system

Abstract

To provide a system that can realize efficient production and delivery by grouping a plurality of users who desire the same type of food based on the user's preference. [Solution] The system includes: a user terminal means for inputting the user's desired eating style and delivery time slot; a server means for receiving the input information and saving it in a database; a means for using AI to calculate the efficiency of meal production and delivery routes based on the saved information; a means for sending orders to affiliated stores based on the calculation results; a means for receiving the estimated time of completion of the food from the affiliated stores; a means for calculating the optimal delivery route based on the estimated time of completion and the user's address information and issuing instructions to the delivery person; a means for receiving delivery completion reports from the delivery person and saving them on a server; a means for receiving feedback from the user and saving them on a server; and a means for adjusting the AI ​​algorithm and improving the quality of service based on the feedback data.

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] With conventional delivery services, users order the food they want individually, and each order is delivered individually, often resulting in high service and delivery fees. This makes it costly for users and discourages frequent use. It also presents a problem for restaurants, as it is difficult to efficiently produce and deliver meals, increasing operational costs when handling large orders. Furthermore, individual deliveries increase the environmental burden, making it a challenge to provide sustainable delivery services. [Means for solving the problem]

[0005] In order to solve these problems, the present invention provides the following means.

[0006] The system provides a user terminal means for inputting the user's desired meal style and delivery time slot. It also provides a server means for receiving the information input from the user terminal means and storing it in a database. It also provides a means for using AI to calculate the efficiency of meal production and delivery routes based on the information stored by the server means. This allows multiple users requesting similar dishes to be grouped together based on the user's preferences, enabling efficient production and delivery.

[0007] The server also includes a means for calculating the optimal delivery route based on the estimated time of completion and the user's address information, and for issuing instructions to the delivery person.

[0008] Furthermore, it provides a means to receive delivery completion reports from delivery personnel and store them on a server. It also includes a means to receive feedback from users, store it on a server, and adjust the AI ​​algorithm to improve the quality of the service. In this way, it is possible to reduce waste in individual deliveries, reduce overall costs, and provide a high-quality delivery service at an affordable price for users.

[0009] "User terminal means" refers to a device that allows a user to access the system and input their own information, desired eating style, and delivery time slot.

[0010] The "server means" is a central processing unit that receives information sent from the user terminal means, stores it in a database, and performs various calculations and instructions.

[0011] The "database" is a storage system for organizing and storing necessary data such as user registration information, desired eating style, delivery time slots, information on affiliated stores, and feedback.

[0012] "Means for calculating the efficiency of meal production and delivery routes using AI" refers to methods and devices that use machine learning or other artificial intelligence technologies to calculate optimal meal production plans and delivery routes based on user preferences and geographical information.

[0013] An "affiliated store" is a cooking facility or restaurant that cooperates with the system to prepare and serve food based on orders from users.

[0014] An "order list" is a list of detailed information such as the type of food required, quantity, and cooking timing, which is sent to the affiliated store.

[0015] A "delivery person" is a person who delivers food from affiliated stores to users and follows delivery route instructions from the system.

[0016] "Means for receiving delivery completion reports and storing them on a server" refers to a method or device for the system to receive a completion report when the delivery person delivers the prepared food to the user and record that information on a server.

[0017] "Means for receiving feedback and storing it on a server" refers to a method or device for collecting opinions and ratings from users regarding meals and deliveries and storing that information on a server.

[0018] "Means for adjusting AI algorithms and improving service quality" refers to methods and devices for improving overall system performance and user experience by updating and adjusting the learning model of artificial intelligence based on collected feedback data.

[0019] "Means for calculating delivery routes and issuing instructions to delivery personnel" refers to a method or device that uses AI to calculate delivery orders and efficient routes for multiple users and communicates the results to delivery personnel. [Brief explanation of the drawings]

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

[0021] 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.

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

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

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

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

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0035] 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.

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

[0037] 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.

[0038] 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.

[0039] 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.

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

[0041] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and delivery time slot. The following is a natural language explanation of the program processing of this system.

[0042] 1. User registration and preference settings

[0043] User Actions

[0044] Users download the app and create an account by entering their name, address, and contact information, as well as their preferred food style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day).

[0045] Server Actions

[0046] The server receives the account information and desired information sent from the user terminal and stores it in a database.

[0047] 2. AI-based list creation and efficiency

[0048] Server Actions

[0049] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[0050] 3. Ordering and adjusting to stores

[0051] Server Actions

[0052] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[0053] Specific examples

[0054] For example, if many users are ordering pizza at 12:00, the server will request a specified Italian restaurant to prepare 10 pizzas by 12:00.

[0055] 4. Delivery route optimization

[0056] Server Actions

[0057] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[0058] 5. Delivery and Reporting

[0059] Delivery person terminal actions

[0060] The delivery person terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery person follows these instructions and delivers the food to each user along the specified route.

[0061] Deliveryman's actions

[0062] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[0063] 6. Feedback and Improvement

[0064] User Actions

[0065] Users can enter feedback through the application about the quality of the delivered food and the delivery time.

[0066] Server Actions

[0067] The server receives user feedback and stores it in a database. The AI ​​uses this feedback data to adjust its algorithms and improve the service for future visits.

[0068] Specific examples and processing flow

[0069] For example, if User A wants Italian food at 12:00 every day and User B wants Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants to deliver Italian food to User A at 12:00 and Western food to User B at 14:00, and the AI ​​calculates the optimal delivery route. In this way, efficient meal production and delivery is achieved.

[0070] This system allows users to order meals at lower prices than usual, streamlining delivery, while also enabling partner restaurants to provide meals as efficiently as possible.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user downloads the app and creates an account. They enter the necessary personal information, such as their name, address, and contact information. They also select their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[0074] Step 2:

[0075] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[0076] Step 3:

[0077] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[0078] Step 4:

[0079] The server generates an order list of the required dishes for each partner store based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner store.

[0080] Step 5:

[0081] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[0082] Step 6:

[0083] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[0084] Step 7:

[0085] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[0086] Step 8:

[0087] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[0088] Step 9:

[0089] The server receives the delivery completion report from the delivery person and stores the information in a database.

[0090] Step 10:

[0091] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[0092] Step 11:

[0093] The terminal sends the user's feedback information to the server, which stores it in a database.

[0094] Step 12:

[0095] The server analyzes the collected feedback data and adjusts the AI ​​algorithms, which will further improve the efficiency of future meal production and delivery routes.

[0096] This series of processing steps allows users to receive high-quality service at a low cost, and enables partner stores to efficiently produce and deliver meals.

[0097] Example 1

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

[0099] Conventional meal delivery systems have difficulty setting efficient delivery routes and production plans that match users' desired meal styles and delivery times. As a result, they have been unable to deliver meals quickly and efficiently according to users' wishes, resulting in problems such as delivery delays and increased delivery costs. Furthermore, they have not been able to fully utilize user feedback to improve the system.

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

[0101] In this invention, the server includes a user terminal means for inputting the user's desired meal style and delivery time slot, an information processing means for receiving the information input from the user terminal means and storing it in a database, and a calculation means for calculating the efficiency of meal production and delivery routes using a generative AI model based on the information stored by the information processing means, thereby enabling efficient meal production and delivery route optimization based on the user's wishes.

[0102] "User terminal means" refers to an electronic terminal for inputting information such as the user's desired meal style and delivery time slot, and includes smartphones, tablets, PCs, etc.

[0103] "Information processing means" refers to a server having the function of receiving information sent from user terminal means and storing and managing it in a database.

[0104] "Generative AI model" refers to an artificial intelligence system that learns from large amounts of data and calculates the efficiency of meal production and delivery routes. Specifically, this includes machine learning frameworks such as TENSORFLOW® and PyTorch.

[0105] "Computational means" refers to a function that uses a generative AI model to calculate efficient meal production lists and delivery routes based on data stored in the information processing means.

[0106] "Communication means" refers to an internet communication function for automatically sending orders to affiliated stores based on the results of the calculation means.

[0107] "Data receiving means" refers to a function that receives the estimated time when the food will be ready from the partner restaurant.

[0108] "Instruction means" refers to a function that calculates the optimal delivery route based on the estimated completion time and the user's address information, and issues instructions to the delivery person.

[0109] The "reporting means" refers to the function of receiving a delivery completion report from the delivery person and storing it in the information processing means.

[0110] "Feedback means" refers to a function that receives feedback from the user and stores it in the information processing means.

[0111] "Improvement measures" refers to the function by which the generative AI model adjusts its algorithm based on feedback data to improve the quality of the service.

[0112] The "processing means" refers to a function that groups multiple users who want the same type of food based on the user's desired eating style and delivery time slot.

[0113] "Scheduling means" refers to the function of generating an order list for affiliated stores and creating a production plan for each store to prepare the necessary dishes.

[0114] This invention relates to a system that realizes efficient meal delivery based on a user's desired eating style and delivery time slot. The system includes a user terminal means, an information processing means, a generative AI model, a calculation means, a communication means, a data receiving means, an instruction means, a reporting means, a feedback means, and an improvement means.

[0115] First, the user downloads a dedicated application onto their user terminal such as a smartphone or tablet. The user launches the application, enters their name, address, and contact information, and sets their preferred dining style and delivery time. For example, they enter information such as "Taro Tanaka, Shinjuku-ku, Tokyo, tanaka@example.com, pasta, 12:00." After completing the input, the user sends this information to the server.

[0116] The server receives the information sent from the user terminal means using the information processing means and stores it in a database. The server maintains reliability and security by using cloud services such as AWS (registered trademark) and Google (registered trademark) Cloud Platform.

[0117] The server then analyzes all user preference information stored in the information processing device using a generative AI model, which uses frameworks such as TensorFlow and PyTorch to calculate an efficient meal production list and delivery route based on the user's preferred eating style and delivery time slot.

[0118] For example, if User A and User C want pasta at 12:00, and User B wants curry rice at 14:00, the server will group them appropriately based on this information.The generative AI model then uses computational tools to calculate an efficient production list and delivery route.

[0119] The server generates an order list for partner stores based on the results of the calculation means and sends it to the store using communication means. The order list contains detailed information about the dish name, quantity, and required time. For example, a list may be generated that says, "Please prepare 10 plates of pasta by 12:00."

[0120] Furthermore, the server receives the estimated time the food will be ready from the restaurant that received the order through the data receiving means. Based on the received estimated time and the user's address information, the server uses an instruction means to generate the optimal delivery route using Google Maps API or OR-Tools. For example, there is a specific example where the server calculates a delivery route from the restaurant to User A, User B, and User C in that order.

[0121] The delivery person's terminal receives delivery instructions sent from the server, which include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Once the delivery is complete, the delivery person reports completion on the terminal.

[0122] Finally, the user provides feedback on the quality and delivery time of the delivered food through the application. The server receives this feedback through the feedback mechanism and stores it in a database. The generative AI model uses this feedback to adjust its algorithm using the refinement mechanism to improve the delivery service next time.

[0123] Prompt Sentence Examples

[0124] User A wants Italian food at 12:00 every day, and User B wants Western food at 14:00. Generate the optimal delivery route and efficient production list.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1: Register your user and set your preferences

[0127] The user downloads the dedicated application and installs it on their device. Next, they launch the application and enter their name, address, and contact information. They then set their preferred dining style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day). Specifically, they enter their information into the input form on the screen and press the "Register" button. Once this information is entered (the registration information is set), the device sends it to the server. The output is the sent user information.

[0128] Step 2: Save user information

[0129] The server receives user information sent from the user terminal means. The received data includes name, address, contact information, meal style, and delivery time slot. Specifically, the server analyzes the received data and stores it in a MySQL (registered trademark) or PostgreSQL database. It inserts information using an SQL query. For example, it executes a command such as "INSERT INTO users (name, address, contact, meal_style, delivery_time) VALUES (...)". The input is the received user information, and the output is the result stored in the database.

[0130] Step 3: Collect user preferences and analyze them with AI

[0131] The server uses information processing means to collect all user preference information from a database. For example, it retrieves data by executing a query such as "SELECT FROM users WHERE delivery_time = '12:00'". It then analyzes the retrieved data using a generative AI model to calculate an efficient meal production list and delivery route. Specifically, it uses TensorFlow and PyTorch to calculate the optimal route based on eating style and delivery time slot. The input is a set of user information retrieved from the database, and the output is a production list and an optimized delivery route.

[0132] Step 4: Generate and send an order list to the store

[0133] The server generates an order list for partner stores based on the calculated production list. The order list includes the dish name, quantity, and required time. Specifically, the server generates order information in JSON format and sends it to the partner store's API endpoint using an HTTP POST request. For example, it sends a request such as "Please prepare 10 plates of pasta by 12:00." The input is the production list, and the output is the order list sent to the partner store.

[0134] Step 5: Calculate delivery routes

[0135] The server receives the estimated time the food will be ready from the partner restaurant. For example, it obtains this information from the restaurant's API via an HTTP GET request. Based on the received estimated time and the user's address information, it calculates the optimal delivery route using Google Maps API or OR-Tools. Specifically, it uses the API to send a request to determine the order of delivery to each destination. The input is the estimated time of completion and the user's address information, and the output is the optimized delivery route.

[0136] Step 6: Delivery and reporting

[0137] The delivery person's terminal receives delivery instructions sent from the server. These instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Specifically, the delivery person uses the terminal to report "delivery complete" upon delivery. The report is made by pressing the "Delivery Complete" button on the dedicated app. The input is the delivery instructions, and the output is the delivery completion report data.

[0138] Step 7: Gather feedback and improve with AI

[0139] The user enters feedback on the quality and delivery time of the delivered food. The server receives this information via the feedback means and stores it in a database. Specifically, the user enters the feedback into an evaluation form within the application and presses the submit button. The received feedback data is analyzed by a generative AI model, and the algorithm is adjusted to improve service quality. The input is the feedback information, and the output is an updated set of parameters for the AI ​​model.

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Conventional meal delivery systems have difficulty effectively reflecting users' preferred eating styles and desired delivery times. They also face the problem of calculating efficient delivery routes for individual orders and coordinating with partner stores, which can be cumbersome. This can lead to problems such as a loss of user satisfaction and reduced work efficiency for delivery staff and stores.

[0143] 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.

[0144] In this invention, the server includes: a user terminal means for inputting a user's desired meal style and desired delivery time; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using a generative AI model to suggest meals based on the information stored by the server means; a means for accepting user orders based on the suggestions from the generative AI model; a means for using AI to calculate the efficiency of meal production and delivery routes based on the order information; a means for generating and sending a bulk order list to affiliated stores based on the calculation results; a means for receiving an estimated meal completion time from the affiliated stores; a means for calculating an optimal delivery route based on the estimated completion time and the user's address information and issuing instructions to the delivery person; a means for tracking and displaying the location information of the delivery person who received the instructions in real time and notifying the user of the delivery status; a means for receiving delivery completion reports from the delivery person and storing them on the server; a means for receiving user feedback and storing them on the server; and a means for adjusting the generative AI model algorithm based on the feedback data to improve service quality. This enables efficient meal delivery according to user preferences, bulk orders, and calculation of the optimal delivery route.

[0145] The "user terminal means" is a device that allows a user to input the desired meal style and desired delivery time.

[0146] The "server means" is a device that receives information input from the user terminal means and stores it in a database.

[0147] A "generative AI model" is an artificial intelligence system that makes meal suggestions and streamlines ordering based on stored user information.

[0148] The "means for making suggestions" is a device or system that uses a generative AI model to suggest meals tailored to the user's preferences.

[0149] "Means for accepting orders" refers to a device or system that accepts orders from users based on suggestions from the generative AI model.

[0150] A "means for performing efficiency calculations" is a device or system that uses AI to calculate the efficiency of meal production and delivery routes based on order information.

[0151] The "means for generating and transmitting a collective order list" is a device or system that generates and transmits a collective order list to affiliated stores.

[0152] The "means for receiving the estimated time of completion of the dish" is a device or system that receives the estimated time of completion of the dish from the affiliated restaurant.

[0153] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a device or system that generates the optimal delivery route based on the estimated completion time and the user's address information and issues instructions to the delivery person.

[0154] The "means for tracking and displaying in real time and notifying the user of the delivery status" is a device or system that tracks the location information of the delivery person in real time and notifies the user of the delivery status.

[0155] The "means for receiving a delivery completion report and storing it on a server" is a device or system that receives a delivery completion report from a delivery person and stores it on a server.

[0156] The "means for receiving feedback and storing it on a server" is a device or system that receives feedback from a user and stores it on a server.

[0157] "Means for adjusting the generative AI model algorithm and improving the quality of the service" refers to a device or system that adjusts the generative AI model based on stored feedback data and improves the quality of the service.

[0158] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and desired delivery time. This system is composed of a user terminal means, a server means, and multiple AI technologies.

[0159] System Overview

[0160] The overall system is made up of the following configuration and processing procedures.

[0161] 1. User registration and preference settings

[0162] Using the user terminal means, the user downloads a dedicated smartphone application and creates an account. The user enters their name, address, contact information, and selects their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day). The server means receives this information and stores it in a database.

[0163] 2. Meal suggestions and ordering

[0164] The server uses a generative AI model based on the stored information to recommend meals that fit the user's preferences. This generative AI model incorporates information such as food and drink types, past order history, and popular menu items to recommend the most suitable meal. The user can then review the suggested meals and place their order through the app.

[0165] 3. AI-powered efficiency and order list generation

[0166] The server uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information. Based on the results of this calculation, it generates and sends a consolidated order list to partner stores. This order list includes details such as the name of the dish, quantity, and required time.

[0167] 4. Delivery route optimization

[0168] The server receives the estimated time of completion of the food from the partner restaurant and calculates the optimal delivery route based on the user's address information. This calculation uses map data such as Google Maps API. The delivery person is provided with information instructing them on the optimized delivery route.

[0169] 5. Delivery and real-time tracking

[0170] The delivery person's device delivers according to the optimal route instructed by the server. The delivery status is tracked in real time, and the user is notified of the delivery person's location and delivery status. This is done using GPS data and push notification technology.

[0171] 6. Completion Report and Feedback

[0172] Once the delivery is complete, the delivery person reports the completion via their device, and the information is saved on the server. The user can then enter feedback on the quality of the delivered food and the delivery time. This feedback data is also saved on the server and used to adjust the generative AI model algorithm and improve service in the future.

[0173] Specific use cases

[0174] For example, if User A requests pizza to be delivered at 12:00 every day, the system will pass this information to the generative AI model to generate the optimal delivery route and place a bulk order with partner stores. The system will also track the delivery person's location in real time and notify the user, visualizing the delivery status.

[0175] Prompt Sentence Examples

[0176] "Based on user A's request for pizza delivery at 12:00 every day, collect information on the delivery time and food style (e.g., Western or Japanese) desired by other users B and C, and combine this information to calculate the optimal delivery route. Also, based on the results, generate an efficient order list for partner stores and send it to each store."

[0177] This system provides users with the benefit of having their desired meals delivered efficiently and quickly, while also enabling partner restaurants to provide meals with maximum efficiency.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] The user terminal means allows the user to input the desired meal style and desired delivery time.

[0181] Input: User's name, address, contact information, eating style, and desired delivery time.

[0182] Output: Sends the input data to the server.

[0183] Specific operation: The user uses the smartphone app to enter the required information into the input form and clicks the submit button to send it to the server.

[0184] Step 2:

[0185] The server means stores the received user information in a database.

