system

Abstract

Provide a system. 【Solution means】 In order to perform demand prediction, means for storing past sales information, seasonal characteristic information, and natural phenomenon information obtained from the outside in a database; Generative artificial intelligence means for calculating a sales prediction according to regional characteristics using the information obtained from the database; Means for proposing an optimal dish configuration for each store based on the sales prediction calculated by the generative artificial intelligence means; Means for monitoring the inventory at a plurality of sales bases in real time and automatically generating an optimized order instruction; Means for displaying the proposed dishes and inventory information generated at each sales base on a terminal; Means for implementing individualized promotional activities based on the usage history information of consumers; Means for promoting sales by making an optimal food proposal in consideration of the past order history of consumers; Means for making real-time menu proposals and promotions based on weather and local event information; Means for monitoring the inventory status and displaying only the dishes that can be ordered; A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the food service industry, while consumers' choices are increasing, food raw material costs and labor costs are soaring, and efficient inventory management and business strategies are required. Also, due to the increase in online orders and delivery services, consumers' behaviors are diversifying. Therefore, flexible responses according to the demand of each store are necessary, and improving efficiency and expanding sales by making the most of new technologies are important issues.

Means for Solving the Problems

[0005] This invention provides a system that constructs a database for demand forecasting using past sales information, seasonal trend information, and weather information, and calculates sales forecasts tailored to regional characteristics using artificial intelligence based on that information. Furthermore, it proposes an optimal menu configuration for each store and enables real-time inventory monitoring and automatic ordering. The system then solves the problem by displaying the generated proposed menu and inventory information on the store's terminal and implementing personalized promotional activities based on the customer's behavior history.

[0006] "Demand forecasting" is the process of estimating the appropriate supply quantity of a product by analyzing past sales data and external factors to predict future sales trends.

[0007] "Sales information" refers to sales data of products recorded from past transactions, and is important information that shows the quantity and timing of sales.

[0008] "Seasonal trend information" refers to information that shows trends and patterns that influence the demand for products during specific seasons or periods.

[0009] "Weather information" refers to information about local weather, temperature, humidity, and other climatic conditions, and is a factor that influences consumer purchasing behavior.

[0010] A "database" is a system that efficiently organizes vast amounts of digitized information and stores it in an accessible format.

[0011] "Generative artificial intelligence" is a computer program that analyzes and learns from vast amounts of data, and makes intelligent judgments and predictions to solve specific problems.

[0012] "Sales forecasting" is predictive information that helps in on-site decision-making by estimating the demand for a product over a specific period in the future.

[0013] "Menu composition" is the process of determining the list of food and beverages offered at a particular store, and it should be optimized according to customer needs.

[0014] "Inventory monitoring" is the process of tracking the quantity of goods currently held in real time and maintaining appropriate inventory levels.

[0015] "Automatic ordering" is a process in which the system automatically instructs the replenishment of goods when inventory levels reach a specific threshold.

[0016] A "terminal" is a device used for operating information systems and displaying information, and plays a role in supporting information sharing and decision-making within a store.

[0017] A "suggested menu" is a list of products deemed optimal for each store based on demand forecasts and regional characteristics, and is designed with the aim of increasing sales.

[0018] "Operation history information" refers to information about past customer purchasing behavior and order history, and serves as the basis for personalized sales promotion activities.

[0019] "Sales promotion activities" refer to marketing and campaign activities conducted to promote the sale of products, with the aim of attracting consumer attention and increasing their desire to purchase. [Brief explanation of the drawing]

[0020] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] This invention provides a system that integrates demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system efficiently supports the food service industry through information exchange between servers, terminals, and users.

[0042] The server first aggregates sales information from each store into a database. For example, it collects past sales data for each menu item and sales trends for specific periods. Furthermore, the server obtains weather data from an external weather information service, analyzes it in combination with historical data, and performs demand forecasting. The server uses an AI algorithm to calculate sales forecasts based on regional characteristics and proposes the optimal menu configuration for each store. These regional characteristics include seasonal events and local holidays.

[0043] The server also has the ability to monitor inventory levels in real time using data from IoT sensors installed in each store. When inventory levels drop, the server automatically generates an order instruction and places an order with the designated supplier.

[0044] Terminals placed in stores receive menu suggestions and inventory information from the server and notify store staff. For example, by displaying today's suggested menu and a list of items that need to be ordered next on the terminal screen, staff can check their goals in a timely manner.

[0045] Finally, users can browse the menu and place orders via their smart devices. Here, personalized promotional information based on past purchase history is presented to users, allowing them to receive discounts and benefits. In this way, personalized service is achieved for each individual consumer, leading to increased customer satisfaction.

[0046] This system enables proper inventory management, cost reduction, and increased sales in the food service industry, while also allowing for flexible business operations.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects and stores past sales information from each store in a database. This includes basic information such as the quantity sold and sales amount for each menu item.

[0050] Step 2:

[0051] The server obtains regional weather data through an external weather information service API. This adds daily temperature, precipitation, and weather conditions to the database.

[0052] Step 3:

[0053] The server combines acquired sales information and weather data to perform demand forecasting using an AI algorithm. In this process, it analyzes sales trends under specific weather conditions and predicts future demand.

[0054] Step 4:

[0055] The server proposes the optimal menu configuration for each store based on demand forecasts. This proposal is adjusted to take into account the regional characteristics and customer preferences of each store.

[0056] Step 5:

[0057] The server monitors the inventory levels of each store in real time via IoT sensors and records them in a database. When inventory reaches its lowest level, the server automatically generates an order.

[0058] Step 6:

[0059] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff. Staff can also check daily sales strategies through the terminal.

[0060] Step 7:

[0061] Users can view the store's menu and place online orders through a smartphone app. Users are notified of personalized promotions based on their past order history, which can be applied when placing an order.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In the food service industry, it is crucial to respond quickly to changes in demand and manage inventory appropriately, but currently, vast amounts of data such as sales information, weather information, and customer behavior history are not being fully utilized. Furthermore, demand forecasting that takes regional characteristics into account and personalized sales promotion activities cannot be efficiently implemented with current systems.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for storing large amounts of past sales information, weather information, and user activity information in a storage device; means for running a program that includes a generating intelligence that analyzes the information obtained from the storage device and performs demand forecasting based on regional characteristics; and means for providing the optimal product configuration for each store in accordance with the demand forecast calculated using the generating intelligence. This enables efficient inventory management that responds quickly to changes in demand and sales promotion activities based on regional characteristics and individual user history.

[0067] A "storage device" is a physical or virtual medium for storing data, and is a device that enables the storage of large amounts of information.

[0068] "Generative intelligence" refers to a program function that utilizes machine learning and artificial intelligence technologies to identify patterns in data and make predictions.

[0069] "Means for running a program" refers to technical means for processing and analyzing data by executing a program within a computer.

[0070] "Product mix" refers to the combination and arrangement of products and services selected based on a specific market or consumer needs.

[0071] "Inventory management" is a management method that tracks the quantity, condition, and movement of goods held as inventory, and makes appropriate decisions regarding replenishment and ordering.

[0072] "Sales information" refers to all data related to product sales, including past sales data, purchase history, and sales performance by sales channel.

[0073] This invention is a system for achieving efficient demand forecasting and inventory management in the food service industry. This system achieves its objectives by exchanging information between a server, terminals, and users.

[0074] Server operation:

[0075] The server first collects sales information from each store and stores it in storage. This sales information includes past sales data and sales history for each product. It also obtains weather data by utilizing external weather information services. Services such as WeatherAPI can be used for this purpose. The server uses the collected data to perform demand forecasting based on regional characteristics, employing generative intelligence. This generative intelligence uses machine learning algorithms to identify specific consumption patterns.

[0076] As a concrete example, the server can analyze historical data and weather data to predict which products will be in high demand under specific weather conditions. For instance, in a store in a region experiencing a prolonged cold climate, it might predict increased demand for hot beverages and food, and inventory levels could be optimized accordingly.

[0077] Device operation:

[0078] The terminal receives menu suggestions and inventory information from the server and displays them on the displays in each store. This allows store staff to check the suggested menu configurations and the list of items to be ordered next time in real time.

[0079] User actions:

[0080] Users can browse menus using their smart devices and receive personalized promotional information. This allows for sales promotions optimized for each customer, stimulating their desire to purchase.

[0081] Specific examples of prompt messages include: "Based on this data, predict demand for each region. Factors to consider include season, weather, and past sales trends."

[0082] Implementing this system enables strategies to maximize sales while preventing stockouts, resulting in management that dynamically optimizes sales conditions.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects historical sales data from each store's POS system. The input includes sales information such as the quantity sold, sales amount, and date / time for each product. This data is aggregated and stored in a memory device. Specifically, the server periodically retrieves data using an API, standardizes the format, and registers it in the database.

[0086] Step 2:

[0087] The server obtains weather data from external weather information services. Input includes weather information based on location and date / time (temperature, probability of precipitation, weather conditions, etc.). The server stores this data in storage and combines it with sales data for analysis. Specifically, it uses the WeatherAPI to obtain the latest weather data based on the location information of each store.

[0088] Step 3:

[0089] The server inputs collected sales information and weather data into a generating AI model to perform demand forecasting. The aforementioned sales information and weather data are used as input. The AI ​​algorithm analyzes this data and outputs future demand patterns and sales forecasts. Specifically, it uses a machine learning model to analyze trends and predict demand under specific conditions.

[0090] Step 4:

[0091] The server creates the optimal product configuration for each store based on demand forecast results calculated by the generating intelligence. The input is the demand forecast results, and the output is the creation of proposed menus and inventory configurations. Specifically, it generates an optimal product placement list based on the forecast data and sends the results to the terminals in each store.

[0092] Step 5:

[0093] The terminal displays suggested menus and inventory information sent from the server. Input is optimized information from the server, and output is a display that staff can review. Specifically, the terminal's UI presents information such as "Today's Recommended Menu" and "List of Items Needing to Be Ordered This Week."

[0094] Step 6:

[0095] Users browse menus using their smart devices and receive personalized promotions. Inputs include past purchase history and current promotion information, and output provides users with personalized coupons and discount information. Specifically, the app analyzes user history and suggests personalized benefits.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] In the restaurant and food delivery industries, achieving efficient demand forecasting, inventory management, and personalized promotional activities is not easy. In particular, optimizing sales strategies in response to natural phenomena and local events, as well as dynamic inventory management, significantly impacts operational efficiency and customer satisfaction. Therefore, a comprehensive system is needed to provide rapid menu suggestions and promotional presentations based on customer trends.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for storing past sales information, seasonal characteristics information, and natural phenomenon information obtained from external sources in a database in order to perform demand forecasting; means for generating artificial intelligence to calculate sales forecasts according to regional characteristics; and means for monitoring inventory at multiple sales locations in real time and automatically generating optimized order instructions. This enables the suggestion of appropriate dishes according to consumer trends, efficient inventory management, and personalized sales promotion.

[0101] "Demand forecasting" is the act of predicting future product demand based on past sales data, weather, and seasonal characteristics.

[0102] "Sales information" is a general term for information related to the transaction of goods or services, such as past sales data and transaction history.

[0103] "Seasonal characteristic information" refers to information about consumer behavior and preferences during specific seasons or periods.

[0104] "Natural phenomenon information" refers to information about the external environment, such as weather data.

[0105] A "database" is a digital recording device or system that allows for the systematic storage and easy retrieval of information.

[0106] "Generative artificial intelligence means" refers to algorithms that analyze vast amounts of data and automatically generate specific patterns or predictions.

[0107] "Inventory management" is the process of monitoring the supply status of goods and replenishing or adjusting them as needed.

[0108] An "order instruction" is a directive to purchase specific goods in order to replenish a shortage of inventory.

[0109] "Personalized promotional activities" refer to marketing strategies that are optimized for specific target groups, taking into account each consumer's past behavior and preferences.

[0110] "Cuisine composition" refers to the design of a menu that combines different types of dishes.

[0111] The "Discontinuation Confirmation Display" is a function that, based on inventory, only shows consumers dishes that are still available for order.

[0112] The system for implementing this invention consists mainly of a server, a terminal, and a user's smart device.

[0113] The server first collects historical sales information from multiple sales locations, seasonal characteristics information, and natural phenomenon information obtained from external sources into a database. For example, it collects past sales performance for each dish and sales trends for specific seasons. In addition, the server also acquires weather data from external weather information services and analyzes it comprehensively.

[0114] Next, using generative artificial intelligence methods, the collected information is analyzed to perform sales forecasts based on regional characteristics and automatically propose the optimal menu configuration for each store. This AI model is built using TENSORFLOW® and PyTorch to process data quickly and accurately.

[0115] Furthermore, the server uses IoT sensors to monitor inventory levels at each sales location in real time. This function automatically generates an order instruction and places an order with the relevant supplier if inventory falls below a certain threshold.

[0116] The terminal receives dish suggestions and inventory information from the server and notifies store staff. The terminal screen displays the suggested dishes and a list of items that need to be ordered next time.

[0117] Users can view personalized meal suggestions and place orders via smart devices such as smartphones and tablets. Here, personalized promotional information is presented to users based on their past purchase history, allowing them to receive discounts and benefits.

[0118] A concrete example is a scenario where a consumer orders a "hot soup" suggested based on weather information, and then receives a push notification offering a "10% discount coupon for their next order" based on their subsequent order history.

[0119] An example of a prompt to input into the generating AI model is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would lead to increased sales." This makes it possible to maximize consumer purchasing intent.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server collects historical sales information, seasonal characteristics information, and natural phenomenon information from each sales location and stores it in a database. Various types of information are collected as input, time-series data is formatted as data processing, and structured data is stored in the database as output. This makes it easy to refer to the information.

