[1226] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[1227] User (operation administrator) processing

[1228] Dashboard management process

[1229] Step 1:

[1230] Users access a management dashboard that provides real-time sales information, inventory status, and sentiment data, which is visually displayed in graphs and tables.

[1231] Step 2:

[1232] Users analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including ad content and duration.

[1233] Step 3:

[1234] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[1235] Step 4:

[1236] Through the dashboard, users can optimize the content and timing of their advertising campaigns based on data obtained from the emotion engine, resulting in highly accurate targeting and maximizing user purchasing intent.

[1237] In this way, this system, combined with the emotion engine, enables efficient operation of capsule toy machines and a highly accurate marketing strategy. Inventory management based on sales forecasts and advertising campaigns utilizing emotion data significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[1238] Example 2

[1239] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1240] In the operation of conventional capsule toy machines, it was difficult to collect sales information and environmental data in real time, and marketing strategies utilizing emotional data were limited. As a result, inventory management and replenishment routes were not adequately optimized, leading to reduced operational efficiency, wasted energy, and increased CO2 emissions. Effective targeting of advertising campaigns was also difficult. These issues need to be resolved.

[1241] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1242] In this invention, the server includes means for receiving sales information, means for receiving environmental data, means for receiving emotion data, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for integrating SNS trend information and sales data of related products, means for calculating an optimal replenishment route based on the sales forecast model, means for notifying a delivery company of the optimized replenishment route, means for displaying real-time data on a management dashboard, and means for setting up and distributing advertising campaigns based on the displayed data. This enables efficient inventory management of capsule toy machines, optimization of operations based on environmental data, and realization of marketing strategies utilizing emotion data.

[1243] "Sales information" refers to data indicating the number of capsule products sold by the capsule toy machine and the sales amount.

[1244] "Environmental data" refers to data related to the operating environment of the capsule toy machine, such as temperature, humidity, and foot traffic.

[1245] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[1246] "Packetization" is a process of consolidating multiple pieces of collected data based on a certain standard.

[1247] "Communication lines" refers to communication infrastructure such as the Internet and 4G / 5G networks used to send and receive data.

[1248] A "database" is an information system for efficiently storing, retrieving, and managing structured data.

[1249] A "sales forecasting model" is a statistical model that uses machine learning algorithms to predict future sales based on collected data.

[1250] "Optimization" is the best way to allocate and operate resources to achieve a specific goal.

[1251] A "delivery route" is a route taken by a delivery company set up for replenishing capsule toy machines and managing inventory.

[1252] The "management dashboard" is a web-based interface that allows operations managers to check various data and perform operations in real time.

[1253] An "advertising campaign" is a series of advertising activities to promote a particular product or service.

[1254] A "solar panel" is a device that converts sunlight into electricity.

[1255] A "power storage device" is a device that stores generated electricity and supplies it when needed.

[1256] "SNS trend information" refers to information and content that is trending on social networking services.

[1257] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[1258] The present invention relates to a system for collecting sales information, environmental data, and emotion data from capsule toy machines in real time, and for carrying out efficient inventory management, marketing strategies, and energy management. Specific embodiments of the present invention are described below.

[1259] Server embodiment

[1260] The server operates using the following hardware and software:

[1261] Hardware: Standard server machine (CPU, memory, storage)

[1262] Software: Cloud infrastructure (e.g., Amazon Web Services), machine learning algorithms (e.g., TensorFlow), database management systems (e.g., MySQL, MongoDB)

[1263] The server's main function is to receive sales information, environmental data, and emotion data sent from the device and store it in a database. A sales forecasting model is generated using a machine learning algorithm based on this stored data. It also obtains social media trend information and related product sales data via API and integrates it into the database. This integrated data is used to improve the accuracy of the sales forecasting model.

[1264] The server then calculates the optimal replenishment route based on the sales forecast model. The results are then sent to the delivery company via email, SMS, or a dedicated app, enabling an efficient replenishment schedule.

[1265] An example of a specific prompt is as follows:

[1266] "Please generate a sales forecasting model by integrating capsule toy machine sales information, environmental data, and sentiment data. Please also take into account social media trend information and sales data for related products."

[1267] Embodiment of terminal (capsule toy machine)

[1268] The device is equipped with the following hardware:

[1269] Hardware: Raspberry Pi, sales sensor, environmental sensors (temperature and humidity sensor, human presence sensor), camera, microphone, 4G / 5G communication module, solar panel, power storage device

[1270] The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. Furthermore, an emotion engine is installed to analyze the user's voice and facial expressions to collect emotional data. This data is packetized at regular intervals and sent to a server via a communication line (4G / 5G).

[1271] The solar panel converts sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces the environmental impact. The device always performs optimal power management, ensuring efficient operation.

[1272] An example of a specific prompt is as follows:

[1273] "Please packetize the data obtained from the sales sensor, environmental sensor, and emotion engine and periodically send it to the server. Please use the solar panel to provide energy."

[1274] User (operation manager) implementation form

[1275] The operations manager uses the management dashboard to perform the following operations:

[1276] Real-time confirmation of sales information, inventory status, environmental data, and sentiment data

[1277] Advertisements based on new events and campaigns are set up and delivered to each device via the server.

[1278] Monitor and analyze data to take appropriate action

[1279] The dashboard displays detailed data using interactive charts and graphs, and uses sentiment data to optimize the content and timing of advertising campaigns to maximize user purchase intent.

[1280] An example of a specific prompt is as follows:

[1281] "Check sales information, inventory status, environmental data, and sentiment data from the dashboard and set up new advertising campaigns. The ads you set up are delivered to each device via our server."

[1282] As described above, the embodiment of the present invention collects and analyzes sales information, environmental data, and sentiment data in real time to realize optimal inventory management and marketing strategies, while also saving energy and reducing environmental impact.

[1283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1284] server

[1285] Step 1: Receiving the data

[1286] The server receives packets of sales information, environmental data, and emotion data sent from the device. As input, it receives the data packet containing each data field, analyzes and decomposes the payload, and as output, it extracts the sales information, environmental data, and emotion data as separate datasets.

[1287] Specific operation: The server opens a receiving port and waits for data from each terminal. When the data arrives, it analyzes the payload and extracts each data field.

[1288] Step 2: Save your data

[1289] The server stores the received data in a database. As input, it receives analyzed sales information, environmental data, and emotion data. As output, each piece of information is stored in the corresponding table in the database.

[1290] What happens: The server connects to the database and inserts the data into the appropriate tables using SQL queries or NoSQL document operations.

[1291] Step 3: Generate a sales forecast model

[1292] The server uses machine learning algorithms to generate a sales forecasting model based on the stored data. As input, it takes sales information from the database, environmental data, and sentiment data. The output is a trained sales forecasting model.

[1293] What it does: The server queries the required data from the database, pre-processes the data, and then feeds it to the machine learning algorithm to train the model.

[1294] Step 4: Obtaining trend information

[1295] The server obtains SNS trend information and related product sales data via API. As input, it sends an API request and receives trend information and sales data. The output is to store the obtained data in a database.

[1296] Specific operation: The server sends API requests at specified intervals, converts the received data into an appropriate format, and stores it in the database.

[1297] Step 5: Model integration and analysis

[1298] The server integrates sales data, environmental data, sentiment data, and social media trend information and analyzes the sales forecasting model. It takes all these datasets as input. The output is the integrated dataset and an improved sales forecasting model.

[1299] Specific operation: The server integrates each dataset and uses the integrated data to retrain the model and tune parameters.

[1300] Step 6: Calculate replenishment routes

[1301] The server calculates the optimal replenishment route based on the sales forecast model and real-time emotion data. The inputs are the sales forecast model and real-time emotion data. The output is the optimal replenishment route and time schedule.

[1302] Specific operation: The server analyzes the location information and inventory information of each terminal and calculates the optimal replenishment route using Dijkstra's algorithm or A algorithm.

