System

Abstract

To provide a system.SOLUTION: A system includes: means for receiving a mood and a hope of a user inputted by the user; means for transmitting received data to generative AI as a prompt sentence; means for proposing appropriate cooking by analyzing the prompt sentence by the generative AI; and means for receiving a selection result of a provision method for the proposed cooking and performing settlement.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] There is no system that can consistently suggest dishes that match the user's mood, choose how they want them served (cooked / delivered / restaurant), and even process the payment. [Means for solving the problem]

[0005] The system allows users to communicate their mood in text and makes cooking suggestions based on that. It also allows users to select the delivery method (cooking, delivery, or restaurant) and complete payment. When cooking at home, the system has a function that allows users to order the recipe and necessary ingredients from a nearby supermarket or online supermarket, and when ordering delivery, it has a function that allows users to order from a nearby restaurant. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention has a function that allows users to communicate their mood through text. Specifically, if a user inputs, "I'd like to try a new dish today," the system will suggest dishes that match that mood. For example, it may suggest, "How about a dish using fresh fish?"

[0029] "Example 2"

[0030] The system of the present invention has a function to select how to prepare the proposed dish. Specifically, if the user selects "make it yourself," the system provides a recipe and the necessary ingredients, allowing them to be ordered from a nearby supermarket or online supermarket. For example, if the user selects "dish using fresh fish," the system provides a recipe for that dish and a list of ingredients to make it.

[0031] "Example 3"

[0032] The system of the present invention has a function that allows delivery from nearby restaurants. Specifically, when a user selects "delivery," the system can order the corresponding dish from a nearby restaurant. For example, if a user selects "dishes using fresh fish," the system will arrange for delivery from a nearby restaurant that serves that dish.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user enters their mood into the system in a sentence, for example, "I want to try something new today."

[0036] Step 2: The system makes a dish suggestion based on the user's input, for example, "How about a dish with fresh fish?"

[0037] "Example 2"

[0038] Step 1: The user selects how they would like to serve the proposed dish, specifically, "Make it yourself."

[0039] Step 2: The system will provide a recipe and the necessary ingredients based on the selected delivery method, allowing you to arrange for delivery from a nearby supermarket or online supermarket. For example, if you select "Fresh fish dishes," the system will provide you with a recipe for that dish and a list of ingredients to make it.

[0040] "Example 3"

[0041] Step 1: The user selects the delivery method for the proposed dish, specifically, "Delivery."

[0042] Step 2: The system can order the corresponding dish from a nearby restaurant based on the delivery method selected. For example, if you select "dishes with fresh fish," the system will arrange delivery from a nearby restaurant that serves that dish.

[0043] Example 1

[0044] Next, a description will be given of Example 1 of Form 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."

[0045] Conventional food recommendation systems have difficulty in suggesting specific dishes based on the user's mood or preferences, and are unable to handle delivery methods and payment in a consistent manner. This has resulted in problems such as users having to spend time and effort to find a dish that suits their mood.

[0046] The specification process by the specification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for a user to input their mood and preferences into a terminal, a means for the terminal to send the input data to the server, a means for the server to send the received data to the generative AI model as a prompt sentence, a means for the generative AI model to analyze the prompt sentence and generate an appropriate suggestion, a means for the server to receive the generated suggestion and return it to the user, a means for the user to receive and confirm the suggestion, and a means for selecting a delivery method (make-yourself / delivery / restaurant) and making payment. This allows the user to receive suggestions for dishes that suit their mood, select a delivery method, and make a payment all in one go.

[0047] "User" refers to an individual who uses the system to input their mood and preferences and receive food suggestions.

[0048] A "terminal" is an electronic device used by a user to input their mood or wishes, and includes smartphones, personal computers, etc.

[0049] "Server" refers to the computer system responsible for receiving data submitted by the user, sending prompts to the generative AI model, and sending generated suggestions back to the user.

[0050] "Generative AI model" refers to an artificial intelligence model that analyzes user input data and generates appropriate dish suggestions.

[0051] A "prompt sentence" is an instruction sentence sent to a generative AI model based on user input data, and includes input information for the generative AI model to analyze.

[0052] "Delivery Method" is how a user chooses to obtain the suggested food, including cook-it-yourself, delivery, and restaurant options.

[0053] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[0054] A "recipe" is a document that contains instructions and a list of ingredients for making a dish.

[0055] "Ingredients" refers to the ingredients and seasonings needed to make a dish.

[0056] "Store" means a store where a user can purchase ingredients for cooking, including a local brick-and-mortar store or an online store.

[0057] "Restaurant" refers to establishments such as restaurants and cafes where users can order food for delivery.

[0058] This invention is a system that suggests appropriate dishes based on the user's mood and preferences, and handles everything from the delivery method to payment. The system works by having the user input their mood and preferences into a terminal, and the server receives and analyzes the data, then uses a generative AI model to generate appropriate suggestions.

[0059] Hardware and software used

[0060] Hardware: Servers, user devices (smartphones, personal computers, etc.)

[0061] Software: Generative AI model (e.g., OpenAI® GPT-4®), communication protocol (HTTP / HTTPS)

[0062] Specific operation of the system

[0063] Users use devices such as smartphones or personal computers to input their moods and desires in text, for example, "I want to try a new dish today." The device then sends this input data to the server using the HTTP / HTTPS protocol.

[0064] The server analyzes the received user input data and generates a prompt to send to the generative AI model. For example, if the user input is "I want to try a new dish today," the prompt will be "The user says they want to try a new dish. What kind of dish would you suggest?"

[0065] The generative AI model analyzes the prompt sent from the server and generates a dish suggestion that matches the user's mood. For example, it generates a suggestion such as, "How about a dish using fresh fish?" The server sends the suggestion received from the generative AI model back to the user. At this time, the suggestion is formatted in a way that is easy for the user to understand and sent to the device.

[0066] The user checks the suggestions displayed on the device and selects the delivery method (cooking, delivery, or restaurant). For example, if cooking, the user can arrange for the recipe and necessary ingredients from a nearby store or online store. If delivery is selected, the food can be ordered from a nearby restaurant. The user can then make payment based on the selected delivery method.

[0067] This system allows users to receive suggestions for dishes that suit their mood, choose how they would like them served, and complete the entire process from payment to delivery.

[0068] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0069] Step 1:

[0070] The user inputs their mood and desires into the device. Specifically, the user opens an application on their smartphone or personal computer and enters "I want to try a new dish today" in the text box. The input data is in text format and expresses the user's mood and desires.

[0071] Step 2:

[0072] The terminal sends the entered data to the server. The terminal converts the text data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is the user's request in text format, and the output is the JSON format data sent to the server.

[0073] Step 3:

[0074] The server sends the received data to the generative AI model as a prompt. The server parses the received JSON data and generates a prompt: "The user says they want to try a new dish. What dish would you suggest?" The input data is the user's preference in JSON format, and the output is the prompt sent to the generative AI model.

[0075] Step 4:

[0076] A generative AI model analyzes the prompt and generates an appropriate suggestion. A generative AI model (e.g., OpenAI GPT-4) analyzes the prompt sent from the server and generates a suggestion such as "Would you like a dish with fresh fish?" The input data is the prompt, and the output is the generated suggestion.

[0077] Step 5:

[0078] The server receives the generated proposal and sends it back to the user. The server converts the proposal received from the generative AI model into JSON format and sends it to the terminal as an HTTP response. The input data is the proposal from the generative AI model, and the output is the JSON-formatted proposal that is sent back to the user.

[0079] Step 6:

[0080] The user receives and confirms the suggestion. The user confirms the suggestion "How about a dish with fresh fish?" displayed on a smartphone or personal computer application. The input data is the suggestion from the server, and the output is the user's confirmation.

[0081] Step 7:

[0082] The user selects the delivery method and makes the payment. The user selects the delivery method (make-yourself, delivery, restaurant) based on the proposed dishes and makes the payment through the application. The input data is the user's selection and payment information, and the output is the selected delivery method and the completion of the payment.

[0083] (Application example 1)

[0084] Next, a description will be given of Application Example 1 of Embodiment 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."

[0085] Conventional food recommendation systems have had the problem of being unable to recommend the right food based on the user's mood, and are unable to provide a consistent system for delivery and payment. In particular, there is a need for a system that allows users to easily find the food that best suits their mood that day and order it immediately.

[0086] 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.

[0087] In this invention, the server includes a means for communicating a user's mood in text and suggesting dishes based on that, a means for selecting a delivery method (cooked meal / delivery / restaurant), a means for processing payment, a means for suggesting dishes according to the user's mood using a generative AI model, a means for generating prompt sentences and inputting them into the generative AI model, and a means for obtaining suggestions from the generative AI model. This allows users to easily find dishes that suit their mood that day, select a delivery method, and quickly order and pay.

[0088] "User" refers to an individual who uses the system to receive recipe suggestions.

[0089] "Mood" refers to the emotions and desires a user is feeling at that time.

[0090] "Text interaction" refers to the user providing input to the system in text format and the system responding in text format accordingly.

[0091] "Dish suggestions" refers to the system recommending suitable dishes based on the user's mood.

[0092] "Delivery Method" refers to the options for how the food is delivered to the user, including cook-it-yourself, delivery, or restaurant.

[0093] "Payment" refers to the process of paying for the food and delivery method selected by the user.

[0094] The "system" refers to a device or software with a series of functions that suggests dishes based on the user's mood, selects the serving method, and even handles payment.

[0095] "Generative AI model" refers to an artificial intelligence model that suggests appropriate dishes based on user input.

[0096] A "prompt" refers to a textual instruction to be input to a generative AI model.

[0097] "Getting suggestions" refers to receiving the food recommendation results output by the generative AI model.

[0098] The system for implementing this invention has a set of functions that suggest dishes based on the user's mood, select the delivery method, and handle payment. The system receives user input and uses a generative AI model to suggest appropriate dishes based on that input.

[0099] The server has a means to communicate the user's mood in text and make food suggestions based on that. When a user uses a device such as a smartphone to input "I want to eat spicy food today," the server generates a prompt from that input. An example of a specific prompt is "The user inputted 'I want to eat spicy food today.' Please suggest a dish that matches my mood."

[0100] The generated prompt is input to a generative AI model, which uses an advanced natural language processing model such as OpenAI's GPT-3 (registered trademark). The model suggests appropriate dishes based on the prompt and returns the results to the server. The server retrieves the suggestions from the generative AI model and presents them to the user.

[0101] The user checks the suggested dishes and selects the delivery method (cook it yourself, delivery, or restaurant). For example, if the user is offered a "spicy curry," and selects delivery, the server will place the order using a function that allows it to be ordered from a nearby restaurant.

[0102] Payment is made based on the delivery method selected by the user. The server processes the payment information and provides the user with a means to complete the payment. This allows users to easily find the food they want that day, select the delivery method, and quickly order and pay.

[0103] The system is implemented using devices such as smartphones and tablets connected to the internet. The software uses Python programs and OpenAI APIs. Data processing and calculations are performed by converting user input into prompts, inputting them into a generative AI model, and obtaining generated suggestions.

[0104] As a specific example, if a user inputs "I want to eat spicy food today," the system will suggest "How about spicy curry?" and then proceed to the delivery order screen. An example of a prompt sentence to input into the generative AI model is "The user inputs 'I want to eat spicy food today.' Please suggest a dish that suits their mood."

[0105] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0106] Step 1:

[0107] The user uses a device such as a smartphone to input text that suits their mood.

[0108] Input: A sentence that describes the user's mood (e.g., "I want to eat spicy food today")

[0109] Output: User input data

[0110] Step 2:

[0111] The terminal transmits the user's input data to the server.

[0112] Input: User-entered data

[0113] Output: User input data sent to the server

[0114] Step 3:

[0115] The server receives the user's input data and generates a prompt sentence.

[0116] Input: User-entered data

[0117] Data processing: Generate prompts based on user input (e.g., "The user entered, 'I want to eat spicy food today.' Please suggest dishes that fit that mood.")

[0118] Output: Generated prompt statement

[0119] Step 4:

[0120] The server inputs the generated prompt sentence into the generative AI model.

[0121] Input: Generated prompt text

[0122] Output: The prompt sentence fed into the generative AI model

[0123] Step 5:

[0124] The generative AI model generates appropriate dish suggestions based on the prompt sentence.

[0125] Input: A prompt sentence entered into the generative AI model

[0126] Data computation: A generative AI model analyzes the prompt and generates appropriate dish suggestions

[0127] Output: Generated food suggestions (e.g. "Would you like a spicy curry?")

[0128] Step 6:

[0129] The server retrieves suggestions from the generative AI model and presents them to the user.

[0130] Input: Generated dish suggestions

[0131] Output: Food suggestions presented to the user

[0132] Step 7:

[0133] The user reviews the proposed dishes and selects the delivery method (cook / delivery / restaurant).

[0134] Input: Food suggestions presented to the user

[0135] Output: Delivery method selected by the user

[0136] Step 8:

[0137] The server makes the necessary arrangements based on the delivery method selected by the user.

[0138] Input: User-selected delivery method

[0139] Data processing: Arrangements depending on the method of delivery (e.g., for delivery, ordering from a nearby restaurant)

[0140] Output: Notification of completion of arrangement

[0141] Step 9:

[0142] The user receives the food and makes payment based on the delivery method.

[0143] Input: Notification of completion of arrangement

[0144] Output: Notification of payment completion

[0145] In this way, a system is realized that processes and calculates data based on user input at each step, ultimately suggesting appropriate dishes to the user, and even handles selection of the serving method and payment in an integrated manner.

[0146] Example 2

[0147] Next, a description will be given of Example 2 of Form 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."

[0148] Conventional food recommendation systems have difficulty in consistently suggesting dishes based on the user's mood, selecting the delivery method, and even handling payment. Furthermore, when the user selects "make it yourself," there is a lack of efficient means to provide recipes and necessary ingredients, and to facilitate arrangements at nearby stores or online stores. Furthermore, there is also a lack of functionality to utilize the user's location information to provide optimal store information.

[0149] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a serving method (cook yourself / delivery / restaurant), and even complete payment; means for generating a recipe and a list of necessary ingredients using a generative AI model when the user selects "cook yourself"; and means for acquiring the user's location information and providing information on nearby stores or online stores. This allows the user to receive suggestions for dishes that suit their mood, select a serving method, and efficiently arrange for the necessary ingredients.

[0150] "User" means an individual or organization that uses the system to receive food suggestions and select the delivery method.

[0151] "Mood" refers to a sentence or keyword that indicates the user's current emotion or state, which the user inputs into the system.

[0152] "Cooking suggestions" refer to specific cooking ideas and recipes that the system provides based on the user's mood.

[0153] "Delivery method" refers to the method of receiving food that users can choose from: "Make it yourself," "Delivery," or "Restaurant."

[0154] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[0155] A "generative AI model" is an artificial intelligence algorithm or system that generates recipes and ingredient lists based on user input.

[0156] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[0157] An "ingredient list" is a list of ingredients and seasonings needed to make a particular dish.

[0158] "Location Information" is data that indicates a user's current location and is obtained through GPS and other location-based services.

[0159] "Nearby stores" are physical stores that sell ingredients and seasonings within reach of the user's current location.

[0160] "Online stores" are websites or applications that allow customers to order ingredients and seasonings over the Internet.

[0161] MODE FOR CARRYING OUT THE INVENTION

[0162] This invention is a system that proposes dishes based on the user's mood, selects the serving method, and performs the entire process from payment to completion. A specific embodiment of this system will be described below.

[0163] System configuration

[0164] The system consists of a user's device, a server, a generative AI model, a location service, and an external API.

[0165] User's device: A device used by a user to access the system, such as a smartphone or PC.

[0166] Server: A computer that manages the entire system and processes various data.

[0167] Generative AI models: For example, using a generative AI model such as GPT-4 to generate a recipe and ingredients list based on user input.

[0168] Location services: Obtaining user location information using Google (registered trademark) Maps API, etc.

[0169] External APIs: We use external APIs such as Google Maps API and Amazon API to provide nearby store information and online store information.

[0170] System Operation

[0171] 1. User mood input

[0172] The user uses a terminal to access the system's interface and input their mood in a sentence, for example, "I want to eat something light today."

[0173] 2. Food suggestions

[0174] The server uses a generative AI model to suggest appropriate dishes based on the user's mood. For example, the following prompt sentence is input to the generative AI model:

[0175] Suggest dishes when a user types, "I want something light today."

[0176] The generative AI model suggests dishes such as "refreshing salad."

[0177] 3. Selection of delivery method

[0178] The user can select one of the following delivery methods for the proposed dish: "Make it yourself," "Delivery," or "Restaurant." For example, the user can select "Make it yourself."

[0179] 4. Generate recipes and ingredient lists

[0180] If the user selects "Make it yourself," the server uses a generative AI model to generate a recipe and ingredients list. For example, the following prompt is input to the generative AI model:

[0181] Generate a recipe and ingredient list if the user selects "Light Salad."

[0182] The generative AI model returns a response like this:

[0183] Recipe: "Refreshing Salad"

[0184] Ingredients list: lettuce, tomato, cucumber, olive oil, lemon

[0185] 5. Obtaining location information

[0186] The server obtains location information from the user's device. This process uses location services such as Google Maps API. If the user allows location information to be provided, the server obtains the user's current location.

[0187] 6. Providing information about nearby stores

[0188] The server uses the acquired location information to provide information about nearby stores or online stores. This process uses external APIs such as Google Maps API or Amazon API. The server provides a list of stores closest to the user's current location and links to online stores.

[0189] Specific examples

[0190] For example, if the user selects "dishes using fresh fish," the server performs the following process.

[0191] 1. The user selects "Fresh fish dishes."

[0192] 2. The server uses a generative AI model to generate a recipe and ingredients list like this:

[0193] Recipe: "Grilled Fresh Fish"

[0194] Ingredients: Fish, salt, pepper, olive oil, lemon

[0195] 3. The server uses the Google Maps API to obtain the user's location and provide information about nearby stores.

[0196] 4. Using the information provided, the user can purchase ingredients at a local store or order them online.

[0197] In this way, users can receive recipe suggestions based on their mood, choose how they would like to be served, and efficiently arrange the necessary ingredients.

[0198] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0199] Step 1:

[0200] A user accesses the system interface using a terminal and inputs his / her mood in text.

[0201] Input: A sentence that describes the user's mood (e.g., "I want something light today").

[0202] Output: Data based on user mood.

[0203] Specific actions: A user opens the app on their smartphone, enters "I want to eat something light today" in the text box, and taps the send button.

[0204] Step 2:

[0205] The server uses a generative AI model to suggest appropriate dishes based on the user's mood.

[0206] Input: A sentence that indicates the user's mood.

[0207] Output: A suggested dish (e.g., "a refreshing salad").

[0208] Specific operation: The server sends the following prompt to the generative AI model.

[0209] Suggest dishes when a user types, "I want something light today."

[0210] The generative AI model suggests a "refreshing salad," and the server sends that information to the user's device.

[0211] Step 3:

[0212] The user selects one of the following delivery methods for the proposed dish: "Make it yourself," "Delivery," or "Restaurant."

[0213] Input: Suggested dish and serving options.

[0214] Output: The delivery method selected by the user (e.g., "Build it yourself").

[0215] Specific operation: The user taps and selects "Make it myself" from the delivery method options displayed on the device screen.

[0216] Step 4:

[0217] If the user selects "make it yourself," the server uses a generative AI model to generate a recipe and ingredients list.

[0218] Input: The dish selected by the user (e.g., "light salad").

[0219] Output: A recipe and a list of ingredients (e.g., "Recipe: Light salad, Ingredients: Lettuce, tomato, cucumber, olive oil, lemon").

[0220] Specific operation: The server sends the following prompt to the generative AI model.

[0221] Generate a recipe and ingredient list if the user selects "Light Salad."

[0222] The generative AI model generates the recipe and ingredients list, and the server sends that information to the user's device.

[0223] Step 5:

[0224] The server obtains location information from the user's device.

[0225] Input: User location permission.

[0226] Output: User's current location.

[0227] Specific behavior: The server uses the Google Maps API to obtain the user's current location. The user taps "Allow" in the dialog box that allows them to provide their location information.

[0228] Step 6:

[0229] Based on the location information acquired by the server, information on nearby stores or online stores is provided.

[0230] Input: User's current location.

[0231] Output: A list of nearby stores or a link to the online store.

[0232] Specific operation: The server uses the Google Maps API or Amazon API to obtain a list of stores closest to the user's current location and sends that information to the user's device.

[0233] Step 7:

[0234] Based on the information provided, the user can purchase ingredients at a nearby store or order them from an online store.

[0235] Input: Nearby store list or online store link.

[0236] Output: Purchase of materials or completion of order.

[0237] What happens: The user chooses the closest supermarket from the list of supermarkets provided and either goes to the supermarket to buy the ingredients, or taps a link to an online store to order online.

[0238] (Application example 2)

[0239] Next, a description will be given of Application Example 2 of Form 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."

[0240] In conventional recipe suggestion systems, after the user selects a dish, the process of arranging ingredients and delivery is complicated, resulting in a poor user experience. Also, when users cook their own meals, arranging the necessary ingredients is time-consuming, making it difficult to cook smoothly.

[0241] The specific processing by the specific 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 means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, means for generating a recipe and ingredient list for the selected dish using a generative AI model, means for arranging ingredients through the API of a nearby store or an online store, and means for delivering the ingredients to the user using the API of a delivery service. This makes it possible to provide a recipe and ingredient list for the dish selected by the user, and further arrange for the ingredients to be delivered.

[0242] "User" means an individual or organization that uses the system to suggest dishes and arrange ingredients.

[0243] "Mood" is information that indicates the current emotion or state that the user inputs into the system.

[0244] "Cuisine suggestions" are meal options provided by the system based on the user's mood.

[0245] "Serving Method" indicates how the user will serve the selected dish, and can be "Make Your Own," "Delivery," or "Restaurant."

[0246] "Payment" refers to the payment procedure carried out by the user for the purchase of the dishes and ingredients selected by the user.

[0247] A "generative AI model" is an artificial intelligence model that generates cooking recipes and ingredient lists based on user input.

[0248] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[0249] An "ingredient list" is a list of ingredients needed to make a particular dish.

[0250] A "nearby store" is a physical store that is close to the user's location.

[0251] An "online store" is a store that sells products over the Internet.

[0252] "API" stands for Application Program Interface, a means by which different software systems communicate with each other.

[0253] "Delivery service" refers to a service that delivers ordered products to users.

[0254] The system for implementing this invention has the function of allowing users to communicate their mood in text, suggest dishes based on that, select the delivery method (cook it yourself / delivery / restaurant), and even process payment. A specific embodiment of this system is described below.

[0255] Hardware and software used

[0256] Hardware: Smartphones, servers

[0257] Software: Mobile applications, cloud-based databases, generative AI models (e.g., GPT-4)

[0258] System configuration

[0259] 1. User Interface

[0260] Users access the system through a smartphone application.

[0261] Users enter their mood in text and receive cooking suggestions.

[0262] 2. Recipe generation

[0263] The server uses a generative AI model (e.g., GPT-4) to generate a recipe and ingredient list for the dish selected by the user.

[0264] The generated recipe and ingredient list are displayed to the user.

[0265] 3. Arrange materials

[0266] If the user selects "make it yourself," the server arranges for the ingredients through APIs of nearby stores or online stores.

[0267] If the user selects "delivery," the server uses the delivery service's API to arrange for the materials to be delivered to the user.

[0268] 4. Payment

[0269] Users make payments through the application.

[0270] Once payment is complete, material arrangements and delivery will begin.

[0271] Processing flow

[0272] 1. User Interface

[0273] The user opens the app and enters their mood.

[0274] For example: "I feel like relaxing today."

[0275] 2. Recipe generation

[0276] The server inputs the following prompt sentence into the generative AI model:

[0277] Example prompt: "The user selected 'Dishes with fresh fish.' Generate a recipe for this dish and a list of the ingredients needed."