[0123] Step 2:

[0124] The server analyzes information retrieved from the database using generative artificial intelligence methods. Sales information and weather information are used as input, and data calculations are performed to analyze sales trends and forecast demand. The output is sales forecast results based on the characteristics of each region. The AI ​​builds a model using TensorFlow to achieve rapid analysis.

[0125] Step 3:

[0126] The server proposes the optimal menu configuration for each sales location based on the analysis results. Sales forecast results are used as input, and the proposed optimal menu configuration is generated as output. Specifically, it automatically selects menu items with a high probability of increasing sales.

[0127] Step 4:

[0128] The server uses data acquired from IoT sensors to monitor inventory levels at each sales location in real time. Current inventory data is provided as input, and order instructions corresponding to the inventory level are automatically generated as output. This prevents inventory shortages or surpluses.

[0129] Step 5:

[0130] The terminal receives dish suggestions and inventory information sent from the server and displays them to the store staff. Information from the server is received as input, and suggested menus and order lists are displayed on the terminal screen as output. The terminal plays a role in effectively visualizing the received data.

[0131] Step 6:

[0132] Users view and order food suggestions provided via their smart devices. The menu information displayed on the smart device serves as input, and the user's order is sent to the server as output. This allows for personalized promotions based on past purchase history.

[0133] Step 7:

[0134] The user selects an appropriate dish using AI suggestions generated by prompts and receives promotional information. Past order data and prompts are included as input, and promotional notifications are sent as output. An example prompt is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would boost sales." This makes it possible to maximize consumer purchasing intent.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention integrates an emotion engine that recognizes user emotions into a system that performs demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system aims to improve efficiency in the food service industry through interaction between servers, terminals, and users.

[0137] First, the server collects past sales information from each store and stores it in a database. The collected data includes sales volume and revenue data, and based on this, an AI algorithm is used to forecast demand. In addition, weather data obtained from external weather information services is combined to propose an optimal menu configuration tailored to regional characteristics.

[0138] The server uses inventory data monitored by IoT sensors to perform real-time inventory management and automatically generates order instructions as needed. These order instructions automate the ordering process with suppliers.

[0139] The terminal can display suggested menus and inventory information provided by the server to store staff. To improve operational efficiency within the store, this terminal provides staff with information that helps them review daily sales strategies and adjust inventory.

[0140] Users can interact with the system via smart devices, browse menus, and place orders. During this process, an emotion engine analyzes the user's facial expressions and voice to tailor and deliver personalized promotions and services. For example, if a user expresses satisfaction, they may be notified of a special discount on their next visit.

[0141] This emotion engine collects emotional data provided as user feedback and optimizes future promotional strategies. Furthermore, if negative emotions are detected, it sends feedback to store staff instructing them to improve the service.

[0142] Thus, the system in this invention aims to improve the consumer experience, increase the operational efficiency of stores, and ultimately boost sales.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The server collects historical sales information from each store into a database. This information includes the quantity sold, sales revenue, and sales period for each menu item.

[0146] Step 2:

[0147] The server retrieves regional weather data from an external weather information service. This information is stored in a database and used in conjunction with sales trends to forecast demand.

[0148] Step 3:

[0149] The server uses AI algorithms based on sales information and weather data to forecast demand tailored to regional characteristics. Specifically, it suggests menu items that tend to increase in popularity based on past data, as well as new promotional strategies.

[0150] Step 4:

[0151] The server develops the optimal menu configuration for each store based on the proposed demand forecast and automatically sends the proposal to the store's terminal. This allows stores to offer menus that meet local demand.

[0152] Step 5:

[0153] The server collects real-time inventory information from IoT sensors installed in each store and automatically generates an order when inventory falls below a certain level. The order details are sent directly to the supplier.

[0154] Step 6:

[0155] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff in real time. This allows staff to quickly adjust inventory and prepare orders.

[0156] Step 7:

[0157] Users browse the menu on their smart devices and place orders according to their preferences. During the ordering process, an emotion engine analyzes the user's facial expressions and voice to provide personalized promotional information.

[0158] Step 8:

[0159] The server processes user sentiment data analyzed by the sentiment engine and develops personalized promotional strategies. For example, it offers discounts for future use to users who show positive sentiment, and sends instructions for service improvement to stores if they show negative sentiment.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] In today's highly competitive commercial environment, efficient inventory management, appropriate product supply based on demand forecasting, and sales promotion activities that respond to consumer emotions and individual preferences are crucial challenges for companies to achieve sustainable growth. However, achieving this requires appropriately processing vast amounts of data and providing personalized suggestions to individual consumers in real time. Furthermore, it is necessary to enhance consumer satisfaction and rapidly improve services by recognizing emotions. This invention aims to comprehensively solve these complex requirements, striving to improve the efficiency of store operations and enhance consumer satisfaction.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for storing past sales information, seasonal trend information, and weather information obtained from external sources in a storage device in order to perform demand forecasting; means for generating artificial intelligence that calculates sales forecasts according to regional characteristics using the information obtained from the storage device; and means for proposing the optimal product configuration for each sales base based on the sales forecast calculated by the generating artificial intelligence. This enables efficient resource management and the provision of personalized sales strategies.

[0165] "Demand forecasting" is a technique that predicts future sales volume and demand based on past sales information and data obtained from external sources.

[0166] A "storage device" is a device that stores data and information, and is used to store various types of information such as sales information and weather data.

[0167] "Generative artificial intelligence means" refers to a means of generating sales forecasts based on regional characteristics and historical data, using artificial intelligence technology for data analysis.

[0168] A "sales location" is a place where goods or services are provided, and includes stores and other sales locations.

[0169] Resource management is a management process that involves monitoring resources such as inventory and balancing efficient supply and consumption.

[0170] A "display device" is a device used to visually show digital information, and is a device used by store staff to verify information.

[0171] A "user" is an end-user who uses and purchases goods and services through the system.

[0172] "Emotional analysis methods" refer to technologies that analyze a user's emotions from their facial expressions and voice, and then provide appropriate sales strategies based on the results.

[0173] "Sales promotion activities" refer to activities such as campaigns and discounts that promote the sale of products, and are carried out to attract the interest of consumers.

[0174] This invention provides a system for achieving efficient inventory management, demand forecasting, and personalized sales promotion in a commercial environment, and its effectiveness is realized through the coordinated operation of server, terminal, and user components.

[0175] First, the server plays a central role in information management. The server uses a database system to store historical sales information collected from each sales location in storage. A general database management system (DBMS) can be used for data management at this stage. Furthermore, this system obtains weather information from external weather information services via an API and stores it in storage, enabling correlation analysis with sales data. As a means of generative artificial intelligence, generative AI models such as machine learning models are executed to forecast demand from the collected data. These generative AI models are implemented using advanced data analysis software.

[0176] Next, the terminal plays a supporting role for store staff. It displays suggested products based on demand forecasts sent from the server, as well as current resource information, on its display screen to support staff in working efficiently. It also presents sales promotion strategies to staff through the terminal, providing guidance for implementing personalized sales activities. The terminal's display screen can be built using a standard tablet or desktop device.

[0177] On the other hand, users interact with the system through smart devices. Users can browse digital menus and order products. The ordered data is then transferred back to the server and used for inventory management and updating sales data. In addition, emotion analysis is performed to recognize the user's emotions, and the resulting emotion data is fed back into the system. This provides a personalized customer experience, for example, by suggesting special offers applicable to the next order if the user shows positive emotions. Emotion analysis can be performed in combination with facial recognition software and voice analysis tools.

[0178] As a concrete example, if the server recommends seafood dishes on a sunny day, and a user who visits the restaurant shows a satisfied expression, the emotion analysis system determines that the expression is positive. Then, a promotion is implemented offering a discount on a specific dessert on the user's next visit. Examples of prompts to facilitate this process include, "What menu do you recommend if the weather is sunny today?" or "Please suggest a promotional strategy for the next visit if the user is satisfied." In this way, the system of the present invention aims to improve the quality of the consumer experience by making accurate decisions based on data.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The server collects historical sales information from the POS systems at each sales location. This information includes sales volume and revenue data, and is stored in a database. The input is data from the POS systems, and the output is sales information accumulated in the database. This data is used for subsequent demand forecasting.

[0182] Step 2:

[0183] The server accesses an external weather information service via an API to obtain weather data. This data includes temperature, precipitation, and other information. The input is information from the weather API, and the output is weather data stored in memory. This weather data is used as a necessary element for sales forecasting.

[0184] Step 3:

[0185] The server uses sales information and weather data stored in its storage device to input into a generative AI model and perform sales forecasting. The generative AI model uses machine learning algorithms to analyze this data. The input is all the stored data, and the output is the demand forecast results for each sales location. Based on this forecast information, the optimal product mix is ​​proposed.

[0186] Step 4:

[0187] The server makes product recommendations for each sales location based on sales forecasts generated by an AI model. Specifically, it selects the most suitable products according to specific conditions, such as suggesting cold drinks on sunny days. The input is the result of the demand forecast, and the output is the product recommendations for each store. These recommendations are sent to terminals used by store staff.

[0188] Step 5:

[0189] The terminal displays product suggestions sent from the server on its display screen. Store staff adjust their operations and prepare products based on this information. The input is product suggestion information sent from the server, and the output is the detailed suggestion information that staff view. This allows staff to carry out their work efficiently.

[0190] Step 6:

[0191] Users view the store's digital menu and order items via their smart devices. During this process, the user's facial expressions and voice are analyzed by an emotion analysis tool and recorded as emotion data. Input is the user's emotions and order information, while output is the emotion data and order information from the emotion analysis tool. This data is then used for further personalized promotions.

[0192] Step 7:

[0193] The server develops personalized sales promotion strategies based on the user's order history and sentiment data. Specifically, it might suggest special offers for future visits to users who have expressed satisfaction. The input consists of sentiment data obtained from a sentiment analysis tool and order history, and the output is the next promotion strategy. This aims to improve user satisfaction.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] Traditional sales operations in commercial facilities often failed to adequately optimize services based on fluctuating demand and customer sentiment, resulting in lost sales opportunities and decreased customer satisfaction. Furthermore, material management and procurement were frequently performed manually, leading to operational problems due to a lack of efficiency.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for storing data in an information storage device to perform demand forecasting, means for calculating sales forecasts using machine learning means, and means for conducting sales promotion activities based on consumer sentiment analysis. This makes it possible to individually optimize the consumer purchasing experience and improve the efficiency of material procurement.

[0199] - "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data and external factors.

[0200] "Information storage device" refers to a storage medium or database that stores data and allows it to be retrieved as needed.

[0201] "Machine learning methods" are a type of artificial intelligence technology that analyzes data patterns to perform predictions and classifications.

[0202] A "commercial facility" refers to a store or facility operated to provide goods or services.

[0203] "Materials" refers to raw materials and product inventory necessary for the operation of a commercial facility and the provision of goods.

[0204] A "procurement order" refers to an order or procedure that requests the purchase or supply of specific materials.

[0205] A "display device" refers to an electronic device used to visually convey information to users.

[0206] A "consumer" refers to an individual or group that purchases or uses goods or services.

[0207] "Expression analysis" refers to the process of inferring emotions from consumers' facial expressions and voices and analyzing them as data.

[0208] "Sales promotion activities" refer to methods and campaigns that promote the sale of products.

[0209] "Satisfaction level" refers to the level of satisfaction or evaluation that consumers receive through the use of a product or service.

[0210] "Sentiment analysis" is the process of evaluating emotions through information such as a consumer's facial expressions and voice.

[0211] "Advantages" refer to specific benefits or beneficial perks.

[0212] "Notification" refers to the act of informing a specific individual or group of relevant information.

[0213] The server stores sales and weather information in its data storage device, and analyzes the acquired data using machine learning to perform demand forecasts tailored to regional characteristics. This makes it possible to propose optimal menu configurations to commercial facilities based on sales forecasts.

[0214] The terminals are used within commercial facilities and are responsible for displaying suggested products and material information provided by the server. This allows facility staff to understand inventory levels and optimal product assortments in real time, improving operational efficiency.

[0215] Users interact with the system using smart devices in stores. An emotion engine analyzes consumer expressions and voice to determine emotions, enabling personalized sales promotions. For example, users who are highly satisfied may receive beneficial perks on their next visit.

[0216] For the entire system, it is recommended to use external software such as Amazon Rekognition or Google Cloud Speech-to-Text for facial and speech recognition. This will allow for the acquisition of sentiment analysis data, which will improve sales promotion and the consumer experience.

[0217] As a concrete example, consider a scenario where a user visiting a shopping mall during lunchtime uses smart glasses to receive service. In this case, the system suggests a "pasta and salad" set meal to the user, and because positive emotions are analyzed, a discount coupon for their next visit is issued. An example of a prompt message to the generating AI model would be, "Analyze the emotions of the customer who has just visited the store and suggest the optimal lunch set."

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The server acquires historical sales information from multiple commercial facilities and external weather information into its data storage device. By receiving sales volume, revenue data, and weather data as input and storing them in the data storage device, it constructs a dataset tailored to regional characteristics.

[0221] Step 2:

[0222] The server passes data stored in its information storage device to a machine learning tool for analysis. Sales information and weather information are used as input, and the AI ​​algorithm analyzes them to generate predictive demand data. The output is sales forecast data. This allows for the development of product strategies tailored to each commercial facility.

[0223] Step 3:

[0224] The terminal receives sales forecast data from the server and displays the product mix for sale to the commercial facility staff. Based on the forecast data as input, it presents the optimal menu on the display device. This allows staff to immediately adjust their sales strategy.

[0225] Step 4:

[0226] Users use smart devices to view menus and place orders within commercial facilities. This process inputs the user's facial and voice information, and the device transmits this data to an emotion engine.

[0227] Step 5:

[0228] The server analyzes user expressions using an emotion engine that performs facial recognition and voice analysis. It receives facial and voice data as input and outputs emotion analysis results using a generative AI model. This enables sales promotion activities that match the user's psychological state.