[1303] Step 7: Send notifications

[1304] The server notifies the calculated replenishment route information to the delivery company. As input, it receives the optimized replenishment route information. The output is a notification message to the delivery company.

[1305] Specific operation: Based on the generated replenishment schedule, the server creates a notification message for the delivery company and sends it via email, SMS, or a dedicated app.

[1306] Terminal (capsule toy machine)

[1307] Step 1: Collect data

[1308] The device collects data using sales sensors, environmental sensors, and an emotion engine. The inputs include sales volume, temperature, humidity, foot traffic, and voice and facial expression data. The output is the collected data.

[1309] Specific operation: The sales sensor increments the count each time it is triggered, the environmental sensor periodically captures data and stores it in a buffer, and the emotion engine analyzes audio and video data at regular intervals to generate emotion data.

[1310] Step 2: Packetize and transmit the data

[1311] The terminal packetizes the collected sales information, environmental data, and emotion data and sends them to the server. The terminal receives the collected data as input. The output is the packetized data.

[1312] Specific operation: The terminal collects all sensor data into one packet, properly formats each data field, and then transmits it through the 4G / 5G communication module.

[1313] Step 3: Managing the power supply

[1314] The device uses a solar panel to convert sunlight into electricity and stores it in a storage device. The input is sunlight. The output is the generated electricity.

[1315] How it works: The device's control unit monitors the power generated by the solar panel and controls charging, supplying power from the storage device as needed.

[1316] User (operation administrator)

[1317] Step 1: Use the dashboard

[1318] Users use the management dashboard to view real-time sales information, inventory status, environmental data, and sentiment data. As input, it receives data sent from the server. The output is detailed data displayed on the dashboard.

[1319] What it does: A user opens a web browser and logs into the dashboard, which displays data in sections and allows users to drill down into details using interactive charts and graphs.

[1320] Step 2: Set up your ad campaign

[1321] Users can set up new event- and campaign-based ads through the dashboard and distribute them to devices via the server. The input is the campaign content and conditions. The output is the configured ad campaign.

[1322] How it works: Users enter campaign details, duration, target audience, etc. into the dashboard and save the settings. The server then sends this information to each device, and the ads are displayed and played through the device's display and speaker.

[1323] Step 3: Monitor and analyze the data

[1324] Users can monitor sales statistics, environmental data, and sentiment data on the dashboard, analyze them, and take appropriate actions. The input is real-time data, and the output is analysis results and actions based on them.

[1325] What happens next? Users use the dashboard's filters and analytical tools to narrow down data for specific periods and conditions, discover trends and anomalies, and take action based on those findings.

[1326] Above we have detailed how the overall system functions specifically at each processing step to turn data from input to output.

[1327] (Application example 2)

[1328] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1329] With conventional capsule toy machines, managing sales information and environmental data was complicated, making optimal inventory management and marketing strategies difficult. Furthermore, the accuracy of sales forecasts using emotion data was not sufficiently improved, and there was a need for real-time data confirmation and more efficient replenishment work. In addition, there was a lack of a way for managers to easily access data and check efficient replenishment routes.

[1330] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1331] In this invention, the server includes a means for collecting sales information, a means for collecting environmental data, and a means for collecting emotional data. This allows for real-time analysis of user emotional data along with sales information and environmental data, improving the accuracy of the sales forecast model. Furthermore, by providing the smart glasses with a means for displaying real-time data and replenishment route information, managers can easily access the data and perform efficient replenishment work. This allows for the optimization of inventory management and marketing strategies, and the efficiency of management work to be achieved.

[1332] "Sales information" refers to data such as the number of units sold and the sales amount from capsule toy machines.

[1333] "Environmental data" refers to information such as temperature, humidity, and foot traffic in the area where the capsule toy machine is installed.

[1334] "Packetization" refers to converting collected data into a certain format so that it can be transmitted over a communication line.

[1335] "Communication lines" refer to the infrastructure for sending and receiving data, specifically wireless communication networks such as 4G and 5G.

[1336] "Database" refers to a computer system for storing and managing various collected data.

[1337] A "sales forecasting model" refers to a statistical and machine learning algorithm that predicts future sales based on past sales information, environmental data, and emotional data.

[1338] "Delivery route" refers to the optimized route for replenishing and delivering capsule toys.

[1339] An "administrative dashboard" is a software interface used by operations managers to monitor real-time data and set up advertising campaigns.

[1340] An "advertising campaign" refers to a marketing activity that distributes information for the purpose of sales promotion at a specific time.

[1341] "Emotion data" refers to information that indicates the emotional state of the user analyzed from their voice and facial expressions.

[1342] "Smart glasses" refer to a wearable device that can be worn by a user and display real-time information in their field of vision.

[1343] "Refill route information" refers to data regarding the optimal refill route for a capsule toy machine.

[1344] Terminal embodiment

[1345] The terminal is equipped with various sensors that collect sales information and environmental data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via a communication line (4G / 5G). The terminal is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. The terminal is powered by a solar panel and operates sustainably using the power stored in a power storage device.

[1346] Server embodiment

[1347] The server receives sales information, environmental data, and emotion data sent from the device and stores them in a database. A sales forecasting model is generated based on the stored data, and this model is created using a machine learning algorithm (specifically, TensorFlow or Scikit-learn). Furthermore, by obtaining social media trend information and related product sales data via API and integrating this into the database, more detailed analysis of marketing data becomes possible. Emotional data is also incorporated into the analysis to improve the accuracy of the sales forecasting model. Furthermore, the optimal replenishment route is calculated based on the generated sales forecasting model, and this information is notified to the delivery company.

[1348] User's embodiment

[1349] Operations managers can check various data in real time using the management dashboard. The dashboard displays sales statistics, environmental data, and user sentiment data, allowing managers to quickly grasp the situation. Managers can also set up advertising campaigns from the dashboard and distribute them to each device. Based on data obtained from the sentiment engine, it is also possible to optimize the content and distribution timing of advertising campaigns. Furthermore, by using smart glasses, managers can visually check necessary data in real time during replenishment work and information on the optimal replenishment route.

[1350] Specific examples

[1351] Imagine a scenario where a shopping mall manager wears smart glasses and stands in front of a capsule toy machine to check "sales data" and "stock status." In this case, the manager can display the data directly in his field of vision and immediately begin replenishing the inventory. Examples of specific prompts include "Show me the latest sales figures for the capsule toy machine" and "Tell me the current stock status."

[1352] This system enables efficient capsule toy management, utilizes marketing data, and enables real-time data collection and analysis. Furthermore, by incorporating user sentiment data, the accuracy of sales forecasts can be improved and optimal replenishment schedules and routes can be generated. Furthermore, managers can visually check the data through smart glasses, improving the efficiency of replenishment work and marketing activities.

[1353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1354] Step 1: The device collects sales information

[1355] A sales sensor installed in the terminal measures the number of capsule toys sold.

[1356] (Input) Sales Event

[1357] (Data processing) Add the number of items sold to the counter

[1358] (Output) Updated sales information

[1359] (Specific operation) The sensor detects the release of the toy capsule and updates the count data.

[1360] Step 2: Device collects environmental data

[1361] Environmental sensors installed on the device measure temperature, humidity, foot traffic, etc.

[1362] (Input) Surrounding environmental conditions

[1363] (Data processing) Read sensor data and convert it into a format

[1364] (Output) Environmental data

[1365] (Specific operation) Temperature and humidity sensors and human presence sensors periodically collect environmental information.

[1366] Step 3: The device collects emotion data

[1367] The device's emotion engine analyzes the user's voice and facial expressions to collect emotional data.

[1368] (Input) User's voice and facial expression

[1369] (Data processing) Analysis of audio and image data, conversion to emotional data

[1370] (Output) Emotion data

[1371] (Specific operation) The camera and microphone capture the user's facial expressions and voice, and the analysis engine measures their emotional state.