[0278] The generated recipe and ingredient list are displayed to the user.

[0279] Example: "Recipe: Salmon Meunière, Ingredients: 2 salmon fillets, a pinch of salt, a pinch of pepper, some flour, 30g butter, 1 lemon, some parsley"

[0280] 3. Arrange materials

[0281] When the user selects "Order ingredients," the server orders the ingredients through the API of a nearby store or online store.

[0282] For example: "I ordered salmon fillets and other ingredients from a local supermarket."

[0283] 4. Delivery arrangements

[0284] Use the delivery service's API to arrange for the materials to be delivered to the user.

[0285] For example: "The materials will be delivered to the user via a delivery service."

[0286] 5. Notification

[0287] Inform users about delivery progress.

[0288] For example: "Materials are on their way. Estimated arrival time is 6:00 PM."

[0289] In this way, the system can provide a recipe and ingredient list for the dish selected by the user, and then arrange for the ingredients to be delivered.

[0290] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0291] Step 1:

[0292] The user opens the smartphone application and inputs their mood. The input mood is sent to the server as text data. The server receives this text data and analyzes it. The input data is a sentence such as "I feel like relaxing today."

[0293] Step 2:

[0294] The server inputs a prompt into a generative AI model (e.g., GPT-4) based on the analyzed mood data. An example of a prompt is, "The user selected 'dishes using fresh fish.' Please generate a recipe for this dish and a list of ingredients." The generative AI model generates a recipe and list of ingredients based on this prompt. The output data is "Recipe: Salmon meunière, Ingredients list: 2 salmon fillets, a little salt, a little pepper, flour to taste, 30g butter, 1 lemon, parsley to taste."

[0295] Step 3:

[0296] The server sends the generated recipe and ingredient list to the user's smartphone. The user can view the recipe and ingredient list on their smartphone screen. The input data is the output data from the generative AI model, and the output data is the recipe and ingredient list displayed on the user's smartphone.

[0297] Step 4:

[0298] If the user selects "Make it yourself", the server arranges for the ingredients through the API of a nearby store or online store. The server sends the ingredients list to the API and orders the required ingredients. The input data is the ingredients list, and the output data is the order confirmation from the store.

[0299] Step 5:

[0300] If the user selects "Delivery," the server uses the delivery service's API to arrange for the materials to be delivered to the user. The server sends the materials list and the user's address information to the API to arrange delivery. The input data is the materials list and address information, and the output data is the delivery progress.

[0301] Step 6:

[0302] The server notifies the user of the progress of the delivery. The user can check the progress of the delivery and the estimated arrival time through a smartphone application. The input data is the progress information from the delivery service, and the output data is the notification to the user.

[0303] In this way, the system can provide a recipe and ingredient list for the dish selected by the user, and then arrange for the ingredients to be delivered.

[0304] Example 3

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

[0306] With conventional delivery systems, when a user selects a specific dish, it is difficult to efficiently search for nearby restaurants that serve that dish and place an order. Furthermore, there is a lack of functionality to identify the most suitable restaurant based on the user's location information, which hinders the user experience.

[0307] The specification process by the specification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for a user to select "delivery" on a terminal, a means for the terminal to send a request to the server, a means for the server to obtain the user's location information, a means for the server to search a database for information on nearby restaurants, a means for the server to filter restaurants based on the user's selected dish, a means for the server to return the filtering results to the terminal, a means for the user to select a specific restaurant, a means for the server to send a delivery order to the selected restaurant, and a means for the restaurant to receive the order, prepare the dish, and deliver it. This makes it possible for a user, when selecting a specific dish, to efficiently search for nearby restaurants that provide that dish and place an order.

[0308] "User" means an individual or organization that uses the System to order food delivery.

[0309] "Terminal" refers to a device operated by a user, including a smartphone, tablet, or PC.

[0310] A "server" is a computer system that receives and processes requests from users.

[0311] A "request" is request information that a user sends to a server through a terminal.

[0312] "Location information" is data that indicates a user's current location and includes geographic coordinates such as latitude and longitude.

[0313] A "database" is a collection of data that stores information systematically and can be searched and updated.

[0314] "Eating establishments" are establishments that serve food, including restaurants, cafes, and fast food restaurants.

[0315] "Filtering" is the process of selecting data based on specific conditions.

[0316] "Ordering" refers to the act of requesting a restaurant to provide the food selected by the user.

[0317] "Delivery" refers to the act of delivering food prepared by a restaurant to a location designated by the user.

[0318] This invention is a system that allows users to select a specific dish and arrange for delivery from a nearby restaurant that serves that dish. The system is implemented by users operating a terminal and a server processing requests.

[0319] Hardware and software used

[0320] This system uses the following hardware and software:

[0321] Device: A device operated by a user, such as a smartphone, tablet, or computer.

[0322] Server: A computer system that receives and processes requests from users.

[0323] Database: We use a database management system, such as MySQL®, to store and retrieve restaurant information.

[0324] Programming language: Python is used to implement server-side processing.

[0325] Explanation of program processing

[0326] When a user selects "Delivery" on the device, the device sends a request to the server. The server obtains the user's location information from the request and searches a database for nearby restaurant information. It then filters restaurants that offer the appropriate cuisine based on the type of cuisine selected by the user. The server returns the filtered results to the device and displays them to the user. When the user selects a specific restaurant, the server sends a delivery order to that restaurant. The restaurant receives the order, prepares the food, and delivers it to the address specified by the user.

[0327] Specific examples

[0328] For example, if the user selects "dishes with fresh fish," the system operates as follows:

[0329] 1. The user selects "Delivery" on the device and enters "Fresh fish dishes."

[0330] 2. The device sends a request to the server.

[0331] 3. The server obtains the user's location information and searches a database for information on nearby restaurants.

[0332] 4. The server filters for restaurants that serve "fresh fish dishes."

[0333] 5. The filtered results are returned to the device and displayed to the user.

[0334] 6. Once the user selects a specific restaurant, the server sends a delivery order to that restaurant.

[0335] 7. The restaurant receives the order, prepares the food, and delivers it to the address specified by the user.

[0336] Prompt Sentence Examples

[0337] An example of a prompt sentence to input to the generative AI model is as follows:

[0338] If the user selects "Fresh fish dishes," please explain the steps your system takes to arrange delivery from a nearby restaurant.

[0339] By using this prompt sentence, the generative AI model can generate a sentence that explains the system's operation in detail. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0340] Step 1:

[0341] The user selects "Delivery" on a device. The user opens an application on a device such as a smartphone or PC and selects the "Delivery" option. The input is the user's selection, and the output is a request to select "Delivery."

[0342] Step 2:

[0343] The device sends a request to the server. The device sends a request to the server that includes the user's selection and location information. The input is the user's selection and location information, and the output is an HTTP request to the server.

[0344] Step 3:

[0345] The server obtains the user's location information. The server extracts the user's location information from the request and uses this to identify nearby restaurants. The input is the location information in the request, and the output is the user's location information.

[0346] Step 4:

[0347] The server searches the database for nearby restaurant information. The server queries the MySQL database to retrieve nearby restaurant information based on the user's location. The input is the user's location, and the output is nearby restaurant information.

[0348] Step 5:

[0349] The server filters restaurants based on the user's selected cuisine. The server filters restaurants that serve the user's selected cuisine (e.g., "dishes with fresh fish"). The input is nearby restaurant information and the user's selected cuisine, and the output is a filtered list of restaurants.

[0350] Step 6:

[0351] The server returns the filtering results to the terminal. The server returns the filtering results in JSON format to the terminal, and the terminal displays it to the user. The input is the filtered list of restaurants, and the output is the JSON response to the terminal.

[0352] Step 7:

[0353] The user selects a specific restaurant. The user selects a specific restaurant from the displayed list of restaurants. The input is the list of filtered results, and the output is the user's selection.

[0354] Step 8:

[0355] The server sends a delivery order to the selected restaurant. The server then sends a delivery request to the selected restaurant, including the user's order details. The input is the user's selection, and the output is an order request to the restaurant.

[0356] Step 9:

[0357] The restaurant receives the order, prepares the food, and delivers it. The restaurant receives the order, prepares the food, and delivers it to the specified address. The input is the order request from the server, and the output is the delivery of the food to the user.

[0358] (Application example 3)

[0359] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0360] Conventional food delivery systems have the problem that it is difficult for users to efficiently search for nearby restaurants and place an appropriate order when ordering specific dishes or ingredients. There is also a lack of systems that suggest dishes based on the user's mood and preferences, or provide options for delivery methods.

[0361] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that mood, select a delivery method (cooked at home / delivery / restaurant), and even complete payment; means for acquiring the user's location information and searching for nearby restaurants that serve the specified dish; and means for ordering the dish from a restaurant selected from the search results. This allows the user to easily select a dish based on their mood and preferences, and efficiently place a delivery order from a nearby restaurant.

[0362] "Exchanging user mood through text" is a process in which the user inputs their feelings and preferences in text format, and the system then suggests appropriate dishes based on that.

[0363] "Dish suggestions" means that the system selects appropriate dishes or menus based on the user's input information and presents them to the user.

[0364] "Method of delivery" refers to the means by which food is provided to users, whether that means cooking it yourself, delivery, or eating it at a restaurant.

[0365] "Payment" is the process by which a user pays for the food or service they have selected.

[0366] "Obtaining user location information" means determining the user's current location using GPS or other location information technology.

[0367] "Search for nearby restaurants that serve a specified dish" means using a database or API to find the nearest restaurant that serves the dish selected by the user.

[0368] "Ordering food from a restaurant selected from the search results" means selecting an appropriate restaurant from the restaurants found by the search and sending the user's order to that restaurant.

[0369] The system for implementing this invention has the function of allowing the user to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked / delivered / restaurant), and even process payment.It also has the function of obtaining the user's location information, searching for nearby restaurants that serve the specified dish, and ordering the dish from a restaurant selected from the search results.

[0370] Hardware and software used

[0371] Hardware: Smartphone

[0372] Software: Python, Geopy library, Requests library

[0373] System processing overview

[0374] 1. Communicate your mood through text:

[0375] Users enter their mood and preferences in text form through a smartphone application, for example, "I want to eat something made with fresh fish today."

[0376] 2. Food suggestions:

[0377] The server selects appropriate dishes and menus based on the user's input and presents them to the user, using a generative AI model to suggest dishes that best suit the user's mood.

[0378] 3. Delivery Method Selection:

[0379] The user selects the delivery method (cook / delivery / restaurant) for the proposed dish. For example, select "Delivery."

[0380] 4. Get the user's location:

[0381] The server uses the Geopy library to determine the user's current location. For example, if the user is in "Shibuya Ward, Tokyo," it obtains the latitude and longitude.

[0382] 5. Search for nearby restaurants:

[0383] The server uses the Requests library to search the API for nearby restaurants that serve a specific dish, for example, "dishes made with fresh fish."

[0384] 6. Food Ordering:

[0385] The server sends the user's order to the restaurant selected from the search results. For example, the user orders "dishes using fresh fish" from the first restaurant.

[0386] Specific examples

[0387] User input: A user living in Shibuya, Tokyo wants to order "dishes made with fresh fish."

[0388] API endpoints: https: / / api.example.com / restaurants and https: / / api.example.com / order / {restaurant_id}

[0389] Prompt Sentence Examples

[0390] Create a Python program that gets the user's location, searches for nearby restaurants that serve a specified dish, and arranges delivery. The user's location is "Shibuya-ku, Tokyo," and the dish is "dishes made with fresh fish." Use the Geopy and Requests libraries.

[0391] The above is a specific embodiment for carrying out the present invention.

[0392] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0393] Step 1:

[0394] A user inputs their mood and preferences in text format through a smartphone application. For example, they might input, "Today I want to eat a dish made with fresh fish." The input data is text information that expresses the user's mood and preferences. The output is the transmission of the user's input information to the server.

[0395] Step 2:

[0396] The server uses a generative AI model to select appropriate dishes and menus based on the user's input information and presents them to the user. The input data is text information that describes the user's mood and preferences. As part of the data processing, the generative AI model analyzes the text information and suggests the most suitable dishes. The output is a list of suggested dishes.

[0397] Step 3:

[0398] The user selects the delivery method (make-yourself / delivery / restaurant) for the proposed dish. For example, they select "delivery". The input data is the delivery method selected by the user. The output is that the selected delivery method is sent to the server.

[0399] Step 4:

[0400] The server uses the Geopy library to identify the user's current location. For example, if the user is in "Shibuya Ward, Tokyo," the server obtains the latitude and longitude. The input data is the user's location information. As data processing, the Geopy library analyzes the location information and obtains the latitude and longitude. The output is the user's latitude and longitude information.

[0401] Step 5:

[0402] The server uses the Requests library to search for nearby restaurants that serve a specified dish via an API. For example, searching for restaurants that serve "dishes using fresh fish." The input data is the user's latitude and longitude information and the specified dish. To process the data, an API request is sent to obtain information about nearby restaurants. The output is a list of restaurants as a search result.

[0403] Step 6:

[0404] The server sends the user's order to the restaurant selected from the search results. For example, the user orders a "dish using fresh fish" from the first restaurant. The input data is the list of restaurants as search results and the user's order information. The data is processed by sending an API request to confirm the order. The output is order confirmation information.

[0405] 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.

[0406] "Example 1"

[0407] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system allows the user to communicate their feelings in text, and the emotion engine recognizes those feelings. Based on the recognized emotion, the system makes dish suggestions, allows the user to select the delivery method (cooked at home, delivery, or restaurant), and even handles payment. For example, if a user inputs a feeling of "I want to try something new today," the emotion engine recognizes that feeling as "adventurous" or "a desire to explore new things." Based on the recognition results, the system makes suggestions such as "How about a dish using fresh fish?"

[0408] "Example 2"

[0409] In addition, if the user chooses to cook the food themselves, the system has a function that allows them to order the recipe and necessary ingredients from a nearby supermarket or online supermarket. For example, if the user selects "a dish using fresh fish," the system will provide the recipe for that dish and a list of ingredients to make it. Once the user selects the necessary ingredients, they can order them from a nearby supermarket or online supermarket.

[0410] "Example 3"

[0411] For delivery, the system has a function that allows ordering from nearby restaurants. For example, if you select "dishes using fresh fish," the system can arrange for delivery from a nearby restaurant that serves that dish.

[0412] The processing flow of each embodiment will be described below.

[0413] "Example 1"

[0414] Step 1: The user enters their mood into the system.

[0415] Step 2: The emotion engine recognizes emotions from the user's input.

[0416] Step 3: The system makes food recommendations based on the emotions it recognizes.

[0417] Step 4: The user selects the delivery method (make it yourself / delivery / restaurant). Step 5: The user makes the payment.

[0418] "Example 2"

[0419] Step 1: The user selects a dish suggested by the system.

[0420] Step 2: The system provides the recipe and ingredient list for the selected dish.

[0421] Step 3: The user selects the required material.

[0422] Step 4: The system arranges for the selected ingredients to be purchased from a nearby supermarket or online supermarket.

[0423] "Example 3"

[0424] Step 1: The user selects a dish suggested by the system.

[0425] Step 2: The system arranges delivery from nearby restaurants that serve the selected dish.

[0426] Example 1

[0427] Next, a description will be given of Example 1 of Form 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."

[0428] Conventional food recommendation systems have the problem of not taking into account the user's mood or emotions when making suggestions, resulting in low user satisfaction. In addition, there was no system that could handle everything from selecting the delivery method to payment, which was a hassle for users.

[0429] 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.

[0430] In this invention, the server includes means for a user to input their mood in text using a terminal, means for transmitting the input text data to the server, means for passing the text data received by the server to an emotion engine and analyzing the emotion, means for inputting a prompt sentence into a generative AI model based on the recognition result of the emotion engine and generating a proposal, means for transmitting the generated proposal to the terminal, means for the user to confirm the proposal and select a serving method, and means for the server to process payment through a payment system based on the selection. This makes it possible to suggest dishes based on the user's mood and emotions, and to perform a consistent process from selecting the serving method to paying, thereby increasing user satisfaction.

[0431] "User" refers to an individual who uses the system to input their mood and receive cooking suggestions.

[0432] "Terminal" refers to electronic devices such as PCs and smartphones used by users.

[0433] "Server" refers to the computer system that receives data from users and processes it using an emotion engine and generative AI models.

[0434] An "emotion engine" refers to software that analyzes emotions from text entered by the user.

[0435] A "generative AI model" refers to an artificial intelligence model that inputs prompt sentences based on the recognition results of the emotion engine and generates suggestions.

[0436] A "prompt" is an instruction given to a generative AI model to generate a specific suggestion.

[0437] "Suggestion" refers to the dish suggestion generated by the generative AI model based on the prompt.

[0438] "Delivery method" refers to the means by which the user chooses to deliver the food, which can be either cooking it themselves, delivery, or a restaurant.

[0439] "Payment System" refers to a system for processing payments based on the payment method selected by the User.

[0440] A "recipe" is a list of instructions and ingredients for making a dish.

[0441] "Ingredients" refers to the ingredients and seasonings needed to make a dish.

[0442] "Nearby Store" refers to a physical store near the user's location.

[0443] "Online Store" refers to a website or application that allows you to purchase products over the Internet.

[0444] "Restaurant" refers to a commercial establishment that serves food.

[0445] This invention is a system that allows users to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked food, delivery, or restaurant), and even process payment. This system is realized using hardware and software such as a server, terminals, an emotion engine, a generative AI model, and a database management system (DBMS).

[0446] A user uses a device (PC or smartphone) to input text describing their mood. For example, they might input, "I want to try a new dish today." The device then sends this input text data to the server. The server then passes the received text data to the emotion engine, which uses natural language processing (NLP) technology to analyze the user's emotions from the input text. For example, from the sentence, "I want to try a new dish today," it can recognize emotions such as "a sense of adventure" and "a desire to seek out new things."

[0447] Next, the server inputs a prompt sentence into the generative AI model based on the recognition results of the emotion engine. For example, a prompt sentence such as "Please suggest a dish that you would recommend when the user is feeling adventurous" is used. Based on this prompt sentence, the generative AI model generates a suggestion such as "How about a dish using fresh fish?" The server then sends the generated suggestion to the device.

[0448] The user checks the suggestions on the device and selects the delivery method (make-it-yourself / delivery / restaurant). For example, if the user selects "delivery," the device sends the selection to the server. Based on the user's selection, the server processes the payment through the delivery service's payment system. The server calls the payment system's API and sends the necessary payment information. Once the payment is completed, the server notifies the device of the result.

[0449] As a concrete example, a user uses a smartphone to type "I want to try something new today" and presses the send button. The smartphone converts this sentence into JSON format and sends it to the server using an HTTP POST request. The server parses the received JSON data and extracts the sentence. The sentence is then passed to the emotion engine, which uses NLP technology to analyze the emotion. The emotion engine recognizes "spirit of adventure" and "desire for novelty."

[0450] Based on the recognition results of the emotion engine, the server inputs a prompt sentence to the generative AI model: "Please suggest a dish you would recommend when the user is feeling adventurous." The generative AI model generates a suggestion: "How about a dish using fresh fish?" The server converts this suggestion into JSON format and sends it to the smartphone using an HTTP response. The user reviews the suggestion on the smartphone and selects "Delivery." The selection is converted back into JSON format and sent to the server using an HTTP POST request.

[0451] The server calls the delivery service's API and sends the user's payment information. Once the payment is complete, the server notifies the smartphone of the result in JSON format. The user receives a message on their smartphone saying "Payment completed."

[0452] In this way, the system handles everything from suggesting dishes that suit the user's mood to selecting the delivery method and handling payment.

[0453] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0454] Step 1:

[0455] The user uses the device to input a sentence describing their mood. For example, they might input, "I want to try a new dish today." The input sentence is saved as text data on the device.

[0456] Step 2:

[0457] The terminal sends the input text data to the server. Specifically, it converts the text data into JSON format and sends it to the server using an HTTP POST request. The input is the user's text data, and the output is an HTTP request to the server.

[0458] Step 3:

[0459] The server parses the received JSON data and extracts the text. Specifically, it uses a JSON parser to extract the text data. The input is JSON data, and the output is the extracted text data.

[0460] Step 4:

[0461] The server passes the extracted text data to the emotion engine for emotion analysis. The emotion engine uses natural language processing (NLP) technology to analyze the user's emotion from the input text. For example, from the sentence "I want to try a new dish today," it recognizes emotions such as "adventurous spirit" and "desire for novelty." The input is text data, and the output is recognized emotion data.

[0462] Step 5:

[0463] The server inputs a prompt sentence into the generative AI model based on the recognition results of the emotion engine. For example, a prompt sentence such as "Please suggest a dish that you would recommend when the user is feeling adventurous" can be used. The generative AI model generates a suggestion based on this prompt sentence. The input is emotion data and the prompt sentence, and the output is the generated suggestion.

[0464] Step 6:

[0465] The server sends the generated proposal to the terminal. Specifically, it converts the proposal data into JSON format and sends it to the terminal using an HTTP response. The input is the generated proposal data, and the output is the HTTP response to the terminal.

[0466] Step 7:

[0467] The user checks the suggestions on the device and selects the delivery method (cook / delivery / restaurant). For example, if the user selects "delivery," the selection is saved on the device. The input is the suggestion data, and the output is the user's selection data.

[0468] Step 8:

[0469] The terminal sends the user's selection data to the server. Specifically, the terminal converts the selection data into JSON format and sends it to the server using an HTTP POST request. The input is the user's selection data, and the output is an HTTP request to the server.

[0470] Step 9:

[0471] The server processes the payment through the delivery service's payment system based on the user's selection. Specifically, it calls the payment system's API and sends the necessary payment information. The input is the user's selection data and payment information, and the output is the payment result.

[0472] Step 10:

[0473] When the payment is completed, the server notifies the terminal of the result. Specifically, the server converts the payment result into JSON format and sends it to the terminal using an HTTP response. The input is the payment result, and the output is the HTTP response to the terminal.

[0474] Step 11:

[0475] The user receives a message on the terminal saying "Payment completed." The input is the payment result from the server, and the output is a notification to the user.

[0476] (Application example 1)

[0477] Next, a description will be given of Application Example 1 of Embodiment 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."

[0478] Conventional food delivery systems do not suggest dishes based on the user's mood or emotions, and users must choose the dishes themselves. Furthermore, there was no system that recognized the user's emotions and suggested appropriate dishes, making it difficult to increase user satisfaction. Furthermore, there was a lack of a system that consistently handled delivery methods and payment, resulting in low user convenience.

[0479] 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.

[0480] In this invention, the server includes a means for exchanging a user's mood in text and suggesting dishes based on that, a means for selecting a delivery method (cooking / delivery / restaurant), a means for processing payment, a means for recognizing the user's emotion using an emotion engine, a means for suggesting dishes based on the recognized emotion, and a means for inputting the user's mood via a smartphone or smart glasses. This makes it possible to suggest appropriate dishes based on the user's mood and emotion, and to handle everything from delivery method to payment in an integrated manner, thereby increasing user satisfaction and convenience.

[0481] A "user" is an individual who uses the system to input their mood and emotions and select food suggestions and serving methods.

[0482] "Mood" refers to the emotions and psychological state that a user is feeling at that time.

[0483] "Text interaction" is the process by which a user inputs their moods and emotions into the system in text form, and the system responds accordingly.

[0484] "Dish suggestion" refers to the system recommending appropriate dishes based on the user's mood and emotions.

[0485] "Delivery method" refers to the means by which food is delivered to users, and refers to the choice of making it yourself, delivery, or restaurant.

[0486] "Payment" is the process of paying for the food and delivery method selected by the user.

[0487] An "emotion engine" is software or an algorithm that recognizes emotions from text entered by the user and makes appropriate suggestions based on those emotions.

[0488] A "smartphone" is a mobile phone that can connect to the Internet and run applications.

[0489] "Smart glasses" are wearable devices that have built-in displays and sensors and allow users to operate them while obtaining visual information.

