System

Abstract

A system is provided.SOLUTION: A system including means for registering an inventory status of a restaurant, means for searching for a corresponding inventory by a user, means for recommending a restaurant suitable for the user using a generated AI model, means for making a reservation by the user, means for notifying the restaurant and the user of reservation information, means for calculating a commission of a contingent fee type and donating a part thereof, and means for updating the generated AI model on the basis of learned data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In the restaurant industry, a large amount of food is wasted due to poor inventory management and problems with ingredient management. This food waste is causing major social and economic problems, and is particularly a factor that stands in the way of solving the problem of hunger. Restaurants also face challenges in managing inventory and attracting new customers. Meanwhile, users currently lack efficient ways to use reasonable, high-quality services. There is a need for a system that can solve these issues and benefit restaurants, users, and society as a whole. [Means for solving the problem]

[0005] The present invention provides a system that registers restaurant inventory status and provides that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for using a generative AI model to recommend restaurants suitable for users, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a performance-based commission and donating a portion of it, and a means for updating the generative AI model based on training data. This allows restaurants to effectively utilize excess inventory and increase sales, and users to enjoy high-quality meals at reasonable prices. The system also contributes to society by donating a portion of the commission to hunger relief organizations.

[0006] A "restaurant" is a commercial establishment that serves food and beverages.

[0007] "Inventory status" refers to the quantity and quality information of ingredients and menu items held by a restaurant.

[0008] "User" refers to a consumer who uses the system to reserve food and beverage services based on restaurant inventory information.

[0009] A "generative AI model" is an artificial intelligence algorithm that recommends the most suitable store based on a user's usage history and feedback.

[0010] "Recommendation" refers to the act of a system suggesting the best option to a user.

[0011] "Reservation" means the act of a user reserving restaurant services in advance for a specific date and time.

[0012] "Commission" refers to the remuneration received from restaurants based on the services provided by the system.

[0013] "Contingency fee" refers to a fee model in which a fee is based on a specific outcome or successful transaction.

[0014] "Donation" refers to the act of donating a portion of profits to charitable causes such as hunger relief organizations.

[0015] "Training data" refers to user behavior history and feedback information, and is data used to improve the accuracy of the generative AI model. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system for registering restaurant inventory status and providing that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for recommending restaurants suitable for users using a generative AI model, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission-based fee and donating a portion of it, and a means for updating the generative AI model based on training data.

[0038] System program and processing description

[0039] Restaurant owners register inventory status

[0040] Restaurant owner (terminal): The owner logs in to the system's management screen and registers the inventory status. For example, the owner enters information such as "I have 10 servings of chicken curry left over."

[0041] Server: Saves the registered information to the database and updates the inventory information.

[0042] Users search for stores

[0043] User (terminal): The user accesses the system and logs in. They search for "curry under 700 yen in Shibuya Ward."

[0044] Server: Based on the user's search criteria, the server searches the database for inventory information for the relevant restaurants and displays the results.

[0045] Store recommendations using the recommendation function

[0046] Server: Using a generative AI model, the server recommends the most suitable restaurant based on the user's past usage history and search criteria. For example, if a user has ordered curry before, the server will prioritize restaurants that serve curry.

[0047] User (device): Select the store of interest from the list of suggested stores.

[0048] A user makes a reservation

[0049] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[0050] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0051] A user visits the store and receives a service

[0052] User: Visits the restaurant on the reserved date and time and receives the discounted meal.

[0053] Restaurant owners: Serve meals to customers, and after the service is complete, mark the reservation as "completed" in the admin panel and update inventory information.

[0054] Provider Fee Processing

[0055] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[0056] System Training

[0057] Server: Stores user usage history, reservation information, feedback, etc. as learning data and updates the generative AI model, thereby improving the accuracy of future recommendations.

[0058] Specific examples

[0059] Specifically, when a restaurant owner registers "I have 10 extra chicken curry servings" on the management screen, that information is sent to the server and saved in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria and uses a generative AI model to suggest restaurants that take past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee.

[0060] This system allows restaurants to efficiently utilize their inventory, allows users to enjoy meals at a reasonable price, and even donates a portion of the fees to social contribution activities.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[0064] Server: Verify authentication information and display admin panel to owner.

[0065] Step 2:

[0066] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[0067] Restaurant owner (device): After completing the input, click the "Register" button.

[0068] Server: Saves the entered inventory information to the database and updates the inventory management table.

[0069] Step 3:

[0070] User (device): The user accesses the site and logs in by entering their username and password.

[0071] Server: Verify credentials and view user-specific dashboard.

[0072] Step 4:

[0073] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[0074] Server: Searches the database for relevant store information based on the entered search criteria.

[0075] Server: Displays search results to the user in a list format.

[0076] Step 5:

[0077] Server: Uses generative AI models to analyze users' past usage history and preferences.

[0078] Server: Based on the search results, the server displays a list of recommended stores to the user.

[0079] Step 6:

[0080] User (device): Select the store of interest from the presented recommendation list.

[0081] Step 7:

[0082] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[0083] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[0084] User (device): After checking the input information, press the "Confirm reservation" button.

[0085] Step 8:

[0086] Server: Saves the reservation information in a database and sends a confirmation email to the user.

[0087] Server: Notifies restaurant owner of reservation information.

[0088] Step 9:

[0089] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[0090] Restaurant owners: Providing meals to users.

[0091] Step 10:

[0092] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[0093] Step 11:

[0094] Server: Calculates commission based on completed booking information.

[0095] Server: The fee will be automatically deducted from the restaurant owner.

[0096] Server: Donate a portion of your fees to hunger relief organizations.

[0097] Step 12:

[0098] Server: Stores user usage history, reservation information, and feedback as learning data.

[0099] Server: Updates the generative AI model to improve the accuracy of future recommendations.

[0100] Example 1

[0101] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0102] Effective management of excess inventory in restaurants and the provision of appropriate services to users based on that inventory are key challenges. There is also a lack of means to quickly respond to user requests, improve restaurant management efficiency, and engage in social contribution activities. In particular, there is a need for improved recommendation accuracy that takes into account users' past usage history and search criteria.

[0103] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0104] In this invention, the server includes a means for registering restaurant inventory status, a means for users to search for relevant inventory, and a means for using a generative AI model to recommend suitable establishments to users. This allows restaurants to effectively utilize their inventory and users to enjoy meals at reasonable prices. Furthermore, by generating prompts and providing recommendation results, the system can respond quickly to user requests and improve usability. Furthermore, by calculating commissions based on success and donating a portion of the commissions, it is possible to contribute to society.

[0105] A "restaurant" is a facility that provides food and beverages.

[0106] "Inventory status" refers to information that indicates the quantity and condition of food and ingredients held by a restaurant.

[0107] "User" means an individual or organization that uses the system to search restaurant inventory information and make reservations.

[0108] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and recommends businesses that are suitable for users.

[0109] A "prompt sentence" is an instruction sentence input into a generative AI model, and is generated based on the user's past usage history and search conditions.

[0110] "Recommendation" refers to the use of generative AI models to recommend suitable businesses and products to users.

[0111] "Reservation information" is data containing detailed information about a reservation made by a user.

[0112] A "success-based fee" is a fee calculated based on the success of the services provided.

[0113] "Donation" means providing a portion of a commission fee for social contribution activities, etc.

[0114] A "database" is an information recording medium for storing and managing restaurant inventory and reservation information.

[0115] The system for implementing this invention is configured using the following hardware and software. The main hardware used includes a server computer and terminals for users and restaurant owners. The software used is AWS EC2 (server infrastructure), MySQL (database management system), GPT-4 (generative AI model), and React (front-end development environment).

[0116] Registering restaurant inventory status

[0117] Using a restaurant owner's device, the owner logs into the system's management screen and, as a specific example, enters "I have 10 servings of chicken curry left" when registering inventory status. The information sent from the device is received by the server and stored in a MySQL database. The server then updates the inventory information to reflect the latest inventory status.

[0118] Users search for stores

[0119] A user accesses the system using a terminal and logs in. By entering "curry under 700 yen in Shibuya Ward" into the search bar and pressing the search button, the server searches for relevant inventory information in the database based on these conditions. Information on stores that match the conditions is collected and displayed on the user's terminal.

[0120] Store recommendations using the recommendation function

[0121] Based on the search results, the server sends a prompt to the generative AI model (GPT-4). An example of a specific prompt would be, "Find out whether the user has ordered curry in the past, and based on that, please make a list of recommended curry restaurants in Shibuya Ward that cost under 700 yen." The generative AI model analyzes this prompt and generates a list of restaurants that are optimal for the user. The server sends this list to the user's device and displays it.

[0122] A user makes a reservation

[0123] On the displayed restaurant details page, the user selects "Chicken Curry" and clicks the "Book Now" button. By entering the "Date and Time" and "Number of People" in the reservation form and pressing the "Confirm Reservation" button, the reservation information is sent to the server. The server saves this information in a MySQL database and sends a reservation confirmation email to the user and the restaurant owner.

[0124] A user visits the store and receives a service

[0125] The user visits the restaurant at the reserved date and time and enjoys a meal based on the reservation details. After providing the service, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. This information is sent to the server and the database is updated.

[0126] Success-based commission processing

[0127] The server calculates commissions based on the completed reservation information and donates a portion of the commission to a hunger relief organization. This calculation is also done automatically on the server.

[0128] System Training

[0129] The server stores user usage history, reservation information, feedback, etc. as learning data, which is used to update the generative AI model and improve the accuracy of future recommendations.

[0130] For example, you can use a prompt such as, "Find out whether the user has ordered curry in the past, and based on that, make a list of recommended curry restaurants in Shibuya Ward that cost less than 700 yen." This system allows restaurants to use their inventory efficiently and users to enjoy meals at reasonable prices. It also creates a system where a portion of the commission is used for social contribution activities.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] Restaurant owner (terminal): Logs in to the system's management screen and enters "I have 10 servings of chicken curry left" into the inventory registration form. The entered data is sent to the server.

[0134] Input: Inventory information entered by the restaurant owner (e.g., 10 servings of chicken curry)

[0135] Output: Stock information sent to the server

[0136] Step 2:

[0137] Server: Stores the inventory information received from the device in a database.

[0138] Input: Inventory information sent by restaurant owner

[0139] Output: Stock information stored in a database

[0140] Step 3:

[0141] User (device): Access the system and log in. Enter "Curry under 700 yen in Shibuya Ward" in the search bar and press the search button.

[0142] Input: Search criteria (e.g. Shibuya-ku, under 700 yen, curry)

[0143] Output: The search query sent to the server

[0144] Step 4:

[0145] Server: Searches the database for relevant inventory information based on the received search criteria, collects inventory information, and generates search results.

[0146] Input: The search query sent to the server

[0147] Output: A list of stores searched

[0148] Step 5:

[0149] Server: Generates and sends prompts to the generative AI model (GPT-4) based on the user's past usage history and search conditions.

[0150] Input: User's past usage history, current search criteria

[0151] Output: Prompt (e.g., "Find out whether the user has ordered curry in the past, and based on that, list recommended curry restaurants in Shibuya Ward that cost less than 700 yen.")

[0152] Step 6:

[0153] Generative AI model: Analyzes the prompt text received and generates a list of stores that are best suited to the user.

[0154] Input: prompt statement

[0155] Output: Recommended store list

[0156] Step 7:

[0157] Server: Sends the generated list of recommended stores to the user's device.

[0158] Input: Recommended store list

[0159] Output: A list of recommended stores displayed on the user's device

[0160] Step 8:

[0161] User (device): From the displayed list of stores, check the details page of the store they are interested in, check the "Chicken Curry" option, and click the "Make a Reservation" button. Enter the "Date and Time" and "Number of people" in the reservation form and press the "Confirm Reservation" button.

[0162] Input: Reservation information (e.g. store, menu, date and time, number of people)

[0163] Output: Reservation information sent to the server

[0164] Step 9:

[0165] Server: Stores the received reservation information in a database and sends a reservation confirmation email to the user and restaurant owner.

[0166] Input: Reservation information submitted by the user

[0167] Output: Reservation information stored in the database, reservation confirmation email

[0168] Step 10:

[0169] User (device): Visits the restaurant at the reserved date and time and enjoys the provided meal.

[0170] Input: Reservation date and time and number of people

[0171] Output: Get service

[0172] Step 11:

[0173] Restaurant owner (device): After providing the service, mark the reservation as "completed" on the management screen and update the inventory information.

[0174] Input: Completed reservation information

[0175] Output: Updated inventory information

[0176] Step 12:

[0177] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[0178] Input: Completed reservation information

[0179] Output: Calculated fees and donations made

[0180] Step 13:

[0181] Server: Stores user usage history, reservation information, and feedback as learning data and updates the generative AI model.

[0182] Input: User usage history, reservation information, feedback

[0183] Output: Highly accurate recommendations from an updated generative AI model

[0184] (Application example 1)

[0185] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0186] Inventory management at restaurants presents a major challenge to efficient operation, with issues such as excess or shortage of inventory and rising disposal costs. It is also difficult for users to grasp the inventory status of each restaurant, leaving them without a means to provide appropriate meals at reasonable prices. Furthermore, conventional recommendation functions have low accuracy in reflecting users' past usage history and search conditions, making it difficult to suggest the most suitable restaurant to the user. Solutions to these issues are highly desirable.

[0187] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0188] In this invention, the server includes: a means for registering restaurant inventory status; a means for users to search for relevant inventory; a means for using a generative AI model to recommend restaurants suitable for users; a means for users to make reservations; a means for notifying restaurants and users of reservation information; a means for calculating a performance-based fee and donating a portion of the fee; a means for updating the generative AI model based on training data; a recommendation function that suggests optimal restaurants based on a user's usage history and prioritizes displaying restaurants that reflect the user's past usage history; a means for searching for restaurants based on ingredient price ranges and inventory status; and a means for updating inventory in real time and providing a search function that allows users to directly access inventory information. This allows restaurant owners to efficiently manage their inventory and users to find optimal restaurants based on their past usage history. Furthermore, a portion of the fees is donated, contributing to society.

[0189] "Means for registering inventory status" is a function that allows restaurant owners to input the amount and type of inventory they have on hand each day into the system and store it in a database.

[0190] "A means for users to search for relevant inventory" is a function that searches for inventory information of restaurants that meet the conditions specified by the user and displays appropriate results.

[0191] "Means of using a generative AI model to recommend restaurants suitable for users" is a function that enables a generative AI model to recommend the most suitable restaurant based on the user's past usage history and search conditions.

[0192] "Means for users to make reservations" refers to a function that allows users to secure inventory at the store they select and complete the reservation at the desired date and time.

[0193] The "means for notifying the restaurant and user of reservation information" is a function for notifying the restaurant owner and user of confirmed reservation information by e-mail or the like.

[0194] The "means of calculating commissions based on success and donating a portion of them" is a function that calculates commissions based on completed reservations and automatically donates a portion of them to social contribution activities.

[0195] "Means for updating the generative AI model based on training data" refers to a function that applies training data to improve the accuracy of the generative AI model using collected user usage history and feedback.

[0196] The "recommendation function that suggests the most suitable restaurant based on the user's usage history" is a function that automatically suggests the most suitable restaurant based on the user's past usage data.

[0197] "A means of preferentially displaying restaurants that reflect past usage history" is a function that allows restaurants that a user has used in the past to be preferentially displayed in search results.

[0198] "A means to search for restaurants based on the price range and availability of ingredients" is a function that searches for restaurant inventory information and displays appropriate results based on the user's budget or specific ingredients.

[0199] The "search function that updates inventory in real time and allows users to directly access inventory information" is a function that updates restaurant inventory information in real time and allows users to access and check that information.

[0200] The system for realizing this invention includes the following programs and processes: The server, the restaurant owner's terminal, and the user's terminal work in cooperation to manage restaurant inventory and provide services to users.

[0201] Stock status registration

[0202] Restaurant owners log in to the system's management screen using a terminal and register their inventory status. At this time, they input the menu items they will offer and the quantities they will provide. The server receives this information and stores it in a database.

[0203] Inventory Search

[0204] Users access the system using a terminal and input conditions (e.g., geographical information, price range, type of ingredients, etc.) to search for inventory. The server queries the database based on the conditions entered and provides inventory information for relevant restaurants. For example, if a user searches for "curry under 700 yen in Shibuya Ward," a list of appropriate restaurants will be displayed.

[0205] Recommendation function

[0206] The server uses a generative AI model to recommend the best restaurants based on the user's past visit history and search criteria. This generative AI model takes into account the user's preferences and past choices and prioritizes the display of restaurants that are most relevant.

[0207] Making a reservation

[0208] The user selects the restaurant and menu they want and makes a reservation. The user enters the date, time, and number of people in the reservation form and confirms it. The server stores the information in a database and sends a confirmation email to the user and the restaurant.

[0209] Real-time inventory updates

[0210] The server updates restaurant inventory information in real time, allowing users to directly access the latest inventory information, so users are always provided with the latest inventory status.

[0211] Fee calculations and donations

[0212] After the reservation is completed, the server calculates the commission fee and donates a portion of it, which not only benefits the restaurant and the user but also contributes to society at the same time.

[0213] Update training data

[0214] The server stores the user's usage history and reservation information as learning data and periodically updates the generative AI model, which improves the accuracy of future recommendations.

[0215] Hardware and software used

[0216] The system uses software such as Python, databases (e.g., MySQL), generative AI models (e.g., TensorFlow, PyTorch), and an SMTP server. The server and user and restaurant terminals are connected to the Internet, enabling real-time data processing.

[0217] Examples of concrete examples and prompts

[0218] As a specific example, when a restaurant owner registers "I have 10 extra servings of chicken curry" on the management screen, the information is immediately sent to the server and saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server searches for restaurants that meet the criteria and uses a generative AI model to prioritize and display a list of restaurants that take the user's past history into consideration. When the user reserves "chicken curry," a confirmation email is sent, a commission fee is calculated, and a portion of that fee is automatically donated.

[0219] Example prompt sentence:

[0220] "For users who have ordered curry in the past, please prioritize displaying restaurants in Shibuya Ward that serve curry for under 700 yen."

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1:

[0223] Stock status registration

[0224] Input: The restaurant owner uses the terminal to input the menu name "Chicken Curry" and the quantity "10 servings."

[0225] Specific actions: Fill out the numbered inventory registration form and press the "Register" button.

[0226] Data processing / calculation: Generate input information in JSON format and send it to the server.

[0227] Output: The server stores the received data in the database and the inventory information is updated.

[0228] Step 2:

[0229] Inventory Search

[0230] Input: The user uses a terminal to enter the search criteria "Curry under 700 yen in Shibuya Ward."

[0231] Specific actions: Enter search criteria in the condition input form and press the "Search" button.

[0232] Data processing / calculation: The server queries the database based on the search criteria and searches for inventory information that matches the criteria.

[0233] Output: Search results will be displayed on the user's device, along with the restaurants and their inventory information.

[0234] Step 3:

[0235] Recommendation function

[0236] Input: User's past usage history and current search criteria.

[0237] Specific operation: The server's generated AI model references the user's history and search criteria to recommend the most suitable restaurant.

[0238] Data processing / calculation: The AI ​​model scores historical data and search data to create a list of restaurants in order of relevance.

[0239] Output: The recommended restaurant list is displayed on the user's device.

[0240] Step 4:

[0241] Making a reservation

[0242] Input: The user selects a restaurant and menu (e.g., "chicken curry"), and enters the desired date and time ("December 31, 2022, 18:00") and the number of people ("2").

[0243] Specific actions: Enter the required information in the reservation form and press the "Confirm reservation" button.

[0244] Data processing / calculation: The server stores the reservation information in a database and generates a confirmation email.

[0245] Output: A reservation confirmation email is sent to the user and restaurant.

[0246] Step 5:

[0247] Real-time inventory updates

[0248] Input: Reservation information and inventory consumption status.

[0249] What it does: The server periodically updates the database to maintain the latest inventory status.

[0250] Data processing / calculation: When a reservation is confirmed, the corresponding inventory is reduced.

[0251] Output: The latest inventory information is shared within the system and can be accessed by users in real time.

[0252] Step 6:

[0253] Fee calculations and donations

[0254] Input: Completed reservation information.

[0255] Specific operation: The server calculates the commission based on the information of each reservation and allocates a portion of it to donations.

[0256] Data processing / calculation: Calculate performance-based fees and determine donation amounts.