[0186] Input: User information sent from the user terminal.

[0187] Output: User information stored in the database.

[0188] Specific operation: The server stores the received user information in a database management system (for example, PostgreSQL or MySQL).

[0189] Step 3:

[0190] The server means uses the generative AI model based on the stored information to make meal recommendations.

[0191] Input: User information stored in the database.

[0192] Output: A suggested meal menu.

[0193] Specific operation: The generative AI model generates the optimal meal based on the user's past ordering history and popular menu items, and returns the suggestions to the server.

[0194] Step 4:

[0195] The user terminal means accepts an order from a user based on the proposal from the generative AI model.

[0196] Input: Meal suggestions from a generative AI model.

[0197] Output: User's order data.

[0198] Specific operation: The user selects the desired meal from the proposed menu and confirms the order.

[0199] Step 5:

[0200] The server means uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information.

[0201] Input: User's order information.

[0202] Output: Efficient delivery routes and production lists.

[0203] How it works: The AI ​​algorithm analyzes multiple order information and calculates the optimal delivery route and order list for the store.

[0204] Step 6:

[0205] The server means generates and transmits a bulk order list to the affiliated stores based on the calculation results.

[0206] Input: The results of the efficiency calculation.

[0207] Output: Bulk order list for partner stores.

[0208] Specific operation: The server creates a bulk order list of the required dishes for each store and sends it to the store via API.

[0209] Step 7:

[0210] The server means receives the estimated time when the food is ready from the affiliated store.

[0211] Input: Estimated completion time from partner store.

[0212] Output: Estimated completion time data.

[0213] Specific operation: Receives estimated completion time information from the store via API and saves it on the server.

[0214] Step 8:

[0215] The server means calculates the optimum delivery route and issues instructions to the delivery person.

[0216] Input: Estimated completion time and user address information.

[0217] Output: Optimal delivery route instructions.

[0218] Specific operation: The AI ​​algorithm generates the optimal delivery route based on the estimated completion time and the user's address information and notifies the delivery person's terminal.

[0219] Step 9:

[0220] The delivery person's terminal delivers the parcel according to the optimal route instructed and reports the delivery status to the server in real time.

[0221] Input: Optimal delivery route.

[0222] Output: Real-time delivery status data.

[0223] Specific operation: The delivery person uses GPS to send location information to the server in real time and report the situation.

[0224] Step 10:

[0225] The user terminal means receives feedback from the user after delivery is completed and transmits it to the server.

[0226] Input: User feedback.

[0227] Output: Feedback data.

[0228] What it does: The user uses the app to enter feedback about the quality of the food and delivery time, which is then sent to the server.

[0229] Step 11:

[0230] The server means stores the received feedback data and adjusts the generative AI model algorithm.

[0231] Input: User feedback data.

[0232] Output: The tuned generative AI model.

[0233] Specific operation: Based on the feedback data, the algorithm of the generative AI model is updated and improved to improve the quality of the service.

[0234] 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.

[0235] The present invention relates to a delivery system that incorporates an emotion engine that recognizes the user's emotions. The system of the present invention not only realizes efficient meal delivery based on the user's desired eating style and delivery time slot, but also recognizes the user's emotions to further improve service. The following is a natural language explanation of the program processing of this system.

[0236] 1. User registration and preference settings

[0237] User Actions

[0238] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day).

[0239] Server Actions

[0240] The server receives the account information and desired information sent from the user terminal and stores them in a database.

[0241] 2. AI-based list creation and efficiency

[0242] Server Actions

[0243] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[0244] 3. Ordering and adjusting to stores

[0245] Server Actions

[0246] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[0247] Specific examples

[0248] For example, if many users are ordering pizza at 12:00, the server will request the specified restaurant to prepare 10 pizzas by 12:00.

[0249] 4. Delivery route optimization

[0250] Server Actions

[0251] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[0252] 5. Delivery and Reporting

[0253] Delivery person terminal actions

[0254] The delivery staff terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery staff follows these instructions and delivers the food to each user along the specified route.

[0255] Deliveryman's actions

[0256] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[0257] 6. Feedback and emotion engine analysis

[0258] User Actions

[0259] Users can input their feedback about the quality of the delivered food and the delivery service through the application, and the emotion engine recognizes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) from the user's voice and text.

[0260] Server Actions

[0261] The server receives feedback and emotional data from users and stores it in a database. The AI ​​then adjusts the algorithm based on this feedback and emotional data to improve the service in the future.

[0262] Specific examples and processing flow

[0263] For example, if User A requests Italian food at 12:00 every day, and User B requests Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants, with Italian food delivered to User A at 12:00 and Western food delivered to User B at 14:00, and the AI ​​will calculate the optimal delivery route. Furthermore, if User A enters positive feedback and an emotion such as "very satisfied" in the application, this information is stored on the server and used to improve the service in the future.

[0264] This system allows users to receive high-quality service at a low price, and enables partner restaurants to efficiently produce and deliver meals. In addition, by using an emotion engine, it is possible to more accurately grasp user satisfaction and further improve services.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[0268] Step 2:

[0269] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[0270] Step 3:

[0271] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[0272] Step 4:

[0273] The server generates an order list of the required dishes for partner stores based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner stores.

[0274] Step 5:

[0275] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[0276] Step 6:

[0277] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[0278] Step 7:

[0279] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[0280] Step 8:

[0281] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[0282] Step 9:

[0283] The server receives the delivery completion report from the delivery person and stores the information in a database.

[0284] Step 10:

[0285] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[0286] Step 11:

[0287] The terminal sends the user's feedback information to the server, which stores it in a database.

[0288] Step 12:

[0289] The server recognizes the user's emotions using an emotion engine in addition to the feedback. The emotion engine analyzes the user's voice feedback and text messages to extract emotions such as satisfaction, dissatisfaction, joy, and anger.

[0290] Step 13:

[0291] The server stores the recognized emotion data in a database and incorporates it into the AI ​​algorithm, which uses this data to adjust the AI ​​algorithm and improve the quality of service for future meal production and delivery.

[0292] Step 14:

[0293] The server sends feedback to partner stores based on the emotion data and provides specific instructions for improving service. For example, it can provide feedback such as "Many users are satisfied with the doneness of their pizza, but are dissatisfied with the delivery time," and work with the store to consider improvement measures.

[0294] This series of processing steps enables efficient food preparation and delivery while increasing user satisfaction. Furthermore, the use of an emotion engine improves the quality of feedback, enabling continuous improvement of the service.

[0295] Example 2

[0296] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0297] Conventional delivery systems have difficulty meeting individual user requests and wishes, making it difficult to achieve efficient delivery and reduce costs. Furthermore, feedback to improve services by taking user feelings into consideration has not been fully utilized. As a result, user satisfaction and service quality have declined, making it difficult to maintain competitiveness.

[0298] 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.

[0299] In this invention, the server includes a terminal means for inputting the user's desired meal style and delivery time slot, a server means for receiving the information input from the terminal means and storing it in a database, and a means for calculating the efficiency of meal production and delivery routes using a machine learning model based on the information stored by the server means, thereby enabling efficient meal delivery according to the user's wishes and improving the quality of service.

[0300] The "terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[0301] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[0302] A "machine learning model" is an algorithm used to calculate the efficiency of meal production and delivery routes based on stored information.

[0303] "Partner restaurants" are restaurants that work in conjunction with this system to receive orders and provide food.

[0304] The "estimated completion time" is the time when the food preparation specified by the partner restaurant will be completed.

[0305] An "optimal delivery route" is a route calculated to efficiently deliver food to multiple users.

[0306] A "delivery person" is a person or device whose role is to deliver food received from partner restaurants to each user.

[0307] The "delivery completion report" is a notification of delivery completion sent when the delivery person delivers the food to the user.

[0308] "Feedback" is information that allows users to provide their evaluations and impressions of the delivered food and service.

[0309] An "emotion recognition engine" is a technology for recognizing emotions from a user's voice or text.

[0310] "Artificial intelligence algorithm" refers to an algorithm that uses feedback data and an emotion recognition engine to improve the quality of services.

[0311] The present invention provides a system for providing an efficient and high-quality meal delivery service based on a user's desired eating style and delivery time slot. The system includes a terminal unit, a server unit, a machine learning model, partner restaurants, an optimal delivery route calculation unit, a delivery person unit, and an emotion recognition engine.

[0312] First, users download a dedicated application onto their smartphone or tablet and create an account. They then enter their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day) into the application.

[0313] Next, the server receives the account information and preference information sent by the user and stores them in a database. The user information stored in the database is stored in a "User Information" table, and the preference information is stored in a "Preference Information" table.

[0314] The server collects the stored data at a fixed time each day and uses a machine learning model to analyze the preferences of all users. This machine learning model, built in Python, groups users who have the same meal preferences at the same time and generates an efficient meal production list. For example, if multiple users want pizza at 12:00, this information is organized and they can efficiently place a bulk order.

[0315] The server then generates an order list for the required dishes for partner restaurants based on the production list created by the AI ​​system. The order list includes detailed information such as the name of the dish, the quantity, and the estimated time of completion. The order list is sent to partner restaurants via API, for example, in JSON format using a RESTful API.

[0316] Partner restaurants prepare food based on the received order list and notify the server of the estimated time the food will be ready. The server receives this notification data and calculates the optimal delivery route based on the estimated time of completion and each user's address information. The server uses map services such as Google Maps API to calculate the shortest route, making it possible to deliver to multiple users efficiently.

[0317] At the actual delivery stage, the delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to the user along the specified route. Once the food has been delivered, the delivery person uses a dedicated application to report "delivery completed," which is then updated on the server.

[0318] Finally, the user enters feedback about the food and service through the application, and an emotion recognition engine analyzes the user's emotions via voice and text, resulting in a level of satisfaction (e.g., satisfied, dissatisfied, happy, angry, etc.) being sent to the server.

[0319] The server collects this feedback and emotion data, stores it in a database, and adjusts the AI ​​algorithm. This feedback loop allows for future service quality improvements, continuously increasing user satisfaction.

[0320] Prompt Sentence Examples

[0321] "Imagine a program that takes into account the user's preferred eating style and delivery time slots to create the optimal delivery route and produce list."

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] System program processing flow

[0324] Step 1: Register your user information and set your preferences

[0325] Specific actions

[0326] Input: The user enters their name, address, contact information, preferred eating style, and desired delivery time slot into a dedicated application.

[0327] Output: The user's input data is sent to the server.

[0328] A user opens the app on their smartphone or tablet, taps the "Create a new account" button, enters the required information, and then taps the "Register" button, which sends the entered data to the server via the Internet.

[0329] Data processing or data calculation

[0330] The server receives the data sent by the user and stores the name, address, and contact information in a "User Information" table, and stores the preferred eating style and desired delivery time slot in a "Preference Information" table, thereby organizing individual user information.

[0331] Step 2: Collect and analyze all users' preferences

[0332] Specific actions

[0333] Input: Get all users' preferences from the database.

[0334] Output: A dataset is generated for analysis.

[0335] The server queries the "Preferences" table from the database at a fixed time every day to collect all users' preferences, which are then passed to the AI ​​system as a dataset for analysis.

[0336] Data processing or data calculation

[0337] The server analyzes the dataset using a machine learning model built in Python to calculate the production list and optimal delivery route based on the user's desired delivery time and eating style, enabling efficient production and delivery.

[0338] Step 3: Generate and send an order list to partner restaurants

[0339] Specific actions

[0340] Input: An efficient production list created by an AI system.

[0341] Output: Specific order data sent to partner restaurants.

[0342] The server analyzes the production list received from the machine learning model and generates specific order data for each partner restaurant. For example, the order list includes the name of the dish, the quantity, and the required time. The generated order list is sent to the partner restaurant in JSON format using a RESTful API.

[0343] Data processing or data calculation

[0344] The server divides and organizes the order list by store and sends it to partner restaurants in the appropriate format, allowing the restaurants to efficiently receive orders and begin preparation.

[0345] Step 4: Receive estimated time of completion and calculate optimal delivery route

[0346] Specific actions

[0347] Input: Estimated time of completion of food received from partner restaurant.

[0348] Output: Optimal delivery route to send to delivery driver.

[0349] The server receives the estimated time the food will be ready from partner restaurants via API. Based on the received data and the user's address information, it calculates the optimal delivery route. Using map services such as Google Maps API, the shortest route is generated for efficient delivery to multiple users.

[0350] Data processing or data calculation

[0351] The server calculates the optimal delivery route based on the estimated completion time and the user's address information, and prepares to send the calculation results to the delivery person's terminal.

[0352] Step 5: Delivery and reporting

[0353] Specific actions

[0354] Input: Delivery instructions sent by the server.

[0355] Output: Delivery completion report.

[0356] The delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. When the food is delivered, the delivery person taps the "Delivery Complete" button to report to the server.

[0357] Data processing or data calculation

[0358] The server receives delivery completion reports from delivery personnel and updates the order status in the database, allowing for real-time monitoring of the progress of each delivery and efficient management.

[0359] Step 6: Feedback and Emotion Recognition Engine Analysis

[0360] Specific actions

[0361] Input: Feedback and sentiment data entered by the user through the application.

[0362] Output: Analysis results for improvement.

[0363] Users can enter their feedback about the food delivered and the service through the application, and an emotion recognition engine analyzes the user's emotions using voice and text, and these emotions, such as satisfaction, dissatisfaction, joy, and anger, are sent to the server.

[0364] Data processing or data calculation

[0365] The server stores the acquired feedback and emotion data in a database and uses artificial intelligence algorithms to improve the quality of the service. This feedback loop will further improve the delivery service for future deliveries.

[0366] keyword

[0367] Generative AI model, prompt sentence

[0368] (Application example 2)

[0369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] Conventional delivery systems have struggled to provide efficient delivery services that match users' desired dining style and delivery time slots. While systems exist for incorporating user feedback, they lack sufficient mechanisms for recognizing user emotions and reflecting them in service quality improvements. This makes it difficult to guarantee a consistently high-quality user experience, and service providers lack effective means of improvement.

[0371] 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.

[0372] In this invention, the server includes: a user terminal means for inputting a user's desired eating style and delivery time slot; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using AI to calculate the efficiency of meal production and delivery routes based on the information stored by the server means; a means for sending an order to a partner store based on the calculation results; a means for receiving an estimated time of completion of the food from the partner store; a means for calculating an optimal delivery route based on the estimated time of completion and the user's address information and issuing instructions to a delivery person; a means for receiving a delivery completion report from the delivery person and storing it in the server; a means for receiving feedback from the user and storing it in the server; and a means for adjusting the AI ​​algorithm based on the feedback data and emotion recognition results to improve the quality of the service. This makes it possible to continuously improve the service based on feedback that takes user emotions into consideration.

[0373] The "user terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[0374] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[0375] "Means for calculating efficiency of meal production and delivery routes using AI" means processes and algorithms that use artificial intelligence to improve the efficiency of meal production plans and delivery routes based on stored information.

[0376] The "means for sending orders to affiliated restaurants" is a system for sending the necessary food order information to affiliated restaurants.

[0377] The "means for receiving the estimated time of completion of the dish" is a mechanism for receiving the estimated time of completion of the dish from the affiliated restaurant.

[0378] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a system that calculates the most efficient delivery route based on the estimated time the food is completed and the user's address information, and issues instructions to the delivery person along that route.

[0379] The "means for receiving a delivery completion report and storing it on a server" is a process for receiving a delivery completion report from a delivery person and storing that information on a server.

[0380] The "means for receiving feedback and storing it on a server" is a system that receives feedback from users and stores that information on a server.

[0381] "Means for adjusting AI algorithms based on emotion recognition results and improving service quality" refers to the process of recognizing emotions contained in user feedback and adjusting AI algorithms based on the results to improve service quality.

[0382] The system of the present invention realizes efficient meal delivery based on the user's desired eating style and delivery time slot, and also recognizes the user's emotions to further improve the service.

[0383] Specifically, this system is configured as follows:

[0384] First, users download a dedicated application and create an account. They enter their name, address, contact information, preferred dining style, and desired delivery time. This information is sent from the user's device to the server, which receives it and stores it in a database.

[0385] Next, the server uses AI to calculate the efficiency of meal production and delivery routes based on the information stored in the database. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food during the same delivery time, these preferences will be grouped and ordered together.

[0386] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI ​​and sends it to each restaurant. The order list includes detailed information such as the name of the dish, quantity, and required time. Each restaurant prepares the dishes based on this list.

[0387] The server also receives the estimated time the food will be ready from partner restaurants and calculates the optimal delivery route based on the user's address information. Using AI, it generates the most efficient route for delivering to multiple users and issues instructions to the delivery staff according to that route.

[0388] The delivery person follows the delivery instructions sent from the server and delivers the food to each user along the specified route. When the delivery is complete, the delivery person reports the completion of the delivery on their device, and this information is saved on the server.

[0389] In addition, users can enter feedback about the delivered food through the application. This feedback includes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) that are recognized from the user's voice or text using an emotion engine. The server receives the user's feedback and emotion data and stores it in a database. The AI ​​adjusts its algorithms based on this feedback and emotion data to improve service in the future.

[0390] The hardware and software used include a smartphone, server, AI system, and emotion engine. Information entered by the user using the smartphone is sent to the server, which receives it and stores it in a database. The AI ​​system analyzes the information in the database and calculates efficient production lists and delivery routes. The emotion engine also analyzes user feedback, which the AI ​​uses to improve the service.

[0391] Specific examples

[0392] A user requests Italian food at 12:00 every day and orders pizza and garlic bread. This information is sent to the server, which generates an order list for pizza and garlic bread for partner restaurants. The restaurant prepares the food, and a delivery person delivers it according to the optimal delivery route calculated by the server. The delivery person sends a delivery completion report to the server, and the user enters feedback such as "Today's food was delicious!" Based on this feedback, the emotion engine recognizes the emotion "satisfied," which the server stores and uses to improve the quality of service.

[0393] Prompt Sentence Examples

[0394] "I was happy that the food arrived quickly, but it was a bit spicy."

[0395] This allows the system to provide more appropriate services based on feedback that takes into account the user's emotions.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] Step 1:

[0398] User Registration

[0399] Users create an account by entering their name, address, and contact information into a dedicated application on their smartphone. This information is sent from the user's device to the server, which receives the information and stores it in a database.

[0400] (Input) Name, address, contact information

[0401] (Output) User information stored in the database

[0402] Step 2:

[0403] User preference settings

[0404] Using the same application, users can set their preferred dining style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day) and send this information to the server, which receives it and stores it in a database.

[0405] (Input) Meal style, delivery time

[0406] (Output) Preferences saved in the database

[0407] Step 3:

[0408] AI-powered list creation and efficiency

[0409] The server collects all user preferences from a database and uses an AI system to calculate efficient meal production lists and delivery routes. This AI system performs calculations to optimize delivery routes based on each user's preferred delivery time slot and eating style.

[0410] (Input) Desired information obtained from the database

[0411] (Output) Efficient meal production list and delivery route

[0412] Step 4:

[0413] Generate order list and send it to the store

[0414] The server generates an order list for the required dishes for partner restaurants based on the efficient production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time, and is sent to the partner restaurant.

[0415] (Input) Efficient meal production list

[0416] (Output) Order list sent to partner stores

[0417] Step 5:

[0418] Receive estimated time of completion of dish

[0419] The partner restaurant sends the estimated time the food will be ready to eat to the server, which receives this information and stores it in a database.

