System

Abstract

A system is provided.SOLUTION: Means for causing a user to input a meal content via an input form displayed on a terminal for recording eating habit data of the user, means for transmitting the recorded eating habit data to a server, means for storing the transmitted data in a database and analyzing data accumulated over a certain period, means for proposing a healthy meal based on an analysis result and providing a discount coupon for the proposed meal, and means for confirming that a target coupon has been used, A system comprising: means for awarding points to a user based thereon; and means for detecting signs of stress in a user and providing special coupons to reduce stress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional diet and health management methods require users to record their eating habits and make food choices based on that record. However, in many cases, this process is tedious and difficult to continue. Resisting cravings can be stressful, leading many to give up on dieting midway. Furthermore, simply providing meal suggestions fails to maintain the user's motivation. To solve these issues, a system that allows users to maintain a healthy diet in a fun, game-like way is needed. [Means for solving the problem]

[0005] The present invention provides a system including: a means for having a user input their dietary habits via an input form displayed on a terminal for recording the user's dietary data; a means for transmitting the recorded dietary data to a server; a means for storing the transmitted data in a database and analyzing the data accumulated over a certain period of time; a means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals; a means for confirming that the coupons have been used and awarding points to the user based on that; and a means for detecting signs of stress in the user and providing special coupons for stress reduction. This system allows users to effortlessly record their dietary habits and enjoy the game-like experience of receiving healthy meal suggestions. Furthermore, the coupon and point system have the effect of increasing motivation.

[0006] "Dietary habit data" is data including details of the food the user ate, the amount ingested, the time of ingestion, the stores visited, and so on.

[0007] A "terminal" is a device such as a computer, smartphone, or tablet that is used by a user to input meal details and communicate with the server.

[0008] The "dietary input form" is an interface consisting of a group of input fields on the screen for the user to input their own dietary data.

[0009] The "transmission means" is a part of the system that has the function of transferring the recorded dietary data to a server via the Internet or the like.

[0010] A "database" is a system that runs on a server and stores and manages recorded dietary data.

[0011] The "analysis means" is a function for analyzing the user's eating habits based on the accumulated dietary data and generating appropriate meal suggestions.

[0012] The "suggestion means" is a function for notifying the user of healthy meal suggestions and discount coupons generated by the analysis means.

[0013] A "discount coupon" is a digital or physical coupon offered to a user for a discount on a particular product or service.

[0014] The "points giving means" is a function that adds points to a user's account based on the usage history when the user uses a discount coupon.

[0015] A "special coupon" is a coupon with a special benefit provided to reduce the user's stress, and can be used for specific foods or restaurants.

[0016] "Stress symptoms" are data or patterns that indicate that the user may be experiencing stress associated with ongoing dietary management. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The system of the present invention supports a user's diet and health management by recording and analyzing the user's eating habits and suggesting healthy meals. Specific embodiments will be described below.

[0039] Recording user's dietary data

[0040] 1. Display the meal entry form

[0041] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (convenience store or restaurant name).

[0042] 2. Enter your meal data

[0043] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[0044] Data transmission and storage

[0045] 1. Data transmission

[0046] The device packages the entered meal data and sends it to the server using a POST request in JSON format.

[0047] 2. Data storage

[0048] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[0049] Analysis of dietary data

[0050] 1. Periodic database queries

[0051] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[0052] 2. Data Analysis

[0053] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0054] Healthy meal suggestions and coupons

[0055] 1. Health suggestion generation

[0056] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[0057] 2. Coupon Notification

[0058] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[0059] Coupon use and points

[0060] 1. Using coupons

[0061] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[0062] 2. Check the coupon

[0063] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[0064] 3. Points Award

[0065] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[0066] Special coupon information

[0067] 1. Stress indicator detection

[0068] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[0069] 2. Selection of special coupons

[0070] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[0071] 3. Notification of coupons

[0072] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[0073] In this way, the system of the present invention records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which the user can enjoyably and continuously manage their health, thereby eliminating the stress and difficulty of continuing with conventional methods.

[0074] The processing flow will be explained below.

[0075] Recording user's dietary data

[0076] Step 1:

[0077] The terminal displays a meal entry form.

[0078] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[0079] Step 2:

[0080] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[0081] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[0082] Step 3:

[0083] The device packages the entered meal data in JSON format and sends it to the server.

[0084] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[0085] Data transmission and storage

[0086] Step 4:

[0087] The server analyzes the received data and stores it in a database.

[0088] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[0089] Analysis of dietary data

[0090] Step 5:

[0091] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[0092] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[0093] Step 6:

[0094] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[0095] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[0096] Healthy meal suggestions and coupons

[0097] Step 7:

[0098] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[0099] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[0100] Step 8:

[0101] The server sends the generated proposals and coupon information in JSON format to the terminal.

[0102] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[0103] Coupon use and points

[0104] Step 9:

[0105] The user presents the coupon at the store to use it.

[0106] Specific action: Present the barcode or coupon code in the app at the store.

[0107] Step 10:

[0108] The terminal (store terminal) sends the coupon code to the server and checks its validity.

[0109] Specific behavior: Enter a coupon code and send a validation request to the server.

[0110] Step 11:

[0111] The server checks the validity of the coupon and stores the usage history in a database.

[0112] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[0113] Step 12:

[0114] The server verifies that the coupon has been used and credits the points to the user's account.

[0115] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[0116] Special coupon information

[0117] Step 13:

[0118] The server analyzes the user's dietary data and detects signs of stress.

[0119] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[0120] Step 14:

[0121] The server selects special coupons for stress reduction.

[0122] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[0123] Step 15:

[0124] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[0125] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[0126] Through the above processing steps, the system of the present invention can support the user's eating habits in an enjoyable and healthy way.

[0127] Example 1

[0128] 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."

[0129] In today's busy lifestyles, it is difficult for users to maintain a healthy diet. In particular, recording and managing dietary habits, choosing healthy meals, and maintaining motivation are major challenges. Increased stress can also lead to unhealthy eating habits. Conventional methods are difficult to efficiently solve these challenges, and effective means for users to continuously manage their health are needed.

[0130] 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.

[0131] In this invention, the server includes: means for having the user input dietary details via an input form displayed on a terminal for recording the user's dietary data; means for transmitting the recorded dietary data to the server; means for storing the transmitted data in a database and analyzing the data accumulated over a certain period of time; means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals; means for confirming that the coupons have been used and awarding points to the user based on that; means for detecting signs of stress in the user and providing special coupons for stress reduction; means for having the user input dietary data including the type, content, amount of intake, and store information of the meals; and means for the server to confirm the validity of the coupons. This makes it easier for users to manage their health in an enjoyable and continuous manner, and also reduces unhealthy food choices due to stress, enabling overall health maintenance.

[0132] A "terminal" is a communication device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0133] An "input form" refers to an interface that is displayed on the terminal screen and allows the user to input data such as meal contents.

[0134] "Dietary data" refers to data including information such as the type, content, and amount of food consumed by the user, as well as store information.

[0135] "Server" refers to a central processing unit for receiving, storing, and analyzing data sent by users.

[0136] "Database" refers to an information management system that manages dietary data stored on a server and accumulates it over a certain period of time.

[0137] "Analysis" refers to the process of evaluating the dietary data stored in the database and extracting information such as the user's meal frequency, preferences, and tendencies toward the stores they frequent.

[0138] "Health suggestions" refer to content that recommends healthy eating to the user based on the analysis results.

[0139] "Discount Coupon" refers to an electronic or paper coupon that a user can use to receive a discount.

[0140] "Points" refer to incentives given to users when they use coupons, and are electronic points that can be later exchanged for rewards.

[0141] A "special complimentary coupon" refers to a coupon for receiving special discounts or benefits provided to reduce the user's stress.

[0142] "Validating a coupon" refers to the process by which the server verifies whether a coupon submitted by a user is valid.

[0143] The system of the present invention supports a user's diet and health management by recording and analyzing the user's dietary data and suggesting healthy meals. Specific embodiments will be described below.

[0144] Recording user's dietary data

[0145] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter the details of their meal. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount consumed, and store information (the name of the convenience store or restaurant). The user enters the details of the meal they actually ate and presses the "Submit" button to send the data. For example, if the user ate "chicken salad" for lunch, they enter that information and submit it.

[0146] Data transmission and storage

[0147] The device packages the entered meal data and sends it to the server. This sends JSON format data via a POST request. The server analyzes the received data and stores it in a database. The stored data includes the user ID, meal content, intake amount, time, store information, etc.

[0148] Analysis of dietary data

[0149] The server periodically queries the database for food data and retrieves data accumulated over a certain period of time. For example, it retrieves food records from the past month. The server analyzes the retrieved data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0150] Healthy meal suggestions and coupons

[0151] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it generates a suggestion such as "User A can use a 20 yen discount coupon for a salad today." The server sends the generated suggestion and coupon information to the device in JSON format. The device receives this and displays it to the user as a list of available coupons.

[0152] Coupon use and points

[0153] The user presents and uses a coupon at a participating store. For example, a user uses a "20 yen off salad coupon" at a convenience store. The store's cash register, QR code reader, or other terminal sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server adds points to the user's account. For example, a user who uses a salad coupon receives 5 points.

[0154] Special coupon information

[0155] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. The server selects special coupons to reduce stress. For example, if the user has a sweet tooth, it selects a "50% off dessert coupon." The server sends the selected special coupon in JSON format to the device, which notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain motivation.

[0156] Examples of concrete examples and prompts

[0157] Specific examples

[0158] User A ate "toast and eggs" for breakfast, "chicken salad" for lunch, and "grilled fish" for dinner. The server analyzes this and recognizes the pattern that "he eats salad at least three times a week." As a result, the server offers a 20 yen discount coupon for salad today to help him enjoy it even more.

[0159] Prompt Sentence Examples

[0160] "Please explain the process of providing healthy meal suggestions and a 20 yen discount coupon for salad in JSON format to User A, who eats salad at least three times a week."

[0161] In this way, this system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Step 1:

[0164] The terminal displays an input form in which the user can input the meal details.

[0165] Input: User's diet, type of food, intake amount, store information, etc.

[0166] Output: Meal data entered by the user

[0167] Specific operation: An input form is displayed on the screen of a smartphone or PC, and the user enters the meal details in the designated fields.

[0168] Step 2:

[0169] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data.

[0170] Input: User-entered meal data

[0171] Output: Meal data when the send button was pressed

[0172] Specific actions: The user enters something like "toast and eggs" for breakfast and presses the "Submit" button.

[0173] Step 3:

[0174] The terminal packages the input meal data and sends it to the server.

[0175] Input: Meal data when the submit button is pressed

[0176] Output: Data packaged in JSON format, request sent to server

[0177] Specific operation: The device converts the meal data into JSON format and sends a POST request to the server.

[0178] Step 4:

[0179] The server analyzes the received data and stores it in a database.

[0180] Input: JSON formatted meal data

[0181] Output: Saved data (user ID, meal contents, intake amount, time, store information, etc.)

[0182] Specific operation: The server parses the received data, executes SQL queries and saves them to the database.

[0183] Step 5:

[0184] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[0185] Input: Query from the database

[0186] Output: Food record for the past month

[0187] Specific operation: Every day at 2:00 AM, the database is accessed and an SQL query such as "SELECT FROM Meal Record WHERE Date >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH)" is executed.

[0188] Step 6:

[0189] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[0190] Input: Food record for the past month

[0191] Output: Analysis results (e.g., "User A eats salad at least three times a week" and "User B eats fast food at least twice a week")

[0192] What it does: Analyzes data and extracts patterns and trends using statistical methods and machine learning models.

[0193] Step 7:

[0194] The server then suggests healthy meals suitable for the user based on the analysis results.

[0195] Input: Analysis results

[0196] Output: Suggested healthy meals and corresponding discount coupons

[0197] Specific operation: Generate specific suggestions such as "User A can use a coupon for 20 yen off a salad today" and create coupon data.

[0198] Step 8:

[0199] The server sends the generated proposals and coupon information in JSON format to the terminal.

[0200] Input: Suggested healthy meals and corresponding discount coupons

[0201] Output: The list of offers and coupons displayed on the terminal.

[0202] Specific operation: The offer content and coupon information are converted into JSON format and sent to the device, which receives it and displays it to the user.

[0203] Step 9:

[0204] The user presents and uses the coupon at the target store.

[0205] Input: Coupon information displayed on the device

[0206] Output: Record of coupon usage at store

[0207] Specific operation: The user presents the "20 yen off salad coupon" and uses it at the store.

[0208] Step 10:

[0209] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[0210] Input: Coupon code, User ID

[0211] Output: Coupon validity check result

[0212] Specific operation: The device sends the coupon code and user ID to the server and checks the expiration date and whether it has been used.

[0213] Step 11:

[0214] The server will credit the points to the user's account after verifying the validity of the coupon.

[0215] Input: Coupon validity check result

[0216] Output: Points awarded to the user account

[0217] Specific operation: The server checks the validity of the coupon and sends the data to the points system to add points to the user's account.

[0218] Step 12:

[0219] The server analyzes the user's dietary data and detects signs of stress.

[0220] Input: Past meal data

[0221] Output: Stress symptom analysis results

[0222] Specific behavior: Analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods to detect signs of stress.

[0223] Step 13:

[0224] The server selects special coupons for stress reduction.

[0225] Input: Stress symptom analysis results

[0226] Output: Selected special coupon (e.g. "50% off dessert coupon")

[0227] Specific operation: Based on the analysis results, coupons that are likely to be preferred by the user are selected.

[0228] Step 14:

[0229] The server sends the selected special discount coupon in JSON format to the terminal, and the terminal notifies the user.

[0230] Input: Selected special coupon

[0231] Output: Coupon notification on the terminal

[0232] Specific operation: The special discount coupon information is converted into JSON format and sent to the terminal, which then notifies the user.

[0233] This system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment where users can enjoy continuous health management.

[0234] (Application example 1)

[0235] 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."

[0236] In today's world, maintaining a healthy diet is a major challenge, especially for urban residents and young people who lead busy lives. While there are a wide variety of food options available, there are few systems that can help users choose appropriate meals or nutritionally balanced meals. There is also a need for a system that can increase users' interest and motivation by providing relevant content and discount coupons in a timely manner.

[0237] 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.

[0238] In this invention, the server includes a means for analyzing the accumulated data and delivering content related to healthy meal suggestions, a means for delivering coupons for specific ingredients and dishes, and a means for detecting signs of stress in the user. This allows users to record their own eating habits and receive personalized healthy meal suggestions and useful information based on the records. Furthermore, by appropriately using coupons, users can enjoy economic benefits and promote healthy choices.