[0257] Output: The donation process is executed and a report of the donation amount is generated.

[0258] Step 7:

[0259] Update training data

[0260] Input: User usage history, booking information, feedback.

[0261] Specific operation: The server collects this data and updates the learning data of the generative AI model.

[0262] Data processing / calculation: New usage data is fed back into the AI ​​model to retrain the model.

[0263] Output: The updated generative AI model will improve the accuracy of future recommendations.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for using a generative AI model to recommend restaurants suitable for users, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission based on performance and donating a portion of it, and a means for updating the generative AI model based on learning data. In addition, by incorporating an emotion engine that recognizes user emotions, the system further optimizes recommendations for individual users.

[0266] System program and processing description

[0267] Restaurant owners register inventory status

[0268] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[0269] Server: Verify authentication information and display admin panel to owner.

[0270] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[0271] Restaurant owner (device): After completing the input, click the "Register" button.

[0272] Server: Saves the entered inventory information to the database and updates the inventory management table.

[0273] Users search for stores

[0274] User (Terminal): The user accesses the system and logs in by entering a username and password.

[0275] Server: Verify credentials and view user-specific dashboard.

[0276] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[0277] Server: Searches the database for relevant store information based on the entered search criteria.

[0278] Server: Displays search results to the user in a list format.

[0279] Emotion engine recognizes user emotions

[0280] Server: Uses the emotion engine to analyze emotional data (e.g., emotional states obtained through facial expression analysis or voice recognition) when a user accesses the service from their device.

[0281] Server: Stores user sentiment data and reflects it in future recommendations.

[0282] Store recommendations using the recommendation function

[0283] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[0284] User (device): Select the store of interest from the suggested recommendation list.

[0285] A user makes a reservation

[0286] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[0287] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0288] A user visits the store and receives a service

[0289] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[0290] Restaurant owners: Providing meals to users.

[0291] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[0292] Provider Fee Processing

[0293] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[0294] System Training

[0295] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[0296] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[0297] Specific examples

[0298] Specifically, when a restaurant owner registers "I have 10 servings of chicken curry left over" on the management screen, that information is sent to the server and stored in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria, analyzes their current emotional state using an emotion engine, and uses a generative AI model to recommend the most suitable restaurant, taking past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee. The system also learns from usage history, including emotional data, to improve the accuracy of recommendations for future visits.

[0299] This allows restaurants to efficiently utilize their inventory and users to enjoy meals at more personalized and affordable prices. In addition, a portion of the fees is donated to social contribution activities.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[0303] Server: Verify authentication information and display admin panel to owner.

[0304] Step 2:

[0305] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[0306] Restaurant owner (device): After completing the input, click the "Register" button.

[0307] Server: Saves the entered inventory information to the database and updates the inventory management table.

[0308] Step 3:

[0309] User (device): The user accesses the site and logs in by entering their username and password.

[0310] Server: Verify credentials and view user-specific dashboard.

[0311] Step 4:

[0312] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[0313] Server: Searches the database for relevant store information based on the entered search criteria.

[0314] Server: Displays search results to the user in a list format.

[0315] Step 5:

[0316] Server: Using the emotion engine, analyzes emotion data acquired from the camera and microphone on the user's device. For example, by analyzing the user's facial expressions and tone of voice, it can recognize emotions such as "happiness," "tiredness," and "excitement."

[0317] Server: Stores the recognized emotion data and uses it for future recommendations.

[0318] Step 6:

[0319] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on a user's past usage history and current emotional state. For example, it recommends bright and lively stores to a user in a "happy" state, and quiet and relaxing stores to a user in a "tired" state.

[0320] Step 7:

[0321] User (device): Select the store of interest from the presented recommendation list.

[0322] Step 8:

[0323] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[0324] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[0325] User (device): After checking the input information, press the "Confirm reservation" button.

[0326] Step 9:

[0327] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0328] Step 10:

[0329] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[0330] Restaurant owners: Providing meals to users.

[0331] Step 11:

[0332] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[0333] Step 12:

[0334] Server: Calculates commission based on completed booking information.

[0335] Server: The fee will be automatically deducted from the restaurant owner.

[0336] Server: Donate a portion of your fees to hunger relief organizations.

[0337] Step 13:

[0338] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[0339] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[0340] Example 2

[0341] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0342] Conventional restaurant inventory management systems provide functions such as inventory information registration, search, and reservation, but lack a mechanism for recommending optimal restaurants based on the user's emotional state. As a result, the user experience cannot be improved and restaurant inventory management is inefficient. Furthermore, there is no system for calculating fees or making donations that takes social contributions into consideration, making these systems insufficient for realizing a sustainable society.

[0343] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0344] In this invention, the server includes means for registering restaurant inventory status, means for users to search for relevant inventory, means for recommending restaurants suitable for users using a generative AI model, means for users to make reservations, means for notifying the restaurant and user of the reservation information, means for calculating a performance-based fee and donating a portion of it, means for updating the generative AI model based on training data, and means for acquiring user emotion data and optimizing recommendations based on that data. This makes it possible to suggest optimal restaurants based on the user's emotional state, improving the user experience and streamlining restaurant inventory management, and realizing a fee calculation and donation system that contributes to society.

[0345] The "means for registering restaurant inventory status" is a function that allows restaurant owners to input current inventory information into the system and store it in the database.

[0346] "Means for users to search for relevant inventory" refers to the means by which users can search for inventory information within the system based on specific conditions and find relevant stores.

[0347] "Means of using a generative AI model to recommend stores suitable for users" is a function that uses a generative AI model to analyze a user's past usage history and emotional data, and recommends the most suitable store.

[0348] "Means for users to make reservations" refers to the means by which users make reservations for the store they have selected via the system.

[0349] "Means for notifying restaurants and users of reservation information" is a function for automatically notifying restaurant owners and users of the information when a reservation is confirmed.

[0350] The "means for calculating commissions based on success and donating a portion of the commission" is a function that executes a process for calculating commissions and donating a portion of the commissions when a reservation is successful.

[0351] "Means for updating the generative AI model based on learning data" refers to a means for regularly updating the generative AI model based on user usage history, feedback, emotional data, etc., in order to improve accuracy.

[0352] "Means of obtaining user emotional data and optimizing recommendations based on that" refers to a function that obtains emotional data from the user's facial expressions, voice, etc., and uses that information to further individualize recommendations.

[0353] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. The system mainly includes the following components: a restaurant owner terminal, a user terminal, and a server.

[0354] Sequence for restaurant owner to register inventory status

[0355] Restaurant owner (terminal): The restaurant owner logs in to the system's management screen and enters their username and password. If authentication is successful, the owner's dedicated management dashboard will be displayed. This dashboard has an input form for inventory status, and the owner enters information such as "product name," "quantity," and "discount price," and clicks the "Register" button.

[0356] Server: The server receives the authentication information and the entered inventory information and saves it in the inventory management table of the database. For example, if the entered information is "Product name: Chicken curry", "Quantity: 10", and "Discount price: 700 yen (regular price 1,000 yen)", this will be saved in the database.

[0357] A sequence where a user searches for a store

[0358] User (terminal): The user accesses the system and logs in by entering their username and password. After successful authentication, a dashboard is displayed. This dashboard has a search bar, and the user can enter conditions such as "area," "menu," and "budget," and then click the "Search" button.

[0359] Server: The server queries the database based on the entered criteria and displays the corresponding restaurant information in a list format to the user. For example, search results are displayed based on criteria such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen."

[0360] Emotion engine recognizes user emotions

[0361] Server: Using the emotion engine, the server acquires emotional data when the user accesses the app from their device. It performs facial and voice analysis to analyze the user's emotional state. The acquired emotional data is stored in a database and is reflected in future recommendations.

[0362] Store recommendations using the recommendation function

[0363] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[0364] User (device): The user selects the store of interest from the proposed recommendation list.

[0365] A user makes a reservation

[0366] User (device): The user checks the "Leftover Ingredients Menu" on the details page of the selected restaurant, enters the "Date and Time" and "Number of people" in the reservation form, and presses the "Confirm Reservation" button.

[0367] Server: Saves the reservation information in a database and sends a confirmation email to the user. At the same time, it notifies the restaurant owner of the reservation information.

[0368] Post-service processing

[0369] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[0370] Restaurant owners: Serve food to customers, mark the reservation as "completed" in the extranet, and update inventory information after serving.

[0371] Fee calculation and donations

[0372] Server: Calculates commission based on successful bookings and donates a portion of the commission to hunger relief organizations.

[0373] System Training

[0374] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data and updates the generative AI model and sentiment engine, thereby improving the accuracy of future recommendations.

[0375] Specific examples

[0376] For example, if a restaurant owner registers that they have 10 extra servings of chicken curry, that information is saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that meet the criteria, analyzing the user's current emotional state using an emotion engine. The generative AI model recommends the most suitable restaurant, taking into account past usage history, and the user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The server then calculates a commission-based fee and initiates a process to execute a portion of that fee.

[0377] Example prompt sentence:

[0378] "Analyze the user's emotional state and recommend the best store."

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Step 1: Login process

[0381] Restaurant owner (terminal): The owner accesses the system's management screen and enters their username and password.

[0382] Server: Receives the entered authentication information and checks it against the database. If successful, displays the owner's personal admin dashboard.

[0383] Input: Username, Password

[0384] Output: Authentication result, admin dashboard screen

[0385] Step 2: Enter inventory information

[0386] Restaurant owner (device): After logging in, enter information such as product name, quantity, discount price, etc. into the inventory entry form on the management screen and click the "Register" button.

[0387] Input: Product name (e.g., chicken curry), quantity (e.g., 10), discount price (e.g., 700 yen)

[0388] Output: Inventory information transmission request

[0389] Step 3: Save inventory information

[0390] Server: Receives the entered inventory information and saves it in the inventory management table of the database. If this process is successful, it sends a registration completion message.

[0391] Input: Inventory information (product name, quantity, discount price)

[0392] Output: Registration complete message

[0393] Step 4: User Login

[0394] User (terminal): Accesses the system and attempts to log in by entering a username and password.

[0395] Server: Checks the entered credentials against the database and, if successful, displays a dashboard specific to the user.

[0396] Input: Username, Password

[0397] Output: Authentication results, user dashboard

[0398] Step 5: Enter search criteria

[0399] User (device): Enter conditions such as "area," "menu," and "budget" in the search bar on the dashboard and click the "Search" button.

[0400] Input: Area (e.g. Shibuya), Menu (e.g. Curry), Budget (e.g. Under 700 yen)

[0401] Output: Search request

[0402] Step 6: Viewing search results

[0403] Server: Queries the database based on the entered search criteria and displays matching store information to the user in a list format.

[0404] Input: Search criteria

[0405] Output: A list of matching stores

[0406] Step 7: Obtaining emotion data

[0407] Server: Uses the emotion engine to obtain emotional data when the user accesses the app from their device. It determines the user's emotional state through facial expression and voice analysis.

[0408] Input: User video and audio data

[0409] Output: Emotion data (e.g. happy, tired)

[0410] Step 8: Storing Emotion Data

[0411] Server: Stores the acquired emotion data in a database and updates the user's profile.

[0412] Input: Emotion data

[0413] Output: Updated user profile

[0414] Step 9: Recommendation Generation

[0415] Server: Using a generative AI model, it suggests the most suitable store based on the user's past usage history, current emotional state, and search criteria.

[0416] Input: User usage history, emotion data, search conditions

[0417] Output: Recommendation list

[0418] Step 10: View recommendation results

[0419] User (device): Select the store of interest from the suggested recommendation list.

[0420] Input: Recommendation list

[0421] Output: Selected store information

[0422] Step 11: Enter reservation information

[0423] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected store, enter the required information such as "Date and Time" and "Number of people" in the reservation form, and press the "Confirm Reservation" button.

[0424] Input: Date, time, number of people, selected menu

[0425] Output: Reservation information sending request

[0426] Step 12: Save and notify reservation information

[0427] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0428] Input: Reservation information

[0429] Output: Confirmation email, reservation information notification

[0430] Step 13: Update your visit record

[0431] Restaurant owner: A user visits the restaurant and uses the reserved menu. After providing the service, the user marks the reservation as "completed" on the management screen and updates the inventory information.

[0432] Input: Visit confirmation, menu information

[0433] Output: Updated inventory information

[0434] Step 14: Fee Calculation and Donation

[0435] Server: Calculates commission-based fees based on completed booking information and donates a portion of the fees.

[0436] Input: Completed reservation information

[0437] Output: Fee calculation results, donation process execution

[0438] Step 15: Save the training data

[0439] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[0440] Input: Usage history, reservation information, feedback, emotional data

[0441] Output: Saved training data

[0442] Step 16: Update the generative AI model

[0443] Server: Uses the saved learning data to update the generative AI model and emotion engine, improving the accuracy of future recommendations.

[0444] Input: Training data

[0445] Output: Updated generative AI model and emotion engine

[0446] (Application example 2)

[0447] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0448] Restaurants are required to manage inventory, propose efficient menus, and provide personalized service based on customers' emotional states. Conventional systems are limited in their ability to collect inventory information and propose appropriate menus, making it particularly difficult to optimize services based on customer emotions. This results in problems such as poor customer satisfaction and a tendency for inventory waste. To solve this problem, a system that integrates inventory management and customer emotion recognition is needed.

[0449] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for registering restaurant inventory status, a means for a user to search for relevant inventory, a means for recommending restaurants suitable for the user using a generative AI model, a means for a user to make a reservation, a means for notifying the restaurant and the user of the reservation information, a means for calculating a performance-based fee and donating a portion of it, a means for updating the generative AI model based on training data, a means incorporating an emotion engine that recognizes the customer's emotional state and presents appropriate restaurants and menus, and a means for presenting information to the user via smart glasses. This enables personalized menu suggestions and inventory management according to the customer's emotional state.

[0450] "Means for registering restaurant inventory status" refers to a system or function for inputting the current inventory status of ingredients and products held by a restaurant and storing it in a database.

[0451] "Means for users to search for relevant inventory" refers to a system or function that allows users to input specific conditions or keywords to search for restaurant inventory information and obtain the necessary information.

[0452] "Means of using generative AI models to recommend restaurants suitable for users" refers to systems and functions that use machine learning and AI technology to suggest optimal restaurants and menus based on a user's preferences and history.

[0453] "Means for users to make reservations" refers to systems or functions that allow users to make reservations in advance for the store and menu they have selected by specifying the date, time, and number of people.

[0454] "Means for notifying the restaurant and user of reservation information" refers to a system or function that notifies the restaurant and user of the details of a confirmed reservation and allows them to confirm it.

[0455] "Means for calculating commission fees based on success and donating a portion of the fee" refers to a system or function that calculates the fee incurred when a user completes a reservation and donates a portion of that fee to social contribution activities, etc.

[0456] "Means for updating the generative AI model based on learning data" refers to systems or functions that periodically update the generative AI model based on user usage history and feedback, thereby improving the accuracy of recommendations.

[0457] "Means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus" refers to systems and functions that use technologies such as facial expression analysis and voice recognition to determine the user's current emotional state and suggest the most appropriate stores and menus accordingly.

[0458] "Means for presenting information to users via smart glasses" refers to a system or function that displays information on the display of smart glasses, allowing users to visually confirm and operate the device.

[0459] This invention is a system for efficiently managing restaurant inventory and providing personalized menu suggestions based on the emotional state of customers. The system is comprised of several main functions, each of which works in conjunction with the other functions.

[0460] Key System Features

[0461] 1. Register restaurant inventory status via:

[0462] The server provides a management screen designed to allow restaurant owners to input inventory information for their stores. Owners input information such as product names, quantities, and prices, and save it in a database. This information is updated in real time.

[0463] 2. How users can search for relevant inventory:

[0464] Users access the system through a web browser or mobile application and search for inventory information by entering keywords or conditions in the search bar. The server then retrieves the relevant inventory information from the database and displays it to the user.

[0465] 3. Using generative AI models to recommend suitable stores to users:

[0466] The server uses a generative AI model to recommend appropriate restaurants and menus based on the user's past usage history and current emotional state. The generative AI model uses machine learning algorithms to analyze the user's preferences and behavioral patterns.

[0467] 4. How users can make reservations:

[0468] Users select a restaurant from the reservation screen provided by the server, enter the desired date and time and number of people, and make a reservation. The reservation information is saved in a database.

[0469] 5. Means of notifying restaurants and users of reservation information:

[0470] Once a reservation is confirmed, the server will send an email or push notification to notify the restaurant and user of the reservation, providing two-way confirmation.

[0471] 6. A means of calculating contingency fees and donating a portion of them:

[0472] The server calculates the commission that will be incurred if the reservation is successful and has the function of donating a portion of it to social contribution activities. The commission calculation is done automatically, and the donation is also carried out according to the set rules.

[0473] 7. How to update generative AI models based on training data:

[0474] The server collects user usage history, feedback, and sentiment data and periodically updates the generative AI model to improve the accuracy of recommendations.

[0475] 8. A means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus:

[0476] The server uses smart glasses and other input devices to recognize the customer's emotional state from their facial expressions and voice, and then suggests appropriate menus and restaurants based on that. The emotion engine uses Affectiva and Microsoft Azure Emotion API.

[0477] 9. Means of presenting information to a user via smart glasses:

[0478] The server displays information on the smart glasses' display, allowing users to visually confirm and operate the device, which allows users to easily make menu suggestions and make reservations.

[0479] Specific examples

[0480] For example, if a cafe in Shibuya Ward has leftover chicken curry, the restaurant owner can register that information in the system. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays the relevant inventory information and simultaneously analyzes the user's current emotional state using an emotion engine. For example, if the user's emotional state is "I want to relax," the server will recommend restaurants with a quiet atmosphere and relaxing menus. The user can check this information through the smart glasses and make a reservation on the spot.

[0481] Prompt Sentence Examples

[0482] "Check out today's availability and recommend the perfect menu for your relaxing guest."

[0483] "Please suggest a dessert menu that suits customers with happy emotions at a cafe in Shibuya Ward."

[0484] In this way, a system that integrates inventory management and sentiment analysis can help restaurants operate more efficiently and improve customer satisfaction.

[0485] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0486] Step 1:

[0487] The server authenticates the restaurant owner by allowing them to access the management screen and enter their username and password. If authentication is successful, the management screen is displayed, allowing the owner to enter inventory status. Specifically, the owner enters information such as product name, quantity, and price into the input form and clicks the "Register" button. The server saves the entered inventory information in the database and updates the inventory management table. The input is the restaurant owner's authentication information and inventory data, and the output is the inventory information saved in the database.

[0488] Step 2:

[0489] Users access the system through a web browser or mobile application and log in by entering their username and password. If authentication is successful, the dashboard is displayed. The user enters keywords or conditions in the search bar (e.g., "Area: Shibuya," "Menu: Curry," "Budget: Under 700 yen") and clicks the "Search" button. The server retrieves relevant inventory information from the database based on the entered search conditions and displays it to the user in list form. The input is the username, password, and search conditions, and the output is a list of relevant inventory information that is displayed to the user.

[0490] Step 3:

[0491] The server receives data from smart glasses or other input devices to obtain emotional data when a customer accesses the system. The emotional data is analyzed using facial expression analysis and voice recognition technology. The emotion engine identifies the user's emotional state based on the analyzed emotional data and passes that information to a generative AI model. The input is the emotional data obtained from the smart glasses or other devices, and the output is the identified emotional state.

[0492] Step 4:

[0493] The server uses a generative AI model to recommend optimal restaurants and menus to the user. The generative AI model generates optimal restaurants and menus by taking into account the user's past usage history, current emotional state, and search criteria. The recommendation results are displayed on the smart glasses' display so that the user can visually confirm them. The input is the user's usage history, emotional state, and search criteria, and the output is a list of recommended restaurants and menus.

[0494] Step 5:

[0495] The user uses the smart glasses to check the suggested restaurants and menus and make a reservation on the spot. They select a restaurant from the reservation screen, enter the desired date and time and number of people, and press the "Confirm reservation" button. The server saves the reservation information in a database and notifies the restaurant and the user of the reservation details. The input is the reservation information entered by the user, and the output is the reservation information saved in the database and a notification message.

[0496] Step 6:

[0497] The user visits the restaurant at the reserved date and time and uses the discount menu. After serving the food, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. The server reflects this information in the database. The input is the restaurant's completed serving information, and the output is the updated inventory information.