[0229] Step 6:

[0230] The terminal displays personalized discount information and promotions for specific users based on the analysis results. A digital coupon is generated when certain conditions are met, using the sentiment analysis results as input. The user is then offered benefits for their next visit.

[0231] Step 7:

[0232] The server sends a notification to the staff of the commercial facility when it receives negative sentiment analysis results from consumers. It takes sentiment data as input and outputs a notification of areas for improvement. This can be used to improve service quality.

[0233] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0239] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0240] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0241] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0243] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0244] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0247] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0248] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0249] This invention provides a system that integrates demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system efficiently supports the food service industry through information exchange between servers, terminals, and users.

[0250] The server first aggregates sales information from each store into a database. For example, it collects past sales data for each menu item and sales trends for specific periods. Furthermore, the server obtains weather data from an external weather information service, analyzes it in combination with historical data, and performs demand forecasting. The server uses an AI algorithm to calculate sales forecasts based on regional characteristics and proposes the optimal menu configuration for each store. These regional characteristics include seasonal events and local holidays.

[0251] The server also has the ability to monitor inventory levels in real time using data from IoT sensors installed in each store. When inventory levels drop, the server automatically generates an order instruction and places an order with the designated supplier.

[0252] Terminals placed in stores receive menu suggestions and inventory information from the server and notify store staff. For example, by displaying today's suggested menu and a list of items that need to be ordered next on the terminal screen, staff can check their goals in a timely manner.

[0253] Finally, users can browse the menu and place orders via their smart devices. Here, personalized promotional information based on past purchase history is presented to users, allowing them to receive discounts and benefits. In this way, personalized service is achieved for each individual consumer, leading to increased customer satisfaction.

[0254] This system enables proper inventory management, cost reduction, and increased sales in the food service industry, while also allowing for flexible business operations.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The server collects and stores past sales information from each store in a database. This includes basic information such as the quantity sold and sales amount for each menu item.

[0258] Step 2:

[0259] The server obtains regional weather data through an external weather information service API. This adds daily temperature, precipitation, and weather conditions to the database.

[0260] Step 3:

[0261] The server combines acquired sales information and weather data to perform demand forecasting using an AI algorithm. In this process, it analyzes sales trends under specific weather conditions and predicts future demand.

[0262] Step 4:

[0263] The server proposes the optimal menu configuration for each store based on demand forecasts. This proposal is adjusted to take into account the regional characteristics and customer preferences of each store.

[0264] Step 5:

[0265] The server monitors the inventory levels of each store in real time via IoT sensors and records them in a database. When inventory reaches its lowest level, the server automatically generates an order.

[0266] Step 6:

[0267] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff. Staff can also check daily sales strategies through the terminal.

[0268] Step 7:

[0269] Users can view the store's menu and place online orders through a smartphone app. Users are notified of personalized promotions based on their past order history, which can be applied when placing an order.

[0270] (Example 1)

[0271] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0272] In the food service industry, it is crucial to respond quickly to changes in demand and manage inventory appropriately, but currently, vast amounts of data such as sales information, weather information, and customer behavior history are not being fully utilized. Furthermore, demand forecasting that takes regional characteristics into account and personalized sales promotion activities cannot be efficiently implemented with current systems.

[0273] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0274] In this invention, the server includes means for storing large amounts of past sales information, weather information, and user activity information in a storage device; means for running a program that includes a generating intelligence that analyzes the information obtained from the storage device and performs demand forecasting based on regional characteristics; and means for providing the optimal product configuration for each store in accordance with the demand forecast calculated using the generating intelligence. This enables efficient inventory management that responds quickly to changes in demand and sales promotion activities based on regional characteristics and individual user history.

[0275] A "storage device" is a physical or virtual medium for storing data, and is a device that enables the storage of large amounts of information.

[0276] "Generative intelligence" refers to a program function that utilizes machine learning and artificial intelligence technologies to identify patterns in data and make predictions.

[0277] "Means for running a program" refers to technical means for processing and analyzing data by executing a program within a computer.

[0278] "Product composition" means a combination of products and services selected based on specific markets and consumer needs and their arrangement.

[0279] "Goods management" is a management method for tracking the quantity, status, and movement of goods held in stock and making appropriate replenishment and ordering decisions.

[0280] "Sales information" refers to all data related to product sales, such as past sales data, purchase history, and sales performance for each sales channel.

[0281] This invention is a system for achieving efficient demand forecasting and inventory management in the food service industry. This system achieves its purpose by exchanging information among servers, terminals, and users.

[0282] Server operations:

[0283] The server first collects sales information from each store and stores it in a storage device. The sales information includes past sales data and the sales history of each product. In addition, by utilizing an external weather information service, weather data is acquired. Services such as WeatherAPI can be used for this. The server uses the generated intelligence with the collected data to perform demand forecasting based on regional characteristics. This generated intelligence uses a machine learning algorithm to identify specific consumption patterns.

[0284] As a specific example, the server can analyze past data and weather data to predict products with increased demand under specific weather conditions. For example, in stores in regions with continuous cold weather, it is predicted that the demand for warm beverages and dishes may increase, and inventory is optimized based on this.

[0285] Terminal operations:

[0286] The terminal receives the menu suggestions and inventory information provided by the server and displays them on the displays of each store. This enables the store staff to check the proposed menu configurations and the list of products to be reordered in real time.

[0287] User actions:

[0288] The user can browse the menu using a smart device and receive personalized promotion information. This enables optimized promotions for each customer and stimulates the desire to purchase.

[0289] Specific examples of prompt sentences include "Please predict the demand for each region based on this data. The factors to be considered include seasons, weather, past sales trends, etc."

[0290] By implementing this system, a strategy to maximize sales while preventing stockouts becomes possible, and dynamic optimization of the sales state is realized.

[0291] The flow of the specific process in Example 1 will be described using FIG. 11.

[0292] Step 1:

[0293] The server collects past sales data from the POS systems of each store. As input, the sales information includes the quantity sold, sales amount, date and time for each product. These data are aggregated and stored in a storage device. As a specific operation, the server periodically obtains data using an API, unifies the format, and registers it in the database.

[0294] Step 2:

[0295] The server obtains weather data from external weather information services. Input includes weather information based on location and date / time (temperature, probability of precipitation, weather conditions, etc.). The server stores this data in storage and combines it with sales data for analysis. Specifically, it uses the WeatherAPI to obtain the latest weather data based on the location information of each store.

[0296] Step 3:

[0297] The server inputs collected sales information and weather data into a generating AI model to perform demand forecasting. The aforementioned sales information and weather data are used as input. The AI ​​algorithm analyzes this data and outputs future demand patterns and sales forecasts. Specifically, it uses a machine learning model to analyze trends and predict demand under specific conditions.

[0298] Step 4:

[0299] The server creates the optimal product configuration for each store based on demand forecast results calculated by the generating intelligence. The input is the demand forecast results, and the output is the creation of proposed menus and inventory configurations. Specifically, it generates an optimal product placement list based on the forecast data and sends the results to the terminals in each store.

[0300] Step 5:

[0301] The terminal displays suggested menus and inventory information sent from the server. Input is optimized information from the server, and output is a display that staff can review. Specifically, the terminal's UI presents information such as "Today's Recommended Menu" and "List of Items Needing to Be Ordered This Week."

[0302] Step 6:

[0303] The user browses the menu using a smart device and receives personalized promotions. The inputs include past purchase history and current promotion information, and the outputs include personalized coupon information and discount information provided to the user. As a specific operation, the user history is analyzed within the app to propose personalized benefits.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] In the food service industry and food delivery services, it is not easy to achieve efficient demand forecasting, inventory management, and individualized promotion activities. In particular, the optimization of sales strategies corresponding to natural phenomena and regional event information, and the dynamic management of inventory have a great impact on business efficiency and customer satisfaction. Therefore, an integrated system for quickly proposing menus and presenting promotions based on customer trends is required.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes means for storing past sales information, seasonal characteristic information, and natural phenomenon information acquired from the outside in a database to perform demand forecasting, generation artificial intelligence means for calculating sales forecasts according to regional characteristics, and means for monitoring the inventory at a plurality of sales bases in real time and automatically generating optimized order instructions. Thereby, it becomes possible to propose appropriate dishes according to consumer trends, efficiently manage inventory, and individualize sales promotion.

[0309] "Demand forecasting" is an act of predicting future product demand based on past sales information, weather, and seasonal characteristic information.

[0310] "Sales information" is a general term for information related to the transaction of goods or services, such as past sales data and transaction history.

[0311] "Seasonal characteristic information" refers to information about consumer behavior and preferences during specific seasons or periods.

[0312] "Natural phenomenon information" refers to information about the external environment, such as weather data.

[0313] A "database" is a digital recording device or system that allows for the systematic storage and easy retrieval of information.

[0314] "Generative artificial intelligence means" refers to algorithms that analyze vast amounts of data and automatically generate specific patterns or predictions.

[0315] "Inventory management" is the process of monitoring the supply status of goods and replenishing or adjusting them as needed.

[0316] An "order instruction" is a directive to purchase specific goods in order to replenish a shortage of inventory.

[0317] "Personalized promotional activities" refer to marketing strategies that are optimized for specific target groups, taking into account each consumer's past behavior and preferences.

[0318] "Cuisine composition" refers to the design of a menu that combines different types of dishes.

[0319] The "Discontinuation Confirmation Display" is a function that, based on inventory, only shows consumers dishes that are still available for order.

[0320] The system for implementing this invention consists mainly of a server, a terminal, and a user's smart device.

[0321] The server first collects historical sales information from multiple sales locations, seasonal characteristics information, and natural phenomenon information obtained from external sources into a database. For example, it collects past sales performance for each dish and sales trends for specific seasons. In addition, the server also acquires weather data from external weather information services and analyzes it comprehensively.

[0322] Next, using generative artificial intelligence methods, the collected information is analyzed to perform sales forecasts based on regional characteristics and automatically propose the optimal menu configuration for each store. This AI model is built using TensorFlow and PyTorch to process data quickly and accurately.

[0323] Furthermore, the server uses IoT sensors to monitor inventory levels at each sales location in real time. This function automatically generates an order instruction and places an order with the relevant supplier if inventory falls below a certain threshold.

[0324] The terminal receives dish suggestions and inventory information from the server and notifies store staff. The terminal screen displays the suggested dishes and a list of items that need to be ordered next time.

[0325] Users can view personalized meal suggestions and place orders via smart devices such as smartphones and tablets. Here, personalized promotional information is presented to users based on their past purchase history, allowing them to receive discounts and benefits.

[0326] A concrete example is a scenario where a consumer orders a "hot soup" suggested based on weather information, and then receives a push notification offering a "10% discount coupon for their next order" based on their subsequent order history.

[0327] An example of a prompt to input into the generating AI model is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would lead to increased sales." This makes it possible to maximize consumer purchasing intent.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The server collects historical sales information, seasonal characteristics information, and natural phenomenon information from each sales location and stores it in a database. Various types of information are collected as input, time-series data is formatted as data processing, and structured data is stored in the database as output. This makes it easy to refer to the information.

[0331] Step 2:

[0332] The server analyzes information retrieved from the database using generative artificial intelligence methods. Sales information and weather information are used as input, and data calculations are performed to analyze sales trends and forecast demand. The output is sales forecast results based on the characteristics of each region. The AI ​​builds a model using TensorFlow to achieve rapid analysis.

[0333] Step 3:

[0334] The server proposes the optimal menu configuration for each sales location based on the analysis results. Sales forecast results are used as input, and the proposed optimal menu configuration is generated as output. Specifically, it automatically selects menu items with a high probability of increasing sales.

[0335] Step 4:

[0336] The server uses data acquired from IoT sensors to monitor inventory levels at each sales location in real time. Current inventory data is provided as input, and order instructions corresponding to the inventory level are automatically generated as output. This prevents inventory shortages or surpluses.

[0337] Step 5:

[0338] The terminal receives dish suggestions and inventory information sent from the server and displays them to the store staff. Information from the server is received as input, and suggested menus and order lists are displayed on the terminal screen as output. The terminal plays a role in effectively visualizing the received data.

[0339] Step 6:

[0340] Users view and order food suggestions provided via their smart devices. The menu information displayed on the smart device serves as input, and the user's order is sent to the server as output. This allows for personalized promotions based on past purchase history.

[0341] Step 7:

[0342] The user selects an appropriate dish using AI suggestions generated by prompts and receives promotional information. Past order data and prompts are included as input, and promotional notifications are sent as output. An example prompt is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would boost sales." This makes it possible to maximize consumer purchasing intent.

[0343] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0344] This invention integrates an emotion engine that recognizes user emotions into a system that performs demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system aims to improve efficiency in the food service industry through interaction between servers, terminals, and users.

[0345] First, the server collects past sales information from each store and stores it in a database. The collected data includes sales volume and revenue data, and based on this, an AI algorithm is used to forecast demand. In addition, weather data obtained from external weather information services is combined to propose an optimal menu configuration tailored to regional characteristics.

[0346] The server uses inventory data monitored by IoT sensors to perform real-time inventory management and automatically generates order instructions as needed. These order instructions automate the ordering process with suppliers.

[0347] The terminal can display suggested menus and inventory information provided by the server to store staff. To improve operational efficiency within the store, this terminal provides staff with information that helps them review daily sales strategies and adjust inventory.

[0348] Users can interact with the system via smart devices, browse menus, and place orders. During this process, an emotion engine analyzes the user's facial expressions and voice to tailor and deliver personalized promotions and services. For example, if a user expresses satisfaction, they may be notified of a special discount on their next visit.

[0349] This emotion engine collects emotional data provided as user feedback and optimizes future promotional strategies. Furthermore, if negative emotions are detected, it sends feedback to store staff instructing them to improve the service.