[0239] A "terminal" is an electronic device that a user uses to input dietary data and receive meal suggestions and coupons.

[0240] The "input form" is a data input screen displayed on the terminal for the user to record the details of their meal.

[0241] The "server" is a central management device that receives, stores, and analyzes the user's dietary data and generates suggestions and notifications.

[0242] "Database" refers to a data repository within the system that stores dietary data submitted by users.

[0243] The "analysis results" are information about the user's eating habits and health condition obtained by the server analyzing data acquired from the database.

[0244] "Healthy meal suggestions" are specific meal suggestions to the user to maintain good health based on the analysis results of the server.

[0245] "Discount Coupon" means an electronic coupon containing a discount offer that can be used when purchasing a suggested meal or ingredient.

[0246] "Points" are incentives given to users for using coupons or taking specific actions.

[0247] "Signs of stress" are possible signs of mental stress detected from fluctuations in the user's dietary data or changes in specific patterns.

[0248] A "special coupon" is a coupon that provides discounts and special benefits to reduce the user's stress.

[0249] "Related content" includes videos, articles, recipes, and other informational materials related to healthy eating.

[0250] The system of the present invention assists the user in managing his or her health by recording and analyzing the user's eating habits and suggesting healthy meals. Specific embodiments will be described below.

[0251] Recording user's dietary data

[0252] 1. Display the meal entry form

[0253] Users record their meal details through an application installed on a device such as a smartphone. A meal entry form is displayed on the device, and users enter information such as the type of meal (breakfast, lunch, dinner, snack, etc.), meal details, intake amount, and restaurant information.

[0254] 2. Enter your meal data

[0255] The user enters the details of their meal into the input form and presses the "Submit" button to send the data. For example, if a user ate "chicken salad" for lunch, they enter that information and submit it.

[0256] Data transmission and storage

[0257] 1. Data transmission

[0258] The device packages the entered meal data in JSON format and sends it to the server via an HTTP POST request.

[0259] 2. Data storage

[0260] The server analyzes the received data and stores it in a database, including the user ID, meal details, intake amount, time, and store information.

[0261] Analysis of dietary data

[0262] 1. Periodic database queries

[0263] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[0264] 2. Data Analysis

[0265] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0266] Healthy eating suggestions and content distribution

[0267] 1. Health suggestion generation

[0268] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[0269] 2. Content Delivery

[0270] Deliver videos and articles related to the suggestions. For example, if a user likes salads, provide new salad recipe videos and health articles.

[0271] Offering coupons and awarding points

[0272] 1. Coupon Notification

[0273] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[0274] 2. Using coupons

[0275] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[0276] 3. Points Award

[0277] After the server verifies the validity of the coupon, it will add points to the user's account. For example, if a user uses a salad coupon, they will receive 5 points.

[0278] Special coupon information

[0279] 1. Stress indicator detection

[0280] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[0281] 2. Selection of special coupons

[0282] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[0283] 3. Notification of coupons

[0284] The server sends the selected special coupon in JSON format to the device, and the device notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[0285] This provides an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[0286] Hardware and Software Used

[0287] Smartphone: iOS or Android device

[0288] Server: A remote server (AWS, Google Cloud, etc.) that analyzes data and distributes coupons

[0289] Database: MySQL or PostgreSQL

[0290] Communication protocol: HTTP / HTTPS, JSON format data

[0291] Examples of prompt statements

[0292] Example prompts to input to a generative AI model:

[0293] A user inputs "I had a salad for lunch" into a smartphone app. Based on this information, the server should perform the following analysis and generate healthy meal suggestions and coupons accordingly.

[0294] Input data:

[0295] User ID: 12345

[0296] Meal: Chicken salad

[0297] Serving size: 200g

[0298] Store name: Salad Stop

[0299] Example output:

[0300] Healthy Eating Suggestion: This Week's New Salad Recipe

[0301] Proposal content: Video link, related article URL

[0302] Coupon: 20 yen off salad coupon

[0303] In this way, the system of the present invention records and analyzes the user's dietary habits and delivers healthy meal suggestions and related content, making it easier for users to make healthy choices and manage their health sustainably.

[0304] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0305] Step 1:

[0306] The user enters the details of their meal into the device. The user opens the application installed on their smartphone and enters the type of meal (e.g., breakfast, lunch, dinner, snack), meal details, intake amount, and store information into the meal entry form. Once the input is complete, the user presses the "Submit" button. The input data includes the user ID, meal details, intake amount, and store information.

[0307] Input: Meal details, intake amount, store information

[0308] Output: Input food data

[0309] Step 2:

[0310] The device packages the entered food data in JSON format and sends it to the server via an HTTP POST request, where it is processed in real time and delivered to the server.

[0311] Input: JSON formatted meal data

[0312] Output: Data sent to the server

[0313] Step 3:

[0314] The server analyzes the received meal data and saves it in a database. The server converts the received data into a specific format and stores the user ID, meal details, intake amount, store information, etc. in the database.

[0315] Input: Data sent to the server

[0316] Output: Data stored in the database

[0317] Step 4:

[0318] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[0319] Input: Query dietary data from the database

[0320] Output: Food record for the past month

[0321] Step 5:

[0322] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0323] Input: Food record for the past month

[0324] Output: User's meal frequency, preferences, and store preferences

[0325] Step 6:

[0326] The server generates healthy eating suggestions based on the analysis results and delivers videos and articles related to the suggestions. For example, if a user likes salads, it will provide new salad recipe videos and health-related articles.

[0327] Input: User's meal frequency, preferences, and store preferences

[0328] Output: Healthy eating suggestions and related content

[0329] Step 7:

[0330] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[0331] Input: Healthy eating suggestions and coupon information

[0332] Output: Coupon sent to the terminal

[0333] Step 8:

[0334] The user presents and uses the coupon at a participating store. For example, they use a "20 yen off salad coupon" at a convenience store. The store's cash register or QR code reader sends the coupon code to the server and verifies its validity.

[0335] Input: Coupon presented at the participating store

[0336] Output: Coupon validity check

[0337] Step 9:

[0338] After the server verifies the validity of the coupon, it will add points to the user's account. For example, if a user uses a salad coupon, they will receive 5 points.

[0339] Input: Check coupon validity

[0340] Output: Points awarded to user account

[0341] Step 10:

[0342] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. Based on the analysis results, it selects special coupons for stress reduction and sends them to the device in JSON format.

[0343] Input: Analysis results of dietary data

[0344] Output: Select and send special coupons

[0345] Step 11:

[0346] The terminal displays the special coupon received from the server and notifies the user. The user can use the special coupon to enjoy an appropriate cheat day and maintain motivation.

[0347] Input: Special coupon information

[0348] Output: Special coupon notification displayed on the terminal

[0349] In this way, the system of the invention records and analyzes the user's eating habits, and delivers healthy meal suggestions and related content. Users can also use coupons and points to enjoyably manage their health on an ongoing basis.

[0350] 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.

[0351] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and suggesting healthy meals, and by combining it with an emotion engine, it can more effectively maintain the motivation of users. Specific embodiments are described below.

[0352] Recording user's dietary data

[0353] 1. Display the meal entry form

[0354] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (convenience store or restaurant name).

[0355] 2. Enter your meal data

[0356] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[0357] Data transmission and storage

[0358] 1. Data transmission

[0359] The device packages the entered meal data in JSON format and sends it to the server using a POST request.

[0360] 2. Data storage

[0361] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[0362] Analysis of dietary data

[0363] 1. Periodic database queries

[0364] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[0365] 2. Data Analysis

[0366] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0367] Healthy meal suggestions and coupons

[0368] 1. Health suggestion generation

[0369] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[0370] 2. Coupon Notification

[0371] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[0372] Coupon use and points

[0373] 1. Using coupons

[0374] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[0375] 2. Check the coupon

[0376] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[0377] 3. Points Award

[0378] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[0379] Special coupon information

[0380] 1. Stress indicator detection

[0381] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[0382] 2. Selection of special coupons

[0383] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[0384] 3. Notification of coupons

[0385] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[0386] Implementing the Emotion Engine

[0387] 1. Entering emotion data

[0388] Users can input their emotional state within the app, choosing from options such as "I'm feeling stressed today" or "I'm in a happy mood."

[0389] 2. Emotion Data Analysis

[0390] The server analyzes the input emotion data and evaluates the user's current emotional state.

[0391] 3. Emotion-based recommendation optimization

[0392] The server optimizes healthy meal suggestions and special coupons based on the user's emotional state: for example, if the user is feeling stressed, it will suggest more relaxing meals and coupons.

[0393] 4. Emotion-based notifications

[0394] The server then sends the optimized recommendations to the device, which then notifies the user. For example, a user who is feeling stressed can be offered a discount coupon for relaxing herbal tea.

[0395] In this way, the system of the present invention records and analyzes the user's eating habits, and by suggesting healthy meals and providing coupons, provides an environment where users can enjoy managing their health. In addition, by combining it with an emotion engine, it is possible to make more personalized suggestions according to the user's emotional state, contributing to maintaining motivation and reducing stress.

[0396] The processing flow will be explained below.

[0397] Processing of systems with emotion engines

[0398] Recording user's dietary data

[0399] Step 1:

[0400] The terminal displays a meal entry form.

[0401] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[0402] Step 2:

[0403] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[0404] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[0405] Step 3:

[0406] The device packages the entered meal data in JSON format and sends it to the server.

[0407] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[0408] Data transmission and storage

[0409] Step 4:

[0410] The server analyzes the received data and stores it in a database.

[0411] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[0412] Entering emotion data

[0413] Step 5:

[0414] The terminal displays an emotion input form.

[0415] Specific behavior: Display options on the device screen to select an emotional state (e.g., "I feel stressed today" or "I feel happy").

[0416] Step 6:

[0417] The user inputs their emotional state and presses the "send" button.

[0418] Specific operation: The user selects today's emotional state and presses the send button.

[0419] Step 7:

[0420] The device packages the input emotion data in JSON format and sends it to the server.

[0421] Specific operation: Emotion data is packaged in JSON format and sent to the server via a POST request.

[0422] Emotional Data Analysis

[0423] Step 8:

[0424] The server analyzes the received emotional data and evaluates the current emotional state.

[0425] Specific operation: Emotional data is passed through an analysis algorithm to assess whether the user is stressed or relaxed.

[0426] Analysis of dietary data

[0427] Step 9:

[0428] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[0429] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[0430] Step 10:

[0431] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[0432] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[0433] Healthy meal suggestions and coupons

[0434] Step 11:

[0435] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[0436] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[0437] Step 12:

[0438] The server optimizes the suggestions taking into account the emotional state.

[0439] What it does: If the user is feeling stressed, it will adjust its suggestions to suggest relaxing meals or coupons.

[0440] Step 13:

[0441] The server sends the generated proposals and coupon information in JSON format to the terminal.

[0442] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[0443] Coupon use and points

[0444] Step 14:

[0445] The user presents the coupon at the store to use it.

[0446] Specific action: Present the barcode or coupon code in the app at the store.

[0447] Step 15:

[0448] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[0449] Specific behavior: Enter a coupon code and send a validation request to the server.

[0450] Step 16:

[0451] The server checks the validity of the coupon and stores the usage history in a database.

[0452] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[0453] Step 17:

[0454] The server verifies that the coupon has been used and credits the points to the user's account.

[0455] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[0456] Special coupon information

[0457] Step 18:

[0458] The server analyzes the user's dietary data and detects signs of stress.

[0459] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[0460] Step 19:

[0461] The server selects special coupons for stress reduction.

[0462] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[0463] Step 20:

[0464] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[0465] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[0466] In this way, by combining an emotion engine, the system of the present invention supports the user's enjoyable and healthy eating habits and can make more personalized suggestions based on the user's emotional state, contributing to maintaining motivation and reducing stress.

[0467] Example 2

[0468] 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."

[0469] Conventional health management systems were able to collect and analyze users' dietary data, but they struggled to provide personalized recommendations that took into account the user's emotional state. This made it difficult to maintain user motivation over the long term, making effective health management difficult to achieve. Furthermore, they lacked a mechanism for early detection of stress symptoms and the provision of appropriate coupons and recommendations.

[0470] 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.

[0471] In this invention, the server is configured to record the user's dietary habit data.

[0472] A means for allowing a user to input meal details via an input form displayed on a terminal;

[0473] means for transmitting the recorded dietary habit data to a server;

[0474] A means for storing the transmitted data in a database and analyzing the data accumulated over a period of time;

[0475] A means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals;

[0476] a means for confirming that the coupon has been used and awarding points to the user based on the confirmation;

[0477] means for inputting the user's emotional state and transmitting the emotional data to a server;

[0478] A means of analyzing emotional data and optimizing healthy meal suggestions and special coupons based on the analysis results;

[0479] A means for notifying the user of sentiment-based optimized offers and special coupons;

[0480] This will enable a comprehensive analysis of the user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons. This will help maintain user motivation and reduce stress, enabling long-term health management.

[0481] "User" refers to an individual who uses this system to input dietary data and emotional data.

[0482] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[0483] "Input form" refers to an on-screen form that allows the user to input dietary details and emotional state.

[0484] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[0485] "Server" refers to the computer on the system that receives transmitted data and stores and analyzes the data.

[0486] "Database" refers to a storage device for storing dietary data and emotional data.

[0487] "Analysis results" refers to information obtained by the server analyzing the accumulated data.

[0488] "Health Suggestions" refers to suggestions for healthy eating and behaviors provided to users based on the analysis results.

[0489] "Coupon" refers to an electronic voucher that offers discounts or special offers on specific meals or products.

[0490] "Points" refers to rewards credited to a user's account based on the use of a coupon.

[0491] "Emotional state" refers to the psychological state such as stress or mood felt by the user.

[0492] "Emotional data" refers to information about an emotional state entered by a user.

[0493] "Discount coupon" refers to an electronic coupon that comes with special discounts or benefits.

[0494] "Notification" refers to a message or alert sent from the server to the device.

[0495] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and providing appropriate healthy meal suggestions and coupons. Furthermore, by combining it with an emotion engine, it is possible to more effectively maintain the user's motivation. Specific embodiments are described below.

[0496] Hardware and Software Use

[0497] Device: Use a device such as a smartphone, tablet, or PC.

[0498] Server: A computer on a network used to receive, analyze, and store data.

[0499] Database: Uses a storage device to store dietary and emotional data.