[0498] Step 7:

[0499] The server calculates a commission based on the completed reservation information and donates a portion of it to social contribution activities according to set rules. The calculated commission and donation amount are recorded in a database. The input is the completed reservation information, and the output is the calculated commission and donation amount.

[0500] Step 8:

[0501] The server collects the user's usage history, feedback, and emotional data and periodically updates the generative AI model. This improves the accuracy of recommendations, making them more appropriate for future use. The input is the user's usage history, feedback, and emotional data, and the output is the updated generative AI model.

[0502] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0503] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0504] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0505] [Second embodiment]

[0506] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0507] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0508] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0509] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0510] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0511] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0512] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0513] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0514] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0515] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0516] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0517] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0518] The present invention relates to a system for registering restaurant inventory status and providing that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for recommending restaurants suitable for users using a generative AI model, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission-based fee and donating a portion of it, and a means for updating the generative AI model based on training data.

[0519] System program and processing description

[0520] Restaurant owners register inventory status

[0521] Restaurant owner (terminal): The owner logs in to the system's management screen and registers the inventory status. For example, the owner enters information such as "I have 10 servings of chicken curry left over."

[0522] Server: Saves the registered information to the database and updates the inventory information.

[0523] Users search for stores

[0524] User (terminal): The user accesses the system and logs in. They search for "curry under 700 yen in Shibuya Ward."

[0525] Server: Based on the user's search criteria, the server searches the database for inventory information for the relevant restaurants and displays the results.

[0526] Store recommendations using the recommendation function

[0527] Server: Using a generative AI model, the server recommends the most suitable restaurant based on the user's past usage history and search criteria. For example, if a user has ordered curry before, the server will prioritize restaurants that serve curry.

[0528] User (device): Select the store of interest from the list of suggested stores.

[0529] A user makes a reservation

[0530] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[0531] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0532] A user visits the store and receives a service

[0533] User: Visits the restaurant on the reserved date and time and receives the discounted meal.

[0534] Restaurant owners: Serve meals to customers, and after the service is complete, mark the reservation as "completed" in the admin panel and update inventory information.

[0535] Provider Fee Processing

[0536] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[0537] System Training

[0538] Server: Stores user usage history, reservation information, feedback, etc. as learning data and updates the generative AI model, thereby improving the accuracy of future recommendations.

[0539] Specific examples

[0540] Specifically, when a restaurant owner registers "I have 10 extra chicken curry servings" on the management screen, that information is sent to the server and saved in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria and uses a generative AI model to suggest restaurants that take past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee.

[0541] This system allows restaurants to efficiently utilize their inventory, allows users to enjoy meals at reasonable prices, and even donates a portion of the fees to social contribution activities.

[0542] The processing flow will be explained below.

[0543] Step 1:

[0544] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[0545] Server: Verify authentication information and display admin panel to owner.

[0546] Step 2:

[0547] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[0548] Restaurant owner (device): After completing the input, click the "Register" button.

[0549] Server: Saves the entered inventory information to the database and updates the inventory management table.

[0550] Step 3:

[0551] User (device): The user accesses the site and logs in by entering their username and password.

[0552] Server: Verify credentials and view user-specific dashboard.

[0553] Step 4:

[0554] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[0555] Server: Searches the database for relevant store information based on the entered search criteria.

[0556] Server: Displays search results to the user in a list format.

[0557] Step 5:

[0558] Server: Uses generative AI models to analyze users' past usage history and preferences.

[0559] Server: Based on the search results, the server displays a list of recommended stores to the user.

[0560] Step 6:

[0561] User (device): Select the store of interest from the presented recommendation list.

[0562] Step 7:

[0563] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[0564] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[0565] User (device): After checking the input information, press the "Confirm reservation" button.

[0566] Step 8:

[0567] Server: Saves the reservation information in a database and sends a confirmation email to the user.

[0568] Server: Notifies restaurant owner of reservation information.

[0569] Step 9:

[0570] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[0571] Restaurant owners: Providing meals to users.

[0572] Step 10:

[0573] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[0574] Step 11:

[0575] Server: Calculates commission based on completed booking information.

[0576] Server: The fee will be automatically deducted from the restaurant owner.

[0577] Server: Donate a portion of your fees to hunger relief organizations.

[0578] Step 12:

[0579] Server: Stores user usage history, reservation information, and feedback as learning data.

[0580] Server: Updates the generative AI model to improve the accuracy of future recommendations.

[0581] Example 1

[0582] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0583] Effective management of excess inventory in restaurants and the provision of appropriate services to users based on that inventory are key challenges. There is also a lack of means to quickly respond to user requests, improve restaurant management efficiency, and engage in social contribution activities. In particular, there is a need for improved recommendation accuracy that takes into account users' past usage history and search criteria.

[0584] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0585] In this invention, the server includes a means for registering restaurant inventory status, a means for users to search for relevant inventory, and a means for using a generative AI model to recommend suitable establishments to users. This allows restaurants to effectively utilize their inventory and users to enjoy meals at reasonable prices. Furthermore, by generating prompts and providing recommendation results, the system can respond quickly to user requests and improve usability. Furthermore, by calculating commissions based on success and donating a portion of the commissions, it is possible to contribute to society.

[0586] A "restaurant" is a facility that provides food and beverages.

[0587] "Inventory status" refers to information that indicates the quantity and condition of food and ingredients held by a restaurant.

[0588] "User" means an individual or organization that uses the system to search restaurant inventory information and make reservations.

[0589] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and recommends businesses that are suitable for users.

[0590] A "prompt sentence" is an instruction sentence input into a generative AI model, and is generated based on the user's past usage history and search conditions.

[0591] "Recommendation" refers to the use of generative AI models to recommend suitable businesses and products to users.

[0592] "Reservation information" is data containing detailed information about a reservation made by a user.

[0593] A "success-based fee" is a fee calculated based on the success of the services provided.

[0594] "Donation" means providing a portion of a commission fee for social contribution activities, etc.

[0595] A "database" is an information recording medium for storing and managing restaurant inventory and reservation information.

[0596] The system for implementing this invention is configured using the following hardware and software. The main hardware used includes a server computer and terminals for users and restaurant owners. The software used is AWS EC2 (server infrastructure), MySQL (database management system), GPT-4 (generative AI model), and React (front-end development environment).

[0597] Registering restaurant inventory status

[0598] Using a restaurant owner's device, the owner logs into the system's management screen and, as a specific example, enters "I have 10 servings of chicken curry left" when registering inventory status. The information sent from the device is received by the server and stored in a MySQL database. The server then updates the inventory information to reflect the latest inventory status.

[0599] Users search for stores

[0600] A user accesses the system using a terminal and logs in. By entering "curry under 700 yen in Shibuya Ward" into the search bar and pressing the search button, the server searches for relevant inventory information in the database based on these conditions. Information on stores that match the conditions is collected and displayed on the user's terminal.

[0601] Store recommendations using the recommendation function

[0602] Based on the search results, the server sends a prompt to the generative AI model (GPT-4). An example of a specific prompt would be, "Find out whether the user has ordered curry in the past, and based on that, please make a list of recommended curry restaurants in Shibuya Ward that cost under 700 yen." The generative AI model analyzes this prompt and generates a list of restaurants that are optimal for the user. The server sends this list to the user's device and displays it.

[0603] A user makes a reservation

[0604] On the displayed restaurant details page, the user selects "Chicken Curry" and clicks the "Book Now" button. By entering the "Date and Time" and "Number of People" in the reservation form and pressing the "Confirm Reservation" button, the reservation information is sent to the server. The server saves this information in a MySQL database and sends a reservation confirmation email to the user and the restaurant owner.

[0605] A user visits the store and receives a service

[0606] The user visits the restaurant at the reserved date and time and enjoys a meal based on the reservation details. After providing the service, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. This information is sent to the server and the database is updated.

[0607] Success-based commission processing

[0608] The server calculates commissions based on the completed reservation information and donates a portion of the commission to a hunger relief organization. This calculation is also done automatically on the server.

[0609] System Training

[0610] The server stores user usage history, reservation information, feedback, etc. as learning data, which is used to update the generative AI model and improve the accuracy of future recommendations.

[0611] For example, you can use a prompt such as, "Find out whether the user has ordered curry in the past, and based on that, make a list of recommended curry restaurants in Shibuya Ward that cost less than 700 yen." This system allows restaurants to use their inventory efficiently and users to enjoy meals at reasonable prices. It also creates a system where a portion of the commission is used for social contribution activities.

[0612] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0613] Step 1:

[0614] Restaurant owner (terminal): Logs in to the system's management screen and enters "I have 10 servings of chicken curry left" into the inventory registration form. The entered data is sent to the server.

[0615] Input: Inventory information entered by the restaurant owner (e.g., 10 servings of chicken curry)

[0616] Output: Stock information sent to the server

[0617] Step 2:

[0618] Server: Stores the inventory information received from the device in a database.

[0619] Input: Inventory information sent by restaurant owner

[0620] Output: Stock information stored in a database

[0621] Step 3:

[0622] User (device): Access the system and log in. Enter "Curry under 700 yen in Shibuya Ward" in the search bar and press the search button.

[0623] Input: Search criteria (e.g. Shibuya-ku, under 700 yen, curry)

[0624] Output: The search query sent to the server

[0625] Step 4:

[0626] Server: Searches the database for relevant inventory information based on the received search criteria, collects inventory information, and generates search results.

[0627] Input: The search query sent to the server

[0628] Output: A list of stores searched

[0629] Step 5:

[0630] Server: Generates and sends prompts to the generative AI model (GPT-4) based on the user's past usage history and search conditions.

[0631] Input: User's past usage history, current search criteria

[0632] Output: Prompt (e.g., "Find out whether the user has ordered curry in the past, and based on that, list recommended curry restaurants in Shibuya Ward that cost less than 700 yen.")

[0633] Step 6:

[0634] Generative AI model: Analyzes the prompt text received and generates a list of stores that are best suited to the user.

[0635] Input: prompt statement

[0636] Output: Recommended store list

[0637] Step 7:

[0638] Server: Sends the generated list of recommended stores to the user's device.

[0639] Input: Recommended store list

[0640] Output: A list of recommended stores displayed on the user's device

[0641] Step 8:

[0642] User (device): From the displayed list of stores, check the details page of the store they are interested in, check the "Chicken Curry" option, and click the "Make a Reservation" button. Enter the "Date and Time" and "Number of people" in the reservation form and press the "Confirm Reservation" button.

[0643] Input: Reservation information (e.g. store, menu, date and time, number of people)

[0644] Output: Reservation information sent to the server

[0645] Step 9:

[0646] Server: Stores the received reservation information in a database and sends a reservation confirmation email to the user and restaurant owner.

[0647] Input: Reservation information submitted by the user

[0648] Output: Reservation information stored in the database, reservation confirmation email

[0649] Step 10:

[0650] User (device): Visits the restaurant at the reserved date and time and enjoys the provided meal.

[0651] Input: Reservation date and time and number of people

[0652] Output: Get service

[0653] Step 11:

[0654] Restaurant owner (device): After providing the service, mark the reservation as "completed" on the management screen and update the inventory information.

[0655] Input: Completed reservation information

[0656] Output: Updated inventory information

[0657] Step 12:

[0658] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[0659] Input: Completed reservation information

[0660] Output: Calculated fees and donations made

[0661] Step 13:

[0662] Server: Stores user usage history, reservation information, and feedback as learning data and updates the generative AI model.

[0663] Input: User usage history, reservation information, feedback

[0664] Output: Highly accurate recommendations from an updated generative AI model

[0665] (Application example 1)

[0666] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0667] Inventory management at restaurants presents a major challenge to efficient operation, with issues such as excess or shortage of inventory and rising disposal costs. It is also difficult for users to grasp the inventory status of each restaurant, leaving them without a means to provide appropriate meals at reasonable prices. Furthermore, conventional recommendation functions have low accuracy in reflecting users' past usage history and search conditions, making it difficult to suggest the most suitable restaurant to the user. Solutions to these issues are highly desirable.

[0668] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0669] In this invention, the server includes: a means for registering restaurant inventory status; a means for users to search for relevant inventory; a means for using a generative AI model to recommend restaurants suitable for users; a means for users to make reservations; a means for notifying restaurants and users of reservation information; a means for calculating a performance-based fee and donating a portion of the fee; a means for updating the generative AI model based on training data; a recommendation function that suggests optimal restaurants based on a user's usage history and prioritizes displaying restaurants that reflect the user's past usage history; a means for searching for restaurants based on ingredient price ranges and inventory status; and a means for updating inventory in real time and providing a search function that allows users to directly access inventory information. This allows restaurant owners to efficiently manage their inventory and users to find optimal restaurants based on their past usage history. Furthermore, a portion of the fees is donated, contributing to society.

[0670] "Means for registering inventory status" is a function that allows restaurant owners to input the amount and type of inventory they have on hand each day into the system and store it in a database.

[0671] "A means for users to search for relevant inventory" is a function that searches for inventory information of restaurants that meet the conditions specified by the user and displays appropriate results.

[0672] "Means of using a generative AI model to recommend restaurants suitable for users" is a function that enables a generative AI model to recommend the most suitable restaurant based on the user's past usage history and search conditions.

[0673] "Means for users to make reservations" refers to a function that allows users to secure inventory at the store they select and complete the reservation at the desired date and time.

[0674] The "means for notifying the restaurant and user of reservation information" is a function for notifying the restaurant owner and user of confirmed reservation information by e-mail or the like.

[0675] The "means of calculating commissions based on success and donating a portion of them" is a function that calculates commissions based on completed reservations and automatically donates a portion of them to social contribution activities.

[0676] "Means for updating the generative AI model based on training data" refers to a function that applies training data to improve the accuracy of the generative AI model using collected user usage history and feedback.

[0677] The "recommendation function that suggests the most suitable restaurant based on the user's usage history" is a function that automatically suggests the most suitable restaurant based on the user's past usage data.

[0678] "A means of preferentially displaying restaurants that reflect past usage history" is a function that allows restaurants that a user has used in the past to be preferentially displayed in search results.

[0679] "A means to search for restaurants based on the price range and availability of ingredients" is a function that searches for restaurant inventory information and displays appropriate results based on the user's budget or specific ingredients.

[0680] The "search function that updates inventory in real time and allows users to directly access inventory information" is a function that updates restaurant inventory information in real time and allows users to access and check that information.

[0681] The system for realizing this invention includes the following programs and processes: The server, the restaurant owner's terminal, and the user's terminal work in cooperation to manage restaurant inventory and provide services to users.

[0682] Stock status registration

[0683] Restaurant owners log in to the system's management screen using a terminal and register their inventory status. At this time, they input the menu items they will offer and the quantities they will provide. The server receives this information and stores it in a database.

[0684] Inventory Search

[0685] Users access the system using a terminal and input conditions (e.g., geographical information, price range, type of ingredients, etc.) to search for inventory. The server queries the database based on the conditions entered and provides inventory information for relevant restaurants. For example, if a user searches for "curry under 700 yen in Shibuya Ward," a list of appropriate restaurants will be displayed.

[0686] Recommendation function

[0687] The server uses a generative AI model to recommend the best restaurants based on the user's past visit history and search criteria. This generative AI model takes into account the user's preferences and past choices and prioritizes the display of restaurants that are most relevant.

[0688] Making a reservation

[0689] The user selects the restaurant and menu they want and makes a reservation. The user enters the date, time, and number of people in the reservation form and confirms it. The server stores the information in a database and sends a confirmation email to the user and the restaurant.

[0690] Real-time inventory updates

[0691] The server updates restaurant inventory information in real time, allowing users to directly access the latest inventory information, so users are always provided with the latest inventory status.

[0692] Fee calculations and donations

[0693] After the reservation is completed, the server calculates the commission fee and donates a portion of it, which not only benefits the restaurant and the user but also contributes to society at the same time.

[0694] Update training data

[0695] The server stores the user's usage history and reservation information as learning data and periodically updates the generative AI model, which improves the accuracy of future recommendations.

[0696] Hardware and software used

[0697] The system uses software such as Python, databases (e.g., MySQL), generative AI models (e.g., TensorFlow, PyTorch), and an SMTP server. The server and user and restaurant terminals are connected to the Internet, enabling real-time data processing.

[0698] Examples of concrete examples and prompts

[0699] As a specific example, when a restaurant owner registers "I have 10 extra servings of chicken curry" on the management screen, the information is immediately sent to the server and saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server searches for restaurants that meet the criteria and uses a generative AI model to prioritize and display a list of restaurants that take the user's past history into consideration. When the user reserves "chicken curry," a confirmation email is sent, a commission fee is calculated, and a portion of that fee is automatically donated.

[0700] Example prompt sentence:

[0701] "For users who have ordered curry in the past, please prioritize displaying restaurants in Shibuya Ward that serve curry for under 700 yen."

[0702] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0703] Step 1:

[0704] Stock status registration

[0705] Input: The restaurant owner uses the terminal to input the menu name "Chicken Curry" and the quantity "10 servings."

[0706] Specific actions: Fill out the numbered inventory registration form and press the "Register" button.

[0707] Data processing / calculation: Generate input information in JSON format and send it to the server.

[0708] Output: The server stores the received data in the database and the inventory information is updated.

[0709] Step 2:

[0710] Inventory Search

[0711] Input: The user uses a terminal to enter the search criteria "Curry under 700 yen in Shibuya Ward."

[0712] Specific actions: Enter search criteria in the condition input form and press the "Search" button.

[0713] Data processing / calculation: The server queries the database based on the search criteria and searches for inventory information that matches the criteria.

[0714] Output: Search results will be displayed on the user's device, along with the restaurants and their inventory information.

[0715] Step 3:

[0716] Recommendation function

[0717] Input: User's past usage history and current search criteria.

[0718] Specific operation: The server's generated AI model references the user's history and search criteria to recommend the most suitable restaurant.

[0719] Data processing / calculation: The AI ​​model scores historical data and search data to create a list of restaurants in order of relevance.

[0720] Output: The recommended restaurant list is displayed on the user's device.

[0721] Step 4:

[0722] Making a reservation

[0723] Input: The user selects a restaurant and menu (e.g., "chicken curry"), and enters the desired date and time ("December 31, 2022, 18:00") and the number of people ("2").

[0724] Specific actions: Enter the required information in the reservation form and press the "Confirm reservation" button.

[0725] Data processing / calculation: The server stores the reservation information in a database and generates a confirmation email.

[0726] Output: A reservation confirmation email is sent to the user and restaurant.

[0727] Step 5:

[0728] Real-time inventory updates

[0729] Input: Reservation information and inventory consumption status.

[0730] What it does: The server periodically updates the database to maintain the latest inventory status.

[0731] Data processing / calculation: When a reservation is confirmed, the corresponding inventory is reduced.

[0732] Output: The latest inventory information is shared within the system and can be accessed by users in real time.

[0733] Step 6:

[0734] Fee calculations and donations

[0735] Input: Completed reservation information.

[0736] Specific operation: The server calculates the commission based on the information of each reservation and allocates a portion of it to donations.

[0737] Data processing / calculation: Calculate performance-based fees and determine donation amounts.

[0738] Output: The donation process is executed and a report of the donation amount is generated.

[0739] Step 7:

[0740] Update training data

[0741] Input: User usage history, booking information, feedback.

[0742] Specific operation: The server collects this data and updates the learning data of the generative AI model.

[0743] Data processing / calculation: New usage data is fed back into the AI ​​model to retrain the model.

[0744] Output: The updated generative AI model will improve the accuracy of future recommendations.

[0745] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0746] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for using a generative AI model to recommend restaurants suitable for users, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission based on performance and donating a portion of it, and a means for updating the generative AI model based on learning data. In addition, by incorporating an emotion engine that recognizes user emotions, the system further optimizes recommendations for individual users.

[0747] System program and processing description

[0748] Restaurant owners register inventory status

[0749] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[0750] Server: Verify authentication information and display admin panel to owner.

[0751] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[0752] Restaurant owner (device): After completing the input, click the "Register" button.

[0753] Server: Saves the entered inventory information to the database and updates the inventory management table.

[0754] Users search for stores

[0755] User (Terminal): The user accesses the system and logs in by entering a username and password.

[0756] Server: Verify credentials and view user-specific dashboard.

[0757] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[0758] Server: Searches the database for relevant store information based on the entered search criteria.

[0759] Server: Displays search results to the user in a list format.