[0350] Thus, the system in this invention aims to improve the consumer experience, increase the operational efficiency of stores, and ultimately boost sales.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The server collects historical sales information from each store into a database. This information includes the quantity sold, sales revenue, and sales period for each menu item.

[0354] Step 2:

[0355] The server retrieves regional weather data from an external weather information service. This information is stored in a database and used in conjunction with sales trends to forecast demand.

[0356] Step 3:

[0357] The server uses AI algorithms based on sales information and weather data to forecast demand tailored to regional characteristics. Specifically, it suggests menu items that tend to increase in popularity based on past data, as well as new promotional strategies.

[0358] Step 4:

[0359] The server develops the optimal menu configuration for each store based on the proposed demand forecast and automatically sends the proposal to the store's terminal. This allows stores to offer menus that meet local demand.

[0360] Step 5:

[0361] The server collects real-time inventory information from IoT sensors installed in each store and automatically generates an order when inventory falls below a certain level. The order details are sent directly to the supplier.

[0362] Step 6:

[0363] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff in real time. This allows staff to quickly adjust inventory and prepare orders.

[0364] Step 7:

[0365] Users browse the menu on their smart devices and place orders according to their preferences. During the ordering process, an emotion engine analyzes the user's facial expressions and voice to provide personalized promotional information.

[0366] Step 8:

[0367] The server processes user sentiment data analyzed by the sentiment engine and develops personalized promotional strategies. For example, it offers discounts for future use to users who show positive sentiment, and sends instructions for service improvement to stores if they show negative sentiment.

[0368] (Example 2)

[0369] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0370] In today's highly competitive commercial environment, efficient inventory management, appropriate product supply based on demand forecasting, and sales promotion activities that respond to consumer emotions and individual preferences are crucial challenges for companies to achieve sustainable growth. However, achieving this requires appropriately processing vast amounts of data and providing personalized suggestions to individual consumers in real time. Furthermore, it is necessary to enhance consumer satisfaction and rapidly improve services by recognizing emotions. This invention aims to comprehensively solve these complex requirements, striving to improve the efficiency of store operations and enhance consumer satisfaction.

[0371] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0372] In this invention, the server includes means for storing past sales information, seasonal trend information, and weather information obtained from external sources in a storage device in order to perform demand forecasting; means for generating artificial intelligence that calculates sales forecasts according to regional characteristics using the information obtained from the storage device; and means for proposing the optimal product configuration for each sales base based on the sales forecast calculated by the generating artificial intelligence. This enables efficient resource management and the provision of personalized sales strategies.

[0373] "Demand forecasting" is a technique that predicts future sales volume and demand based on past sales information and data obtained from external sources.

[0374] A "storage device" is a device that stores data and information, and is used to store various types of information such as sales information and weather data.

[0375] "Generative artificial intelligence means" refers to a means of generating sales forecasts based on regional characteristics and historical data, using artificial intelligence technology for data analysis.

[0376] A "sales location" is a place where goods or services are provided, and includes stores and other sales locations.

[0377] Resource management is a management process that involves monitoring resources such as inventory and balancing efficient supply and consumption.

[0378] A "display device" is a device used to visually show digital information, and is a device used by store staff to verify information.

[0379] A "user" is an end-user who uses and purchases goods and services through the system.

[0380] "Emotional analysis methods" refer to technologies that analyze a user's emotions from their facial expressions and voice, and then provide appropriate sales strategies based on the results.

[0381] "Sales promotion activities" refer to activities such as campaigns and discounts that promote the sale of products, and are carried out to attract the interest of consumers.

[0382] This invention provides a system for achieving efficient inventory management, demand forecasting, and personalized sales promotion in a commercial environment, and its effectiveness is realized through the coordinated operation of server, terminal, and user components.

[0383] First, the server plays a central role in information management. The server uses a database system to store historical sales information collected from each sales location in storage. A general database management system (DBMS) can be used for data management at this stage. Furthermore, this system obtains weather information from external weather information services via an API and stores it in storage, enabling correlation analysis with sales data. As a means of generative artificial intelligence, generative AI models such as machine learning models are executed to forecast demand from the collected data. These generative AI models are implemented using advanced data analysis software.

[0384] Next, the terminal plays a supporting role for store staff. It displays suggested products based on demand forecasts sent from the server, as well as current resource information, on its display screen to support staff in working efficiently. It also presents sales promotion strategies to staff through the terminal, providing guidance for implementing personalized sales activities. The terminal's display screen can be built using a standard tablet or desktop device.

[0385] On the other hand, users interact with the system through smart devices. Users can browse digital menus and order products. The ordered data is then transferred back to the server and used for inventory management and updating sales data. In addition, emotion analysis is performed to recognize the user's emotions, and the resulting emotion data is fed back into the system. This provides a personalized customer experience, for example, by suggesting special offers applicable to the next order if the user shows positive emotions. Emotion analysis can be performed in combination with facial recognition software and voice analysis tools.

[0386] As a concrete example, if the server recommends seafood dishes on a sunny day, and a user who visits the restaurant shows a satisfied expression, the emotion analysis system determines that the expression is positive. Then, a promotion is implemented offering a discount on a specific dessert on the user's next visit. Examples of prompts to facilitate this process include, "What menu do you recommend if the weather is sunny today?" or "Please suggest a promotional strategy for the next visit if the user is satisfied." In this way, the system of the present invention aims to improve the quality of the consumer experience by making accurate decisions based on data.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The server collects historical sales information from the POS systems at each sales location. This information includes sales volume and revenue data, and is stored in a database. The input is data from the POS systems, and the output is sales information accumulated in the database. This data is used for subsequent demand forecasting.

[0390] Step 2:

[0391] The server accesses an external weather information service via an API to obtain weather data. This data includes temperature, precipitation, and other information. The input is information from the weather API, and the output is weather data stored in memory. This weather data is used as a necessary element for sales forecasting.

[0392] Step 3:

[0393] The server uses sales information and weather data stored in its storage device to input into a generative AI model and perform sales forecasting. The generative AI model uses machine learning algorithms to analyze this data. The input is all the stored data, and the output is the demand forecast results for each sales location. Based on this forecast information, the optimal product mix is ​​proposed.

[0394] Step 4:

[0395] The server makes product recommendations for each sales location based on sales forecasts generated by an AI model. Specifically, it selects the most suitable products according to specific conditions, such as suggesting cold drinks on sunny days. The input is the result of the demand forecast, and the output is the product recommendations for each store. These recommendations are sent to terminals used by store staff.

[0396] Step 5:

[0397] The terminal displays product suggestions sent from the server on its display screen. Store staff adjust their operations and prepare products based on this information. The input is product suggestion information sent from the server, and the output is the detailed suggestion information that staff view. This allows staff to carry out their work efficiently.

[0398] Step 6:

[0399] Users view the store's digital menu and order items via their smart devices. During this process, the user's facial expressions and voice are analyzed by an emotion analysis tool and recorded as emotion data. Input is the user's emotions and order information, while output is the emotion data and order information from the emotion analysis tool. This data is then used for further personalized promotions.

[0400] Step 7:

[0401] The server develops personalized sales promotion strategies based on the user's order history and sentiment data. Specifically, it might suggest special offers for future visits to users who have expressed satisfaction. The input consists of sentiment data obtained from a sentiment analysis tool and order history, and the output is the next promotion strategy. This aims to improve user satisfaction.

[0402] (Application Example 2)

[0403] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0404] Traditional sales operations in commercial facilities often failed to adequately optimize services based on fluctuating demand and customer sentiment, resulting in lost sales opportunities and decreased customer satisfaction. Furthermore, material management and procurement were frequently performed manually, leading to operational problems due to a lack of efficiency.

[0405] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0406] In this invention, the server includes means for storing data in an information storage device to perform demand forecasting, means for calculating sales forecasts using machine learning means, and means for conducting sales promotion activities based on consumer sentiment analysis. This makes it possible to individually optimize the consumer purchasing experience and improve the efficiency of material procurement.

[0407] - "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data and external factors.

[0408] "Information storage device" refers to a storage medium or database that stores data and allows it to be retrieved as needed.

[0409] "Machine learning methods" are a type of artificial intelligence technology that analyzes data patterns to perform predictions and classifications.

[0410] A "commercial facility" refers to a store or facility operated to provide goods or services.

[0411] "Materials" refers to raw materials and product inventory necessary for the operation of a commercial facility and the provision of goods.

[0412] A "procurement order" refers to an order or procedure that requests the purchase or supply of specific materials.

[0413] A "display device" refers to an electronic device used to visually convey information to users.

[0414] A "consumer" refers to an individual or group that purchases or uses goods or services.

[0415] "Expression analysis" refers to the process of inferring emotions from consumers' facial expressions and voices and analyzing them as data.

[0416] "Sales promotion activities" refer to methods and campaigns that promote the sale of products.

[0417] "Satisfaction level" refers to the level of satisfaction or evaluation that consumers receive through the use of a product or service.

[0418] "Sentiment analysis" is the process of evaluating emotions through information such as a consumer's facial expressions and voice.

[0419] "Advantages" refer to specific benefits or beneficial perks.

[0420] "Notification" refers to the act of informing a specific individual or group of relevant information.

[0421] The server stores sales and weather information in its data storage device, and analyzes the acquired data using machine learning to perform demand forecasts tailored to regional characteristics. This makes it possible to propose optimal menu configurations to commercial facilities based on sales forecasts.

[0422] The terminals are used within commercial facilities and are responsible for displaying suggested products and material information provided by the server. This allows facility staff to understand inventory levels and optimal product assortments in real time, improving operational efficiency.

[0423] Users interact with the system using smart devices in stores. An emotion engine analyzes consumer expressions and voice to determine emotions, enabling personalized sales promotions. For example, users who are highly satisfied may receive beneficial perks on their next visit.

[0424] For the entire system, it is recommended to use external software such as Amazon Rekognition or Google Cloud Speech-to-Text for facial and speech recognition. This will allow for the acquisition of sentiment analysis data, which will improve sales promotion and the consumer experience.

[0425] As a concrete example, consider a scenario where a user visiting a shopping mall during lunchtime uses smart glasses to receive service. In this case, the system suggests a "pasta and salad" set meal to the user, and because positive emotions are analyzed, a discount coupon for their next visit is issued. An example of a prompt message to the generating AI model would be, "Analyze the emotions of the customer who has just visited the store and suggest the optimal lunch set."

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] The server acquires historical sales information from multiple commercial facilities and external weather information into its data storage device. By receiving sales volume, revenue data, and weather data as input and storing them in the data storage device, it constructs a dataset tailored to regional characteristics.

[0429] Step 2:

[0430] The server passes data stored in its information storage device to a machine learning tool for analysis. Sales information and weather information are used as input, and the AI ​​algorithm analyzes them to generate predictive demand data. The output is sales forecast data. This allows for the development of product strategies tailored to each commercial facility.

[0431] Step 3:

[0432] The terminal receives sales forecast data from the server and displays the product mix for sale to the commercial facility staff. Based on the forecast data as input, it presents the optimal menu on the display device. This allows staff to immediately adjust their sales strategy.

[0433] Step 4:

[0434] Users use smart devices to view menus and place orders within commercial facilities. This process inputs the user's facial and voice information, and the device transmits this data to an emotion engine.

[0435] Step 5:

[0436] The server analyzes user expressions using an emotion engine that performs facial recognition and voice analysis. It receives facial and voice data as input and outputs emotion analysis results using a generative AI model. This enables sales promotion activities that match the user's psychological state.

[0437] Step 6:

[0438] The terminal displays personalized discount information and promotions for specific users based on the analysis results. A digital coupon is generated when certain conditions are met, using the sentiment analysis results as input. The user is then offered benefits for their next visit.

[0439] Step 7:

[0440] The server sends a notification to the staff of the commercial facility when it receives negative sentiment analysis results from consumers. It takes sentiment data as input and outputs a notification of areas for improvement. This can be used to improve service quality.

[0441] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0442] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0443] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0444] [Third Embodiment]

[0445] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0446] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0447] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0448] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0449] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0450] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0451] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0452] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0453] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0454] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0455] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0456] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0457] This invention provides a system that integrates demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system efficiently supports the food service industry through information exchange between servers, terminals, and users.

[0458] The server first aggregates sales information from each store into a database. For example, it collects past sales data for each menu item and sales trends for specific periods. Furthermore, the server obtains weather data from an external weather information service, analyzes it in combination with historical data, and performs demand forecasting. The server uses an AI algorithm to calculate sales forecasts based on regional characteristics and proposes the optimal menu configuration for each store. These regional characteristics include seasonal events and local holidays.

[0459] The server also has the ability to monitor inventory levels in real time using data from IoT sensors installed in each store. When inventory levels drop, the server automatically generates an order instruction and places an order with the designated supplier.

[0460] Terminals placed in stores receive menu suggestions and inventory information from the server and notify store staff. For example, by displaying today's suggested menu and a list of items that need to be ordered next on the terminal screen, staff can check their goals in a timely manner.

[0461] Finally, users can browse the menu and place orders via their smart devices. Here, personalized promotional information based on past purchase history is presented to users, allowing them to receive discounts and benefits. In this way, personalized service is achieved for each individual consumer, leading to increased customer satisfaction.

[0462] This system enables proper inventory management, cost reduction, and increased sales in the food service industry, while also allowing for flexible business operations.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The server collects and stores past sales information from each store in a database. This includes basic information such as the quantity sold and sales amount for each menu item.

[0466] Step 2:

[0467] The server obtains regional weather data through an external weather information service API. This adds daily temperature, precipitation, and weather conditions to the database.

[0468] Step 3:

[0469] The server combines acquired sales information and weather data to perform demand forecasting using an AI algorithm. In this process, it analyzes sales trends under specific weather conditions and predicts future demand.

[0470] Step 4:

[0471] The server proposes the optimal menu configuration for each store based on demand forecasts. This proposal is adjusted to take into account the regional characteristics and customer preferences of each store.