[0500] Data processing and calculation

[0501] Data collection:

[0502] The terminal collects user input data and receives dietary details and emotional state in an input form.

[0503] The user enters their dietary habits and emotional state into an input form and submits it.

[0504] Data transmission and storage:

[0505] The terminal sends the data entered by the user to the server in JSON format.

[0506] The server analyzes the received data and stores it in a database, including the user ID, meal content, intake amount, time, store information, emotional state, etc.

[0507] Data Analysis:

[0508] The server periodically queries and analyzes the data stored in the database, including the user's eating habits, preferences, restaurant preferences, and emotional state.

[0509] For example, if user A tends to eat salad more than three times a week, this pattern will be recognized.

[0510] Dining suggestions and coupons:

[0511] The server generates healthy meal suggestions based on the analysis results and provides discount coupons for the suggested meals.

[0512] The server sends the generated offer and coupon information to the terminal, which receives it and notifies the user.

[0513] Coupon use and points awarded:

[0514] The user presents the coupon and uses it at the target store.

[0515] The terminal (store device) sends the coupon code to the server and checks its validity.

[0516] The server awards points to the user based on valid coupons.

[0517] Detecting stress indicators and providing special coupons:

[0518] The server analyzes the user's dietary and emotional data to detect stress indicators. For example, if the user's intake of high-calorie foods increases, the server determines that the user is feeling stressed.

[0519] The server sends the selected special coupon to the terminal and notifies the user, helping them enjoy their cheat day.

[0520] Specific prompt examples

[0521] Food data entry prompt:

[0522] What did you have for lunch today? Please enter the exact menu item and portion size.

[0523] Example: Grilled Chicken Salad, 200g

[0524] Emotional state prompt:

[0525] Please tell us how you're feeling right now. Please choose from the options below.

[0526] 1. Fun

[0527] 2. Normal

[0528] 3. You feel stressed

[0529] 4. Feeling depressed

[0530] Example

[0531] For example, if a user has "grilled chicken salad" for lunch, they enter and submit the details as "grilled chicken salad, 200g, salad shop ABC." The server analyzes the data and recognizes the user's eating habits. If the user tends to eat salad more than three times a week, the server will suggest a "50 yen discount coupon" for the next salad purchase and notify the user's device.

[0532] This will enable comprehensive analysis of a user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons, which is expected to help maintain user motivation and reduce stress, enabling long-term health management.

[0533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0534] Step 1:

[0535] Display of meal entry form

[0536] The terminal displays an input form that allows the user to enter meal details. The form includes radio buttons such as "Breakfast," "Lunch," "Dinner," and "Snack," as well as text input fields. The input form is displayed when the program is started or when a specific button is pressed.

[0537] Input: Launching a program or pressing a button

[0538] Output: Display of meal entry form

[0539] Step 2:

[0540] Entering meal data

[0541] The user inputs the meal details using the input form on the terminal and presses the "Submit" button to send the data. For example, the user inputs "Grilled chicken salad, 200g, salad shop ABC" for lunch.

[0542] Input: The meal details entered by the user in the input form

[0543] Output: Pressing the send button puts the data into a waiting state for transmission.

[0544] Step 3:

[0545] Data transmission

[0546] The device packages the meal data entered by the user into JSON format and sends it to the server using an HTTP POST request.

[0547] Input: User-entered meal data (e.g., Grilled Chicken Salad, 200g, Salad Shop ABC)

[0548] Output: Meal data is sent to the server in JSON format

[0549] Step 4:

[0550] Data storage

[0551] The server analyzes the received meal data and stores it in a database, including the user ID, meal content, intake amount, meal time, and restaurant information.

[0552] Input: Meal data sent in JSON format

[0553] Output: The data is saved correctly in the database

[0554] Step 5:

[0555] Periodic database queries

[0556] The server periodically queries the database to obtain meal data for a certain period of time, for example, to obtain meal data for the past month.

[0557] Input: A trigger that runs a periodic query from the database

[0558] Output: Meal data for a certain period of time is obtained.

[0559] Step 6:

[0560] Data analysis

[0561] The server analyzes the food data retrieved from the database to extract the user's meal frequency, preferences, and restaurant preferences. This analysis uses standard statistical analysis methods and machine learning algorithms. For example, the server can determine that User A eats salad at least three times a week.

[0562] Input: Acquired dietary data over a period of time

[0563] Output: Analysis results of each user's meal frequency, preferences, and store usage trends

[0564] Step 7:

[0565] Health suggestion generation

[0566] The server generates healthy meal suggestions based on the analysis results. For example, it might suggest to User A, "A 50 yen discount coupon for the next salad purchase." The suggestions are generated using pre-defined rules and machine learning models.

[0567] Input: Analysis results of dietary data

[0568] Output: Health recommendations and coupons tailored to each individual user

[0569] Step 8:

[0570] Coupon Notifications

[0571] The server sends the generated offers and coupon information to the device, which receives it in JSON format and displays a notification to the user.

[0572] Input: Offer and coupon information

[0573] Output: Offer and coupon information is sent to the terminal and the user is notified

[0574] Step 9:

[0575] Use coupon

[0576] The user presents the coupon at the store. For example, the user uses a "20 yen off salad coupon" at a convenience store. The coupon is presented using a QR code or barcode.

[0577] Input: Present coupon

[0578] Output: A confirmation of coupon redemption is displayed to the user

[0579] Step 10:

[0580] Check coupons

[0581] The terminal (such as a store cash register or QR code reader) sends the coupon code to a server to verify its validity, which typically involves checking a database of coupons.

[0582] Enter: Coupon Code

[0583] Output: Coupon code validation result

[0584] Step 11:

[0585] Points awarded

[0586] After verifying the validity of the coupon, the server adds points to the user's account. For example, if a user uses a "salad coupon," 5 points are added. The point addition involves updating the user's account.

[0587] Input: Coupon validity check result

[0588] Output: Points are credited to the user account.

[0589] Step 12:

[0590] Input of user emotion data

[0591] The user inputs their emotional state using an input form on the device. For example, they answer "yes" to the question "Are you feeling stressed today?"

[0592] Input: User's emotional state input

[0593] Output: Emotional state data ready to send

[0594] Step 13:

[0595] Sending emotional data

[0596] The device sends the emotion data entered by the user to the server in JSON format.

[0597] Input: Emotion data entered by the user

[0598] Output: Emotion data is sent to the server in JSON format.

[0599] Step 14:

[0600] Emotional Data Analysis

[0601] The server analyzes the received emotion data and evaluates the user's current emotional state. The analysis uses natural language processing and machine learning models. For example, if a user inputs "stress," the server recognizes the user as being in a high-stress state.

[0602] Input: Received emotion data

[0603] Output: Emotion evaluation result

[0604] Step 15:

[0605] Emotion-based suggestion optimization

[0606] The server optimizes health suggestions and special coupons based on emotional data. For example, if a user is under high stress, it will suggest relaxing suggestions. Machine learning algorithms are used for optimization.

[0607] Input: Emotion evaluation result

[0608] Output: Optimized health recommendations and coupon information

[0609] Step 16:

[0610] Emotion-based notifications

[0611] The server transmits the optimized proposal and the special coupon to the terminal, which notifies the user of the same.

[0612] Input: Optimized health recommendations and coupon information

[0613] Output: Offer and coupon information is sent to the terminal and notified to the user

[0614] (Application example 2)

[0615] 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."

[0616] Conventional dietary management systems can record a user's dietary data and suggest healthy meals, but they have difficulty making personalized suggestions that take into account the user's emotional state. Furthermore, there is a lack of effective health management methods, particularly for specific occupational environments such as factory workers. This has led to issues such as not being able to adequately maintain user motivation or reduce stress.

[0617] 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.

[0618] In this invention, the server includes a means for inputting and analyzing the user's emotional state and optimizing healthy meal suggestions and special coupons based on the input, a means for combining the analysis results with the emotional state to make health management suggestions for factory workers, and a means for analyzing the accumulated data, thereby enabling personalized health management suggestions according to the user's emotional state and work environment.

[0619] "User" refers to an individual or worker who uses the system of the present invention.

[0620] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[0621] "Input form" refers to the screen displayed on the terminal for the user to input their dietary details and emotional state.

[0622] "Device" means a smartphone, tablet, personal computer, or other digital device.

[0623] "Server" refers to a central system for receiving, storing, and analyzing data submitted by users.

[0624] "Database" refers to a storage system for storing accumulated dietary data and analysis results.

[0625] "Healthy meal suggestions" refer to meal suggestions that are effective for maintaining health and dieting, based on the user's dietary data and emotional state.

[0626] "Discount Coupon" means a coupon for applying a discount to a proposed meal.

[0627] "Special coupons" refer to special coupons provided to users to reduce stress and increase motivation.

[0628] "Emotional state" refers to the emotional or mental state a user is experiencing.

[0629] "Personalized suggestions" refers to health suggestions or coupon offers that are tailored based on an individual user's specific situation or emotional state.

[0630] "Factory workers" refers to people who work in factories and who require health care in a specific occupational environment.

[0631] The system of the present invention supports users in dieting and health management by recording and analyzing their dietary habits and suggesting healthy meals. Furthermore, by combining it with an emotion engine, it is possible to maintain the user's motivation.

[0632] Hardware and Software Configuration

[0633] The system is configured so that users can input data using devices such as smartphones, tablets, and personal computers, and the server analyzes the data. Communication between the server and devices and data storage are performed using an internet connection and a database management system.

[0634] Recording user's dietary data

[0635] The user inputs the details of their meal and their emotional state via an input form displayed on the device. The input form includes fields for the type of meal (breakfast, lunch, dinner, snack, etc.), the details of the meal, the amount of intake, and store information (such as the name of the restaurant or convenience store). The user can also input their emotional state. For example, options such as "I feel stressed today" and "I'm in a happy mood" are provided.

[0636] Data transmission and storage

[0637] The device converts the entered dietary data and emotional state into JSON format and sends it to the server using a POST request. The server then stores the received data in a database. This data includes the user ID, meal content, intake amount, time, store information, emotional state, etc.

[0638] Analysis of dietary data

[0639] The server periodically queries the database for dietary data and retrieves data accumulated over a certain period of time. For example, it retrieves a meal record for the past month. Based on this retrieved data, it extracts the user's meal frequency, preferences, and restaurant preferences. It also analyzes the user's emotional state and detects signs of stress.

[0640] Healthy meal suggestions and coupons

[0641] The server then suggests healthy meals suitable for the user based on the analysis results and generates discount coupons for the suggested meals. The generated coupon information is sent to the device in JSON format and displayed on the device as a coupon list.

[0642] Coupon use and points

[0643] The user presents and uses the coupon at the applicable store. The terminal (store cash register or QR code reader) sends the coupon code to the server to check its validity. After the server checks the validity of the coupon, points are added to the user's account.

[0644] Optimizing recommendations with an emotion engine

[0645] The server analyzes the input emotional data and optimizes healthy meal suggestions and special coupons based on the user's current emotional state. For example, if the user is feeling stressed, it will suggest relaxing meals and coupons.

[0646] Specific examples

[0647] For example, if a user with user ID 123 ate "chicken salad" for lunch and entered their emotional state as "feeling stressed," the server would generate healthy meal suggestions and relaxation coupons based on this data. An example of a prompt sentence entered in the system is as follows:

[0648] User ID: 123

[0649] Meal Type: Lunch

[0650] Meal: Chicken salad

[0651] Quantity: 1

[0652] Store name: Convenience store A

[0653] Emotional state: Stressed

[0654] Based on this prompt, appropriate suggestions can be made to the user.

[0655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0656] Step 1:

[0657] Data Entry

[0658] The user inputs information such as meal details, intake amount, restaurant information, and emotional state into an input form on the device. The input data includes specific information such as "Lunch," "Chicken Salad," "Quantity: 1," "Convenience Store A," and "Feeling Stressed." This information is transmitted from the device as dietary and emotional data.

[0659] Step 2:

[0660] Data transmission

[0661] The device converts the entered dietary and emotional data into JSON format and sends it to the server as a POST request. Examples of the data sent include the following JSON format data:

[0662] json

[0663] {

[0664] "user_id": "123",

[0665] "meal_type": "Lunch",

[0666] "meal_content": "Chicken Salad",

[0667] "quantity": "1",

[0668] "store_name": "Convenience store A",

[0669] "emotion_state": "stressed"

[0670] }

[0671] The server receives this and proceeds to the next step.

[0672] Step 3:

[0673] Data storage

[0674] The server parses the received JSON data and breaks it down into items such as user ID, meal content, intake amount, time, store information, and emotional state. The broken down data is stored in a database, which accumulates data for subsequent data analysis and health recommendations.

[0675] Step 4:

[0676] Data Query and Analysis

[0677] The server periodically queries the database for accumulated dietary and emotional data, obtaining data from a certain period of time in the past. For example, it obtains data from the past month's worth of food records and emotional state. The obtained data is processed by an analysis engine to extract the user's eating habits, preferences, restaurant habits, and signs of stress.

[0678] Step 5:

[0679] Generate healthy eating suggestions

[0680] Based on the analysis results, the server will suggest healthy meals suitable for the user. For example, it will generate specific suggestions such as "We recommend herbal tea with a relaxing effect for user 123." This information includes food names, quantities, recommended stores, and even discount coupons.

[0681] Step 6:

[0682] Generate and send coupons

[0683] Taking into account the analysis results and the user's emotional state, the server generates discount coupons and special offers for healthy eating suggestions. The generated coupon information is then packaged in JSON format and sent to the device.

[0684] Step 7:

[0685] Coupon and Offer Notifications

[0686] The device notifies the user of the offer information and coupons received from the server. The user can check and use the received coupons. The notifications are displayed as push notifications or in-app notifications.

[0687] Step 8:

[0688] Coupon usage and confirmation

[0689] The user presents the coupon at a participating store to use it. The store's terminal (e.g., QR code reader or cash register) sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server records the successful use of the coupon in the database.

[0690] Step 9:

[0691] Points awarded

[0692] After verifying the validity of the coupon, the server will add points to the user's account. This completes the coupon usage and provides an incentive to the user. For example, an operation such as "add 5 points to user 123" is executed.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] [Second embodiment]

[0697] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0698] 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.

[0699] 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).

[0700] 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.

[0701] 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.

[0702] 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).

[0703] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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."

[0709] The system of the present invention records and analyzes the user's eating habits and suggests healthy meals to support the user in dieting and health management. Specific embodiments will be described below.