[0760] Emotion engine recognizes user emotions

[0761] Server: Uses the emotion engine to analyze emotional data (e.g., emotional states obtained through facial expression analysis or voice recognition) when a user accesses the service from their device.

[0762] Server: Stores user sentiment data and reflects it in future recommendations.

[0763] Store recommendations using the recommendation function

[0764] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[0765] User (device): Select the store of interest from the suggested recommendation list.

[0766] A user makes a reservation

[0767] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[0768] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0769] A user visits the store and receives a service

[0770] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[0771] Restaurant owners: Providing meals to users.

[0772] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[0773] Provider Fee Processing

[0774] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[0775] System Training

[0776] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[0777] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[0778] Specific examples

[0779] Specifically, when a restaurant owner registers "I have 10 servings of chicken curry left over" on the management screen, that information is sent to the server and stored in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria, analyzes their current emotional state using an emotion engine, and uses a generative AI model to recommend the most suitable restaurant, taking past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee. The system also learns from usage history, including emotional data, to improve the accuracy of recommendations for future visits.

[0780] This allows restaurants to efficiently utilize their inventory and users to enjoy meals at more personalized and affordable prices. In addition, a portion of the fees is donated to social contribution activities.

[0781] The processing flow will be explained below.

[0782] Step 1:

[0783] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[0784] Server: Verify authentication information and display admin panel to owner.

[0785] Step 2:

[0786] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[0787] Restaurant owner (device): After completing the input, click the "Register" button.

[0788] Server: Saves the entered inventory information to the database and updates the inventory management table.

[0789] Step 3:

[0790] User (device): The user accesses the site and logs in by entering their username and password.

[0791] Server: Verify credentials and view user-specific dashboard.

[0792] Step 4:

[0793] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[0794] Server: Searches the database for relevant store information based on the entered search criteria.

[0795] Server: Displays search results to the user in a list format.

[0796] Step 5:

[0797] Server: Using the emotion engine, analyzes emotion data acquired from the camera and microphone on the user's device. For example, by analyzing the user's facial expressions and tone of voice, it can recognize emotions such as "happiness," "tiredness," and "excitement."

[0798] Server: Stores the recognized emotion data and uses it for future recommendations.

[0799] Step 6:

[0800] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on a user's past usage history and current emotional state. For example, it recommends bright and lively stores to a user in a "happy" state, and quiet and relaxing stores to a user in a "tired" state.

[0801] Step 7:

[0802] User (device): Select the store of interest from the presented recommendation list.

[0803] Step 8:

[0804] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[0805] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[0806] User (device): After checking the input information, press the "Confirm reservation" button.

[0807] Step 9:

[0808] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0809] Step 10:

[0810] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[0811] Restaurant owners: Providing meals to users.

[0812] Step 11:

[0813] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[0814] Step 12:

[0815] Server: Calculates commission based on completed booking information.

[0816] Server: The fee will be automatically deducted from the restaurant owner.

[0817] Server: Donate a portion of your fees to hunger relief organizations.

[0818] Step 13:

[0819] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[0820] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[0821] Example 2

[0822] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0823] Conventional restaurant inventory management systems provide functions such as inventory information registration, search, and reservation, but lack a mechanism for recommending optimal restaurants based on the user's emotional state. As a result, the user experience cannot be improved and restaurant inventory management is inefficient. Furthermore, there is no system for calculating fees or making donations that takes social contributions into consideration, making these systems insufficient for realizing a sustainable society.

[0824] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0825] In this invention, the server includes means for registering restaurant inventory status, means for users to search for relevant inventory, means for recommending restaurants suitable for users using a generative AI model, means for users to make reservations, means for notifying the restaurant and user of the reservation information, means for calculating a performance-based fee and donating a portion of it, means for updating the generative AI model based on training data, and means for acquiring user emotion data and optimizing recommendations based on that data. This makes it possible to suggest optimal restaurants based on the user's emotional state, improving the user experience and streamlining restaurant inventory management, and realizing a fee calculation and donation system that contributes to society.

[0826] The "means for registering restaurant inventory status" is a function that allows restaurant owners to input current inventory information into the system and store it in the database.

[0827] "Means for users to search for relevant inventory" refers to the means by which users can search for inventory information within the system based on specific conditions and find relevant stores.

[0828] "Means of using a generative AI model to recommend stores suitable for users" is a function that uses a generative AI model to analyze a user's past usage history and emotional data, and recommends the most suitable store.

[0829] "Means for users to make reservations" refers to the means by which users make reservations for the store they have selected via the system.

[0830] "Means for notifying restaurants and users of reservation information" is a function for automatically notifying restaurant owners and users of the information when a reservation is confirmed.

[0831] The "means for calculating commissions based on success and donating a portion of the commission" is a function that executes a process for calculating commissions and donating a portion of the commissions when a reservation is successful.

[0832] "Means for updating the generative AI model based on learning data" refers to a means for regularly updating the generative AI model based on user usage history, feedback, emotional data, etc., in order to improve accuracy.

[0833] "Means of obtaining user emotional data and optimizing recommendations based on that" refers to a function that obtains emotional data from the user's facial expressions, voice, etc., and uses that information to further individualize recommendations.

[0834] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. The system mainly includes the following components: a restaurant owner terminal, a user terminal, and a server.

[0835] Sequence for restaurant owner to register inventory status

[0836] Restaurant owner (terminal): The restaurant owner logs in to the system's management screen and enters their username and password. If authentication is successful, the owner's dedicated management dashboard will be displayed. This dashboard has an input form for inventory status, and the owner enters information such as "product name," "quantity," and "discount price," and clicks the "Register" button.

[0837] Server: The server receives the authentication information and the entered inventory information and saves it in the inventory management table of the database. For example, if the entered information is "Product name: Chicken curry", "Quantity: 10", and "Discount price: 700 yen (regular price 1,000 yen)", this will be saved in the database.

[0838] A sequence where a user searches for a store

[0839] User (terminal): The user accesses the system and logs in by entering their username and password. After successful authentication, a dashboard is displayed. This dashboard has a search bar, and the user can enter conditions such as "area," "menu," and "budget," and then click the "Search" button.

[0840] Server: The server queries the database based on the entered criteria and displays the corresponding restaurant information in a list format to the user. For example, search results are displayed based on criteria such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen."

[0841] Emotion engine recognizes user emotions

[0842] Server: Using the emotion engine, the server acquires emotional data when the user accesses the app from their device. It performs facial and voice analysis to analyze the user's emotional state. The acquired emotional data is stored in a database and is reflected in future recommendations.

[0843] Store recommendations using the recommendation function

[0844] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[0845] User (device): The user selects the store of interest from the proposed recommendation list.

[0846] A user makes a reservation

[0847] User (device): The user checks the "Leftover Ingredients Menu" on the details page of the selected restaurant, enters the "Date and Time" and "Number of people" in the reservation form, and presses the "Confirm Reservation" button.

[0848] Server: Saves the reservation information in a database and sends a confirmation email to the user. At the same time, it notifies the restaurant owner of the reservation information.

[0849] Post-service processing

[0850] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[0851] Restaurant owners: Serve food to customers, mark the reservation as "completed" in the extranet, and update inventory information after serving.

[0852] Fee calculation and donations

[0853] Server: Calculates commission based on successful bookings and donates a portion of the commission to hunger relief organizations.

[0854] System Training

[0855] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data and updates the generative AI model and sentiment engine, thereby improving the accuracy of future recommendations.

[0856] Specific examples

[0857] For example, if a restaurant owner registers that they have 10 extra servings of chicken curry, that information is saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that meet the criteria, analyzing the user's current emotional state using an emotion engine. The generative AI model recommends the most suitable restaurant, taking into account past usage history, and the user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The server then calculates a commission-based fee and initiates a process to execute a portion of that fee.

[0858] Example prompt sentence:

[0859] "Analyze the user's emotional state and recommend the best store."

[0860] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0861] Step 1: Login process

[0862] Restaurant owner (terminal): The owner accesses the system's management screen and enters their username and password.

[0863] Server: Receives the entered authentication information and checks it against the database. If successful, displays the owner's personal admin dashboard.

[0864] Input: Username, Password

[0865] Output: Authentication result, admin dashboard screen

[0866] Step 2: Enter inventory information

[0867] Restaurant owner (device): After logging in, enter information such as product name, quantity, discount price, etc. into the inventory entry form on the management screen, and click the "Register" button.

[0868] Input: Product name (e.g., chicken curry), quantity (e.g., 10), discount price (e.g., 700 yen)

[0869] Output: Inventory information transmission request

[0870] Step 3: Save inventory information

[0871] Server: Receives the entered inventory information and saves it in the inventory management table of the database. If this process is successful, it sends a registration completion message.

[0872] Input: Inventory information (product name, quantity, discount price)

[0873] Output: Registration complete message

[0874] Step 4: User Login

[0875] User (terminal): Accesses the system and attempts to log in by entering a username and password.

[0876] Server: Checks the entered credentials against the database and, if successful, displays a dashboard specific to the user.

[0877] Input: Username, Password

[0878] Output: Authentication results, user dashboard

[0879] Step 5: Enter search criteria

[0880] User (device): Enter conditions such as "area," "menu," and "budget" in the search bar on the dashboard and click the "Search" button.

[0881] Input: Area (e.g. Shibuya), Menu (e.g. Curry), Budget (e.g. Under 700 yen)

[0882] Output: Search request

[0883] Step 6: Viewing search results

[0884] Server: Queries the database based on the entered search criteria and displays matching store information to the user in a list format.

[0885] Input: Search criteria

[0886] Output: A list of matching stores

[0887] Step 7: Obtaining emotion data

[0888] Server: Uses the emotion engine to obtain emotional data when the user accesses the app from their device. It determines the user's emotional state through facial expression and voice analysis.

[0889] Input: User video and audio data

[0890] Output: Emotion data (e.g. happy, tired)

[0891] Step 8: Storing Emotion Data

[0892] Server: Stores the acquired emotion data in a database and updates the user's profile.

[0893] Input: Emotion data

[0894] Output: Updated user profile

[0895] Step 9: Recommendation Generation

[0896] Server: Using a generative AI model, it suggests the most suitable store based on the user's past usage history, current emotional state, and search criteria.

[0897] Input: User usage history, emotion data, search conditions

[0898] Output: Recommendation list

[0899] Step 10: View recommendation results

[0900] User (device): Select the store of interest from the suggested recommendation list.

[0901] Input: Recommendation list

[0902] Output: Selected store information

[0903] Step 11: Enter reservation information

[0904] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected store, enter the required information such as "Date and Time" and "Number of people" in the reservation form, and press the "Confirm Reservation" button.

[0905] Input: Date, time, number of people, selected menu

[0906] Output: Reservation information sending request

[0907] Step 12: Save and notify reservation information

[0908] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[0909] Input: Reservation information

[0910] Output: Confirmation email, reservation information notification

[0911] Step 13: Update your visit record

[0912] Restaurant owner: A user visits the restaurant and uses the reserved menu. After providing the service, the user marks the reservation as "completed" on the management screen and updates the inventory information.

[0913] Input: Visit confirmation, menu information

[0914] Output: Updated inventory information

[0915] Step 14: Fee Calculation and Donation

[0916] Server: Calculates commission-based fees based on completed booking information and donates a portion of the fees.

[0917] Input: Completed reservation information

[0918] Output: Fee calculation results, donation process execution

[0919] Step 15: Save the training data

[0920] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[0921] Input: Usage history, reservation information, feedback, emotional data

[0922] Output: Saved training data

[0923] Step 16: Update the generative AI model

[0924] Server: Uses the saved learning data to update the generative AI model and emotion engine, improving the accuracy of future recommendations.

[0925] Input: Training data

[0926] Output: Updated generative AI model and emotion engine

[0927] (Application example 2)

[0928] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0929] Restaurants are required to manage inventory, propose efficient menus, and provide personalized service based on customers' emotional states. Conventional systems are limited in their ability to collect inventory information and propose appropriate menus, making it particularly difficult to optimize services based on customer emotions. This results in problems such as poor customer satisfaction and a tendency for inventory waste. To solve this problem, a system that integrates inventory management and customer emotion recognition is needed.

[0930] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for registering restaurant inventory status, a means for a user to search for relevant inventory, a means for recommending restaurants suitable for the user using a generative AI model, a means for a user to make a reservation, a means for notifying the restaurant and the user of the reservation information, a means for calculating a performance-based fee and donating a portion of it, a means for updating the generative AI model based on training data, a means incorporating an emotion engine that recognizes the customer's emotional state and presents appropriate restaurants and menus, and a means for presenting information to the user via smart glasses. This enables personalized menu suggestions and inventory management according to the customer's emotional state.

[0931] "Means for registering restaurant inventory status" refers to a system or function for inputting the current inventory status of ingredients and products held by a restaurant and storing it in a database.

[0932] "Means for users to search for relevant inventory" refers to a system or function that allows users to input specific conditions or keywords to search for restaurant inventory information and obtain the necessary information.

[0933] "Means of using generative AI models to recommend restaurants suitable for users" refers to systems and functions that use machine learning and AI technology to suggest optimal restaurants and menus based on a user's preferences and history.

[0934] "Means for users to make reservations" refers to systems or functions that allow users to make reservations in advance for the store and menu they have selected by specifying the date, time, and number of people.

[0935] "Means for notifying the restaurant and user of reservation information" refers to a system or function that notifies the restaurant and user of the details of a confirmed reservation and allows them to confirm it.

[0936] "Means for calculating commission fees based on success and donating a portion of the fee" refers to a system or function that calculates the fee incurred when a user completes a reservation and donates a portion of that fee to social contribution activities, etc.

[0937] "Means for updating the generative AI model based on learning data" refers to systems or functions that periodically update the generative AI model based on user usage history and feedback, thereby improving the accuracy of recommendations.

[0938] "Means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus" refers to systems and functions that use technologies such as facial expression analysis and voice recognition to determine the user's current emotional state and suggest the most appropriate stores and menus accordingly.

[0939] "Means for presenting information to users via smart glasses" refers to a system or function that displays information on the display of smart glasses, allowing users to visually confirm and operate the device.

[0940] This invention is a system for efficiently managing restaurant inventory and providing personalized menu suggestions based on the emotional state of customers. The system is comprised of several main functions, each of which works in conjunction with the other functions.

[0941] Key System Features

[0942] 1. Register restaurant inventory status via:

[0943] The server provides a management screen designed to allow restaurant owners to input inventory information for their stores. Owners input information such as product names, quantities, and prices, and save it in a database. This information is updated in real time.

[0944] 2. How users can search for relevant inventory:

[0945] Users access the system through a web browser or mobile application and search for inventory information by entering keywords or conditions in the search bar. The server then retrieves the relevant inventory information from the database and displays it to the user.

[0946] 3. Using generative AI models to recommend suitable stores to users:

[0947] The server uses a generative AI model to recommend appropriate restaurants and menus based on the user's past usage history and current emotional state. The generative AI model uses machine learning algorithms to analyze the user's preferences and behavioral patterns.

[0948] 4. How users can make reservations:

[0949] Users select a restaurant from the reservation screen provided by the server, enter the desired date and time and number of people, and make a reservation. The reservation information is saved in a database.

[0950] 5. Means of notifying restaurants and users of reservation information:

[0951] Once a reservation is confirmed, the server will send an email or push notification to notify the restaurant and user of the reservation, providing two-way confirmation.

[0952] 6. A means of calculating contingency fees and donating a portion of them:

[0953] The server calculates the commission that will be incurred if the reservation is successful and has the function of donating a portion of it to social contribution activities. The commission calculation is done automatically, and the donation is also carried out according to the set rules.

[0954] 7. How to update generative AI models based on training data:

[0955] The server collects user usage history, feedback, and sentiment data and periodically updates the generative AI model to improve the accuracy of recommendations.

[0956] 8. A means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus:

[0957] The server uses smart glasses and other input devices to recognize the customer's emotional state from their facial expressions and voice, and then suggests appropriate menus and restaurants based on that. The emotion engine uses Affectiva and Microsoft Azure Emotion API.

[0958] 9. Means of presenting information to a user via smart glasses:

[0959] The server displays information on the smart glasses' display, allowing users to visually confirm and operate the device, which allows users to easily make menu suggestions and make reservations.

[0960] Specific examples

[0961] For example, if a cafe in Shibuya Ward has leftover chicken curry, the restaurant owner can register that information in the system. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays the relevant inventory information and simultaneously analyzes the user's current emotional state using an emotion engine. For example, if the user's emotional state is "I want to relax," the server will recommend restaurants with a quiet atmosphere and relaxing menus. The user can check this information through the smart glasses and make a reservation on the spot.

[0962] Prompt Sentence Examples

[0963] "Check out today's availability and recommend the perfect menu for your relaxing guest."

[0964] "Please suggest a dessert menu that suits customers with happy emotions at a cafe in Shibuya Ward."

[0965] In this way, a system that integrates inventory management and sentiment analysis can help restaurants operate more efficiently and improve customer satisfaction.

[0966] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0967] Step 1:

[0968] The server authenticates the restaurant owner by allowing them to access the management screen and enter their username and password. If authentication is successful, the management screen is displayed, allowing the owner to enter inventory status. Specifically, the owner enters information such as product name, quantity, and price into the input form and clicks the "Register" button. The server saves the entered inventory information in the database and updates the inventory management table. The input is the restaurant owner's authentication information and inventory data, and the output is the inventory information saved in the database.

[0969] Step 2:

[0970] Users access the system through a web browser or mobile application and log in by entering their username and password. If authentication is successful, the dashboard is displayed. The user enters keywords or conditions in the search bar (e.g., "Area: Shibuya," "Menu: Curry," "Budget: Under 700 yen") and clicks the "Search" button. The server retrieves relevant inventory information from the database based on the entered search conditions and displays it to the user in list form. The input is the username, password, and search conditions, and the output is a list of relevant inventory information that is displayed to the user.

[0971] Step 3:

[0972] The server receives data from smart glasses or other input devices to obtain emotional data when a customer accesses the system. The emotional data is analyzed using facial expression analysis and voice recognition technology. The emotion engine identifies the user's emotional state based on the analyzed emotional data and passes that information to a generative AI model. The input is the emotional data obtained from the smart glasses or other devices, and the output is the identified emotional state.

[0973] Step 4:

[0974] The server uses a generative AI model to recommend optimal restaurants and menus to the user. The generative AI model generates optimal restaurants and menus by taking into account the user's past usage history, current emotional state, and search criteria. The recommendation results are displayed on the smart glasses' display so that the user can visually confirm them. The input is the user's usage history, emotional state, and search criteria, and the output is a list of recommended restaurants and menus.

[0975] Step 5:

[0976] The user uses the smart glasses to check the suggested restaurants and menus and make a reservation on the spot. They select a restaurant from the reservation screen, enter the desired date and time and number of people, and press the "Confirm reservation" button. The server saves the reservation information in a database and notifies the restaurant and the user of the reservation details. The input is the reservation information entered by the user, and the output is the reservation information saved in the database and a notification message.

[0977] Step 6:

[0978] The user visits the restaurant at the reserved date and time and uses the discount menu. After serving the food, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. The server reflects this information in the database. The input is the restaurant's completed serving information, and the output is the updated inventory information.

[0979] Step 7:

[0980] The server calculates a commission based on the completed reservation information and donates a portion of it to social contribution activities according to set rules. The calculated commission and donation amount are recorded in a database. The input is the completed reservation information, and the output is the calculated commission and donation amount.

[0981] Step 8:

[0982] The server collects the user's usage history, feedback, and emotional data and periodically updates the generative AI model. This improves the accuracy of recommendations, making them more appropriate for future use. The input is the user's usage history, feedback, and emotional data, and the output is the updated generative AI model.

[0983] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0984] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0985] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0986] [Third embodiment]

[0987] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0988] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0989] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0990] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0991] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0992] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0993] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0994] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0995] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0996] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0997] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0998] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0999] The present invention relates to a system for registering restaurant inventory status and providing that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for recommending restaurants suitable for users using a generative AI model, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission-based fee and donating a portion of it, and a means for updating the generative AI model based on training data.

[1000] System program and processing description

[1001] Restaurant owners register inventory status

[1002] Restaurant owner (terminal): The owner logs in to the system's management screen and registers the inventory status. For example, the owner enters information such as "I have 10 servings of chicken curry left over."

[1003] Server: Saves the registered information to the database and updates the inventory information.