[0420] (Input) Estimated time of completion of food from partner restaurant

[0421] (Output) Estimated completion time information saved in the database

[0422] Step 6:

[0423] Calculating and directing optimal delivery routes

[0424] The server uses an AI system to calculate the optimal delivery route based on the estimated time the food is ready and the user's address information, and then gives instructions to the delivery person to follow that route.

[0425] (Input) Estimated time of completion of the dish, user's address information

[0426] (Output) Optimal delivery route and instructions for delivery personnel

[0427] Step 7:

[0428] Receiving and storing delivery completion reports

[0429] Once the delivery person has delivered the food, they use their smartphone to report that the delivery is complete. This report is sent to the server, which then stores the information in a database.

[0430] (Input) Delivery completion report

[0431] (Output) Delivery completion information saved in the database

[0432] Step 8:

[0433] User Feedback and Emotion Recognition

[0434] The user inputs feedback about the delivered food through the application. This feedback includes emotions recognized from the user's voice and text using an emotion engine. The server receives the feedback and emotion data from the user and stores them in a database.

[0435] (Input) User feedback and sentiment data

[0436] (Output) Feedback and emotion data stored in a database

[0437] Step 9:

[0438] Adjusting AI algorithms and improving service quality

[0439] The server uses the stored feedback and emotional data to adjust its AI algorithms and improve the quality of the service, making future visits even more user-friendly.

[0440] (Input) Feedback and emotion data

[0441] (Output) Adjusted AI algorithms and service improvement measures

[0442] Through the above steps, an efficient and high-quality delivery service that takes user emotions into consideration is realized.

[0443] 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.

[0444] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] 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.

[0446] [Second embodiment]

[0447] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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).

[0453] 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. 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.

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0459] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and delivery time slot. The following is a natural language explanation of the program processing of this system.

[0460] 1. User registration and preference settings

[0461] User Actions

[0462] Users download the app and create an account by entering their name, address, and contact information, as well as their preferred food style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day).

[0463] Server Actions

[0464] The server receives the account information and desired information sent from the user terminal and stores it in a database.

[0465] 2. AI-based list creation and efficiency

[0466] Server Actions

[0467] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[0468] 3. Ordering and adjusting to stores

[0469] Server Actions

[0470] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[0471] Specific examples

[0472] For example, if many users are ordering pizza at 12:00, the server will request a specified Italian restaurant to prepare 10 pizzas by 12:00.

[0473] 4. Delivery route optimization

[0474] Server Actions

[0475] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[0476] 5. Delivery and Reporting

[0477] Delivery person terminal actions

[0478] The delivery person terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery person follows these instructions and delivers the food to each user along the specified route.

[0479] Deliveryman's actions

[0480] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[0481] 6. Feedback and Improvement

[0482] User Actions

[0483] Users can enter feedback through the application about the quality of the delivered food and the delivery time.

[0484] Server Actions

[0485] The server receives user feedback and stores it in a database. The AI ​​uses this feedback data to adjust its algorithms and improve the service for future visits.

[0486] Specific examples and processing flow

[0487] For example, if User A wants Italian food at 12:00 every day and User B wants Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants to deliver Italian food to User A at 12:00 and Western food to User B at 14:00, and the AI ​​calculates the optimal delivery route. In this way, efficient meal production and delivery is achieved.

[0488] This system allows users to order meals at lower prices than usual, streamlining delivery, while also enabling partner restaurants to provide meals as efficiently as possible.

[0489] The processing flow will be explained below.

[0490] Step 1:

[0491] The user downloads the app and creates an account. They enter the necessary personal information, such as their name, address, and contact information. They also select their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[0492] Step 2:

[0493] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[0494] Step 3:

[0495] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[0496] Step 4:

[0497] The server generates an order list of the required dishes for each partner store based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner store.

[0498] Step 5:

[0499] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[0500] Step 6:

[0501] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[0502] Step 7:

[0503] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[0504] Step 8:

[0505] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[0506] Step 9:

[0507] The server receives the delivery completion report from the delivery person and stores the information in a database.

[0508] Step 10:

[0509] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[0510] Step 11:

[0511] The terminal sends the user's feedback information to the server, which stores it in a database.

[0512] Step 12:

[0513] The server analyzes the collected feedback data and adjusts the AI ​​algorithms, which will further improve the efficiency of future meal production and delivery routes.

[0514] This series of processing steps allows users to receive high-quality service at a low cost, and enables partner stores to efficiently produce and deliver meals.

[0515] Example 1

[0516] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0517] Conventional meal delivery systems have difficulty setting efficient delivery routes and production plans that match users' desired meal styles and delivery times. As a result, they have been unable to deliver meals quickly and efficiently according to users' wishes, resulting in problems such as delivery delays and increased delivery costs. Furthermore, they have not been able to fully utilize user feedback to improve the system.

[0518] 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.

[0519] In this invention, the server includes a user terminal means for inputting the user's desired meal style and delivery time slot, an information processing means for receiving the information input from the user terminal means and storing it in a database, and a calculation means for calculating the efficiency of meal production and delivery routes using a generative AI model based on the information stored by the information processing means, thereby enabling efficient meal production and delivery route optimization based on the user's wishes.

[0520] "User terminal means" refers to an electronic terminal for inputting information such as the user's desired meal style and delivery time slot, and includes smartphones, tablets, PCs, etc.

[0521] "Information processing means" refers to a server having the function of receiving information sent from user terminal means and storing and managing it in a database.

[0522] "Generative AI models" refer to artificial intelligence systems that learn from large amounts of data and calculate the efficiency of meal production and delivery routes. Specifically, this includes machine learning frameworks such as TensorFlow and PyTorch.

[0523] "Computational means" refers to a function that uses a generative AI model to calculate efficient meal production lists and delivery routes based on data stored in the information processing means.

[0524] "Communication means" refers to an internet communication function for automatically sending orders to affiliated stores based on the results of the calculation means.

[0525] "Data receiving means" refers to a function that receives the estimated time when the food will be ready from the partner restaurant.

[0526] "Instruction means" refers to a function that calculates the optimal delivery route based on the estimated completion time and the user's address information, and issues instructions to the delivery person.

[0527] The "reporting means" refers to the function of receiving a delivery completion report from the delivery person and storing it in the information processing means.

[0528] "Feedback means" refers to a function that receives feedback from the user and stores it in the information processing means.

[0529] "Improvement measures" refers to the function by which the generative AI model adjusts its algorithm based on feedback data to improve the quality of the service.

[0530] The "processing means" refers to a function that groups multiple users who want the same type of food based on the user's desired eating style and delivery time slot.

[0531] "Scheduling means" refers to the function of generating an order list for affiliated stores and creating a production plan for each store to prepare the necessary dishes.

[0532] This invention relates to a system that realizes efficient meal delivery based on a user's desired eating style and delivery time slot. The system includes a user terminal means, an information processing means, a generative AI model, a calculation means, a communication means, a data receiving means, an instruction means, a reporting means, a feedback means, and an improvement means.

[0533] First, the user downloads a dedicated application onto their user terminal such as a smartphone or tablet. The user launches the application, enters their name, address, and contact information, and sets their preferred dining style and delivery time. For example, they enter information such as "Taro Tanaka, Shinjuku-ku, Tokyo, tanaka@example.com, pasta, 12:00." After completing the input, the user sends this information to the server.

[0534] The server receives the information sent from the user terminal means using the information processing means and stores it in a database. The server maintains reliability and security by using cloud services such as AWS and Google Cloud Platform.

[0535] The server then analyzes all user preference information stored in the information processing device using a generative AI model, which uses frameworks such as TensorFlow and PyTorch to calculate an efficient meal production list and delivery route based on the user's preferred eating style and delivery time slot.

[0536] For example, if User A and User C want pasta at 12:00, and User B wants curry rice at 14:00, the server will group them appropriately based on this information.The generative AI model then uses computational tools to calculate an efficient production list and delivery route.

[0537] The server generates an order list for partner stores based on the results of the calculation means and sends it to the store using communication means. The order list contains detailed information about the dish name, quantity, and required time. For example, a list may be generated that says, "Please prepare 10 plates of pasta by 12:00."

[0538] Furthermore, the server receives the estimated time the food will be ready from the restaurant that received the order through the data receiving means. Based on the received estimated time and the user's address information, the server uses an instruction means to generate the optimal delivery route using Google Maps API or OR-Tools. For example, there is a specific example where the server calculates a delivery route from the restaurant to User A, User B, and User C in that order.

[0539] The delivery person's terminal receives delivery instructions sent from the server, which include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Once the delivery is complete, the delivery person reports completion on the terminal.

[0540] Finally, the user provides feedback on the quality and delivery time of the delivered food through the application. The server receives this feedback through the feedback mechanism and stores it in a database. The generative AI model uses this feedback to adjust its algorithm using the refinement mechanism to improve the delivery service next time.

[0541] Prompt Sentence Examples

[0542] User A wants Italian food at 12:00 every day, and User B wants Western food at 14:00. Generate the optimal delivery route and efficient production list.

[0543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0544] Step 1: Register your user and set your preferences

[0545] The user downloads the dedicated application and installs it on their device. Next, they launch the application and enter their name, address, and contact information. They then set their preferred dining style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day). Specifically, they enter their information into the input form on the screen and press the "Register" button. Once this information is entered (the registration information is set), the device sends it to the server. The output is the sent user information.

[0546] Step 2: Save user information

[0547] The server receives user information sent from the user terminal means. The received data includes name, address, contact information, meal style, and delivery time slot. Specifically, the server analyzes the received data and stores it in a MySQL or PostgreSQL database. It inserts the information using an SQL query. For example, it executes a command such as "INSERT INTO users (name, address, contact, meal_style, delivery_time) VALUES (...)". The input is the received user information, and the output is the result stored in the database.

[0548] Step 3: Collect user preferences and analyze them with AI

[0549] The server uses information processing means to collect all user preference information from a database. For example, it retrieves data by executing a query such as "SELECT FROM users WHERE delivery_time = '12:00'". It then analyzes the retrieved data using a generative AI model to calculate an efficient meal production list and delivery route. Specifically, it uses TensorFlow and PyTorch to calculate the optimal route based on eating style and delivery time slot. The input is a set of user information retrieved from the database, and the output is a production list and an optimized delivery route.

[0550] Step 4: Generate and send an order list to the store

[0551] The server generates an order list for partner stores based on the calculated production list. The order list includes the dish name, quantity, and required time. Specifically, the server generates order information in JSON format and sends it to the partner store's API endpoint using an HTTP POST request. For example, it sends a request such as "Please prepare 10 plates of pasta by 12:00." The input is the production list, and the output is the order list sent to the partner store.

[0552] Step 5: Calculate delivery routes

[0553] The server receives the estimated time the food will be ready from the partner restaurant. For example, it obtains this information from the restaurant's API via an HTTP GET request. Based on the received estimated time and the user's address information, it calculates the optimal delivery route using Google Maps API or OR-Tools. Specifically, it uses the API to send a request to determine the order of delivery to each destination. The input is the estimated time of completion and the user's address information, and the output is the optimized delivery route.

[0554] Step 6: Delivery and reporting

[0555] The delivery person's terminal receives delivery instructions sent from the server. These instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Specifically, the delivery person uses the terminal to report "delivery complete" upon delivery. The report is made by pressing the "Delivery Complete" button on the dedicated app. The input is the delivery instructions, and the output is the delivery completion report data.

[0556] Step 7: Gather feedback and improve with AI

[0557] The user enters feedback on the quality and delivery time of the delivered food. The server receives this information via the feedback means and stores it in a database. Specifically, the user enters the feedback into an evaluation form within the application and presses the submit button. The received feedback data is analyzed by a generative AI model, and the algorithm is adjusted to improve service quality. The input is the feedback information, and the output is an updated set of parameters for the AI ​​model.

[0558] (Application example 1)

[0559] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0560] Conventional meal delivery systems have difficulty effectively reflecting users' preferred eating styles and desired delivery times. They also face the problem of calculating efficient delivery routes for individual orders and coordinating with partner stores, which can be cumbersome. This can lead to problems such as a loss of user satisfaction and reduced work efficiency for delivery staff and stores.

[0561] 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.

[0562] In this invention, the server includes: a user terminal means for inputting a user's desired meal style and desired delivery time; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using a generative AI model to suggest meals based on the information stored by the server means; a means for accepting user orders based on the suggestions from the generative AI model; a means for using AI to calculate the efficiency of meal production and delivery routes based on the order information; a means for generating and sending a bulk order list to affiliated stores based on the calculation results; a means for receiving an estimated meal completion time from the affiliated stores; a means for calculating an optimal delivery route based on the estimated completion time and the user's address information and issuing instructions to the delivery person; a means for tracking and displaying the location information of the delivery person who received the instructions in real time and notifying the user of the delivery status; a means for receiving delivery completion reports from the delivery person and storing them on the server; a means for receiving user feedback and storing them on the server; and a means for adjusting the generative AI model algorithm based on the feedback data to improve service quality. This enables efficient meal delivery according to user preferences, bulk orders, and calculation of the optimal delivery route.

[0563] The "user terminal means" is a device that allows a user to input the desired meal style and desired delivery time.

[0564] The "server means" is a device that receives information input from the user terminal means and stores it in a database.

[0565] A "generative AI model" is an artificial intelligence system that makes meal suggestions and streamlines ordering based on stored user information.

[0566] The "means for making suggestions" is a device or system that uses a generative AI model to suggest meals tailored to the user's preferences.

[0567] "Means for accepting orders" refers to a device or system that accepts orders from users based on suggestions from the generative AI model.

[0568] A "means for performing efficiency calculations" is a device or system that uses AI to calculate the efficiency of meal production and delivery routes based on order information.

[0569] The "means for generating and transmitting a collective order list" is a device or system that generates and transmits a collective order list to affiliated stores.

[0570] The "means for receiving the estimated time of completion of the dish" is a device or system that receives the estimated time of completion of the dish from the affiliated restaurant.

[0571] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a device or system that generates the optimal delivery route based on the estimated completion time and the user's address information and issues instructions to the delivery person.

[0572] The "means for tracking and displaying in real time and notifying the user of the delivery status" is a device or system that tracks the location information of the delivery person in real time and notifies the user of the delivery status.

[0573] The "means for receiving a delivery completion report and storing it on a server" is a device or system that receives a delivery completion report from a delivery person and stores it on a server.

[0574] The "means for receiving feedback and storing it on a server" is a device or system that receives feedback from a user and stores it on a server.

[0575] "Means for adjusting the generative AI model algorithm and improving the quality of the service" refers to a device or system that adjusts the generative AI model based on stored feedback data and improves the quality of the service.

[0576] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and desired delivery time. This system is composed of a user terminal means, a server means, and multiple AI technologies.

[0577] System Overview

[0578] The overall system is made up of the following configuration and processing procedures.

[0579] 1. User registration and preference settings

[0580] Using the user terminal means, the user downloads a dedicated smartphone application and creates an account. The user enters their name, address, contact information, and selects their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day). The server means receives this information and stores it in a database.

[0581] 2. Meal suggestions and ordering

[0582] The server uses a generative AI model based on the stored information to recommend meals that fit the user's preferences. This generative AI model incorporates information such as food and drink types, past order history, and popular menu items to recommend the most suitable meal. The user can then review the suggested meals and place their order through the app.

[0583] 3. AI-powered efficiency and order list generation

[0584] The server uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information. Based on the results of this calculation, it generates and sends a consolidated order list to partner stores. This order list includes details such as the name of the dish, quantity, and required time.

[0585] 4. Delivery route optimization

[0586] The server receives the estimated time of completion of the food from the partner restaurant and calculates the optimal delivery route based on the user's address information. This calculation uses map data such as Google Maps API. The delivery person is provided with information instructing them on the optimized delivery route.

[0587] 5. Delivery and real-time tracking

[0588] The delivery person's device delivers according to the optimal route instructed by the server. The delivery status is tracked in real time, and the user is notified of the delivery person's location and delivery status. This is done using GPS data and push notification technology.

[0589] 6. Completion Report and Feedback

[0590] Once the delivery is complete, the delivery person reports the completion via their device, and the information is saved on the server. The user can then enter feedback on the quality of the delivered food and the delivery time. This feedback data is also saved on the server and used to adjust the generative AI model algorithm and improve service in the future.

[0591] Specific use cases

[0592] For example, if User A requests pizza to be delivered at 12:00 every day, the system will pass this information to the generative AI model to generate the optimal delivery route and place a bulk order with partner stores. The system will also track the delivery person's location in real time and notify the user, visualizing the delivery status.

[0593] Prompt Sentence Examples

[0594] "Based on user A's request for pizza delivery at 12:00 every day, collect information on the delivery time and food style (e.g., Western or Japanese) desired by other users B and C, and combine this information to calculate the optimal delivery route. Also, based on the results, generate an efficient order list for partner stores and send it to each store."

[0595] This system provides users with the benefit of having their desired meals delivered efficiently and quickly, while also enabling partner restaurants to provide meals with maximum efficiency.

[0596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0597] Step 1:

[0598] The user terminal means allows the user to input the desired meal style and desired delivery time.

[0599] Input: User's name, address, contact information, eating style, and desired delivery time.

[0600] Output: Sends the input data to the server.

[0601] Specific operation: The user uses the smartphone app to enter the required information into the input form and clicks the submit button to send it to the server.

[0602] Step 2:

[0603] The server means stores the received user information in a database.

[0604] Input: User information sent from the user terminal.

[0605] Output: User information stored in the database.

[0606] Specific operation: The server stores the received user information in a database management system (for example, PostgreSQL or MySQL).

[0607] Step 3:

[0608] The server means uses the generative AI model based on the stored information to make meal recommendations.

[0609] Input: User information stored in the database.

[0610] Output: A suggested meal menu.

[0611] Specific operation: The generative AI model generates the optimal meal based on the user's past ordering history and popular menu items, and returns the suggestions to the server.

[0612] Step 4:

[0613] The user terminal means accepts an order from a user based on the proposal from the generative AI model.

[0614] Input: Meal suggestions from a generative AI model.

[0615] Output: User's order data.

[0616] Specific operation: The user selects the desired meal from the proposed menu and confirms the order.

[0617] Step 5:

[0618] The server means uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information.

[0619] Input: User's order information.

[0620] Output: Efficient delivery routes and production lists.

[0621] How it works: The AI ​​algorithm analyzes multiple order information and calculates the optimal delivery route and order list for the store.

[0622] Step 6:

[0623] The server means generates and transmits a bulk order list to the affiliated stores based on the calculation results.

[0624] Input: The results of the efficiency calculation.

[0625] Output: Bulk order list for partner stores.

[0626] Specific operation: The server creates a bulk order list of the required dishes for each store and sends it to the store via API.

[0627] Step 7:

[0628] The server means receives the estimated time when the food is ready from the affiliated store.

[0629] Input: Estimated completion time from partner store.

[0630] Output: Estimated completion time data.

[0631] Specific operation: Receives estimated completion time information from the store via API and saves it on the server.

[0632] Step 8:

[0633] The server means calculates the optimum delivery route and issues instructions to the delivery person.

[0634] Input: Estimated completion time and user address information.

[0635] Output: Optimal delivery route instructions.

[0636] Specific operation: The AI ​​algorithm generates the optimal delivery route based on the estimated completion time and the user's address information and notifies the delivery person's terminal.

[0637] Step 9:

[0638] The delivery person's terminal delivers the parcel according to the optimal route instructed and reports the delivery status to the server in real time.