[0710] Recording user's dietary data

[0711] 1. Display the meal entry form

[0712] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (the name of the convenience store or restaurant).

[0713] 2. Enter your meal data

[0714] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[0715] Data transmission and storage

[0716] 1. Data transmission

[0717] The device packages the entered meal data and sends it to the server using a POST request in JSON format.

[0718] 2. Data storage

[0719] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[0720] Analysis of dietary data

[0721] 1. Periodic database queries

[0722] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[0723] 2. Data Analysis

[0724] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0725] Healthy meal suggestions and coupons

[0726] 1. Health suggestion generation

[0727] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[0728] 2. Coupon Notification

[0729] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[0730] Coupon use and points

[0731] 1. Using coupons

[0732] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[0733] 2. Check the coupon

[0734] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[0735] 3. Points Award

[0736] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[0737] Special coupon information

[0738] 1. Stress indicator detection

[0739] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[0740] 2. Selection of special coupons

[0741] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[0742] 3. Notification of coupons

[0743] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[0744] In this way, the system of the present invention records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which the user can enjoyably and continuously manage their health, thereby eliminating the stress and difficulty of continuing with conventional methods.

[0745] The processing flow will be explained below.

[0746] Recording user's dietary data

[0747] Step 1:

[0748] The terminal displays a meal entry form.

[0749] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[0750] Step 2:

[0751] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[0752] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[0753] Step 3:

[0754] The device packages the entered meal data in JSON format and sends it to the server.

[0755] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[0756] Data transmission and storage

[0757] Step 4:

[0758] The server analyzes the received data and stores it in a database.

[0759] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[0760] Analysis of dietary data

[0761] Step 5:

[0762] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[0763] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[0764] Step 6:

[0765] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[0766] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[0767] Healthy meal suggestions and coupons

[0768] Step 7:

[0769] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[0770] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[0771] Step 8:

[0772] The server sends the generated proposals and coupon information in JSON format to the terminal.

[0773] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[0774] Coupon use and points

[0775] Step 9:

[0776] The user presents the coupon at the store to use it.

[0777] Specific action: Present the barcode or coupon code in the app at the store.

[0778] Step 10:

[0779] The terminal (store terminal) sends the coupon code to the server and checks its validity.

[0780] Specific behavior: Enter a coupon code and send a validation request to the server.

[0781] Step 11:

[0782] The server checks the validity of the coupon and stores the usage history in a database.

[0783] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[0784] Step 12:

[0785] The server verifies that the coupon has been used and credits the points to the user's account.

[0786] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[0787] Special coupon information

[0788] Step 13:

[0789] The server analyzes the user's dietary data and detects signs of stress.

[0790] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[0791] Step 14:

[0792] The server selects special coupons for stress reduction.

[0793] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[0794] Step 15:

[0795] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[0796] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[0797] Through the above processing steps, the system of the present invention can support the user's eating habits in an enjoyable and healthy way.

[0798] Example 1

[0799] 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."

[0800] In today's busy lifestyles, it is difficult for users to maintain a healthy diet. In particular, recording and managing dietary habits, choosing healthy meals, and maintaining motivation are major challenges. Increased stress can also lead to unhealthy eating habits. Conventional methods are difficult to efficiently solve these challenges, and effective means for users to continuously manage their health are needed.

[0801] 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.

[0802] In this invention, the server includes: means for having the user input dietary details via an input form displayed on a terminal for recording the user's dietary data; means for transmitting the recorded dietary data to the server; means for storing the transmitted data in a database and analyzing the data accumulated over a certain period of time; means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals; means for confirming that the coupons have been used and awarding points to the user based on that; means for detecting signs of stress in the user and providing special coupons for stress reduction; means for having the user input dietary data including the type, content, amount of intake, and store information of the meals; and means for the server to confirm the validity of the coupons. This makes it easier for users to manage their health in an enjoyable and continuous manner, and also reduces unhealthy food choices due to stress, enabling overall health maintenance.

[0803] A "terminal" is a communication device used by a user, and includes a smartphone, tablet, personal computer, etc.

[0804] An "input form" refers to an interface that is displayed on the terminal screen and allows the user to input data such as meal details.

[0805] "Dietary data" refers to data including information such as the type, content, and amount of food consumed by the user, as well as store information.

[0806] "Server" refers to a central processing unit for receiving, storing, and analyzing data sent by users.

[0807] "Database" refers to an information management system that manages dietary data stored on a server and accumulates it over a certain period of time.

[0808] "Analysis" refers to the process of evaluating the dietary data stored in the database and extracting information such as the user's meal frequency, preferences, and tendencies toward the stores they frequent.

[0809] "Health suggestions" refer to content that recommends healthy eating to the user based on the analysis results.

[0810] "Discount Coupon" refers to an electronic or paper coupon that a user can use to receive a discount.

[0811] "Points" refer to incentives given to users when they use coupons, and are electronic points that can be later exchanged for rewards.

[0812] A "special complimentary coupon" refers to a coupon for receiving special discounts or benefits provided to reduce the user's stress.

[0813] "Validating a coupon" refers to the process by which the server verifies whether a coupon submitted by a user is valid.

[0814] The system of the present invention supports a user's diet and health management by recording and analyzing the user's dietary data and suggesting healthy meals. Specific embodiments will be described below.

[0815] Recording user's dietary data

[0816] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter the details of their meal. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount consumed, and store information (the name of the convenience store or restaurant). The user enters the details of the meal they actually ate and presses the "Submit" button to send the data. For example, if the user ate "chicken salad" for lunch, they enter that information and submit it.

[0817] Data transmission and storage

[0818] The device packages the entered meal data and sends it to the server. This sends JSON format data via a POST request. The server analyzes the received data and stores it in a database. The stored data includes the user ID, meal content, intake amount, time, store information, etc.

[0819] Analysis of dietary data

[0820] The server periodically queries the database for food data and retrieves data accumulated over a certain period of time. For example, it retrieves food records from the past month. The server analyzes the retrieved data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0821] Healthy meal suggestions and coupons

[0822] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it generates a suggestion such as "User A can use a 20 yen discount coupon for a salad today." The server sends the generated suggestion and coupon information to the device in JSON format. The device receives this and displays it to the user as a list of available coupons.

[0823] Coupon use and points

[0824] The user presents and uses a coupon at a participating store. For example, a user uses a "20 yen off salad coupon" at a convenience store. The store's cash register, QR code reader, or other terminal sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server adds points to the user's account. For example, a user who uses a salad coupon receives 5 points.

[0825] Special coupon information

[0826] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. The server selects special coupons to reduce stress. For example, if the user has a sweet tooth, it selects a "50% off dessert coupon." The server sends the selected special coupon in JSON format to the device, which notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain motivation.

[0827] Examples of specific examples and prompts

[0828] Specific examples

[0829] User A ate "toast and eggs" for breakfast, "chicken salad" for lunch, and "grilled fish" for dinner. The server analyzes this and recognizes the pattern that "he eats salad at least three times a week." As a result, the server offers a 20 yen discount coupon for salad today to help him enjoy it even more.

[0830] Prompt Sentence Examples

[0831] "Please explain the process of providing healthy meal suggestions and a 20 yen discount coupon for salad in JSON format to User A, who eats salad at least three times a week."

[0832] In this way, this system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[0833] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0834] Step 1:

[0835] The terminal displays an input form in which the user can input the meal details.

[0836] Input: User's diet, type of food, intake amount, store information, etc.

[0837] Output: Meal data entered by the user

[0838] Specific operation: An input form is displayed on the screen of a smartphone or PC, and the user enters the meal details in the designated fields.

[0839] Step 2:

[0840] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data.

[0841] Input: User-entered meal data

[0842] Output: Meal data when the send button was pressed

[0843] Specific actions: The user enters something like "toast and eggs" for breakfast and presses the "Submit" button.

[0844] Step 3:

[0845] The terminal packages the input meal data and sends it to the server.

[0846] Input: Meal data when the submit button is pressed

[0847] Output: Data packaged in JSON format, request sent to server

[0848] Specific operation: The device converts the meal data into JSON format and sends a POST request to the server.

[0849] Step 4:

[0850] The server analyzes the received data and stores it in a database.

[0851] Input: JSON formatted meal data

[0852] Output: Saved data (user ID, meal contents, intake amount, time, store information, etc.)

[0853] Specific operation: The server analyzes the received data, executes SQL queries and saves them to the database.

[0854] Step 5:

[0855] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[0856] Input: Query from the database

[0857] Output: Food record for the past month

[0858] Specific operation: Every day at 2:00 AM, the database is accessed and an SQL query such as "SELECT FROM Meal Record WHERE Date >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH)" is executed.

[0859] Step 6:

[0860] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[0861] Input: Food record for the past month

[0862] Output: Analysis results (e.g., "User A eats salad at least three times a week" and "User B eats fast food at least twice a week")

[0863] What it does: Analyzes data and extracts patterns and trends using statistical methods and machine learning models.

[0864] Step 7:

[0865] The server then suggests healthy meals suitable for the user based on the analysis results.

[0866] Input: Analysis results

[0867] Output: Suggested healthy meals and corresponding discount coupons

[0868] Specific operation: Generate specific suggestions such as "User A can use a coupon for 20 yen off a salad today" and create coupon data.

[0869] Step 8:

[0870] The server sends the generated proposals and coupon information in JSON format to the terminal.

[0871] Input: Suggested healthy meals and corresponding discount coupons

[0872] Output: The list of offers and coupons displayed on the terminal.

[0873] Specific operation: The offer content and coupon information are converted into JSON format and sent to the device, which receives it and displays it to the user.

[0874] Step 9:

[0875] The user presents and uses the coupon at the target store.

[0876] Input: Coupon information displayed on the device

[0877] Output: Record of coupon usage at store

[0878] Specific operation: The user presents the "20 yen off salad coupon" and uses it at the store.

[0879] Step 10:

[0880] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[0881] Input: Coupon code, User ID

[0882] Output: Coupon validity check result

[0883] Specific operation: The device sends the coupon code and user ID to the server and checks the expiration date and whether it has been used.

[0884] Step 11:

[0885] The server will credit the points to the user's account after verifying the validity of the coupon.

[0886] Input: Coupon validity check result

[0887] Output: Points awarded to the user account

[0888] Specific operation: The server checks the validity of the coupon and sends the data to the points system to add points to the user's account.

[0889] Step 12:

[0890] The server analyzes the user's dietary data and detects signs of stress.

[0891] Input: Past meal data

[0892] Output: Stress symptom analysis results

[0893] Specific behavior: Analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods to detect signs of stress.

[0894] Step 13:

[0895] The server selects special coupons for stress reduction.

[0896] Input: Stress symptom analysis results

[0897] Output: Selected special coupon (e.g. "50% off dessert coupon")

[0898] Specific operation: Based on the analysis results, coupons that are likely to be preferred by the user are selected.

[0899] Step 14:

[0900] The server sends the selected special discount coupon in JSON format to the terminal, and the terminal notifies the user.

[0901] Input: Selected special coupon

[0902] Output: Coupon notification on the terminal

[0903] Specific operation: The special discount coupon information is converted into JSON format and sent to the terminal, which then notifies the user.

[0904] This system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment where users can enjoyably and continuously manage their health.

[0905] (Application example 1)

[0906] 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."

[0907] In today's world, maintaining a healthy diet is a major challenge, especially for urban residents and young people who lead busy lives. While there are a wide variety of food options available, there are few systems that can help users choose appropriate meals or nutritionally balanced meals. There is also a need for a system that can increase users' interest and motivation by providing relevant content and discount coupons in a timely manner.

[0908] 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.

[0909] In this invention, the server includes a means for analyzing the accumulated data and delivering content related to healthy meal suggestions, a means for delivering coupons for specific ingredients and dishes, and a means for detecting signs of stress in the user. This allows users to record their own eating habits and receive personalized healthy meal suggestions and useful information based on the records. Furthermore, by appropriately using coupons, users can enjoy economic benefits and promote healthy choices.

[0910] A "terminal" is an electronic device that a user uses to input dietary data and receive meal suggestions and coupons.

[0911] The "input form" is a data input screen displayed on the terminal for the user to record the details of their meal.

[0912] The "server" is a central management device that receives, stores, and analyzes the user's dietary data and generates suggestions and notifications.

[0913] "Database" refers to a data repository within the system that stores dietary data submitted by users.

[0914] The "analysis results" are information about the user's eating habits and health condition obtained by the server analyzing data acquired from the database.

[0915] "Healthy meal suggestions" are specific meal suggestions to the user to maintain good health based on the analysis results of the server.

[0916] "Discount Coupon" means an electronic coupon containing a discount offer that can be used when purchasing a suggested meal or ingredient.

[0917] "Points" are incentives given to users for using coupons or taking specific actions.

[0918] "Signs of stress" are possible signs of mental stress detected from fluctuations in the user's dietary data or changes in specific patterns.

[0919] A "special coupon" is a coupon that provides discounts and special benefits to reduce the user's stress.

[0920] "Related content" includes videos, articles, recipes, and other informational materials related to healthy eating.

[0921] The system of the present invention assists the user in managing his or her health by recording and analyzing the user's eating habits and suggesting healthy meals. Specific embodiments will be described below.

[0922] Recording user's dietary data

[0923] 1. Display the meal entry form

[0924] Users record their meal details through an application installed on a device such as a smartphone. A meal entry form is displayed on the device, and users enter information such as the type of meal (breakfast, lunch, dinner, snack, etc.), meal details, intake amount, and restaurant information.

[0925] 2. Enter your meal data

[0926] The user enters the details of their meal into the input form and presses the "Submit" button to send the data. For example, if a user ate "chicken salad" for lunch, they enter that information and submit it.

[0927] Data transmission and storage

[0928] 1. Data transmission

[0929] The device packages the entered meal data in JSON format and sends it to the server via an HTTP POST request.

[0930] 2. Data storage

[0931] The server analyzes the received data and stores it in a database, including the user ID, meal details, intake amount, time, and store information.

[0932] Analysis of dietary data

[0933] 1. Periodic database queries

[0934] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[0935] 2. Data Analysis

[0936] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0937] Healthy eating suggestions and content distribution

[0938] 1. Health suggestion generation

[0939] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[0940] 2. Content Delivery

[0941] Deliver videos and articles related to the suggestions. For example, if a user likes salads, provide new salad recipe videos and health articles.