[1004] Users search for stores

[1005] User (terminal): The user accesses the system and logs in. They search for "curry under 700 yen in Shibuya Ward."

[1006] Server: Based on the user's search criteria, the server searches the database for inventory information for the relevant restaurants and displays the results.

[1007] Store recommendations using the recommendation function

[1008] Server: Using a generative AI model, the server recommends the most suitable restaurant based on the user's past usage history and search criteria. For example, if a user has ordered curry before, the server will prioritize restaurants that serve curry.

[1009] User (device): Select the store of interest from the list of suggested stores.

[1010] A user makes a reservation

[1011] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[1012] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1013] A user visits the store and receives a service

[1014] User: Visits the restaurant on the reserved date and time and receives the discounted meal.

[1015] Restaurant owners: Serve meals to customers, and after the service is complete, mark the reservation as "completed" in the admin panel and update inventory information.

[1016] Provider Fee Processing

[1017] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[1018] System Training

[1019] Server: Stores user usage history, reservation information, feedback, etc. as learning data and updates the generative AI model, thereby improving the accuracy of future recommendations.

[1020] Specific examples

[1021] Specifically, when a restaurant owner registers "I have 10 extra chicken curry servings" on the management screen, that information is sent to the server and saved in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria and uses a generative AI model to suggest restaurants that take past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee.

[1022] This system allows restaurants to efficiently utilize their inventory, allows users to enjoy meals at a reasonable price, and even donates a portion of the fees to social contribution activities.

[1023] The processing flow will be explained below.

[1024] Step 1:

[1025] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[1026] Server: Verify authentication information and display admin panel to owner.

[1027] Step 2:

[1028] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[1029] Restaurant owner (device): After completing the input, click the "Register" button.

[1030] Server: Saves the entered inventory information to the database and updates the inventory management table.

[1031] Step 3:

[1032] User (device): The user accesses the site and logs in by entering their username and password.

[1033] Server: Verify credentials and view user-specific dashboard.

[1034] Step 4:

[1035] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[1036] Server: Searches the database for relevant store information based on the entered search criteria.

[1037] Server: Displays search results to the user in a list format.

[1038] Step 5:

[1039] Server: Uses generative AI models to analyze users' past usage history and preferences.

[1040] Server: Based on the search results, the server displays a list of recommended stores to the user.

[1041] Step 6:

[1042] User (device): Select the store of interest from the presented recommendation list.

[1043] Step 7:

[1044] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[1045] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[1046] User (device): After checking the input information, press the "Confirm reservation" button.

[1047] Step 8:

[1048] Server: Saves the reservation information in a database and sends a confirmation email to the user.

[1049] Server: Notifies restaurant owner of reservation information.

[1050] Step 9:

[1051] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[1052] Restaurant owners: Providing meals to users.

[1053] Step 10:

[1054] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[1055] Step 11:

[1056] Server: Calculates commission based on completed booking information.

[1057] Server: The fee will be automatically deducted from the restaurant owner.

[1058] Server: Donate a portion of your fees to hunger relief organizations.

[1059] Step 12:

[1060] Server: Stores user usage history, reservation information, and feedback as learning data.

[1061] Server: Updates the generative AI model to improve the accuracy of future recommendations.

[1062] Example 1

[1063] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1064] Effective management of excess inventory in restaurants and the provision of appropriate services to users based on that inventory are key challenges. There is also a lack of means to quickly respond to user requests, improve restaurant management efficiency, and engage in social contribution activities. In particular, there is a need for improved recommendation accuracy that takes into account users' past usage history and search criteria.

[1065] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1066] In this invention, the server includes a means for registering restaurant inventory status, a means for users to search for relevant inventory, and a means for using a generative AI model to recommend suitable establishments to users. This allows restaurants to effectively utilize their inventory and users to enjoy meals at reasonable prices. Furthermore, by generating prompts and providing recommendation results, the system can respond quickly to user requests and improve usability. Furthermore, by calculating commissions based on success and donating a portion of the commissions, it is possible to contribute to society.

[1067] A "restaurant" is a facility that provides food and beverages.

[1068] "Inventory status" refers to information that indicates the quantity and condition of food and ingredients held by a restaurant.

[1069] "User" means an individual or organization that uses the system to search restaurant inventory information and make reservations.

[1070] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and recommends businesses that are suitable for users.

[1071] A "prompt sentence" is an instruction sentence input into a generative AI model, and is generated based on the user's past usage history and search conditions.

[1072] "Recommendation" refers to the use of generative AI models to recommend suitable businesses and products to users.

[1073] "Reservation information" is data containing detailed information about a reservation made by a user.

[1074] A "success-based fee" is a fee calculated based on the success of the services provided.

[1075] "Donation" means providing a portion of a commission fee for social contribution activities, etc.

[1076] A "database" is an information recording medium for storing and managing restaurant inventory and reservation information.

[1077] The system for implementing this invention is configured using the following hardware and software. The main hardware used includes a server computer and terminals for users and restaurant owners. The software used is AWS EC2 (server infrastructure), MySQL (database management system), GPT-4 (generative AI model), and React (front-end development environment).

[1078] Registering restaurant inventory status

[1079] Using a restaurant owner's device, the owner logs into the system's management screen and, as a specific example, enters "I have 10 servings of chicken curry left" when registering inventory status. The information sent from the device is received by the server and stored in a MySQL database. The server then updates the inventory information to reflect the latest inventory status.

[1080] Users search for stores

[1081] A user accesses the system using a terminal and logs in. By entering "curry under 700 yen in Shibuya Ward" into the search bar and pressing the search button, the server searches for relevant inventory information in the database based on these conditions. Information on stores that match the conditions is collected and displayed on the user's terminal.

[1082] Store recommendations using the recommendation function

[1083] Based on the search results, the server sends a prompt to the generative AI model (GPT-4). An example of a specific prompt would be, "Find out whether the user has ordered curry in the past, and based on that, please make a list of recommended curry restaurants in Shibuya Ward that cost under 700 yen." The generative AI model analyzes this prompt and generates a list of restaurants that are optimal for the user. The server sends this list to the user's device and displays it.

[1084] A user makes a reservation

[1085] On the displayed restaurant details page, the user selects "Chicken Curry" and clicks the "Book Now" button. By entering the "Date and Time" and "Number of People" in the reservation form and pressing the "Confirm Reservation" button, the reservation information is sent to the server. The server saves this information in a MySQL database and sends a reservation confirmation email to the user and the restaurant owner.

[1086] A user visits the store and receives a service

[1087] The user visits the restaurant at the reserved date and time and enjoys a meal based on the reservation details. After providing the service, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. This information is sent to the server and the database is updated.

[1088] Success-based commission processing

[1089] The server calculates commissions based on the completed reservation information and donates a portion of the commission to a hunger relief organization. This calculation is also done automatically on the server.

[1090] System Training

[1091] The server stores user usage history, reservation information, feedback, etc. as learning data, which is used to update the generative AI model and improve the accuracy of future recommendations.

[1092] For example, you can use a prompt such as, "Find out whether the user has ordered curry in the past, and based on that, make a list of recommended curry restaurants in Shibuya Ward that cost less than 700 yen." This system allows restaurants to use their inventory efficiently and users to enjoy meals at reasonable prices. It also creates a system where a portion of the commission is used for social contribution activities.

[1093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1094] Step 1:

[1095] Restaurant owner (terminal): Logs in to the system's management screen and enters "I have 10 servings of chicken curry left" into the inventory registration form. The entered data is sent to the server.

[1096] Input: Inventory information entered by the restaurant owner (e.g., 10 servings of chicken curry)

[1097] Output: Stock information sent to the server

[1098] Step 2:

[1099] Server: Stores the inventory information received from the device in a database.

[1100] Input: Inventory information sent by restaurant owner

[1101] Output: Stock information stored in a database

[1102] Step 3:

[1103] User (device): Access the system and log in. Enter "Curry under 700 yen in Shibuya Ward" in the search bar and press the search button.

[1104] Input: Search criteria (e.g. Shibuya-ku, under 700 yen, curry)

[1105] Output: The search query sent to the server

[1106] Step 4:

[1107] Server: Searches the database for relevant inventory information based on the received search criteria, collects inventory information, and generates search results.

[1108] Input: The search query sent to the server

[1109] Output: A list of stores searched

[1110] Step 5:

[1111] Server: Generates and sends prompts to the generative AI model (GPT-4) based on the user's past usage history and search conditions.

[1112] Input: User's past usage history, current search criteria

[1113] Output: Prompt (e.g., "Find out whether the user has ordered curry in the past, and based on that, list recommended curry restaurants in Shibuya Ward that cost less than 700 yen.")

[1114] Step 6:

[1115] Generative AI model: Analyzes the prompt text received and generates a list of stores that are best suited to the user.

[1116] Input: prompt statement

[1117] Output: Recommended store list

[1118] Step 7:

[1119] Server: Sends the generated list of recommended stores to the user's device.

[1120] Input: Recommended store list

[1121] Output: A list of recommended stores displayed on the user's device

[1122] Step 8:

[1123] User (device): From the displayed list of stores, check the details page of the store they are interested in, check the "Chicken Curry" option, and click the "Make a Reservation" button. Enter the "Date and Time" and "Number of people" in the reservation form and press the "Confirm Reservation" button.

[1124] Input: Reservation information (e.g. store, menu, date and time, number of people)

[1125] Output: Reservation information sent to the server

[1126] Step 9:

[1127] Server: Stores the received reservation information in a database and sends a reservation confirmation email to the user and restaurant owner.

[1128] Input: Reservation information submitted by the user

[1129] Output: Reservation information stored in the database, reservation confirmation email

[1130] Step 10:

[1131] User (device): Visits the restaurant at the reserved date and time and enjoys the provided meal.

[1132] Input: Reservation date and time and number of people

[1133] Output: Get service

[1134] Step 11:

[1135] Restaurant owner (device): After providing the service, mark the reservation as "completed" on the management screen and update the inventory information.

[1136] Input: Completed reservation information

[1137] Output: Updated inventory information

[1138] Step 12:

[1139] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[1140] Input: Completed reservation information

[1141] Output: Calculated fees and donations made

[1142] Step 13:

[1143] Server: Stores user usage history, reservation information, and feedback as learning data and updates the generative AI model.

[1144] Input: User usage history, reservation information, feedback

[1145] Output: Highly accurate recommendations from an updated generative AI model

[1146] (Application example 1)

[1147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1148] Inventory management at restaurants presents a major challenge to efficient operation, with issues such as excess or shortage of inventory and rising disposal costs. It is also difficult for users to grasp the inventory status of each restaurant, leaving them without a means to provide appropriate meals at reasonable prices. Furthermore, conventional recommendation functions have low accuracy in reflecting users' past usage history and search conditions, making it difficult to suggest the most suitable restaurant to the user. Solutions to these issues are highly desirable.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1150] In this invention, the server includes: a means for registering restaurant inventory status; a means for users to search for relevant inventory; a means for using a generative AI model to recommend restaurants suitable for users; a means for users to make reservations; a means for notifying restaurants and users of reservation information; a means for calculating a performance-based fee and donating a portion of the fee; a means for updating the generative AI model based on training data; a recommendation function that suggests optimal restaurants based on a user's usage history and prioritizes displaying restaurants that reflect the user's past usage history; a means for searching for restaurants based on ingredient price ranges and inventory status; and a means for updating inventory in real time and providing a search function that allows users to directly access inventory information. This allows restaurant owners to efficiently manage their inventory and users to find optimal restaurants based on their past usage history. Furthermore, a portion of the fees is donated, contributing to society.

[1151] "Means for registering inventory status" is a function that allows restaurant owners to input the amount and type of inventory they have on hand each day into the system and store it in a database.

[1152] "A means for users to search for relevant inventory" is a function that searches for inventory information of restaurants that meet the conditions specified by the user and displays appropriate results.

[1153] "Means of using a generative AI model to recommend restaurants suitable for users" is a function that enables a generative AI model to recommend the most suitable restaurant based on the user's past usage history and search conditions.

[1154] "Means for users to make reservations" refers to a function that allows users to secure inventory at the store they select and complete the reservation at the desired date and time.

[1155] The "means for notifying the restaurant and user of reservation information" is a function for notifying the restaurant owner and user of confirmed reservation information by e-mail or the like.

[1156] The "means of calculating commissions based on success and donating a portion of them" is a function that calculates commissions based on completed reservations and automatically donates a portion of them to social contribution activities.

[1157] "Means for updating the generative AI model based on training data" refers to a function that applies training data to improve the accuracy of the generative AI model using collected user usage history and feedback.

[1158] The "recommendation function that suggests the most suitable restaurant based on the user's usage history" is a function that automatically suggests the most suitable restaurant based on the user's past usage data.

[1159] "A means of preferentially displaying restaurants that reflect past usage history" is a function that allows restaurants that a user has used in the past to be preferentially displayed in search results.

[1160] "A means to search for restaurants based on the price range and availability of ingredients" is a function that searches for restaurant inventory information and displays appropriate results based on the user's budget or specific ingredients.

[1161] The "search function that updates inventory in real time and allows users to directly access inventory information" is a function that updates restaurant inventory information in real time and allows users to access and check that information.

[1162] The system for realizing this invention includes the following programs and processes: The server, the restaurant owner's terminal, and the user's terminal work in cooperation to manage restaurant inventory and provide services to users.

[1163] Stock status registration

[1164] Restaurant owners log in to the system's management screen using a terminal and register their inventory status. At this time, they input the menu items they will offer and the quantities they will provide. The server receives this information and stores it in a database.

[1165] Inventory Search

[1166] Users access the system using a terminal and input conditions (e.g., geographical information, price range, type of ingredients, etc.) to search for inventory. The server queries the database based on the conditions entered and provides inventory information for relevant restaurants. For example, if a user searches for "curry under 700 yen in Shibuya Ward," a list of appropriate restaurants will be displayed.

[1167] Recommendation function

[1168] The server uses a generative AI model to recommend the best restaurants based on the user's past visit history and search criteria. This generative AI model takes into account the user's preferences and past choices and prioritizes the display of restaurants that are most relevant.

[1169] Making a reservation

[1170] The user selects the restaurant and menu they want and makes a reservation. The user enters the date, time, and number of people in the reservation form and confirms it. The server stores the information in a database and sends a confirmation email to the user and the restaurant.

[1171] Real-time inventory updates

[1172] The server updates restaurant inventory information in real time, allowing users to directly access the latest inventory information, so users are always provided with the latest inventory status.

[1173] Fee calculations and donations

[1174] After the reservation is completed, the server calculates the commission fee and donates a portion of it, which not only benefits the restaurant and the user but also contributes to society at the same time.

[1175] Update training data

[1176] The server stores the user's usage history and reservation information as learning data and periodically updates the generative AI model, which improves the accuracy of future recommendations.

[1177] Hardware and software used

[1178] The system uses software such as Python, databases (e.g., MySQL), generative AI models (e.g., TensorFlow, PyTorch), and an SMTP server. The server and user and restaurant terminals are connected to the Internet, enabling real-time data processing.

[1179] Examples of concrete examples and prompts

[1180] As a specific example, when a restaurant owner registers "I have 10 extra servings of chicken curry" on the management screen, the information is immediately sent to the server and saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server searches for restaurants that meet the criteria and uses a generative AI model to prioritize and display a list of restaurants that take the user's past history into consideration. When the user reserves "chicken curry," a confirmation email is sent, a commission fee is calculated, and a portion of that fee is automatically donated.

[1181] Example prompt sentence:

[1182] "For users who have ordered curry in the past, please prioritize displaying restaurants in Shibuya Ward that serve curry for under 700 yen."

[1183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1184] Step 1:

[1185] Stock status registration

[1186] Input: The restaurant owner uses the terminal to input the menu name "Chicken Curry" and the quantity "10 servings."

[1187] Specific actions: Fill out the numbered inventory registration form and press the "Register" button.

[1188] Data processing / calculation: Generate input information in JSON format and send it to the server.

[1189] Output: The server stores the received data in the database and the inventory information is updated.

[1190] Step 2:

[1191] Inventory Search

[1192] Input: The user uses a terminal to enter the search criteria "Curry under 700 yen in Shibuya Ward."

[1193] Specific actions: Enter search criteria in the condition input form and press the "Search" button.

[1194] Data processing / calculation: The server queries the database based on the search criteria and searches for inventory information that matches the criteria.

[1195] Output: Search results will be displayed on the user's device, along with the restaurants and their inventory information.

[1196] Step 3:

[1197] Recommendation function

[1198] Input: User's past usage history and current search criteria.

[1199] Specific operation: The server's generated AI model references the user's history and search criteria to recommend the most suitable restaurant.

[1200] Data processing / calculation: The AI ​​model scores historical data and search data to create a list of restaurants in order of relevance.

[1201] Output: The recommended restaurant list is displayed on the user's device.

[1202] Step 4:

[1203] Making a reservation

[1204] Input: The user selects a restaurant and menu (e.g., "chicken curry"), and enters the desired date and time ("December 31, 2022, 18:00") and the number of people ("2").

[1205] Specific actions: Enter the required information in the reservation form and press the "Confirm reservation" button.

[1206] Data processing / calculation: The server stores the reservation information in a database and generates a confirmation email.

[1207] Output: A reservation confirmation email is sent to the user and restaurant.

[1208] Step 5:

[1209] Real-time inventory updates

[1210] Input: Reservation information and inventory consumption status.

[1211] What it does: The server periodically updates the database to maintain the latest inventory status.

[1212] Data processing / calculation: When a reservation is confirmed, the corresponding inventory is reduced.

[1213] Output: The latest inventory information is shared within the system and can be accessed by users in real time.

[1214] Step 6:

[1215] Fee calculations and donations

[1216] Input: Completed reservation information.

[1217] Specific operation: The server calculates the commission based on the information of each reservation and allocates a portion of it to donations.

[1218] Data processing / calculation: Calculate performance-based fees and determine donation amounts.

[1219] Output: The donation process is executed and a report of the donation amount is generated.

[1220] Step 7:

[1221] Update training data

[1222] Input: User usage history, booking information, feedback.

[1223] Specific operation: The server collects this data and updates the learning data of the generative AI model.

[1224] Data processing / calculation: New usage data is fed back into the AI ​​model to retrain the model.

[1225] Output: The updated generative AI model will improve the accuracy of future recommendations.

[1226] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1227] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for using a generative AI model to recommend restaurants suitable for users, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission based on performance and donating a portion of it, and a means for updating the generative AI model based on learning data. In addition, by incorporating an emotion engine that recognizes user emotions, the system further optimizes recommendations for individual users.

[1228] System program and processing description

[1229] Restaurant owners register inventory status

[1230] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[1231] Server: Verify authentication information and display admin panel to owner.

[1232] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[1233] Restaurant owner (device): After completing the input, click the "Register" button.

[1234] Server: Saves the entered inventory information to the database and updates the inventory management table.

[1235] Users search for stores

[1236] User (Terminal): The user accesses the system and logs in by entering a username and password.

[1237] Server: Verify credentials and view user-specific dashboard.

[1238] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[1239] Server: Searches the database for relevant store information based on the entered search criteria.

[1240] Server: Displays search results to the user in a list format.

[1241] Emotion engine recognizes user emotions

[1242] Server: Uses the emotion engine to analyze emotional data (e.g., emotional states obtained through facial expression analysis or voice recognition) when a user accesses the service from their device.

[1243] Server: Stores user sentiment data and reflects it in future recommendations.

[1244] Store recommendations using the recommendation function

[1245] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[1246] User (device): Select the store of interest from the suggested recommendation list.

[1247] A user makes a reservation

[1248] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[1249] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1250] A user visits the store and receives a service

[1251] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[1252] Restaurant owners: Providing meals to users.

[1253] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[1254] Provider Fee Processing

[1255] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[1256] System Training

[1257] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[1258] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[1259] Specific examples

[1260] Specifically, when a restaurant owner registers "I have 10 servings of chicken curry left over" on the management screen, that information is sent to the server and stored in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria, analyzes their current emotional state using an emotion engine, and uses a generative AI model to recommend the most suitable restaurant, taking past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee. The system also learns from usage history, including emotional data, to improve the accuracy of recommendations for future visits.

[1261] This allows restaurants to efficiently utilize their inventory and users to enjoy meals at more personalized and affordable prices. In addition, a portion of the fees is donated to social contribution activities.

[1262] The processing flow will be explained below.