[0472] Step 5:

[0473] The server monitors the inventory levels of each store in real time via IoT sensors and records them in a database. When inventory reaches its lowest level, the server automatically generates an order.

[0474] Step 6:

[0475] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff. Staff can also check daily sales strategies through the terminal.

[0476] Step 7:

[0477] Users can view the store's menu and place online orders through a smartphone app. Users are notified of personalized promotions based on their past order history, which can be applied when placing an order.

[0478] (Example 1)

[0479] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] In the food service industry, it is crucial to respond quickly to changes in demand and manage inventory appropriately, but currently, vast amounts of data such as sales information, weather information, and customer behavior history are not being fully utilized. Furthermore, demand forecasting that takes regional characteristics into account and personalized sales promotion activities cannot be efficiently implemented with current systems.

[0481] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0482] In this invention, the server includes means for storing large amounts of past sales information, weather information, and user activity information in a storage device; means for running a program that includes a generating intelligence that analyzes the information obtained from the storage device and performs demand forecasting based on regional characteristics; and means for providing the optimal product configuration for each store in accordance with the demand forecast calculated using the generating intelligence. This enables efficient inventory management that responds quickly to changes in demand and sales promotion activities based on regional characteristics and individual user history.

[0483] A "storage device" is a physical or virtual medium for storing data, and is a device that enables the storage of large amounts of information.

[0484] "Generative intelligence" refers to a program function that utilizes machine learning and artificial intelligence technologies to identify patterns in data and make predictions.

[0485] "Means for running a program" refers to technical means for processing and analyzing data by executing a program within a computer.

[0486] "Product mix" refers to the combination and arrangement of products and services selected based on a specific market or consumer needs.

[0487] "Inventory management" is a management method that tracks the quantity, condition, and movement of goods held as inventory, and makes appropriate decisions regarding replenishment and ordering.

[0488] "Sales information" refers to all data related to product sales, including past sales data, purchase history, and sales performance by sales channel.

[0489] This invention is a system for achieving efficient demand forecasting and inventory management in the food service industry. This system achieves its objectives by exchanging information between a server, terminals, and users.

[0490] Server operation:

[0491] The server first collects sales information from each store and stores it in storage. This sales information includes past sales data and sales history for each product. It also obtains weather data by utilizing external weather information services. Services such as WeatherAPI can be used for this purpose. The server uses the collected data to perform demand forecasting based on regional characteristics, employing generative intelligence. This generative intelligence uses machine learning algorithms to identify specific consumption patterns.

[0492] As a concrete example, the server can analyze historical data and weather data to predict which products will be in high demand under specific weather conditions. For instance, in a store in a region experiencing a prolonged cold climate, it might predict increased demand for hot beverages and food, and inventory levels could be optimized accordingly.

[0493] Device operation:

[0494] The terminal receives menu suggestions and inventory information from the server and displays them on the displays in each store. This allows store staff to check the suggested menu configurations and the list of items to be ordered next time in real time.

[0495] User actions:

[0496] Users can browse menus using their smart devices and receive personalized promotional information. This allows for sales promotions optimized for each customer, stimulating their desire to purchase.

[0497] Specific examples of prompt messages include: "Based on this data, predict demand for each region. Factors to consider include season, weather, and past sales trends."

[0498] Implementing this system enables strategies to maximize sales while preventing stockouts, resulting in management that dynamically optimizes sales conditions.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] The server collects historical sales data from each store's POS system. The input includes sales information such as the quantity sold, sales amount, and date / time for each product. This data is aggregated and stored in a memory device. Specifically, the server periodically retrieves data using an API, standardizes the format, and registers it in the database.

[0502] Step 2:

[0503] The server obtains weather data from external weather information services. Input includes weather information based on location and date / time (temperature, probability of precipitation, weather conditions, etc.). The server stores this data in storage and combines it with sales data for analysis. Specifically, it uses the WeatherAPI to obtain the latest weather data based on the location information of each store.

[0504] Step 3:

[0505] The server inputs collected sales information and weather data into a generating AI model to perform demand forecasting. The aforementioned sales information and weather data are used as input. The AI ​​algorithm analyzes this data and outputs future demand patterns and sales forecasts. Specifically, it uses a machine learning model to analyze trends and predict demand under specific conditions.

[0506] Step 4:

[0507] The server creates the optimal product configuration for each store based on demand forecast results calculated by the generating intelligence. The input is the demand forecast results, and the output is the creation of proposed menus and inventory configurations. Specifically, it generates an optimal product placement list based on the forecast data and sends the results to the terminals in each store.

[0508] Step 5:

[0509] The terminal displays suggested menus and inventory information sent from the server. Input is optimized information from the server, and output is a display that staff can review. Specifically, the terminal's UI presents information such as "Today's Recommended Menu" and "List of Items Needing to Be Ordered This Week."

[0510] Step 6:

[0511] Users browse menus using their smart devices and receive personalized promotions. Inputs include past purchase history and current promotion information, and output provides users with personalized coupons and discount information. Specifically, the app analyzes user history and suggests personalized benefits.

[0512] (Application Example 1)

[0513] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0514] In the restaurant and food delivery industries, achieving efficient demand forecasting, inventory management, and personalized promotional activities is not easy. In particular, optimizing sales strategies in response to natural phenomena and local events, as well as dynamic inventory management, significantly impacts operational efficiency and customer satisfaction. Therefore, a comprehensive system is needed to provide rapid menu suggestions and promotional presentations based on customer trends.

[0515] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0516] In this invention, the server includes means for storing past sales information, seasonal characteristics information, and natural phenomenon information obtained from external sources in a database in order to perform demand forecasting; means for generating artificial intelligence to calculate sales forecasts according to regional characteristics; and means for monitoring inventory at multiple sales locations in real time and automatically generating optimized order instructions. This enables the suggestion of appropriate dishes according to consumer trends, efficient inventory management, and personalized sales promotion.

[0517] "Demand forecasting" is the act of predicting future product demand based on past sales data, weather, and seasonal characteristics.

[0518] "Sales information" is a general term for information related to the transaction of goods or services, such as past sales data and transaction history.

[0519] "Seasonal characteristic information" refers to information about consumer behavior and preferences during specific seasons or periods.

[0520] "Natural phenomenon information" refers to information about the external environment, such as weather data.

[0521] A "database" is a digital recording device or system that allows for the systematic storage and easy retrieval of information.

[0522] "Generative artificial intelligence means" refers to algorithms that analyze vast amounts of data and automatically generate specific patterns or predictions.

[0523] "Inventory management" is the process of monitoring the supply status of goods and replenishing or adjusting them as needed.

[0524] An "order instruction" is a directive to purchase specific goods in order to replenish a shortage of inventory.

[0525] "Personalized promotional activities" refer to marketing strategies that are optimized for specific target groups, taking into account each consumer's past behavior and preferences.

[0526] "Cuisine composition" refers to the design of a menu that combines different types of dishes.

[0527] The "Discontinuation Confirmation Display" is a function that, based on inventory, only shows consumers dishes that are still available for order.

[0528] The system for implementing this invention consists mainly of a server, a terminal, and a user's smart device.

[0529] The server first collects historical sales information from multiple sales locations, seasonal characteristics information, and natural phenomenon information obtained from external sources into a database. For example, it collects past sales performance for each dish and sales trends for specific seasons. In addition, the server also acquires weather data from external weather information services and analyzes it comprehensively.

[0530] Next, using generative artificial intelligence methods, the collected information is analyzed to perform sales forecasts based on regional characteristics and automatically propose the optimal menu configuration for each store. This AI model is built using TensorFlow and PyTorch to process data quickly and accurately.

[0531] Furthermore, the server uses IoT sensors to monitor inventory levels at each sales location in real time. This function automatically generates an order instruction and places an order with the relevant supplier if inventory falls below a certain threshold.

[0532] The terminal receives dish suggestions and inventory information from the server and notifies store staff. The terminal screen displays the suggested dishes and a list of items that need to be ordered next time.

[0533] Users can view personalized meal suggestions and place orders via smart devices such as smartphones and tablets. Here, personalized promotional information is presented to users based on their past purchase history, allowing them to receive discounts and benefits.

[0534] A concrete example is a scenario where a consumer orders a "hot soup" suggested based on weather information, and then receives a push notification offering a "10% discount coupon for their next order" based on their subsequent order history.

[0535] An example of a prompt to input into the generating AI model is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would lead to increased sales." This makes it possible to maximize consumer purchasing intent.

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0537] Step 1:

[0538] The server collects historical sales information, seasonal characteristics information, and natural phenomenon information from each sales location and stores it in a database. Various types of information are collected as input, time-series data is formatted as data processing, and structured data is stored in the database as output. This makes it easy to refer to the information.

[0539] Step 2:

[0540] The server analyzes information retrieved from the database using generative artificial intelligence methods. Sales information and weather information are used as input, and data calculations are performed to analyze sales trends and forecast demand. The output is sales forecast results based on the characteristics of each region. The AI ​​builds a model using TensorFlow to achieve rapid analysis.

[0541] Step 3:

[0542] The server proposes the optimal menu configuration for each sales location based on the analysis results. Sales forecast results are used as input, and the proposed optimal menu configuration is generated as output. Specifically, it automatically selects menu items with a high probability of increasing sales.

[0543] Step 4:

[0544] The server uses data acquired from IoT sensors to monitor inventory levels at each sales location in real time. Current inventory data is provided as input, and order instructions corresponding to the inventory level are automatically generated as output. This prevents inventory shortages or surpluses.

[0545] Step 5:

[0546] The terminal receives dish suggestions and inventory information sent from the server and displays them to the store staff. Information from the server is received as input, and suggested menus and order lists are displayed on the terminal screen as output. The terminal plays a role in effectively visualizing the received data.

[0547] Step 6:

[0548] Users view and order food suggestions provided via their smart devices. The menu information displayed on the smart device serves as input, and the user's order is sent to the server as output. This allows for personalized promotions based on past purchase history.

[0549] Step 7:

[0550] The user selects an appropriate dish using AI suggestions generated by prompts and receives promotional information. Past order data and prompts are included as input, and promotional notifications are sent as output. An example prompt is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would boost sales." This makes it possible to maximize consumer purchasing intent.

[0551] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0552] This invention integrates an emotion engine that recognizes user emotions into a system that performs demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system aims to improve efficiency in the food service industry through interaction between servers, terminals, and users.

[0553] First, the server collects past sales information from each store and stores it in a database. The collected data includes sales volume and revenue data, and based on this, an AI algorithm is used to forecast demand. In addition, weather data obtained from external weather information services is combined to propose an optimal menu configuration tailored to regional characteristics.

[0554] The server uses inventory data monitored by IoT sensors to perform real-time inventory management and automatically generates order instructions as needed. These order instructions automate the ordering process with suppliers.

[0555] The terminal can display suggested menus and inventory information provided by the server to store staff. To improve operational efficiency within the store, this terminal provides staff with information that helps them review daily sales strategies and adjust inventory.

[0556] Users can interact with the system via smart devices, browse menus, and place orders. During this process, an emotion engine analyzes the user's facial expressions and voice to tailor and deliver personalized promotions and services. For example, if a user expresses satisfaction, they may be notified of a special discount on their next visit.

[0557] This emotion engine collects emotional data provided as user feedback and optimizes future promotional strategies. Furthermore, if negative emotions are detected, it sends feedback to store staff instructing them to improve the service.

[0558] Thus, the system in this invention aims to improve the consumer experience, increase the operational efficiency of stores, and ultimately boost sales.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The server collects historical sales information from each store into a database. This information includes the quantity sold, sales revenue, and sales period for each menu item.

[0562] Step 2:

[0563] The server retrieves regional weather data from an external weather information service. This information is stored in a database and used in conjunction with sales trends to forecast demand.

[0564] Step 3:

[0565] The server uses AI algorithms based on sales information and weather data to forecast demand tailored to regional characteristics. Specifically, it suggests menu items that tend to increase in popularity based on past data, as well as new promotional strategies.

[0566] Step 4:

[0567] The server develops the optimal menu configuration for each store based on the proposed demand forecast and automatically sends the proposal to the store's terminal. This allows stores to offer menus that meet local demand.

[0568] Step 5:

[0569] The server collects real-time inventory information from IoT sensors installed in each store and automatically generates an order when inventory falls below a certain level. The order details are sent directly to the supplier.

[0570] Step 6:

[0571] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff in real time. This allows staff to quickly adjust inventory and prepare orders.

[0572] Step 7:

[0573] Users browse the menu on their smart devices and place orders according to their preferences. During the ordering process, an emotion engine analyzes the user's facial expressions and voice to provide personalized promotional information.

[0574] Step 8:

[0575] The server processes user sentiment data analyzed by the sentiment engine and develops personalized promotional strategies. For example, it offers discounts for future use to users who show positive sentiment, and sends instructions for service improvement to stores if they show negative sentiment.

[0576] (Example 2)

[0577] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] In today's highly competitive commercial environment, efficient inventory management, appropriate product supply based on demand forecasting, and sales promotion activities that respond to consumer emotions and individual preferences are crucial challenges for companies to achieve sustainable growth. However, achieving this requires appropriately processing vast amounts of data and providing personalized suggestions to individual consumers in real time. Furthermore, it is necessary to enhance consumer satisfaction and rapidly improve services by recognizing emotions. This invention aims to comprehensively solve these complex requirements, striving to improve the efficiency of store operations and enhance consumer satisfaction.

[0579] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0580] In this invention, the server includes means for storing past sales information, seasonal trend information, and weather information obtained from external sources in a storage device in order to perform demand forecasting; means for generating artificial intelligence that calculates sales forecasts according to regional characteristics using the information obtained from the storage device; and means for proposing the optimal product configuration for each sales base based on the sales forecast calculated by the generating artificial intelligence. This enables efficient resource management and the provision of personalized sales strategies.