[0490] "Input" is the act of a user providing data such as text or voice to a system.

[0491] The system for implementing this invention allows users to communicate their mood in text, suggests dishes based on that, selects the delivery method (cooking / delivery / restaurant), and even handles payment. A specific embodiment of this system is described below.

[0492] System configuration

[0493] The system consists of the following main components:

[0494] 1. User terminal: A device through which the user inputs their mood, such as a smartphone or smart glasses.

[0495] 2. Server: The central system including the emotion engine, food suggestion engine, delivery service API, etc.

[0496] 3. Emotion engine: Software that recognizes emotions from text entered by the user.

[0497] 4. Food suggestion engine: Software that suggests appropriate dishes based on recognized emotions.

[0498] 5. Delivery Service API: An interface for delivering the food selected by the user.

[0499] Program processing

[0500] The server receives the mood statement sent from the user's device and recognizes the emotion using the emotion engine. Based on the recognized emotion, the food recommendation engine suggests appropriate dishes. If the user accepts the suggestion, they select the delivery method and complete the order through the delivery service API.

[0501] Hardware and software used

[0502] Hardware: Smartphones, smart glasses, servers

[0503] Software: Emotion Engine, Food Suggestion Engine, Delivery Service API

[0504] Specific examples

[0505] A user uses their smartphone to input, "I'd like to try something new today." The server receives this input and uses an emotion engine to recognize emotions such as "adventurous spirit" and "desire for novelty." The food suggestion engine then suggests, "How about a dish made with fresh fish?" If the user accepts this suggestion, the order is completed via the delivery service API.

[0506] Example prompt sentence:

[0507] User: I want to try something new today

[0508] System: You have a sense of adventure and a desire for novelty. How about some fresh fish?

[0509] User: I'll take that.

[0510] System: Your order has been placed. Please wait a moment for delivery.

[0511] In this way, it becomes possible to suggest appropriate dishes based on the user's mood and emotions, and the delivery method and payment can all be handled consistently, thereby increasing user satisfaction and convenience.

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] Users input their mood using a smartphone or smart glasses.

[0515] Input: Text that describes the user's mood (e.g., "I want to try something new today")

[0516] Output: User's mood text data

[0517] Specific operation: The user inputs their mood through the input interface of their smartphone or smart glasses, and sends it as text data to the server.

[0518] Step 2:

[0519] The server receives the user's mood text data and passes it to the emotion engine.

[0520] Input: User's mood text data

[0521] Output: Text data passed to the emotion engine

[0522] Specific operation: The server receives the text data sent by the user and passes it to the emotion engine as input data.

[0523] Step 3:

[0524] The emotion engine analyzes the user's mood text data and recognizes emotions.

[0525] Input: User's mood text data

[0526] Output: Recognized emotion data (e.g., "adventurous spirit" or "seeking novelty")

[0527] Specific operation: The emotion engine uses natural language processing technology to analyze text data and recognize the user's emotions. The recognized emotion data is returned to the server.

[0528] Step 4:

[0529] The server receives the recognized emotion data and passes it to the food suggestion engine.

[0530] Input: Recognized emotion data

[0531] Output: Emotion data passed to the food suggestion engine

[0532] Specific operation: The server receives the emotion data returned from the emotion engine and passes it as input data to the dish suggestion engine.

[0533] Step 5:

[0534] The food suggestion engine suggests appropriate dishes based on the recognized emotion data.

[0535] Input: Recognized emotion data

[0536] Output: Suggested dish data (e.g., "Dishes made with fresh fish")

[0537] Specific operation: The recipe suggestion engine analyzes the emotion data, selects appropriate recipes from the database, and returns the suggested recipe data to the server.

[0538] Step 6:

[0539] The server sends the suggested recipe data to the user's terminal.

[0540] Input: Suggested dish data

[0541] Output: A recipe suggestion message sent to the user device

[0542] Specific operation: The server receives the recipe data returned from the recipe suggestion engine and sends it as a message to the user device.

[0543] Step 7:

[0544] The user reviews the suggested dishes and chooses how they are served.

[0545] Input: Suggested dish data

[0546] Output: Selected delivery method data (e.g. "Delivery")

[0547] Specific operation: The user reviews the proposed dishes through the interface of their smartphone or smart glasses and selects the serving method.

[0548] Step 8:

[0549] The server receives the selected delivery method data and passes it to the delivery service API.

[0550] Input: Selected delivery method data

[0551] Output: Order data passed to the delivery service API

[0552] Specific operation: The server receives the delivery method data sent by the user and passes it as input data to the delivery service API.

[0553] Step 9:

[0554] The delivery service API processes the order and returns the order status to the server.

[0555] Input: Order data

[0556] Output: Order status data (e.g. "Order completed")

[0557] Specific operation: The delivery service API processes the order data and returns the order status to the server.

[0558] Step 10:

[0559] The server sends the order status data to the user terminal.

[0560] Input: Order status data

[0561] Output: Order status message sent to user terminal

[0562] Specific operation: The server receives the order status data returned from the delivery service API and sends it as a message to the user device.

[0563] Example 2

[0564] Next, a description will be given of Example 2 of Form 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."

[0565] Conventional recipe suggestion systems have the drawback of being cumbersome after users select a dish, requiring them to make specific arrangements and purchase ingredients, resulting in low user convenience. Furthermore, when users cook their own meals, there is a lack of efficient ways to arrange the necessary ingredients. Furthermore, when users select delivery, there is also the issue of being unable to smoothly order food from nearby restaurants.

[0566] 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. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment; means for retrieving the corresponding recipe and ingredient list from a database when the user selects a dish; and means for arranging the ingredients selected by the user from a nearby store or online store. This allows the user to easily obtain the recipe and ingredients for the dish and efficiently arrange the necessary ingredients. Furthermore, even if delivery is selected, the user can smoothly order the ingredients from a nearby restaurant.

[0567] "User" means an individual or organization that uses the system to receive food suggestions, select the delivery method, and make payment.

[0568] "Mood" is a sentence that the user inputs into the system to express their current feelings and wishes.

[0569] "Cuisine Suggestions" are specific cuisine options provided by the system based on the user's mood.

[0570] "Delivery method" refers to the means of delivery of food selected by the user, and can be one of "make it yourself," "delivery," or "restaurant."

[0571] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[0572] A "database" is a collection of information that allows the system to store cooking recipes and ingredient lists and retrieve them as needed.

[0573] A "recipe" is a document that details the steps and ingredients for making a particular dish.

[0574] An "ingredient list" is a list of ingredients needed to make a particular dish.

[0575] "Nearby stores" are physical stores that are close to the user's residence or current location.

[0576] An "online store" is a virtual store where products can be purchased over the Internet.

[0577] "Arrangement" refers to the process of purchasing the materials selected by the user and delivering them to the user.

[0578] "Delivery" is a service that delivers the food selected by the user to a specified location.

[0579] A "restaurant" is a physical establishment that serves the food selected by the user.

[0580] This invention is a system that allows users to receive recipe suggestions, select the serving method, and arrange the necessary ingredients. This system is realized using a server, user terminals, a database management system, an online supermarket API, and a generative AI model.

[0581] Hardware and software used

[0582] Hardware: Servers, user devices (smartphones, tablets, PCs)

[0583] Software: Web applications, database management systems (e.g., MySQL), online supermarket APIs, generative AI models (e.g., GPT-4)

[0584] Specific operation of the system

[0585] 1. The user selects a dish

[0586] A user accesses the system's web application using a terminal and searches for a food category or a specific food name. For example, the user selects "Fresh fish dishes."

[0587] 2. The server generates the recipe and ingredients list

[0588] Based on the user's selection, the server retrieves the appropriate recipe and ingredient list from a database management system. If necessary, it uses a generative AI model to complete the recipe details. For example, the server retrieves the recipe and ingredient list for "dishes using fresh fish" from the database and then uses a generative AI model to detail the recipe steps.

[0589] 3. Provide users with a recipe and ingredient list

[0590] The server sends the retrieved recipe and ingredient list to the user's device, where the user can check the recipe and ingredient list on the device screen. For example, the server generates a web page with the recipe and ingredient list for "dishes using fresh fish" and displays it on the user's device.

[0591] 4. The user selects the material

[0592] The user selects the ingredients they need from a provided list of ingredients. Selections are made using checkboxes and drop-down menus. For example, the user clicks the checkbox for "fresh fish, vegetables, and seasonings."

[0593] 5. The server arranges for the ingredients.

[0594] The server uses the online supermarket API to place an order to arrange for the ingredients selected by the user to be delivered to a nearby store or online store. The order details are notified to the user. For example, the server orders "fresh fish, vegetables, and seasonings" through the online supermarket API and sends an order confirmation email to the user.

[0595] Specific examples

[0596] User Action: The user selects "Fresh Fish Dishes" and sees the recipe and ingredients list.

[0597] Server processing: The server retrieves the relevant recipe and ingredient list from the database and provides them to the user.

[0598] Arranging materials: The user selects the required materials, and the server arranges the materials using the NetSupermarket API.

[0599] Example prompts for generative AI models

[0600] "Please provide a recipe and ingredient list for a dish using fresh fish."

[0601] "How can I order the ingredients selected by the user from a nearby supermarket or online supermarket?"

[0602] In this way, users can easily get cooking recipes and ingredients and arrange the necessary ingredients.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604] Step 1:

[0605] The user selects a dish

[0606] Input: A user uses a terminal to access the system's web application and search for a food category or a specific food name.

[0607] Data processing: Based on the user's input, the system displays food options.

[0608] Output: The user selects "Dishes with fresh fish."

[0609] What happens: A user types "fresh fish" into the search bar of a web application and clicks "fresh fish dishes" from the results.

[0610] Step 2:

[0611] The server generates the recipe and ingredients list

[0612] Input: Information about the dish selected by the user.

[0613] Data processing: The server retrieves the relevant recipe and ingredient list from a database management system (e.g., MySQL). If necessary, it uses a generative AI model (e.g., GPT-4) to complete the recipe details.

[0614] Output: Recipe and ingredients list.

[0615] What it does: The server retrieves the recipe and ingredient list for a "dish using fresh fish" from a database and uses a generative AI model to write detailed instructions for the recipe.

[0616] Step 3:

[0617] Providing users with recipes and ingredient lists

[0618] Input: A server-generated recipe and ingredient list.

[0619] Data processing: The server sends the acquired recipe and ingredient list to the user's device.

[0620] Output: The recipe and ingredients list displayed on the user's device.

[0621] Specific operation: The server generates a web page with a recipe and ingredient list for "dishes using fresh fish" and displays it on the user's device.

[0622] Step 4:

[0623] User selects material

[0624] Input: User provided materials list.

[0625] Data processing: The user selects the required materials from a list of materials provided, using checkboxes and drop-down menus.

[0626] Output: A list of materials selected by the user.

[0627] Specific Action: The user clicks the checkbox for "Fresh fish, vegetables, and seasonings" to select it.

[0628] Step 5:

[0629] The server arranges the ingredients

[0630] Input: A list of materials selected by the user.

[0631] Data processing: The server uses the NetSupermarket API to place an order to arrange for the ingredients selected by the user to be delivered to a nearby store or online store.

[0632] Output: Order confirmation notification.

[0633] Specific operation: The server orders "fresh fish, vegetables, and seasonings" through the online supermarket API and sends an order confirmation email to the user.

[0634] (Application example 2)

[0635] Next, a description will be given of Application Example 2 of Form 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."

[0636] Conventional food recommendation systems have had issues with being unable to centrally recommend dishes based on the user's mood, select the serving method, or arrange ingredients. Furthermore, when users cook their own meals, it takes time to arrange the necessary ingredients, and when ordering delivery, it is difficult to smoothly order from nearby restaurants.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0638] In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook-it-yourself / delivery / restaurant), and even complete payment; means for arranging the recipe and necessary ingredients from a nearby retail store or online retailer if cooking-it-yourself, and means for ordering from a nearby restaurant if delivery is selected; and means for providing the recipe and a list of necessary ingredients and arranging the ingredients from a nearby retail store or online retailer when the user selects a dish and chooses the "cook-it-yourself" option using a smartphone application. This makes it possible to centrally handle everything from suggesting dishes based on the user's mood to selecting a delivery method, arranging the ingredients, and arranging delivery.

[0639] A "user" is an individual who uses the system to suggest dishes and select the method of serving them.

[0640] "Mood" refers to the current feelings or state that a user expresses in writing.

[0641] "Dish suggestion" refers to the system recommending appropriate dishes based on the user's mood.

[0642] "Delivery method" refers to the method of receiving food that users can choose, and can be one of "make it yourself," "delivery," or "restaurant."

[0643] "Payment" means the act of paying a fee based on the delivery method selected by the user.

[0644] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[0645] An "ingredient list" is a list of ingredients needed to make a particular dish.

[0646] "Retail Store" refers to a nearby store where users can purchase the materials they need.

[0647] "Online retail store" refers to a store where users can purchase the materials they need via the Internet.

[0648] "Restaurant" refers to a nearby establishment that provides food when a user selects delivery.

[0649] A "smartphone application" is software that allows users to use the system via their smartphone.

[0650] A "generative AI model" is an artificial intelligence model that makes recipe suggestions based on user input.

[0651] A "prompt sentence" is an input sentence that provides a recipe and ingredient list based on the user's selection.

[0652] As an embodiment of the present invention, the following system is constructed.

[0653] System configuration

[0654] The system consists of a server that allows users to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked at home / delivery / restaurant), and even process payment, as well as a smartphone application used by the user.

[0655] Program processing

[0656] The server uses a generative AI model to suggest dishes based on user input. Once the user selects a dish and chooses the "make it yourself" option, the server generates a recipe and a list of ingredients, which the user can then use through a smartphone application, along with the means to source the ingredients from a local or online retailer.

[0657] In the case of delivery, the server will arrange for food to be delivered from a nearby restaurant, allowing users to enjoy their meal smoothly whether they choose to cook it themselves or have it delivered.

[0658] Hardware and software used

[0659] Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

[0660] Smartphone application: Mobile application for iOS and ANDROID (registered trademark)

[0661] Generative AI models: AI models for natural language processing (e.g., GPT-3, BERT)

[0662] Prompt: A prompt to provide a recipe and ingredient list based on the user's selection.

[0663] Specific examples

[0664] A user launches a smartphone application and enters a prompt like the following:

[0665] Prompt Sentence Examples

[0666] Enter the name of the dish: Fresh fish dish

[0667] Enter '1' for Build Your Own, '2' for Delivery: 1

[0668] Recipe:

[0669] 1. Wash the fish

[0670] 2. Cut the fish

[0671] 3. Grilling fish

[0672] 4. Add seasonings

[0673] Material List:

[0674] ['Fresh fish', 'Salt', 'Pepper', 'Olive oil']

[0675] Would you like to arrange materials? (y / n): y

[0676] The materials have been arranged.

[0677] Based on this prompt, the server uses a generative AI model to analyze the user's input, suggest an appropriate dish, and arrange the ingredients. The smartphone application displays the recipe and ingredients list to the user, and notifies them when the ingredients have been arranged.

[0678] In this way, users can easily choose the dish that best suits their mood and arrange the necessary ingredients. In addition, if they select delivery, they can have the food delivered from a nearby restaurant, which is extremely convenient.

[0679] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0680] Step 1:

[0681] The user launches the smartphone application and inputs the name of the dish and the serving method. As input, the user enters "Enter the name of the dish: Fresh fish dish" and "Enter '1' for cooking it yourself or '2' for delivery: 1" into the application. This allows the application to select a dish based on the user's mood and preferences.

[0682] Step 2:

[0683] The device sends user input, including the name of the dish and the serving method, to the server. The server receives this data and uses a generative AI model to generate prompts to suggest dishes.

[0684] Step 3:

[0685] The server uses a generative AI model to suggest dishes based on user input. As input, a prompt based on the user's mood and preferences is passed to the generative AI model. The generative AI model analyzes this prompt and generates an appropriate recipe and ingredient list. The output is the recipe and ingredient list.

[0686] Step 4:

[0687] The server sends the generated recipe and ingredient list to the device. The input includes the recipe and ingredient list obtained from the generative AI model. The device receives this data and displays it to the user. As an output, the user can view the recipe and ingredient list.

[0688] Step 5:

[0689] When a user wants to order a material, the terminal sends a request to the server, which includes the user's request as input. The server receives this request and arranges for the material to be ordered from a nearby retailer or an online retailer.

[0690] Step 6:

[0691] The server arranges for ingredients from a retail store or online retailer. The input includes a list of ingredients desired by the user. The server sends an order to the retail store or online retailer through an API. The output is that the ingredients have been arranged.

[0692] Step 7:

[0693] The server notifies the terminal that the material has been ordered. The input includes a notification from the retail store or online retailer that the material has been ordered. The terminal receives this notification and displays it to the user. The output is a confirmation to the user that the material has been ordered.

[0694] Step 8:

[0695] If the user selects delivery, the device sends a delivery request to the server, which includes the user's delivery preference as input. The server receives this request and places an order for the food from a nearby restaurant.

[0696] Step 9:

[0697] The server places a food order with a restaurant. The input includes the name of the dish desired by the user. The server sends the order to the restaurant through an API. The output is a completed food order.

[0698] Step 10:

[0699] The server notifies the terminal that the food order has been completed. The input includes a notification from the restaurant that the order has been completed. The terminal receives this notification and displays it to the user. As an output, the user can see that the food order has been completed.

[0700] Example 3

[0701] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0702] Conventional food recommendation systems have limited options for suggesting dishes and serving methods based on the user's mood, and when arranging delivery in particular, it is difficult to efficiently order from nearby restaurants based on the user's current location. Additionally, arranging the ingredients needed for cooking by the user is also complicated. To solve these issues, it is necessary to suggest dishes and select serving methods based on the user's mood, as well as to streamline delivery and ingredient arrangements.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0704] In this invention, the server includes means for allowing a user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, means for acquiring the user's current location information when the user selects delivery, means for acquiring information on nearby restaurants from a database based on the acquired current location information, means for filtering restaurants that serve the dishes selected by the user, means for displaying the filtered restaurant information to the user, and means for sending a delivery order to the restaurant when the user selects the desired restaurant. This makes it possible to suggest dishes and select a delivery method according to the user's mood, and further realizes more efficient delivery and ingredient arrangements.

[0705] "User" refers to an individual who uses the system to suggest dishes or arrange delivery.

[0706] "Delivery" refers to a service in which the food selected by the user is delivered from a nearby restaurant to a location specified by the user.

[0707] "Current location information" refers to the latitude and longitude location information obtained by the user's device.

[0708] "Eating and drinking establishments" refers to establishments that serve food, including restaurants and cafes.

[0709] "Database" refers to an information management system that allows a system to store information and search and retrieve it as needed.

[0710] "Filtering" refers to the process of sorting data based on specific conditions and extracting only the necessary information.

[0711] A "recipe" is a list of instructions and ingredients for making a dish.

[0712] "Retail store" refers to a store that sells goods to consumers, including supermarkets and online retailers.

[0713] "Order" means a request to purchase a dish or product selected by a User.

[0714] "Payment" refers to the process of paying for the food or product selected by the user.

[0715] This invention is a system that proposes dishes according to the user's mood, selects the serving method, and also improves the efficiency of delivery and ingredient arrangement. Specific embodiments of this system are described below.

[0716] System configuration

[0717] This system includes a user terminal, a server, a database, and an API provided by each restaurant. The user terminal is a mobile device such as a smartphone or tablet, and the server is located on the cloud. The database uses MySQL, and the restaurant API is provided by each restaurant.

[0718] Program processing

[0719] When a user selects "Delivery" using the device, the device uses GPS to obtain the user's current location. The device then sends a request containing the user's current location and the selected dish to the server. The server then queries a MySQL database to obtain information about restaurants within a certain distance of the user's current location.

[0720] The server filters the acquired restaurant information to restaurants that offer the food selected by the user. The filtered restaurant information is returned to the device in JSON format. The device displays the received list of restaurants to the user, and when the user selects the desired restaurant, the server sends a delivery order to that restaurant.

[0721] Specific examples

[0722] For example, if a user selects "dishes using fresh fish," the device obtains the user's current location and sends a request to the server. The server retrieves nearby restaurant information from a database and filters out restaurants that offer "dishes using fresh fish." The filtered restaurant information is returned to the device and displayed to the user. Once the user selects the desired restaurant, the server sends a delivery order to that restaurant.

[0723] Prompt Sentence Examples

[0724] Example prompts to input to a generative AI model:

[0725] If the user selects "Fresh Fish," how would the system arrange for delivery from a nearby restaurant? Please explain the specific steps.

[0726] In this way, a system is provided that can suggest dishes and select serving methods according to the user's mood, and also improve the efficiency of delivery and material arrangements. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0727] Step 1:

[0728] User selects delivery

[0729] The user opens the application on the device and selects the "Delivery" option. As input, the user's selection information is obtained. As output, the delivery selection flag is set.

[0730] Step 2:

[0731] The device obtains the user's current location information

[0732] The device uses its GPS function to obtain the user's current location information. Specifically, it uses the device's location information service to obtain latitude and longitude data. The device's location information service is used as input, and the user's current location information (latitude and longitude) is obtained as output.

[0733] Step 3:

[0734] The device sends a request to the server

[0735] The device sends a request to the server containing the user's current location and the selected dish. The request is sent using the HTTP protocol. The user's current location and the selected dish are used as input, and the request sent to the server is generated as output.

[0736] Step 4:

[0737] The server retrieves information about nearby restaurants from the database.

[0738] The server queries the MySQL database to get information about restaurants within a certain distance of the user's current location. Specifically, it executes a query such as "SELECT FROM restaurants WHERE distance < 5km". The user's current location is used as input, and nearby restaurant information is obtained as output.

[0739] Step 5:

[0740] The server filters restaurants that serve the user's selected dish.

[0741] The server filters the acquired restaurant information to find restaurants that offer the "dishes using fresh fish" selected by the user. Information about nearby restaurants and the dish information selected by the user are used as input, and the filtered restaurant information is obtained as output.

[0742] Step 6:

[0743] The server returns the filtering results to the device

[0744] The server returns the filtered list of restaurants in JSON format to the terminal. The filtered restaurant information is used as input and JSON format data is generated as output.

[0745] Step 7:

[0746] The device displays a list of restaurants to the user.

[0747] The terminal displays the received list of restaurants to the user. The list includes information such as the restaurant's name, rating, and distance. The JSON format data received from the server is used as input, and the list of restaurants displayed to the user is generated as output.

[0748] Step 8:

[0749] The user selects the restaurant they want to eat at.

[0750] The user selects a desired restaurant from the displayed list. The user's selection information is obtained as input, and information about the selected restaurant is obtained as output.

[0751] Step 9:

[0752] The server sends the delivery order to the selected restaurant.

[0753] The server sends a delivery request to the selected restaurant, including the user's order information. The request is sent using the restaurant's API. The selected restaurant information and the user's order information are used as input, and a delivery request is generated as output to be sent to the restaurant.

[0754] (Application example 3)

[0755] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0756] With conventional food delivery systems, it was difficult for users to specify the specific food they wanted to eat, and it was also difficult to efficiently search for that food from nearby restaurants and arrange delivery. Furthermore, there was a lack of a system that could consistently handle everything from suggesting dishes based on the user's mood and preferences, selecting the delivery method, and even payment. This resulted in a decrease in user convenience and a lack of satisfaction.

[0757] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0758] In this invention, the server includes a means for allowing a user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, a means for inputting the type of food the user wants to eat, searching for nearby restaurants, and listing restaurants that serve that food, and a means for arranging delivery from the restaurant selected by the user. This allows a user to easily specify the food they want to eat based on their mood and preferences, and efficiently arrange for delivery from a nearby restaurant.

[0759] "User" refers to an individual who uses the system to receive food suggestions and delivery.

[0760] "Mood" refers to the emotions the user is feeling at that time and their desire for food.

[0761] "Cuisine suggestions" refers to the cuisine options provided by the system based on the user's mood and preferences.