[0639] Input: Optimal delivery route.

[0640] Output: Real-time delivery status data.

[0641] Specific operation: The delivery person uses GPS to send location information to the server in real time and report the situation.

[0642] Step 10:

[0643] The user terminal means receives feedback from the user after delivery is completed and transmits it to the server.

[0644] Input: User feedback.

[0645] Output: Feedback data.

[0646] What it does: The user uses the app to enter feedback about the quality of the food and delivery time, which is then sent to the server.

[0647] Step 11:

[0648] The server means stores the received feedback data and adjusts the generative AI model algorithm.

[0649] Input: User feedback data.

[0650] Output: The tuned generative AI model.

[0651] Specific operation: Based on the feedback data, the algorithm of the generative AI model is updated and improved to improve the quality of the service.

[0652] 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.

[0653] The present invention relates to a delivery system that incorporates an emotion engine that recognizes the user's emotions. The system of the present invention not only realizes efficient meal delivery based on the user's desired eating style and delivery time slot, but also recognizes the user's emotions to further improve service. The following is a natural language explanation of the program processing of this system.

[0654] 1. User registration and preference settings

[0655] User Actions

[0656] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day).

[0657] Server Actions

[0658] The server receives the account information and desired information sent from the user terminal and stores them in a database.

[0659] 2. AI-based list creation and efficiency

[0660] Server Actions

[0661] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[0662] 3. Ordering and adjusting to stores

[0663] Server Actions

[0664] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[0665] Specific examples

[0666] For example, if many users are ordering pizza at 12:00, the server will request the specified restaurant to prepare 10 pizzas by 12:00.

[0667] 4. Delivery route optimization

[0668] Server Actions

[0669] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[0670] 5. Delivery and Reporting

[0671] Delivery person terminal actions

[0672] The delivery staff terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery staff follows these instructions and delivers the food to each user along the specified route.

[0673] Deliveryman's actions

[0674] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[0675] 6. Feedback and emotion engine analysis

[0676] User Actions

[0677] Users can input their feedback about the quality of the delivered food and the delivery service through the application, and the emotion engine recognizes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) from the user's voice and text.

[0678] Server Actions

[0679] The server receives feedback and emotional data from users and stores it in a database. The AI ​​then adjusts the algorithm based on this feedback and emotional data to improve the service in the future.

[0680] Specific examples and processing flow

[0681] For example, if User A requests Italian food at 12:00 every day, and User B requests Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants, with Italian food delivered to User A at 12:00 and Western food delivered to User B at 14:00, and the AI ​​will calculate the optimal delivery route. Furthermore, if User A enters positive feedback and an emotion such as "very satisfied" in the application, this information is stored on the server and used to improve the service in the future.

[0682] This system allows users to receive high-quality service at a low price, and enables partner restaurants to efficiently produce and deliver meals. In addition, by using an emotion engine, it is possible to more accurately grasp user satisfaction and further improve services.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[0686] Step 2:

[0687] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[0688] Step 3:

[0689] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[0690] Step 4:

[0691] The server generates an order list of the required dishes for partner stores based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner stores.

[0692] Step 5:

[0693] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[0694] Step 6:

[0695] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[0696] Step 7:

[0697] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[0698] Step 8:

[0699] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[0700] Step 9:

[0701] The server receives the delivery completion report from the delivery person and stores the information in a database.

[0702] Step 10:

[0703] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[0704] Step 11:

[0705] The terminal sends the user's feedback information to the server, which stores it in a database.

[0706] Step 12:

[0707] The server recognizes the user's emotions using an emotion engine in addition to the feedback. The emotion engine analyzes the user's voice feedback and text messages to extract emotions such as satisfaction, dissatisfaction, joy, and anger.

[0708] Step 13:

[0709] The server stores the recognized emotion data in a database and incorporates it into the AI ​​algorithm, which uses this data to adjust the AI ​​algorithm and improve the quality of service for future meal production and delivery.

[0710] Step 14:

[0711] The server sends feedback to partner stores based on the emotion data and provides specific instructions for improving service. For example, it can provide feedback such as "Many users are satisfied with the doneness of their pizza, but are dissatisfied with the delivery time," and work with the store to consider improvement measures.

[0712] This series of processing steps enables efficient food preparation and delivery while increasing user satisfaction. Furthermore, the use of an emotion engine improves the quality of feedback, enabling continuous improvement of the service.

[0713] Example 2

[0714] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0715] Conventional delivery systems have difficulty meeting individual user requests and wishes, making it difficult to achieve efficient delivery and reduce costs. Furthermore, feedback to improve services by taking user feelings into consideration has not been fully utilized. As a result, user satisfaction and service quality have declined, making it difficult to maintain competitiveness.

[0716] 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.

[0717] In this invention, the server includes a terminal means for inputting the user's desired meal style and delivery time slot, a server means for receiving the information input from the terminal means and storing it in a database, and a means for calculating the efficiency of meal production and delivery routes using a machine learning model based on the information stored by the server means, thereby enabling efficient meal delivery according to the user's wishes and improving the quality of service.

[0718] The "terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[0719] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[0720] A "machine learning model" is an algorithm used to calculate the efficiency of meal production and delivery routes based on stored information.

[0721] "Partner restaurants" are restaurants that work in conjunction with this system to receive orders and provide food.

[0722] The "estimated completion time" is the time when the food preparation specified by the partner restaurant will be completed.

[0723] An "optimal delivery route" is a route calculated to efficiently deliver food to multiple users.

[0724] A "delivery person" is a person or device whose role is to deliver food received from partner restaurants to each user.

[0725] The "delivery completion report" is a notification of delivery completion sent when the delivery person delivers the food to the user.

[0726] "Feedback" is information that allows users to provide their evaluations and impressions of the delivered food and service.

[0727] An "emotion recognition engine" is a technology for recognizing emotions from a user's voice or text.

[0728] "Artificial intelligence algorithm" refers to an algorithm that uses feedback data and an emotion recognition engine to improve the quality of services.

[0729] The present invention provides a system for providing an efficient and high-quality meal delivery service based on a user's desired eating style and delivery time slot. The system includes a terminal unit, a server unit, a machine learning model, partner restaurants, an optimal delivery route calculation unit, a delivery person unit, and an emotion recognition engine.

[0730] First, users download a dedicated application onto their smartphone or tablet and create an account. They then enter their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day) into the application.

[0731] Next, the server receives the account information and preference information sent by the user and stores them in a database. The user information stored in the database is stored in a "User Information" table, and the preference information is stored in a "Preference Information" table.

[0732] The server collects the stored data at a fixed time each day and uses a machine learning model to analyze the preferences of all users. This machine learning model, built in Python, groups users who have the same meal preferences at the same time and generates an efficient meal production list. For example, if multiple users want pizza at 12:00, this information is organized and they can efficiently place a bulk order.

[0733] The server then generates an order list for the required dishes for partner restaurants based on the production list created by the AI ​​system. The order list includes detailed information such as the name of the dish, the quantity, and the estimated time of completion. The order list is sent to partner restaurants via API, for example, in JSON format using a RESTful API.

[0734] Partner restaurants prepare food based on the received order list and notify the server of the estimated time the food will be ready. The server receives this notification data and calculates the optimal delivery route based on the estimated time of completion and each user's address information. The server uses map services such as Google Maps API to calculate the shortest route, making it possible to deliver to multiple users efficiently.

[0735] At the actual delivery stage, the delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to the user along the specified route. Once the food has been delivered, the delivery person uses a dedicated application to report "delivery completed," which is then updated on the server.

[0736] Finally, the user enters feedback about the food and service through the application, and an emotion recognition engine analyzes the user's emotions via voice and text, resulting in a level of satisfaction (e.g., satisfied, dissatisfied, happy, angry, etc.) being sent to the server.

[0737] The server collects this feedback and emotion data, stores it in a database, and adjusts the AI ​​algorithm. This feedback loop allows for future service quality improvements, continuously increasing user satisfaction.

[0738] Prompt Sentence Examples

[0739] "Imagine a program that takes into account the user's preferred eating style and delivery time slots to create the optimal delivery route and produce list."

[0740] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0741] System program processing flow

[0742] Step 1: Register your user information and set your preferences

[0743] Specific actions

[0744] Input: The user enters their name, address, contact information, preferred eating style, and desired delivery time slot into a dedicated application.

[0745] Output: The user's input data is sent to the server.

[0746] A user opens the app on their smartphone or tablet, taps the "Create a new account" button, enters the required information, and then taps the "Register" button, which sends the entered data to the server via the Internet.

[0747] Data processing or data calculation

[0748] The server receives the data sent by the user and stores the name, address, and contact information in a "User Information" table, and stores the preferred eating style and desired delivery time slot in a "Preference Information" table, thereby organizing individual user information.

[0749] Step 2: Collect and analyze all users' preferences

[0750] Specific actions

[0751] Input: Get all users' preferences from the database.

[0752] Output: A dataset is generated for analysis.

[0753] The server queries the "Preferences" table from the database at a fixed time every day to collect all users' preferences, which are then passed to the AI ​​system as a dataset for analysis.

[0754] Data processing or data calculation

[0755] The server analyzes the dataset using a machine learning model built in Python to calculate the production list and optimal delivery route based on the user's desired delivery time and eating style, enabling efficient production and delivery.

[0756] Step 3: Generate and send an order list to partner restaurants

[0757] Specific actions

[0758] Input: An efficient production list created by an AI system.

[0759] Output: Specific order data sent to partner restaurants.

[0760] The server analyzes the production list received from the machine learning model and generates specific order data for each partner restaurant. For example, the order list includes the name of the dish, the quantity, and the required time. The generated order list is sent to the partner restaurant in JSON format using a RESTful API.

[0761] Data processing or data calculation

[0762] The server divides and organizes the order list by store and sends it to partner restaurants in the appropriate format, allowing the restaurants to efficiently receive orders and begin preparation.

[0763] Step 4: Receive estimated time of completion and calculate optimal delivery route

[0764] Specific actions

[0765] Input: Estimated time of completion of food received from partner restaurant.

[0766] Output: Optimal delivery route to send to delivery driver.

[0767] The server receives the estimated time the food will be ready from partner restaurants via API. Based on the received data and the user's address information, it calculates the optimal delivery route. Using map services such as Google Maps API, the shortest route is generated for efficient delivery to multiple users.

[0768] Data processing or data calculation

[0769] The server calculates the optimal delivery route based on the estimated completion time and the user's address information, and prepares to send the calculation results to the delivery person's terminal.

[0770] Step 5: Delivery and reporting

[0771] Specific actions

[0772] Input: Delivery instructions sent by the server.

[0773] Output: Delivery completion report.

[0774] The delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. When the food is delivered, the delivery person taps the "Delivery Complete" button to report to the server.

[0775] Data processing or data calculation

[0776] The server receives delivery completion reports from delivery personnel and updates the order status in the database, allowing for real-time monitoring of the progress of each delivery and efficient management.

[0777] Step 6: Feedback and Emotion Recognition Engine Analysis

[0778] Specific actions

[0779] Input: Feedback and sentiment data entered by the user through the application.

[0780] Output: Analysis results for improvement.

[0781] Users can enter their feedback about the food delivered and the service through the application, and an emotion recognition engine analyzes the user's emotions using voice and text, and these emotions, such as satisfaction, dissatisfaction, joy, and anger, are sent to the server.

[0782] Data processing or data calculation

[0783] The server stores the acquired feedback and emotion data in a database and uses artificial intelligence algorithms to improve the quality of the service. This feedback loop will further improve the delivery service for future deliveries.

[0784] keyword

[0785] Generative AI model, prompt sentence

[0786] (Application example 2)

[0787] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0788] Conventional delivery systems have struggled to provide efficient delivery services that match users' desired dining style and delivery time slots. While systems exist for incorporating user feedback, they lack sufficient mechanisms for recognizing user emotions and reflecting them in service quality improvements. This makes it difficult to guarantee a consistently high-quality user experience, and service providers lack effective means of improvement.

[0789] 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.

[0790] In this invention, the server includes: a user terminal means for inputting a user's desired eating style and delivery time slot; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using AI to calculate the efficiency of meal production and delivery routes based on the information stored by the server means; a means for sending an order to a partner store based on the calculation results; a means for receiving an estimated time of completion of the food from the partner store; a means for calculating an optimal delivery route based on the estimated time of completion and the user's address information and issuing instructions to a delivery person; a means for receiving a delivery completion report from the delivery person and storing it in the server; a means for receiving feedback from the user and storing it in the server; and a means for adjusting the AI ​​algorithm based on the feedback data and emotion recognition results to improve the quality of the service. This makes it possible to continuously improve the service based on feedback that takes user emotions into consideration.

[0791] The "user terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[0792] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[0793] "Means for calculating efficiency of meal production and delivery routes using AI" means processes and algorithms that use artificial intelligence to improve the efficiency of meal production plans and delivery routes based on stored information.

[0794] The "means for sending orders to affiliated restaurants" is a system for sending the necessary food order information to affiliated restaurants.

[0795] The "means for receiving the estimated time of completion of the dish" is a mechanism for receiving the estimated time of completion of the dish from the affiliated restaurant.

[0796] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a system that calculates the most efficient delivery route based on the estimated time the food is completed and the user's address information, and issues instructions to the delivery person along that route.

[0797] The "means for receiving a delivery completion report and storing it on a server" is a process for receiving a delivery completion report from a delivery person and storing that information on a server.

[0798] The "means for receiving feedback and storing it on a server" is a system that receives feedback from users and stores that information on a server.

[0799] "Means for adjusting AI algorithms based on emotion recognition results and improving service quality" refers to the process of recognizing emotions contained in user feedback and adjusting AI algorithms based on the results to improve service quality.

[0800] The system of the present invention realizes efficient meal delivery based on the user's desired eating style and delivery time slot, and also recognizes the user's emotions to further improve the service.

[0801] Specifically, this system is configured as follows:

[0802] First, users download a dedicated application and create an account. They enter their name, address, contact information, preferred dining style, and desired delivery time. This information is sent from the user's device to the server, which receives it and stores it in a database.

[0803] Next, the server uses AI to calculate the efficiency of meal production and delivery routes based on the information stored in the database. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food during the same delivery time, these preferences will be grouped and ordered together.

[0804] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI ​​and sends it to each restaurant. The order list includes detailed information such as the name of the dish, quantity, and required time. Each restaurant prepares the dishes based on this list.

[0805] The server also receives the estimated time the food will be ready from partner restaurants and calculates the optimal delivery route based on the user's address information. Using AI, it generates the most efficient route for delivering to multiple users and issues instructions to the delivery staff according to that route.

[0806] The delivery person follows the delivery instructions sent from the server and delivers the food to each user along the specified route. When the delivery is complete, the delivery person reports the completion of the delivery on their device, and this information is saved on the server.

[0807] In addition, users can enter feedback about the delivered food through the application. This feedback includes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) that are recognized from the user's voice or text using an emotion engine. The server receives the user's feedback and emotion data and stores it in a database. The AI ​​adjusts its algorithms based on this feedback and emotion data to improve service in the future.

[0808] The hardware and software used include a smartphone, server, AI system, and emotion engine. Information entered by the user using the smartphone is sent to the server, which receives it and stores it in a database. The AI ​​system analyzes the information in the database and calculates efficient production lists and delivery routes. The emotion engine also analyzes user feedback, which the AI ​​uses to improve the service.

[0809] Specific examples

[0810] A user requests Italian food at 12:00 every day and orders pizza and garlic bread. This information is sent to the server, which generates an order list for pizza and garlic bread for partner restaurants. The restaurant prepares the food, and a delivery person delivers it according to the optimal delivery route calculated by the server. The delivery person sends a delivery completion report to the server, and the user enters feedback such as "Today's food was delicious!" Based on this feedback, the emotion engine recognizes the emotion "satisfied," which the server stores and uses to improve the quality of service.

[0811] Prompt Sentence Examples

[0812] "I was happy that the food arrived quickly, but it was a bit spicy."

[0813] This allows the system to provide more appropriate services based on feedback that takes into account the user's emotions.

[0814] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0815] Step 1:

[0816] User Registration

[0817] Users create an account by entering their name, address, and contact information into a dedicated application on their smartphone. This information is sent from the user's device to the server, which receives the information and stores it in a database.

[0818] (Input) Name, address, contact information

[0819] (Output) User information stored in the database

[0820] Step 2:

[0821] User preference settings

[0822] Using the same application, users can set their preferred dining style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day) and send this information to the server, which receives it and stores it in a database.

[0823] (Input) Meal style, delivery time

[0824] (Output) Preferences saved in the database

[0825] Step 3:

[0826] AI-powered list creation and efficiency

[0827] The server collects all user preferences from a database and uses an AI system to calculate efficient meal production lists and delivery routes. This AI system performs calculations to optimize delivery routes based on each user's preferred delivery time slot and eating style.

[0828] (Input) Desired information obtained from the database

[0829] (Output) Efficient meal production list and delivery route

[0830] Step 4:

[0831] Generate order list and send it to the store

[0832] The server generates an order list for the required dishes for partner restaurants based on the efficient production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time, and is sent to the partner restaurant.

[0833] (Input) Efficient meal production list

[0834] (Output) Order list sent to partner stores

[0835] Step 5:

[0836] Receive estimated time of completion of dish

[0837] The partner restaurant sends the estimated time the food will be ready to eat to the server, which receives this information and stores it in a database.

[0838] (Input) Estimated time of completion of food from partner restaurant

[0839] (Output) Estimated completion time information saved in the database

[0840] Step 6:

[0841] Calculating and directing optimal delivery routes

[0842] The server uses an AI system to calculate the optimal delivery route based on the estimated time the food is ready and the user's address information, and then gives instructions to the delivery person to follow that route.

[0843] (Input) Estimated time of completion of the dish, user's address information

[0844] (Output) Optimal delivery route and instructions for delivery personnel

[0845] Step 7:

[0846] Receiving and storing delivery completion reports

[0847] Once the delivery person has delivered the food, they use their smartphone to report that the delivery is complete. This report is sent to the server, which then stores the information in a database.

[0848] (Input) Delivery completion report

[0849] (Output) Delivery completion information saved in the database

[0850] Step 8:

[0851] User Feedback and Emotion Recognition

[0852] The user inputs feedback about the delivered food through the application. This feedback includes emotions recognized from the user's voice and text using an emotion engine. The server receives the feedback and emotion data from the user and stores them in a database.

[0853] (Input) User feedback and sentiment data

[0854] (Output) Feedback and emotion data stored in a database

[0855] Step 9:

[0856] Adjusting AI algorithms and improving service quality

[0857] The server uses the stored feedback and emotional data to adjust its AI algorithms and improve the quality of the service, making future visits even more user-friendly.

[0858] (Input) Feedback and emotion data

[0859] (Output) Adjusted AI algorithms and service improvement measures

[0860] Through the above steps, an efficient and high-quality delivery service that takes user emotions into consideration is realized.

[0861] 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.

[0862] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0863] 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.

[0864] [Third embodiment]

[0865] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0866] 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.

[0867] 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).

[0868] 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.

[0869] 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.

[0870] 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).

[0871] 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. 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0877] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and delivery time slot. The following is a natural language explanation of the program processing of this system.

[0878] 1. User registration and preference settings

[0879] User Actions

[0880] Users download the app and create an account by entering their name, address, and contact information, as well as their preferred food style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day).