[0942] Offering coupons and awarding points

[0943] 1. Coupon Notification

[0944] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[0945] 2. Using coupons

[0946] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[0947] 3. Points Award

[0948] After the server verifies the validity of the coupon, it will add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[0949] Special coupon information

[0950] 1. Stress indicator detection

[0951] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[0952] 2. Selection of special coupons

[0953] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[0954] 3. Notification of coupons

[0955] The server sends the selected special coupon in JSON format to the device, and the device notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[0956] This provides an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[0957] Hardware and Software Used

[0958] Smartphone: iOS or Android device

[0959] Server: A remote server (AWS, Google Cloud, etc.) that analyzes data and distributes coupons

[0960] Database: MySQL or PostgreSQL

[0961] Communication protocol: HTTP / HTTPS, JSON format data

[0962] Examples of prompt statements

[0963] Example prompts to input to the generative AI model:

[0964] A user inputs "I had a salad for lunch" into a smartphone app. Based on this information, the server should perform the following analysis and generate healthy meal suggestions and coupons accordingly.

[0965] Input data:

[0966] User ID: 12345

[0967] Meal: Chicken salad

[0968] Serving size: 200g

[0969] Store name: Salad Stop

[0970] Example output:

[0971] Healthy Eating Suggestion: This Week's New Salad Recipe

[0972] Proposal content: Video link, related article URL

[0973] Coupon: Salad 20 yen discount coupon

[0974] In this way, the system of the present invention records and analyzes the user's eating habits and delivers healthy meal suggestions and related content, making it easier for users to make healthy choices and manage their health sustainably.

[0975] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0976] Step 1:

[0977] The user enters the details of their meal into the device. The user opens the application installed on their smartphone and enters the type of meal (e.g., breakfast, lunch, dinner, snack), meal details, intake amount, and store information into the meal entry form. Once the input is complete, the user presses the "Submit" button. The input data includes the user ID, meal details, intake amount, and store information.

[0978] Input: Meal details, intake amount, store information

[0979] Output: Input food data

[0980] Step 2:

[0981] The device packages the entered food data in JSON format and sends it to the server via an HTTP POST request, where it is processed in real time and delivered to the server.

[0982] Input: JSON formatted meal data

[0983] Output: Data sent to the server

[0984] Step 3:

[0985] The server analyzes the received meal data and saves it in a database. The server converts the received data into a specific format and stores the user ID, meal details, intake amount, store information, etc. in the database.

[0986] Input: Data sent to the server

[0987] Output: Data stored in the database

[0988] Step 4:

[0989] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[0990] Input: Query dietary data from the database

[0991] Output: Food record for the past month

[0992] Step 5:

[0993] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[0994] Input: Food record for the past month

[0995] Output: User's meal frequency, preferences, and store preferences

[0996] Step 6:

[0997] The server generates healthy eating suggestions based on the analysis results and delivers videos and articles related to the suggestions. For example, if a user likes salads, it will provide new salad recipe videos and health-related articles.

[0998] Input: User's meal frequency, preferences, and store preferences

[0999] Output: Healthy eating suggestions and related content

[1000] Step 7:

[1001] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[1002] Input: Healthy eating suggestions and coupon information

[1003] Output: Coupon sent to the terminal

[1004] Step 8:

[1005] The user presents the coupon at a participating store and uses it. For example, they use a "20 yen off salad coupon" at a convenience store. The store's cash register or QR code reader sends the coupon code to the server and verifies its validity.

[1006] Input: Coupon presented at the participating store

[1007] Output: Coupon validity check

[1008] Step 9:

[1009] After the server verifies the validity of the coupon, it will add points to the user's account. For example, if a user uses a salad coupon, they will receive 5 points.

[1010] Input: Check coupon validity

[1011] Output: Points awarded to user account

[1012] Step 10:

[1013] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. Based on the analysis results, it selects special coupons for stress reduction and sends them to the device in JSON format.

[1014] Input: Analysis results of dietary data

[1015] Output: Select and send special coupons

[1016] Step 11:

[1017] The terminal displays the special coupon received from the server and notifies the user. The user can use the special coupon to enjoy an appropriate cheat day and maintain motivation.

[1018] Input: Special coupon information

[1019] Output: Special coupon notification displayed on the terminal

[1020] In this way, the system of the invention records and analyzes the user's eating habits, and delivers healthy meal suggestions and related content. Users can also use coupons and points to enjoyably manage their health on an ongoing basis.

[1021] 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.

[1022] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and suggesting healthy meals, and by combining it with an emotion engine, it can more effectively maintain the motivation of users. Specific embodiments are described below.

[1023] Recording user's dietary data

[1024] 1. Display the meal entry form

[1025] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (convenience store or restaurant name).

[1026] 2. Enter your meal data

[1027] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[1028] Data transmission and storage

[1029] 1. Data transmission

[1030] The device packages the entered meal data in JSON format and sends it to the server using a POST request.

[1031] 2. Data storage

[1032] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[1033] Analysis of dietary data

[1034] 1. Periodic database queries

[1035] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[1036] 2. Data Analysis

[1037] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[1038] Healthy meal suggestions and coupons

[1039] 1. Health suggestion generation

[1040] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[1041] 2. Coupon Notification

[1042] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[1043] Coupon use and points

[1044] 1. Using coupons

[1045] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[1046] 2. Check the coupon

[1047] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[1048] 3. Points Award

[1049] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[1050] Special coupon information

[1051] 1. Stress indicator detection

[1052] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[1053] 2. Selection of special coupons

[1054] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[1055] 3. Notification of coupons

[1056] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[1057] Implementing the Emotion Engine

[1058] 1. Entering emotion data

[1059] Users can input their emotional state within the app, choosing from options such as "I'm feeling stressed today" or "I'm in a happy mood."

[1060] 2. Emotion Data Analysis

[1061] The server analyzes the input emotion data and evaluates the user's current emotional state.

[1062] 3. Emotion-based recommendation optimization

[1063] The server optimizes healthy meal suggestions and special coupons based on the user's emotional state: for example, if the user is feeling stressed, it will suggest more relaxing meals and coupons.

[1064] 4. Emotion-based notifications

[1065] The server then sends the optimized recommendations to the device, which then notifies the user. For example, a user who is feeling stressed can be offered a discount coupon for relaxing herbal tea.

[1066] In this way, the system of the present invention records and analyzes the user's eating habits, and by suggesting healthy meals and providing coupons, provides an environment where users can enjoy managing their health. In addition, by combining it with an emotion engine, it is possible to make more personalized suggestions according to the user's emotional state, contributing to maintaining motivation and reducing stress.

[1067] The processing flow will be explained below.

[1068] Processing of systems with emotion engines

[1069] Recording user's dietary data

[1070] Step 1:

[1071] The terminal displays a meal entry form.

[1072] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[1073] Step 2:

[1074] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[1075] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[1076] Step 3:

[1077] The device packages the entered meal data in JSON format and sends it to the server.

[1078] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[1079] Data transmission and storage

[1080] Step 4:

[1081] The server analyzes the received data and stores it in a database.

[1082] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[1083] Entering emotion data

[1084] Step 5:

[1085] The terminal displays an emotion input form.

[1086] Specific behavior: Display options on the device screen to select an emotional state (e.g., "I feel stressed today" or "I feel happy").

[1087] Step 6:

[1088] The user inputs their emotional state and presses the "send" button.

[1089] Specific operation: The user selects today's emotional state and presses the send button.

[1090] Step 7:

[1091] The device packages the input emotion data in JSON format and sends it to the server.

[1092] Specific operation: Emotion data is packaged in JSON format and sent to the server via a POST request.

[1093] Emotional Data Analysis

[1094] Step 8:

[1095] The server analyzes the received emotional data and evaluates the current emotional state.

[1096] Specific operation: Emotional data is passed through an analysis algorithm to assess whether the user is stressed or relaxed.

[1097] Analysis of dietary data

[1098] Step 9:

[1099] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[1100] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[1101] Step 10:

[1102] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[1103] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[1104] Healthy meal suggestions and coupons

[1105] Step 11:

[1106] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[1107] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[1108] Step 12:

[1109] The server optimizes the suggestions taking into account the emotional state.

[1110] What it does: If the user is feeling stressed, it will adjust its suggestions to suggest relaxing meals or coupons.

[1111] Step 13:

[1112] The server sends the generated proposals and coupon information in JSON format to the terminal.

[1113] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[1114] Coupon use and points

[1115] Step 14:

[1116] The user presents the coupon at the store to use it.

[1117] Specific action: Present the barcode or coupon code in the app at the store.

[1118] Step 15:

[1119] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[1120] Specific behavior: Enter a coupon code and send a validation request to the server.

[1121] Step 16:

[1122] The server checks the validity of the coupon and stores the usage history in a database.

[1123] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[1124] Step 17:

[1125] The server verifies that the coupon has been used and credits the points to the user's account.

[1126] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[1127] Special coupon information

[1128] Step 18:

[1129] The server analyzes the user's dietary data and detects signs of stress.

[1130] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[1131] Step 19:

[1132] The server selects special coupons for stress reduction.

[1133] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[1134] Step 20:

[1135] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[1136] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[1137] In this way, by combining an emotion engine, the system of the present invention supports the user's enjoyable and healthy eating habits and can make more personalized suggestions based on the user's emotional state, contributing to maintaining motivation and reducing stress.

[1138] Example 2

[1139] 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."

[1140] Conventional health management systems were able to collect and analyze users' dietary data, but they struggled to provide personalized recommendations that took into account the user's emotional state. This made it difficult to maintain user motivation over the long term, making effective health management difficult to achieve. Furthermore, they lacked a mechanism for early detection of stress symptoms and the provision of appropriate coupons and recommendations.

[1141] 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.

[1142] In this invention, the server is configured to record the user's dietary habit data.

[1143] A means for allowing a user to input meal details via an input form displayed on a terminal;

[1144] means for transmitting the recorded dietary habit data to a server;

[1145] A means for storing the transmitted data in a database and analyzing the data accumulated over a period of time;

[1146] A means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals;

[1147] a means for confirming that the coupon has been used and awarding points to the user based on the confirmation;

[1148] means for inputting the user's emotional state and transmitting the emotional data to a server;

[1149] A means of analyzing emotional data and optimizing healthy meal suggestions and special coupons based on the analysis results;

[1150] A means for notifying the user of sentiment-based optimized offers and special coupons;

[1151] This will enable a comprehensive analysis of the user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons. This will help maintain user motivation and reduce stress, enabling long-term health management.

[1152] "User" refers to an individual who uses this system to input dietary data and emotional data.

[1153] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1154] "Input form" refers to an on-screen form that allows the user to input dietary details and emotional state.

[1155] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[1156] "Server" refers to the computer on the system that receives transmitted data and stores and analyzes the data.

[1157] "Database" refers to a storage device for storing dietary data and emotional data.

[1158] "Analysis results" refers to information obtained by the server analyzing the accumulated data.

[1159] "Health Suggestions" refers to suggestions for healthy eating and behaviors provided to users based on the analysis results.

[1160] "Coupon" refers to an electronic voucher that offers discounts or special offers on specific meals or products.

[1161] "Points" refers to rewards credited to a user's account based on the use of a coupon.

[1162] "Emotional state" refers to the psychological state such as stress or mood felt by the user.

[1163] "Emotional data" refers to information about an emotional state entered by a user.

[1164] "Discount coupon" refers to an electronic coupon that comes with special discounts or benefits.

[1165] "Notification" refers to a message or alert sent from the server to the device.

[1166] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and providing appropriate healthy meal suggestions and coupons. Furthermore, by combining it with an emotion engine, it is possible to more effectively maintain the user's motivation. Specific embodiments are described below.

[1167] Hardware and Software Use

[1168] Device: Use a device such as a smartphone, tablet, or PC.

[1169] Server: A computer on a network used to receive, analyze, and store data.

[1170] Database: Uses a storage device to store dietary and emotional data.

[1171] Data processing and calculation

[1172] Data collection:

[1173] The terminal collects user input data and receives dietary details and emotional state in an input form.

[1174] The user enters their dietary habits and emotional state into an input form and submits it.

[1175] Data transmission and storage:

[1176] The terminal sends the data entered by the user to the server in JSON format.

[1177] The server analyzes the received data and stores it in a database, including the user ID, meal content, intake amount, time, store information, emotional state, etc.

[1178] Data Analysis:

[1179] The server periodically queries and analyzes the data stored in the database, including the user's eating habits, preferences, restaurant preferences, and emotional state.

[1180] For example, if user A tends to eat salad more than three times a week, this pattern will be recognized.

[1181] Dining suggestions and coupons:

[1182] The server generates healthy meal suggestions based on the analysis results and provides discount coupons for the suggested meals.

[1183] The server sends the generated offer and coupon information to the terminal, which receives it and notifies the user.

[1184] Coupon use and points awarded:

[1185] The user presents the coupon and uses it at the target store.

[1186] The terminal (store device) sends the coupon code to the server and checks its validity.

[1187] The server awards points to the user based on valid coupons.

[1188] Detecting stress indicators and providing special coupons:

[1189] The server analyzes the user's dietary and emotional data to detect stress indicators. For example, if the user's intake of high-calorie foods increases, the server determines that the user is feeling stressed.

[1190] The server sends the selected special coupon to the terminal and notifies the user, helping them enjoy their cheat day.

[1191] Specific prompt examples

[1192] Food data entry prompt:

[1193] What did you have for lunch today? Please enter the exact menu item and portion size.

[1194] Example: Grilled Chicken Salad, 200g

[1195] Emotional state prompt:

[1196] Please tell us how you're feeling right now. Please choose from the options below.

[1197] 1. Fun

[1198] 2. Normal

[1199] 3. You feel stressed

[1200] 4. Feeling depressed

[1201] Example

[1202] For example, if a user has "grilled chicken salad" for lunch, they enter and submit the details as "grilled chicken salad, 200g, salad shop ABC." The server analyzes the data and recognizes the user's eating habits. If the user tends to eat salad more than three times a week, the server will suggest a "50 yen discount coupon" for the next salad purchase and notify the user's device.

[1203] This will enable comprehensive analysis of a user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons, which is expected to help maintain user motivation and reduce stress, enabling long-term health management.

[1204] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1205] Step 1:

[1206] Display of meal entry form

[1207] The terminal displays an input form that allows the user to enter meal details. The form includes radio buttons such as "Breakfast," "Lunch," "Dinner," and "Snack," as well as text input fields. The input form is displayed when the program is started or when a specific button is pressed.