[1372] Step 4: Packetize the data and send it over the communication line to the server

[1373] The sales information, environmental data, and emotional data collected by the device are packaged into packets and sent to a server via 4G / 5G lines.

[1374] (Input) Sales information, environmental data, emotional data

[1375] (Data processing) Data formatting and packetization

[1376] (Output) Outgoing packets

[1377] (Specific operation) The data is formatted into a specific format, and the communication module forms a transmission packet, which is then sent to the server via the line.

[1378] Step 5: The server saves the received data to the database

[1379] The server receives the data sent from the terminal and stores it in a database.

[1380] (Input) Outgoing packets

[1381] (Data processing) Packet analysis and data insertion into database

[1382] (Output) Database records

[1383] (Specific Operation) The receiving module of the server analyzes the packet and inserts the data into the corresponding table.

[1384] Step 6: The server generates the sales forecast model

[1385] A sales forecasting model is generated using machine learning algorithms based on the data stored in the database.

[1386] (Input) Stored sales information, environmental data, and emotional data

[1387] (Data processing) Training machine learning models

[1388] (Output) Sales forecast model

[1389] (Specific operation) Using libraries such as TensorFlow and Scikit-learn, the algorithm analyzes the training dataset and generates a predictive model.

[1390] Step 7: The server calculates the optimal delivery route and notifies the delivery company.

[1391] The server calculates the optimal replenishment route based on the generated sales forecast model and notifies the delivery company.

[1392] (Input) Sales forecast model, geographic information

[1393] (Data processing) Application of optimization algorithms

[1394] (Output) Replenishment route information

[1395] (Specific operation) The server's calculation module optimizes the replenishment route, and the notification system sends instructions to the delivery company.

[1396] Step 8: Users review data via the admin dashboard

[1397] Operations managers view real-time data via an administrative dashboard.

[1398] (Input) Latest sales information, environmental data, emotional data

[1399] (Data processing) Data visualization

[1400] (Output) Dashboard screen

[1401] (Specific operation) The dashboard interface retrieves the latest data and displays it in graph and table format.

[1402] Step 9: User sets up ad campaign and sends it to each device

[1403] The operations manager sets up advertising campaigns from the dashboard and sends them to each device.

[1404] (Input) Advertising campaign setting information

[1405] (Data processing) Formatting and sending campaign data

[1406] (Output) Campaign instructions to each device

[1407] (Specific operation) Campaign information set from the dashboard is saved in a database and sent to each device for distribution.

[1408] Step 10: The server uses the emotional data to optimize the content and timing of the ad campaign.

[1409] Optimize the content and timing of advertising campaigns based on emotional data.

[1410] (Input) Emotion data, campaign setting information

[1411] (Data processing) Data analysis and optimization calculations

[1412] (Output) Optimized campaign timing and content

[1413] (Specific operation) A machine learning algorithm analyzes emotional data and calculates the optimal timing and content.

[1414] Step 11: The administrator checks the replenishment route information using the smart glasses.

[1415] Managers use smart glasses to check replenishment route information in real time.

[1416] (Input) Replenishment route information

[1417] (Data processing) Real-time display of data

[1418] (Output) HUD (Head-Up Display) for smart glasses

[1419] (Specific operation) The smart glasses display the replenishment route information sent from the server on the HUD.

[1420] Example prompt sentence:

[1421] "Show me the latest sales for capsule toy machines."

[1422] "Please let me know the current stock situation."

[1423] "Check the optimal replenishment route"

[1424] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1425] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1426] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1427] [Fourth embodiment]

[1428] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1429] 7, a 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.

[1430] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1431] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1432] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1435] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1436] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1437] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1438] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1439] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1440] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] This invention is a system that equips capsule toy machines with a power source and IoT devices to operate as standalone machines. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information in real time, utilizing marketing data, and calculating optimal replenishment routes.

[1442] Embodiment of terminal (capsule toy machine)

[1443] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[1444] The terminal is also equipped with a solar panel that uses sunlight to generate electricity and stores it in a storage device, ensuring a sustainable power supply and reducing the burden on the environment.

[1445] Server embodiment

[1446] The server receives the sales information and environmental data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[1447] The server also retrieves social media trend information and related product sales data via API and integrates it into the database, enabling more detailed analysis of marketing data.

[1448] The optimal replenishment route is calculated based on the sales forecast model. The server notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[1449] User (operation manager) implementation form

[1450] Operations managers can check real-time sales information and inventory status using the management dashboard, which also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation.

[1451] Administrators can also set up new event- or campaign-based advertising through the dashboard, which is then distributed to each device via the server, maximizing sales during specific event periods.

[1452] For example, if a campaign for a specific product is to be carried out at a station or event venue where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends and surrounding environmental data, and launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[1453] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and the implementation of campaigns using marketing data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[1454] The processing flow will be explained below.

[1455] Processing of terminals (capsule toy machines)

[1456] Sales information collection and transmission process

[1457] Step 1:

[1458] The terminal uses a sales sensor to measure the number of capsule toys sold. For example, each time a capsule toy is dispensed, a counter increases by one.

[1459] Step 2:

[1460] The device uses environmental sensors to capture data on the surrounding environment, such as temperature, humidity, and foot traffic, which then records the local environmental conditions.

[1461] Step 3:

[1462] The terminals then packetize the collected sales and environmental data, and these packets also contain meta-information such as the type of data and a timestamp.

[1463] Step 4:

[1464] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is performed at regular intervals, and the data is updated in real time.

[1465] Powered by solar panels

[1466] Step 1:

[1467] The device converts sunlight into electricity using solar panels, which are designed to efficiently absorb sunlight during the day.

[1468] Step 2:

[1469] The generated power is stored in a power storage device and used to operate sensors and communication devices.

[1470] Server Processing

[1471] Data Receipt and Storage Process

[1472] Step 1:

[1473] The server receives the data packets sent from the terminal, and the reception process is performed according to the communication protocol, checking that the data is not lost or distorted.

[1474] Step 2:

[1475] The received data is analyzed to extract sales information and environmental data, which are then stored in a database.

[1476] Generate a sales forecast model

[1477] Step 1:

[1478] The server retrieves existing sales and environmental data from the database. The retrieved data must be up-to-date.

[1479] Step 2:

[1480] The server applies machine learning algorithms to the acquired data to generate a sales forecasting model, which is used to predict future sales.

[1481] Step 3:

[1482] Evaluate the generated sales forecast model and check its accuracy. If the model accuracy is not above a certain level, retrain it.

[1483] Delivery route optimization

[1484] Step 1:

[1485] The server creates a list of devices that need replenishment based on the sales forecast model, and prevents unnecessary replenishment by predicting when inventory will be low.

[1486] Step 2:

[1487] The server calculates the optimal replenishment route for the delivery company, taking into account the distance to each replenishment point and traffic conditions.

[1488] Step 3:

[1489] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[1490] User (operation administrator) processing

[1491] Dashboard management process

[1492] Step 1:

[1493] Users access a management dashboard to view real-time sales information and inventory status, which is visually displayed in graphs and tables.

[1494] Step 2:

[1495] Users can analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including the content and duration of advertising sent to devices.

[1496] Step 3:

[1497] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[1498] This processing flow enables efficient operation of capsule toy machines and optimization of marketing strategies.

[1499] Example 1

[1500] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1501] Conventional capsule toy machines often required manual inventory management and sales data collection, making efficient operation difficult. Furthermore, environmental data was not collected and energy was not used effectively, making sustainable operation difficult. Furthermore, there was no system in place for real-time advertising tailored to customer needs, making it difficult to maximize sales.

[1502] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1503] In this invention, the server includes means for collecting sales data, means for collecting environmental data, means for packetizing the collected sales data and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management display device, means for setting advertising activities based on the displayed data, and means for transmitting the set advertising activities to multiple terminals. This enables efficient management and sustainable operation of capsule toy machines. Furthermore, real-time data collection and analysis enables optimal inventory management and advertising activities, thereby maximizing sales.