[0881] Server Actions

[0882] The server receives the account information and desired information sent from the user terminal and stores it in a database.

[0883] 2. AI-based list creation and efficiency

[0884] Server Actions

[0885] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[0886] 3. Ordering and adjusting to stores

[0887] Server Actions

[0888] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[0889] Specific examples

[0890] For example, if many users are ordering pizza at 12:00, the server will request a specified Italian restaurant to prepare 10 pizzas by 12:00.

[0891] 4. Delivery route optimization

[0892] Server Actions

[0893] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[0894] 5. Delivery and Reporting

[0895] Delivery person terminal actions

[0896] The delivery person terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery person follows these instructions and delivers the food to each user along the specified route.

[0897] Deliveryman's actions

[0898] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[0899] 6. Feedback and Improvement

[0900] User Actions

[0901] Users can enter feedback through the application about the quality of the delivered food and the delivery time.

[0902] Server Actions

[0903] The server receives user feedback and stores it in a database. The AI ​​uses this feedback data to adjust its algorithms and improve the service for future visits.

[0904] Specific examples and processing flow

[0905] For example, if User A wants Italian food at 12:00 every day and User B wants Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants to deliver Italian food to User A at 12:00 and Western food to User B at 14:00, and the AI ​​calculates the optimal delivery route. In this way, efficient meal production and delivery is achieved.

[0906] This system allows users to order meals at lower prices than usual, streamlining delivery, while also enabling partner restaurants to provide meals as efficiently as possible.

[0907] The processing flow will be explained below.

[0908] Step 1:

[0909] The user downloads the app and creates an account. They enter the necessary personal information, such as their name, address, and contact information. They also select their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[0910] Step 2:

[0911] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[0912] Step 3:

[0913] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[0914] Step 4:

[0915] The server generates an order list of the required dishes for each partner store based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner store.

[0916] Step 5:

[0917] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[0918] Step 6:

[0919] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[0920] Step 7:

[0921] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[0922] Step 8:

[0923] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[0924] Step 9:

[0925] The server receives the delivery completion report from the delivery person and stores the information in a database.

[0926] Step 10:

[0927] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[0928] Step 11:

[0929] The terminal sends the user's feedback information to the server, which stores it in a database.

[0930] Step 12:

[0931] The server analyzes the collected feedback data and adjusts the AI ​​algorithms, which will further improve the efficiency of future meal production and delivery routes.

[0932] This series of processing steps allows users to receive high-quality service at a low cost, and enables partner stores to efficiently produce and deliver meals.

[0933] Example 1

[0934] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0935] Conventional meal delivery systems have difficulty setting efficient delivery routes and production plans that match users' desired meal styles and delivery times. As a result, they have been unable to deliver meals quickly and efficiently according to users' wishes, resulting in problems such as delivery delays and increased delivery costs. Furthermore, they have not been able to fully utilize user feedback to improve the system.

[0936] 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.

[0937] In this invention, the server includes a user terminal means for inputting the user's desired meal style and delivery time slot, an information processing means for receiving the information input from the user terminal means and storing it in a database, and a calculation means for calculating the efficiency of meal production and delivery routes using a generative AI model based on the information stored by the information processing means, thereby enabling efficient meal production and delivery route optimization based on the user's wishes.

[0938] "User terminal means" refers to an electronic terminal for inputting information such as the user's desired meal style and delivery time slot, and includes smartphones, tablets, PCs, etc.

[0939] "Information processing means" refers to a server having the function of receiving information sent from user terminal means and storing and managing it in a database.

[0940] "Generative AI models" refer to artificial intelligence systems that learn from large amounts of data and calculate the efficiency of meal production and delivery routes. Specifically, this includes machine learning frameworks such as TensorFlow and PyTorch.

[0941] "Computational means" refers to a function that uses a generative AI model to calculate efficient meal production lists and delivery routes based on data stored in the information processing means.

[0942] "Communication means" refers to an internet communication function for automatically sending orders to affiliated stores based on the results of the calculation means.

[0943] "Data receiving means" refers to a function that receives the estimated time when the food will be ready from the partner restaurant.

[0944] "Instruction means" refers to a function that calculates the optimal delivery route based on the estimated completion time and the user's address information, and issues instructions to the delivery person.

[0945] The "reporting means" refers to the function of receiving a delivery completion report from the delivery person and storing it in the information processing means.

[0946] "Feedback means" refers to a function that receives feedback from the user and stores it in the information processing means.

[0947] "Improvement measures" refers to the function by which the generative AI model adjusts its algorithm based on feedback data to improve the quality of the service.

[0948] The "processing means" refers to a function that groups multiple users who want the same type of food based on the user's desired eating style and delivery time slot.

[0949] "Scheduling means" refers to the function of generating an order list for affiliated stores and creating a production plan for each store to prepare the necessary dishes.

[0950] This invention relates to a system that realizes efficient meal delivery based on a user's desired eating style and delivery time slot. The system includes a user terminal means, an information processing means, a generative AI model, a calculation means, a communication means, a data receiving means, an instruction means, a reporting means, a feedback means, and an improvement means.

[0951] First, the user downloads a dedicated application onto their user terminal such as a smartphone or tablet. The user launches the application, enters their name, address, and contact information, and sets their preferred dining style and delivery time. For example, they enter information such as "Taro Tanaka, Shinjuku-ku, Tokyo, tanaka@example.com, pasta, 12:00." After completing the input, the user sends this information to the server.

[0952] The server receives the information sent from the user terminal means using the information processing means and stores it in a database. The server maintains reliability and security by using cloud services such as AWS and Google Cloud Platform.

[0953] The server then analyzes all user preference information stored in the information processing device using a generative AI model, which uses frameworks such as TensorFlow and PyTorch to calculate an efficient meal production list and delivery route based on the user's preferred eating style and delivery time slot.

[0954] For example, if User A and User C want pasta at 12:00, and User B wants curry rice at 14:00, the server will group them appropriately based on this information.The generative AI model then uses computational tools to calculate an efficient production list and delivery route.

[0955] The server generates an order list for partner stores based on the results of the calculation means and sends it to the store using communication means. The order list contains detailed information about the dish name, quantity, and required time. For example, a list may be generated that says, "Please prepare 10 plates of pasta by 12:00."

[0956] Furthermore, the server receives the estimated time the food will be ready from the restaurant that received the order through the data receiving means. Based on the received estimated time and the user's address information, the server uses an instruction means to generate the optimal delivery route using Google Maps API or OR-Tools. For example, there is a specific example where the server calculates a delivery route from the restaurant to User A, User B, and User C in that order.

[0957] The delivery person's terminal receives delivery instructions sent from the server, which include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Once the delivery is complete, the delivery person reports completion on the terminal.

[0958] Finally, the user provides feedback on the quality and delivery time of the delivered food through the application. The server receives this feedback through the feedback mechanism and stores it in a database. The generative AI model uses this feedback to adjust its algorithm using the refinement mechanism to improve the delivery service next time.

[0959] Prompt Sentence Examples

[0960] User A wants Italian food at 12:00 every day, and User B wants Western food at 14:00. Generate the optimal delivery route and efficient production list.

[0961] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0962] Step 1: Register your user and set your preferences

[0963] The user downloads the dedicated application and installs it on their device. Next, they launch the application and enter their name, address, and contact information. They then set their preferred dining style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day). Specifically, they enter their information into the input form on the screen and press the "Register" button. Once this information is entered (the registration information is set), the device sends it to the server. The output is the sent user information.

[0964] Step 2: Save user information

[0965] The server receives user information sent from the user terminal means. The received data includes name, address, contact information, meal style, and delivery time slot. Specifically, the server analyzes the received data and stores it in a MySQL or PostgreSQL database. It inserts the information using an SQL query. For example, it executes a command such as "INSERT INTO users (name, address, contact, meal_style, delivery_time) VALUES (...)". The input is the received user information, and the output is the result stored in the database.

[0966] Step 3: Collect user preferences and analyze them with AI

[0967] The server uses information processing means to collect all user preference information from a database. For example, it retrieves data by executing a query such as "SELECT FROM users WHERE delivery_time = '12:00'". It then analyzes the retrieved data using a generative AI model to calculate an efficient meal production list and delivery route. Specifically, it uses TensorFlow and PyTorch to calculate the optimal route based on eating style and delivery time slot. The input is a set of user information retrieved from the database, and the output is a production list and an optimized delivery route.

[0968] Step 4: Generate and send an order list to the store

[0969] The server generates an order list for partner stores based on the calculated production list. The order list includes the dish name, quantity, and required time. Specifically, the server generates order information in JSON format and sends it to the partner store's API endpoint using an HTTP POST request. For example, it sends a request such as "Please prepare 10 plates of pasta by 12:00." The input is the production list, and the output is the order list sent to the partner store.

[0970] Step 5: Calculate delivery routes

[0971] The server receives the estimated time the food will be ready from the partner restaurant. For example, it obtains this information from the restaurant's API via an HTTP GET request. Based on the received estimated time and the user's address information, it calculates the optimal delivery route using Google Maps API or OR-Tools. Specifically, it uses the API to send a request to determine the order of delivery to each destination. The input is the estimated time of completion and the user's address information, and the output is the optimized delivery route.

[0972] Step 6: Delivery and reporting

[0973] The delivery person's terminal receives delivery instructions sent from the server. These instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Specifically, the delivery person uses the terminal to report "delivery complete" upon delivery. The report is made by pressing the "Delivery Complete" button on the dedicated app. The input is the delivery instructions, and the output is the delivery completion report data.

[0974] Step 7: Gather feedback and improve with AI

[0975] The user enters feedback on the quality and delivery time of the delivered food. The server receives this information via the feedback means and stores it in a database. Specifically, the user enters the feedback into an evaluation form within the application and presses the submit button. The received feedback data is analyzed by a generative AI model, and the algorithm is adjusted to improve service quality. The input is the feedback information, and the output is an updated set of parameters for the AI ​​model.

[0976] (Application example 1)

[0977] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0978] Conventional meal delivery systems have difficulty effectively reflecting users' preferred eating styles and desired delivery times. They also face the problem of calculating efficient delivery routes for individual orders and coordinating with partner stores, which can be cumbersome. This can lead to problems such as a loss of user satisfaction and reduced work efficiency for delivery staff and stores.

[0979] 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.

[0980] In this invention, the server includes: a user terminal means for inputting a user's desired meal style and desired delivery time; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using a generative AI model to suggest meals based on the information stored by the server means; a means for accepting user orders based on the suggestions from the generative AI model; a means for using AI to calculate the efficiency of meal production and delivery routes based on the order information; a means for generating and sending a bulk order list to affiliated stores based on the calculation results; a means for receiving an estimated meal completion time from the affiliated stores; a means for calculating an optimal delivery route based on the estimated completion time and the user's address information and issuing instructions to the delivery person; a means for tracking and displaying the location information of the delivery person who received the instructions in real time and notifying the user of the delivery status; a means for receiving delivery completion reports from the delivery person and storing them on the server; a means for receiving user feedback and storing them on the server; and a means for adjusting the generative AI model algorithm based on the feedback data to improve service quality. This enables efficient meal delivery according to user preferences, bulk orders, and calculation of the optimal delivery route.

[0981] The "user terminal means" is a device that allows a user to input the desired meal style and desired delivery time.

[0982] The "server means" is a device that receives information input from the user terminal means and stores it in a database.

[0983] A "generative AI model" is an artificial intelligence system that makes meal suggestions and streamlines ordering based on stored user information.

[0984] The "means for making suggestions" is a device or system that uses a generative AI model to suggest meals tailored to the user's preferences.

[0985] "Means for accepting orders" refers to a device or system that accepts orders from users based on suggestions from the generative AI model.

[0986] A "means for performing efficiency calculations" is a device or system that uses AI to calculate the efficiency of meal production and delivery routes based on order information.

[0987] The "means for generating and transmitting a collective order list" is a device or system that generates and transmits a collective order list to affiliated stores.

[0988] The "means for receiving the estimated time of completion of the dish" is a device or system that receives the estimated time of completion of the dish from the affiliated restaurant.

[0989] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a device or system that generates the optimal delivery route based on the estimated completion time and the user's address information and issues instructions to the delivery person.

[0990] The "means for tracking and displaying in real time and notifying the user of the delivery status" is a device or system that tracks the location information of the delivery person in real time and notifies the user of the delivery status.

[0991] The "means for receiving a delivery completion report and storing it on a server" is a device or system that receives a delivery completion report from a delivery person and stores it on a server.

[0992] The "means for receiving feedback and storing it on a server" is a device or system that receives feedback from a user and stores it on a server.

[0993] "Means for adjusting the generative AI model algorithm and improving the quality of the service" refers to a device or system that adjusts the generative AI model based on stored feedback data and improves the quality of the service.

[0994] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and desired delivery time. This system is composed of a user terminal means, a server means, and multiple AI technologies.

[0995] System Overview

[0996] The overall system is made up of the following configuration and processing procedures.

[0997] 1. User registration and preference settings

[0998] Using the user terminal means, the user downloads a dedicated smartphone application and creates an account. The user enters their name, address, contact information, and selects their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day). The server means receives this information and stores it in a database.

[0999] 2. Meal suggestions and ordering

[1000] The server uses a generative AI model based on the stored information to recommend meals that fit the user's preferences. This generative AI model incorporates information such as food and drink types, past order history, and popular menu items to recommend the most suitable meal. The user can then review the suggested meals and place their order through the app.

[1001] 3. AI-powered efficiency and order list generation

[1002] The server uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information. Based on the results of this calculation, it generates and sends a consolidated order list to partner stores. This order list includes details such as the name of the dish, quantity, and required time.

[1003] 4. Delivery route optimization

[1004] The server receives the estimated time of completion of the food from the partner restaurant and calculates the optimal delivery route based on the user's address information. This calculation uses map data such as Google Maps API. The delivery person is provided with information instructing them on the optimized delivery route.

[1005] 5. Delivery and real-time tracking

[1006] The delivery person's device delivers according to the optimal route instructed by the server. The delivery status is tracked in real time, and the user is notified of the delivery person's location and delivery status. This is done using GPS data and push notification technology.

[1007] 6. Completion Report and Feedback

[1008] Once the delivery is complete, the delivery person reports the completion via their device, and the information is saved on the server. The user can then enter feedback on the quality of the delivered food and the delivery time. This feedback data is also saved on the server and used to adjust the generative AI model algorithm and improve service in the future.

[1009] Specific use cases

[1010] For example, if User A requests pizza to be delivered at 12:00 every day, the system will pass this information to the generative AI model to generate the optimal delivery route and place a bulk order with partner stores. The system will also track the delivery person's location in real time and notify the user, visualizing the delivery status.

[1011] Prompt Sentence Examples

[1012] "Based on user A's request for pizza delivery at 12:00 every day, collect information on the delivery time and food style (e.g., Western or Japanese) desired by other users B and C, and combine this information to calculate the optimal delivery route. Also, based on the results, generate an efficient order list for partner stores and send it to each store."

[1013] This system provides users with the benefit of having their desired meals delivered efficiently and quickly, while also enabling partner restaurants to provide meals with maximum efficiency.

[1014] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1015] Step 1:

[1016] The user terminal means allows the user to input the desired meal style and desired delivery time.

[1017] Input: User's name, address, contact information, eating style, and desired delivery time.

[1018] Output: Sends the input data to the server.

[1019] Specific operation: The user uses the smartphone app to enter the required information into the input form and clicks the submit button to send it to the server.

[1020] Step 2:

[1021] The server means stores the received user information in a database.

[1022] Input: User information sent from the user terminal.

[1023] Output: User information stored in the database.

[1024] Specific operation: The server stores the received user information in a database management system (for example, PostgreSQL or MySQL).

[1025] Step 3:

[1026] The server means uses the generative AI model based on the stored information to make meal recommendations.

[1027] Input: User information stored in the database.

[1028] Output: A suggested meal menu.

[1029] Specific operation: The generative AI model generates the optimal meal based on the user's past ordering history and popular menu items, and returns the suggestions to the server.

[1030] Step 4:

[1031] The user terminal means accepts an order from a user based on the proposal from the generative AI model.

[1032] Input: Meal suggestions from a generative AI model.

[1033] Output: User's order data.

[1034] Specific operation: The user selects the desired meal from the proposed menu and confirms the order.

[1035] Step 5:

[1036] The server means uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information.

[1037] Input: User's order information.

[1038] Output: Efficient delivery routes and production lists.

[1039] How it works: The AI ​​algorithm analyzes multiple order information and calculates the optimal delivery route and order list for the store.

[1040] Step 6:

[1041] The server means generates and transmits a bulk order list to the affiliated stores based on the calculation results.

[1042] Input: The results of the efficiency calculation.

[1043] Output: Bulk order list for partner stores.

[1044] Specific operation: The server creates a bulk order list of the required dishes for each store and sends it to the store via API.

[1045] Step 7:

[1046] The server means receives the estimated time when the food is ready from the affiliated store.

[1047] Input: Estimated completion time from partner store.

[1048] Output: Estimated completion time data.

[1049] Specific operation: Receives estimated completion time information from the store via API and saves it on the server.

[1050] Step 8:

[1051] The server means calculates the optimum delivery route and issues instructions to the delivery person.

[1052] Input: Estimated completion time and user address information.

[1053] Output: Optimal delivery route instructions.

[1054] Specific operation: The AI ​​algorithm generates the optimal delivery route based on the estimated completion time and the user's address information and notifies the delivery person's terminal.

[1055] Step 9:

[1056] The delivery person's terminal delivers the parcel according to the optimal route instructed and reports the delivery status to the server in real time.

[1057] Input: Optimal delivery route.

[1058] Output: Real-time delivery status data.

[1059] Specific operation: The delivery person uses GPS to send location information to the server in real time and report the situation.

[1060] Step 10:

[1061] The user terminal means receives feedback from the user after delivery is completed and transmits it to the server.

[1062] Input: User feedback.

[1063] Output: Feedback data.

[1064] What it does: The user uses the app to enter feedback about the quality of the food and delivery time, which is then sent to the server.

[1065] Step 11:

[1066] The server means stores the received feedback data and adjusts the generative AI model algorithm.

[1067] Input: User feedback data.

[1068] Output: The tuned generative AI model.

[1069] Specific operation: Based on the feedback data, the algorithm of the generative AI model is updated and improved to improve the quality of the service.

[1070] 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.

[1071] The present invention relates to a delivery system that incorporates an emotion engine that recognizes the user's emotions. The system of the present invention not only realizes efficient meal delivery based on the user's desired eating style and delivery time slot, but also recognizes the user's emotions to further improve service. The following is a natural language explanation of the program processing of this system.

[1072] 1. User registration and preference settings

[1073] User Actions

[1074] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day).

[1075] Server Actions

[1076] The server receives the account information and desired information sent from the user terminal and stores them in a database.

[1077] 2. AI-based list creation and efficiency

[1078] Server Actions

[1079] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[1080] 3. Ordering and adjusting to stores

[1081] Server Actions

[1082] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[1083] Specific examples

[1084] For example, if many users are ordering pizza at 12:00, the server will request the specified restaurant to prepare 10 pizzas by 12:00.

[1085] 4. Delivery route optimization

[1086] Server Actions

[1087] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[1088] 5. Delivery and Reporting

[1089] Delivery person terminal actions

[1090] The delivery staff terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery staff follows these instructions and delivers the food to each user along the specified route.