[1208] Input: Launching a program or pressing a button

[1209] Output: Display of meal entry form

[1210] Step 2:

[1211] Entering meal data

[1212] The user inputs the meal details using the input form on the terminal and presses the "Submit" button to send the data. For example, the user inputs "Grilled chicken salad, 200g, salad shop ABC" for lunch.

[1213] Input: The meal details entered by the user in the input form

[1214] Output: Pressing the send button puts the data into a waiting state for transmission.

[1215] Step 3:

[1216] Data transmission

[1217] The device packages the meal data entered by the user into JSON format and sends it to the server using an HTTP POST request.

[1218] Input: User-entered meal data (e.g., Grilled Chicken Salad, 200g, Salad Shop ABC)

[1219] Output: Meal data is sent to the server in JSON format

[1220] Step 4:

[1221] Data storage

[1222] The server analyzes the received meal data and stores it in a database, including the user ID, meal content, intake amount, meal time, and restaurant information.

[1223] Input: Meal data sent in JSON format

[1224] Output: The data is saved correctly in the database

[1225] Step 5:

[1226] Periodic database queries

[1227] The server periodically queries the database to obtain meal data for a certain period of time, for example, to obtain meal data for the past month.

[1228] Input: A trigger that runs a periodic query from the database

[1229] Output: Meal data for a certain period of time is obtained.

[1230] Step 6:

[1231] Data analysis

[1232] The server analyzes the food data retrieved from the database to extract the user's meal frequency, preferences, and restaurant preferences. This analysis uses standard statistical analysis methods and machine learning algorithms. For example, the server can determine that User A eats salad at least three times a week.

[1233] Input: Acquired dietary data over a period of time

[1234] Output: Analysis results of each user's meal frequency, preferences, and store usage trends

[1235] Step 7:

[1236] Health suggestion generation

[1237] The server generates healthy meal suggestions based on the analysis results. For example, it might suggest to User A, "A 50 yen discount coupon for the next salad purchase." The suggestions are generated using pre-defined rules and machine learning models.

[1238] Input: Analysis results of dietary data

[1239] Output: Health recommendations and coupons tailored to each individual user

[1240] Step 8:

[1241] Coupon Notifications

[1242] The server sends the generated offers and coupon information to the device, which receives it in JSON format and displays a notification to the user.

[1243] Input: Offer and coupon information

[1244] Output: Offer and coupon information is sent to the terminal and the user is notified

[1245] Step 9:

[1246] Use coupon

[1247] The user presents the coupon at the store. For example, the user uses a "20 yen off salad coupon" at a convenience store. The coupon is presented using a QR code or barcode.

[1248] Input: Present coupon

[1249] Output: A confirmation of coupon redemption is displayed to the user

[1250] Step 10:

[1251] Check coupons

[1252] The terminal (such as a store cash register or QR code reader) sends the coupon code to a server to verify its validity, which typically involves checking a database of coupons.

[1253] Enter: Coupon Code

[1254] Output: Coupon code validation result

[1255] Step 11:

[1256] Points awarded

[1257] After verifying the validity of the coupon, the server adds points to the user's account. For example, if a user uses a "salad coupon," 5 points are added. The point addition involves updating the user's account.

[1258] Input: Coupon validity check result

[1259] Output: Points are credited to the user account.

[1260] Step 12:

[1261] Input of user emotion data

[1262] The user inputs their emotional state using an input form on the device. For example, they answer "yes" to the question "Are you feeling stressed today?"

[1263] Input: User's emotional state input

[1264] Output: Emotional state data ready to send

[1265] Step 13:

[1266] Sending emotional data

[1267] The device sends the emotion data entered by the user to the server in JSON format.

[1268] Input: Emotion data entered by the user

[1269] Output: Emotion data is sent to the server in JSON format.

[1270] Step 14:

[1271] Emotional Data Analysis

[1272] The server analyzes the received emotion data and evaluates the user's current emotional state. The analysis uses natural language processing and machine learning models. For example, if a user inputs "stress," the server recognizes the user as being in a high-stress state.

[1273] Input: Received emotion data

[1274] Output: Emotion evaluation result

[1275] Step 15:

[1276] Emotion-based suggestion optimization

[1277] The server optimizes health suggestions and special coupons based on emotional data. For example, if a user is under high stress, it will suggest relaxing suggestions. Machine learning algorithms are used for optimization.

[1278] Input: Emotion evaluation result

[1279] Output: Optimized health recommendations and coupon information

[1280] Step 16:

[1281] Emotion-based notifications

[1282] The server transmits the optimized proposal and the special coupon to the terminal, which notifies the user of the same.

[1283] Input: Optimized health recommendations and coupon information

[1284] Output: Offer and coupon information is sent to the terminal and notified to the user

[1285] (Application example 2)

[1286] 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."

[1287] Conventional dietary management systems can record a user's dietary data and suggest healthy meals, but they have difficulty making personalized suggestions that take into account the user's emotional state. Furthermore, there is a lack of effective health management methods, particularly for specific occupational environments such as factory workers. This has led to issues such as not being able to adequately maintain user motivation or reduce stress.

[1288] 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.

[1289] In this invention, the server includes a means for inputting and analyzing the user's emotional state and optimizing healthy meal suggestions and special coupons based on the input, a means for combining the analysis results with the emotional state to make health management suggestions for factory workers, and a means for analyzing the accumulated data, thereby enabling personalized health management suggestions according to the user's emotional state and work environment.

[1290] "User" refers to an individual or worker who uses the system of the present invention.

[1291] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[1292] "Input form" refers to the screen displayed on the terminal for the user to input their dietary details and emotional state.

[1293] "Device" means a smartphone, tablet, personal computer, or other digital device.

[1294] "Server" refers to a central system for receiving, storing, and analyzing data submitted by users.

[1295] "Database" refers to a storage system for storing accumulated dietary data and analysis results.

[1296] "Healthy meal suggestions" refer to meal suggestions that are effective for maintaining health and dieting, based on the user's dietary data and emotional state.

[1297] "Discount Coupon" means a coupon for applying a discount to a proposed meal.

[1298] "Special coupons" refer to special coupons provided to users to reduce stress and increase motivation.

[1299] "Emotional state" refers to the emotional or mental state a user is experiencing.

[1300] "Personalized suggestions" refers to health suggestions or coupon offers that are tailored based on an individual user's specific situation or emotional state.

[1301] "Factory workers" refers to people who work in factories and who require health care in a specific occupational environment.

[1302] The system of the present invention supports users in dieting and health management by recording and analyzing their dietary habits and suggesting healthy meals. Furthermore, by combining it with an emotion engine, it is possible to maintain the user's motivation.

[1303] Hardware and Software Configuration

[1304] The system is configured so that users can input data using devices such as smartphones, tablets, and personal computers, and the server analyzes the data. Communication between the server and devices and data storage are performed using an internet connection and a database management system.

[1305] Recording user's dietary data

[1306] The user inputs the details of their meal and their emotional state via an input form displayed on the device. The input form includes fields for the type of meal (breakfast, lunch, dinner, snack, etc.), the details of the meal, the amount of intake, and store information (such as the name of the restaurant or convenience store). The user can also input their emotional state. For example, options such as "I feel stressed today" and "I'm in a happy mood" are provided.

[1307] Data transmission and storage

[1308] The device converts the entered dietary data and emotional state into JSON format and sends it to the server using a POST request. The server then stores the received data in a database. This data includes the user ID, meal content, intake amount, time, store information, emotional state, etc.

[1309] Analysis of dietary data

[1310] The server periodically queries the database for dietary data and retrieves data accumulated over a certain period of time. For example, it retrieves a meal record for the past month. Based on this retrieved data, it extracts the user's meal frequency, preferences, and restaurant preferences. It also analyzes the user's emotional state and detects signs of stress.

[1311] Healthy meal suggestions and coupons

[1312] The server then suggests healthy meals suitable for the user based on the analysis results and generates discount coupons for the suggested meals. The generated coupon information is sent to the device in JSON format and displayed on the device as a coupon list.

[1313] Coupon use and points

[1314] The user presents and uses the coupon at the applicable store. The terminal (store cash register or QR code reader) sends the coupon code to the server to check its validity. After the server checks the validity of the coupon, points are added to the user's account.

[1315] Optimizing recommendations with an emotion engine

[1316] The server analyzes the input emotional data and optimizes healthy meal suggestions and special coupons based on the user's current emotional state. For example, if the user is feeling stressed, it will suggest relaxing meals and coupons.

[1317] Specific examples

[1318] For example, if a user with user ID 123 ate "chicken salad" for lunch and entered their emotional state as "feeling stressed," the server would generate healthy meal suggestions and relaxation coupons based on this data. An example of a prompt sentence entered in the system is as follows:

[1319] User ID: 123

[1320] Meal Type: Lunch

[1321] Meal: Chicken salad

[1322] Quantity: 1

[1323] Store name: Convenience store A

[1324] Emotional state: Stressed

[1325] Based on this prompt, appropriate suggestions can be made to the user.

[1326] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1327] Step 1:

[1328] Data Entry

[1329] The user inputs information such as meal details, intake amount, restaurant information, and emotional state into an input form on the device. The input data includes specific information such as "Lunch," "Chicken Salad," "Quantity: 1," "Convenience Store A," and "Feeling Stressed." This information is transmitted from the device as dietary and emotional data.

[1330] Step 2:

[1331] Data transmission

[1332] The device converts the entered dietary and emotional data into JSON format and sends it to the server as a POST request. Examples of the data sent include the following JSON format data:

[1333] json

[1334] {

[1335] "user_id": "123",

[1336] "meal_type": "Lunch",

[1337] "meal_content": "Chicken Salad",

[1338] "quantity": "1",

[1339] "store_name": "Convenience store A",

[1340] "emotion_state": "stressed"

[1341] }

[1342] The server receives this and proceeds to the next step.

[1343] Step 3:

[1344] Data storage

[1345] The server parses the received JSON data and breaks it down into items such as user ID, meal content, intake amount, time, store information, and emotional state. The broken down data is stored in a database, which accumulates data for subsequent data analysis and health recommendations.

[1346] Step 4:

[1347] Data Query and Analysis

[1348] The server periodically queries the database for accumulated dietary and emotional data, obtaining data from a certain period of time in the past. For example, it obtains data from the past month's worth of food records and emotional state. The obtained data is processed by an analysis engine to extract the user's eating habits, preferences, restaurant habits, and signs of stress.

[1349] Step 5:

[1350] Generate healthy eating suggestions

[1351] Based on the analysis results, the server will suggest healthy meals suitable for the user. For example, it will generate specific suggestions such as "We recommend herbal tea with a relaxing effect for user 123." This information includes food names, quantities, recommended stores, and even discount coupons.

[1352] Step 6:

[1353] Generate and send coupons

[1354] Taking into account the analysis results and the user's emotional state, the server generates discount coupons and special offers for healthy eating suggestions. The generated coupon information is then packaged in JSON format and sent to the device.

[1355] Step 7:

[1356] Coupon and Offer Notifications

[1357] The device notifies the user of the offer information and coupons received from the server. The user can check and use the received coupons. The notifications are displayed as push notifications or in-app notifications.

[1358] Step 8:

[1359] Coupon usage and confirmation

[1360] The user presents the coupon at a participating store to use it. The store's terminal (e.g., QR code reader or cash register) sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server records the successful use of the coupon in the database.

[1361] Step 9:

[1362] Points awarded

[1363] After verifying the validity of the coupon, the server will add points to the user's account. This completes the coupon usage and provides an incentive to the user. For example, an operation such as "add 5 points to user 123" is executed.

[1364] 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.

[1365] 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.

[1366] 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.

[1367] [Third embodiment]

[1368] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1369] 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.

[1370] 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).

[1371] 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.

[1372] 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.

[1373] 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).

[1374] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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."

[1380] The system of the present invention records and analyzes the user's eating habits and suggests healthy meals to support the user in dieting and health management. Specific embodiments will be described below.

[1381] Recording user's dietary data

[1382] 1. Display the meal entry form

[1383] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (the name of the convenience store or restaurant).

[1384] 2. Enter your meal data

[1385] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[1386] Data transmission and storage

[1387] 1. Data transmission

[1388] The device packages the entered meal data and sends it to the server using a POST request in JSON format.

[1389] 2. Data storage

[1390] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[1391] Analysis of dietary data

[1392] 1. Periodic database queries

[1393] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[1394] 2. Data Analysis

[1395] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[1396] Healthy meal suggestions and coupons

[1397] 1. Health suggestion generation

[1398] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[1399] 2. Coupon Notification

[1400] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[1401] Coupon use and points

[1402] 1. Using coupons

[1403] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[1404] 2. Check the coupon

[1405] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[1406] 3. Points Award

[1407] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[1408] Special coupon information

[1409] 1. Stress indicator detection

[1410] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[1411] 2. Selection of special coupons

[1412] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[1413] 3. Notification of coupons

[1414] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[1415] In this way, the system of the present invention records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which the user can enjoyably and continuously manage their health, thereby eliminating the stress and difficulty of continuing with conventional methods.

[1416] The processing flow will be explained below.

[1417] Recording user's dietary data

[1418] Step 1:

[1419] The terminal displays a meal entry form.

[1420] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[1421] Step 2:

[1422] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[1423] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[1424] Step 3:

[1425] The device packages the entered meal data in JSON format and sends it to the server.

[1426] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[1427] Data transmission and storage

[1428] Step 4:

[1429] The server analyzes the received data and stores it in a database.

[1430] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[1431] Analysis of dietary data

[1432] Step 5:

[1433] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[1434] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[1435] Step 6:

[1436] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[1437] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[1438] Healthy meal suggestions and coupons

[1439] Step 7:

[1440] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[1441] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[1442] Step 8:

[1443] The server sends the generated proposals and coupon information in JSON format to the terminal.

[1444] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[1445] Coupon use and points

[1446] Step 9:

[1447] The user presents the coupon at the store to use it.

[1448] Specific action: Present the barcode or coupon code in the app at the store.

[1449] Step 10:

[1450] The terminal (store terminal) sends the coupon code to the server and checks its validity.

[1451] Specific behavior: Enter a coupon code and send a validation request to the server.

[1452] Step 11:

[1453] The server checks the validity of the coupon and stores the usage history in a database.

[1454] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[1455] Step 12:

[1456] The server verifies that the coupon has been used and credits the points to the user's account.

[1457] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[1458] Special coupon information

[1459] Step 13:

[1460] The server analyzes the user's dietary data and detects signs of stress.

[1461] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[1462] Step 14:

[1463] The server selects special coupons for stress reduction.