[1504] "Sales data" is information regarding the number and price of products sold in a capsule toy machine.

[1505] "Environmental data" refers to information regarding the temperature, humidity, foot traffic, etc. at the location where the capsule toy machine is installed.

[1506] "Packetization" is the process of dividing multiple pieces of data into a certain format or unit so that they can be sent over a communication line.

[1507] A "communication line" refers to the network infrastructure for sending and receiving data, and includes, for example, 4G and 5G mobile communication networks.

[1508] A "terminal" is a device for collecting sales data and environmental data, and in the present invention refers to a capsule toy machine.

[1509] A "database" is a system for storing and managing data electronically in an organized form.

[1510] A "sales forecasting model" is a mathematical model for predicting future sales based on past sales data.

[1511] A "delivery route" is the optimal route for replenishing or delivering products.

[1512] The "management display device" is an interface for displaying sales data, inventory status, and the like in real time, and includes, for example, a dashboard.

[1513] "Advertising activities" are marketing activities aimed at promoting the sale of specific products or services.

[1514] A "photovoltaic power generation device" is a device that generates electricity using sunlight.

[1515] A "power storage device" is a device that can store generated electricity and supply it when needed.

[1516] A "social network service" is a platform that allows users to share information and interact with each other via the Internet.

[1517] An "application programming interface" is a set of definitions and protocols that allow software to interact with other software through an interface.

[1518] This invention is a system that equips a capsule toy machine with a power source and IoT devices, allowing it to operate as a standalone machine. Specifically, the capsule toy machine is equipped with sales sensors and environmental sensors, which collect sales data and environmental data and send it to a server via a communication line. The terminal is equipped with a solar power generation device, and the generated electricity is stored in a power storage device.

[1519] Terminal embodiment

[1520] The terminal is equipped with a sales sensor and an environmental sensor, which collect the respective data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, if a capsule toy machine installed at a station sells 100 capsule toys in a day during the summer, and the environmental data at that time is a temperature of 30°C and humidity of 70%, that information will be recorded by the sensors.

[1521] The device periodically converts the collected sales and environmental data into packets and transmits them to a server via 4G or 5G communication lines, where the data is accumulated in real time and used for further analysis.

[1522] Server embodiment

[1523] The server receives the sales data and environmental data sent from the device and stores it in a database. This data is stored and managed using specific software such as Python and MySQL. Based on the stored data, a sales forecasting model is generated using a machine learning algorithm (e.g., SKlearn). This forecasting model is used to forecast sales for the next week and achieve optimal inventory management.

[1524] The server also obtains social media trend information and sales data for related products via APIs (e.g., Twitter API) and integrates this data into the database. This enables multifaceted data analysis and improves the accuracy of the sales forecast model.

[1525] The server then calculates the optimal delivery route based on the sales forecast model. For example, it uses the Google Maps API to calculate the shortest distance and optimal time between points, and notifies the delivery company of this information. This allows for efficient replenishment.

[1526] User's embodiment

[1527] Operations managers can view real-time sales information and inventory status using a management dashboard, which displays sales data, environmental data, and analysis results from sales forecasting models.

[1528] Users can also set up new advertising activities through the dashboard. Once an advertising campaign is set up, it is distributed to each device via the server. For example, a campaign for a specific product during the summer vacation can be set up, and the information will be immediately notified to customers via the display and voice message of each capsule toy machine.

[1529] Prompt Sentence Examples

[1530] "Calculate the next week's sales forecast and optimal replenishment route based on capsule toy machine sales data and environmental data."

[1531] Introducing such a system will enable efficient management and sustainable operation of capsule toy machines, while also improving energy efficiency and reducing operating costs.

[1532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1533] Step 1:

[1534] The terminal collects sales data and environmental data. A sales sensor attached to the terminal measures the number of capsule toys sold, and an environmental sensor collects information such as the temperature, humidity, and foot traffic of the installation location. For example, the sales sensor measures the sales of 100 capsule toys, and the temperature at that time is 30°C and the humidity is 70%. This becomes the input data.

[1535] Step 2:

[1536] The data collected by the device is packetized. Specifically, sales data and environmental data are combined into a single data packet, which can then be sent to the server. For example, the data is packetized in JSON format as follows: {"sales": 100, "temperature": 30, "humidity": 70}.

[1537] Step 3:

[1538] The device transmits the packetized data over a communication line to the server, for example, using a 4G or 5G network. The output of this step is data packets that are received by the server.

[1539] Step 4:

[1540] The server stores the received data in a database. Specifically, it writes the received data to a database management system (e.g., MySQL). For example, it inserts the data into the database using an SQL query. The query executed is "INSERT INTO sales_data (sales, temperature, humidity) VALUES (100, 30, 70)".

[1541] Step 5:

[1542] The server generates a sales forecasting model based on the stored data. Specifically, it uses a machine learning algorithm (e.g., SKlearn) to train a model that predicts future sales from past data. The inputs to the forecasting model are past sales data and environmental data, and the output is future sales forecasts.

[1543] Step 6:

[1544] The server obtains social media trend information and sales data for related products via API and integrates them into a database. For example, use the Twitter API to obtain trend information related to capsule toys and store it in a database. The query "INSERT INTO trend_data (trend_info) VALUES ('Capsule Toy Boom')" is executed.

[1545] Step 7:

[1546] The server calculates the optimal delivery route based on the sales forecast model. Specifically, it uses the Google Maps API to calculate the delivery distance and time and identify the most efficient route. For example, it calculates the "shortest route from point A to point B" and notifies the delivery company of the results.

[1547] Step 8:

[1548] The server notifies the delivery company of the optimal delivery route information, for example, via email or a dedicated application. The output of this step is the route information received by the delivery company.

[1549] Step 9:

[1550] A user uses the management dashboard to check real-time sales information and inventory status. The user opens a web browser and accesses the management dashboard, where the latest sales data and environmental data are displayed as graphs and tables.

[1551] Step 10:

[1552] The user sets up a new advertising activity via the dashboard. For example, the user sets up a "summer vacation campaign" using the dashboard interface. This setting information is sent to the server.

[1553] Step 11:

[1554] The server sends the configured advertising activities to each terminal, for example, specific appealing messages or visual advertisements are instantly displayed on the display of each capsule toy machine.

[1555] (Application example 1)

[1556] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1557] Conventional capsule toy machines have limited collection of sales information and environmental data, making it difficult to optimize inventory management and marketing strategies. It has also been difficult for managers to obtain accurate data in real time and effectively set up advertising campaigns. Furthermore, there are issues with the power supply to each terminal, and improvements are needed from a sustainability perspective. The purpose of this invention is to solve these issues and realize efficient and sustainable capsule toy machine operation.

[1558] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1559] In this invention, the server includes means for collecting sales information, means for collecting environmental data, means for packetizing the collected sales information and environmental data, means for transmitting the packetized data via a communication line, means for receiving data transmitted from a terminal, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for optimizing a delivery route based on the generated sales forecast model, means for notifying a delivery company of the optimized delivery route, means for displaying real-time data on a management dashboard, means for setting up an advertising campaign based on the displayed data, means for transmitting the set advertising campaign to multiple terminals, means for adding a visualization means for displaying data collected on the terminals to facilitate data display, means for providing an input means for an administrator to set up an advertising campaign, means for transmitting event data set based on the advertising campaign to the server and managing it, means for acquiring real-time sales information and environmental data and analyzing and visualizing the data to support administrator decision-making, means for supporting marketing optimization using a generative AI model on the data, and means for automatically setting up a campaign based on the generated prompt sentences.This enables efficient inventory management, marketing campaign optimization, sustainable power supply, and support for administrator decision-making.

[1560] "Sales information" is data regarding the quantity and price of products sold in capsule toy machines.

[1561] "Environmental data" refers to data related to the temperature, humidity, foot traffic, etc. of the location where the capsule toy machine is installed.