[0581] "Demand forecasting" is a technique that predicts future sales volume and demand based on past sales information and data obtained from external sources.

[0582] A "storage device" is a device that stores data and information, and is used to store various types of information such as sales information and weather data.

[0583] "Generative artificial intelligence means" refers to a means of generating sales forecasts based on regional characteristics and historical data, using artificial intelligence technology for data analysis.

[0584] A "sales location" is a place where goods or services are provided, and includes stores and other sales locations.

[0585] Resource management is a management process that involves monitoring resources such as inventory and balancing efficient supply and consumption.

[0586] A "display device" is a device used to visually show digital information, and is a device used by store staff to verify information.

[0587] A "user" is an end-user who uses and purchases goods and services through the system.

[0588] "Emotional analysis methods" refer to technologies that analyze a user's emotions from their facial expressions and voice, and then provide appropriate sales strategies based on the results.

[0589] "Sales promotion activities" refer to activities such as campaigns and discounts that promote the sale of products, and are carried out to attract the interest of consumers.

[0590] This invention provides a system for achieving efficient inventory management, demand forecasting, and personalized sales promotion in a commercial environment, and its effectiveness is realized through the coordinated operation of server, terminal, and user components.

[0591] First, the server plays a central role in information management. The server uses a database system to store historical sales information collected from each sales location in storage. A general database management system (DBMS) can be used for data management at this stage. Furthermore, this system obtains weather information from external weather information services via an API and stores it in storage, enabling correlation analysis with sales data. As a means of generative artificial intelligence, generative AI models such as machine learning models are executed to forecast demand from the collected data. These generative AI models are implemented using advanced data analysis software.

[0592] Next, the terminal plays a supporting role for store staff. It displays suggested products based on demand forecasts sent from the server, as well as current resource information, on its display screen to support staff in working efficiently. It also presents sales promotion strategies to staff through the terminal, providing guidance for implementing personalized sales activities. The terminal's display screen can be built using a standard tablet or desktop device.

[0593] On the other hand, users interact with the system through smart devices. Users can browse digital menus and order products. The ordered data is then transferred back to the server and used for inventory management and updating sales data. In addition, emotion analysis is performed to recognize the user's emotions, and the resulting emotion data is fed back into the system. This provides a personalized customer experience, for example, by suggesting special offers applicable to the next order if the user shows positive emotions. Emotion analysis can be performed in combination with facial recognition software and voice analysis tools.

[0594] As a concrete example, if the server recommends seafood dishes on a sunny day, and a user who visits the restaurant shows a satisfied expression, the emotion analysis system determines that the expression is positive. Then, a promotion is implemented offering a discount on a specific dessert on the user's next visit. Examples of prompts to facilitate this process include, "What menu do you recommend if the weather is sunny today?" or "Please suggest a promotional strategy for the next visit if the user is satisfied." In this way, the system of the present invention aims to improve the quality of the consumer experience by making accurate decisions based on data.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] The server collects historical sales information from the POS systems at each sales location. This information includes sales volume and revenue data, and is stored in a database. The input is data from the POS systems, and the output is sales information accumulated in the database. This data is used for subsequent demand forecasting.

[0598] Step 2:

[0599] The server accesses an external weather information service via an API to obtain weather data. This data includes temperature, precipitation, and other information. The input is information from the weather API, and the output is weather data stored in memory. This weather data is used as a necessary element for sales forecasting.

[0600] Step 3:

[0601] The server uses sales information and weather data stored in its storage device to input into a generative AI model and perform sales forecasting. The generative AI model uses machine learning algorithms to analyze this data. The input is all the stored data, and the output is the demand forecast results for each sales location. Based on this forecast information, the optimal product mix is ​​proposed.

[0602] Step 4:

[0603] The server makes product recommendations for each sales location based on sales forecasts generated by an AI model. Specifically, it selects the most suitable products according to specific conditions, such as suggesting cold drinks on sunny days. The input is the result of the demand forecast, and the output is the product recommendations for each store. These recommendations are sent to terminals used by store staff.

[0604] Step 5:

[0605] The terminal displays product suggestions sent from the server on its display screen. Store staff adjust their operations and prepare products based on this information. The input is product suggestion information sent from the server, and the output is the detailed suggestion information that staff view. This allows staff to carry out their work efficiently.

[0606] Step 6:

[0607] Users view the store's digital menu and order items via their smart devices. During this process, the user's facial expressions and voice are analyzed by an emotion analysis tool and recorded as emotion data. Input is the user's emotions and order information, while output is the emotion data and order information from the emotion analysis tool. This data is then used for further personalized promotions.

[0608] Step 7:

[0609] The server develops personalized sales promotion strategies based on the user's order history and sentiment data. Specifically, it might suggest special offers for future visits to users who have expressed satisfaction. The input consists of sentiment data obtained from a sentiment analysis tool and order history, and the output is the next promotion strategy. This aims to improve user satisfaction.

[0610] (Application Example 2)

[0611] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0612] Traditional sales operations in commercial facilities often failed to adequately optimize services based on fluctuating demand and customer sentiment, resulting in lost sales opportunities and decreased customer satisfaction. Furthermore, material management and procurement were frequently performed manually, leading to operational problems due to a lack of efficiency.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0614] In this invention, the server includes means for storing data in an information storage device to perform demand forecasting, means for calculating sales forecasts using machine learning means, and means for conducting sales promotion activities based on consumer sentiment analysis. This makes it possible to individually optimize the consumer purchasing experience and improve the efficiency of material procurement.

[0615] - "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data and external factors.

[0616] "Information storage device" refers to a storage medium or database that stores data and allows it to be retrieved as needed.

[0617] "Machine learning methods" are a type of artificial intelligence technology that analyzes data patterns to perform predictions and classifications.

[0618] A "commercial facility" refers to a store or facility operated to provide goods or services.

[0619] "Materials" refers to raw materials and product inventory necessary for the operation of a commercial facility and the provision of goods.

[0620] A "procurement order" refers to an order or procedure that requests the purchase or supply of specific materials.

[0621] A "display device" refers to an electronic device used to visually convey information to users.

[0622] A "consumer" refers to an individual or group that purchases or uses goods or services.

[0623] "Expression analysis" refers to the process of inferring emotions from consumers' facial expressions and voices and analyzing them as data.

[0624] "Sales promotion activities" refer to methods and campaigns that promote the sale of products.

[0625] "Satisfaction level" refers to the level of satisfaction or evaluation that consumers receive through the use of a product or service.

[0626] "Sentiment analysis" is the process of evaluating emotions through information such as a consumer's facial expressions and voice.

[0627] "Advantages" refer to specific benefits or beneficial perks.

[0628] "Notification" refers to the act of informing a specific individual or group of relevant information.

[0629] The server stores sales and weather information in its data storage device, and analyzes the acquired data using machine learning to perform demand forecasts tailored to regional characteristics. This makes it possible to propose optimal menu configurations to commercial facilities based on sales forecasts.

[0630] The terminals are used within commercial facilities and are responsible for displaying suggested products and material information provided by the server. This allows facility staff to understand inventory levels and optimal product assortments in real time, improving operational efficiency.

[0631] Users interact with the system using smart devices in stores. An emotion engine analyzes consumer expressions and voice to determine emotions, enabling personalized sales promotions. For example, users who are highly satisfied may receive beneficial perks on their next visit.

[0632] For the entire system, it is recommended to use external software such as Amazon Rekognition or Google Cloud Speech-to-Text for facial and speech recognition. This will allow for the acquisition of sentiment analysis data, which will improve sales promotion and the consumer experience.

[0633] As a concrete example, consider a scenario where a user visiting a shopping mall during lunchtime uses smart glasses to receive service. In this case, the system suggests a "pasta and salad" set meal to the user, and because positive emotions are analyzed, a discount coupon for their next visit is issued. An example of a prompt message to the generating AI model would be, "Analyze the emotions of the customer who has just visited the store and suggest the optimal lunch set."

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The server acquires historical sales information from multiple commercial facilities and external weather information into its data storage device. By receiving sales volume, revenue data, and weather data as input and storing them in the data storage device, it constructs a dataset tailored to regional characteristics.

[0637] Step 2:

[0638] The server passes data stored in its information storage device to a machine learning tool for analysis. Sales information and weather information are used as input, and the AI ​​algorithm analyzes them to generate predictive demand data. The output is sales forecast data. This allows for the development of product strategies tailored to each commercial facility.

[0639] Step 3:

[0640] The terminal receives sales forecast data from the server and displays the product mix for sale to the commercial facility staff. Based on the forecast data as input, it presents the optimal menu on the display device. This allows staff to immediately adjust their sales strategy.

[0641] Step 4:

[0642] Users use smart devices to view menus and place orders within commercial facilities. This process inputs the user's facial and voice information, and the device transmits this data to an emotion engine.

[0643] Step 5:

[0644] The server analyzes user expressions using an emotion engine that performs facial recognition and voice analysis. It receives facial and voice data as input and outputs emotion analysis results using a generative AI model. This enables sales promotion activities that match the user's psychological state.

[0645] Step 6:

[0646] The terminal displays personalized discount information and promotions for specific users based on the analysis results. A digital coupon is generated when certain conditions are met, using the sentiment analysis results as input. The user is then offered benefits for their next visit.

[0647] Step 7:

[0648] The server sends a notification to the staff of the commercial facility when it receives negative sentiment analysis results from consumers. It takes sentiment data as input and outputs a notification of areas for improvement. This can be used to improve service quality.

[0649] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0650] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0651] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0652] [Fourth Embodiment]

[0653] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0654] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0655] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0656] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0657] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0658] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0659] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0660] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0661] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0662] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0663] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0665] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] This invention provides a system that integrates demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system efficiently supports the food service industry through information exchange between servers, terminals, and users.

[0667] The server first aggregates sales information from each store into a database. For example, it collects past sales data for each menu item and sales trends for specific periods. Furthermore, the server obtains weather data from an external weather information service, analyzes it in combination with historical data, and performs demand forecasting. The server uses an AI algorithm to calculate sales forecasts based on regional characteristics and proposes the optimal menu configuration for each store. These regional characteristics include seasonal events and local holidays.

[0668] The server also has the ability to monitor inventory levels in real time using data from IoT sensors installed in each store. When inventory levels drop, the server automatically generates an order instruction and places an order with the designated supplier.

[0669] Terminals placed in stores receive menu suggestions and inventory information from the server and notify store staff. For example, by displaying today's suggested menu and a list of items that need to be ordered next on the terminal screen, staff can check their goals in a timely manner.

[0670] Finally, users can browse the menu and place orders via their smart devices. Here, personalized promotional information based on past purchase history is presented to users, allowing them to receive discounts and benefits. In this way, personalized service is achieved for each individual consumer, leading to increased customer satisfaction.

[0671] This system enables proper inventory management, cost reduction, and increased sales in the food service industry, while also allowing for flexible business operations.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The server collects and stores past sales information from each store in a database. This includes basic information such as the quantity sold and sales amount for each menu item.

[0675] Step 2:

[0676] The server obtains regional weather data through an external weather information service API. This adds daily temperature, precipitation, and weather conditions to the database.

[0677] Step 3:

[0678] The server combines acquired sales information and weather data to perform demand forecasting using an AI algorithm. In this process, it analyzes sales trends under specific weather conditions and predicts future demand.

[0679] Step 4:

[0680] The server proposes the optimal menu configuration for each store based on demand forecasts. This proposal is adjusted to take into account the regional characteristics and customer preferences of each store.

[0681] Step 5:

[0682] The server monitors the inventory levels of each store in real time via IoT sensors and records them in a database. When inventory reaches its lowest level, the server automatically generates an order.

[0683] Step 6:

[0684] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff. Staff can also check daily sales strategies through the terminal.

[0685] Step 7:

[0686] Users can view the store's menu and place online orders through a smartphone app. Users are notified of personalized promotions based on their past order history, which can be applied when placing an order.

[0687] (Example 1)

[0688] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0689] In the food service industry, it is crucial to respond quickly to changes in demand and manage inventory appropriately, but currently, vast amounts of data such as sales information, weather information, and customer behavior history are not being fully utilized. Furthermore, demand forecasting that takes regional characteristics into account and personalized sales promotion activities cannot be efficiently implemented with current systems.

[0690] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0691] In this invention, the server includes means for storing large amounts of past sales information, weather information, and user activity information in a storage device; means for running a program that includes a generating intelligence that analyzes the information obtained from the storage device and performs demand forecasting based on regional characteristics; and means for providing the optimal product configuration for each store in accordance with the demand forecast calculated using the generating intelligence. This enables efficient inventory management that responds quickly to changes in demand and sales promotion activities based on regional characteristics and individual user history.

[0692] A "storage device" is a physical or virtual medium for storing data, and is a device that enables the storage of large amounts of information.

[0693] "Generative intelligence" refers to a program function that utilizes machine learning and artificial intelligence technologies to identify patterns in data and make predictions.

[0694] "Means for running a program" refers to technical means for processing and analyzing data by executing a program within a computer.

[0695] "Product mix" refers to the combination and arrangement of products and services selected based on a specific market or consumer needs.

[0696] "Inventory management" is a management method that tracks the quantity, condition, and movement of goods held as inventory, and makes appropriate decisions regarding replenishment and ordering.

[0697] "Sales information" refers to all data related to product sales, including past sales data, purchase history, and sales performance by sales channel.

[0698] This invention is a system for achieving efficient demand forecasting and inventory management in the food service industry. This system achieves its objectives by exchanging information between a server, terminals, and users.

[0699] Server operation:

[0700] The server first collects sales information from each store and stores it in storage. This sales information includes past sales data and sales history for each product. It also obtains weather data by utilizing external weather information services. Services such as WeatherAPI can be used for this purpose. The server uses the collected data to perform demand forecasting based on regional characteristics, employing generative intelligence. This generative intelligence uses machine learning algorithms to identify specific consumption patterns.