[1263] Step 1:

[1264] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[1265] Server: Verify authentication information and display admin panel to owner.

[1266] Step 2:

[1267] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[1268] Restaurant owner (device): After completing the input, click the "Register" button.

[1269] Server: Saves the entered inventory information to the database and updates the inventory management table.

[1270] Step 3:

[1271] User (device): The user accesses the site and logs in by entering their username and password.

[1272] Server: Verify credentials and view user-specific dashboard.

[1273] Step 4:

[1274] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[1275] Server: Searches the database for relevant store information based on the entered search criteria.

[1276] Server: Displays search results to the user in a list format.

[1277] Step 5:

[1278] Server: Using the emotion engine, analyzes emotion data acquired from the camera and microphone on the user's device. For example, by analyzing the user's facial expressions and tone of voice, it can recognize emotions such as "happiness," "tiredness," and "excitement."

[1279] Server: Stores the recognized emotion data and uses it for future recommendations.

[1280] Step 6:

[1281] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on a user's past usage history and current emotional state. For example, it recommends bright and lively stores to a user in a "happy" state, and quiet and relaxing stores to a user in a "tired" state.

[1282] Step 7:

[1283] User (device): Select the store of interest from the presented recommendation list.

[1284] Step 8:

[1285] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[1286] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[1287] User (device): After checking the input information, press the "Confirm reservation" button.

[1288] Step 9:

[1289] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1290] Step 10:

[1291] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[1292] Restaurant owners: Providing meals to users.

[1293] Step 11:

[1294] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[1295] Step 12:

[1296] Server: Calculates commission based on completed booking information.

[1297] Server: The fee will be automatically deducted from the restaurant owner.

[1298] Server: Donate a portion of your fees to hunger relief organizations.

[1299] Step 13:

[1300] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[1301] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[1302] Example 2

[1303] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1304] Conventional restaurant inventory management systems provide functions such as inventory information registration, search, and reservation, but lack a mechanism for recommending optimal restaurants based on the user's emotional state. As a result, the user experience cannot be improved and restaurant inventory management is inefficient. Furthermore, there is no system for calculating fees or making donations that takes social contributions into consideration, making these systems insufficient for realizing a sustainable society.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1306] In this invention, the server includes means for registering restaurant inventory status, means for users to search for relevant inventory, means for recommending restaurants suitable for users using a generative AI model, means for users to make reservations, means for notifying the restaurant and user of the reservation information, means for calculating a performance-based fee and donating a portion of it, means for updating the generative AI model based on training data, and means for acquiring user emotion data and optimizing recommendations based on that data. This makes it possible to suggest optimal restaurants based on the user's emotional state, improving the user experience and streamlining restaurant inventory management, and realizing a fee calculation and donation system that contributes to society.

[1307] The "means for registering restaurant inventory status" is a function that allows restaurant owners to input current inventory information into the system and store it in the database.

[1308] "Means for users to search for relevant inventory" refers to the means by which users can search for inventory information within the system based on specific conditions and find relevant stores.

[1309] "Means of using a generative AI model to recommend stores suitable for users" is a function that uses a generative AI model to analyze a user's past usage history and emotional data, and recommends the most suitable store.

[1310] "Means for users to make reservations" refers to the means by which users make reservations for the store they have selected via the system.

[1311] "Means for notifying restaurants and users of reservation information" is a function for automatically notifying restaurant owners and users of the information when a reservation is confirmed.

[1312] The "means for calculating commissions based on success and donating a portion of the commission" is a function that executes a process for calculating commissions and donating a portion of the commissions when a reservation is successful.

[1313] "Means for updating the generative AI model based on learning data" refers to a means for regularly updating the generative AI model based on user usage history, feedback, emotional data, etc., in order to improve accuracy.

[1314] "Means of obtaining user emotional data and optimizing recommendations based on that" refers to a function that obtains emotional data from the user's facial expressions, voice, etc., and uses that information to further individualize recommendations.

[1315] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. The system mainly includes the following components: a restaurant owner terminal, a user terminal, and a server.

[1316] Sequence for restaurant owner to register inventory status

[1317] Restaurant owner (terminal): The restaurant owner logs in to the system's management screen and enters their username and password. If authentication is successful, the owner's dedicated management dashboard will be displayed. This dashboard has an input form for inventory status, and the owner enters information such as "product name," "quantity," and "discount price," and clicks the "Register" button.

[1318] Server: The server receives the authentication information and the entered inventory information and saves it in the inventory management table of the database. For example, if the entered information is "Product name: Chicken curry", "Quantity: 10", and "Discount price: 700 yen (regular price 1,000 yen)", this will be saved in the database.

[1319] A sequence where a user searches for a store

[1320] User (terminal): The user accesses the system and logs in by entering their username and password. After successful authentication, a dashboard is displayed. This dashboard has a search bar, and the user can enter conditions such as "area," "menu," and "budget," and then click the "Search" button.

[1321] Server: The server queries the database based on the entered criteria and displays the corresponding restaurant information in a list format to the user. For example, search results are displayed based on criteria such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen."

[1322] Emotion engine recognizes user emotions

[1323] Server: Using the emotion engine, the server acquires emotional data when the user accesses the app from their device. It performs facial and voice analysis to analyze the user's emotional state. The acquired emotional data is stored in a database and is reflected in future recommendations.

[1324] Store recommendations using the recommendation function

[1325] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[1326] User (device): The user selects the store of interest from the proposed recommendation list.

[1327] A user makes a reservation

[1328] User (device): The user checks the "Leftover Ingredients Menu" on the details page of the selected restaurant, enters the "Date and Time" and "Number of people" in the reservation form, and presses the "Confirm Reservation" button.

[1329] Server: Saves the reservation information in a database and sends a confirmation email to the user. At the same time, it notifies the restaurant owner of the reservation information.

[1330] Post-service processing

[1331] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[1332] Restaurant owners: Serve food to customers, mark the reservation as "completed" in the extranet, and update inventory information after serving.

[1333] Fee calculation and donations

[1334] Server: Calculates commission based on successful bookings and donates a portion of the commission to hunger relief organizations.

[1335] System Training

[1336] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data and updates the generative AI model and sentiment engine, thereby improving the accuracy of future recommendations.

[1337] Specific examples

[1338] For example, if a restaurant owner registers that they have 10 extra servings of chicken curry, that information is saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that meet the criteria, analyzing the user's current emotional state using an emotion engine. The generative AI model recommends the most suitable restaurant, taking into account past usage history, and the user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The server then calculates a commission-based fee and initiates a process to execute a portion of that fee.

[1339] Example prompt sentence:

[1340] "Analyze the user's emotional state and recommend the best store."

[1341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1342] Step 1: Login process

[1343] Restaurant owner (terminal): The owner accesses the system's management screen and enters their username and password.

[1344] Server: Receives the entered authentication information and checks it against the database. If successful, displays the owner's personal admin dashboard.

[1345] Input: Username, Password

[1346] Output: Authentication result, admin dashboard screen

[1347] Step 2: Enter inventory information

[1348] Restaurant owner (device): After logging in, enter information such as product name, quantity, discount price, etc. into the inventory entry form on the management screen, and click the "Register" button.

[1349] Input: Product name (e.g., chicken curry), quantity (e.g., 10), discount price (e.g., 700 yen)

[1350] Output: Inventory information transmission request

[1351] Step 3: Save inventory information

[1352] Server: Receives the entered inventory information and saves it in the inventory management table of the database. If this process is successful, it sends a registration completion message.

[1353] Input: Inventory information (product name, quantity, discount price)

[1354] Output: Registration complete message

[1355] Step 4: User Login

[1356] User (terminal): Accesses the system and attempts to log in by entering a username and password.

[1357] Server: Checks the entered credentials against the database and, if successful, displays a dashboard specific to the user.

[1358] Input: Username, Password

[1359] Output: Authentication results, user dashboard

[1360] Step 5: Enter search criteria

[1361] User (device): Enter conditions such as "area," "menu," and "budget" in the search bar on the dashboard and click the "Search" button.

[1362] Input: Area (e.g. Shibuya), Menu (e.g. Curry), Budget (e.g. Under 700 yen)

[1363] Output: Search request

[1364] Step 6: Viewing search results

[1365] Server: Queries the database based on the entered search criteria and displays matching store information to the user in a list format.

[1366] Input: Search criteria

[1367] Output: A list of matching stores

[1368] Step 7: Obtaining emotion data

[1369] Server: Uses the emotion engine to obtain emotional data when the user accesses the app from their device. It determines the user's emotional state through facial expression and voice analysis.

[1370] Input: User video and audio data

[1371] Output: Emotion data (e.g. happy, tired)

[1372] Step 8: Storing Emotion Data

[1373] Server: Stores the acquired emotion data in a database and updates the user's profile.

[1374] Input: Emotion data

[1375] Output: Updated user profile

[1376] Step 9: Recommendation Generation

[1377] Server: Using a generative AI model, it suggests the most suitable store based on the user's past usage history, current emotional state, and search criteria.

[1378] Input: User usage history, emotion data, search conditions

[1379] Output: Recommendation list

[1380] Step 10: View recommendation results

[1381] User (device): Select the store of interest from the suggested recommendation list.

[1382] Input: Recommendation list

[1383] Output: Selected store information

[1384] Step 11: Enter reservation information

[1385] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected store, enter the required information such as "Date and Time" and "Number of people" in the reservation form, and press the "Confirm Reservation" button.

[1386] Input: Date, time, number of people, selected menu

[1387] Output: Reservation information sending request

[1388] Step 12: Save and notify reservation information

[1389] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1390] Input: Reservation information

[1391] Output: Confirmation email, reservation information notification

[1392] Step 13: Update your visit record

[1393] Restaurant owner: A user visits the restaurant and uses the reserved menu. After providing the service, the user marks the reservation as "completed" on the management screen and updates the inventory information.

[1394] Input: Visit confirmation, menu information

[1395] Output: Updated inventory information

[1396] Step 14: Fee Calculation and Donation

[1397] Server: Calculates commission-based fees based on completed booking information and donates a portion of the fees.

[1398] Input: Completed reservation information

[1399] Output: Fee calculation results, donation process execution

[1400] Step 15: Save the training data

[1401] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[1402] Input: Usage history, reservation information, feedback, emotional data

[1403] Output: Saved training data

[1404] Step 16: Update the generative AI model

[1405] Server: Uses the saved learning data to update the generative AI model and emotion engine, improving the accuracy of future recommendations.

[1406] Input: Training data

[1407] Output: Updated generative AI model and emotion engine

[1408] (Application example 2)

[1409] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1410] Restaurants are required to manage inventory, propose efficient menus, and provide personalized service based on customers' emotional states. Conventional systems are limited in their ability to collect inventory information and propose appropriate menus, making it particularly difficult to optimize services based on customer emotions. This results in problems such as poor customer satisfaction and a tendency for inventory waste. To solve this problem, a system that integrates inventory management and customer emotion recognition is needed.

[1411] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for registering restaurant inventory status, a means for a user to search for relevant inventory, a means for recommending restaurants suitable for the user using a generative AI model, a means for a user to make a reservation, a means for notifying the restaurant and the user of the reservation information, a means for calculating a performance-based fee and donating a portion of it, a means for updating the generative AI model based on training data, a means incorporating an emotion engine that recognizes the customer's emotional state and presents appropriate restaurants and menus, and a means for presenting information to the user via smart glasses. This enables personalized menu suggestions and inventory management according to the customer's emotional state.

[1412] "Means for registering restaurant inventory status" refers to a system or function for inputting the current inventory status of ingredients and products held by a restaurant and storing it in a database.

[1413] "Means for users to search for relevant inventory" refers to a system or function that allows users to input specific conditions or keywords to search for restaurant inventory information and obtain the necessary information.

[1414] "Means of using generative AI models to recommend restaurants suitable for users" refers to systems and functions that use machine learning and AI technology to suggest optimal restaurants and menus based on a user's preferences and history.

[1415] "Means for users to make reservations" refers to systems or functions that allow users to make reservations in advance for the store and menu they have selected by specifying the date, time, and number of people.

[1416] "Means for notifying the restaurant and user of reservation information" refers to a system or function that notifies the restaurant and user of the details of a confirmed reservation and allows them to confirm it.

[1417] "Means for calculating commission fees based on success and donating a portion of the fee" refers to a system or function that calculates the fee incurred when a user completes a reservation and donates a portion of that fee to social contribution activities, etc.

[1418] "Means for updating the generative AI model based on learning data" refers to systems or functions that periodically update the generative AI model based on user usage history and feedback, thereby improving the accuracy of recommendations.

[1419] "Means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus" refers to systems and functions that use technologies such as facial expression analysis and voice recognition to determine the user's current emotional state and suggest the most appropriate stores and menus accordingly.

[1420] "Means for presenting information to users via smart glasses" refers to a system or function that displays information on the display of smart glasses, allowing users to visually confirm and operate the device.

[1421] This invention is a system for efficiently managing restaurant inventory and providing personalized menu suggestions based on the emotional state of customers. The system is comprised of several main functions, each of which works in conjunction with the other functions.

[1422] Key System Features

[1423] 1. Register restaurant inventory status via:

[1424] The server provides a management screen designed to allow restaurant owners to input inventory information for their stores. Owners input information such as product names, quantities, and prices, and save it in a database. This information is updated in real time.

[1425] 2. How users can search for relevant inventory:

[1426] Users access the system through a web browser or mobile application and search for inventory information by entering keywords or conditions in the search bar. The server then retrieves the relevant inventory information from the database and displays it to the user.

[1427] 3. Using generative AI models to recommend suitable stores to users:

[1428] The server uses a generative AI model to recommend appropriate restaurants and menus based on the user's past usage history and current emotional state. The generative AI model uses machine learning algorithms to analyze the user's preferences and behavioral patterns.

[1429] 4. How users can make reservations:

[1430] Users select a restaurant from the reservation screen provided by the server, enter the desired date and time and number of people, and make a reservation. The reservation information is saved in a database.

[1431] 5. Means of notifying restaurants and users of reservation information:

[1432] Once a reservation is confirmed, the server will send an email or push notification to notify the restaurant and user of the reservation, providing two-way confirmation.

[1433] 6. A means of calculating contingency fees and donating a portion of them:

[1434] The server calculates the commission that will be incurred if the reservation is successful and has the function of donating a portion of it to social contribution activities. The commission calculation is done automatically, and the donation is also carried out according to the set rules.

[1435] 7. How to update generative AI models based on training data:

[1436] The server collects user usage history, feedback, and sentiment data and periodically updates the generative AI model to improve the accuracy of recommendations.

[1437] 8. A means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus:

[1438] The server uses smart glasses and other input devices to recognize the customer's emotional state from their facial expressions and voice, and then suggests appropriate menus and restaurants based on that. The emotion engine uses Affectiva and Microsoft Azure Emotion API.

[1439] 9. Means of presenting information to a user via smart glasses:

[1440] The server displays information on the smart glasses' display, allowing users to visually confirm and operate the device, which allows users to easily make menu suggestions and make reservations.

[1441] Specific examples

[1442] For example, if a cafe in Shibuya Ward has leftover chicken curry, the restaurant owner can register that information in the system. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays the relevant inventory information and simultaneously analyzes the user's current emotional state using an emotion engine. For example, if the user's emotional state is "I want to relax," the server will recommend restaurants with a quiet atmosphere and relaxing menus. The user can check this information through the smart glasses and make a reservation on the spot.

[1443] Prompt Sentence Examples

[1444] "Check out today's availability and recommend the perfect menu for your relaxing guest."

[1445] "Please suggest a dessert menu that suits customers with happy emotions at a cafe in Shibuya Ward."

[1446] In this way, a system that integrates inventory management and sentiment analysis can help restaurants operate more efficiently and improve customer satisfaction.

[1447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1448] Step 1:

[1449] The server authenticates the restaurant owner by allowing them to access the management screen and enter their username and password. If authentication is successful, the management screen is displayed, allowing the owner to enter inventory status. Specifically, the owner enters information such as product name, quantity, and price into the input form and clicks the "Register" button. The server saves the entered inventory information in the database and updates the inventory management table. The input is the restaurant owner's authentication information and inventory data, and the output is the inventory information saved in the database.

[1450] Step 2:

[1451] Users access the system through a web browser or mobile application and log in by entering their username and password. If authentication is successful, the dashboard is displayed. The user enters keywords or conditions in the search bar (e.g., "Area: Shibuya," "Menu: Curry," "Budget: Under 700 yen") and clicks the "Search" button. The server retrieves relevant inventory information from the database based on the entered search conditions and displays it to the user in list form. The input is the username, password, and search conditions, and the output is a list of relevant inventory information that is displayed to the user.

[1452] Step 3:

[1453] The server receives data from smart glasses or other input devices to obtain emotional data when a customer accesses the system. The emotional data is analyzed using facial expression analysis and voice recognition technology. The emotion engine identifies the user's emotional state based on the analyzed emotional data and passes that information to a generative AI model. The input is the emotional data obtained from the smart glasses or other devices, and the output is the identified emotional state.

[1454] Step 4:

[1455] The server uses a generative AI model to recommend optimal restaurants and menus to the user. The generative AI model generates optimal restaurants and menus by taking into account the user's past usage history, current emotional state, and search criteria. The recommendation results are displayed on the smart glasses' display so that the user can visually confirm them. The input is the user's usage history, emotional state, and search criteria, and the output is a list of recommended restaurants and menus.

[1456] Step 5:

[1457] The user uses the smart glasses to check the suggested restaurants and menus and make a reservation on the spot. They select a restaurant from the reservation screen, enter the desired date and time and number of people, and press the "Confirm reservation" button. The server saves the reservation information in a database and notifies the restaurant and the user of the reservation details. The input is the reservation information entered by the user, and the output is the reservation information saved in the database and a notification message.

[1458] Step 6:

[1459] The user visits the restaurant at the reserved date and time and uses the discount menu. After serving the food, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. The server reflects this information in the database. The input is the restaurant's completed serving information, and the output is the updated inventory information.

[1460] Step 7:

[1461] The server calculates a commission based on the completed reservation information and donates a portion of it to social contribution activities according to set rules. The calculated commission and donation amount are recorded in a database. The input is the completed reservation information, and the output is the calculated commission and donation amount.

[1462] Step 8:

[1463] The server collects the user's usage history, feedback, and emotional data and periodically updates the generative AI model. This improves the accuracy of recommendations, making them more appropriate for future use. The input is the user's usage history, feedback, and emotional data, and the output is the updated generative AI model.

[1464] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1465] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1466] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1467] [Fourth embodiment]

[1468] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1469] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1470] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1471] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1472] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1473] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1474] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1475] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1476] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1477] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1478] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1479] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1480] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1481] The present invention relates to a system for registering restaurant inventory status and providing that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for recommending restaurants suitable for users using a generative AI model, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission-based fee and donating a portion of it, and a means for updating the generative AI model based on training data.

[1482] System program and processing description

[1483] Restaurant owners register inventory status

[1484] Restaurant owner (terminal): The owner logs in to the system's management screen and registers the inventory status. For example, the owner enters information such as "I have 10 servings of chicken curry left over."

[1485] Server: Saves the registered information to the database and updates the inventory information.

[1486] Users search for stores

[1487] User (terminal): The user accesses the system and logs in. They search for "curry under 700 yen in Shibuya Ward."

[1488] Server: Based on the user's search criteria, the server searches the database for inventory information for the relevant restaurants and displays the results.

[1489] Store recommendations using the recommendation function

[1490] Server: Using a generative AI model, the server recommends the most suitable restaurant based on the user's past usage history and search criteria. For example, if a user has ordered curry before, the server will prioritize restaurants that serve curry.

[1491] User (device): Select the store of interest from the list of suggested stores.

[1492] A user makes a reservation

[1493] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[1494] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1495] A user visits the store and receives a service

[1496] User: Visits the restaurant on the reserved date and time and receives the discounted meal.

[1497] Restaurant owners: Serve meals to customers, and after the service is complete, mark the reservation as "completed" in the admin panel and update inventory information.

[1498] Provider Fee Processing

[1499] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[1500] System Training

[1501] Server: Stores user usage history, reservation information, feedback, etc. as learning data and updates the generative AI model, thereby improving the accuracy of future recommendations.

[1502] Specific examples

[1503] Specifically, when a restaurant owner registers "I have 10 extra chicken curry servings" on the management screen, that information is sent to the server and saved in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria and uses a generative AI model to suggest restaurants that take past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee.