[1562] "Packetization" means organizing collected sales information and environmental data into a fixed format and breaking the data into smaller pieces for transmission over communication lines.

[1563] "Communication lines" refer to wireless networks such as 4G and 5G, which are the infrastructure for sending and receiving data.

[1564] A "database" is an information management system that effectively stores, searches, and analyzes collected data.

[1565] A "sales forecasting model" is an algorithm or statistical model for predicting future sales based on past sales data and environmental data.

[1566] "Delivery route optimization" is the calculation of the most efficient delivery route for replenishing products.

[1567] A "management dashboard" is a graphical user interface for displaying and analyzing sales information and environmental data in real time.

[1568] An "advertising campaign" is a sales promotion activity for a specific product or event, and is a means of increasing sales by offering special prices or benefits for a specific period of time.

[1569] A "visualization tool" is a method or technique for visually displaying data as graphs or charts.

[1570] "Input means" is an interface through which an administrator can set up advertising campaigns and other settings.

[1571] A "generative AI model" is a model created using machine learning and deep learning algorithms to perform data analysis and predictions.

[1572] A "prompt sentence" is an instruction sentence that prompts a specific operation or input.

[1573] A "solar panel" is a device that converts sunlight into electricity.

[1574] "Electricity storage device" refers to a battery or electrical equipment for storing generated electricity.

[1575] An "API" is an interface for exchanging data between different software.

[1576] This invention is a system for achieving efficient operation and sustainability of capsule toy machines. The system includes the following main components:

[1577] Embodiment of terminal (capsule toy machine)

[1578] The terminals are equipped with sales sensors and environmental sensors. The sales sensors measure the number of capsule toys sold, while the environmental sensors collect data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via communication lines (4G / 5G). The terminals are also equipped with solar panels that use sunlight to generate electricity, which is then stored in a power storage device. This ensures a sustainable power supply and reduces the environmental impact.

[1579] Server embodiment

[1580] The server receives sales information and environmental data sent from the device and stores them in a database. A sales forecasting model is generated based on this stored data. This model uses machine learning algorithms to predict future sales and enable effective inventory management. The server also obtains social media trend information and related product sales data via API and integrates it into the database. This enables more detailed analysis of marketing data. The server also obtains real-time sales information and environmental data, and performs data analysis and visualization to support managerial decision-making.

[1581] The server calculates the optimal replenishment route based on the generated sales forecast model. This replenishment route information is notified to delivery companies, realizing efficient replenishment without waste. It also provides an input method for administrators to set up advertising campaigns.

[1582] User (operation manager) implementation form

[1583] Operations managers can use the management dashboard to check real-time sales information and inventory status. The dashboard also displays sales statistics and environmental data, allowing managers to quickly and accurately grasp the situation. Managers can also use the dashboard to set up advertisements based on new events and campaigns. These set advertising campaigns are distributed to each terminal via the server and notified to customers via display screens and voice messages.

[1584] For example, when setting up a Christmas campaign, the administrator can apply discounts to specific products and send that information to each terminal via the server, thereby attracting interest in the specific products and increasing sales.

[1585] Hardware and software used

[1586] Hardware: smartphones, IoT sensors, solar panels, energy storage devices

[1587] Software: Python, Flask (web framework), Pandas (data analysis library), Matplotlib (data visualization library), Requests (API communication library)

[1588] Examples of prompt statements

[1589] Prompt to retrieve sales data from the server: "Please retrieve capsule toy sales data and visualize it."

[1590] Prompt to set up a campaign: "For product ID 1234, set up a campaign offering 20% ​​off from December 20th to 25th."

[1591] This enables efficient inventory management, optimized marketing campaigns, sustainable power supply, and managerial decision support.

[1592] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1593] Step 1:

[1594] The server receives sales information and environmental data from the terminal.

[1595] Input: Sales information and environmental data sent from the terminal

[1596] Output: Raw sales and environmental data

[1597] The server receives data from the device in real time and performs initial processing to store it in a database. Specifically, the server receives data packets sent from the device via 4G or 5G communication lines and analyzes the contents. The received data includes sales volume, temperature, humidity, foot traffic, etc., and identifies and classifies each data point.

[1598] Step 2:

[1599] The server stores the received sales information and environmental data in a database.

[1600] Input: Identified and classified sales information and environmental data

[1601] Output: Sales information and environmental data stored in a database

[1602] The server converts the received data into an appropriate format and stores it in the database. Specifically, it establishes a connection to the database and writes the sales information to the sales table and the environmental data to the environmental table.

[1603] Step 3:

[1604] The server generates a sales forecast model based on the stored data.

[1605] Input: Sales information and environmental data stored in a database

[1606] Output: Generated sales forecast model

[1607] The server analyzes the stored past sales data and environmental data using a machine learning algorithm to generate a sales forecasting model. Specifically, it creates a training dataset using a Python machine learning library (such as scikit-learn) and builds a forecasting model based on that data.

[1608] Step 4:

[1609] The server calculates the optimal delivery route based on the generated sales forecast model.

[1610] Input: Generated sales forecast model

[1611] Output: Optimal delivery route

[1612] The server calculates the predicted stock-out timing for each terminal based on the sales forecast model, and generates the optimal delivery route based on this.Specifically, it uses a Python route optimization library (such as ORTools) to calculate a route that minimizes delivery cost and time.

[1613] Step 5:

[1614] The server notifies the delivery company of the optimized delivery route.

[1615] Input: Optimal delivery route

[1616] Output: Delivery route notified to the delivery company

[1617] The server sends the calculated delivery route information to the specified delivery company API and notifies it. Specifically, it sends the optimized route information via a POST request to the delivery company's specific API endpoint.

[1618] Step 6:

[1619] Users can view real-time sales information and inventory status on an administrative dashboard.

[1620] Input: Real-time data stored in a database

[1621] Output: Sales information and inventory status displayed on a dashboard

[1622] Users can access the dashboard via a web browser and check real-time data. Specifically, sales information and environmental data are displayed in graphs on the dashboard, allowing users to check them.

[1623] Step 7:

[1624] A user sets up an advertising campaign through a dashboard.

[1625] Input: Campaign setting information entered by the administrator

[1626] Output: Configured ad campaigns

[1627] Users use the dashboard's input form to set up advertising campaigns for specific products and time periods. Specifically, they enter campaign information into the form and press the "Set" button, which sends the information to the server.

[1628] Step 8:

[1629] A server transmits a configured advertising campaign to multiple terminals.

[1630] Input: Set advertising campaign information

[1631] Output: Advertising campaign information sent to each device

[1632] The server distributes advertising campaign information set by the user to each terminal. Specifically, the server sends the campaign information to the communication address of each terminal and sets the terminal to display the content on its display or as a voice message.

[1633] Step 9:

[1634] The server provides marketing optimization support using generative AI models.

[1635] Input: Sales information, environmental data, and social media trend information stored in the database

[1636] Output: Optimized marketing strategies

[1637] The server uses the generative AI model to analyze sales data, environmental data, and social media trend information to optimize marketing strategies. Specifically, it analyzes data using Python's machine learning library and recommends optimal marketing actions.

[1638] Step 10:

[1639] The server automatically sets up a campaign based on the generated prompt text.

[1640] Input: A prompt generated by a generative AI model

[1641] Output: Automated ad campaign

[1642] The server automatically sets up advertising campaigns based on the prompts created by the generative AI model. Specifically, it analyzes the generated prompts, creates campaign setting information with appropriate content, and sends it to each device.

[1643] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1644] This invention is a system that combines a capsule toy machine with a power source, IoT devices, and an emotion engine to operate as an autonomous machine. The system aims to efficiently manage and sustainably operate capsule toys by collecting sales information and environmental data in real time, analyzing emotion data, utilizing marketing data, and calculating optimal replenishment routes.