[0762] "Method of delivery" refers to the means by which food is delivered to the user, and can be cooking it yourself, having it delivered, or eating it at a restaurant.

[0763] "Payment" refers to the process by which a user makes payment for the food or service they have selected.

[0764] "Type of food you want to eat" refers to the category of food or a specific dish that the user specifically wants to eat.

[0765] "Nearby restaurants" refers to restaurants located within a certain distance from the user's current location.

[0766] "Listing" refers to the system displaying a list of restaurants that match the search results.

[0767] "Delivery" refers to a service in which the food selected by the user is delivered from the restaurant to the location specified by the user.

[0768] "Generative AI model" refers to an artificial intelligence model used to search for suitable restaurants and make dish suggestions based on user input.

[0769] A "prompt" is an instruction entered into a generative AI model to prompt it to take a specific action or respond.

[0770] The system for implementing this invention has the function of allowing users to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked / delivered / restaurant), and even process payment.It also has the function of allowing users to input the type of food they want to eat, search for nearby restaurants, list restaurants that serve the food, and arrange for delivery from the restaurant selected by the user.

[0771] Hardware and software used

[0772] Hardware: Smartphones, servers

[0773] Software: Python, Requests library, generative AI models

[0774] Data processing and calculation

[0775] 1. User input: Users input their mood and the type of food they want to eat through a smartphone application.

[0776] 2. Use of generative AI model: The server uses a generative AI model to suggest appropriate dishes based on the user's input. For example, if you input "dishes with fresh fish," the generative AI model will suggest dishes related to that dish.

[0777] 3. Find nearby restaurants: The server uses the Requests library to send a request to the Find Nearby Restaurants API and get a list of restaurants that serve the specified dish.

[0778] 4. Arrange Delivery: Send a request to arrange delivery from the restaurant of your choice and arrange delivery.

[0779] Specific examples

[0780] The user opens the smartphone application and inputs "dishes using fresh fish." The generative AI model suggests appropriate dishes based on the user's input and searches for nearby restaurants. For example, "Sushi Restaurant A" and "Seafood Restaurant B" are listed. The user selects "Sushi Restaurant A" and arranges for delivery. The server arranges for delivery, and the dish is delivered to the user.

[0781] Prompt Sentence Examples

[0782] When a user enters "dishes using fresh fish," generate a Python program that searches for nearby restaurants and returns a list of restaurants that serve that dish.

[0783] In this way, users can easily specify the food they want to eat based on their mood and preferences, and efficiently arrange delivery from nearby restaurants.

[0784] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0785] Step 1:

[0786] Users open the smartphone application and input their mood and the type of food they want to eat.

[0787] Input: User's mood or type of food they want to eat (e.g., "Fresh fish").

[0788] Output: User input data

[0789] What happens: The user enters "Fresh fish dishes" into the application's input field and presses the submit button.

[0790] Step 2:

[0791] The server uses a generative AI model to suggest appropriate dishes based on user input.

[0792] Input: User-entered data

[0793] Output: Recipe suggestions data

[0794] How it works: The server inputs prompts into the generative AI model, which then generates food suggestions that match the user's mood and preferences. For example, suggestions might be made such as "sushi made with fresh fish" or "seafood bowl."

[0795] Step 3:

[0796] The server sends a request to an API that searches for nearby restaurants and lists restaurants that serve the specified dish.

[0797] Input: Food recommendation data, user location information

[0798] Output: List of restaurants

[0799] Specific operation: The server sends a request to the API based on the user's current location and food suggestion data to search for nearby restaurants. For example, "Sushi Restaurant A" and "Seafood Restaurant B" are listed.

[0800] Step 4:

[0801] Users can select from a list of restaurants and arrange for delivery.

[0802] Input: List of restaurants, user selection

[0803] Output: Delivery arrangement data

[0804] Specific operation: The user selects "Sushi Restaurant A" on the application and presses the delivery order button. The server sends a delivery order request.

[0805] Step 5:

[0806] The server arranges delivery and the food is delivered to the user.

[0807] Input: Delivery arrangement data

[0808] Output: Delivery completion notification

[0809] Specific operation: The server sends a delivery request to the restaurant, the restaurant prepares the food and delivers it to the location specified by the user, and the user is notified that the delivery is complete.

[0810] 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.

[0811] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0812] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0813] 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.

[0814] [Second embodiment]

[0815] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0816] 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.

[0817] 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).

[0818] 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.

[0819] 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.

[0820] 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).

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0827] "Example 1"

[0828] The system of the present invention has a function that allows users to communicate their mood through text. Specifically, if a user inputs, "I'd like to try a new dish today," the system will suggest dishes that match that mood. For example, it may suggest, "How about a dish using fresh fish?"

[0829] "Example 2"

[0830] The system of the present invention has a function to select how to prepare the proposed dish. Specifically, if the user selects "make it yourself," the system provides a recipe and the necessary ingredients, allowing them to be ordered from a nearby supermarket or online supermarket. For example, if the user selects "dish using fresh fish," the system provides a recipe for that dish and a list of ingredients to make it.

[0831] "Example 3"

[0832] The system of the present invention has a function that allows delivery from nearby restaurants. Specifically, when a user selects "delivery," the system can order the corresponding dish from a nearby restaurant. For example, if a user selects "dishes using fresh fish," the system will arrange for delivery from a nearby restaurant that serves that dish.

[0833] The processing flow of each embodiment will be described below.

[0834] "Example 1"

[0835] Step 1: The user enters their mood into the system in a sentence, for example, "I want to try something new today."

[0836] Step 2: The system makes a dish suggestion based on the user's input, for example, "How about a dish with fresh fish?"

[0837] "Example 2"

[0838] Step 1: The user selects how they would like to serve the proposed dish, specifically, "Make it yourself."

[0839] Step 2: The system will provide a recipe and the necessary ingredients based on the selected delivery method, allowing you to arrange for delivery from a nearby supermarket or online supermarket. For example, if you select "Fresh fish dishes," the system will provide you with a recipe for that dish and a list of ingredients to make it.

[0840] "Example 3"

[0841] Step 1: The user selects the delivery method for the proposed dish, specifically, "Delivery."

[0842] Step 2: The system can order the corresponding dish from a nearby restaurant based on the delivery method selected. For example, if you select "dishes with fresh fish," the system will arrange delivery from a nearby restaurant that serves that dish.

[0843] Example 1

[0844] Next, a description will be given of Example 1 of Form 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."

[0845] Conventional food recommendation systems have difficulty in suggesting specific dishes based on the user's mood or preferences, and are unable to handle delivery methods and payment in a consistent manner. This has resulted in problems such as users having to spend time and effort to find a dish that suits their mood.

[0846] The specification process by the specification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for a user to input their mood and preferences into a terminal, a means for the terminal to send the input data to the server, a means for the server to send the received data to the generative AI model as a prompt sentence, a means for the generative AI model to analyze the prompt sentence and generate an appropriate suggestion, a means for the server to receive the generated suggestion and return it to the user, a means for the user to receive and confirm the suggestion, and a means for selecting a delivery method (make-yourself / delivery / restaurant) and making payment. This allows the user to receive suggestions for dishes that suit their mood, select a delivery method, and make a payment all in one go.

[0847] "User" refers to an individual who uses the system to input their mood and preferences and receive food suggestions.

[0848] A "terminal" is an electronic device used by a user to input their mood or wishes, and includes smartphones, personal computers, etc.

[0849] "Server" refers to the computer system responsible for receiving data submitted by the user, sending prompts to the generative AI model, and sending generated suggestions back to the user.

[0850] "Generative AI model" refers to an artificial intelligence model that analyzes user input data and generates appropriate dish suggestions.

[0851] A "prompt sentence" is an instruction sentence sent to a generative AI model based on user input data, and includes input information for the generative AI model to analyze.

[0852] "Delivery Method" is how a user chooses to obtain the suggested food, including cook-it-yourself, delivery, and restaurant options.

[0853] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[0854] A "recipe" is a document that contains instructions and a list of ingredients for making a dish.

[0855] "Ingredients" refers to the ingredients and seasonings needed to make a dish.

[0856] "Store" means a store where a user can purchase ingredients for cooking, including a local brick-and-mortar store or an online store.

[0857] "Restaurant" refers to establishments such as restaurants and cafes where users can order food for delivery.

[0858] This invention is a system that suggests appropriate dishes based on the user's mood and preferences, and handles everything from the delivery method to payment. The system works by having the user input their mood and preferences into a terminal, and the server receives and analyzes the data, then uses a generative AI model to generate appropriate suggestions.

[0859] Hardware and software used

[0860] Hardware: Servers, user devices (smartphones, personal computers, etc.)

[0861] Software: Generative AI models (e.g., OpenAI GPT-4), communication protocols (HTTP / HTTPS)

[0862] Specific operation of the system

[0863] Users use devices such as smartphones or personal computers to input their moods and desires in text, for example, "I want to try a new dish today." The device then sends this input data to the server using the HTTP / HTTPS protocol.

[0864] The server analyzes the received user input data and generates a prompt to send to the generative AI model. For example, if the user input is "I want to try a new dish today," the prompt will be "The user says they want to try a new dish. What kind of dish would you suggest?"

[0865] The generative AI model analyzes the prompt sent from the server and generates a dish suggestion that matches the user's mood. For example, it generates a suggestion such as, "How about a dish using fresh fish?" The server sends the suggestion received from the generative AI model back to the user. At this time, the suggestion is formatted in a way that is easy for the user to understand and sent to the device.

[0866] The user checks the suggestions displayed on the device and selects the delivery method (cooking, delivery, or restaurant). For example, if cooking, the user can arrange for the recipe and necessary ingredients from a nearby store or online store. If delivery is selected, the food can be ordered from a nearby restaurant. The user can then make payment based on the selected delivery method.

[0867] This system allows users to receive suggestions for dishes that suit their mood, choose how they would like them served, and complete the entire process from payment to delivery.

[0868] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0869] Step 1:

[0870] The user inputs their mood and desires into the device. Specifically, the user opens an application on their smartphone or personal computer and enters "I want to try a new dish today" in the text box. The input data is in text format and expresses the user's mood and desires.

[0871] Step 2:

[0872] The terminal sends the entered data to the server. The terminal converts the text data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is the user's request in text format, and the output is the JSON format data sent to the server.

[0873] Step 3:

[0874] The server sends the received data to the generative AI model as a prompt. The server parses the received JSON data and generates a prompt: "The user says they want to try a new dish. What dish would you suggest?" The input data is the user's preference in JSON format, and the output is the prompt sent to the generative AI model.

[0875] Step 4:

[0876] A generative AI model analyzes the prompt and generates an appropriate suggestion. A generative AI model (e.g., OpenAI GPT-4) analyzes the prompt sent from the server and generates a suggestion such as "Would you like a dish with fresh fish?" The input data is the prompt, and the output is the generated suggestion.

[0877] Step 5:

[0878] The server receives the generated proposal and sends it back to the user. The server converts the proposal received from the generative AI model into JSON format and sends it to the terminal as an HTTP response. The input data is the proposal from the generative AI model, and the output is the JSON-formatted proposal that is sent back to the user.

[0879] Step 6:

[0880] The user receives and confirms the suggestion. The user confirms the suggestion "How about a dish with fresh fish?" displayed on a smartphone or personal computer application. The input data is the suggestion from the server, and the output is the user's confirmation.

[0881] Step 7:

[0882] The user selects the delivery method and makes the payment. The user selects the delivery method (make-yourself, delivery, restaurant) based on the proposed dishes and makes the payment through the application. The input data is the user's selection and payment information, and the output is the selected delivery method and the completion of the payment.

[0883] (Application example 1)

[0884] Next, a description will be given of Application Example 1 of Form 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."

[0885] Conventional food recommendation systems have had the problem of being unable to recommend the right food based on the user's mood, and are unable to provide a consistent system for delivery and payment. In particular, there is a need for a system that allows users to easily find the food that best suits their mood that day and order it immediately.

[0886] 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.

[0887] In this invention, the server includes a means for communicating a user's mood in text and suggesting dishes based on that, a means for selecting a delivery method (cooked meal / delivery / restaurant), a means for processing payment, a means for suggesting dishes according to the user's mood using a generative AI model, a means for generating prompt sentences and inputting them into the generative AI model, and a means for obtaining suggestions from the generative AI model. This allows users to easily find dishes that suit their mood that day, select a delivery method, and quickly order and pay.

[0888] "User" refers to an individual who uses the system to receive recipe suggestions.

[0889] "Mood" refers to the emotions and desires a user is feeling at that time.

[0890] "Text interaction" refers to the user providing input to the system in text format and the system responding in text format accordingly.

[0891] "Dish suggestions" refers to the system recommending suitable dishes based on the user's mood.

[0892] "Delivery Method" refers to the options for how the food is delivered to the user, including cook-it-yourself, delivery, or restaurant.

[0893] "Payment" refers to the process of paying for the food and delivery method selected by the user.

[0894] The "system" refers to a device or software with a series of functions that suggests dishes based on the user's mood, selects the serving method, and even handles payment.

[0895] "Generative AI model" refers to an artificial intelligence model that suggests appropriate dishes based on user input.

[0896] A "prompt" refers to a textual instruction to be input to a generative AI model.

[0897] "Getting suggestions" refers to receiving the food recommendation results output by the generative AI model.

[0898] The system for implementing this invention has a set of functions that suggest dishes based on the user's mood, select the delivery method, and handle payment. The system receives user input and uses a generative AI model to suggest appropriate dishes based on that input.

[0899] The server has a means to communicate the user's mood in text and make food suggestions based on that. When a user uses a device such as a smartphone to input "I want to eat spicy food today," the server generates a prompt from that input. An example of a specific prompt is "The user inputted 'I want to eat spicy food today.' Please suggest a dish that matches my mood."

[0900] The generated prompt is input into a generative AI model, which uses an advanced natural language processing model such as OpenAI's GPT-3. The model suggests appropriate dishes based on the prompt and returns the results to the server. The server retrieves the suggestions from the generative AI model and presents them to the user.

[0901] The user checks the suggested dishes and selects the delivery method (cook it yourself, delivery, or restaurant). For example, if the user is offered a "spicy curry," and selects delivery, the server will place the order using a function that allows it to be ordered from a nearby restaurant.

[0902] Payment is made based on the delivery method selected by the user. The server processes the payment information and provides the user with a means to complete the payment. This allows users to easily find the food they want that day, select the delivery method, and quickly order and pay.

[0903] The system is implemented using devices such as smartphones and tablets connected to the internet. The software uses Python programs and OpenAI APIs. Data processing and calculations are performed by converting user input into prompts, inputting them into a generative AI model, and obtaining generated suggestions.

[0904] As a specific example, if a user inputs "I want to eat spicy food today," the system will suggest "How about spicy curry?" and then proceed to the delivery order screen. An example of a prompt sentence to input into the generative AI model is "The user inputs 'I want to eat spicy food today.' Please suggest a dish that suits their mood."

[0905] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0906] Step 1:

[0907] The user uses a device such as a smartphone to input text that suits their mood.

[0908] Input: A sentence that describes the user's mood (e.g., "I want to eat spicy food today")

[0909] Output: User input data

[0910] Step 2:

[0911] The terminal transmits the user's input data to the server.

[0912] Input: User-entered data

[0913] Output: User input data sent to the server

[0914] Step 3:

[0915] The server receives the user's input data and generates a prompt sentence.

[0916] Input: User-entered data

[0917] Data processing: Generate prompts based on user input (e.g., "The user entered, 'I want to eat spicy food today.' Please suggest dishes that fit that mood.")

[0918] Output: Generated prompt statement

[0919] Step 4:

[0920] The server inputs the generated prompt sentence into the generative AI model.

[0921] Input: Generated prompt text

[0922] Output: The prompt sentence fed into the generative AI model

[0923] Step 5:

[0924] The generative AI model generates appropriate dish suggestions based on the prompt sentence.

[0925] Input: A prompt sentence entered into the generative AI model

[0926] Data computation: A generative AI model analyzes the prompt and generates appropriate dish suggestions

[0927] Output: Generated food suggestions (e.g. "Would you like a spicy curry?")

[0928] Step 6:

[0929] The server retrieves suggestions from the generative AI model and presents them to the user.

[0930] Input: Generated dish suggestions

[0931] Output: Food suggestions presented to the user

[0932] Step 7:

[0933] The user reviews the proposed dishes and selects the delivery method (cook / delivery / restaurant).

[0934] Input: Food suggestions presented to the user

[0935] Output: Delivery method selected by the user

[0936] Step 8:

[0937] The server makes the necessary arrangements based on the delivery method selected by the user.

[0938] Input: User-selected delivery method

[0939] Data processing: Arrangements depending on the method of delivery (e.g., for delivery, ordering from a nearby restaurant)

[0940] Output: Notification of completion of arrangement

[0941] Step 9:

[0942] The user receives the food and makes payment based on the delivery method.

[0943] Input: Notification of completion of arrangement

[0944] Output: Notification of payment completion

[0945] In this way, a system is realized that processes and calculates data based on user input at each step, ultimately suggesting appropriate dishes to the user, and even handles selection of the serving method and payment in an integrated manner.

[0946] Example 2

[0947] Next, a description will be given of Example 2 of Form 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."

[0948] Conventional food recommendation systems have difficulty in consistently suggesting dishes based on the user's mood, selecting the delivery method, and even handling payment. Furthermore, when the user selects "make it yourself," there is a lack of efficient means to provide recipes and necessary ingredients, and to facilitate arrangements at nearby stores or online stores. Furthermore, there is also a lack of functionality to utilize the user's location information to provide optimal store information.

[0949] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a serving method (cook yourself / delivery / restaurant), and even complete payment; means for generating a recipe and a list of necessary ingredients using a generative AI model when the user selects "cook yourself"; and means for acquiring the user's location information and providing information on nearby stores or online stores. This allows the user to receive suggestions for dishes that suit their mood, select a serving method, and efficiently arrange for the necessary ingredients.

[0950] "User" means an individual or organization that uses the system to receive food suggestions and select the delivery method.

[0951] "Mood" refers to a sentence or keyword that indicates the user's current emotion or state, which the user inputs into the system.

[0952] "Cooking suggestions" refer to specific cooking ideas and recipes that the system provides based on the user's mood.

[0953] "Delivery method" refers to the method of receiving food that users can choose from: "Make it yourself," "Delivery," or "Restaurant."

[0954] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[0955] A "generative AI model" is an artificial intelligence algorithm or system that generates recipes and ingredient lists based on user input.

[0956] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[0957] An "ingredient list" is a list of ingredients and seasonings needed to make a particular dish.

[0958] "Location Information" is data that indicates a user's current location and is obtained through GPS and other location-based services.

[0959] "Nearby stores" are physical stores that sell ingredients and seasonings within reach of the user's current location.

[0960] "Online stores" are websites or applications that allow customers to order ingredients and seasonings over the Internet.

[0961] MODE FOR CARRYING OUT THE INVENTION

[0962] This invention is a system that proposes dishes based on the user's mood, selects the serving method, and performs the entire process from payment to completion. A specific embodiment of this system will be described below.

[0963] System configuration

[0964] The system consists of a user's device, a server, a generative AI model, a location service, and an external API.

[0965] User's device: A device used by a user to access the system, such as a smartphone or PC.

[0966] Server: A computer that manages the entire system and processes various data.

[0967] Generative AI models: For example, using a generative AI model such as GPT-4 to generate a recipe and ingredients list based on user input.

[0968] Location services: Obtaining user location information using Google Maps API, etc.

[0969] External APIs: We use external APIs such as Google Maps API and Amazon API to provide nearby store information and online store information.

[0970] System Operation

[0971] 1. User mood input

[0972] The user uses a terminal to access the system's interface and input their mood in a sentence, for example, "I want to eat something light today."

[0973] 2. Food suggestions

[0974] The server uses a generative AI model to suggest appropriate dishes based on the user's mood. For example, the following prompt sentence is input to the generative AI model:

[0975] Suggest dishes when a user types, "I want something light today."

[0976] The generative AI model suggests dishes such as "refreshing salad."

[0977] 3. Selection of delivery method

[0978] The user can select one of the following delivery methods for the proposed dish: "Make it yourself," "Delivery," or "Restaurant." For example, the user can select "Make it yourself."

[0979] 4. Generate recipes and ingredient lists

[0980] If the user selects "Make it yourself," the server uses a generative AI model to generate a recipe and ingredients list. For example, the following prompt is input to the generative AI model:

[0981] Generate a recipe and ingredient list if the user selects "Light Salad."

[0982] The generative AI model returns a response like this:

[0983] Recipe: "Refreshing Salad"

[0984] Ingredients list: lettuce, tomato, cucumber, olive oil, lemon

[0985] 5. Obtaining location information

[0986] The server obtains location information from the user's device. This process uses location services such as Google Maps API. If the user allows location information to be provided, the server obtains the user's current location.

[0987] 6. Providing information about nearby stores

[0988] The server uses the acquired location information to provide information about nearby stores or online stores. This process uses external APIs such as Google Maps API or Amazon API. The server provides a list of stores closest to the user's current location and links to online stores.

[0989] Specific examples

[0990] For example, if the user selects "dishes using fresh fish," the server performs the following process.

[0991] 1. The user selects "Fresh fish dishes."

[0992] 2. The server uses a generative AI model to generate a recipe and ingredients list like this:

[0993] Recipe: "Grilled Fresh Fish"

[0994] Ingredients: Fish, salt, pepper, olive oil, lemon

[0995] 3. The server uses the Google Maps API to obtain the user's location and provide information about nearby stores.

[0996] 4. Using the information provided, the user can purchase ingredients at a local store or order them online.

[0997] In this way, users can receive recipe suggestions based on their mood, choose how they would like to be served, and efficiently arrange the necessary ingredients.

[0998] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0999] Step 1:

[1000] A user accesses the system interface using a terminal and inputs his / her mood in text.

[1001] Input: A sentence that describes the user's mood (e.g., "I want something light today").

[1002] Output: Data based on user mood.

[1003] Specific actions: A user opens the app on their smartphone, enters "I want to eat something light today" in the text box, and taps the send button.

[1004] Step 2:

[1005] The server uses a generative AI model to suggest appropriate dishes based on the user's mood.

[1006] Input: A sentence that indicates the user's mood.

[1007] Output: A suggested dish (e.g., "a refreshing salad").

[1008] Specific operation: The server sends the following prompt to the generative AI model.

[1009] Suggest dishes when a user types, "I want something light today."

[1010] The generative AI model suggests a "refreshing salad," and the server sends that information to the user's device.

[1011] Step 3:

[1012] The user selects one of the following delivery methods for the proposed dish: "Make it yourself," "Delivery," or "Restaurant."

[1013] Input: Suggested dish and serving options.

[1014] Output: The delivery method selected by the user (e.g., "Build it yourself").

[1015] Specific operation: The user taps and selects "Make it myself" from the delivery method options displayed on the device screen.

[1016] Step 4:

[1017] If the user selects "make it yourself," the server uses a generative AI model to generate a recipe and ingredients list.

[1018] Input: The dish selected by the user (e.g., "light salad").

[1019] Output: A recipe and a list of ingredients (e.g., "Recipe: Light salad, Ingredients: Lettuce, tomato, cucumber, olive oil, lemon").

[1020] Specific operation: The server sends the following prompt to the generative AI model.

[1021] Generate a recipe and ingredient list if the user selects "Light Salad."

[1022] The generative AI model generates the recipe and ingredients list, and the server sends that information to the user's device.

[1023] Step 5:

[1024] The server obtains location information from the user's device.

[1025] Input: User location permission.

[1026] Output: User's current location.

[1027] Specific behavior: The server uses the Google Maps API to obtain the user's current location. The user taps "Allow" in the dialog box that allows them to provide their location information.

[1028] Step 6:

[1029] Based on the location information acquired by the server, information on nearby stores or online stores is provided.

[1030] Input: User's current location.

[1031] Output: A list of nearby stores or a link to the online store.