[1504] This system allows restaurants to efficiently utilize their inventory, allows users to enjoy meals at a reasonable price, and even donates a portion of the fees to social contribution activities.

[1505] The processing flow will be explained below.

[1506] Step 1:

[1507] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[1508] Server: Verify authentication information and display admin panel to owner.

[1509] Step 2:

[1510] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[1511] Restaurant owner (device): After completing the input, click the "Register" button.

[1512] Server: Saves the entered inventory information to the database and updates the inventory management table.

[1513] Step 3:

[1514] User (device): The user accesses the site and logs in by entering their username and password.

[1515] Server: Verify credentials and view user-specific dashboard.

[1516] Step 4:

[1517] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[1518] Server: Searches the database for relevant store information based on the entered search criteria.

[1519] Server: Displays search results to the user in a list format.

[1520] Step 5:

[1521] Server: Uses generative AI models to analyze users' past usage history and preferences.

[1522] Server: Based on the search results, the server displays a list of recommended stores to the user.

[1523] Step 6:

[1524] User (device): Select the store of interest from the presented recommendation list.

[1525] Step 7:

[1526] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[1527] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[1528] User (device): After checking the input information, press the "Confirm reservation" button.

[1529] Step 8:

[1530] Server: Saves the reservation information in a database and sends a confirmation email to the user.

[1531] Server: Notifies restaurant owner of reservation information.

[1532] Step 9:

[1533] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[1534] Restaurant owners: Providing meals to users.

[1535] Step 10:

[1536] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[1537] Step 11:

[1538] Server: Calculates commission based on completed booking information.

[1539] Server: The fee will be automatically deducted from the restaurant owner.

[1540] Server: Donate a portion of your fees to hunger relief organizations.

[1541] Step 12:

[1542] Server: Stores user usage history, reservation information, and feedback as learning data.

[1543] Server: Updates the generative AI model to improve the accuracy of future recommendations.

[1544] Example 1

[1545] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1546] Effective management of excess inventory in restaurants and the provision of appropriate services to users based on that inventory are key challenges. There is also a lack of means to quickly respond to user requests, improve restaurant management efficiency, and engage in social contribution activities. In particular, there is a need for improved recommendation accuracy that takes into account users' past usage history and search criteria.

[1547] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1548] In this invention, the server includes a means for registering restaurant inventory status, a means for users to search for relevant inventory, and a means for using a generative AI model to recommend suitable establishments to users. This allows restaurants to effectively utilize their inventory and users to enjoy meals at reasonable prices. Furthermore, by generating prompts and providing recommendation results, the system can respond quickly to user requests and improve usability. Furthermore, by calculating commissions based on success and donating a portion of the commissions, it is possible to contribute to society.

[1549] A "restaurant" is a facility that provides food and beverages.

[1550] "Inventory status" refers to information that indicates the quantity and condition of food and ingredients held by a restaurant.

[1551] "User" means an individual or organization that uses the system to search restaurant inventory information and make reservations.

[1552] A "generative AI model" is an artificial intelligence model that learns patterns from large amounts of data and recommends businesses that are suitable for users.

[1553] A "prompt sentence" is an instruction sentence input into a generative AI model, and is generated based on the user's past usage history and search conditions.

[1554] "Recommendation" refers to the use of generative AI models to recommend suitable businesses and products to users.

[1555] "Reservation information" is data containing detailed information about a reservation made by a user.

[1556] A "success-based fee" is a fee calculated based on the success of the services provided.

[1557] "Donation" means providing a portion of a commission fee for social contribution activities, etc.

[1558] A "database" is an information recording medium for storing and managing restaurant inventory and reservation information.

[1559] The system for implementing this invention is configured using the following hardware and software. The main hardware used includes a server computer and terminals for users and restaurant owners. The software used is AWS EC2 (server infrastructure), MySQL (database management system), GPT-4 (generative AI model), and React (front-end development environment).

[1560] Registering restaurant inventory status

[1561] Using a restaurant owner's device, the owner logs into the system's management screen and, as a specific example, enters "I have 10 servings of chicken curry left" when registering inventory status. The information sent from the device is received by the server and stored in a MySQL database. The server then updates the inventory information to reflect the latest inventory status.

[1562] Users search for stores

[1563] A user accesses the system using a terminal and logs in. By entering "curry under 700 yen in Shibuya Ward" into the search bar and pressing the search button, the server searches for relevant inventory information in the database based on these conditions. Information on stores that match the conditions is collected and displayed on the user's terminal.

[1564] Store recommendations using the recommendation function

[1565] Based on the search results, the server sends a prompt to the generative AI model (GPT-4). An example of a specific prompt would be, "Find out whether the user has ordered curry in the past, and based on that, please make a list of recommended curry restaurants in Shibuya Ward that cost under 700 yen." The generative AI model analyzes this prompt and generates a list of restaurants that are optimal for the user. The server sends this list to the user's device and displays it.

[1566] A user makes a reservation

[1567] On the displayed restaurant details page, the user selects "Chicken Curry" and clicks the "Book Now" button. By entering the "Date and Time" and "Number of People" in the reservation form and pressing the "Confirm Reservation" button, the reservation information is sent to the server. The server saves this information in a MySQL database and sends a reservation confirmation email to the user and the restaurant owner.

[1568] A user visits the store and receives a service

[1569] The user visits the restaurant at the reserved date and time and enjoys a meal based on the reservation details. After providing the service, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. This information is sent to the server and the database is updated.

[1570] Success-based commission processing

[1571] The server calculates commissions based on the completed reservation information and donates a portion of the commission to a hunger relief organization. This calculation is also done automatically on the server.

[1572] System Training

[1573] The server stores user usage history, reservation information, feedback, etc. as learning data, which is used to update the generative AI model and improve the accuracy of future recommendations.

[1574] For example, you can use a prompt such as, "Find out whether the user has ordered curry in the past, and based on that, make a list of recommended curry restaurants in Shibuya Ward that cost less than 700 yen." This system allows restaurants to use their inventory efficiently and users to enjoy meals at reasonable prices. It also creates a system where a portion of the commission is used for social contribution activities.

[1575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1576] Step 1:

[1577] Restaurant owner (terminal): Logs in to the system's management screen and enters "I have 10 servings of chicken curry left" into the inventory registration form. The entered data is sent to the server.

[1578] Input: Inventory information entered by the restaurant owner (e.g., 10 servings of chicken curry)

[1579] Output: Stock information sent to the server

[1580] Step 2:

[1581] Server: Stores the inventory information received from the device in a database.

[1582] Input: Inventory information sent by restaurant owner

[1583] Output: Stock information stored in a database

[1584] Step 3:

[1585] User (device): Access the system and log in. Enter "Curry under 700 yen in Shibuya Ward" in the search bar and press the search button.

[1586] Input: Search criteria (e.g. Shibuya-ku, under 700 yen, curry)

[1587] Output: The search query sent to the server

[1588] Step 4:

[1589] Server: Searches the database for relevant inventory information based on the received search criteria, collects inventory information, and generates search results.

[1590] Input: The search query sent to the server

[1591] Output: A list of stores searched

[1592] Step 5:

[1593] Server: Generates and sends prompts to the generative AI model (GPT-4) based on the user's past usage history and search conditions.

[1594] Input: User's past usage history, current search criteria

[1595] Output: Prompt (e.g., "Find out whether the user has ordered curry in the past, and based on that, list recommended curry restaurants in Shibuya Ward that cost less than 700 yen.")

[1596] Step 6:

[1597] Generative AI model: Analyzes the prompt text received and generates a list of stores that are best suited to the user.

[1598] Input: prompt statement

[1599] Output: Recommended store list

[1600] Step 7:

[1601] Server: Sends the generated list of recommended stores to the user's device.

[1602] Input: Recommended store list

[1603] Output: A list of recommended stores displayed on the user's device

[1604] Step 8:

[1605] User (device): From the displayed list of stores, check the details page of the store they are interested in, check the "Chicken Curry" option, and click the "Make a Reservation" button. Enter the "Date and Time" and "Number of people" in the reservation form and press the "Confirm Reservation" button.

[1606] Input: Reservation information (e.g. store, menu, date and time, number of people)

[1607] Output: Reservation information sent to the server

[1608] Step 9:

[1609] Server: Stores the received reservation information in a database and sends a reservation confirmation email to the user and restaurant owner.

[1610] Input: Reservation information submitted by the user

[1611] Output: Reservation information stored in the database, reservation confirmation email

[1612] Step 10:

[1613] User (device): Visits the restaurant at the reserved date and time and enjoys the provided meal.

[1614] Input: Reservation date and time and number of people

[1615] Output: Get service

[1616] Step 11:

[1617] Restaurant owner (device): After providing the service, mark the reservation as "completed" on the management screen and update the inventory information.

[1618] Input: Completed reservation information

[1619] Output: Updated inventory information

[1620] Step 12:

[1621] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[1622] Input: Completed reservation information

[1623] Output: Calculated fees and donations made

[1624] Step 13:

[1625] Server: Stores user usage history, reservation information, and feedback as learning data and updates the generative AI model.

[1626] Input: User usage history, reservation information, feedback

[1627] Output: Highly accurate recommendations from an updated generative AI model

[1628] (Application example 1)

[1629] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1630] Inventory management at restaurants presents a major challenge to efficient operation, with issues such as excess or shortage of inventory and rising disposal costs. It is also difficult for users to grasp the inventory status of each restaurant, leaving them without a means to provide appropriate meals at reasonable prices. Furthermore, conventional recommendation functions have low accuracy in reflecting users' past usage history and search conditions, making it difficult to suggest the most suitable restaurant to the user. Solutions to these issues are highly desirable.

[1631] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1632] In this invention, the server includes: a means for registering restaurant inventory status; a means for users to search for relevant inventory; a means for using a generative AI model to recommend restaurants suitable for users; a means for users to make reservations; a means for notifying restaurants and users of reservation information; a means for calculating a performance-based fee and donating a portion of the fee; a means for updating the generative AI model based on training data; a recommendation function that suggests optimal restaurants based on a user's usage history and prioritizes displaying restaurants that reflect the user's past usage history; a means for searching for restaurants based on ingredient price ranges and inventory status; and a means for updating inventory in real time and providing a search function that allows users to directly access inventory information. This allows restaurant owners to efficiently manage their inventory and users to find optimal restaurants based on their past usage history. Furthermore, a portion of the fees is donated, contributing to society.

[1633] "Means for registering inventory status" is a function that allows restaurant owners to input the amount and type of inventory they have on hand each day into the system and store it in a database.

[1634] "A means for users to search for relevant inventory" is a function that searches for inventory information of restaurants that meet the conditions specified by the user and displays appropriate results.

[1635] "Means of using a generative AI model to recommend restaurants suitable for users" is a function that enables a generative AI model to recommend the most suitable restaurant based on the user's past usage history and search conditions.

[1636] "Means for users to make reservations" refers to a function that allows users to secure inventory at the store they select and complete the reservation at the desired date and time.

[1637] The "means for notifying the restaurant and user of reservation information" is a function for notifying the restaurant owner and user of confirmed reservation information by e-mail or the like.

[1638] The "means of calculating commissions based on success and donating a portion of them" is a function that calculates commissions based on completed reservations and automatically donates a portion of them to social contribution activities.

[1639] "Means for updating the generative AI model based on training data" refers to a function that applies training data to improve the accuracy of the generative AI model using collected user usage history and feedback.

[1640] The "recommendation function that suggests the most suitable restaurant based on the user's usage history" is a function that automatically suggests the most suitable restaurant based on the user's past usage data.

[1641] "A means of preferentially displaying restaurants that reflect past usage history" is a function that allows restaurants that a user has used in the past to be preferentially displayed in search results.

[1642] "A means to search for restaurants based on the price range and availability of ingredients" is a function that searches for restaurant inventory information and displays appropriate results based on the user's budget or specific ingredients.

[1643] The "search function that updates inventory in real time and allows users to directly access inventory information" is a function that updates restaurant inventory information in real time and allows users to access and check that information.

[1644] The system for realizing this invention includes the following programs and processes: The server, the restaurant owner's terminal, and the user's terminal work in cooperation to manage restaurant inventory and provide services to users.

[1645] Stock status registration

[1646] Restaurant owners log in to the system's management screen using a terminal and register their inventory status. At this time, they input the menu items they will offer and the quantities they will provide. The server receives this information and stores it in a database.

[1647] Inventory Search

[1648] Users access the system using a terminal and input conditions (e.g., geographical information, price range, type of ingredients, etc.) to search for inventory. The server queries the database based on the conditions entered and provides inventory information for relevant restaurants. For example, if a user searches for "curry under 700 yen in Shibuya Ward," a list of appropriate restaurants will be displayed.

[1649] Recommendation function

[1650] The server uses a generative AI model to recommend the best restaurants based on the user's past visit history and search criteria. This generative AI model takes into account the user's preferences and past choices and prioritizes the display of restaurants that are most relevant.

[1651] Making a reservation

[1652] The user selects the restaurant and menu they want and makes a reservation. The user enters the date, time, and number of people in the reservation form and confirms it. The server stores the information in a database and sends a confirmation email to the user and the restaurant.

[1653] Real-time inventory updates

[1654] The server updates restaurant inventory information in real time, allowing users to directly access the latest inventory information, so users are always provided with the latest inventory status.

[1655] Fee calculations and donations

[1656] After the reservation is completed, the server calculates the commission fee and donates a portion of it, which not only benefits the restaurant and the user but also contributes to society at the same time.

[1657] Update training data

[1658] The server stores the user's usage history and reservation information as learning data and periodically updates the generative AI model, which improves the accuracy of future recommendations.

[1659] Hardware and software used

[1660] The system uses software such as Python, databases (e.g., MySQL), generative AI models (e.g., TensorFlow, PyTorch), and an SMTP server. The server and user and restaurant terminals are connected to the Internet, enabling real-time data processing.

[1661] Examples of concrete examples and prompts

[1662] As a specific example, when a restaurant owner registers "I have 10 extra servings of chicken curry" on the management screen, the information is immediately sent to the server and saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server searches for restaurants that meet the criteria and uses a generative AI model to prioritize and display a list of restaurants that take the user's past history into consideration. When the user reserves "chicken curry," a confirmation email is sent, a commission fee is calculated, and a portion of that fee is automatically donated.

[1663] Example prompt sentence:

[1664] "For users who have ordered curry in the past, please prioritize displaying restaurants in Shibuya Ward that serve curry for under 700 yen."

[1665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1666] Step 1:

[1667] Stock status registration

[1668] Input: The restaurant owner uses the terminal to input the menu name "Chicken Curry" and the quantity "10 servings."

[1669] Specific actions: Fill out the numbered inventory registration form and press the "Register" button.

[1670] Data processing / calculation: Generate input information in JSON format and send it to the server.

[1671] Output: The server stores the received data in the database and the inventory information is updated.

[1672] Step 2:

[1673] Inventory Search

[1674] Input: The user uses a terminal to enter the search criteria "Curry under 700 yen in Shibuya Ward."

[1675] Specific actions: Enter search criteria in the condition input form and press the "Search" button.

[1676] Data processing / calculation: The server queries the database based on the search criteria and searches for inventory information that matches the criteria.

[1677] Output: Search results will be displayed on the user's device, along with the restaurants and their inventory information.

[1678] Step 3:

[1679] Recommendation function

[1680] Input: User's past usage history and current search criteria.

[1681] Specific operation: The server's generated AI model references the user's history and search criteria to recommend the most suitable restaurant.

[1682] Data processing / calculation: The AI ​​model scores historical data and search data to create a list of restaurants in order of relevance.

[1683] Output: The recommended restaurant list is displayed on the user's device.

[1684] Step 4:

[1685] Making a reservation

[1686] Input: The user selects a restaurant and menu (e.g., "chicken curry"), and enters the desired date and time ("December 31, 2022, 18:00") and the number of people ("2").

[1687] Specific actions: Enter the required information in the reservation form and press the "Confirm reservation" button.

[1688] Data processing / calculation: The server stores the reservation information in a database and generates a confirmation email.

[1689] Output: A reservation confirmation email is sent to the user and restaurant.

[1690] Step 5:

[1691] Real-time inventory updates

[1692] Input: Reservation information and inventory consumption status.

[1693] What it does: The server periodically updates the database to maintain the latest inventory status.

[1694] Data processing / calculation: When a reservation is confirmed, the corresponding inventory is reduced.

[1695] Output: The latest inventory information is shared within the system and can be accessed by users in real time.

[1696] Step 6:

[1697] Fee calculations and donations

[1698] Input: Completed reservation information.

[1699] Specific operation: The server calculates the commission based on the information of each reservation and allocates a portion of it to donations.

[1700] Data processing / calculation: Calculate performance-based fees and determine donation amounts.

[1701] Output: The donation process is executed and a report of the donation amount is generated.

[1702] Step 7:

[1703] Update training data

[1704] Input: User usage history, booking information, feedback.

[1705] Specific operation: The server collects this data and updates the learning data of the generative AI model.

[1706] Data processing / calculation: New usage data is fed back into the AI ​​model to retrain the model.

[1707] Output: The updated generative AI model will improve the accuracy of future recommendations.

[1708] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1709] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. This system includes a means for restaurant owners to register inventory status, a means for users to search for relevant inventory, a means for using a generative AI model to recommend restaurants suitable for users, a means for users to make reservations, a means for notifying restaurants and users of reservation information, a means for calculating a commission based on performance and donating a portion of it, and a means for updating the generative AI model based on learning data. In addition, by incorporating an emotion engine that recognizes user emotions, the system further optimizes recommendations for individual users.

[1710] System program and processing description

[1711] Restaurant owners register inventory status

[1712] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[1713] Server: Verify authentication information and display admin panel to owner.

[1714] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[1715] Restaurant owner (device): After completing the input, click the "Register" button.

[1716] Server: Saves the entered inventory information to the database and updates the inventory management table.

[1717] Users search for stores

[1718] User (Terminal): The user accesses the system and logs in by entering a username and password.

[1719] Server: Verify credentials and view user-specific dashboard.

[1720] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[1721] Server: Searches the database for relevant store information based on the entered search criteria.

[1722] Server: Displays search results to the user in a list format.

[1723] Emotion engine recognizes user emotions

[1724] Server: Uses the emotion engine to analyze emotional data (e.g., emotional states obtained through facial expression analysis or voice recognition) when a user accesses the service from their device.

[1725] Server: Stores user sentiment data and reflects it in future recommendations.

[1726] Store recommendations using the recommendation function

[1727] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[1728] User (device): Select the store of interest from the suggested recommendation list.

[1729] A user makes a reservation

[1730] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected restaurant, select "Chicken Curry" and make a reservation. Enter the "Date and Time" and "Number of people" in the reservation form, confirm, and press the "Confirm Reservation" button.

[1731] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1732] A user visits the store and receives a service

[1733] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[1734] Restaurant owners: Providing meals to users.

[1735] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[1736] Provider Fee Processing

[1737] Server: Calculates commission based on completed bookings and donates a portion of the commission to hunger relief organizations.

[1738] System Training

[1739] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[1740] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[1741] Specific examples

[1742] Specifically, when a restaurant owner registers "I have 10 servings of chicken curry left over" on the management screen, that information is sent to the server and stored in a database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that match the criteria, analyzes their current emotional state using an emotion engine, and uses a generative AI model to recommend the most suitable restaurant, taking past usage history into consideration. The user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The restaurant then updates its inventory information after providing the service, and the server calculates and executes a commission-based fee. The system also learns from usage history, including emotional data, to improve the accuracy of recommendations for future visits.

[1743] This allows restaurants to efficiently utilize their inventory and users to enjoy meals at more personalized and affordable prices. In addition, a portion of the fees is donated to social contribution activities.

[1744] The processing flow will be explained below.

[1745] Step 1:

[1746] Restaurant owner (terminal): The owner logs in to the system's management screen and authenticates by entering their username and password.

[1747] Server: Verify authentication information and display admin panel to owner.

[1748] Step 2:

[1749] Restaurant owner (device): Enter information such as "Product name: Chicken curry," "Quantity: 10," and "Discount price: 700 yen (regular price: 1,000 yen)" into the inventory status input form on the management screen.

[1750] Restaurant owner (device): After completing the input, click the "Register" button.

[1751] Server: Saves the entered inventory information to the database and updates the inventory management table.