[1645] Embodiment of terminal (capsule toy machine)

[1646] The terminal is equipped with a sales sensor and an environmental sensor. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the location where it is installed. This data is periodically packetized and sent to a server via a communication line (4G / 5G).

[1647] The device is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. This emotional data is transmitted along with sales information and environmental data. Solar panels are used to convert sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces environmental impact.

[1648] Server embodiment

[1649] The server receives the sales information, environmental data, and emotion data sent from the device and stores them in a database. Based on this stored data, a sales forecasting model is generated. The sales forecasting model uses machine learning algorithms to predict future sales and enable effective inventory management.

[1650] The server also retrieves social media trend information and related product sales data via API and integrates it into the database. This allows for more detailed analysis of marketing data. Furthermore, incorporating emotion data obtained from the emotion engine into the analysis improves the accuracy of the sales forecast model.

[1651] The optimal replenishment route is calculated based on a sales forecast model. By taking into account emotional data, the system predicts when customers' purchasing motivation will be highest and sets an optimal replenishment schedule. The server then notifies the delivery company of this replenishment route information, ensuring efficient replenishment without waste.

[1652] User (operation manager) implementation form

[1653] Operations managers can check real-time sales information and inventory status using the management dashboard, which displays sales statistics, environmental data, and even user sentiment data, allowing managers to quickly and accurately grasp the situation.

[1654] Administrators can also use the dashboard to set up advertisements based on new events or campaigns. These set up advertising campaigns are then distributed to each device via the server. Furthermore, the content and timing of advertising campaigns can be optimized based on data obtained from the emotion engine. This allows for more precise targeting and maximizes user purchasing intent.

[1655] For example, if a campaign for a specific product is to be carried out at a shopping mall where terminals are installed, the administrator can check sales data from the dashboard, analyze social media trends, surrounding environmental data, and even sentiment data, and then launch the campaign at the appropriate time. This set advertising campaign is instantly distributed to each terminal, and customers are notified via display screens and voice messages.

[1656] In this way, capsule toy machines can collect data in real time and operate efficiently based on that data. Optimal inventory management based on sales forecasts and marketing strategies that utilize user sentiment data will significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[1657] The processing flow will be explained below.

[1658] Processing of terminals (capsule toy machines)

[1659] Sales information collection and transmission process

[1660] Step 1:

[1661] The terminal uses a sales sensor to measure the number of capsule toys sold. Specifically, it counts each time a capsule toy is dispensed.

[1662] Step 2:

[1663] The device uses environmental sensors to capture surrounding environmental data such as temperature, humidity, and foot traffic, allowing it to record the environmental conditions of the location in real time.

[1664] Step 3:

[1665] The device uses an emotion engine to analyze the user's voice and facial expressions to collect emotional data, for example, analyzing voice to determine whether the user is having fun.

[1666] Step 4:

[1667] The device then packets the collected sales data, environmental data, and emotion data, each of which includes the type of data and a timestamp.

[1668] Step 5:

[1669] The device transmits packetized data to the server via a communication line (4G or 5G). The transmission is done at regular intervals, and the data is always kept up to date.

[1670] Powered by solar panels

[1671] Step 1:

[1672] The device uses solar panels to convert sunlight into electricity, and is designed to efficiently absorb sunlight during the day.

[1673] Step 2:

[1674] The generated power is stored in a power storage device, and is used to operate sensors and communication devices.

[1675] Server Processing

[1676] Data Receipt and Storage Process

[1677] Step 1:

[1678] The server receives the data packets sent from the device and checks that the data is not lost or distorted according to the communication protocol.

[1679] Step 2:

[1680] The server analyzes the received data and extracts sales information, environmental data, and emotional data, which are then stored in a database.

[1681] Generate a sales forecast model

[1682] Step 1:

[1683] The server retrieves historical sales data, environmental data, and emotional data stored in the database, providing the most up-to-date information.

[1684] Step 2:

[1685] The server uses the acquired data to apply machine learning algorithms to generate a sales forecasting model, which is used to predict future sales.

[1686] Step 3:

[1687] The generated sales forecast model is evaluated to check its accuracy. If the accuracy is insufficient, the model is retrained to improve it.

[1688] Delivery route optimization

[1689] Step 1:

[1690] The server creates a list of devices that need replenishment based on the sales forecast model, predicts when inventory will be low, and prevents unnecessary replenishment.

[1691] Step 2:

[1692] The server calculates the optimal replenishment route, taking into account the distance to each replenishment point and traffic conditions.

[1693] Step 3:

[1694] The server notifies the delivery company of the optimized replenishment route information in real time, enabling efficient replenishment of products.

[1695] User (operation administrator) processing

[1696] Dashboard management process

[1697] Step 1:

[1698] Users access a management dashboard that provides real-time sales information, inventory status, and sentiment data, which is visually displayed in graphs and tables.

[1699] Step 2:

[1700] Users analyze trending information for events and promotions and set up advertising campaigns from the dashboard, including ad content and duration.

[1701] Step 3:

[1702] The advertising campaigns set by the user are delivered to each device via the server, and the device updates its display and voice messages based on the received campaign information.

[1703] Step 4:

[1704] Through the dashboard, users can optimize the content and timing of their advertising campaigns based on data obtained from the emotion engine, resulting in highly accurate targeting and maximizing user purchasing intent.

[1705] In this way, this system, combined with the emotion engine, enables efficient operation of capsule toy machines and a highly accurate marketing strategy. Inventory management based on sales forecasts and advertising campaigns utilizing emotion data significantly improve operational efficiency, while also contributing to energy savings and reduced CO2 emissions.

[1706] Example 2

[1707] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1708] In the operation of conventional capsule toy machines, it was difficult to collect sales information and environmental data in real time, and marketing strategies utilizing emotional data were limited. As a result, inventory management and replenishment routes were not adequately optimized, leading to reduced operational efficiency, wasted energy, and increased CO2 emissions. Effective targeting of advertising campaigns was also difficult. These issues need to be resolved.

[1709] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1710] In this invention, the server includes means for receiving sales information, means for receiving environmental data, means for receiving emotion data, means for storing the received data in a database, means for generating a sales forecast model based on the stored data, means for integrating SNS trend information and sales data of related products, means for calculating an optimal replenishment route based on the sales forecast model, means for notifying a delivery company of the optimized replenishment route, means for displaying real-time data on a management dashboard, and means for setting up and distributing advertising campaigns based on the displayed data. This enables efficient inventory management of capsule toy machines, optimization of operations based on environmental data, and realization of marketing strategies utilizing emotion data.

[1711] "Sales information" refers to data indicating the number of capsule products sold by the capsule toy machine and the sales amount.

[1712] "Environmental data" refers to data related to the operating environment of the capsule toy machine, such as temperature, humidity, and foot traffic.

[1713] "Emotion data" is data that indicates the emotional state of the user, obtained by analyzing the user's voice and facial expressions.

[1714] "Packetization" is a process of consolidating multiple pieces of collected data based on a certain standard.

[1715] "Communication lines" refers to communication infrastructure such as the Internet and 4G / 5G networks used to send and receive data.

[1716] A "database" is an information system for efficiently storing, retrieving, and managing structured data.

[1717] A "sales forecasting model" is a statistical model that uses machine learning algorithms to predict future sales based on collected data.

[1718] "Optimization" is the best way to allocate and operate resources to achieve a specific goal.

[1719] A "delivery route" is a route taken by a delivery company set up for replenishing capsule toy machines and managing inventory.

[1720] The "management dashboard" is a web-based interface that allows operations managers to check various data and perform operations in real time.

[1721] An "advertising campaign" is a series of advertising activities to promote a particular product or service.

[1722] A "solar panel" is a device that converts sunlight into electricity.

[1723] A "power storage device" is a device that stores generated electricity and supplies it when needed.

[1724] "SNS trend information" refers to information and content that is trending on social networking services.

[1725] "API" stands for Application Programming Interface, an interface that allows different software systems to communicate with each other.