[1091] Deliveryman's actions

[1092] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[1093] 6. Feedback and emotion engine analysis

[1094] User Actions

[1095] Users can input their feedback about the quality of the delivered food and the delivery service through the application, and the emotion engine recognizes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) from the user's voice and text.

[1096] Server Actions

[1097] The server receives feedback and emotional data from users and stores it in a database. The AI ​​then adjusts the algorithm based on this feedback and emotional data to improve the service in the future.

[1098] Specific examples and processing flow

[1099] For example, if User A requests Italian food at 12:00 every day, and User B requests Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants, with Italian food delivered to User A at 12:00 and Western food delivered to User B at 14:00, and the AI ​​will calculate the optimal delivery route. Furthermore, if User A enters positive feedback and an emotion such as "very satisfied" in the application, this information is stored on the server and used to improve the service in the future.

[1100] This system allows users to receive high-quality service at a low price, and enables partner restaurants to efficiently produce and deliver meals. In addition, by using an emotion engine, it is possible to more accurately grasp user satisfaction and further improve services.

[1101] The processing flow will be explained below.

[1102] Step 1:

[1103] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[1104] Step 2:

[1105] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[1106] Step 3:

[1107] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[1108] Step 4:

[1109] The server generates an order list of the required dishes for partner stores based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner stores.

[1110] Step 5:

[1111] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[1112] Step 6:

[1113] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[1114] Step 7:

[1115] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[1116] Step 8:

[1117] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[1118] Step 9:

[1119] The server receives the delivery completion report from the delivery person and stores the information in a database.

[1120] Step 10:

[1121] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[1122] Step 11:

[1123] The terminal sends the user's feedback information to the server, which stores it in a database.

[1124] Step 12:

[1125] The server recognizes the user's emotions using an emotion engine in addition to the feedback. The emotion engine analyzes the user's voice feedback and text messages to extract emotions such as satisfaction, dissatisfaction, joy, and anger.

[1126] Step 13:

[1127] The server stores the recognized emotion data in a database and incorporates it into the AI ​​algorithm, which uses this data to adjust the AI ​​algorithm and improve the quality of service for future meal production and delivery.

[1128] Step 14:

[1129] The server sends feedback to partner stores based on the emotion data and provides specific instructions for improving service. For example, it can provide feedback such as "Many users are satisfied with the doneness of their pizza, but are dissatisfied with the delivery time," and work with the store to consider improvement measures.

[1130] This series of processing steps enables efficient food preparation and delivery while increasing user satisfaction. Furthermore, the use of an emotion engine improves the quality of feedback, enabling continuous improvement of the service.

[1131] Example 2

[1132] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1133] Conventional delivery systems have difficulty meeting individual user requests and wishes, making it difficult to achieve efficient delivery and reduce costs. Furthermore, feedback to improve services by taking user feelings into consideration has not been fully utilized. As a result, user satisfaction and service quality have declined, making it difficult to maintain competitiveness.

[1134] 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.

[1135] In this invention, the server includes a terminal means for inputting the user's desired meal style and delivery time slot, a server means for receiving the information input from the terminal means and storing it in a database, and a means for calculating the efficiency of meal production and delivery routes using a machine learning model based on the information stored by the server means, thereby enabling efficient meal delivery according to the user's wishes and improving the quality of service.

[1136] The "terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[1137] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[1138] A "machine learning model" is an algorithm used to calculate the efficiency of meal production and delivery routes based on stored information.

[1139] "Partner restaurants" are restaurants that work in conjunction with this system to receive orders and provide food.

[1140] The "estimated completion time" is the time when the food preparation specified by the partner restaurant will be completed.

[1141] An "optimal delivery route" is a route calculated to efficiently deliver food to multiple users.

[1142] A "delivery person" is a person or device whose role is to deliver food received from partner restaurants to each user.

[1143] The "delivery completion report" is a notification of delivery completion sent when the delivery person delivers the food to the user.

[1144] "Feedback" is information that allows users to provide their evaluations and impressions of the delivered food and service.

[1145] An "emotion recognition engine" is a technology for recognizing emotions from a user's voice or text.

[1146] "Artificial intelligence algorithm" refers to an algorithm that uses feedback data and an emotion recognition engine to improve the quality of services.

[1147] The present invention provides a system for providing an efficient and high-quality meal delivery service based on a user's desired eating style and delivery time slot. The system includes a terminal unit, a server unit, a machine learning model, partner restaurants, an optimal delivery route calculation unit, a delivery person unit, and an emotion recognition engine.

[1148] First, users download a dedicated application onto their smartphone or tablet and create an account. They then enter their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day) into the application.

[1149] Next, the server receives the account information and preference information sent by the user and stores them in a database. The user information stored in the database is stored in a "User Information" table, and the preference information is stored in a "Preference Information" table.

[1150] The server collects the stored data at a fixed time each day and uses a machine learning model to analyze the preferences of all users. This machine learning model, built in Python, groups users who have the same meal preferences at the same time and generates an efficient meal production list. For example, if multiple users want pizza at 12:00, this information is organized and they can efficiently place a bulk order.

[1151] The server then generates an order list for the required dishes for partner restaurants based on the production list created by the AI ​​system. The order list includes detailed information such as the name of the dish, the quantity, and the estimated time of completion. The order list is sent to partner restaurants via API, for example, in JSON format using a RESTful API.

[1152] Partner restaurants prepare food based on the received order list and notify the server of the estimated time the food will be ready. The server receives this notification data and calculates the optimal delivery route based on the estimated time of completion and each user's address information. The server uses map services such as Google Maps API to calculate the shortest route, making it possible to deliver to multiple users efficiently.

[1153] At the actual delivery stage, the delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to the user along the specified route. Once the food has been delivered, the delivery person uses a dedicated application to report "delivery completed," which is then updated on the server.

[1154] Finally, the user enters feedback about the food and service through the application, and an emotion recognition engine analyzes the user's emotions via voice and text, resulting in a level of satisfaction (e.g., satisfied, dissatisfied, happy, angry, etc.) being sent to the server.

[1155] The server collects this feedback and emotion data, stores it in a database, and adjusts the AI ​​algorithm. This feedback loop allows for future service quality improvements, continuously increasing user satisfaction.

[1156] Prompt Sentence Examples

[1157] "Imagine a program that takes into account the user's preferred eating style and delivery time slots to create the optimal delivery route and produce list."

[1158] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1159] System program processing flow

[1160] Step 1: Register your user information and set your preferences

[1161] Specific actions

[1162] Input: The user enters their name, address, contact information, preferred eating style, and desired delivery time slot into a dedicated application.

[1163] Output: The user's input data is sent to the server.

[1164] A user opens the app on their smartphone or tablet, taps the "Create a new account" button, enters the required information, and then taps the "Register" button, which sends the entered data to the server via the Internet.

[1165] Data processing or data calculation

[1166] The server receives the data sent by the user and stores the name, address, and contact information in a "User Information" table, and stores the preferred eating style and desired delivery time slot in a "Preference Information" table, thereby organizing individual user information.

[1167] Step 2: Collect and analyze all users' preferences

[1168] Specific actions

[1169] Input: Get all users' preferences from the database.

[1170] Output: A dataset is generated for analysis.

[1171] The server queries the "Preferences" table from the database at a fixed time every day to collect all users' preferences, which are then passed to the AI ​​system as a dataset for analysis.

[1172] Data processing or data calculation

[1173] The server analyzes the dataset using a machine learning model built in Python to calculate the production list and optimal delivery route based on the user's desired delivery time and eating style, enabling efficient production and delivery.

[1174] Step 3: Generate and send an order list to partner restaurants

[1175] Specific actions

[1176] Input: An efficient production list created by an AI system.

[1177] Output: Specific order data sent to partner restaurants.

[1178] The server analyzes the production list received from the machine learning model and generates specific order data for each partner restaurant. For example, the order list includes the name of the dish, the quantity, and the required time. The generated order list is sent to the partner restaurant in JSON format using a RESTful API.

[1179] Data processing or data calculation

[1180] The server divides and organizes the order list by store and sends it to partner restaurants in the appropriate format, allowing the restaurants to efficiently receive orders and begin preparation.

[1181] Step 4: Receive estimated time of completion and calculate optimal delivery route

[1182] Specific actions

[1183] Input: Estimated time of completion of food received from partner restaurant.

[1184] Output: Optimal delivery route to send to delivery driver.

[1185] The server receives the estimated time the food will be ready from partner restaurants via API. Based on the received data and the user's address information, it calculates the optimal delivery route. Using map services such as Google Maps API, the shortest route is generated for efficient delivery to multiple users.

[1186] Data processing or data calculation

[1187] The server calculates the optimal delivery route based on the estimated completion time and the user's address information, and prepares to send the calculation results to the delivery person's terminal.

[1188] Step 5: Delivery and reporting

[1189] Specific actions

[1190] Input: Delivery instructions sent by the server.

[1191] Output: Delivery completion report.

[1192] The delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. When the food is delivered, the delivery person taps the "Delivery Complete" button to report to the server.

[1193] Data processing or data calculation

[1194] The server receives delivery completion reports from delivery personnel and updates the order status in the database, allowing for real-time monitoring of the progress of each delivery and efficient management.

[1195] Step 6: Feedback and Emotion Recognition Engine Analysis

[1196] Specific actions

[1197] Input: Feedback and sentiment data entered by the user through the application.

[1198] Output: Analysis results for improvement.

[1199] Users can enter their feedback about the food delivered and the service through the application, and an emotion recognition engine analyzes the user's emotions using voice and text, and these emotions, such as satisfaction, dissatisfaction, joy, and anger, are sent to the server.

[1200] Data processing or data calculation

[1201] The server stores the acquired feedback and emotion data in a database and uses artificial intelligence algorithms to improve the quality of the service. This feedback loop will further improve the delivery service for future deliveries.

[1202] keyword

[1203] Generative AI model, prompt sentence

[1204] (Application example 2)

[1205] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1206] Conventional delivery systems have struggled to provide efficient delivery services that match users' desired dining style and delivery time slots. While systems exist for incorporating user feedback, they lack sufficient mechanisms for recognizing user emotions and reflecting them in service quality improvements. This makes it difficult to guarantee a consistently high-quality user experience, and service providers lack effective means of improvement.

[1207] 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.

[1208] In this invention, the server includes: a user terminal means for inputting a user's desired eating style and delivery time slot; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using AI to calculate the efficiency of meal production and delivery routes based on the information stored by the server means; a means for sending an order to a partner store based on the calculation results; a means for receiving an estimated time of completion of the food from the partner store; a means for calculating an optimal delivery route based on the estimated time of completion and the user's address information and issuing instructions to a delivery person; a means for receiving a delivery completion report from the delivery person and storing it in the server; a means for receiving feedback from the user and storing it in the server; and a means for adjusting the AI ​​algorithm based on the feedback data and emotion recognition results to improve the quality of the service. This makes it possible to continuously improve the service based on feedback that takes user emotions into consideration.

[1209] The "user terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[1210] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[1211] "Means for calculating efficiency of meal production and delivery routes using AI" means processes and algorithms that use artificial intelligence to improve the efficiency of meal production plans and delivery routes based on stored information.

[1212] The "means for sending orders to affiliated restaurants" is a system for sending the necessary food order information to affiliated restaurants.

[1213] The "means for receiving the estimated time of completion of the dish" is a mechanism for receiving the estimated time of completion of the dish from the affiliated restaurant.

[1214] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a system that calculates the most efficient delivery route based on the estimated time the food is completed and the user's address information, and issues instructions to the delivery person along that route.

[1215] The "means for receiving a delivery completion report and storing it on a server" is a process for receiving a delivery completion report from a delivery person and storing that information on a server.

[1216] The "means for receiving feedback and storing it on a server" is a system that receives feedback from users and stores that information on a server.

[1217] "Means for adjusting AI algorithms based on emotion recognition results and improving service quality" refers to the process of recognizing emotions contained in user feedback and adjusting AI algorithms based on the results to improve service quality.

[1218] The system of the present invention realizes efficient meal delivery based on the user's desired eating style and delivery time slot, and also recognizes the user's emotions to further improve the service.

[1219] Specifically, this system is configured as follows:

[1220] First, users download a dedicated application and create an account. They enter their name, address, contact information, preferred dining style, and desired delivery time. This information is sent from the user's device to the server, which receives it and stores it in a database.

[1221] Next, the server uses AI to calculate the efficiency of meal production and delivery routes based on the information stored in the database. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food during the same delivery time, these preferences will be grouped and ordered together.

[1222] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI ​​and sends it to each restaurant. The order list includes detailed information such as the name of the dish, quantity, and required time. Each restaurant prepares the dishes based on this list.

[1223] The server also receives the estimated time the food will be ready from partner restaurants and calculates the optimal delivery route based on the user's address information. Using AI, it generates the most efficient route for delivering to multiple users and issues instructions to the delivery staff according to that route.

[1224] The delivery person follows the delivery instructions sent from the server and delivers the food to each user along the specified route. When the delivery is complete, the delivery person reports the completion of the delivery on their device, and this information is saved on the server.

[1225] In addition, users can enter feedback about the delivered food through the application. This feedback includes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) that are recognized from the user's voice or text using an emotion engine. The server receives the user's feedback and emotion data and stores it in a database. The AI ​​adjusts its algorithms based on this feedback and emotion data to improve service in the future.

[1226] The hardware and software used include a smartphone, server, AI system, and emotion engine. Information entered by the user using the smartphone is sent to the server, which receives it and stores it in a database. The AI ​​system analyzes the information in the database and calculates efficient production lists and delivery routes. The emotion engine also analyzes user feedback, which the AI ​​uses to improve the service.

[1227] Specific examples

[1228] A user requests Italian food at 12:00 every day and orders pizza and garlic bread. This information is sent to the server, which generates an order list for pizza and garlic bread for partner restaurants. The restaurant prepares the food, and a delivery person delivers it according to the optimal delivery route calculated by the server. The delivery person sends a delivery completion report to the server, and the user enters feedback such as "Today's food was delicious!" Based on this feedback, the emotion engine recognizes the emotion "satisfied," which the server stores and uses to improve the quality of service.

[1229] Prompt Sentence Examples

[1230] "I was happy that the food arrived quickly, but it was a bit spicy."

[1231] This allows the system to provide more appropriate services based on feedback that takes into account the user's emotions.

[1232] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1233] Step 1:

[1234] User Registration

[1235] Users create an account by entering their name, address, and contact information into a dedicated application on their smartphone. This information is sent from the user's device to the server, which receives the information and stores it in a database.

[1236] (Input) Name, address, contact information

[1237] (Output) User information stored in the database

[1238] Step 2:

[1239] User preference settings

[1240] Using the same application, users can set their preferred dining style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day) and send this information to the server, which receives it and stores it in a database.

[1241] (Input) Meal style, delivery time

[1242] (Output) Preferences saved in the database

[1243] Step 3:

[1244] AI-powered list creation and efficiency

[1245] The server collects all user preferences from a database and uses an AI system to calculate efficient meal production lists and delivery routes. This AI system performs calculations to optimize delivery routes based on each user's preferred delivery time slot and eating style.

[1246] (Input) Desired information obtained from the database

[1247] (Output) Efficient meal production list and delivery route

[1248] Step 4:

[1249] Generate order list and send it to the store

[1250] The server generates an order list for the required dishes for partner restaurants based on the efficient production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time, and is sent to the partner restaurant.

[1251] (Input) Efficient meal production list

[1252] (Output) Order list sent to partner stores

[1253] Step 5:

[1254] Receive estimated time of completion of dish

[1255] The partner restaurant sends the estimated time the food will be ready to eat to the server, which receives this information and stores it in a database.

[1256] (Input) Estimated time of completion of food from partner restaurant

[1257] (Output) Estimated completion time information saved in the database

[1258] Step 6:

[1259] Calculating and directing optimal delivery routes

[1260] The server uses an AI system to calculate the optimal delivery route based on the estimated time the food is ready and the user's address information, and then gives instructions to the delivery person to follow that route.

[1261] (Input) Estimated time of completion of the dish, user's address information

[1262] (Output) Optimal delivery route and instructions for delivery personnel

[1263] Step 7:

[1264] Receiving and storing delivery completion reports

[1265] Once the delivery person has delivered the food, they use their smartphone to report that the delivery is complete. This report is sent to the server, which then stores the information in a database.

[1266] (Input) Delivery completion report

[1267] (Output) Delivery completion information saved in the database

[1268] Step 8:

[1269] User Feedback and Emotion Recognition

[1270] The user inputs feedback about the delivered food through the application. This feedback includes emotions recognized from the user's voice and text using an emotion engine. The server receives the feedback and emotion data from the user and stores them in a database.

[1271] (Input) User feedback and sentiment data

[1272] (Output) Feedback and emotion data stored in a database

[1273] Step 9:

[1274] Adjusting AI algorithms and improving service quality

[1275] The server uses the stored feedback and emotional data to adjust its AI algorithms and improve the quality of the service, making future visits even more user-friendly.

[1276] (Input) Feedback and emotion data

[1277] (Output) Adjusted AI algorithms and service improvement measures

[1278] Through the above steps, an efficient and high-quality delivery service that takes user emotions into consideration is realized.

[1279] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1280] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1281] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1282] [Fourth embodiment]

[1283] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1284] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1285] 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).

[1286] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1287] 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.

[1288] 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).

[1289] 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. 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.

[1290] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1291] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1292] 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.

[1293] 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.

[1294] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1295] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1296] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and delivery time slot. The following is a natural language explanation of the program processing of this system.

[1297] 1. User registration and preference settings

[1298] User Actions

[1299] Users download the app and create an account by entering their name, address, and contact information, as well as their preferred food style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day).

[1300] Server Actions

[1301] The server receives the account information and desired information sent from the user terminal and stores it in a database.

[1302] 2. AI-based list creation and efficiency

[1303] Server Actions

[1304] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[1305] 3. Ordering and adjusting to stores

[1306] Server Actions

[1307] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[1308] Specific examples

[1309] For example, if many users are ordering pizza at 12:00, the server will request a specified Italian restaurant to prepare 10 pizzas by 12:00.

[1310] 4. Delivery route optimization

[1311] Server Actions

[1312] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[1313] 5. Delivery and Reporting

[1314] Delivery person terminal actions

[1315] The delivery person terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery person follows these instructions and delivers the food to each user along the specified route.

[1316] Deliveryman's actions

[1317] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[1318] 6. Feedback and Improvement

[1319] User Actions

[1320] Users can enter feedback through the application about the quality of the delivered food and the delivery time.

[1321] Server Actions

[1322] The server receives user feedback and stores it in a database. The AI ​​uses this feedback data to adjust its algorithms and improve the service for future visits.

[1323] Specific examples and processing flow

[1324] For example, if User A wants Italian food at 12:00 every day and User B wants Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants to deliver Italian food to User A at 12:00 and Western food to User B at 14:00, and the AI ​​calculates the optimal delivery route. In this way, efficient meal production and delivery is achieved.

[1325] This system allows users to order meals at lower prices than usual, streamlining delivery, while also enabling partner restaurants to provide meals as efficiently as possible.

[1326] The processing flow will be explained below.

[1327] Step 1:

[1328] The user downloads the app and creates an account. They enter the necessary personal information, such as their name, address, and contact information. They also select their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[1329] Step 2:

[1330] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[1331] Step 3:

[1332] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[1333] Step 4:

[1334] The server generates an order list of the required dishes for each partner store based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner store.