[1752] Step 3:

[1753] User (device): The user accesses the site and logs in by entering their username and password.

[1754] Server: Verify credentials and view user-specific dashboard.

[1755] Step 4:

[1756] User (device): Enter conditions such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen" in the search bar on the dashboard and click the "Search" button.

[1757] Server: Searches the database for relevant store information based on the entered search criteria.

[1758] Server: Displays search results to the user in a list format.

[1759] Step 5:

[1760] Server: Using the emotion engine, analyzes emotion data acquired from the camera and microphone on the user's device. For example, by analyzing the user's facial expressions and tone of voice, it can recognize emotions such as "happiness," "tiredness," and "excitement."

[1761] Server: Stores the recognized emotion data and uses it for future recommendations.

[1762] Step 6:

[1763] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on a user's past usage history and current emotional state. For example, it recommends bright and lively stores to a user in a "happy" state, and quiet and relaxing stores to a user in a "tired" state.

[1764] Step 7:

[1765] User (device): Select the store of interest from the presented recommendation list.

[1766] Step 8:

[1767] User (device): Check the "Leftover Ingredients Menu" provided on the details page of the selected store and select "Chicken Curry."

[1768] User (device): Enter the date and time and number of people in the reservation form and click the "Confirm" button.

[1769] User (device): After checking the input information, press the "Confirm reservation" button.

[1770] Step 9:

[1771] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1772] Step 10:

[1773] User: Visits the restaurant on the reserved date and time and uses the reserved discount menu.

[1774] Restaurant owners: Providing meals to users.

[1775] Step 11:

[1776] Restaurant owner (device): After serving the food, mark the reservation as "completed" on the management screen and update the inventory information.

[1777] Step 12:

[1778] Server: Calculates commission based on completed booking information.

[1779] Server: The fee will be automatically deducted from the restaurant owner.

[1780] Server: Donate a portion of your fees to hunger relief organizations.

[1781] Step 13:

[1782] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[1783] Server: Updates the generative AI model and emotion engine to improve the accuracy of future recommendations.

[1784] Example 2

[1785] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1786] Conventional restaurant inventory management systems provide functions such as inventory information registration, search, and reservation, but lack a mechanism for recommending optimal restaurants based on the user's emotional state. As a result, the user experience cannot be improved and restaurant inventory management is inefficient. Furthermore, there is no system for calculating fees or making donations that takes social contributions into consideration, making these systems insufficient for realizing a sustainable society.

[1787] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1788] In this invention, the server includes means for registering restaurant inventory status, means for users to search for relevant inventory, means for recommending restaurants suitable for users using a generative AI model, means for users to make reservations, means for notifying the restaurant and user of the reservation information, means for calculating a performance-based fee and donating a portion of it, means for updating the generative AI model based on training data, and means for acquiring user emotion data and optimizing recommendations based on that data. This makes it possible to suggest optimal restaurants based on the user's emotional state, improving the user experience and streamlining restaurant inventory management, and realizing a fee calculation and donation system that contributes to society.

[1789] The "means for registering restaurant inventory status" is a function that allows restaurant owners to input current inventory information into the system and store it in the database.

[1790] "Means for users to search for relevant inventory" refers to the means by which users can search for inventory information within the system based on specific conditions and find relevant stores.

[1791] "Means of using a generative AI model to recommend stores suitable for users" is a function that uses a generative AI model to analyze a user's past usage history and emotional data, and recommends the most suitable store.

[1792] "Means for users to make reservations" refers to the means by which users make reservations for the store they have selected via the system.

[1793] "Means for notifying restaurants and users of reservation information" is a function for automatically notifying restaurant owners and users of the information when a reservation is confirmed.

[1794] The "means for calculating commissions based on success and donating a portion of the commission" is a function that executes a process for calculating commissions and donating a portion of the commissions when a reservation is successful.

[1795] "Means for updating the generative AI model based on learning data" refers to a means for regularly updating the generative AI model based on user usage history, feedback, emotional data, etc., in order to improve accuracy.

[1796] "Means of obtaining user emotional data and optimizing recommendations based on that" refers to a function that obtains emotional data from the user's facial expressions, voice, etc., and uses that information to further individualize recommendations.

[1797] This invention combines an emotion engine with a system that registers restaurant inventory status and provides that information to users. The system mainly includes the following components: a restaurant owner terminal, a user terminal, and a server.

[1798] Sequence for restaurant owner to register inventory status

[1799] Restaurant owner (terminal): The restaurant owner logs in to the system's management screen and enters their username and password. If authentication is successful, the owner's dedicated management dashboard will be displayed. This dashboard has an input form for inventory status, and the owner enters information such as "product name," "quantity," and "discount price," and clicks the "Register" button.

[1800] Server: The server receives the authentication information and the entered inventory information and saves it in the inventory management table of the database. For example, if the entered information is "Product name: Chicken curry", "Quantity: 10", and "Discount price: 700 yen (regular price 1,000 yen)", this will be saved in the database.

[1801] A sequence where a user searches for a store

[1802] User (terminal): The user accesses the system and logs in by entering their username and password. After successful authentication, a dashboard is displayed. This dashboard has a search bar, and the user can enter conditions such as "area," "menu," and "budget," and then click the "Search" button.

[1803] Server: The server queries the database based on the entered criteria and displays the corresponding restaurant information in a list format to the user. For example, search results are displayed based on criteria such as "Area: Shibuya," "Menu: Curry," and "Budget: Under 700 yen."

[1804] Emotion engine recognizes user emotions

[1805] Server: Using the emotion engine, the server acquires emotional data when the user accesses the app from their device. It performs facial and voice analysis to analyze the user's emotional state. The acquired emotional data is stored in a database and is reflected in future recommendations.

[1806] Store recommendations using the recommendation function

[1807] Server: Combines a generative AI model with an emotion engine to suggest optimal stores based on the user's past usage history and current emotional state. For example, if the user is in a "happy" state, it will recommend stores with a bright atmosphere, and if the user is in a "tired" state, it will prioritize suggesting stores where they can relax.

[1808] User (device): The user selects the store of interest from the proposed recommendation list.

[1809] A user makes a reservation

[1810] User (device): The user checks the "Leftover Ingredients Menu" on the details page of the selected restaurant, enters the "Date and Time" and "Number of people" in the reservation form, and presses the "Confirm Reservation" button.

[1811] Server: Saves the reservation information in a database and sends a confirmation email to the user. At the same time, it notifies the restaurant owner of the reservation information.

[1812] Post-service processing

[1813] User: Visits the store on the reserved date and time and uses the reserved discount menu.

[1814] Restaurant owners: Serve food to customers, mark the reservation as "completed" in the extranet, and update inventory information after serving.

[1815] Fee calculation and donations

[1816] Server: Calculates commission based on successful bookings and donates a portion of the commission to hunger relief organizations.

[1817] System Training

[1818] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data and updates the generative AI model and sentiment engine, thereby improving the accuracy of future recommendations.

[1819] Specific examples

[1820] For example, if a restaurant owner registers that they have 10 extra servings of chicken curry, that information is saved in the database. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays restaurants that meet the criteria, analyzing the user's current emotional state using an emotion engine. The generative AI model recommends the most suitable restaurant, taking into account past usage history, and the user makes a reservation for "chicken curry," visits the restaurant on the specified date and time, and enjoys a meal at a discounted price. The server then calculates a commission-based fee and initiates a process to execute a portion of that fee.

[1821] Example prompt sentence:

[1822] "Analyze the user's emotional state and recommend the best store."

[1823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1824] Step 1: Login process

[1825] Restaurant owner (terminal): The owner accesses the system's management screen and enters their username and password.

[1826] Server: Receives the entered authentication information and checks it against the database. If successful, displays the owner's personal admin dashboard.

[1827] Input: Username, Password

[1828] Output: Authentication result, admin dashboard screen

[1829] Step 2: Enter inventory information

[1830] Restaurant owner (device): After logging in, enter information such as product name, quantity, discount price, etc. into the inventory entry form on the management screen and click the "Register" button.

[1831] Input: Product name (e.g., chicken curry), quantity (e.g., 10), discount price (e.g., 700 yen)

[1832] Output: Inventory information transmission request

[1833] Step 3: Save inventory information

[1834] Server: Receives the entered inventory information and saves it in the inventory management table of the database. If this process is successful, it sends a registration completion message.

[1835] Input: Inventory information (product name, quantity, discount price)

[1836] Output: Registration complete message

[1837] Step 4: User Login

[1838] User (terminal): Accesses the system and attempts to log in by entering a username and password.

[1839] Server: Checks the entered credentials against the database and, if successful, displays a dashboard specific to the user.

[1840] Input: Username, Password

[1841] Output: Authentication results, user dashboard

[1842] Step 5: Enter search criteria

[1843] User (device): Enter conditions such as "area," "menu," and "budget" in the search bar on the dashboard and click the "Search" button.

[1844] Input: Area (e.g. Shibuya), Menu (e.g. Curry), Budget (e.g. Under 700 yen)

[1845] Output: Search request

[1846] Step 6: Viewing search results

[1847] Server: Queries the database based on the entered search criteria and displays matching store information to the user in a list format.

[1848] Input: Search criteria

[1849] Output: A list of matching stores

[1850] Step 7: Obtaining emotion data

[1851] Server: Uses the emotion engine to obtain emotional data when the user accesses the app from their device. It determines the user's emotional state through facial expression and voice analysis.

[1852] Input: User video and audio data

[1853] Output: Emotion data (e.g. happy, tired)

[1854] Step 8: Storing Emotion Data

[1855] Server: Stores the acquired emotion data in a database and updates the user's profile.

[1856] Input: Emotion data

[1857] Output: Updated user profile

[1858] Step 9: Recommendation Generation

[1859] Server: Using a generative AI model, it suggests the most suitable store based on the user's past usage history, current emotional state, and search criteria.

[1860] Input: User usage history, emotion data, search conditions

[1861] Output: Recommendation list

[1862] Step 10: View recommendation results

[1863] User (device): Select the store of interest from the suggested recommendation list.

[1864] Input: Recommendation list

[1865] Output: Selected store information

[1866] Step 11: Enter reservation information

[1867] User (device): Check the "Leftover Ingredients Menu" on the details page of the selected store, enter the required information such as "Date and Time" and "Number of people" in the reservation form, and press the "Confirm Reservation" button.

[1868] Input: Date, time, number of people, selected menu

[1869] Output: Reservation information sending request

[1870] Step 12: Save and notify reservation information

[1871] Server: Saves the reservation information in a database, sends a confirmation email to the user, and notifies the restaurant owner of the reservation information.

[1872] Input: Reservation information

[1873] Output: Confirmation email, reservation information notification

[1874] Step 13: Update your visit record

[1875] Restaurant owner: A user visits the restaurant and uses the reserved menu. After providing the service, the user marks the reservation as "completed" on the management screen and updates the inventory information.

[1876] Input: Visit confirmation, menu information

[1877] Output: Updated inventory information

[1878] Step 14: Fee Calculation and Donation

[1879] Server: Calculates commission-based fees based on completed booking information and donates a portion of the fees.

[1880] Input: Completed reservation information

[1881] Output: Fee calculation results, donation process execution

[1882] Step 15: Save the training data

[1883] Server: Stores user usage history, reservation information, feedback, and sentiment data as learning data.

[1884] Input: Usage history, reservation information, feedback, emotional data

[1885] Output: Saved training data

[1886] Step 16: Update the generative AI model

[1887] Server: Uses the saved learning data to update the generative AI model and emotion engine, improving the accuracy of future recommendations.

[1888] Input: Training data

[1889] Output: Updated generative AI model and emotion engine

[1890] (Application example 2)

[1891] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1892] Restaurants are required to manage inventory, propose efficient menus, and provide personalized service based on customers' emotional states. Conventional systems are limited in their ability to collect inventory information and propose appropriate menus, making it particularly difficult to optimize services based on customer emotions. This results in problems such as poor customer satisfaction and a tendency for inventory waste. To solve this problem, a system that integrates inventory management and customer emotion recognition is needed.

[1893] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for registering restaurant inventory status, a means for a user to search for relevant inventory, a means for recommending restaurants suitable for the user using a generative AI model, a means for a user to make a reservation, a means for notifying the restaurant and the user of the reservation information, a means for calculating a performance-based fee and donating a portion of it, a means for updating the generative AI model based on training data, a means incorporating an emotion engine that recognizes the customer's emotional state and presents appropriate restaurants and menus, and a means for presenting information to the user via smart glasses. This enables personalized menu suggestions and inventory management according to the customer's emotional state.

[1894] "Means for registering restaurant inventory status" refers to a system or function for inputting the current inventory status of ingredients and products held by a restaurant and storing it in a database.

[1895] "Means for users to search for relevant inventory" refers to a system or function that allows users to input specific conditions or keywords to search for restaurant inventory information and obtain the necessary information.

[1896] "Means of using generative AI models to recommend restaurants suitable for users" refers to systems and functions that use machine learning and AI technology to suggest optimal restaurants and menus based on a user's preferences and history.

[1897] "Means for users to make reservations" refers to systems or functions that allow users to make reservations in advance for the store and menu they have selected by specifying the date, time, and number of people.

[1898] "Means for notifying the restaurant and user of reservation information" refers to a system or function that notifies the restaurant and user of the details of a confirmed reservation and allows them to confirm it.

[1899] "Means for calculating commission fees based on success and donating a portion of the fee" refers to a system or function that calculates the fee incurred when a user completes a reservation and donates a portion of that fee to social contribution activities, etc.

[1900] "Means for updating the generative AI model based on learning data" refers to systems or functions that periodically update the generative AI model based on user usage history and feedback, thereby improving the accuracy of recommendations.

[1901] "Means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus" refers to systems and functions that use technologies such as facial expression analysis and voice recognition to determine the user's current emotional state and suggest the most appropriate stores and menus accordingly.

[1902] "Means for presenting information to users via smart glasses" refers to a system or function that displays information on the display of smart glasses, allowing users to visually confirm and operate the device.

[1903] This invention is a system for efficiently managing restaurant inventory and providing personalized menu suggestions based on the emotional state of customers. The system is comprised of several main functions, each of which works in conjunction with the other functions.

[1904] Key System Features

[1905] 1. Register restaurant inventory status via:

[1906] The server provides a management screen designed to allow restaurant owners to input inventory information for their stores. Owners input information such as product names, quantities, and prices, and save it in a database. This information is updated in real time.

[1907] 2. How users can search for relevant inventory:

[1908] Users access the system through a web browser or mobile application and search for inventory information by entering keywords or conditions in the search bar. The server then retrieves the relevant inventory information from the database and displays it to the user.

[1909] 3. Using generative AI models to recommend suitable stores to users:

[1910] The server uses a generative AI model to recommend appropriate restaurants and menus based on the user's past usage history and current emotional state. The generative AI model uses machine learning algorithms to analyze the user's preferences and behavioral patterns.

[1911] 4. How users can make reservations:

[1912] Users select a restaurant from the reservation screen provided by the server, enter the desired date and time and number of people, and make a reservation. The reservation information is saved in a database.

[1913] 5. Means of notifying restaurants and users of reservation information:

[1914] Once a reservation is confirmed, the server will send an email or push notification to notify the restaurant and user of the reservation, providing two-way confirmation.

[1915] 6. A means of calculating contingency fees and donating a portion of them:

[1916] The server calculates the commission that will be incurred if the reservation is successful and has the function of donating a portion of it to social contribution activities. The commission calculation is done automatically, and the donation is also carried out according to the set rules.

[1917] 7. How to update generative AI models based on training data:

[1918] The server collects user usage history, feedback, and sentiment data and periodically updates the generative AI model to improve the accuracy of recommendations.

[1919] 8. A means incorporating an emotion engine that recognizes the customer's emotional state and suggests appropriate stores and menus:

[1920] The server uses smart glasses and other input devices to recognize the customer's emotional state from their facial expressions and voice, and then suggests appropriate menus and restaurants based on that. The emotion engine uses Affectiva and Microsoft Azure Emotion API.

[1921] 9. Means of presenting information to a user via smart glasses:

[1922] The server displays information on the smart glasses' display, allowing users to visually confirm and operate the device, which allows users to easily make menu suggestions and make reservations.

[1923] Specific examples

[1924] For example, if a cafe in Shibuya Ward has leftover chicken curry, the restaurant owner can register that information in the system. When a user searches for "curry under 700 yen in Shibuya Ward," the server displays the relevant inventory information and simultaneously analyzes the user's current emotional state using an emotion engine. For example, if the user's emotional state is "I want to relax," the server will recommend restaurants with a quiet atmosphere and relaxing menus. The user can check this information through the smart glasses and make a reservation on the spot.

[1925] Prompt Sentence Examples

[1926] "Check out today's availability and recommend the perfect menu for your relaxing guest."

[1927] "Please suggest a dessert menu that suits customers with happy emotions at a cafe in Shibuya Ward."

[1928] In this way, a system that integrates inventory management and sentiment analysis can help restaurants operate more efficiently and improve customer satisfaction.

[1929] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1930] Step 1:

[1931] The server authenticates the restaurant owner by allowing them to access the management screen and enter their username and password. If authentication is successful, the management screen is displayed, allowing the owner to enter inventory status. Specifically, the owner enters information such as product name, quantity, and price into the input form and clicks the "Register" button. The server saves the entered inventory information in the database and updates the inventory management table. The input is the restaurant owner's authentication information and inventory data, and the output is the inventory information saved in the database.

[1932] Step 2:

[1933] Users access the system through a web browser or mobile application and log in by entering their username and password. If authentication is successful, the dashboard is displayed. The user enters keywords or conditions in the search bar (e.g., "Area: Shibuya," "Menu: Curry," "Budget: Under 700 yen") and clicks the "Search" button. The server retrieves relevant inventory information from the database based on the entered search conditions and displays it to the user in list form. The input is the username, password, and search conditions, and the output is a list of relevant inventory information that is displayed to the user.

[1934] Step 3:

[1935] The server receives data from smart glasses or other input devices to obtain emotional data when a customer accesses the system. The emotional data is analyzed using facial expression analysis and voice recognition technology. The emotion engine identifies the user's emotional state based on the analyzed emotional data and passes that information to a generative AI model. The input is the emotional data obtained from the smart glasses or other devices, and the output is the identified emotional state.

[1936] Step 4:

[1937] The server uses a generative AI model to recommend optimal restaurants and menus to the user. The generative AI model generates optimal restaurants and menus by taking into account the user's past usage history, current emotional state, and search criteria. The recommendation results are displayed on the smart glasses' display so that the user can visually confirm them. The input is the user's usage history, emotional state, and search criteria, and the output is a list of recommended restaurants and menus.

[1938] Step 5:

[1939] The user uses the smart glasses to check the suggested restaurants and menus and make a reservation on the spot. They select a restaurant from the reservation screen, enter the desired date and time and number of people, and press the "Confirm reservation" button. The server saves the reservation information in a database and notifies the restaurant and the user of the reservation details. The input is the reservation information entered by the user, and the output is the reservation information saved in the database and a notification message.

[1940] Step 6:

[1941] The user visits the restaurant at the reserved date and time and uses the discount menu. After serving the food, the restaurant owner marks the reservation as "completed" on the management screen and updates the inventory information. The server reflects this information in the database. The input is the restaurant's completed serving information, and the output is the updated inventory information.

[1942] Step 7:

[1943] The server calculates a commission based on the completed reservation information and donates a portion of it to social contribution activities according to set rules. The calculated commission and donation amount are recorded in a database. The input is the completed reservation information, and the output is the calculated commission and donation amount.

[1944] Step 8:

[1945] The server collects the user's usage history, feedback, and emotional data and periodically updates the generative AI model. This improves the accuracy of recommendations, making them more appropriate for future use. The input is the user's usage history, feedback, and emotional data, and the output is the updated generative AI model.

[1946] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1947] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1948] In the above embodiment, an example was given in which the specific processing ...

Claims

1. A means of registering restaurant inventory status; A way for users to search for relevant inventory; A means of using generative AI models to recommend stores suitable for users, a means by which users can make reservations; A means of notifying restaurants and users of reservation information; A method to calculate a success-based fee and donate a portion of it; A means for updating the generative AI model based on the training data; and A system including:

2. 10. The system according to claim 1, further comprising a database for managing restaurant inventory and reservation information.

3. The system according to claim 1, further comprising a recommendation function for recommending more suitable stores based on the user's past usage history.

Images