[1464] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[1465] Step 15:

[1466] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[1467] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[1468] Through the above processing steps, the system of the present invention can support the user's eating habits in an enjoyable and healthy way.

[1469] Example 1

[1470] 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."

[1471] In today's busy lifestyles, it is difficult for users to maintain a healthy diet. In particular, recording and managing dietary habits, choosing healthy meals, and maintaining motivation are major challenges. Increased stress can also lead to unhealthy eating habits. Conventional methods are difficult to efficiently solve these challenges, and effective means for users to continuously manage their health are needed.

[1472] 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.

[1473] In this invention, the server includes: means for having the user input dietary details via an input form displayed on a terminal for recording the user's dietary data; means for transmitting the recorded dietary data to the server; means for storing the transmitted data in a database and analyzing the data accumulated over a certain period of time; means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals; means for confirming that the coupons have been used and awarding points to the user based on that; means for detecting signs of stress in the user and providing special coupons for stress reduction; means for having the user input dietary data including the type, content, amount of intake, and store information of the meals; and means for the server to confirm the validity of the coupons. This makes it easier for users to manage their health in an enjoyable and continuous manner, and also reduces unhealthy food choices due to stress, enabling overall health maintenance.

[1474] A "terminal" is a communication device used by a user, and includes a smartphone, tablet, personal computer, etc.

[1475] An "input form" refers to an interface that is displayed on the terminal screen and allows the user to input data such as meal details.

[1476] "Dietary data" refers to data including information such as the type, content, and amount of food consumed by the user, as well as store information.

[1477] "Server" refers to a central processing unit for receiving, storing, and analyzing data sent by users.

[1478] "Database" refers to an information management system that manages dietary data stored on a server and accumulates it over a certain period of time.

[1479] "Analysis" refers to the process of evaluating the dietary data stored in the database and extracting information such as the user's meal frequency, preferences, and tendencies toward the stores they frequent.

[1480] "Health suggestions" refer to content that recommends healthy eating to the user based on the analysis results.

[1481] "Discount Coupon" refers to an electronic or paper coupon that a user can use to receive a discount.

[1482] "Points" refer to incentives given to users when they use coupons, and are electronic points that can be later exchanged for rewards.

[1483] A "special complimentary coupon" refers to a coupon for receiving special discounts or benefits provided to reduce the user's stress.

[1484] "Validating a coupon" refers to the process by which the server verifies whether a coupon submitted by a user is valid.

[1485] The system of the present invention supports a user's diet and health management by recording and analyzing the user's dietary data and suggesting healthy meals. Specific embodiments will be described below.

[1486] Recording user's dietary data

[1487] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter the details of their meal. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount consumed, and store information (the name of the convenience store or restaurant). The user enters the details of the meal they actually ate and presses the "Submit" button to send the data. For example, if the user ate "chicken salad" for lunch, they enter that information and submit it.

[1488] Data transmission and storage

[1489] The device packages the entered meal data and sends it to the server. This sends JSON format data via a POST request. The server analyzes the received data and stores it in a database. The stored data includes the user ID, meal content, intake amount, time, store information, etc.

[1490] Analysis of dietary data

[1491] The server periodically queries the database for food data and retrieves data accumulated over a certain period of time. For example, it retrieves food records from the past month. The server analyzes the retrieved data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[1492] Healthy meal suggestions and coupons

[1493] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it generates a suggestion such as "User A can use a 20 yen discount coupon for a salad today." The server sends the generated suggestion and coupon information to the device in JSON format. The device receives this and displays it to the user as a list of available coupons.

[1494] Coupon use and points

[1495] The user presents and uses a coupon at a participating store. For example, a user uses a "20 yen off salad coupon" at a convenience store. The store's cash register, QR code reader, or other terminal sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server adds points to the user's account. For example, a user who uses a salad coupon receives 5 points.

[1496] Special coupon information

[1497] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. The server selects special coupons to reduce stress. For example, if the user has a sweet tooth, it selects a "50% off dessert coupon." The server sends the selected special coupon in JSON format to the device, which notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain motivation.

[1498] Examples of specific examples and prompts

[1499] Specific examples

[1500] User A ate "toast and eggs" for breakfast, "chicken salad" for lunch, and "grilled fish" for dinner. The server analyzes this and recognizes the pattern that "he eats salad at least three times a week." As a result, the server offers a 20 yen discount coupon for salad today to help him enjoy it even more.

[1501] Prompt Sentence Examples

[1502] "Please explain the process of providing healthy meal suggestions and a 20 yen discount coupon for salad in JSON format to User A, who eats salad at least three times a week."

[1503] In this way, this system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[1504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1505] Step 1:

[1506] The terminal displays an input form in which the user can input the meal details.

[1507] Input: User's diet, type of food, intake amount, store information, etc.

[1508] Output: Meal data entered by the user

[1509] Specific operation: An input form is displayed on the screen of a smartphone or PC, and the user enters the meal details in the designated fields.

[1510] Step 2:

[1511] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data.

[1512] Input: User-entered meal data

[1513] Output: Meal data when the send button was pressed

[1514] Specific actions: The user enters something like "toast and eggs" for breakfast and presses the "Submit" button.

[1515] Step 3:

[1516] The terminal packages the input meal data and sends it to the server.

[1517] Input: Meal data when the submit button is pressed

[1518] Output: Data packaged in JSON format, request sent to server

[1519] Specific operation: The device converts the meal data into JSON format and sends a POST request to the server.

[1520] Step 4:

[1521] The server analyzes the received data and stores it in a database.

[1522] Input: JSON formatted meal data

[1523] Output: Saved data (user ID, meal contents, intake amount, time, store information, etc.)

[1524] Specific operation: The server analyzes the received data, executes SQL queries and saves them to the database.

[1525] Step 5:

[1526] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[1527] Input: Query from the database

[1528] Output: Food record for the past month

[1529] Specific operation: Every day at 2:00 AM, the database is accessed and an SQL query such as "SELECT FROM Meal Record WHERE Date >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH)" is executed.

[1530] Step 6:

[1531] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[1532] Input: Food record for the past month

[1533] Output: Analysis results (e.g., "User A eats salad at least three times a week" and "User B eats fast food at least twice a week")

[1534] What it does: Analyzes data and extracts patterns and trends using statistical methods and machine learning models.

[1535] Step 7:

[1536] The server then suggests healthy meals suitable for the user based on the analysis results.

[1537] Input: Analysis results

[1538] Output: Suggested healthy meals and corresponding discount coupons

[1539] Specific operation: Generate specific suggestions such as "User A can use a coupon for 20 yen off a salad today" and create coupon data.

[1540] Step 8:

[1541] The server sends the generated proposals and coupon information in JSON format to the terminal.

[1542] Input: Suggested healthy meals and corresponding discount coupons

[1543] Output: The list of offers and coupons displayed on the terminal.

[1544] Specific operation: The offer content and coupon information are converted into JSON format and sent to the device, which receives it and displays it to the user.

[1545] Step 9:

[1546] The user presents and uses the coupon at the target store.

[1547] Input: Coupon information displayed on the device

[1548] Output: Record of coupon usage at store

[1549] Specific operation: The user presents the "20 yen off salad coupon" and uses it at the store.

[1550] Step 10:

[1551] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[1552] Input: Coupon code, User ID

[1553] Output: Coupon validity check result

[1554] Specific operation: The device sends the coupon code and user ID to the server and checks the expiration date and whether it has been used.

[1555] Step 11:

[1556] The server will credit the points to the user's account after verifying the validity of the coupon.

[1557] Input: Coupon validity check result

[1558] Output: Points awarded to the user account

[1559] Specific operation: The server checks the validity of the coupon and sends the data to the points system to add points to the user's account.

[1560] Step 12:

[1561] The server analyzes the user's dietary data and detects signs of stress.

[1562] Input: Past meal data

[1563] Output: Stress symptom analysis results

[1564] Specific behavior: Analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods to detect signs of stress.

[1565] Step 13:

[1566] The server selects special coupons for stress reduction.

[1567] Input: Stress symptom analysis results

[1568] Output: Selected special coupon (e.g. "50% off dessert coupon")

[1569] Specific operation: Based on the analysis results, coupons that are likely to be preferred by the user are selected.

[1570] Step 14:

[1571] The server sends the selected special discount coupon in JSON format to the terminal, and the terminal notifies the user.

[1572] Input: Selected special coupon

[1573] Output: Coupon notification on the terminal

[1574] Specific operation: The special discount coupon information is converted into JSON format and sent to the terminal, which then notifies the user.

[1575] This system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment where users can enjoyably and continuously manage their health.

[1576] (Application example 1)

[1577] 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."

[1578] In today's world, maintaining a healthy diet is a major challenge, especially for urban residents and young people who lead busy lives. While there are a wide variety of food options available, there are few systems that can help users choose appropriate meals or nutritionally balanced meals. There is also a need for a system that can increase users' interest and motivation by providing relevant content and discount coupons in a timely manner.

[1579] 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.

[1580] In this invention, the server includes a means for analyzing the accumulated data and delivering content related to healthy meal suggestions, a means for delivering coupons for specific ingredients and dishes, and a means for detecting signs of stress in the user. This allows users to record their own eating habits and receive personalized healthy meal suggestions and useful information based on the records. Furthermore, by appropriately using coupons, users can enjoy economic benefits and promote healthy choices.

[1581] A "terminal" is an electronic device that a user uses to input dietary data and receive meal suggestions and coupons.

[1582] The "input form" is a data input screen displayed on the terminal for the user to record the details of their meal.

[1583] The "server" is a central management device that receives, stores, and analyzes the user's dietary data and generates suggestions and notifications.

[1584] "Database" refers to a data repository within the system that stores dietary data submitted by users.

[1585] The "analysis results" are information about the user's eating habits and health condition obtained by the server analyzing data acquired from the database.

[1586] "Healthy meal suggestions" are specific meal suggestions to the user to maintain good health based on the analysis results of the server.

[1587] "Discount Coupon" means an electronic coupon containing a discount offer that can be used when purchasing a suggested meal or ingredient.

[1588] "Points" are incentives given to users for using coupons or taking specific actions.

[1589] "Signs of stress" are possible signs of mental stress detected from fluctuations in the user's dietary data or changes in specific patterns.

[1590] A "special coupon" is a coupon that provides discounts and special benefits to reduce the user's stress.

[1591] "Related content" includes videos, articles, recipes, and other informational materials related to healthy eating.

[1592] The system of the present invention assists the user in managing his or her health by recording and analyzing the user's eating habits and suggesting healthy meals. Specific embodiments will be described below.

[1593] Recording user's dietary data

[1594] 1. Display the meal entry form

[1595] Users record their meal details through an application installed on a device such as a smartphone. A meal entry form is displayed on the device, and users enter information such as the type of meal (breakfast, lunch, dinner, snack, etc.), meal details, intake amount, and restaurant information.

[1596] 2. Enter your meal data

[1597] The user enters the details of their meal into the input form and presses the "Submit" button to send the data. For example, if a user ate "chicken salad" for lunch, they enter that information and submit it.

[1598] Data transmission and storage

[1599] 1. Data transmission

[1600] The device packages the entered meal data in JSON format and sends it to the server via an HTTP POST request.

[1601] 2. Data storage

[1602] The server analyzes the received data and stores it in a database, including the user ID, meal details, intake amount, time, and store information.

[1603] Analysis of dietary data

[1604] 1. Periodic database queries

[1605] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[1606] 2. Data Analysis

[1607] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[1608] Healthy eating suggestions and content distribution

[1609] 1. Health suggestion generation

[1610] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[1611] 2. Content Delivery

[1612] Deliver videos and articles related to the suggestions. For example, if a user likes salads, provide new salad recipe videos and health articles.

[1613] Offering coupons and awarding points

[1614] 1. Coupon Notification

[1615] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[1616] 2. Using coupons

[1617] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[1618] 3. Points Award

[1619] After the server verifies the validity of the coupon, it will add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[1620] Special coupon information

[1621] 1. Stress indicator detection

[1622] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[1623] 2. Selection of special coupons

[1624] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[1625] 3. Notification of coupons

[1626] The server sends the selected special coupon in JSON format to the device, and the device notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[1627] This provides an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[1628] Hardware and Software Used

[1629] Smartphone: iOS or Android device

[1630] Server: A remote server (AWS, Google Cloud, etc.) that analyzes data and distributes coupons

[1631] Database: MySQL or PostgreSQL

[1632] Communication protocol: HTTP / HTTPS, JSON format data

[1633] Examples of prompt statements

[1634] Example prompts to input to the generative AI model:

[1635] A user inputs "I had a salad for lunch" into a smartphone app. Based on this information, the server should perform the following analysis and generate healthy meal suggestions and coupons accordingly.

[1636] Input data:

[1637] User ID: 12345

[1638] Meal: Chicken salad

[1639] Serving size: 200g

[1640] Store name: Salad Stop

[1641] Example output:

[1642] Healthy Eating Suggestion: This Week's New Salad Recipe

[1643] Proposal content: Video link, related article URL

[1644] Coupon: Salad 20 yen discount coupon

[1645] In this way, the system of the present invention records and analyzes the user's eating habits and delivers healthy meal suggestions and related content, making it easier for users to make healthy choices and manage their health sustainably.

[1646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1647] Step 1:

[1648] The user enters the details of their meal into the device. The user opens the application installed on their smartphone and enters the type of meal (e.g., breakfast, lunch, dinner, snack), meal details, intake amount, and store information into the meal entry form. Once the input is complete, the user presses the "Submit" button. The input data includes the user ID, meal details, intake amount, and store information.

[1649] Input: Meal details, intake amount, store information

[1650] Output: Input food data

[1651] Step 2:

[1652] The device packages the entered food data in JSON format and sends it to the server via an HTTP POST request, where it is processed in real time and delivered to the server.

[1653] Input: JSON formatted meal data

[1654] Output: Data sent to the server

[1655] Step 3:

[1656] The server analyzes the received meal data and saves it in a database. The server converts the received data into a specific format and stores the user ID, meal details, intake amount, store information, etc. in the database.

[1657] Input: Data sent to the server

[1658] Output: Data stored in the database

[1659] Step 4:

[1660] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[1661] Input: Query dietary data from the database

[1662] Output: Food record for the past month

[1663] Step 5:

[1664] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[1665] Input: Food record for the past month

[1666] Output: User's meal frequency, preferences, and store preferences

[1667] Step 6:

[1668] The server generates healthy eating suggestions based on the analysis results and delivers videos and articles related to the suggestions. For example, if a user likes salads, it will provide new salad recipe videos and health-related articles.