[0701] As a concrete example, the server can analyze historical data and weather data to predict which products will be in high demand under specific weather conditions. For instance, in a store in a region experiencing a prolonged cold climate, it might predict increased demand for hot beverages and food, and inventory levels could be optimized accordingly.

[0702] Device operation:

[0703] The terminal receives menu suggestions and inventory information from the server and displays them on the displays in each store. This allows store staff to check the suggested menu configurations and the list of items to be ordered next time in real time.

[0704] User actions:

[0705] Users can browse menus using their smart devices and receive personalized promotional information. This allows for sales promotions optimized for each customer, stimulating their desire to purchase.

[0706] Specific examples of prompt messages include: "Based on this data, predict demand for each region. Factors to consider include season, weather, and past sales trends."

[0707] Implementing this system enables strategies to maximize sales while preventing stockouts, resulting in management that dynamically optimizes sales conditions.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1:

[0710] The server collects historical sales data from each store's POS system. The input includes sales information such as the quantity sold, sales amount, and date / time for each product. This data is aggregated and stored in a memory device. Specifically, the server periodically retrieves data using an API, standardizes the format, and registers it in the database.

[0711] Step 2:

[0712] The server obtains weather data from external weather information services. Input includes weather information based on location and date / time (temperature, probability of precipitation, weather conditions, etc.). The server stores this data in storage and combines it with sales data for analysis. Specifically, it uses the WeatherAPI to obtain the latest weather data based on the location information of each store.

[0713] Step 3:

[0714] The server inputs collected sales information and weather data into a generating AI model to perform demand forecasting. The aforementioned sales information and weather data are used as input. The AI ​​algorithm analyzes this data and outputs future demand patterns and sales forecasts. Specifically, it uses a machine learning model to analyze trends and predict demand under specific conditions.

[0715] Step 4:

[0716] The server creates the optimal product configuration for each store based on demand forecast results calculated by the generating intelligence. The input is the demand forecast results, and the output is the creation of proposed menus and inventory configurations. Specifically, it generates an optimal product placement list based on the forecast data and sends the results to the terminals in each store.

[0717] Step 5:

[0718] The terminal displays suggested menus and inventory information sent from the server. Input is optimized information from the server, and output is a display that staff can review. Specifically, the terminal's UI presents information such as "Today's Recommended Menu" and "List of Items Needing to Be Ordered This Week."

[0719] Step 6:

[0720] Users browse menus using their smart devices and receive personalized promotions. Inputs include past purchase history and current promotion information, and output provides users with personalized coupons and discount information. Specifically, the app analyzes user history and suggests personalized benefits.

[0721] (Application Example 1)

[0722] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] In the restaurant and food delivery industries, achieving efficient demand forecasting, inventory management, and personalized promotional activities is not easy. In particular, optimizing sales strategies in response to natural phenomena and local events, as well as dynamic inventory management, significantly impacts operational efficiency and customer satisfaction. Therefore, a comprehensive system is needed to provide rapid menu suggestions and promotional presentations based on customer trends.

[0724] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0725] In this invention, the server includes means for storing past sales information, seasonal characteristics information, and natural phenomenon information obtained from external sources in a database in order to perform demand forecasting; means for generating artificial intelligence to calculate sales forecasts according to regional characteristics; and means for monitoring inventory at multiple sales locations in real time and automatically generating optimized order instructions. This enables the suggestion of appropriate dishes according to consumer trends, efficient inventory management, and personalized sales promotion.

[0726] "Demand forecasting" is the act of predicting future product demand based on past sales data, weather, and seasonal characteristics.

[0727] "Sales information" is a general term for information related to the transaction of goods or services, such as past sales data and transaction history.

[0728] "Seasonal characteristic information" refers to information about consumer behavior and preferences during specific seasons or periods.

[0729] "Natural phenomenon information" refers to information about the external environment, such as weather data.

[0730] A "database" is a digital recording device or system that allows for the systematic storage and easy retrieval of information.

[0731] "Generative artificial intelligence means" refers to algorithms that analyze vast amounts of data and automatically generate specific patterns or predictions.

[0732] "Inventory management" is the process of monitoring the supply status of goods and replenishing or adjusting them as needed.

[0733] An "order instruction" is a directive to purchase specific goods in order to replenish a shortage of inventory.

[0734] "Personalized promotional activities" refer to marketing strategies that are optimized for specific target groups, taking into account each consumer's past behavior and preferences.

[0735] "Cuisine composition" refers to the design of a menu that combines different types of dishes.

[0736] The "Discontinuation Confirmation Display" is a function that, based on inventory, only shows consumers dishes that are still available for order.

[0737] The system for implementing this invention consists mainly of a server, a terminal, and a user's smart device.

[0738] The server first collects historical sales information from multiple sales locations, seasonal characteristics information, and natural phenomenon information obtained from external sources into a database. For example, it collects past sales performance for each dish and sales trends for specific seasons. In addition, the server also acquires weather data from external weather information services and analyzes it comprehensively.

[0739] Next, using generative artificial intelligence methods, the collected information is analyzed to perform sales forecasts based on regional characteristics and automatically propose the optimal menu configuration for each store. This AI model is built using TensorFlow and PyTorch to process data quickly and accurately.

[0740] Furthermore, the server uses IoT sensors to monitor inventory levels at each sales location in real time. This function automatically generates an order instruction and places an order with the relevant supplier if inventory falls below a certain threshold.

[0741] The terminal receives dish suggestions and inventory information from the server and notifies store staff. The terminal screen displays the suggested dishes and a list of items that need to be ordered next time.

[0742] Users can view personalized meal suggestions and place orders via smart devices such as smartphones and tablets. Here, personalized promotional information is presented to users based on their past purchase history, allowing them to receive discounts and benefits.

[0743] A concrete example is a scenario where a consumer orders a "hot soup" suggested based on weather information, and then receives a push notification offering a "10% discount coupon for their next order" based on their subsequent order history.

[0744] An example of a prompt to input into the generating AI model is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would lead to increased sales." This makes it possible to maximize consumer purchasing intent.

[0745] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0746] Step 1:

[0747] The server collects historical sales information, seasonal characteristics information, and natural phenomenon information from each sales location and stores it in a database. Various types of information are collected as input, time-series data is formatted as data processing, and structured data is stored in the database as output. This makes it easy to refer to the information.

[0748] Step 2:

[0749] The server analyzes information retrieved from the database using generative artificial intelligence methods. Sales information and weather information are used as input, and data calculations are performed to analyze sales trends and forecast demand. The output is sales forecast results based on the characteristics of each region. The AI ​​builds a model using TensorFlow to achieve rapid analysis.

[0750] Step 3:

[0751] The server proposes the optimal menu configuration for each sales location based on the analysis results. Sales forecast results are used as input, and the proposed optimal menu configuration is generated as output. Specifically, it automatically selects menu items with a high probability of increasing sales.

[0752] Step 4:

[0753] The server uses data acquired from IoT sensors to monitor inventory levels at each sales location in real time. Current inventory data is provided as input, and order instructions corresponding to the inventory level are automatically generated as output. This prevents inventory shortages or surpluses.

[0754] Step 5:

[0755] The terminal receives dish suggestions and inventory information sent from the server and displays them to the store staff. Information from the server is received as input, and suggested menus and order lists are displayed on the terminal screen as output. The terminal plays a role in effectively visualizing the received data.

[0756] Step 6:

[0757] Users view and order food suggestions provided via their smart devices. The menu information displayed on the smart device serves as input, and the user's order is sent to the server as output. This allows for personalized promotions based on past purchase history.

[0758] Step 7:

[0759] The user selects an appropriate dish using AI suggestions generated by prompts and receives promotional information. Past order data and prompts are included as input, and promotional notifications are sent as output. An example prompt is, "Based on the user's past order history and the current weather, please suggest which dishes and promotions would boost sales." This makes it possible to maximize consumer purchasing intent.

[0760] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0761] This invention integrates an emotion engine that recognizes user emotions into a system that performs demand forecasting, inventory management, automated ordering, and personalized promotional activities. This system aims to improve efficiency in the food service industry through interaction between servers, terminals, and users.

[0762] First, the server collects past sales information from each store and stores it in a database. The collected data includes sales volume and revenue data, and based on this, an AI algorithm is used to forecast demand. In addition, weather data obtained from external weather information services is combined to propose an optimal menu configuration tailored to regional characteristics.

[0763] The server uses inventory data monitored by IoT sensors to perform real-time inventory management and automatically generates order instructions as needed. These order instructions automate the ordering process with suppliers.

[0764] The terminal can display suggested menus and inventory information provided by the server to store staff. To improve operational efficiency within the store, this terminal provides staff with information that helps them review daily sales strategies and adjust inventory.

[0765] Users can interact with the system via smart devices, browse menus, and place orders. During this process, an emotion engine analyzes the user's facial expressions and voice to tailor and deliver personalized promotions and services. For example, if a user expresses satisfaction, they may be notified of a special discount on their next visit.

[0766] This emotion engine collects emotional data provided as user feedback and optimizes future promotional strategies. Furthermore, if negative emotions are detected, it sends feedback to store staff instructing them to improve the service.

[0767] Thus, the system in this invention aims to improve the consumer experience, increase the operational efficiency of stores, and ultimately boost sales.

[0768] The following describes the processing flow.

[0769] Step 1:

[0770] The server collects historical sales information from each store into a database. This information includes the quantity sold, sales revenue, and sales period for each menu item.

[0771] Step 2:

[0772] The server retrieves regional weather data from an external weather information service. This information is stored in a database and used in conjunction with sales trends to forecast demand.

[0773] Step 3:

[0774] The server uses AI algorithms based on sales information and weather data to forecast demand tailored to regional characteristics. Specifically, it suggests menu items that tend to increase in popularity based on past data, as well as new promotional strategies.

[0775] Step 4:

[0776] The server develops the optimal menu configuration for each store based on the proposed demand forecast and automatically sends the proposal to the store's terminal. This allows stores to offer menus that meet local demand.

[0777] Step 5:

[0778] The server collects real-time inventory information from IoT sensors installed in each store and automatically generates an order when inventory falls below a certain level. The order details are sent directly to the supplier.

[0779] Step 6:

[0780] The terminal receives suggested menus and inventory information sent from the server and displays them to store staff in real time. This allows staff to quickly adjust inventory and prepare orders.

[0781] Step 7:

[0782] Users browse the menu on their smart devices and place orders according to their preferences. During the ordering process, an emotion engine analyzes the user's facial expressions and voice to provide personalized promotional information.

[0783] Step 8:

[0784] The server processes user sentiment data analyzed by the sentiment engine and develops personalized promotional strategies. For example, it offers discounts for future use to users who show positive sentiment, and sends instructions for service improvement to stores if they show negative sentiment.

[0785] (Example 2)

[0786] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0787] In today's highly competitive commercial environment, efficient inventory management, appropriate product supply based on demand forecasting, and sales promotion activities that respond to consumer emotions and individual preferences are crucial challenges for companies to achieve sustainable growth. However, achieving this requires appropriately processing vast amounts of data and providing personalized suggestions to individual consumers in real time. Furthermore, it is necessary to enhance consumer satisfaction and rapidly improve services by recognizing emotions. This invention aims to comprehensively solve these complex requirements, striving to improve the efficiency of store operations and enhance consumer satisfaction.

[0788] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0789] In this invention, the server includes means for storing past sales information, seasonal trend information, and weather information obtained from external sources in a storage device in order to perform demand forecasting; means for generating artificial intelligence that calculates sales forecasts according to regional characteristics using the information obtained from the storage device; and means for proposing the optimal product configuration for each sales base based on the sales forecast calculated by the generating artificial intelligence. This enables efficient resource management and the provision of personalized sales strategies.

[0790] "Demand forecasting" is a technique that predicts future sales volume and demand based on past sales information and data obtained from external sources.

[0791] A "storage device" is a device that stores data and information, and is used to store various types of information such as sales information and weather data.

[0792] "Generative artificial intelligence means" refers to a means of generating sales forecasts based on regional characteristics and historical data, using artificial intelligence technology for data analysis.

[0793] A "sales location" is a place where goods or services are provided, and includes stores and other sales locations.

[0794] Resource management is a management process that involves monitoring resources such as inventory and balancing efficient supply and consumption.

[0795] A "display device" is a device used to visually show digital information, and is a device used by store staff to verify information.

[0796] A "user" is an end-user who uses and purchases goods and services through the system.

[0797] "Emotional analysis methods" refer to technologies that analyze a user's emotions from their facial expressions and voice, and then provide appropriate sales strategies based on the results.

[0798] "Sales promotion activities" refer to activities such as campaigns and discounts that promote the sale of products, and are carried out to attract the interest of consumers.

[0799] This invention provides a system for achieving efficient inventory management, demand forecasting, and personalized sales promotion in a commercial environment, and its effectiveness is realized through the coordinated operation of server, terminal, and user components.

[0800] First, the server plays a central role in information management. The server uses a database system to store historical sales information collected from each sales location in storage. A general database management system (DBMS) can be used for data management at this stage. Furthermore, this system obtains weather information from external weather information services via an API and stores it in storage, enabling correlation analysis with sales data. As a means of generative artificial intelligence, generative AI models such as machine learning models are executed to forecast demand from the collected data. These generative AI models are implemented using advanced data analysis software.

[0801] Next, the terminal plays a supporting role for store staff. It displays suggested products based on demand forecasts sent from the server, as well as current resource information, on its display screen to support staff in working efficiently. It also presents sales promotion strategies to staff through the terminal, providing guidance for implementing personalized sales activities. The terminal's display screen can be built using a standard tablet or desktop device.