[1032] Specific operation: The server uses the Google Maps API or Amazon API to obtain a list of stores closest to the user's current location and sends that information to the user's device.

[1033] Step 7:

[1034] Based on the information provided, the user can purchase ingredients at a nearby store or order them from an online store.

[1035] Input: Nearby store list or online store link.

[1036] Output: Purchase of materials or completion of order.

[1037] What happens: The user chooses the closest supermarket from the list of supermarkets provided and either goes to the supermarket to buy the ingredients, or taps a link to an online store to order online.

[1038] (Application example 2)

[1039] Next, a description will be given of Application Example 2 of Form 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."

[1040] In conventional recipe suggestion systems, after the user selects a dish, the process of arranging ingredients and delivery is complicated, resulting in a poor user experience. Also, when users cook their own meals, arranging the necessary ingredients is time-consuming, making it difficult to cook smoothly.

[1041] The specific processing by the specific 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 means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, means for generating a recipe and ingredient list for the selected dish using a generative AI model, means for arranging ingredients through the API of a nearby store or an online store, and means for delivering the ingredients to the user using the API of a delivery service. This makes it possible to provide a recipe and ingredient list for the dish selected by the user, and further arrange for the ingredients to be delivered.

[1042] "User" means an individual or organization that uses the system to suggest dishes and arrange ingredients.

[1043] "Mood" is information that indicates the current emotion or state that the user inputs into the system.

[1044] "Cuisine suggestions" are meal options provided by the system based on the user's mood.

[1045] "Serving Method" indicates how the user will serve the selected dish, and can be "Make Your Own," "Delivery," or "Restaurant."

[1046] "Payment" refers to the payment procedure carried out by the user for the purchase of the dishes and ingredients selected by the user.

[1047] A "generative AI model" is an artificial intelligence model that generates cooking recipes and ingredient lists based on user input.

[1048] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[1049] An "ingredient list" is a list of ingredients needed to make a particular dish.

[1050] A "nearby store" is a physical store that is close to the user's location.

[1051] An "online store" is a store that sells products over the Internet.

[1052] "API" stands for Application Program Interface, a means by which different software systems communicate with each other.

[1053] "Delivery service" refers to a service that delivers ordered products to users.

[1054] The system for implementing this invention has the function of allowing users to communicate their mood in text, suggest dishes based on that, select the delivery method (cook it yourself / delivery / restaurant), and even process payment. A specific embodiment of this system is described below.

[1055] Hardware and software used

[1056] Hardware: Smartphones, servers

[1057] Software: Mobile applications, cloud-based databases, generative AI models (e.g., GPT-4)

[1058] System configuration

[1059] 1. User Interface

[1060] Users access the system through a smartphone application.

[1061] Users enter their mood in text and receive cooking suggestions.

[1062] 2. Recipe generation

[1063] The server uses a generative AI model (e.g., GPT-4) to generate a recipe and ingredient list for the dish selected by the user.

[1064] The generated recipe and ingredient list are displayed to the user.

[1065] 3. Arrange materials

[1066] If the user selects "make it yourself," the server arranges for the ingredients through APIs of nearby stores or online stores.

[1067] If the user selects "delivery," the server uses the delivery service's API to arrange for the materials to be delivered to the user.

[1068] 4. Payment

[1069] Users make payments through the application.

[1070] Once payment is complete, material arrangements and delivery will begin.

[1071] Processing flow

[1072] 1. User Interface

[1073] The user opens the app and enters their mood.

[1074] For example: "I feel like relaxing today."

[1075] 2. Recipe generation

[1076] The server inputs the following prompt sentence into the generative AI model:

[1077] Example prompt: "The user selected 'Dishes with fresh fish.' Generate a recipe for this dish and a list of the ingredients needed."

[1078] The generated recipe and ingredient list are displayed to the user.

[1079] Example: "Recipe: Salmon Meunière, Ingredients: 2 salmon fillets, a pinch of salt, a pinch of pepper, some flour, 30g butter, 1 lemon, some parsley"

[1080] 3. Arrange materials

[1081] When the user selects "Order ingredients," the server orders the ingredients through the API of a nearby store or online store.

[1082] For example: "I ordered salmon fillets and other ingredients from a local supermarket."

[1083] 4. Delivery arrangements

[1084] Use the delivery service's API to arrange for the materials to be delivered to the user.

[1085] For example: "The materials will be delivered to the user via a delivery service."

[1086] 5. Notification

[1087] Inform users about delivery progress.

[1088] For example: "Materials are on their way. Estimated arrival time is 6:00 PM."

[1089] In this way, the system can provide a recipe and ingredient list for the dish selected by the user, and then arrange for the ingredients to be delivered.

[1090] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1091] Step 1:

[1092] The user opens the smartphone application and inputs their mood. The input mood is sent to the server as text data. The server receives this text data and analyzes it. The input data is a sentence such as "I feel like relaxing today."

[1093] Step 2:

[1094] The server inputs a prompt into a generative AI model (e.g., GPT-4) based on the analyzed mood data. An example of a prompt is, "The user selected 'dishes using fresh fish.' Please generate a recipe for this dish and a list of ingredients." The generative AI model generates a recipe and list of ingredients based on this prompt. The output data is "Recipe: Salmon meunière, Ingredients list: 2 salmon fillets, a little salt, a little pepper, flour to taste, 30g butter, 1 lemon, parsley to taste."

[1095] Step 3:

[1096] The server sends the generated recipe and ingredient list to the user's smartphone. The user can view the recipe and ingredient list on their smartphone screen. The input data is the output data from the generative AI model, and the output data is the recipe and ingredient list displayed on the user's smartphone.

[1097] Step 4:

[1098] If the user selects "Make it yourself", the server arranges for the ingredients through the API of a nearby store or online store. The server sends the ingredients list to the API and orders the required ingredients. The input data is the ingredients list, and the output data is the order confirmation from the store.

[1099] Step 5:

[1100] If the user selects "Delivery," the server uses the delivery service's API to arrange for the materials to be delivered to the user. The server sends the materials list and the user's address information to the API to arrange delivery. The input data is the materials list and address information, and the output data is the delivery progress.

[1101] Step 6:

[1102] The server notifies the user of the progress of the delivery. The user can check the progress of the delivery and the estimated arrival time through a smartphone application. The input data is the progress information from the delivery service, and the output data is the notification to the user.

[1103] In this way, the system can provide a recipe and ingredient list for the dish selected by the user, and then arrange for the ingredients to be delivered.

[1104] Example 3

[1105] Next, a description will be given of Example 3 of Form Example 3. 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."

[1106] With conventional delivery systems, when a user selects a specific dish, it is difficult to efficiently search for nearby restaurants that serve that dish and place an order. Furthermore, there is a lack of functionality to identify the most suitable restaurant based on the user's location information, which hinders the user experience.

[1107] The specification process by the specification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for a user to select "delivery" on a terminal, a means for the terminal to send a request to the server, a means for the server to obtain the user's location information, a means for the server to search a database for information on nearby restaurants, a means for the server to filter restaurants based on the user's selected dish, a means for the server to return the filtering results to the terminal, a means for the user to select a specific restaurant, a means for the server to send a delivery order to the selected restaurant, and a means for the restaurant to receive the order, prepare the dish, and deliver it. This makes it possible for a user, when selecting a specific dish, to efficiently search for nearby restaurants that provide that dish and place an order.

[1108] "User" means an individual or organization that uses the System to order food delivery.

[1109] "Terminal" refers to a device operated by a user, including a smartphone, tablet, or PC.

[1110] A "server" is a computer system that receives and processes requests from users.

[1111] A "request" is request information that a user sends to a server through a terminal.

[1112] "Location information" is data that indicates a user's current location and includes geographic coordinates such as latitude and longitude.

[1113] A "database" is a collection of data that stores information systematically and can be searched and updated.

[1114] "Eating establishments" are establishments that serve food, including restaurants, cafes, and fast food restaurants.

[1115] "Filtering" is the process of selecting data based on specific conditions.

[1116] "Ordering" refers to the act of requesting a restaurant to provide the food selected by the user.

[1117] "Delivery" refers to the act of delivering food prepared by a restaurant to a location designated by the user.

[1118] This invention is a system that allows users to select a specific dish and arrange for delivery from a nearby restaurant that serves that dish. The system is implemented by users operating a terminal and a server processing requests.

[1119] Hardware and software used

[1120] This system uses the following hardware and software:

[1121] Device: A device operated by a user, such as a smartphone, tablet, or computer.

[1122] Server: A computer system that receives and processes requests from users.

[1123] Database: Use a database management system such as MySQL to store and retrieve restaurant information.

[1124] Programming language: Python is used to implement server-side processing.

[1125] Explanation of program processing

[1126] When a user selects "Delivery" on the device, the device sends a request to the server. The server obtains the user's location information from the request and searches a database for nearby restaurant information. It then filters restaurants that offer the appropriate cuisine based on the type of cuisine selected by the user. The server returns the filtered results to the device and displays them to the user. When the user selects a specific restaurant, the server sends a delivery order to that restaurant. The restaurant receives the order, prepares the food, and delivers it to the address specified by the user.

[1127] Specific examples

[1128] For example, if the user selects "dishes with fresh fish," the system operates as follows:

[1129] 1. The user selects "Delivery" on the device and enters "Fresh fish dishes."

[1130] 2. The device sends a request to the server.

[1131] 3. The server obtains the user's location information and searches a database for information on nearby restaurants.

[1132] 4. The server filters for restaurants that serve "fresh fish dishes."

[1133] 5. The filtered results are returned to the device and displayed to the user.

[1134] 6. Once the user selects a specific restaurant, the server sends a delivery order to that restaurant.

[1135] 7. The restaurant receives the order, prepares the food, and delivers it to the address specified by the user.

[1136] Prompt Sentence Examples

[1137] An example of a prompt sentence to input to the generative AI model is as follows:

[1138] If the user selects "Fresh fish dishes," please explain the steps your system takes to arrange delivery from a nearby restaurant.

[1139] By using this prompt sentence, the generative AI model can generate a sentence that explains the system's operation in detail. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1140] Step 1:

[1141] The user selects "Delivery" on a device. The user opens an application on a device such as a smartphone or PC and selects the "Delivery" option. The input is the user's selection, and the output is a request to select "Delivery."

[1142] Step 2:

[1143] The device sends a request to the server. The device sends a request to the server that includes the user's selection and location information. The input is the user's selection and location information, and the output is an HTTP request to the server.

[1144] Step 3:

[1145] The server obtains the user's location information. The server extracts the user's location information from the request and uses this to identify nearby restaurants. The input is the location information in the request, and the output is the user's location information.

[1146] Step 4:

[1147] The server searches the database for nearby restaurant information. The server queries the MySQL database to retrieve nearby restaurant information based on the user's location. The input is the user's location, and the output is nearby restaurant information.

[1148] Step 5:

[1149] The server filters restaurants based on the user's selected cuisine. The server filters restaurants that serve the user's selected cuisine (e.g., "dishes with fresh fish"). The input is nearby restaurant information and the user's selected cuisine, and the output is a filtered list of restaurants.

[1150] Step 6:

[1151] The server returns the filtering results to the terminal. The server returns the filtering results in JSON format to the terminal, and the terminal displays it to the user. The input is the filtered list of restaurants, and the output is the JSON response to the terminal.

[1152] Step 7:

[1153] The user selects a specific restaurant. The user selects a specific restaurant from the displayed list of restaurants. The input is the list of filtered results, and the output is the user's selection.

[1154] Step 8:

[1155] The server sends a delivery order to the selected restaurant. The server then sends a delivery request to the selected restaurant, including the user's order details. The input is the user's selection, and the output is an order request to the restaurant.

[1156] Step 9:

[1157] The restaurant receives the order, prepares the food, and delivers it. The restaurant receives the order, prepares the food, and delivers it to the specified address. The input is the order request from the server, and the output is the delivery of the food to the user.

[1158] (Application example 3)

[1159] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1160] Conventional food delivery systems have the problem that it is difficult for users to efficiently search for nearby restaurants and place an appropriate order when ordering specific dishes or ingredients. There is also a lack of systems that suggest dishes based on the user's mood and preferences, or provide options for delivery methods.

[1161] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that mood, select a delivery method (cooked at home / delivery / restaurant), and even complete payment; means for acquiring the user's location information and searching for nearby restaurants that serve the specified dish; and means for ordering the dish from a restaurant selected from the search results. This allows the user to easily select a dish based on their mood and preferences, and efficiently place a delivery order from a nearby restaurant.

[1162] "Exchanging user mood through text" is a process in which the user inputs their feelings and preferences in text format, and the system then suggests appropriate dishes based on that.

[1163] "Dish suggestions" means that the system selects appropriate dishes or menus based on the user's input information and presents them to the user.

[1164] "Method of delivery" refers to the means by which food is provided to users, whether that means cooking it yourself, delivery, or eating it at a restaurant.

[1165] "Payment" is the process by which a user pays for the food or service they have selected.

[1166] "Obtaining user location information" means determining the user's current location using GPS or other location information technology.

[1167] "Search for nearby restaurants that serve a specified dish" means using a database or API to find the nearest restaurant that serves the dish selected by the user.

[1168] "Ordering food from a restaurant selected from the search results" means selecting an appropriate restaurant from the restaurants found by the search and sending the user's order to that restaurant.

[1169] The system for implementing this invention has the function of allowing the user to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked / delivered / restaurant), and even process payment.It also has the function of obtaining the user's location information, searching for nearby restaurants that serve the specified dish, and ordering the dish from a restaurant selected from the search results.

[1170] Hardware and software used

[1171] Hardware: Smartphone

[1172] Software: Python, Geopy library, Requests library

[1173] System processing overview

[1174] 1. Communicate your mood through text:

[1175] Users enter their mood and preferences in text form through a smartphone application, for example, "I want to eat something made with fresh fish today."

[1176] 2. Food suggestions:

[1177] The server selects appropriate dishes and menus based on the user's input and presents them to the user, using a generative AI model to suggest dishes that best suit the user's mood.

[1178] 3. Delivery Method Selection:

[1179] The user selects the delivery method (cook / delivery / restaurant) for the proposed dish. For example, select "Delivery."

[1180] 4. Get the user's location:

[1181] The server uses the Geopy library to determine the user's current location. For example, if the user is in "Shibuya Ward, Tokyo," it obtains the latitude and longitude.

[1182] 5. Search for nearby restaurants:

[1183] The server uses the Requests library to search the API for nearby restaurants that serve a specific dish, for example, "dishes made with fresh fish."

[1184] 6. Food Ordering:

[1185] The server sends the user's order to the restaurant selected from the search results. For example, the user orders "dishes using fresh fish" from the first restaurant.

[1186] Specific examples

[1187] User input: A user living in Shibuya, Tokyo wants to order "dishes made with fresh fish."

[1188] API endpoints: https: / / api.example.com / restaurants and https: / / api.example.com / order / {restaurant_id}

[1189] Prompt Sentence Examples

[1190] Create a Python program that gets the user's location, searches for nearby restaurants that serve a specified dish, and arranges delivery. The user's location is "Shibuya-ku, Tokyo," and the dish is "dishes made with fresh fish." Use the Geopy and Requests libraries.

[1191] The above is a specific embodiment for carrying out the present invention.

[1192] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1193] Step 1:

[1194] A user inputs their mood and preferences in text format through a smartphone application. For example, they might input, "Today I want to eat a dish made with fresh fish." The input data is text information that expresses the user's mood and preferences. The output is the transmission of the user's input information to the server.

[1195] Step 2:

[1196] The server uses a generative AI model to select appropriate dishes and menus based on the user's input information and presents them to the user. The input data is text information that describes the user's mood and preferences. As part of the data processing, the generative AI model analyzes the text information and suggests the most suitable dishes. The output is a list of suggested dishes.

[1197] Step 3:

[1198] The user selects the delivery method (make-yourself / delivery / restaurant) for the proposed dish. For example, they select "delivery". The input data is the delivery method selected by the user. The output is that the selected delivery method is sent to the server.

[1199] Step 4:

[1200] The server uses the Geopy library to identify the user's current location. For example, if the user is in "Shibuya Ward, Tokyo," the server obtains the latitude and longitude. The input data is the user's location information. As data processing, the Geopy library analyzes the location information and obtains the latitude and longitude. The output is the user's latitude and longitude information.

[1201] Step 5:

[1202] The server uses the Requests library to search for nearby restaurants that serve a specified dish via an API. For example, searching for restaurants that serve "dishes using fresh fish." The input data is the user's latitude and longitude information and the specified dish. To process the data, an API request is sent to obtain information about nearby restaurants. The output is a list of restaurants as a search result.

[1203] Step 6:

[1204] The server sends the user's order to the restaurant selected from the search results. For example, the user orders a "dish using fresh fish" from the first restaurant. The input data is the list of restaurants as search results and the user's order information. The data is processed by sending an API request to confirm the order. The output is order confirmation information.

[1205] 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.

[1206] "Example 1"

[1207] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system allows the user to communicate their feelings in text, and the emotion engine recognizes those feelings. Based on the recognized emotion, the system makes dish suggestions, allows the user to select the delivery method (cooked at home, delivery, or restaurant), and even handles payment. For example, if a user inputs a feeling of "I want to try something new today," the emotion engine recognizes that feeling as "adventurous" or "a desire to explore new things." Based on the recognition results, the system makes suggestions such as "How about a dish using fresh fish?"

[1208] "Example 2"

[1209] In addition, if the user chooses to cook the food themselves, the system has a function that allows them to order the recipe and necessary ingredients from a nearby supermarket or online supermarket. For example, if the user selects "a dish using fresh fish," the system will provide the recipe for that dish and a list of ingredients to make it. Once the user selects the necessary ingredients, they can order them from a nearby supermarket or online supermarket.

[1210] "Example 3"

[1211] For delivery, the system has a function that allows ordering from nearby restaurants. For example, if you select "dishes using fresh fish," the system can arrange for delivery from a nearby restaurant that serves that dish.

[1212] The processing flow of each embodiment will be described below.

[1213] "Example 1"

[1214] Step 1: The user enters their mood into the system.

[1215] Step 2: The emotion engine recognizes emotions from the user's input.

[1216] Step 3: The system makes food recommendations based on the emotions it recognizes.

[1217] Step 4: The user selects the delivery method (make it yourself / delivery / restaurant). Step 5: The user makes the payment.

[1218] "Example 2"

[1219] Step 1: The user selects a dish suggested by the system.

[1220] Step 2: The system provides the recipe and ingredient list for the selected dish.

[1221] Step 3: The user selects the required material.

[1222] Step 4: The system arranges for the selected ingredients to be purchased from a nearby supermarket or online supermarket.

[1223] "Example 3"

[1224] Step 1: The user selects a dish suggested by the system.

[1225] Step 2: The system arranges delivery from nearby restaurants that serve the selected dish.

[1226] Example 1

[1227] Next, a description will be given of Example 1 of Form 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."

[1228] Conventional food recommendation systems have the problem of not taking into account the user's mood or emotions when making suggestions, resulting in low user satisfaction. In addition, there was no system that could handle everything from selecting the delivery method to payment, which was a hassle for users.

[1229] 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.

[1230] In this invention, the server includes means for a user to input their mood in text using a terminal, means for transmitting the input text data to the server, means for passing the text data received by the server to an emotion engine and analyzing the emotion, means for inputting a prompt sentence into a generative AI model based on the recognition result of the emotion engine and generating a proposal, means for transmitting the generated proposal to the terminal, means for the user to confirm the proposal and select a serving method, and means for the server to process payment through a payment system based on the selection. This makes it possible to suggest dishes based on the user's mood and emotions, and to perform a consistent process from selecting the serving method to paying, thereby increasing user satisfaction.

[1231] "User" refers to an individual who uses the system to input their mood and receive cooking suggestions.

[1232] "Terminal" refers to electronic devices such as PCs and smartphones used by users.

[1233] "Server" refers to the computer system that receives data from users and processes it using an emotion engine and generative AI models.

[1234] An "emotion engine" refers to software that analyzes emotions from text entered by the user.

[1235] A "generative AI model" refers to an artificial intelligence model that inputs prompt sentences based on the recognition results of the emotion engine and generates suggestions.

[1236] A "prompt" is an instruction given to a generative AI model to generate a specific suggestion.

[1237] "Suggestion" refers to the dish suggestion generated by the generative AI model based on the prompt.

[1238] "Delivery method" refers to the means by which the user chooses to deliver the food, which can be either cooking it themselves, delivery, or a restaurant.

[1239] "Payment System" refers to a system for processing payments based on the payment method selected by the User.

[1240] A "recipe" is a list of instructions and ingredients for making a dish.

[1241] "Ingredients" refers to the ingredients and seasonings needed to make a dish.

[1242] "Nearby Store" refers to a physical store near the user's location.

[1243] "Online Store" refers to a website or application that allows you to purchase products over the Internet.

[1244] "Restaurant" refers to a commercial establishment that serves food.

[1245] This invention is a system that allows users to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked food, delivery, or restaurant), and even process payment. This system is realized using hardware and software such as a server, terminals, an emotion engine, a generative AI model, and a database management system (DBMS).

[1246] A user uses a device (PC or smartphone) to input text describing their mood. For example, they might input, "I want to try a new dish today." The device then sends this input text data to the server. The server then passes the received text data to the emotion engine, which uses natural language processing (NLP) technology to analyze the user's emotions from the input text. For example, from the sentence, "I want to try a new dish today," it can recognize emotions such as "a sense of adventure" and "a desire to seek out new things."

[1247] Next, the server inputs a prompt sentence into the generative AI model based on the recognition results of the emotion engine. For example, a prompt sentence such as "Please suggest a dish that you would recommend when the user is feeling adventurous" is used. Based on this prompt sentence, the generative AI model generates a suggestion such as "How about a dish using fresh fish?" The server then sends the generated suggestion to the device.

[1248] The user checks the suggestions on the device and selects the delivery method (make-it-yourself / delivery / restaurant). For example, if the user selects "delivery," the device sends the selection to the server. Based on the user's selection, the server processes the payment through the delivery service's payment system. The server calls the payment system's API and sends the necessary payment information. Once the payment is completed, the server notifies the device of the result.

[1249] As a concrete example, a user uses a smartphone to type "I want to try something new today" and presses the send button. The smartphone converts this sentence into JSON format and sends it to the server using an HTTP POST request. The server parses the received JSON data and extracts the sentence. The sentence is then passed to the emotion engine, which uses NLP technology to analyze the emotion. The emotion engine recognizes "spirit of adventure" and "desire for novelty."

[1250] Based on the recognition results of the emotion engine, the server inputs a prompt sentence to the generative AI model: "Please suggest a dish you would recommend when the user is feeling adventurous." The generative AI model generates a suggestion: "How about a dish using fresh fish?" The server converts this suggestion into JSON format and sends it to the smartphone using an HTTP response. The user reviews the suggestion on the smartphone and selects "Delivery." The selection is converted back into JSON format and sent to the server using an HTTP POST request.

[1251] The server calls the delivery service's API and sends the user's payment information. Once the payment is complete, the server notifies the smartphone of the result in JSON format. The user receives a message on their smartphone saying "Payment completed."

[1252] In this way, the system handles everything from suggesting dishes that suit the user's mood to selecting the delivery method and handling payment.

[1253] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1254] Step 1:

[1255] The user uses the device to input a sentence describing their mood. For example, they might input, "I want to try a new dish today." The input sentence is saved as text data on the device.

[1256] Step 2:

[1257] The terminal sends the input text data to the server. Specifically, it converts the text data into JSON format and sends it to the server using an HTTP POST request. The input is the user's text data, and the output is an HTTP request to the server.

[1258] Step 3:

[1259] The server parses the received JSON data and extracts the text. Specifically, it uses a JSON parser to extract the text data. The input is JSON data, and the output is the extracted text data.