[1335] Step 5:

[1336] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[1337] Step 6:

[1338] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[1339] Step 7:

[1340] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[1341] Step 8:

[1342] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[1343] Step 9:

[1344] The server receives the delivery completion report from the delivery person and stores the information in a database.

[1345] Step 10:

[1346] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[1347] Step 11:

[1348] The terminal sends the user's feedback information to the server, which stores it in a database.

[1349] Step 12:

[1350] The server analyzes the collected feedback data and adjusts the AI ​​algorithms, which will further improve the efficiency of future meal production and delivery routes.

[1351] This series of processing steps allows users to receive high-quality service at a low cost, and enables partner stores to efficiently produce and deliver meals.

[1352] Example 1

[1353] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1354] Conventional meal delivery systems have difficulty setting efficient delivery routes and production plans that match users' desired meal styles and delivery times. As a result, they have been unable to deliver meals quickly and efficiently according to users' wishes, resulting in problems such as delivery delays and increased delivery costs. Furthermore, they have not been able to fully utilize user feedback to improve the system.

[1355] 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.

[1356] In this invention, the server includes a user terminal means for inputting the user's desired meal style and delivery time slot, an information processing means for receiving the information input from the user terminal means and storing it in a database, and a calculation means for calculating the efficiency of meal production and delivery routes using a generative AI model based on the information stored by the information processing means, thereby enabling efficient meal production and delivery route optimization based on the user's wishes.

[1357] "User terminal means" refers to an electronic terminal for inputting information such as the user's desired meal style and delivery time slot, and includes smartphones, tablets, PCs, etc.

[1358] "Information processing means" refers to a server having the function of receiving information sent from user terminal means and storing and managing it in a database.

[1359] "Generative AI models" refer to artificial intelligence systems that learn from large amounts of data and calculate the efficiency of meal production and delivery routes. Specifically, this includes machine learning frameworks such as TensorFlow and PyTorch.

[1360] "Computational means" refers to a function that uses a generative AI model to calculate efficient meal production lists and delivery routes based on data stored in the information processing means.

[1361] "Communication means" refers to an internet communication function for automatically sending orders to affiliated stores based on the results of the calculation means.

[1362] "Data receiving means" refers to a function that receives the estimated time when the food will be ready from the partner restaurant.

[1363] "Instruction means" refers to a function that calculates the optimal delivery route based on the estimated completion time and the user's address information, and issues instructions to the delivery person.

[1364] The "reporting means" refers to the function of receiving a delivery completion report from the delivery person and storing it in the information processing means.

[1365] "Feedback means" refers to a function that receives feedback from the user and stores it in the information processing means.

[1366] "Improvement measures" refers to the function by which the generative AI model adjusts its algorithm based on feedback data to improve the quality of the service.

[1367] The "processing means" refers to a function that groups multiple users who want the same type of food based on the user's desired eating style and delivery time slot.

[1368] "Scheduling means" refers to the function of generating an order list for affiliated stores and creating a production plan for each store to prepare the necessary dishes.

[1369] This invention relates to a system that realizes efficient meal delivery based on a user's desired eating style and delivery time slot. The system includes a user terminal means, an information processing means, a generative AI model, a calculation means, a communication means, a data receiving means, an instruction means, a reporting means, a feedback means, and an improvement means.

[1370] First, the user downloads a dedicated application onto their user terminal such as a smartphone or tablet. The user launches the application, enters their name, address, and contact information, and sets their preferred dining style and delivery time. For example, they enter information such as "Taro Tanaka, Shinjuku-ku, Tokyo, tanaka@example.com, pasta, 12:00." After completing the input, the user sends this information to the server.

[1371] The server receives the information sent from the user terminal means using the information processing means and stores it in a database. The server maintains reliability and security by using cloud services such as AWS and Google Cloud Platform.

[1372] The server then analyzes all user preference information stored in the information processing device using a generative AI model, which uses frameworks such as TensorFlow and PyTorch to calculate an efficient meal production list and delivery route based on the user's preferred eating style and delivery time slot.

[1373] For example, if User A and User C want pasta at 12:00, and User B wants curry rice at 14:00, the server will group them appropriately based on this information.The generative AI model then uses computational tools to calculate an efficient production list and delivery route.

[1374] The server generates an order list for partner stores based on the results of the calculation means and sends it to the store using communication means. The order list contains detailed information about the dish name, quantity, and required time. For example, a list may be generated that says, "Please prepare 10 plates of pasta by 12:00."

[1375] Furthermore, the server receives the estimated time the food will be ready from the restaurant that received the order through the data receiving means. Based on the received estimated time and the user's address information, the server uses an instruction means to generate the optimal delivery route using Google Maps API or OR-Tools. For example, there is a specific example where the server calculates a delivery route from the restaurant to User A, User B, and User C in that order.

[1376] The delivery person's terminal receives delivery instructions sent from the server, which include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Once the delivery is complete, the delivery person reports completion on the terminal.

[1377] Finally, the user provides feedback on the quality and delivery time of the delivered food through the application. The server receives this feedback through the feedback mechanism and stores it in a database. The generative AI model uses this feedback to adjust its algorithm using the refinement mechanism to improve the delivery service next time.

[1378] Prompt Sentence Examples

[1379] User A wants Italian food at 12:00 every day, and User B wants Western food at 14:00. Generate the optimal delivery route and efficient production list.

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Step 1: Register your user and set your preferences

[1382] The user downloads the dedicated application and installs it on their device. Next, they launch the application and enter their name, address, and contact information. They then set their preferred dining style (e.g., Italian, Western, etc.) and delivery time (e.g., 12:00 every day). Specifically, they enter their information into the input form on the screen and press the "Register" button. Once this information is entered (the registration information is set), the device sends it to the server. The output is the sent user information.

[1383] Step 2: Save user information

[1384] The server receives user information sent from the user terminal means. The received data includes name, address, contact information, meal style, and delivery time slot. Specifically, the server analyzes the received data and stores it in a MySQL or PostgreSQL database. It inserts the information using an SQL query. For example, it executes a command such as "INSERT INTO users (name, address, contact, meal_style, delivery_time) VALUES (...)". The input is the received user information, and the output is the result stored in the database.

[1385] Step 3: Collect user preferences and analyze them with AI

[1386] The server uses information processing means to collect all user preference information from a database. For example, it retrieves data by executing a query such as "SELECT FROM users WHERE delivery_time = '12:00'". It then analyzes the retrieved data using a generative AI model to calculate an efficient meal production list and delivery route. Specifically, it uses TensorFlow and PyTorch to calculate the optimal route based on eating style and delivery time slot. The input is a set of user information retrieved from the database, and the output is a production list and an optimized delivery route.

[1387] Step 4: Generate and send an order list to the store

[1388] The server generates an order list for partner stores based on the calculated production list. The order list includes the dish name, quantity, and required time. Specifically, the server generates order information in JSON format and sends it to the partner store's API endpoint using an HTTP POST request. For example, it sends a request such as "Please prepare 10 plates of pasta by 12:00." The input is the production list, and the output is the order list sent to the partner store.

[1389] Step 5: Calculate delivery routes

[1390] The server receives the estimated time the food will be ready from the partner restaurant. For example, it obtains this information from the restaurant's API via an HTTP GET request. Based on the received estimated time and the user's address information, it calculates the optimal delivery route using Google Maps API or OR-Tools. Specifically, it uses the API to send a request to determine the order of delivery to each destination. The input is the estimated time of completion and the user's address information, and the output is the optimized delivery route.

[1391] Step 6: Delivery and reporting

[1392] The delivery person's terminal receives delivery instructions sent from the server. These instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. Specifically, the delivery person uses the terminal to report "delivery complete" upon delivery. The report is made by pressing the "Delivery Complete" button on the dedicated app. The input is the delivery instructions, and the output is the delivery completion report data.

[1393] Step 7: Gather feedback and improve with AI

[1394] The user enters feedback on the quality and delivery time of the delivered food. The server receives this information via the feedback means and stores it in a database. Specifically, the user enters the feedback into an evaluation form within the application and presses the submit button. The received feedback data is analyzed by a generative AI model, and the algorithm is adjusted to improve service quality. The input is the feedback information, and the output is an updated set of parameters for the AI ​​model.

[1395] (Application example 1)

[1396] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1397] Conventional meal delivery systems have difficulty effectively reflecting users' preferred eating styles and desired delivery times. They also face the problem of calculating efficient delivery routes for individual orders and coordinating with partner stores, which can be cumbersome. This can lead to problems such as a loss of user satisfaction and reduced work efficiency for delivery staff and stores.

[1398] 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.

[1399] In this invention, the server includes: a user terminal means for inputting a user's desired meal style and desired delivery time; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using a generative AI model to suggest meals based on the information stored by the server means; a means for accepting user orders based on the suggestions from the generative AI model; a means for using AI to calculate the efficiency of meal production and delivery routes based on the order information; a means for generating and sending a bulk order list to affiliated stores based on the calculation results; a means for receiving an estimated meal completion time from the affiliated stores; a means for calculating an optimal delivery route based on the estimated completion time and the user's address information and issuing instructions to the delivery person; a means for tracking and displaying the location information of the delivery person who received the instructions in real time and notifying the user of the delivery status; a means for receiving delivery completion reports from the delivery person and storing them on the server; a means for receiving user feedback and storing them on the server; and a means for adjusting the generative AI model algorithm based on the feedback data to improve service quality. This enables efficient meal delivery according to user preferences, bulk orders, and calculation of the optimal delivery route.

[1400] The "user terminal means" is a device that allows a user to input the desired meal style and desired delivery time.

[1401] The "server means" is a device that receives information input from the user terminal means and stores it in a database.

[1402] A "generative AI model" is an artificial intelligence system that makes meal suggestions and streamlines ordering based on stored user information.

[1403] The "means for making suggestions" is a device or system that uses a generative AI model to suggest meals tailored to the user's preferences.

[1404] "Means for accepting orders" refers to a device or system that accepts orders from users based on suggestions from the generative AI model.

[1405] A "means for performing efficiency calculations" is a device or system that uses AI to calculate the efficiency of meal production and delivery routes based on order information.

[1406] The "means for generating and transmitting a collective order list" is a device or system that generates and transmits a collective order list to affiliated stores.

[1407] The "means for receiving the estimated time of completion of the dish" is a device or system that receives the estimated time of completion of the dish from the affiliated restaurant.

[1408] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a device or system that generates the optimal delivery route based on the estimated completion time and the user's address information and issues instructions to the delivery person.

[1409] The "means for tracking and displaying in real time and notifying the user of the delivery status" is a device or system that tracks the location information of the delivery person in real time and notifies the user of the delivery status.

[1410] The "means for receiving a delivery completion report and storing it on a server" is a device or system that receives a delivery completion report from a delivery person and stores it on a server.

[1411] The "means for receiving feedback and storing it on a server" is a device or system that receives feedback from a user and stores it on a server.

[1412] "Means for adjusting the generative AI model algorithm and improving the quality of the service" refers to a device or system that adjusts the generative AI model based on stored feedback data and improves the quality of the service.

[1413] The present invention relates to a system that realizes efficient meal delivery based on the user's desired eating style and desired delivery time. This system is composed of a user terminal means, a server means, and multiple AI technologies.

[1414] System Overview

[1415] The overall system is made up of the following configuration and processing procedures.

[1416] 1. User registration and preference settings

[1417] Using the user terminal means, the user downloads a dedicated smartphone application and creates an account. The user enters their name, address, contact information, and selects their preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day). The server means receives this information and stores it in a database.

[1418] 2. Meal suggestions and ordering

[1419] The server uses a generative AI model based on the stored information to recommend meals that fit the user's preferences. This generative AI model incorporates information such as food and drink types, past order history, and popular menu items to recommend the most suitable meal. The user can then review the suggested meals and place their order through the app.

[1420] 3. AI-powered efficiency and order list generation

[1421] The server uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information. Based on the results of this calculation, it generates and sends a consolidated order list to partner stores. This order list includes details such as the name of the dish, quantity, and required time.

[1422] 4. Delivery route optimization

[1423] The server receives the estimated time of completion of the food from the partner restaurant and calculates the optimal delivery route based on the user's address information. This calculation uses map data such as Google Maps API. The delivery person is provided with information instructing them on the optimized delivery route.

[1424] 5. Delivery and real-time tracking

[1425] The delivery person's device delivers according to the optimal route instructed by the server. The delivery status is tracked in real time, and the user is notified of the delivery person's location and delivery status. This is done using GPS data and push notification technology.

[1426] 6. Completion Report and Feedback

[1427] Once the delivery is complete, the delivery person reports the completion via their device, and the information is saved on the server. The user can then enter feedback on the quality of the delivered food and the delivery time. This feedback data is also saved on the server and used to adjust the generative AI model algorithm and improve service in the future.

[1428] Specific use cases

[1429] For example, if User A requests pizza to be delivered at 12:00 every day, the system will pass this information to the generative AI model to generate the optimal delivery route and place a bulk order with partner stores. The system will also track the delivery person's location in real time and notify the user, visualizing the delivery status.

[1430] Prompt Sentence Examples

[1431] "Based on user A's request for pizza delivery at 12:00 every day, collect information on the delivery time and food style (e.g., Western or Japanese) desired by other users B and C, and combine this information to calculate the optimal delivery route. Also, based on the results, generate an efficient order list for partner stores and send it to each store."

[1432] This system provides users with the benefit of having their desired meals delivered efficiently and quickly, while also enabling partner restaurants to provide meals with maximum efficiency.

[1433] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1434] Step 1:

[1435] The user terminal means allows the user to input the desired meal style and desired delivery time.

[1436] Input: User's name, address, contact information, eating style, and desired delivery time.

[1437] Output: Sends the input data to the server.

[1438] Specific operation: The user uses the smartphone app to enter the required information into the input form and clicks the submit button to send it to the server.

[1439] Step 2:

[1440] The server means stores the received user information in a database.

[1441] Input: User information sent from the user terminal.

[1442] Output: User information stored in the database.

[1443] Specific operation: The server stores the received user information in a database management system (for example, PostgreSQL or MySQL).

[1444] Step 3:

[1445] The server means uses the generative AI model based on the stored information to make meal recommendations.

[1446] Input: User information stored in the database.

[1447] Output: A suggested meal menu.

[1448] Specific operation: The generative AI model generates the optimal meal based on the user's past ordering history and popular menu items, and returns the suggestions to the server.

[1449] Step 4:

[1450] The user terminal means accepts an order from a user based on the proposal from the generative AI model.

[1451] Input: Meal suggestions from a generative AI model.

[1452] Output: User's order data.

[1453] Specific operation: The user selects the desired meal from the proposed menu and confirms the order.

[1454] Step 5:

[1455] The server means uses AI to calculate the efficiency of meal production and delivery routes based on the user's order information.

[1456] Input: User's order information.

[1457] Output: Efficient delivery routes and production lists.

[1458] How it works: The AI ​​algorithm analyzes multiple order information and calculates the optimal delivery route and order list for the store.

[1459] Step 6:

[1460] The server means generates and transmits a bulk order list to the affiliated stores based on the calculation results.

[1461] Input: The results of the efficiency calculation.

[1462] Output: Bulk order list for partner stores.

[1463] Specific operation: The server creates a bulk order list of the required dishes for each store and sends it to the store via API.

[1464] Step 7:

[1465] The server means receives the estimated time when the food is ready from the affiliated store.

[1466] Input: Estimated completion time from partner store.

[1467] Output: Estimated completion time data.

[1468] Specific operation: Receives estimated completion time information from the store via API and saves it on the server.

[1469] Step 8:

[1470] The server means calculates the optimum delivery route and issues instructions to the delivery person.

[1471] Input: Estimated completion time and user address information.

[1472] Output: Optimal delivery route instructions.

[1473] Specific operation: The AI ​​algorithm generates the optimal delivery route based on the estimated completion time and the user's address information and notifies the delivery person's terminal.

[1474] Step 9:

[1475] The delivery person's terminal delivers the parcel according to the optimal route instructed and reports the delivery status to the server in real time.

[1476] Input: Optimal delivery route.

[1477] Output: Real-time delivery status data.

[1478] Specific operation: The delivery person uses GPS to send location information to the server in real time and report the situation.

[1479] Step 10:

[1480] The user terminal means receives feedback from the user after delivery is completed and transmits it to the server.

[1481] Input: User feedback.

[1482] Output: Feedback data.

[1483] What it does: The user uses the app to enter feedback about the quality of the food and delivery time, which is then sent to the server.

[1484] Step 11:

[1485] The server means stores the received feedback data and adjusts the generative AI model algorithm.

[1486] Input: User feedback data.

[1487] Output: The tuned generative AI model.

[1488] Specific operation: Based on the feedback data, the algorithm of the generative AI model is updated and improved to improve the quality of the service.

[1489] 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.

[1490] The present invention relates to a delivery system that incorporates an emotion engine that recognizes the user's emotions. The system of the present invention not only realizes efficient meal delivery based on the user's desired eating style and delivery time slot, but also recognizes the user's emotions to further improve service. The following is a natural language explanation of the program processing of this system.

[1491] 1. User registration and preference settings

[1492] User Actions

[1493] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day).

[1494] Server Actions

[1495] The server receives the account information and desired information sent from the user terminal and stores them in a database.

[1496] 2. AI-based list creation and efficiency

[1497] Server Actions

[1498] The server collects all user preferences from the database and begins analysis using an AI system. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food for the same delivery time, these preferences will be grouped and ordered together.

[1499] 3. Ordering and adjusting to stores

[1500] Server Actions

[1501] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI. This order list includes detailed information such as the name of the dish, quantity, and required time. The order list is then sent to the partner restaurant.

[1502] Specific examples

[1503] For example, if many users are ordering pizza at 12:00, the server will request the specified restaurant to prepare 10 pizzas by 12:00.

[1504] 4. Delivery route optimization

[1505] Server Actions

[1506] The server receives the estimated time the food will be ready from the restaurant that received the order, and calculates the optimal delivery route based on the user's address. Using AI, it generates the most efficient route for delivering to multiple users.

[1507] 5. Delivery and Reporting

[1508] Delivery person terminal actions

[1509] The delivery staff terminal receives the delivery instructions (including the order of visits and details of the food to be delivered) sent from the server. The delivery staff follows these instructions and delivers the food to each user along the specified route.

[1510] Deliveryman's actions

[1511] The delivery person will make deliveries along the specified route and report "delivery completed" on the terminal when the food has been delivered to each user.

[1512] 6. Feedback and emotion engine analysis

[1513] User Actions

[1514] Users can input their feedback about the quality of the delivered food and the delivery service through the application, and the emotion engine recognizes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) from the user's voice and text.

[1515] Server Actions

[1516] The server receives feedback and emotional data from users and stores it in a database. The AI ​​then adjusts the algorithm based on this feedback and emotional data to improve the service in the future.

[1517] Specific examples and processing flow

[1518] For example, if User A requests Italian food at 12:00 every day, and User B requests Western food at 14:00, the server will collect this information and group them appropriately. Next, a bulk order is placed with partner restaurants, with Italian food delivered to User A at 12:00 and Western food delivered to User B at 14:00, and the AI ​​will calculate the optimal delivery route. Furthermore, if User A enters positive feedback and an emotion such as "very satisfied" in the application, this information is stored on the server and used to improve the service in the future.