[0802] On the other hand, users interact with the system through smart devices. Users can browse digital menus and order products. The ordered data is then transferred back to the server and used for inventory management and updating sales data. In addition, emotion analysis is performed to recognize the user's emotions, and the resulting emotion data is fed back into the system. This provides a personalized customer experience, for example, by suggesting special offers applicable to the next order if the user shows positive emotions. Emotion analysis can be performed in combination with facial recognition software and voice analysis tools.

[0803] As a concrete example, if the server recommends seafood dishes on a sunny day, and a user who visits the restaurant shows a satisfied expression, the emotion analysis system determines that the expression is positive. Then, a promotion is implemented offering a discount on a specific dessert on the user's next visit. Examples of prompts to facilitate this process include, "What menu do you recommend if the weather is sunny today?" or "Please suggest a promotional strategy for the next visit if the user is satisfied." In this way, the system of the present invention aims to improve the quality of the consumer experience by making accurate decisions based on data.

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] The server collects historical sales information from the POS systems at each sales location. This information includes sales volume and revenue data, and is stored in a database. The input is data from the POS systems, and the output is sales information accumulated in the database. This data is used for subsequent demand forecasting.

[0807] Step 2:

[0808] The server accesses an external weather information service via an API to obtain weather data. This data includes temperature, precipitation, and other information. The input is information from the weather API, and the output is weather data stored in memory. This weather data is used as a necessary element for sales forecasting.

[0809] Step 3:

[0810] The server uses sales information and weather data stored in its storage device to input into a generative AI model and perform sales forecasting. The generative AI model uses machine learning algorithms to analyze this data. The input is all the stored data, and the output is the demand forecast results for each sales location. Based on this forecast information, the optimal product mix is ​​proposed.

[0811] Step 4:

[0812] The server makes product recommendations for each sales location based on sales forecasts generated by an AI model. Specifically, it selects the most suitable products according to specific conditions, such as suggesting cold drinks on sunny days. The input is the result of the demand forecast, and the output is the product recommendations for each store. These recommendations are sent to terminals used by store staff.

[0813] Step 5:

[0814] The terminal displays product suggestions sent from the server on its display screen. Store staff adjust their operations and prepare products based on this information. The input is product suggestion information sent from the server, and the output is the detailed suggestion information that staff view. This allows staff to carry out their work efficiently.

[0815] Step 6:

[0816] Users view the store's digital menu and order items via their smart devices. During this process, the user's facial expressions and voice are analyzed by an emotion analysis tool and recorded as emotion data. Input is the user's emotions and order information, while output is the emotion data and order information from the emotion analysis tool. This data is then used for further personalized promotions.

[0817] Step 7:

[0818] The server develops personalized sales promotion strategies based on the user's order history and sentiment data. Specifically, it might suggest special offers for future visits to users who have expressed satisfaction. The input consists of sentiment data obtained from a sentiment analysis tool and order history, and the output is the next promotion strategy. This aims to improve user satisfaction.

[0819] (Application Example 2)

[0820] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] Traditional sales operations in commercial facilities often failed to adequately optimize services based on fluctuating demand and customer sentiment, resulting in lost sales opportunities and decreased customer satisfaction. Furthermore, material management and procurement were frequently performed manually, leading to operational problems due to a lack of efficiency.

[0822] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0823] In this invention, the server includes means for storing data in an information storage device to perform demand forecasting, means for calculating sales forecasts using machine learning means, and means for conducting sales promotion activities based on consumer sentiment analysis. This makes it possible to individually optimize the consumer purchasing experience and improve the efficiency of material procurement.

[0824] - "Demand forecasting" is the process of estimating future demand for a product by taking into account past sales data and external factors.

[0825] "Information storage device" refers to a storage medium or database that stores data and allows it to be retrieved as needed.

[0826] "Machine learning methods" are a type of artificial intelligence technology that analyzes data patterns to perform predictions and classifications.

[0827] A "commercial facility" refers to a store or facility operated to provide goods or services.

[0828] "Materials" refers to raw materials and product inventory necessary for the operation of a commercial facility and the provision of goods.

[0829] A "procurement order" refers to an order or procedure that requests the purchase or supply of specific materials.

[0830] A "display device" refers to an electronic device used to visually convey information to users.

[0831] A "consumer" refers to an individual or group that purchases or uses goods or services.

[0832] "Expression analysis" refers to the process of inferring emotions from consumers' facial expressions and voices and analyzing them as data.

[0833] "Sales promotion activities" refer to methods and campaigns that promote the sale of products.

[0834] "Satisfaction level" refers to the level of satisfaction or evaluation that consumers receive through the use of a product or service.

[0835] "Sentiment analysis" is the process of evaluating emotions through information such as a consumer's facial expressions and voice.

[0836] "Advantages" refer to specific benefits or beneficial perks.

[0837] "Notification" refers to the act of informing a specific individual or group of relevant information.

[0838] The server stores sales and weather information in its data storage device, and analyzes the acquired data using machine learning to perform demand forecasts tailored to regional characteristics. This makes it possible to propose optimal menu configurations to commercial facilities based on sales forecasts.

[0839] The terminals are used within commercial facilities and are responsible for displaying suggested products and material information provided by the server. This allows facility staff to understand inventory levels and optimal product assortments in real time, improving operational efficiency.

[0840] Users interact with the system using smart devices in stores. An emotion engine analyzes consumer expressions and voice to determine emotions, enabling personalized sales promotions. For example, users who are highly satisfied may receive beneficial perks on their next visit.

[0841] For the entire system, it is recommended to use external software such as Amazon Rekognition or Google Cloud Speech-to-Text for facial and speech recognition. This will allow for the acquisition of sentiment analysis data, which will improve sales promotion and the consumer experience.

[0842] As a concrete example, consider a scenario where a user visiting a shopping mall during lunchtime uses smart glasses to receive service. In this case, the system suggests a "pasta and salad" set meal to the user, and because positive emotions are analyzed, a discount coupon for their next visit is issued. An example of a prompt message to the generating AI model would be, "Analyze the emotions of the customer who has just visited the store and suggest the optimal lunch set."

[0843] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0844] Step 1:

[0845] The server acquires historical sales information from multiple commercial facilities and external weather information into its data storage device. By receiving sales volume, revenue data, and weather data as input and storing them in the data storage device, it constructs a dataset tailored to regional characteristics.

[0846] Step 2:

[0847] The server passes data stored in its information storage device to a machine learning tool for analysis. Sales information and weather information are used as input, and the AI ​​algorithm analyzes them to generate predictive demand data. The output is sales forecast data. This allows for the development of product strategies tailored to each commercial facility.

[0848] Step 3:

[0849] The terminal receives sales forecast data from the server and displays the product mix for sale to the commercial facility staff. Based on the forecast data as input, it presents the optimal menu on the display device. This allows staff to immediately adjust their sales strategy.

[0850] Step 4:

[0851] Users use smart devices to view menus and place orders within commercial facilities. This process inputs the user's facial and voice information, and the device transmits this data to an emotion engine.

[0852] Step 5:

[0853] The server analyzes user expressions using an emotion engine that performs facial recognition and voice analysis. It receives facial and voice data as input and outputs emotion analysis results using a generative AI model. This enables sales promotion activities that match the user's psychological state.

[0854] Step 6:

[0855] The terminal displays personalized discount information and promotions for specific users based on the analysis results. A digital coupon is generated when certain conditions are met, using the sentiment analysis results as input. The user is then offered benefits for their next visit.

[0856] Step 7:

[0857] The server sends a notification to the staff of the commercial facility when it receives negative sentiment analysis results from consumers. It takes sentiment data as input and outputs a notification of areas for improvement. This can be used to improve service quality.

[0858] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0859] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0860] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0861] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0862] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0863] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0864] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0865] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0866] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0867] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0868] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0869] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0870] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0871] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0872] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0873] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0874] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0875] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0876] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0877] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0878] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0879] The following is further disclosed regarding the embodiments described above.

[0880] (Claim 1)

[0881] In order to perform demand forecasting, a means of storing past sales information, seasonal trend information, and weather information obtained from external sources in a database,

[0882] A generative artificial intelligence means that calculates sales forecasts according to regional characteristics using information obtained from the aforementioned database,

[0883] A means for proposing the optimal menu configuration for each store based on the sales forecast calculated by the aforementioned artificial intelligence generation means,

[0884] A means to monitor inventory across multiple stores in real time and automatically generate optimized order instructions,

[0885] A means of displaying suggested menus and inventory information generated at each store on the store's terminal,

[0886] A means of conducting personalized promotional activities based on customer activity history information,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which adjusts the information stored in the database based on successful sales cases and makes further sales promotion suggestions.

[0890] (Claim 3)

[0891] The system according to claim 1, which dynamically optimizes the product composition and inventory status in response to increasing local demand by the means described above.

[0892] "Example 1"

[0893] (Claim 1)

[0894] A means for storing a large amount of past sales information, weather information, and user activity information in a storage device,

[0895] A means for operating a program that includes a generating intelligence that analyzes information acquired from a storage device and performs demand forecasting based on regional characteristics,

[0896] A means of providing the optimal product mix for each store in accordance with demand forecasts calculated using generative intelligence,

[0897] A means of monitoring inventory management at each store in real time and generating efficient procurement instructions,

[0898] A means for displaying generated product suggestions and inventory management information on a display device installed in the store,

[0899] A means of implementing personalized sales promotion based on user activity history,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, which adjusts the information stored in a storage device based on cases where sales increased, and generates proposals for more efficient sales promotion.

[0903] (Claim 3)

[0904] The system according to claim 1, which dynamically optimizes product composition and inventory management in response to a significant increase in local demand through a generation means.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] In order to forecast demand, a means of storing past sales information, seasonal characteristics information, and natural phenomenon information obtained from external sources in a database,

[0908] A generative artificial intelligence means that calculates sales forecasts according to regional characteristics using information obtained from the aforementioned database,

[0909] A means for proposing the optimal menu composition for each store based on the sales forecast calculated by the aforementioned artificial intelligence generation means,

[0910] A means to monitor inventory at multiple sales locations in real time and automatically generate optimized order instructions,

[0911] A means for displaying suggested dishes and inventory information generated at each sales location on a terminal,

[0912] A means of conducting personalized promotional activities based on consumer usage history information,

[0913] A means of promoting sales by considering consumers' past order history and making optimal food recommendations,

[0914] A method for providing real-time menu suggestions and promotions based on weather and local event information,

[0915] A means of monitoring inventory status and displaying only dishes that are available for order,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, which adjusts the information stored in the database based on successful sales cases and makes further sales promotion suggestions.

[0919] (Claim 3)

[0920] The system according to claim 1, which dynamically optimizes the product composition and inventory status in response to increasing local demand by the means described above.

[0921] "Example 2 of combining an emotion engine"

[0922] (Claim 1)

[0923] In order to perform demand forecasting, means for storing past sales information, seasonal trend information, and weather information obtained from external sources in a storage device,

[0924] A generative artificial intelligence means that calculates sales forecasts according to regional characteristics using information acquired from the aforementioned storage device,

[0925] A means for proposing the optimal product configuration for each sales location based on the sales forecast calculated by the aforementioned artificial intelligence generation means,

[0926] A means for monitoring resources at multiple sales locations in real time and automatically generating optimized order instructions,

[0927] A means for displaying proposed products and resource information generated at each sales base on a display device at the base,

[0928] A means of conducting personalized sales promotion activities based on user activity history information,

[0929] A sentiment analysis tool that analyzes user emotions and provides personalized sales strategies,

[0930] A means of sending instructions for service improvement to sales staff when negative sentiment data is detected,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1, which adjusts the information stored in the storage device based on successful sales cases and makes further sales promotion proposals.

[0934] (Claim 3)

[0935] The system according to claim 1, which dynamically optimizes the product composition and resource situation in response to increasing local demand by the means described above.

[0936] "Application example 2 when combining with an emotional engine"

[0937] (Claim 1)

[0938] A means for storing past sales information, seasonal trend information, and weather information obtained from external sources in an information storage device in order to perform demand forecasting,

[0939] A machine learning means for calculating sales forecasts according to regional characteristics using information acquired from the aforementioned information storage device,

[0940] A means for proposing the optimal product mix for each commercial facility based on the sales forecast calculated by the aforementioned machine learning means,

[0941] A means for continuously monitoring materials at multiple commercial facilities and automatically generating optimized procurement instructions,

[0942] A means for displaying proposed product and material information generated at each commercial facility on a display device at the commercial facility,

[0943] Means for implementing personalized sales promotion activities based on consumer expression analysis,

[0944] A means of providing benefits for future use when customer satisfaction is high,

[0945] A means of notifying staff of areas for improvement based on consumer sentiment analysis,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, which adjusts the information stored in the information storage device based on successful sales cases and makes further sales promotion proposals.

[0949] (Claim 3)

[0950] The system according to claim 1, wherein the means described above dynamically optimizes the product configuration and material status in response to increasing local demand. [Explanation of Symbols]

[0951] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In order to forecast demand, a means of storing past sales information, seasonal characteristics information, and natural phenomenon information obtained from external sources in a database, A generative artificial intelligence means that calculates sales forecasts according to regional characteristics using information obtained from the aforementioned database, A means for proposing the optimal menu composition for each store based on the sales forecast calculated by the aforementioned artificial intelligence generation means, A means to monitor inventory at multiple sales locations in real time and automatically generate optimized order instructions, A means for displaying suggested dishes and inventory information generated at each sales location on a terminal, A means of conducting personalized promotional activities based on consumer usage history information, A means of promoting sales by considering consumers' past order history and making optimal food recommendations, A method for providing real-time menu suggestions and promotions based on weather and local event information, A means of monitoring inventory status and displaying only dishes that are available for order, A system that includes this.

2. The system according to claim 1, which adjusts the information stored in the database based on successful sales cases and makes suggestions for further sales promotion.

3. The system according to claim 1, which dynamically optimizes the product composition and inventory status in response to increasing local demand by the means described above.

Images