[1260] Step 4:

[1261] The server passes the extracted text data to the emotion engine for emotion analysis. The emotion engine uses natural language processing (NLP) technology to analyze the user's emotion from the input text. For example, from the sentence "I want to try a new dish today," it recognizes emotions such as "adventurous spirit" and "desire for novelty." The input is text data, and the output is recognized emotion data.

[1262] Step 5:

[1263] The server inputs a prompt sentence into the generative AI model based on the recognition results of the emotion engine. For example, a prompt sentence such as "Please suggest a dish that you would recommend when the user is feeling adventurous" can be used. The generative AI model generates a suggestion based on this prompt sentence. The input is emotion data and the prompt sentence, and the output is the generated suggestion.

[1264] Step 6:

[1265] The server sends the generated proposal to the terminal. Specifically, it converts the proposal data into JSON format and sends it to the terminal using an HTTP response. The input is the generated proposal data, and the output is the HTTP response to the terminal.

[1266] Step 7:

[1267] The user checks the suggestions on the device and selects the delivery method (cook / delivery / restaurant). For example, if the user selects "delivery," the selection is saved on the device. The input is the suggestion data, and the output is the user's selection data.

[1268] Step 8:

[1269] The terminal sends the user's selection data to the server. Specifically, the terminal converts the selection data into JSON format and sends it to the server using an HTTP POST request. The input is the user's selection data, and the output is an HTTP request to the server.

[1270] Step 9:

[1271] The server processes the payment through the delivery service's payment system based on the user's selection. Specifically, it calls the payment system's API and sends the necessary payment information. The input is the user's selection data and payment information, and the output is the payment result.

[1272] Step 10:

[1273] When the payment is completed, the server notifies the terminal of the result. Specifically, the server converts the payment result into JSON format and sends it to the terminal using an HTTP response. The input is the payment result, and the output is the HTTP response to the terminal.

[1274] Step 11:

[1275] The user receives a message on the terminal saying "Payment completed." The input is the payment result from the server, and the output is a notification to the user.

[1276] (Application example 1)

[1277] Next, a description will be given of Application Example 1 of Form 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."

[1278] Conventional food delivery systems do not suggest dishes based on the user's mood or emotions, and users must choose the dishes themselves. Furthermore, there was no system that recognized the user's emotions and suggested appropriate dishes, making it difficult to increase user satisfaction. Furthermore, there was a lack of a system that consistently handled delivery methods and payment, resulting in low user convenience.

[1279] 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.

[1280] In this invention, the server includes a means for exchanging a user's mood in text and suggesting dishes based on that, a means for selecting a delivery method (cooking / delivery / restaurant), a means for processing payment, a means for recognizing the user's emotion using an emotion engine, a means for suggesting dishes based on the recognized emotion, and a means for inputting the user's mood via a smartphone or smart glasses. This makes it possible to suggest appropriate dishes based on the user's mood and emotion, and to handle everything from delivery method to payment in an integrated manner, thereby increasing user satisfaction and convenience.

[1281] A "user" is an individual who uses the system to input their mood and emotions and select food suggestions and serving methods.

[1282] "Mood" refers to the emotions and psychological state that a user is feeling at that time.

[1283] "Text interaction" is the process by which a user inputs their moods and emotions into the system in text form, and the system responds accordingly.

[1284] "Dish suggestion" refers to the system recommending appropriate dishes based on the user's mood and emotions.

[1285] "Delivery method" refers to the means by which food is delivered to users, and refers to the choice of making it yourself, delivery, or restaurant.

[1286] "Payment" is the process of paying for the food and delivery method selected by the user.

[1287] An "emotion engine" is software or an algorithm that recognizes emotions from text entered by the user and makes appropriate suggestions based on those emotions.

[1288] A "smartphone" is a mobile phone that can connect to the Internet and run applications.

[1289] "Smart glasses" are wearable devices that have built-in displays and sensors and allow users to operate them while obtaining visual information.

[1290] "Input" is the act of a user providing data such as text or voice to a system.

[1291] The system for implementing this invention allows users to communicate their mood in text, suggests dishes based on that, selects the delivery method (cooking / delivery / restaurant), and even handles payment. A specific embodiment of this system is described below.

[1292] System configuration

[1293] The system consists of the following main components:

[1294] 1. User terminal: A device through which the user inputs their mood, such as a smartphone or smart glasses.

[1295] 2. Server: The central system including the emotion engine, food suggestion engine, delivery service API, etc.

[1296] 3. Emotion engine: Software that recognizes emotions from text entered by the user.

[1297] 4. Food suggestion engine: Software that suggests appropriate dishes based on recognized emotions.

[1298] 5. Delivery Service API: An interface for delivering the food selected by the user.

[1299] Program processing

[1300] The server receives the mood statement sent from the user's device and recognizes the emotion using the emotion engine. Based on the recognized emotion, the food recommendation engine suggests appropriate dishes. If the user accepts the suggestion, they select the delivery method and complete the order through the delivery service API.

[1301] Hardware and software used

[1302] Hardware: Smartphones, smart glasses, servers

[1303] Software: Emotion Engine, Food Suggestion Engine, Delivery Service API

[1304] Specific examples

[1305] A user uses their smartphone to input, "I'd like to try something new today." The server receives this input and uses an emotion engine to recognize emotions such as "adventurous spirit" and "desire for novelty." The food suggestion engine then suggests, "How about a dish made with fresh fish?" If the user accepts this suggestion, the order is completed via the delivery service API.

[1306] Example prompt sentence:

[1307] User: I want to try something new today

[1308] System: You have a sense of adventure and a desire for novelty. How about some fresh fish?

[1309] User: I'll take that.

[1310] System: Your order has been placed. Please wait a moment for delivery.

[1311] In this way, it becomes possible to suggest appropriate dishes based on the user's mood and emotions, and the delivery method and payment can all be handled consistently, thereby increasing user satisfaction and convenience.

[1312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1313] Step 1:

[1314] Users input their mood using a smartphone or smart glasses.

[1315] Input: Text that describes the user's mood (e.g., "I want to try something new today")

[1316] Output: User's mood text data

[1317] Specific operation: The user inputs their mood through the input interface of their smartphone or smart glasses, and sends it as text data to the server.

[1318] Step 2:

[1319] The server receives the user's mood text data and passes it to the emotion engine.

[1320] Input: User's mood text data

[1321] Output: Text data passed to the emotion engine

[1322] Specific operation: The server receives the text data sent by the user and passes it to the emotion engine as input data.

[1323] Step 3:

[1324] The emotion engine analyzes the user's mood text data and recognizes emotions.

[1325] Input: User's mood text data

[1326] Output: Recognized emotion data (e.g., "adventurous spirit" or "seeking novelty")

[1327] Specific operation: The emotion engine uses natural language processing technology to analyze text data and recognize the user's emotions. The recognized emotion data is returned to the server.

[1328] Step 4:

[1329] The server receives the recognized emotion data and passes it to the food suggestion engine.

[1330] Input: Recognized emotion data

[1331] Output: Emotion data passed to the food suggestion engine

[1332] Specific operation: The server receives the emotion data returned from the emotion engine and passes it as input data to the dish suggestion engine.

[1333] Step 5:

[1334] The food suggestion engine suggests appropriate dishes based on the recognized emotion data.

[1335] Input: Recognized emotion data

[1336] Output: Suggested dish data (e.g., "Dishes made with fresh fish")

[1337] Specific operation: The recipe suggestion engine analyzes the emotion data, selects appropriate recipes from the database, and returns the suggested recipe data to the server.

[1338] Step 6:

[1339] The server sends the suggested recipe data to the user's terminal.

[1340] Input: Suggested dish data

[1341] Output: A recipe suggestion message sent to the user device

[1342] Specific operation: The server receives the recipe data returned from the recipe suggestion engine and sends it as a message to the user device.

[1343] Step 7:

[1344] The user reviews the suggested dishes and chooses how they are served.

[1345] Input: Suggested dish data

[1346] Output: Selected delivery method data (e.g. "Delivery")

[1347] Specific operation: The user reviews the proposed dishes through the interface of their smartphone or smart glasses and selects the serving method.

[1348] Step 8:

[1349] The server receives the selected delivery method data and passes it to the delivery service API.

[1350] Input: Selected delivery method data

[1351] Output: Order data passed to the delivery service API

[1352] Specific operation: The server receives the delivery method data sent by the user and passes it as input data to the delivery service API.

[1353] Step 9:

[1354] The delivery service API processes the order and returns the order status to the server.

[1355] Input: Order data

[1356] Output: Order status data (e.g. "Order completed")

[1357] Specific operation: The delivery service API processes the order data and returns the order status to the server.

[1358] Step 10:

[1359] The server sends the order status data to the user terminal.

[1360] Input: Order status data

[1361] Output: Order status message sent to user terminal

[1362] Specific operation: The server receives the order status data returned from the delivery service API and sends it as a message to the user device.

[1363] Example 2

[1364] Next, a description will be given of Example 2 of Form 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."

[1365] Conventional recipe suggestion systems have the drawback of being cumbersome after users select a dish, requiring them to make specific arrangements and purchase ingredients, resulting in low user convenience. Furthermore, when users cook their own meals, there is a lack of efficient ways to arrange the necessary ingredients. Furthermore, when users select delivery, there is also the issue of being unable to smoothly order food from nearby restaurants.

[1366] 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. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment; means for retrieving the corresponding recipe and ingredient list from a database when the user selects a dish; and means for arranging the ingredients selected by the user from a nearby store or online store. This allows the user to easily obtain the recipe and ingredients for the dish and efficiently arrange the necessary ingredients. Furthermore, even if delivery is selected, the user can smoothly order the ingredients from a nearby restaurant.

[1367] "User" means an individual or organization that uses the system to receive food suggestions, select the delivery method, and make payment.

[1368] "Mood" is a sentence that the user inputs into the system to express their current feelings and wishes.

[1369] "Cuisine Suggestions" are specific cuisine options provided by the system based on the user's mood.

[1370] "Delivery method" refers to the means of delivery of food selected by the user, and can be one of "make it yourself," "delivery," or "restaurant."

[1371] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[1372] A "database" is a collection of information that allows the system to store cooking recipes and ingredient lists and retrieve them as needed.

[1373] A "recipe" is a document that details the steps and ingredients for making a particular dish.

[1374] An "ingredient list" is a list of ingredients needed to make a particular dish.

[1375] "Nearby stores" are physical stores that are close to the user's residence or current location.

[1376] An "online store" is a virtual store where products can be purchased over the Internet.

[1377] "Arrangement" refers to the process of purchasing the materials selected by the user and delivering them to the user.

[1378] "Delivery" is a service that delivers the food selected by the user to a specified location.

[1379] A "restaurant" is a physical establishment that serves the food selected by the user.

[1380] This invention is a system that allows users to receive recipe suggestions, select the serving method, and arrange the necessary ingredients. This system is realized using a server, user terminals, a database management system, an online supermarket API, and a generative AI model.

[1381] Hardware and software used

[1382] Hardware: Servers, user devices (smartphones, tablets, PCs)

[1383] Software: Web applications, database management systems (e.g., MySQL), online supermarket APIs, generative AI models (e.g., GPT-4)

[1384] Specific operation of the system

[1385] 1. The user selects a dish

[1386] A user accesses the system's web application using a terminal and searches for a food category or a specific food name. For example, the user selects "Fresh fish dishes."

[1387] 2. The server generates the recipe and ingredients list

[1388] Based on the user's selection, the server retrieves the appropriate recipe and ingredient list from a database management system. If necessary, it uses a generative AI model to complete the recipe details. For example, the server retrieves the recipe and ingredient list for "dishes using fresh fish" from the database and then uses a generative AI model to detail the recipe steps.

[1389] 3. Provide users with a recipe and ingredient list

[1390] The server sends the retrieved recipe and ingredient list to the user's device, where the user can check the recipe and ingredient list on the device screen. For example, the server generates a web page with the recipe and ingredient list for "dishes using fresh fish" and displays it on the user's device.

[1391] 4. The user selects the material

[1392] The user selects the ingredients they need from a provided list of ingredients. Selections are made using checkboxes and drop-down menus. For example, the user clicks the checkbox for "fresh fish, vegetables, and seasonings."

[1393] 5. The server arranges for the ingredients.

[1394] The server uses the online supermarket API to place an order to arrange for the ingredients selected by the user to be delivered to a nearby store or online store. The order details are notified to the user. For example, the server orders "fresh fish, vegetables, and seasonings" through the online supermarket API and sends an order confirmation email to the user.

[1395] Specific examples

[1396] User Action: The user selects "Fresh Fish Dishes" and sees the recipe and ingredients list.

[1397] Server processing: The server retrieves the relevant recipe and ingredient list from the database and provides them to the user.

[1398] Arranging materials: The user selects the required materials, and the server arranges the materials using the NetSupermarket API.

[1399] Example prompts for generative AI models

[1400] "Please provide a recipe and ingredient list for a dish using fresh fish."

[1401] "How can I order the ingredients selected by the user from a nearby supermarket or online supermarket?"

[1402] In this way, users can easily get cooking recipes and ingredients and arrange the necessary ingredients.

[1403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1404] Step 1:

[1405] The user selects a dish

[1406] Input: A user uses a terminal to access the system's web application and search for a food category or a specific food name.

[1407] Data processing: Based on the user's input, the system displays food options.

[1408] Output: The user selects "Dishes with fresh fish."

[1409] What happens: A user types "fresh fish" into the search bar of a web application and clicks "fresh fish dishes" from the results.

[1410] Step 2:

[1411] The server generates the recipe and ingredients list

[1412] Input: Information about the dish selected by the user.

[1413] Data processing: The server retrieves the relevant recipe and ingredient list from a database management system (e.g., MySQL). If necessary, it uses a generative AI model (e.g., GPT-4) to complete the recipe details.

[1414] Output: Recipe and ingredients list.

[1415] What it does: The server retrieves the recipe and ingredient list for a "dish using fresh fish" from a database and uses a generative AI model to write detailed instructions for the recipe.

[1416] Step 3:

[1417] Providing users with recipes and ingredient lists

[1418] Input: A server-generated recipe and ingredient list.

[1419] Data processing: The server sends the acquired recipe and ingredient list to the user's device.

[1420] Output: The recipe and ingredients list displayed on the user's device.

[1421] Specific operation: The server generates a web page with a recipe and ingredient list for "dishes using fresh fish" and displays it on the user's device.

[1422] Step 4:

[1423] User selects material

[1424] Input: User provided materials list.

[1425] Data processing: The user selects the required materials from a list of materials provided, using checkboxes and drop-down menus.

[1426] Output: A list of materials selected by the user.

[1427] Specific Action: The user clicks the checkbox for "Fresh fish, vegetables, and seasonings" to select it.

[1428] Step 5:

[1429] The server arranges the ingredients

[1430] Input: A list of materials selected by the user.

[1431] Data processing: The server uses the NetSupermarket API to place an order to arrange for the ingredients selected by the user to be delivered to a nearby store or online store.

[1432] Output: Order confirmation notification.

[1433] Specific operation: The server orders "fresh fish, vegetables, and seasonings" through the online supermarket API and sends an order confirmation email to the user.

[1434] (Application example 2)

[1435] Next, a description will be given of Application Example 2 of Form 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."

[1436] Conventional food recommendation systems have had issues with being unable to centrally recommend dishes based on the user's mood, select the serving method, or arrange ingredients. Furthermore, when users cook their own meals, it takes time to arrange the necessary ingredients, and when ordering delivery, it is difficult to smoothly order from nearby restaurants.

[1437] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1438] In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook-it-yourself / delivery / restaurant), and even complete payment; means for arranging the recipe and necessary ingredients from a nearby retail store or online retailer if cooking-it-yourself, and means for ordering from a nearby restaurant if delivery is selected; and means for providing the recipe and a list of necessary ingredients and arranging the ingredients from a nearby retail store or online retailer when the user selects a dish and chooses the "cook-it-yourself" option using a smartphone application. This makes it possible to centrally handle everything from suggesting dishes based on the user's mood to selecting a delivery method, arranging the ingredients, and arranging delivery.

[1439] A "user" is an individual who uses the system to suggest dishes and select the method of serving them.

[1440] "Mood" refers to the current feelings or state that a user expresses in writing.

[1441] "Dish suggestion" refers to the system recommending appropriate dishes based on the user's mood.

[1442] "Delivery method" refers to the method of receiving food that users can choose, and can be one of "make it yourself," "delivery," or "restaurant."

[1443] "Payment" means the act of paying a fee based on the delivery method selected by the user.

[1444] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[1445] An "ingredient list" is a list of ingredients needed to make a particular dish.

[1446] "Retail Store" refers to a nearby store where users can purchase the materials they need.

[1447] "Online retail store" refers to a store where users can purchase the materials they need via the Internet.

[1448] "Restaurant" refers to a nearby establishment that provides food when a user selects delivery.

[1449] A "smartphone application" is software that allows users to use the system via their smartphone.

[1450] A "generative AI model" is an artificial intelligence model that makes recipe suggestions based on user input.

[1451] A "prompt sentence" is an input sentence that provides a recipe and ingredient list based on the user's selection.

[1452] As an embodiment of the present invention, the following system is constructed.

[1453] System configuration

[1454] The system consists of a server that allows users to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked at home / delivery / restaurant), and even process payment, as well as a smartphone application used by the user.

[1455] Program processing

[1456] The server uses a generative AI model to suggest dishes based on user input. Once the user selects a dish and chooses the "make it yourself" option, the server generates a recipe and a list of ingredients, which the user can then use through a smartphone application, along with the means to source the ingredients from a local or online retailer.

[1457] In the case of delivery, the server will arrange for food to be delivered from a nearby restaurant, allowing users to enjoy their meal smoothly whether they choose to cook it themselves or have it delivered.

[1458] Hardware and software used

[1459] Server: High-performance cloud server (e.g., Amazon Web Services, Google Cloud Platform)

[1460] Smartphone application: Mobile application for iOS and Android

[1461] Generative AI models: AI models for natural language processing (e.g., GPT-3, BERT)

[1462] Prompt: A prompt to provide a recipe and ingredient list based on the user's selection.

[1463] Specific examples

[1464] A user launches a smartphone application and enters a prompt like the following:

[1465] Prompt Sentence Examples

[1466] Enter the name of the dish: Fresh fish dish

[1467] Enter '1' for Build Your Own, '2' for Delivery: 1

[1468] Recipe:

[1469] 1. Wash the fish

[1470] 2. Cut the fish

[1471] 3. Grilling fish

[1472] 4. Add seasonings

[1473] Material List:

[1474] ['Fresh fish', 'Salt', 'Pepper', 'Olive oil']

[1475] Would you like to arrange materials? (y / n): y

[1476] The materials have been arranged.

[1477] Based on this prompt, the server uses a generative AI model to analyze the user's input, suggest an appropriate dish, and arrange the ingredients. The smartphone application displays the recipe and ingredients list to the user, and notifies them when the ingredients have been arranged.

[1478] In this way, users can easily choose the dish that best suits their mood and arrange the necessary ingredients. In addition, if they select delivery, they can have the food delivered from a nearby restaurant, which is extremely convenient.

[1479] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1480] Step 1:

[1481] The user launches the smartphone application and inputs the name of the dish and the serving method. As input, the user enters "Enter the name of the dish: Fresh fish dish" and "Enter '1' for cooking it yourself or '2' for delivery: 1" into the application. This allows the application to select a dish based on the user's mood and preferences.

[1482] Step 2:

[1483] The device sends user input, including the name of the dish and the serving method, to the server. The server receives this data and uses a generative AI model to generate prompts to suggest dishes.

[1484] Step 3:

[1485] The server uses a generative AI model to suggest dishes based on user input. As input, a prompt based on the user's mood and preferences is passed to the generative AI model. The generative AI model analyzes this prompt and generates an appropriate recipe and ingredient list. The output is the recipe and ingredient list.

[1486] Step 4:

[1487] The server sends the generated recipe and ingredient list to the device. The input includes the recipe and ingredient list obtained from the generative AI model. The device receives this data and displays it to the user. As an output, the user can view the recipe and ingredient list.

[1488] Step 5:

[1489] When a user wants to order a material, the terminal sends a request to the server, which includes the user's request as input. The server receives this request and arranges for the material to be ordered from a nearby retailer or an online retailer.

[1490] Step 6:

[1491] The server arranges for ingredients from a retail store or online retailer. The input includes a list of ingredients desired by the user. The server sends an order to the retail store or online retailer through an API. The output is that the ingredients have been arranged.

[1492] Step 7:

[1493] The server notifies the terminal that the material has been ordered. The input includes a notification from the retail store or online retailer that the material has been ordered. The terminal receives this notification and displays it to the user. The output is a confirmation to the user that the material has been ordered.

[1494] Step 8:

[1495] If the user selects delivery, the device sends a delivery request to the server, which includes the user's delivery preference as input. The server receives this request and places an order for the food from a nearby restaurant.

[1496] Step 9:

[1497] The server places a food order with a restaurant. The input includes the name of the dish desired by the user. The server sends the order to the restaurant through an API. The output is a completed food order.

[1498] Step 10:

[1499] The server notifies the terminal that the food order has been completed. The input includes a notification from the restaurant that the order has been completed. The terminal receives this notification and displays it to the user. As an output, the user can see that the food order has been completed.

[1500] Example 3

[1501] Next, a description will be given of Example 3 of Form Example 3. 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."

[1502] Conventional food recommendation systems have limited options for suggesting dishes and serving methods based on the user's mood, and when arranging delivery in particular, it is difficult to efficiently order from nearby restaurants based on the user's current location. Additionally, arranging the ingredients needed for cooking by the user is also complicated. To solve these issues, it is necessary to suggest dishes and select serving methods based on the user's mood, as well as to streamline delivery and ingredient arrangements.

[1503] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1504] In this invention, the server includes means for allowing a user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, means for acquiring the user's current location information when the user selects delivery, means for acquiring information on nearby restaurants from a database based on the acquired current location information, means for filtering restaurants that serve the dishes selected by the user, means for displaying the filtered restaurant information to the user, and means for sending a delivery order to the restaurant when the user selects the desired restaurant. This makes it possible to suggest dishes and select a delivery method according to the user's mood, and further realizes more efficient delivery and ingredient arrangements.

[1505] "User" refers to an individual who uses the system to suggest dishes or arrange delivery.

[1506] "Delivery" refers to a service in which the food selected by the user is delivered from a nearby restaurant to a location specified by the user.

[1507] "Current location information" refers to the latitude and longitude location information obtained by the user's device.

[1508] "Eating and drinking establishments" refers to establishments that serve food, including restaurants and cafes.

[1509] "Database" refers to an information management system that allows a system to store information and search and retrieve it as needed.

[1510] "Filtering" refers to the process of sorting data based on specific conditions and extracting only the necessary information.

[1511] A "recipe" is a list of instructions and ingredients for making a dish.

[1512] "Retail store" refers to a store that sells goods to consumers, including supermarkets and online retailers.

[1513] "Order" means a request to purchase a dish or product selected by a User.

[1514] "Payment" refers to the process of paying for the food or product selected by the user.

[1515] This invention is a system that proposes dishes according to the user's mood, selects the serving method, and also improves the efficiency of delivery and ingredient arrangement. Specific embodiments of this system are described below.

[1516] System configuration

[1517] This system includes a user terminal, a server, a database, and an API provided by each restaurant. The user terminal is a mobile device such as a smartphone or tablet, and the server is located on the cloud. The database uses MySQL, and the restaurant API is provided by each restaurant.

[1518] Program processing

[1519] When a user selects "Delivery" using the device, the device uses GPS to obtain the user's current location. The device then sends a request containing the user's current location and the selected dish to the server. The server then queries a MySQL database to obtain information about restaurants within a certain distance of the user's current location.

[1520] The server filters the acquired restaurant information to restaurants that offer the food selected by the user. The filtered restaurant information is returned to the device in JSON format. The device displays the received list of restaurants to the user, and when the user selects the desired restaurant, the server sends a delivery order to that restaurant.