[1519] This system allows users to receive high-quality service at a low price, and enables partner restaurants to efficiently produce and deliver meals. In addition, by using an emotion engine, it is possible to more accurately grasp user satisfaction and further improve services.

[1520] The processing flow will be explained below.

[1521] Step 1:

[1522] Users download the app and create an account, entering their name, address, contact information, preferred food style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day).

[1523] Step 2:

[1524] The terminal receives the information entered by the user and transmits it to the server, which stores the received user information and desired information in a database.

[1525] Step 3:

[1526] The server collects all user preferences from the database and begins analysis using an AI system, which calculates an efficient meal production list and delivery route based on the user's desired delivery time slot and eating style.

[1527] Step 4:

[1528] The server generates an order list of the required dishes for partner stores based on the production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time. The server then sends this order list to the partner stores.

[1529] Step 5:

[1530] The store terminal receives the order list sent from the server and starts preparing the food. The store calculates the estimated time of completion and notifies the server of that estimated time.

[1531] Step 6:

[1532] The server uses AI calculations to optimize delivery routes based on the estimated completion time of the food received from each restaurant and each user's address information. Based on the calculation results, it generates detailed delivery instructions for the delivery person.

[1533] Step 7:

[1534] The delivery person's terminal receives the delivery instructions sent from the server. These instructions include the order of visits, details of the food to be delivered, the user's address, etc. The delivery person makes preparations based on these instructions.

[1535] Step 8:

[1536] The delivery person follows the instructions and delivers the food to the user via the optimal route. When the food is delivered to each user, the delivery person inputs a "delivery completed" report into the terminal and sends this information to the server.

[1537] Step 9:

[1538] The server receives the delivery completion report from the delivery person and stores the information in a database.

[1539] Step 10:

[1540] The user enters feedback about the delivered food and delivery service through the application, including taste, temperature, delivery time, etc.

[1541] Step 11:

[1542] The terminal sends the user's feedback information to the server, which stores it in a database.

[1543] Step 12:

[1544] The server recognizes the user's emotions using an emotion engine in addition to the feedback. The emotion engine analyzes the user's voice feedback and text messages to extract emotions such as satisfaction, dissatisfaction, joy, and anger.

[1545] Step 13:

[1546] The server stores the recognized emotion data in a database and incorporates it into the AI ​​algorithm, which uses this data to adjust the AI ​​algorithm and improve the quality of service for future meal production and delivery.

[1547] Step 14:

[1548] The server sends feedback to partner stores based on the emotion data and provides specific instructions for improving service. For example, it can provide feedback such as "Many users are satisfied with the doneness of their pizza, but are dissatisfied with the delivery time," and work with the store to consider improvement measures.

[1549] This series of processing steps enables efficient food preparation and delivery while increasing user satisfaction. Furthermore, the use of an emotion engine improves the quality of feedback, enabling continuous improvement of the service.

[1550] Example 2

[1551] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1552] Conventional delivery systems have difficulty meeting individual user requests and wishes, making it difficult to achieve efficient delivery and reduce costs. Furthermore, feedback to improve services by taking user feelings into consideration has not been fully utilized. As a result, user satisfaction and service quality have declined, making it difficult to maintain competitiveness.

[1553] 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.

[1554] In this invention, the server includes a terminal means for inputting the user's desired meal style and delivery time slot, a server means for receiving the information input from the terminal means and storing it in a database, and a means for calculating the efficiency of meal production and delivery routes using a machine learning model based on the information stored by the server means, thereby enabling efficient meal delivery according to the user's wishes and improving the quality of service.

[1555] The "terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[1556] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[1557] A "machine learning model" is an algorithm used to calculate the efficiency of meal production and delivery routes based on stored information.

[1558] "Partner restaurants" are restaurants that work in conjunction with this system to receive orders and provide food.

[1559] The "estimated completion time" is the time when the food preparation specified by the partner restaurant will be completed.

[1560] An "optimal delivery route" is a route calculated to efficiently deliver food to multiple users.

[1561] A "delivery person" is a person or device whose role is to deliver food received from partner restaurants to each user.

[1562] The "delivery completion report" is a notification of delivery completion sent when the delivery person delivers the food to the user.

[1563] "Feedback" is information that allows users to provide their evaluations and impressions of the delivered food and service.

[1564] An "emotion recognition engine" is a technology for recognizing emotions from a user's voice or text.

[1565] "Artificial intelligence algorithm" refers to an algorithm that uses feedback data and an emotion recognition engine to improve the quality of services.

[1566] The present invention provides a system for providing an efficient and high-quality meal delivery service based on a user's desired eating style and delivery time slot. The system includes a terminal unit, a server unit, a machine learning model, partner restaurants, an optimal delivery route calculation unit, a delivery person unit, and an emotion recognition engine.

[1567] First, users download a dedicated application onto their smartphone or tablet and create an account. They then enter their name, address, contact information, preferred food style (e.g., Italian, Western, etc.), and desired delivery time (e.g., 12:00 every day) into the application.

[1568] Next, the server receives the account information and preference information sent by the user and stores them in a database. The user information stored in the database is stored in a "User Information" table, and the preference information is stored in a "Preference Information" table.

[1569] The server collects the stored data at a fixed time each day and uses a machine learning model to analyze the preferences of all users. This machine learning model, built in Python, groups users who have the same meal preferences at the same time and generates an efficient meal production list. For example, if multiple users want pizza at 12:00, this information is organized and they can efficiently place a bulk order.

[1570] The server then generates an order list for the required dishes for partner restaurants based on the production list created by the AI ​​system. The order list includes detailed information such as the name of the dish, the quantity, and the estimated time of completion. The order list is sent to partner restaurants via API, for example, in JSON format using a RESTful API.

[1571] Partner restaurants prepare food based on the received order list and notify the server of the estimated time the food will be ready. The server receives this notification data and calculates the optimal delivery route based on the estimated time of completion and each user's address information. The server uses map services such as Google Maps API to calculate the shortest route, making it possible to deliver to multiple users efficiently.

[1572] At the actual delivery stage, the delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to the user along the specified route. Once the food has been delivered, the delivery person uses a dedicated application to report "delivery completed," which is then updated on the server.

[1573] Finally, the user enters feedback about the food and service through the application, and an emotion recognition engine analyzes the user's emotions via voice and text, resulting in a level of satisfaction (e.g., satisfied, dissatisfied, happy, angry, etc.) being sent to the server.

[1574] The server collects this feedback and emotion data, stores it in a database, and adjusts the AI ​​algorithm. This feedback loop allows for future service quality improvements, continuously increasing user satisfaction.

[1575] Prompt Sentence Examples

[1576] "Imagine a program that takes into account the user's preferred eating style and delivery time slots to create the optimal delivery route and produce list."

[1577] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1578] System program processing flow

[1579] Step 1: Register your user information and set your preferences

[1580] Specific actions

[1581] Input: The user enters their name, address, contact information, preferred eating style, and desired delivery time slot into a dedicated application.

[1582] Output: The user's input data is sent to the server.

[1583] A user opens the app on their smartphone or tablet, taps the "Create a new account" button, enters the required information, and then taps the "Register" button, which sends the entered data to the server via the Internet.

[1584] Data processing or data calculation

[1585] The server receives the data sent by the user and stores the name, address, and contact information in a "User Information" table, and stores the preferred eating style and desired delivery time slot in a "Preference Information" table, thereby organizing individual user information.

[1586] Step 2: Collect and analyze all users' preferences

[1587] Specific actions

[1588] Input: Get all users' preferences from the database.

[1589] Output: A dataset is generated for analysis.

[1590] The server queries the "Preferences" table from the database at a fixed time every day to collect all users' preferences, which are then passed to the AI ​​system as a dataset for analysis.

[1591] Data processing or data calculation

[1592] The server analyzes the dataset using a machine learning model built in Python to calculate the production list and optimal delivery route based on the user's desired delivery time and eating style, enabling efficient production and delivery.

[1593] Step 3: Generate and send an order list to partner restaurants

[1594] Specific actions

[1595] Input: An efficient production list created by an AI system.

[1596] Output: Specific order data sent to partner restaurants.

[1597] The server analyzes the production list received from the machine learning model and generates specific order data for each partner restaurant. For example, the order list includes the name of the dish, the quantity, and the required time. The generated order list is sent to the partner restaurant in JSON format using a RESTful API.

[1598] Data processing or data calculation

[1599] The server divides and organizes the order list by store and sends it to partner restaurants in the appropriate format, allowing the restaurants to efficiently receive orders and begin preparation.

[1600] Step 4: Receive estimated time of completion and calculate optimal delivery route

[1601] Specific actions

[1602] Input: Estimated time of completion of food received from partner restaurant.

[1603] Output: Optimal delivery route to send to delivery driver.

[1604] The server receives the estimated time the food will be ready from partner restaurants via API. Based on the received data and the user's address information, it calculates the optimal delivery route. Using map services such as Google Maps API, the shortest route is generated for efficient delivery to multiple users.

[1605] Data processing or data calculation

[1606] The server calculates the optimal delivery route based on the estimated completion time and the user's address information, and prepares to send the calculation results to the delivery person's terminal.

[1607] Step 5: Delivery and reporting

[1608] Specific actions

[1609] Input: Delivery instructions sent by the server.

[1610] Output: Delivery completion report.

[1611] The delivery person's terminal receives delivery instructions sent from the server. The delivery instructions include the order of visits and details of the food to be delivered. The delivery person follows these instructions and delivers the food to each user along the specified route. When the food is delivered, the delivery person taps the "Delivery Complete" button to report to the server.

[1612] Data processing or data calculation

[1613] The server receives delivery completion reports from delivery personnel and updates the order status in the database, allowing for real-time monitoring of the progress of each delivery and efficient management.

[1614] Step 6: Feedback and Emotion Recognition Engine Analysis

[1615] Specific actions

[1616] Input: Feedback and sentiment data entered by the user through the application.

[1617] Output: Analysis results for improvement.

[1618] Users can enter their feedback about the food delivered and the service through the application, and an emotion recognition engine analyzes the user's emotions using voice and text, and these emotions, such as satisfaction, dissatisfaction, joy, and anger, are sent to the server.

[1619] Data processing or data calculation

[1620] The server stores the acquired feedback and emotion data in a database and uses artificial intelligence algorithms to improve the quality of the service. This feedback loop will further improve the delivery service for future deliveries.

[1621] keyword

[1622] Generative AI model, prompt sentence

[1623] (Application example 2)

[1624] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1625] Conventional delivery systems have struggled to provide efficient delivery services that match users' desired dining style and delivery time slots. While systems exist for incorporating user feedback, they lack sufficient mechanisms for recognizing user emotions and reflecting them in service quality improvements. This makes it difficult to guarantee a consistently high-quality user experience, and service providers lack effective means of improvement.

[1626] 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.

[1627] In this invention, the server includes: a user terminal means for inputting a user's desired eating style and delivery time slot; a server means for receiving the information input from the user terminal means and storing it in a database; a means for using AI to calculate the efficiency of meal production and delivery routes based on the information stored by the server means; a means for sending an order to a partner store based on the calculation results; a means for receiving an estimated time of completion of the food from the partner store; a means for calculating an optimal delivery route based on the estimated time of completion and the user's address information and issuing instructions to a delivery person; a means for receiving a delivery completion report from the delivery person and storing it in the server; a means for receiving feedback from the user and storing it in the server; and a means for adjusting the AI ​​algorithm based on the feedback data and emotion recognition results to improve the quality of the service. This makes it possible to continuously improve the service based on feedback that takes user emotions into consideration.

[1628] The "user terminal means" is a device that allows a user to input their desired meal style and delivery time slot.

[1629] The "server means" is a system for receiving information input from the user terminal means and storing it in a database.

[1630] "Means for calculating efficiency of meal production and delivery routes using AI" means processes and algorithms that use artificial intelligence to improve the efficiency of meal production plans and delivery routes based on stored information.

[1631] The "means for sending orders to affiliated restaurants" is a system for sending the necessary food order information to affiliated restaurants.

[1632] The "means for receiving the estimated time of completion of the dish" is a mechanism for receiving the estimated time of completion of the dish from the affiliated restaurant.

[1633] The "means for calculating the optimal delivery route and issuing instructions to the delivery person" is a system that calculates the most efficient delivery route based on the estimated time the food is completed and the user's address information, and issues instructions to the delivery person along that route.

[1634] The "means for receiving a delivery completion report and storing it on a server" is a process for receiving a delivery completion report from a delivery person and storing that information on a server.

[1635] The "means for receiving feedback and storing it on a server" is a system that receives feedback from users and stores that information on a server.

[1636] "Means for adjusting AI algorithms based on emotion recognition results and improving service quality" refers to the process of recognizing emotions contained in user feedback and adjusting AI algorithms based on the results to improve service quality.

[1637] The system of the present invention realizes efficient meal delivery based on the user's desired eating style and delivery time slot, and also recognizes the user's emotions to further improve the service.

[1638] Specifically, this system is configured as follows:

[1639] First, users download a dedicated application and create an account. They enter their name, address, contact information, preferred dining style, and desired delivery time. This information is sent from the user's device to the server, which receives it and stores it in a database.

[1640] Next, the server uses AI to calculate the efficiency of meal production and delivery routes based on the information stored in the database. This AI system calculates an efficient meal production list and delivery route based on the user's desired delivery time and eating style. For example, if multiple users want Italian food during the same delivery time, these preferences will be grouped and ordered together.

[1641] The server generates an order list of the necessary dishes for partner restaurants based on the efficient production list created by the AI ​​and sends it to each restaurant. The order list includes detailed information such as the name of the dish, quantity, and required time. Each restaurant prepares the dishes based on this list.

[1642] The server also receives the estimated time the food will be ready from partner restaurants and calculates the optimal delivery route based on the user's address information. Using AI, it generates the most efficient route for delivering to multiple users and issues instructions to the delivery staff according to that route.

[1643] The delivery person follows the delivery instructions sent from the server and delivers the food to each user along the specified route. When the delivery is complete, the delivery person reports the completion of the delivery on their device, and this information is saved on the server.

[1644] In addition, users can enter feedback about the delivered food through the application. This feedback includes emotions (e.g., satisfaction, dissatisfaction, joy, anger, etc.) that are recognized from the user's voice or text using an emotion engine. The server receives the user's feedback and emotion data and stores it in a database. The AI ​​adjusts its algorithms based on this feedback and emotion data to improve service in the future.

[1645] The hardware and software used include a smartphone, server, AI system, and emotion engine. Information entered by the user using the smartphone is sent to the server, which receives it and stores it in a database. The AI ​​system analyzes the information in the database and calculates efficient production lists and delivery routes. The emotion engine also analyzes user feedback, which the AI ​​uses to improve the service.

[1646] Specific examples

[1647] A user requests Italian food at 12:00 every day and orders pizza and garlic bread. This information is sent to the server, which generates an order list for pizza and garlic bread for partner restaurants. The restaurant prepares the food, and a delivery person delivers it according to the optimal delivery route calculated by the server. The delivery person sends a delivery completion report to the server, and the user enters feedback such as "Today's food was delicious!" Based on this feedback, the emotion engine recognizes the emotion "satisfied," which the server stores and uses to improve the quality of service.

[1648] Prompt Sentence Examples

[1649] "I was happy that the food arrived quickly, but it was a bit spicy."

[1650] This allows the system to provide more appropriate services based on feedback that takes into account the user's emotions.

[1651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1652] Step 1:

[1653] User Registration

[1654] Users create an account by entering their name, address, and contact information into a dedicated application on their smartphone. This information is sent from the user's device to the server, which receives the information and stores it in a database.

[1655] (Input) Name, address, contact information

[1656] (Output) User information stored in the database

[1657] Step 2:

[1658] User preference settings

[1659] Using the same application, users can set their preferred dining style (e.g., Italian, Western, etc.) and desired delivery time (e.g., 12:00 every day) and send this information to the server, which receives it and stores it in a database.

[1660] (Input) Meal style, delivery time

[1661] (Output) Preferences saved in the database

[1662] Step 3:

[1663] AI-powered list creation and efficiency

[1664] The server collects all user preferences from a database and uses an AI system to calculate efficient meal production lists and delivery routes. This AI system performs calculations to optimize delivery routes based on each user's preferred delivery time slot and eating style.

[1665] (Input) Desired information obtained from the database

[1666] (Output) Efficient meal production list and delivery route

[1667] Step 4:

[1668] Generate order list and send it to the store

[1669] The server generates an order list for the required dishes for partner restaurants based on the efficient production list created by the AI. The order list includes detailed information such as the name of the dish, quantity, and required time, and is sent to the partner restaurant.

[1670] (Input) Efficient meal production list

[1671] (Output) Order list sent to partner stores

[1672] Step 5:

[1673] Receive estimated time of completion of dish

[1674] The partner restaurant sends the estimated time the food will be ready to eat to the server, which receives this information and stores it in a database.

[1675] (Input) Estimated time of completion of food from partner restaurant

[1676] (Output) Estimated completion time information saved in the database

[1677] Step 6:

[1678] Calculating and directing optimal delivery routes

[1679] The server uses an AI system to calculate the optimal delivery route based on the estimated time the food is ready and the user's address information, and then gives instructions to the delivery person to follow that route.

[1680] (Input) Estimated time of completion of the dish, user's address information

[1681] (Output) Optimal delivery route and instructions for delivery personnel

[1682] Step 7:

[1683] Receiving and storing delivery completion reports

[1684] Once the delivery person has delivered the food, they use their smartphone to report that the delivery is complete. This report is sent to the server, which then stores the information in a database.

[1685] (Input) Delivery completion report

[1686] (Output) Delivery completion information saved in the database

[1687] Step 8:

[1688] User Feedback and Emotion Recognition

[1689] The user inputs feedback about the delivered food through the application. This feedback includes emotions recognized from the user's voice and text using an emotion engine. The server receives the feedback and emotion data from the user and stores them in a database.

[1690] (Input) User feedback and sentiment data

[1691] (Output) Feedback and emotion data stored in a database

[1692] Step 9:

[1693] Adjusting AI algorithms and improving service quality

[1694] The server uses the stored feedback and emotional data to adjust its AI algorithms and improve the quality of the service, making future visits even more user-friendly.

[1695] (Input) Feedback and emotion data

[1696] (Output) Adjusted AI algorithms and service improvement measures

[1697] Through the above steps, an efficient and high-quality delivery service that takes user emotions into consideration is realized.

[1698] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1699] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1700] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1701] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1702] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1703] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1704] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1705] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1706] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1707] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1708] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1709] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1710] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1711] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1712] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1713] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate ...

Claims

1. a user terminal means for inputting a user's desired meal style and delivery time slot; a server means for receiving information input from the user terminal means and storing the information in a database; A means for calculating the efficiency of meal production and delivery routes using AI based on the information stored by the server means; A means for sending an order to an affiliated store based on the calculation result; A means for receiving an estimated time when the food is ready from the affiliated store; a means for calculating an optimal delivery route based on the estimated completion time and the user's address information and issuing instructions to a delivery person; A means for receiving a delivery completion report from a delivery person and storing the report in a server; a means for receiving and storing user feedback on a server; A means for adjusting the AI ​​algorithm based on the feedback data to improve the quality of the service; A system including:

2. The system according to claim 1 , further comprising means for grouping a plurality of users who desire the same type of food based on the user's desired eating style and delivery time slot.

3. The system according to claim 1, further comprising means for generating an order list for the affiliated stores and making a production plan for each store to prepare the required dishes.

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