[1726] The present invention relates to a system for collecting sales information, environmental data, and emotion data from capsule toy machines in real time, and for carrying out efficient inventory management, marketing strategies, and energy management. Specific embodiments of the present invention are described below.

[1727] Server embodiment

[1728] The server operates using the following hardware and software:

[1729] Hardware: Standard server machine (CPU, memory, storage)

[1730] Software: Cloud infrastructure (e.g., Amazon Web Services), machine learning algorithms (e.g., TensorFlow), database management systems (e.g., MySQL, MongoDB)

[1731] The server's main function is to receive sales information, environmental data, and emotion data sent from the device and store it in a database. A sales forecasting model is generated using a machine learning algorithm based on this stored data. It also obtains social media trend information and related product sales data via API and integrates it into the database. This integrated data is used to improve the accuracy of the sales forecasting model.

[1732] The server then calculates the optimal replenishment route based on the sales forecast model. The results are then sent to the delivery company via email, SMS, or a dedicated app, enabling an efficient replenishment schedule.

[1733] An example of a specific prompt is as follows:

[1734] "Please generate a sales forecasting model by integrating capsule toy machine sales information, environmental data, and sentiment data. Please also take into account social media trend information and sales data for related products."

[1735] Embodiment of terminal (capsule toy machine)

[1736] The device is equipped with the following hardware:

[1737] Hardware: Raspberry Pi, sales sensor, environmental sensors (temperature and humidity sensor, human presence sensor), camera, microphone, 4G / 5G communication module, solar panel, power storage device

[1738] The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. Furthermore, an emotion engine is installed to analyze the user's voice and facial expressions to collect emotional data. This data is packetized at regular intervals and sent to a server via a communication line (4G / 5G).

[1739] The solar panel converts sunlight into electricity, which is then stored in a storage device. This ensures a sustainable power supply and reduces the environmental impact. The device always performs optimal power management, ensuring efficient operation.

[1740] An example of a specific prompt is as follows:

[1741] "Please packetize the data obtained from the sales sensor, environmental sensor, and emotion engine and periodically send it to the server. Please use the solar panel to provide energy."

[1742] User (operation manager) implementation form

[1743] The operations manager uses the management dashboard to perform the following operations:

[1744] Real-time confirmation of sales information, inventory status, environmental data, and sentiment data

[1745] Advertisements based on new events and campaigns are set up and delivered to each device via the server.

[1746] Monitor and analyze data to take appropriate action

[1747] The dashboard displays detailed data using interactive charts and graphs, and uses sentiment data to optimize the content and timing of advertising campaigns to maximize user purchase intent.

[1748] An example of a specific prompt is as follows:

[1749] "Check sales information, inventory status, environmental data, and sentiment data from the dashboard and set up new advertising campaigns. The ads you set up are delivered to each device via our server."

[1750] As described above, the embodiment of the present invention collects and analyzes sales information, environmental data, and sentiment data in real time to realize optimal inventory management and marketing strategies, while also saving energy and reducing environmental impact.

[1751] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1752] server

[1753] Step 1: Receiving the data

[1754] The server receives packets of sales information, environmental data, and emotion data sent from the device. As input, it receives the data packet containing each data field, analyzes and decomposes the payload, and as output, it extracts the sales information, environmental data, and emotion data as separate datasets.

[1755] Specific operation: The server opens a receiving port and waits for data from each terminal. When the data arrives, it analyzes the payload and extracts each data field.

[1756] Step 2: Save your data

[1757] The server stores the received data in a database. As input, it receives analyzed sales information, environmental data, and emotion data. As output, each piece of information is stored in the corresponding table in the database.

[1758] What happens: The server connects to the database and inserts the data into the appropriate tables using SQL queries or NoSQL document operations.

[1759] Step 3: Generate a sales forecast model

[1760] The server uses machine learning algorithms to generate a sales forecasting model based on the stored data. As input, it takes sales information from the database, environmental data, and sentiment data. The output is a trained sales forecasting model.

[1761] What it does: The server queries the required data from the database, pre-processes the data, and then feeds it to the machine learning algorithm to train the model.

[1762] Step 4: Obtaining trend information

[1763] The server obtains SNS trend information and related product sales data via API. As input, it sends an API request and receives trend information and sales data. The output is to store the obtained data in a database.

[1764] Specific operation: The server sends API requests at specified intervals, converts the received data into an appropriate format, and stores it in the database.

[1765] Step 5: Model integration and analysis

[1766] The server integrates sales data, environmental data, sentiment data, and social media trend information and analyzes the sales forecasting model. It takes all these datasets as input. The output is the integrated dataset and an improved sales forecasting model.

[1767] Specific operation: The server integrates each dataset and uses the integrated data to retrain the model and tune parameters.

[1768] Step 6: Calculate replenishment routes

[1769] The server calculates the optimal replenishment route based on the sales forecast model and real-time emotion data. The inputs are the sales forecast model and real-time emotion data. The output is the optimal replenishment route and time schedule.

[1770] Specific operation: The server analyzes the location information and inventory information of each terminal and calculates the optimal replenishment route using Dijkstra's algorithm or A algorithm.

[1771] Step 7: Send notifications

[1772] The server notifies the calculated replenishment route information to the delivery company. As input, it receives the optimized replenishment route information. The output is a notification message to the delivery company.

[1773] Specific operation: Based on the generated replenishment schedule, the server creates a notification message for the delivery company and sends it via email, SMS, or a dedicated app.

[1774] Terminal (capsule toy machine)

[1775] Step 1: Collect data

[1776] The device collects data using sales sensors, environmental sensors, and an emotion engine. The inputs include sales volume, temperature, humidity, foot traffic, and voice and facial expression data. The output is the collected data.

[1777] Specific operation: The sales sensor increments the count each time it is triggered, the environmental sensor periodically captures data and stores it in a buffer, and the emotion engine analyzes audio and video data at regular intervals to generate emotion data.

[1778] Step 2: Packetize and transmit the data

[1779] The terminal packetizes the collected sales information, environmental data, and emotion data and sends them to the server. The terminal receives the collected data as input. The output is the packetized data.

[1780] Specific operation: The terminal collects all sensor data into one packet, properly formats each data field, and then transmits it through the 4G / 5G communication module.

[1781] Step 3: Managing the power supply

[1782] The device uses a solar panel to convert sunlight into electricity and stores it in a storage device. The input is sunlight. The output is the generated electricity.

[1783] How it works: The device's control unit monitors the power generated by the solar panel and controls charging, supplying power from the storage device as needed.

[1784] User (operation administrator)

[1785] Step 1: Use the dashboard

[1786] Users use the management dashboard to view real-time sales information, inventory status, environmental data, and sentiment data. As input, it receives data sent from the server. The output is detailed data displayed on the dashboard.

[1787] What it does: A user opens a web browser and logs into the dashboard, which displays data in sections and allows users to drill down into details using interactive charts and graphs.

[1788] Step 2: Set up your ad campaign

[1789] Users can set up new event- and campaign-based ads through the dashboard and distribute them to devices via the server. The input is the campaign content and conditions. The output is the configured ad campaign.

[1790] How it works: Users enter campaign details, duration, target audience, etc. into the dashboard and save the settings. The server then sends this information to each device, and the ads are displayed and played through the device's display and speaker.

[1791] Step 3: Monitor and analyze the data

[1792] Users can monitor sales statistics, environmental data, and sentiment data on the dashboard, analyze them, and take appropriate actions. The input is real-time data, and the output is analysis results and actions based on them.

[1793] What happens next? Users use the dashboard's filters and analytical tools to narrow down data for specific periods and conditions, discover trends and anomalies, and take action based on those findings.

[1794] Above we have detailed how the overall system functions specifically at each processing step to turn data from input to output.