[1521] Specific examples

[1522] For example, if a user selects "dishes using fresh fish," the device obtains the user's current location and sends a request to the server. The server retrieves nearby restaurant information from a database and filters out restaurants that offer "dishes using fresh fish." The filtered restaurant information is returned to the device and displayed to the user. Once the user selects the desired restaurant, the server sends a delivery order to that restaurant.

[1523] Prompt Sentence Examples

[1524] Example prompts to input to a generative AI model:

[1525] If the user selects "Fresh Fish," how would the system arrange for delivery from a nearby restaurant? Please explain the specific steps.

[1526] In this way, a system is provided that can suggest dishes and select serving methods according to the user's mood, and also improve the efficiency of delivery and material arrangements. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1527] Step 1:

[1528] User selects delivery

[1529] The user opens the application on the device and selects the "Delivery" option. As input, the user's selection information is obtained. As output, the delivery selection flag is set.

[1530] Step 2:

[1531] The device obtains the user's current location information

[1532] The device uses its GPS function to obtain the user's current location information. Specifically, it uses the device's location information service to obtain latitude and longitude data. The device's location information service is used as input, and the user's current location information (latitude and longitude) is obtained as output.

[1533] Step 3:

[1534] The device sends a request to the server

[1535] The device sends a request to the server containing the user's current location and the selected dish. The request is sent using the HTTP protocol. The user's current location and the selected dish are used as input, and the request sent to the server is generated as output.

[1536] Step 4:

[1537] The server retrieves information about nearby restaurants from the database.

[1538] The server queries the MySQL database to get information about restaurants within a certain distance of the user's current location. Specifically, it executes a query such as "SELECT FROM restaurants WHERE distance < 5km". The user's current location is used as input, and nearby restaurant information is obtained as output.

[1539] Step 5:

[1540] The server filters restaurants that serve the user's selected dish.

[1541] The server filters the acquired restaurant information to find restaurants that offer the "dishes using fresh fish" selected by the user. Information about nearby restaurants and the dish information selected by the user are used as input, and the filtered restaurant information is obtained as output.

[1542] Step 6:

[1543] The server returns the filtering results to the device

[1544] The server returns the filtered list of restaurants in JSON format to the terminal. The filtered restaurant information is used as input and JSON format data is generated as output.

[1545] Step 7:

[1546] The device displays a list of restaurants to the user.

[1547] The terminal displays the received list of restaurants to the user. The list includes information such as the restaurant's name, rating, and distance. The JSON format data received from the server is used as input, and the list of restaurants displayed to the user is generated as output.

[1548] Step 8:

[1549] The user selects the restaurant they want to eat at.

[1550] The user selects a desired restaurant from the displayed list. The user's selection information is obtained as input, and information about the selected restaurant is obtained as output.

[1551] Step 9:

[1552] The server sends the delivery order to the selected restaurant.

[1553] The server sends a delivery request to the selected restaurant, including the user's order information. The request is sent using the restaurant's API. The selected restaurant information and the user's order information are used as input, and a delivery request is generated as output to be sent to the restaurant.

[1554] (Application example 3)

[1555] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1556] With conventional food delivery systems, it was difficult for users to specify the specific food they wanted to eat, and it was also difficult to efficiently search for that food from nearby restaurants and arrange delivery. Furthermore, there was a lack of a system that could consistently handle everything from suggesting dishes based on the user's mood and preferences, selecting the delivery method, and even payment. This resulted in a decrease in user convenience and a lack of satisfaction.

[1557] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1558] In this invention, the server includes a means for allowing a user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, a means for inputting the type of food the user wants to eat, searching for nearby restaurants, and listing restaurants that serve that food, and a means for arranging delivery from the restaurant selected by the user. This allows a user to easily specify the food they want to eat based on their mood and preferences, and efficiently arrange for delivery from a nearby restaurant.

[1559] "User" refers to an individual who uses the system to receive food suggestions and delivery.

[1560] "Mood" refers to the emotions the user is feeling at that time and their desire for food.

[1561] "Cuisine suggestions" refers to the cuisine options provided by the system based on the user's mood and preferences.

[1562] "Method of delivery" refers to the means by which food is delivered to the user, and can be cooking it yourself, having it delivered, or eating it at a restaurant.

[1563] "Payment" refers to the process by which a user makes payment for the food or service they have selected.

[1564] "Type of food you want to eat" refers to the category of food or a specific dish that the user specifically wants to eat.

[1565] "Nearby restaurants" refers to restaurants located within a certain distance from the user's current location.

[1566] "Listing" refers to the system displaying a list of restaurants that match the search results.

[1567] "Delivery" refers to a service in which the food selected by the user is delivered from the restaurant to the location specified by the user.

[1568] "Generative AI model" refers to an artificial intelligence model used to search for suitable restaurants and make dish suggestions based on user input.

[1569] A "prompt" is an instruction entered into a generative AI model to prompt it to take a specific action or respond.

[1570] The system for implementing this invention has the function of allowing users to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked / delivered / restaurant), and even process payment.It also has the function of allowing users to input the type of food they want to eat, search for nearby restaurants, list restaurants that serve the food, and arrange for delivery from the restaurant selected by the user.

[1571] Hardware and software used

[1572] Hardware: Smartphones, servers

[1573] Software: Python, Requests library, generative AI models

[1574] Data processing and calculation

[1575] 1. User input: Users input their mood and the type of food they want to eat through a smartphone application.

[1576] 2. Use of generative AI model: The server uses a generative AI model to suggest appropriate dishes based on the user's input. For example, if you input "dishes with fresh fish," the generative AI model will suggest dishes related to that dish.

[1577] 3. Find nearby restaurants: The server uses the Requests library to send a request to the Find Nearby Restaurants API and get a list of restaurants that serve the specified dish.

[1578] 4. Arrange Delivery: Send a request to arrange delivery from the restaurant of your choice and arrange delivery.

[1579] Specific examples

[1580] The user opens the smartphone application and inputs "dishes using fresh fish." The generative AI model suggests appropriate dishes based on the user's input and searches for nearby restaurants. For example, "Sushi Restaurant A" and "Seafood Restaurant B" are listed. The user selects "Sushi Restaurant A" and arranges for delivery. The server arranges for delivery, and the dish is delivered to the user.

[1581] Prompt Sentence Examples

[1582] When a user enters "dishes using fresh fish," generate a Python program that searches for nearby restaurants and returns a list of restaurants that serve that dish.

[1583] In this way, users can easily specify the food they want to eat based on their mood and preferences, and efficiently arrange delivery from nearby restaurants.

[1584] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1585] Step 1:

[1586] Users open the smartphone application and input their mood and the type of food they want to eat.

[1587] Input: User's mood or type of food they want to eat (e.g., "Fresh fish").

[1588] Output: User input data

[1589] What happens: The user enters "Fresh fish dishes" into the application's input field and presses the submit button.

[1590] Step 2:

[1591] The server uses a generative AI model to suggest appropriate dishes based on user input.

[1592] Input: User-entered data

[1593] Output: Recipe suggestions data

[1594] How it works: The server inputs prompts into the generative AI model, which then generates food suggestions that match the user's mood and preferences. For example, suggestions might be made such as "sushi made with fresh fish" or "seafood bowl."

[1595] Step 3:

[1596] The server sends a request to an API that searches for nearby restaurants and lists restaurants that serve the specified dish.

[1597] Input: Food recommendation data, user location information

[1598] Output: List of restaurants

[1599] Specific operation: The server sends a request to the API based on the user's current location and food suggestion data to search for nearby restaurants. For example, "Sushi Restaurant A" and "Seafood Restaurant B" are listed.

[1600] Step 4:

[1601] Users can select from a list of restaurants and arrange for delivery.

[1602] Input: List of restaurants, user selection

[1603] Output: Delivery arrangement data

[1604] Specific operation: The user selects "Sushi Restaurant A" on the application and presses the delivery order button. The server sends a delivery order request.

[1605] Step 5:

[1606] The server arranges delivery and the food is delivered to the user.

[1607] Input: Delivery arrangement data

[1608] Output: Delivery completion notification

[1609] Specific operation: The server sends a delivery request to the restaurant, the restaurant prepares the food and delivers it to the location specified by the user, and the user is notified that the delivery is complete.

[1610] 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.

[1611] 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> ) 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.

[1612] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1613] 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.

[1614] [Third embodiment]

[1615] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1616] 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.

[1617] 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).

[1618] 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.

[1619] 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.

[1620] 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).

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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.

[1625] 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.

[1626] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1627] "Example 1"

[1628] The system of the present invention has a function that allows users to communicate their mood through text. Specifically, if a user inputs, "I'd like to try a new dish today," the system will suggest dishes that match that mood. For example, it may suggest, "How about a dish using fresh fish?"

[1629] "Example 2"

[1630] The system of the present invention has a function to select how to prepare the proposed dish. Specifically, if the user selects "make it yourself," the system provides a recipe and the necessary ingredients, allowing them to be ordered from a nearby supermarket or online supermarket. For example, if the user selects "dish using fresh fish," the system provides a recipe for that dish and a list of ingredients to make it.

[1631] "Example 3"

[1632] The system of the present invention has a function that allows delivery from nearby restaurants. Specifically, when a user selects "delivery," the system can order the corresponding dish from a nearby restaurant. For example, if a user selects "dishes using fresh fish," the system will arrange for delivery from a nearby restaurant that serves that dish.

[1633] The processing flow of each embodiment will be described below.

[1634] "Example 1"

[1635] Step 1: The user enters their mood into the system in a sentence, for example, "I want to try something new today."

[1636] Step 2: The system makes a dish suggestion based on the user's input, for example, "How about a dish with fresh fish?"

[1637] "Example 2"

[1638] Step 1: The user selects how they would like to serve the proposed dish, specifically, "Make it yourself."

[1639] Step 2: The system will provide a recipe and the necessary ingredients based on the selected delivery method, allowing you to arrange for delivery from a nearby supermarket or online supermarket. For example, if you select "Fresh fish dishes," the system will provide you with a recipe for that dish and a list of ingredients to make it.

[1640] "Example 3"

[1641] Step 1: The user selects the delivery method for the proposed dish, specifically, "Delivery."

[1642] Step 2: The system can order the corresponding dish from a nearby restaurant based on the delivery method selected. For example, if you select "dishes with fresh fish," the system will arrange delivery from a nearby restaurant that serves that dish.

[1643] Example 1

[1644] Next, a description will be given of Example 1 of Form 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."

[1645] Conventional food recommendation systems have difficulty in suggesting specific dishes based on the user's mood or preferences, and are unable to handle delivery methods and payment in a consistent manner. This has resulted in problems such as users having to spend time and effort to find a dish that suits their mood.

[1646] The specification process by the specification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes a means for a user to input their mood and preferences into a terminal, a means for the terminal to send the input data to the server, a means for the server to send the received data to the generative AI model as a prompt sentence, a means for the generative AI model to analyze the prompt sentence and generate an appropriate suggestion, a means for the server to receive the generated suggestion and return it to the user, a means for the user to receive and confirm the suggestion, and a means for selecting a delivery method (make-yourself / delivery / restaurant) and making payment. This allows the user to receive suggestions for dishes that suit their mood, select a delivery method, and make a payment all in one go.

[1647] "User" refers to an individual who uses the system to input their mood and preferences and receive food suggestions.

[1648] A "terminal" is an electronic device used by a user to input their mood or wishes, and includes smartphones, personal computers, etc.

[1649] "Server" refers to the computer system responsible for receiving data submitted by the user, sending prompts to the generative AI model, and sending generated suggestions back to the user.

[1650] "Generative AI model" refers to an artificial intelligence model that analyzes user input data and generates appropriate dish suggestions.

[1651] A "prompt sentence" is an instruction sentence sent to a generative AI model based on user input data, and includes input information for the generative AI model to analyze.

[1652] "Delivery Method" is how a user chooses to obtain the suggested food, including cook-it-yourself, delivery, and restaurant options.

[1653] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[1654] A "recipe" is a document that contains instructions and a list of ingredients for making a dish.

[1655] "Ingredients" refers to the ingredients and seasonings needed to make a dish.

[1656] "Store" means a store where a user can purchase ingredients for cooking, including a local brick-and-mortar store or an online store.

[1657] "Restaurant" refers to establishments such as restaurants and cafes where users can order food for delivery.

[1658] This invention is a system that suggests appropriate dishes based on the user's mood and preferences, and handles everything from the delivery method to payment. The system works by having the user input their mood and preferences into a terminal, and the server receives and analyzes the data, then uses a generative AI model to generate appropriate suggestions.

[1659] Hardware and software used

[1660] Hardware: Servers, user devices (smartphones, personal computers, etc.)

[1661] Software: Generative AI models (e.g., OpenAI GPT-4), communication protocols (HTTP / HTTPS)

[1662] Specific operation of the system

[1663] Users use devices such as smartphones or personal computers to input their moods and desires in text, for example, "I want to try a new dish today." The device then sends this input data to the server using the HTTP / HTTPS protocol.

[1664] The server analyzes the received user input data and generates a prompt to send to the generative AI model. For example, if the user input is "I want to try a new dish today," the prompt will be "The user says they want to try a new dish. What kind of dish would you suggest?"

[1665] The generative AI model analyzes the prompt sent from the server and generates a dish suggestion that matches the user's mood. For example, it generates a suggestion such as, "How about a dish using fresh fish?" The server sends the suggestion received from the generative AI model back to the user. At this time, the suggestion is formatted in a way that is easy for the user to understand and sent to the device.

[1666] The user checks the suggestions displayed on the device and selects the delivery method (cooking, delivery, or restaurant). For example, if cooking, the user can arrange for the recipe and necessary ingredients from a nearby store or online store. If delivery is selected, the food can be ordered from a nearby restaurant. The user can then make payment based on the selected delivery method.

[1667] This system allows users to receive suggestions for dishes that suit their mood, choose how they would like them served, and complete the entire process from payment to delivery.

[1668] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1669] Step 1:

[1670] The user inputs their mood and desires into the device. Specifically, the user opens an application on their smartphone or personal computer and enters "I want to try a new dish today" in the text box. The input data is in text format and expresses the user's mood and desires.

[1671] Step 2:

[1672] The terminal sends the entered data to the server. The terminal converts the text data entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is the user's request in text format, and the output is the JSON format data sent to the server.

[1673] Step 3:

[1674] The server sends the received data to the generative AI model as a prompt. The server parses the received JSON data and generates a prompt: "The user says they want to try a new dish. What dish would you suggest?" The input data is the user's preference in JSON format, and the output is the prompt sent to the generative AI model.

[1675] Step 4:

[1676] A generative AI model analyzes the prompt and generates an appropriate suggestion. A generative AI model (e.g., OpenAI GPT-4) analyzes the prompt sent from the server and generates a suggestion such as "Would you like a dish with fresh fish?" The input data is the prompt, and the output is the generated suggestion.

[1677] Step 5:

[1678] The server receives the generated proposal and sends it back to the user. The server converts the proposal received from the generative AI model into JSON format and sends it to the terminal as an HTTP response. The input data is the proposal from the generative AI model, and the output is the JSON-formatted proposal that is sent back to the user.

[1679] Step 6:

[1680] The user receives and confirms the suggestion. The user confirms the suggestion "How about a dish with fresh fish?" displayed on a smartphone or personal computer application. The input data is the suggestion from the server, and the output is the user's confirmation.

[1681] Step 7:

[1682] The user selects the delivery method and makes the payment. The user selects the delivery method (make-yourself, delivery, restaurant) based on the proposed dishes and makes the payment through the application. The input data is the user's selection and payment information, and the output is the selected delivery method and the completion of the payment.

[1683] (Application example 1)

[1684] Next, a description will be given of Application Example 1 of Form 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."

[1685] Conventional food recommendation systems have had the problem of being unable to recommend the right food based on the user's mood, and are unable to provide a consistent system for delivery and payment. In particular, there is a need for a system that allows users to easily find the food that best suits their mood that day and order it immediately.

[1686] 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.

[1687] In this invention, the server includes a means for communicating a user's mood in text and suggesting dishes based on that, a means for selecting a delivery method (cooked meal / delivery / restaurant), a means for processing payment, a means for suggesting dishes according to the user's mood using a generative AI model, a means for generating prompt sentences and inputting them into the generative AI model, and a means for obtaining suggestions from the generative AI model. This allows users to easily find dishes that suit their mood that day, select a delivery method, and quickly order and pay.

[1688] "User" refers to an individual who uses the system to receive recipe suggestions.

[1689] "Mood" refers to the emotions and desires a user is feeling at that time.

[1690] "Text interaction" refers to the user providing input to the system in text format and the system responding in text format accordingly.

[1691] "Dish suggestions" refers to the system recommending suitable dishes based on the user's mood.

[1692] "Delivery Method" refers to the options for how the food is delivered to the user, including cook-it-yourself, delivery, or restaurant.

[1693] "Payment" refers to the process of paying for the food and delivery method selected by the user.

[1694] The "system" refers to a device or software with a series of functions that suggests dishes based on the user's mood, selects the serving method, and even handles payment.

[1695] "Generative AI model" refers to an artificial intelligence model that suggests appropriate dishes based on user input.

[1696] A "prompt" refers to a textual instruction to be input to a generative AI model.

[1697] "Getting suggestions" refers to receiving the food recommendation results output by the generative AI model.

[1698] The system for implementing this invention has a set of functions that suggest dishes based on the user's mood, select the delivery method, and handle payment. The system receives user input and uses a generative AI model to suggest appropriate dishes based on that input.

[1699] The server has a means to communicate the user's mood in text and make food suggestions based on that. When a user uses a device such as a smartphone to input "I want to eat spicy food today," the server generates a prompt from that input. An example of a specific prompt is "The user inputted 'I want to eat spicy food today.' Please suggest a dish that matches my mood."

[1700] The generated prompt is input into a generative AI model, which uses an advanced natural language processing model such as OpenAI's GPT-3. The model suggests appropriate dishes based on the prompt and returns the results to the server. The server retrieves the suggestions from the generative AI model and presents them to the user.

[1701] The user checks the suggested dishes and selects the delivery method (cook it yourself, delivery, or restaurant). For example, if the user is offered a "spicy curry," and selects delivery, the server will place the order using a function that allows it to be ordered from a nearby restaurant.

[1702] Payment is made based on the delivery method selected by the user. The server processes the payment information and provides the user with a means to complete the payment. This allows users to easily find the food they want that day, select the delivery method, and quickly order and pay.

[1703] The system is implemented using devices such as smartphones and tablets connected to the internet. The software uses Python programs and OpenAI APIs. Data processing and calculations are performed by converting user input into prompts, inputting them into a generative AI model, and obtaining generated suggestions.

[1704] As a specific example, if a user inputs "I want to eat spicy food today," the system will suggest "How about spicy curry?" and then proceed to the delivery order screen. An example of a prompt sentence to input into the generative AI model is "The user inputs 'I want to eat spicy food today.' Please suggest a dish that suits their mood."

[1705] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1706] Step 1:

[1707] The user uses a device such as a smartphone to input text that suits their mood.

[1708] Input: A sentence that describes the user's mood (e.g., "I want to eat spicy food today")

[1709] Output: User input data

[1710] Step 2:

[1711] The terminal transmits the user's input data to the server.

[1712] Input: User-entered data

[1713] Output: User input data sent to the server

[1714] Step 3:

[1715] The server receives the user's input data and generates a prompt sentence.

[1716] Input: User-entered data

[1717] Data processing: Generate prompts based on user input (e.g., "The user entered, 'I want to eat spicy food today.' Please suggest dishes that fit that mood.")

[1718] Output: Generated prompt statement

[1719] Step 4:

[1720] The server inputs the generated prompt sentence into the generative AI model.

[1721] Input: Generated prompt text

[1722] Output: The prompt sentence fed into the generative AI model

[1723] Step 5:

[1724] The generative AI model generates appropriate dish suggestions based on the prompt sentence.

[1725] Input: A prompt sentence entered into the generative AI model

[1726] Data computation: A generative AI model analyzes the prompt and generates appropriate dish suggestions

[1727] Output: Generated food suggestions (e.g. "Would you like a spicy curry?")

[1728] Step 6:

[1729] The server retrieves suggestions from the generative AI model and presents them to the user.

[1730] Input: Generated dish suggestions

[1731] Output: Food suggestions presented to the user

[1732] Step 7:

[1733] The user reviews the proposed dishes and selects the delivery method (cook / delivery / restaurant).

[1734] Input: Food suggestions presented to the user

[1735] Output: Delivery method selected by the user

[1736] Step 8:

[1737] The server makes the necessary arrangements based on the delivery method selected by the user.

[1738] Input: User-selected delivery method

[1739] Data processing: Arrangements depending on the method of delivery (e.g., for delivery, ordering from a nearby restaurant)

[1740] Output: Notification of completion of arrangement

[1741] Step 9:

[1742] The user receives the food and makes payment based on the delivery method.

[1743] Input: Notification of completion of arrangement

[1744] Output: Notification of payment completion

[1745] In this way, a system is realized that processes and calculates data based on user input at each step, ultimately suggesting appropriate dishes to the user, and even handles selection of the serving method and payment in an integrated manner.

[1746] Example 2

[1747] Next, a description will be given of Example 2 of Form 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."

[1748] Conventional food recommendation systems have difficulty in consistently suggesting dishes based on the user's mood, selecting the delivery method, and even handling payment. Furthermore, when the user selects "make it yourself," there is a lack of efficient means to provide recipes and necessary ingredients, and to facilitate arrangements at nearby stores or online stores. Furthermore, there is also a lack of functionality to utilize the user's location information to provide optimal store information.

[1749] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that, select a serving method (cook yourself / delivery / restaurant), and even complete payment; means for generating a recipe and a list of necessary ingredients using a generative AI model when the user selects "cook yourself"; and means for acquiring the user's location information and providing information on nearby stores or online stores. This allows the user to receive suggestions for dishes that suit their mood, select a serving method, and efficiently arrange for the necessary ingredients.

[1750] "User" means an individual or organization that uses the system to receive food suggestions and select the delivery method.

[1751] "Mood" refers to a sentence or keyword that indicates the user's current emotion or state, which the user inputs into the system.

[1752] "Cooking suggestions" refer to specific cooking ideas and recipes that the system provides based on the user's mood.

[1753] "Delivery method" refers to the method of receiving food that users can choose from: "Make it yourself," "Delivery," or "Restaurant."

[1754] "Payment" is the process of paying the fee based on the delivery method selected by the user.

[1755] A "generative AI model" is an artificial intelligence algorithm or system that generates recipes and ingredient lists based on user input.

[1756] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[1757] An "ingredient list" is a list of ingredients and seasonings needed to make a particular dish.

[1758] "Location Information" is data that indicates a user's current location and is obtained through GPS and other location-based services.

[1759] "Nearby stores" are physical stores that sell ingredients and seasonings within reach of the user's current location.

[1760] "Online stores" are websites or applications that allow customers to order ingredients and seasonings over the Internet.

[1761] MODE FOR CARRYING OUT THE INVENTION

[1762] This invention is a system that proposes dishes based on the user's mood, selects the serving method, and performs the entire process from payment to completion. A specific embodiment of this system will be described below.

[1763] System configuration

[1764] The system consists of a user's device, a server, a generative AI model, a location service, and an external API.

[1765] User's device: A device used by a user to access the system, such as a smartphone or PC.

[1766] Server: A computer that manages the entire system and processes various data.

[1767] Generative AI models: For example, using a generative AI model such as GPT-4 to generate a recipe and ingredients list based on user input.

[1768] Location services: Obtaining user location information using Google Maps API, etc.

[1769] External APIs: We use external APIs such as Google Maps API and Amazon API to provide nearby store information and online store information.

[1770] System Operation

[1771] 1. User mood input

[1772] The user uses a terminal to access the system's interface and input their mood in a sentence, for example, "I want to eat something light today."

[1773] 2. Food suggestions

[1774] The server uses a generative AI model to suggest appropriate dishes based on the user's mood. For example, the following prompt sentence is input to the generative AI model:

[1775] Suggest dishes when a user types, "I want something light today."

[1776] The generative AI model suggests dishes such as "refreshing salad."

[1777] 3. Selection of delivery method

[1778] The user can select one of the following delivery methods for the proposed dish: "Make it yourself," "Delivery," or "Restaurant." For example, the user can select "Make it yourself."

[1779] 4. Generate recipes and ingredient lists

[1780] If the user selects "Make it yourself," the server uses a generative AI model to generate a recipe and ingredients list. For example, the following prompt is input to the generative AI model:

[1781] Generate a recipe and ingredient list if the user selects "Light Salad."

[1782] The generative AI model returns a response like this:

[1783] Recipe: "Refreshing Salad"

[1784] Ingredients list: lettuce, tomato, cucumber, olive oil, lemon

[1785] 5. Obtaining location information

[1786] The server obtains location information from the user's device. This process uses location services such as Google Maps API. If the user allows location information to be provided, the server obtains the user's current location.

[1787] 6. Providing information about nearby stores

[1788] The server uses the acquired location information to provide information about nearby stores or online stores. This process uses external APIs such as Google Maps API or Amazon API. The server provides a list of stores closest to the user's current location and links to online stores.

[1789] Specific examples

[1790] For example, if the user selects "dishes using fresh fish," the server performs the following process.

[1791] 1. The user selects "Fresh fish dishes."

[1792] 2. The server uses a generative AI model to generate a recipe and ingredients list like this:

[1793] Recipe: "Grilled Fresh Fish"

[1794] Ingredients: Fish, salt, pepper, olive oil, lemon

[1795] 3. The server uses the Google Maps API to obtain the user's location and provide information about nearby stores.

[1796] 4. Using the information provided, the user can purchase ingredients at a local store or order them online.

[1797] In this way, users can receive recipe suggestions based on their mood, choose how they would like to be served, and efficiently arrange the necessary ingredients.

[1798] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1799] Step 1:

[1800] A user accesses the system interface using a terminal and inputs his / her mood in text.

[1801] Input: A sentence that describes the user's mood (e.g., "I want something light today").

[1802] Output: Data based on user mood.

[1803] Specific actions: A user opens the app on their smartphone, enters "I want to eat something light today" in the text box, and taps the send button.

[1804] Step 2:

[1805] The server uses a generative AI model to suggest appropriate dishes based on the user's mood.

[1806] Input: A sentence that indicates the user's mood.

[1807] Output: A suggested dish (e.g., "a refreshing salad").

[1808] Specific operation: The server sends the following prompt to the generative AI model.

[1809] Suggest dishes when a user types, "I want something light today."

[1810] The generative AI model suggests a "refreshing salad," and the server sends that information to the user's device.

[1811] Step 3:

[1812] The user selects one of the following delivery methods for the proposed dish: "Make it yourself," "Delivery," or "Restaurant."

[1813] Input: Suggested dish and serving options.

[1814] Output: The delivery method selected by the user (e.g., "Build it yourself").

[1815] Specific operation: The user taps and selects "Make it myself" from the delivery method options displayed on the device screen.

[1816] Step 4:

[1817] If the user selects "make it yourself," the server uses a generative AI model to generate a recipe and ingredients list.

[1818] Input: The dish selected by the user (e.g., "light salad").

[1819] Output: A recipe and a list of ingredients (e.g., "Recipe: Light salad, Ingredients: Lettuce, tomato, cucumber, olive oil, lemon").

[1820] Specific operation: The server sends the following prompt to the generative AI model.

[1821] Generate a recipe and ingredient list if the user selects "Light Salad."

[1822] The generative AI model generates the recipe and ingredients list, and the server sends that information to the user's device.

[1823] Step 5:

[1824] The server obtains location information from the user's device.

[1825] Input: User location permission.

[1826] Output: User's current location.

[1827] Specific behavior: The server uses the Google Maps API to obtain the user's current location. The user taps "Allow" in the dialog box that allows them to provide their location information.

[1828] Step 6:

[1829] Based on the location information acquired by the server, information on nearby stores or online stores is provided.

[1830] Input: User's current location.

[1831] Output: A list of nearby stores or a link to the online store.

[1832] Specific operation: The server uses the Google Maps API or Amazon API to obtain a list of stores closest to the user's current location and sends that information to the user's device.

[1833] Step 7:

[1834] Based on the information provided, the user can purchase ingredients at a nearby store or order them from an online store.

[1835] Input: Nearby store list or online store link.

[1836] Output: Purchase of materials or completion of order.

[1837] What happens: The user chooses the closest supermarket from the list of supermarkets provided and either goes to the supermarket to buy the ingredients, or taps a link to an online store to order online.

[1838] (Application example 2)

[1839] Next, a description will be given of Application Example 2 of Form 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."

[1840] In conventional recipe suggestion systems, after the user selects a dish, the process of arranging ingredients and delivery is complicated, resulting in a poor user experience. Also, when users cook their own meals, arranging the necessary ingredients is time-consuming, making it difficult to cook smoothly.

[1841] The specific processing by the specific 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 means for allowing the user to communicate their mood in text, suggest dishes based on that, select a delivery method (cook it yourself / delivery / restaurant), and even complete payment, means for generating a recipe and ingredient list for the selected dish using a generative AI model, means for arranging ingredients through the API of a nearby store or an online store, and means for delivering the ingredients to the user using the API of a delivery service. This makes it possible to provide a recipe and ingredient list for the dish selected by the user, and further arrange for the ingredients to be delivered.

[1842] "User" means an individual or organization that uses the system to suggest dishes and arrange ingredients.

[1843] "Mood" is information that indicates the current emotion or state that the user inputs into the system.

[1844] "Cuisine suggestions" are meal options provided by the system based on the user's mood.

[1845] "Serving Method" indicates how the user will serve the selected dish, and can be "Make Your Own," "Delivery," or "Restaurant."

[1846] "Payment" refers to the payment procedure carried out by the user for the purchase of the dishes and ingredients selected by the user.

[1847] A "generative AI model" is an artificial intelligence model that generates cooking recipes and ingredient lists based on user input.

[1848] A "recipe" is information that includes instructions and a list of ingredients for making a particular dish.

[1849] An "ingredient list" is a list of ingredients needed to make a particular dish.

[1850] A "nearby store" is a physical store that is close to the user's location.

[1851] An "online store" is a store that sells products over the Internet.

[1852] "API" stands for Application Program Interface, a means by which different software systems communicate with each other.

[1853] "Delivery service" refers to a service that delivers ordered products to users.

[1854] The system for implementing this invention has the function of allowing users to communicate their mood in text, suggest dishes based on that, select the delivery method (cook it yourself / delivery / restaurant), and even process payment. A specific embodiment of this system is described below.

[1855] Hardware and software used

[1856] Hardware: Smartphones, servers

[1857] Software: Mobile applications, cloud-based databases, generative AI models (e.g., GPT-4)

[1858] System configuration

[1859] 1. User Interface

[1860] Users access the system through a smartphone application.

[1861] Users enter their mood in text and receive cooking suggestions.

[1862] 2. Recipe generation

[1863] The server uses a generative AI model (e.g., GPT-4) to generate a recipe and ingredient list for the dish selected by the user.

[1864] The generated recipe and ingredient list are displayed to the user.

[1865] 3. Arrange materials

[1866] If the user selects "make it yourself," the server arranges for the ingredients through APIs of nearby stores or online stores.

[1867] If the user selects "delivery," the server uses the delivery service's API to arrange for the materials to be delivered to the user.

[1868] 4. Payment

[1869] Users make payments through the application.

[1870] Once payment is complete, material arrangements and delivery will begin.

[1871] Processing flow

[1872] 1. User Interface

[1873] The user opens the app and enters their mood.

[1874] For example: "I feel like relaxing today."

[1875] 2. Recipe generation

[1876] The server inputs the following prompt sentence into the generative AI model:

[1877] Example prompt: "The user selected 'Dishes with fresh fish.' Generate a recipe for this dish and a list of the ingredients needed."

[1878] The generated recipe and ingredient list are displayed to the user.

[1879] Example: "Recipe: Salmon Meunière, Ingredients: 2 salmon fillets, a pinch of salt, a pinch of pepper, some flour, 30g butter, 1 lemon, some parsley"

[1880] 3. Arrange materials

[1881] When the user selects "Order ingredients," the server orders the ingredients through the API of a nearby store or online store.

[1882] For example: "I ordered salmon fillets and other ingredients from a local supermarket."

[1883] 4. Delivery arrangements

[1884] Use the delivery service's API to arrange for the materials to be delivered to the user.

[1885] For example: "The materials will be delivered to the user via a delivery service."

[1886] 5. Notification

[1887] Inform users about delivery progress.

[1888] For example: "Materials are on their way. Estimated arrival time is 6:00 PM."

[1889] In this way, the system can provide a recipe and ingredient list for the dish selected by the user, and then arrange for the ingredients to be delivered.

[1890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1891] Step 1:

[1892] The user opens the smartphone application and inputs their mood. The input mood is sent to the server as text data. The server receives this text data and analyzes it. The input data is a sentence such as "I feel like relaxing today."

[1893] Step 2:

[1894] The server inputs a prompt into a generative AI model (e.g., GPT-4) based on the analyzed mood data. An example of a prompt is, "The user selected 'dishes using fresh fish.' Please generate a recipe for this dish and a list of ingredients." The generative AI model generates a recipe and list of ingredients based on this prompt. The output data is "Recipe: Salmon meunière, Ingredients list: 2 salmon fillets, a little salt, a little pepper, flour to taste, 30g butter, 1 lemon, parsley to taste."

[1895] Step 3:

[1896] The server sends the generated recipe and ingredient list to the user's smartphone. The user can view the recipe and ingredient list on their smartphone screen. The input data is the output data from the generative AI model, and the output data is the recipe and ingredient list displayed on the user's smartphone.

[1897] Step 4:

[1898] If the user selects "Make it yourself", the server arranges for the ingredients through the API of a nearby store or online store. The server sends the ingredients list to the API and orders the required ingredients. The input data is the ingredients list, and the output data is the order confirmation from the store.

[1899] Step 5:

[1900] If the user selects "Delivery," the server uses the delivery service's API to arrange for the materials to be delivered to the user. The server sends the materials list and the user's address information to the API to arrange delivery. The input data is the materials list and address information, and the output data is the delivery progress.

[1901] Step 6:

[1902] The server notifies the user of the progress of the delivery. The user can check the progress of the delivery and the estimated arrival time through a smartphone application. The input data is the progress information from the delivery service, and the output data is the notification to the user.

[1903] In this way, the system can provide a recipe and ingredient list for the dish selected by the user, and then arrange for the ingredients to be delivered.

[1904] Example 3

[1905] Next, a third embodiment of the third embodiment will be described. 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."

[1906] With conventional delivery systems, when a user selects a specific dish, it is difficult to efficiently search for nearby restaurants that serve that dish and place an order. Furthermore, there is a lack of functionality to identify the most suitable restaurant based on the user's location information, which hinders the user experience.

[1907] The specification process by the specification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for a user to select "delivery" on a terminal, a means for the terminal to send a request to the server, a means for the server to obtain the user's location information, a means for the server to search a database for information on nearby restaurants, a means for the server to filter restaurants based on the user's selected dish, a means for the server to return the filtering results to the terminal, a means for the user to select a specific restaurant, a means for the server to send a delivery order to the selected restaurant, and a means for the restaurant to receive the order, prepare the dish, and deliver it. This makes it possible for a user, when selecting a specific dish, to efficiently search for nearby restaurants that provide that dish and place an order.

[1908] "User" means an individual or organization that uses the System to order food delivery.

[1909] "Terminal" refers to a device operated by a user, including a smartphone, tablet, or PC.

[1910] A "server" is a computer system that receives and processes requests from users.

[1911] A "request" is request information that a user sends to a server through a terminal.

[1912] "Location information" is data that indicates a user's current location and includes geographic coordinates such as latitude and longitude.

[1913] A "database" is a collection of data that stores information systematically and can be searched and updated.

[1914] "Eating establishments" are establishments that serve food, including restaurants, cafes, and fast food restaurants.

[1915] "Filtering" is the process of selecting data based on specific conditions.

[1916] "Ordering" refers to the act of requesting a restaurant to provide the food selected by the user.

[1917] "Delivery" refers to the act of delivering food prepared by a restaurant to a location designated by the user.

[1918] This invention is a system that allows users to select a specific dish and arrange for delivery from a nearby restaurant that serves that dish. The system is implemented by users operating a terminal and a server processing requests.

[1919] Hardware and software used

[1920] This system uses the following hardware and software:

[1921] Device: A device operated by a user, such as a smartphone, tablet, or computer.

[1922] Server: A computer system that receives and processes requests from users.

[1923] Database: Use a database management system such as MySQL to store and retrieve restaurant information.

[1924] Programming language: Python is used to implement server-side processing.

[1925] Explanation of program processing

[1926] When a user selects "Delivery" on the device, the device sends a request to the server. The server obtains the user's location information from the request and searches a database for nearby restaurant information. It then filters restaurants that offer the appropriate cuisine based on the type of cuisine selected by the user. The server returns the filtered results to the device and displays them to the user. When the user selects a specific restaurant, the server sends a delivery order to that restaurant. The restaurant receives the order, prepares the food, and delivers it to the address specified by the user.

[1927] Specific examples

[1928] For example, if the user selects "dishes with fresh fish," the system operates as follows:

[1929] 1. The user selects "Delivery" on the device and enters "Fresh fish dishes."

[1930] 2. The device sends a request to the server.

[1931] 3. The server obtains the user's location information and searches a database for information on nearby restaurants.

[1932] 4. The server filters for restaurants that serve "fresh fish dishes."

[1933] 5. The filtered results are returned to the device and displayed to the user.

[1934] 6. Once the user selects a specific restaurant, the server sends a delivery order to that restaurant.

[1935] 7. The restaurant receives the order, prepares the food, and delivers it to the address specified by the user.

[1936] Prompt Sentence Examples

[1937] An example of a prompt sentence to input to the generative AI model is as follows:

[1938] If the user selects "Fresh fish dishes," please explain the steps your system takes to arrange delivery from a nearby restaurant.

[1939] By using this prompt sentence, the generative AI model can generate a sentence that explains the system's operation in detail. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1940] Step 1:

[1941] The user selects "Delivery" on a device. The user opens an application on a device such as a smartphone or PC and selects the "Delivery" option. The input is the user's selection, and the output is a request to select "Delivery."

[1942] Step 2:

[1943] The device sends a request to the server. The device sends a request to the server that includes the user's selection and location information. The input is the user's selection and location information, and the output is an HTTP request to the server.

[1944] Step 3:

[1945] The server obtains the user's location information. The server extracts the user's location information from the request and uses this to identify nearby restaurants. The input is the location information in the request, and the output is the user's location information.

[1946] Step 4:

[1947] The server searches the database for nearby restaurant information. The server queries the MySQL database to retrieve nearby restaurant information based on the user's location. The input is the user's location, and the output is nearby restaurant information.

[1948] Step 5:

[1949] The server filters restaurants based on the user's selected cuisine. The server filters restaurants that serve the user's selected cuisine (e.g., "dishes with fresh fish"). The input is nearby restaurant information and the user's selected cuisine, and the output is a filtered list of restaurants.

[1950] Step 6:

[1951] The server returns the filtering results to the terminal. The server returns the filtering results in JSON format to the terminal, and the terminal displays it to the user. The input is the filtered list of restaurants, and the output is the JSON response to the terminal.

[1952] Step 7:

[1953] The user selects a specific restaurant. The user selects a specific restaurant from the displayed list of restaurants. The input is the list of filtered results, and the output is the user's selection.

[1954] Step 8:

[1955] The server sends a delivery order to the selected restaurant. The server then sends a delivery request to the selected restaurant, including the user's order details. The input is the user's selection, and the output is an order request to the restaurant.

[1956] Step 9:

[1957] The restaurant receives the order, prepares the food, and delivers it. The restaurant receives the order, prepares the food, and delivers it to the specified address. The input is the order request from the server, and the output is the delivery of the food to the user.

[1958] (Application example 3)

[1959] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1960] Conventional food delivery systems have the problem that it is difficult for users to efficiently search for nearby restaurants and place an appropriate order when ordering specific dishes or ingredients. There is also a lack of systems that suggest dishes based on the user's mood and preferences, or provide options for delivery methods.

[1961] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for allowing the user to communicate their mood in text, suggest dishes based on that mood, select a delivery method (cooked at home / delivery / restaurant), and even complete payment; means for acquiring the user's location information and searching for nearby restaurants that serve the specified dish; and means for ordering the dish from a restaurant selected from the search results. This allows the user to easily select a dish based on their mood and preferences, and efficiently place a delivery order from a nearby restaurant.

[1962] "Exchanging user mood through text" is a process in which the user inputs their feelings and preferences in text format, and the system then suggests appropriate dishes based on that.

[1963] "Dish suggestions" means that the system selects appropriate dishes or menus based on the user's input information and presents them to the user.

[1964] "Method of delivery" refers to the means by which food is provided to users, whether that means cooking it yourself, delivery, or eating it at a restaurant.

[1965] "Payment" is the process by which a user pays for the food or service they have selected.

[1966] "Obtaining user location information" means determining the user's current location using GPS or other location information technology.

[1967] "Search for nearby restaurants that serve a specified dish" means using a database or API to find the nearest restaurant that serves the dish selected by the user.

[1968] "Ordering food from a restaurant selected from the search results" means selecting an appropriate restaurant from the restaurants found by the search and sending the user's order to that restaurant.

[1969] The system for implementing this invention has the function of allowing the user to communicate their mood in text, suggest dishes based on that, select the delivery method (cooked / delivered / restaurant), and even process payment.It also has the function of obtaining the user's location information, searching for nearby restaurants that serve the specified dish, and ordering the dish from a restaurant selected from the search results.

[1970] Hardware and software used

[1971] Hardware: Smartphone

[1972] Software: Python, Geopy library, Requests library

[1973] System processing overview

[1974] 1. Communicate your mood through text:

[1975] Users enter their mood and preferences in text form through a smartphone application, for example, "I want to eat something made with fresh fish today."

[1976] 2. Food suggestions:

[1977] The server selects appropriate dishes and menus based on the user's input and presents them to the user, using a generative AI model to suggest dishes that best suit the user's mood.

[1978] 3. Delivery Method Selection:

[1979] The user selects the delivery method (cook / delivery / restaurant) for the proposed dish. For example, select "Delivery."

[1980] 4. Get the user's location:

[1981] The server uses the Geopy library to determine the user's current location. For example, if the user is in "Shibuya Ward, Tokyo," it obtains the latitude and longitude.

[1982] 5. Search for nearby restaurants:

[1983] The server uses the Requests library to search the API for nearby restaurants that serve a specific dish, for example, "dishes made with fresh fish."

[1984] 6. Food Ordering:

[1985] The server sends the user's order to the restaurant selected from the search results. For example, the user orders "dishes using fresh fish" from the first restaurant.

[1986] Specific examples

[1987] User input: A user living in Shibuya, Tokyo wants to order "dishes made with fresh fish."

[1988] API endpoints: https: / / api.example.com / restaurants and https: / / api.example.com / order / {restaurant_id}

[1989] Prompt Sentence Examples

[1990] Create a Python program that gets the user's location, searches for nearby restaurants that serve a specified dish, and arranges delivery. The user's location is "Shibuya-ku, Tokyo," and the dish is "dishes made with fresh fish." Use the Geopy and Requests libraries.

[1991] The above is a specific embodiment for carrying out the present invention.

[1992] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1993] Step 1:

[1994] A user inputs their mood and preferences in text format through a smartphone application. For example, they might input, "Today I want to eat a dish made with fresh fish." The input data is text information that expresses the user's mood and preferences. The output is the transmission of the user's input information to the server.

[1995] Step 2:

[1996] The server uses a generative AI model to select appropriate dishes and menus based on the user's input information and presents them to the user. The input data is text information that describes the user's mood and preferences. As part of the data processing, the generative AI model analyzes the text information and suggests the most suitable dishes. The output is a list of suggested dishes.

[1997] Step 3:

[1998] The user selects the delivery method (make-yourself / delivery / restaurant) for the proposed dish. For example, they select "delivery". The input data is the delivery method selected by the user. The output is that the selected delivery method is sent to the server.

[1999] Step 4:

[2000] The server uses the Geopy library to identify the user's current location. For example, if the user is in "Shibuya Ward, Tokyo," the server obtains the latitude and longitude. The input data is the user's location information. As data processing, the Geopy library analyzes the location information and obtains the latitude and longitude. The output is the user's latitude and longitude information.

[2001] Step 5:

[2002] The server uses the Requests library to search for nearby restaurants that serve a specified dish via an API. For example, searching for restaurants that serve "dishes using fresh fish." The input data is the user's latitude and longitude information and the specified dish. To process the data, an API request is sent to obtain information about nearby restaurants. The output is a list of restaurants as a search result.

[2003] Step 6:

[2004] The server sends the user's order to the restaurant selected from the search results. For example, the user orders a "dish using fresh fish" from the first restaurant. The input data is the list of restaurants as search results and the user's order information. The data is processed by sending an API request to confirm the order. The output is order confirmation information.

[2005] 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.

[2006] "Example 1"

[2007] One embodiment of the present invention is a system that combines an emotion engine that recognizes the user's emotions. This system allows the user to communicate their feelings in text, and the emotion engine recognizes those feelings. Based on the recognized emotion, the system makes dish suggestions, allows the user to select the delivery method (cooked at home, delivery, or restaurant), and even handles payment. For example, if a user inputs a feeling of "I want to try something new today," the emotion engine recognizes that feeling as "adventurous" or "a desire to explore new things." Based on the recognition results, the system makes suggestions such as "How about a dish using fresh fish?"

[2008] "Example 2"

[2009] In addition, if the user chooses to cook the food themselves, the system has a function that allows them to order the recipe and necessary ingredients from a nearby supermarket or online supermarket. For example, if the user selects "a dish using fresh fish," the system will provide the recipe for that dish and a list of ingredients to make it. Once the user selects the necessary ingredients, they can order them from a nearby supermarket or online supermarket.

[2010] "Example 3"

[2011] For delivery, the system has a function that allows ordering from nearby restaurants. For example, if you select "dishes using fresh fish," the system can arrange for delivery from a nearby restaurant that serves that dish.

[2012] The processing flow of each embodiment will be described below.

[2013] "Example 1"

[2014] Step 1: The user enters their mood into the system.

[2015] Step 2: The emotion engine recognizes emotions from the user's input.

[2016] Step 3: The system makes food recommendations based on the emotions it recognizes.

[2017] Step 4: The user selects the delivery method (make it yourself / delivery / restaurant). Step 5: The user makes the payment.

[2018] "Example 2"

[2019] Step 1: The user selects a dish suggested by the system.

[2020] Step 2: The system provides the recipe and ingredient list for the selected dish.

[2021] Step 3: The user selects the required material.

[2022] Step 4: The system arranges for the selected ingredients to be purchased from a nearby supermarket or online supermarket.

[2023] "Example 3"

[2024] Step 1: The user selects a dish suggested by the system.

[2025] Step 2: The system arranges delivery from nearby restaurants that serve the selected dish.

[2026] Example 1

[2027] Next, a description will be given of Example 1 of Form 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."

[2028] Conventional food recommendation systems have the problem of not taking into account the user's mo...

Claims

1. means for receiving a mood and a desire of the user inputted by the user; A means for transmitting the received data to the generation AI as a prompt sentence; A means for analyzing the prompt sentence by the generating AI and proposing an appropriate dish; A means for accepting a selection of a serving method for the proposed dish and making a payment; A system including:

2. The system according to claim 1, further comprising means for arranging a recipe and necessary ingredients from a nearby store or an online store when the make-it-yourself method is selected as the provision method.

3. The system according to claim 1, further comprising a means for ordering from a nearby restaurant when delivery is selected as the method of provision.

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