[1795] (Application example 2)

[1796] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1797] With conventional capsule toy machines, managing sales information and environmental data was complicated, making optimal inventory management and marketing strategies difficult. Furthermore, the accuracy of sales forecasts using emotion data was not sufficiently improved, and there was a need for real-time data confirmation and more efficient replenishment work. In addition, there was a lack of a way for managers to easily access data and check efficient replenishment routes.

[1798] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1799] In this invention, the server includes a means for collecting sales information, a means for collecting environmental data, and a means for collecting emotional data. This allows for real-time analysis of user emotional data along with sales information and environmental data, improving the accuracy of the sales forecast model. Furthermore, by providing the smart glasses with a means for displaying real-time data and replenishment route information, managers can easily access the data and perform efficient replenishment work. This allows for the optimization of inventory management and marketing strategies, and the efficiency of management work to be achieved.

[1800] "Sales information" refers to data such as the number of units sold and the sales amount from capsule toy machines.

[1801] "Environmental data" refers to information such as temperature, humidity, and foot traffic in the area where the capsule toy machine is installed.

[1802] "Packetization" refers to converting collected data into a certain format so that it can be transmitted over a communication line.

[1803] "Communication lines" refer to the infrastructure for sending and receiving data, specifically wireless communication networks such as 4G and 5G.

[1804] "Database" refers to a computer system for storing and managing various collected data.

[1805] A "sales forecasting model" refers to a statistical and machine learning algorithm that predicts future sales based on past sales information, environmental data, and emotional data.

[1806] "Delivery route" refers to the optimized route for replenishing and delivering capsule toys.

[1807] An "administrative dashboard" is a software interface used by operations managers to monitor real-time data and set up advertising campaigns.

[1808] An "advertising campaign" refers to a marketing activity that distributes information for the purpose of sales promotion at a specific time.

[1809] "Emotion data" refers to information that indicates the emotional state of the user analyzed from their voice and facial expressions.

[1810] "Smart glasses" refer to a wearable device that can be worn by a user and display real-time information in their field of vision.

[1811] "Refill route information" refers to data regarding the optimal refill route for a capsule toy machine.

[1812] Terminal embodiment

[1813] The terminal is equipped with various sensors that collect sales information and environmental data. The sales sensor measures the number of capsule toys sold, while the environmental sensor collects data such as the temperature, humidity, and foot traffic of the installation location. This data is periodically packetized and sent to a server via a communication line (4G / 5G). The terminal is also equipped with an emotion engine that analyzes the user's voice and facial expressions to collect emotional data. The terminal is powered by a solar panel and operates sustainably using the power stored in a power storage device.

[1814] Server embodiment

[1815] The server receives sales information, environmental data, and emotion data sent from the device and stores them in a database. A sales forecasting model is generated based on the stored data, and this model is created using a machine learning algorithm (specifically, TensorFlow or Scikit-learn). Furthermore, by obtaining social media trend information and related product sales data via API and integrating this into the database, more detailed analysis of marketing data becomes possible. Emotional data is also incorporated into the analysis to improve the accuracy of the sales forecasting model. Furthermore, the optimal replenishment route is calculated based on the generated sales forecasting model, and this information is notified to the delivery company.

[1816] User's embodiment

[1817] Operations managers can check various data in real time using the management dashboard. The dashboard displays sales statistics, environmental data, and user sentiment data, allowing managers to quickly grasp the situation. Managers can also set up advertising campaigns from the dashboard and distribute them to each device. Based on data obtained from the sentiment engine, it is also possible to optimize the content and distribution timing of advertising campaigns. Furthermore, by using smart glasses, managers can visually check necessary data in real time during replenishment work and information on the optimal replenishment route.

[1818] Specific examples

[1819] Imagine a scenario where a shopping mall manager wears smart glasses and stands in front of a capsule toy machine to check "sales data" and "stock status." In this case, the manager can display the data directly in his field of vision and immediately begin replenishing the inventory. Examples of specific prompts include "Show me the latest sales figures for the capsule toy machine" and "Tell me the current stock status."

[1820] This system enables efficient capsule toy management, utilizes marketing data, and enables real-time data collection and analysis. Furthermore, by incorporating user sentiment data, the accuracy of sales forecasts can be improved and optimal replenishment schedules and routes can be generated. Furthermore, managers can visually check the data through smart glasses, improving the efficiency of replenishment work and marketing activities.

[1821] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1822] Step 1: The device collects sales information

[1823] A sales sensor installed in the terminal measures the number of capsule toys sold.

[1824] (Input) Sales Event

[1825] (Data processing) Add the number of items sold to the counter

[1826] (Output) Updated sales information

[1827] (Specific operation) The sensor detects the release of the toy capsule and updates the count data.

[1828] Step 2: Device collects environmental data

[1829] Environmental sensors installed on the device measure temperature, humidity, foot traffic, etc.

[1830] (Input) Surrounding environmental conditions

[1831] (Data processing) Read sensor data and convert it into a format

[1832] (Output) Environmental data

[1833] (Specific operation) Temperature and humidity sensors and human presence sensors periodically collect environmental information.

[1834] Step 3: The device collects emotion data

[1835] The device's emotion engine analyzes the user's voice and facial expressions to collect emotional data.

[1836] (Input) User's voice and facial expression

[1837] (Data processing) Analysis of audio and image data, conversion to emotional data

[1838] (Output) Emotion data

[1839] (Specific operation) The camera and microphone capture the user's facial expressions and voice, and the analysis engine measures their emotional state.

[1840] Step 4: Packetize the data and send it over the communication line to the server

[1841] The sales information, environmental data, and emotional data collected by the device are packaged into packets and sent to a server via 4G / 5G lines.

[1842] (Input) Sales information, environmental data, emotional data

[1843] (Data processing) Data formatting and packetization

[1844] (Output) Outgoing packets

[1845] (Specific operation) The data is formatted into a specific format, and the communication module forms a transmission packet, which is then sent to the server via the line.

[1846] Step 5: The server saves the received data to the database

[1847] The server receives the data sent from the terminal and stores it in a database.

[1848] (Input) Outgoing packets

[1849] (Data processing) Packet analysis and data insertion into database

[1850] (Output) Database records

[1851] (Specific Operation) The receiving module of the server analyzes the packet and inserts the data into the corresponding table.

[1852] Step 6: The server generates the sales forecast model

[1853] A sales forecasting model is generated using machine learning algorithms based on the data stored in the database.

[1854] (Input) Stored sales information, environmental data, and emotional data

[1855] (Data processing) Training machine learning models

[1856] (Output) Sales forecast model

[1857] (Specific operation) Using libraries such as TensorFlow and Scikit-learn, the algorithm analyzes the training dataset and generates a predictive model.

[1858] Step 7: The server calculates the optimal delivery route and notifies the delivery company.

[1859] The server calculates the optimal replenishment route based on the generated sales forecast model and notifies the delivery company.

[1860] (Input) Sales forecast model, geographic information

[1861] (Data processing) Application of optimization algorithms

[1862] (Output) Replenishment route information

[1863] (Specific operation) The server's calculation module optimizes the replenishment route, and the notification system sends instructions to the delivery company.

[1864] Step 8: Users review data via the admin dashboard

[1865] Operations managers view real-time data via an administrative dashboard. 【186...

Claims

1. A means of collecting sales information; a means for collecting environmental data; a means for packetizing the collected sales information and environmental data; means for transmitting packetized data over a communication line; means for receiving data transmitted from a terminal; a means for storing the received data in a database; a means for generating a sales forecasting model based on the stored data; A means of optimizing delivery routes based on the generated sales forecast model; a means for informing the delivery company of the optimized delivery route; A means to view real-time data in an administrative dashboard; a means for setting up advertising campaigns based on the displayed data; A means of sending configured advertising campaigns to multiple devices A system including:

2. 2. The system according to claim 1, further comprising means for supplying power by installing a solar panel on the terminal and using a power storage device.

3. The system according to claim 1, further comprising means for acquiring SNS trend information and sales data via an API and integrating the information into a database.

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