[1669] Input: User's meal frequency, preferences, and store preferences

[1670] Output: Healthy eating suggestions and related content

[1671] Step 7:

[1672] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[1673] Input: Healthy eating suggestions and coupon information

[1674] Output: Coupon sent to the terminal

[1675] Step 8:

[1676] The user presents the coupon at a participating store and uses it. For example, they use a "20 yen off salad coupon" at a convenience store. The store's cash register or QR code reader sends the coupon code to the server and verifies its validity.

[1677] Input: Coupon presented at the participating store

[1678] Output: Coupon validity check

[1679] Step 9:

[1680] After the server verifies the validity of the coupon, it will add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[1681] Input: Check coupon validity

[1682] Output: Points awarded to user account

[1683] Step 10:

[1684] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. Based on the analysis results, it selects special coupons for stress reduction and sends them to the device in JSON format.

[1685] Input: Analysis results of dietary data

[1686] Output: Select and send special coupons

[1687] Step 11:

[1688] The terminal displays the special coupon received from the server and notifies the user, who can then use the special coupon to enjoy an appropriate cheat day and maintain motivation.

[1689] Input: Special coupon information

[1690] Output: Special coupon notification displayed on the terminal

[1691] In this way, the system of the invention records and analyzes the user's eating habits, and delivers healthy meal suggestions and related content. Users can also use coupons and points to enjoyably manage their health on an ongoing basis.

[1692] 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.

[1693] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and suggesting healthy meals, and by combining it with an emotion engine, it can more effectively maintain the motivation of users. Specific embodiments are described below.

[1694] Recording user's dietary data

[1695] 1. Display the meal entry form

[1696] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (the name of the convenience store or restaurant).

[1697] 2. Enter your meal data

[1698] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[1699] Data transmission and storage

[1700] 1. Data transmission

[1701] The device packages the entered meal data in JSON format and sends it to the server using a POST request.

[1702] 2. Data storage

[1703] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[1704] Analysis of dietary data

[1705] 1. Periodic database queries

[1706] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[1707] 2. Data Analysis

[1708] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[1709] Healthy meal suggestions and coupons

[1710] 1. Health suggestion generation

[1711] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[1712] 2. Coupon Notification

[1713] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[1714] Coupon use and points

[1715] 1. Using coupons

[1716] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[1717] 2. Check the coupon

[1718] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[1719] 3. Points Award

[1720] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[1721] Special coupon information

[1722] 1. Stress indicator detection

[1723] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[1724] 2. Selection of special coupons

[1725] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[1726] 3. Notification of coupons

[1727] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[1728] Implementing the Emotion Engine

[1729] 1. Entering emotion data

[1730] Users can input their emotional state within the app, choosing from options such as "I'm feeling stressed today" or "I'm in a happy mood."

[1731] 2. Emotion Data Analysis

[1732] The server analyzes the input emotion data and evaluates the user's current emotional state.

[1733] 3. Emotion-based recommendation optimization

[1734] The server optimizes healthy meal suggestions and special coupons based on the user's emotional state: for example, if the user is feeling stressed, it will suggest more relaxing meals and coupons.

[1735] 4. Emotion-based notifications

[1736] The server then sends the optimized recommendations to the device, which then notifies the user. For example, a user who is feeling stressed can be offered a discount coupon for relaxing herbal tea.

[1737] In this way, the system of the present invention records and analyzes the user's eating habits, and by suggesting healthy meals and providing coupons, provides an environment where users can enjoy managing their health. In addition, by combining it with an emotion engine, it is possible to make more personalized suggestions according to the user's emotional state, contributing to maintaining motivation and reducing stress.

[1738] The processing flow will be explained below.

[1739] Processing of systems with emotion engines

[1740] Recording user's dietary data

[1741] Step 1:

[1742] The terminal displays a meal entry form.

[1743] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[1744] Step 2:

[1745] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[1746] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[1747] Step 3:

[1748] The device packages the entered meal data in JSON format and sends it to the server.

[1749] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[1750] Data transmission and storage

[1751] Step 4:

[1752] The server analyzes the received data and stores it in a database.

[1753] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[1754] Entering emotion data

[1755] Step 5:

[1756] The terminal displays an emotion input form.

[1757] Specific behavior: Display options on the device screen to select an emotional state (e.g., "I feel stressed today" or "I feel happy").

[1758] Step 6:

[1759] The user inputs their emotional state and presses the "send" button.

[1760] Specific operation: The user selects today's emotional state and presses the send button.

[1761] Step 7:

[1762] The device packages the input emotion data in JSON format and sends it to the server.

[1763] Specific operation: Emotion data is packaged in JSON format and sent to the server via a POST request.

[1764] Emotional Data Analysis

[1765] Step 8:

[1766] The server analyzes the received emotional data and evaluates the current emotional state.

[1767] Specific operation: Emotional data is run through an analysis algorithm to assess whether the user is stressed or relaxed.

[1768] Analysis of dietary data

[1769] Step 9:

[1770] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[1771] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[1772] Step 10:

[1773] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[1774] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[1775] Healthy meal suggestions and coupons

[1776] Step 11:

[1777] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[1778] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[1779] Step 12:

[1780] The server optimizes the suggestions taking into account the emotional state.

[1781] What it does: If the user is feeling stressed, it will adjust its suggestions to suggest relaxing meals or coupons.

[1782] Step 13:

[1783] The server sends the generated proposals and coupon information in JSON format to the terminal.

[1784] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[1785] Coupon use and points

[1786] Step 14:

[1787] The user presents the coupon at the store to use it.

[1788] Specific action: Present the barcode or coupon code in the app at the store.

[1789] Step 15:

[1790] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[1791] Specific behavior: Enter a coupon code and send a validation request to the server.

[1792] Step 16:

[1793] The server checks the validity of the coupon and stores the usage history in a database.

[1794] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[1795] Step 17:

[1796] The server verifies that the coupon has been used and credits the points to the user's account.

[1797] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[1798] Special coupon information

[1799] Step 18:

[1800] The server analyzes the user's dietary data and detects signs of stress.

[1801] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[1802] Step 19:

[1803] The server selects special coupons for stress reduction.

[1804] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[1805] Step 20:

[1806] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[1807] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[1808] In this way, by combining an emotion engine, the system of the present invention supports the user's enjoyable and healthy eating habits and can make more personalized suggestions based on the user's emotional state, contributing to maintaining motivation and reducing stress.

[1809] Example 2

[1810] 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."

[1811] Conventional health management systems were able to collect and analyze users' dietary data, but they struggled to provide personalized recommendations that took into account the user's emotional state. This made it difficult to maintain user motivation over the long term, making effective health management difficult to achieve. Furthermore, they lacked a mechanism for early detection of stress symptoms and the provision of appropriate coupons and recommendations.

[1812] 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.

[1813] In this invention, the server is configured to record the user's dietary habit data.

[1814] A means for allowing a user to input meal details via an input form displayed on a terminal;

[1815] means for transmitting the recorded dietary habit data to a server;

[1816] A means for storing the transmitted data in a database and analyzing the data accumulated over a period of time;

[1817] A means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals;

[1818] a means for confirming that the coupon has been used and awarding points to the user based on the confirmation;

[1819] means for inputting the user's emotional state and transmitting the emotional data to a server;

[1820] A means of analyzing emotional data and optimizing healthy meal suggestions and special coupons based on the analysis results;

[1821] A means for notifying the user of sentiment-based optimized offers and special offers;

[1822] This will enable a comprehensive analysis of the user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons. This will help maintain user motivation and reduce stress, enabling long-term health management.

[1823] "User" refers to an individual who uses this system to input dietary data and emotional data.

[1824] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[1825] "Input form" refers to an on-screen form that allows the user to input dietary details and emotional state.

[1826] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[1827] "Server" refers to the computer on the system that receives transmitted data and stores and analyzes the data.

[1828] "Database" refers to a storage device for storing dietary data and emotional data.

[1829] "Analysis results" refers to information obtained by the server analyzing the accumulated data.

[1830] "Health Suggestions" refers to suggestions for healthy eating and behaviors provided to users based on the analysis results.

[1831] "Coupon" refers to an electronic voucher that offers discounts or special offers on specific meals or products.

[1832] "Points" refers to rewards credited to a user's account based on the use of a coupon.

[1833] "Emotional state" refers to the psychological state such as stress or mood felt by the user.

[1834] "Emotional data" refers to information about an emotional state entered by a user.

[1835] "Discount coupon" refers to an electronic coupon that comes with special discounts or benefits.

[1836] "Notification" refers to a message or alert sent from the server to the device.

[1837] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and providing appropriate healthy meal suggestions and coupons. Furthermore, by combining it with an emotion engine, it is possible to more effectively maintain the user's motivation. Specific embodiments are described below.

[1838] Hardware and Software Use

[1839] Device: Use a device such as a smartphone, tablet, or PC.

[1840] Server: A computer on a network used to receive, analyze, and store data.

[1841] Database: Uses a storage device to store dietary and emotional data.

[1842] Data processing and calculation

[1843] Data collection:

[1844] The terminal collects user input data and receives dietary details and emotional state in an input form.

[1845] The user enters their dietary habits and emotional state into an input form and submits it.

[1846] Data transmission and storage:

[1847] The terminal sends the data entered by the user to the server in JSON format.

[1848] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, store information, emotional state, etc.

[1849] Data analysis:

[1850] The server periodically queries and analyzes the data stored in the database, including the user's eating habits, preferences, restaurant preferences, and emotional state.

[1851] For example, if user A tends to eat salad more than three times a week, this pattern will be recognized.

[1852] Dining suggestions and coupons:

[1853] The server generates healthy meal suggestions based on the analysis results and provides discount coupons for the suggested meals.

[1854] The server sends the generated offer and coupon information to the terminal, which receives it and notifies the user.

[1855] Coupon use and points awarded:

[1856] The user presents the coupon and uses it at the target store.

[1857] The terminal (store device) sends the coupon code to the server and checks its validity.

[1858] The server awards points to the user based on valid coupons.

[1859] Detecting stress indicators and providing special coupons:

[1860] The server analyzes the user's dietary and emotional data to detect stress indicators. For example, if the user's intake of high-calorie foods increases, the server determines that the user is feeling stressed.

[1861] The server sends the selected special coupon to the terminal and notifies the user, helping them enjoy their cheat day.

[1862] Specific prompt examples

[1863] Food data entry prompt:

[1864] What did you have for lunch today? Please enter the exact menu item and portion size.

[1865] Example: Grilled Chicken Salad, 200g

[1866] Emotional state prompt:

[1867] Please tell us how you're feeling right now. Please choose from the options below.

[1868] 1. Fun

[1869] 2. Normal

[1870] 3. You feel stressed

[1871] 4. Feeling depressed

[1872] Example

[1873] For example, if a user has "grilled chicken salad" for lunch, they enter and submit the details as "grilled chicken salad, 200g, salad shop ABC." The server analyzes the data and recognizes the user's eating habits. If the user tends to eat salad more than three times a week, the server will suggest a "50 yen discount coupon" for the next salad purchase and notify the user's device.

[1874] This will enable comprehensive analysis of a user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons, which is expected to help maintain user motivation and reduce stress, enabling long-term health management.

[1875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1876] Step 1:

[1877] Display of meal entry form

[1878] The terminal displays an input form that allows the user to enter meal details. The form includes radio buttons such as "Breakfast," "Lunch," "Dinner," and "Snack," as well as text input fields. The input form is displayed when the program is started or when a specific button is pressed.

[1879] Input: Launching a program or pressing a button

[1880] Output: Display of meal entry form

[1881] Step 2:

[1882] Entering meal data

[1883] The user inputs the meal details using the input form on the terminal and presses the "Submit" button to send the data. For example, the user inputs "Grilled chicken salad, 200g, salad shop ABC" for lunch.

[1884] Input: The meal details entered by the user in the input form

[1885] Output: Pressing the send button puts the data into a waiting state for transmission.

[1886] Step 3:

[1887] Data transmission

[1888] The device packages the meal data entered by the user into JSON format and sends it to the server using an HTTP POST request.

[1889] Input: User-entered meal data (e.g., Grilled Chicken Salad, 200g, Salad Shop ABC)

[1890] Output: Meal data is sent to the server in JSON format

[1891] Step 4:

[1892] Data storage

[1893] The server analyzes the received meal data and stores it in a database, including the user ID, meal content, intake amount, meal time, and restaurant information.

[1894] Input: Meal data sent in JSON format

[1895] Output: The data is saved correctly in the database

[1896] Step 5:

[1897] Periodic database queries

[1898] The server periodically queries the database to obtain meal data for a certain period of time, for example, to obtain meal data for the past month.

[1899] Input: A trigger that runs a periodic query from the database

[1900] Output: Meal data for a certain period of time is obtained.

[1901] Step 6:

[1902] Data analysis

[1903] The server analyzes the food data retrieved from the database to extract the user's meal frequency, preferences, and restaurant preferences. This analysis uses standard statistical analysis methods and machine learning algorithms. For example, the server can determine that User A eats salad at least three times a week.

[1904] Input: Acquired dietary data over a period of time

[1905] Output: Analysis results of each user's meal frequency, preferences, and store usage trends

[1906] Step 7:

[1907] Health suggestion generation

[1908] The server generates healthy meal suggestions based on the analysis results. For example, it might suggest to User A, "A 50 yen discount coupon for the next salad purchase." The suggestions are generated using pre-defined rules and machine learning models.

[1909] Input: Analysis results of dietary data

[1910] Output: Health recommendations and coupon information tailored to each individual user

[1911] Step 8:

[1912] Coupon Notifications

[1913] The server sends the generated offers and coupon information to the device, which receives it in JSON format and displays a notification to the user.

[1914] Input: Offer and coupon information

[1915] Output: Offer and coupon information is sent to the terminal and the user is notified

[1916] Step 9:

[1917] Use coupon

[1918] The user presents the coupon at the store. For example, the user uses a "20 yen off salad coupon" at a convenience store. The coupon is presented using a QR code or barcode.

[1919] Input: Present coupon

[1920] Output: A confirmation of coupon redemption is displayed to the user

[1921] Step 10:

[1922] Check coupons

[1923] The terminal (such as a store cash register or QR code reader) sends the coupon code to a server to verify its validity, which typically involves checking a database of coupons.

[1924] Enter: Coupon Code

[1925] Output: Coupon code validation result

[1926] Step 11:

[1927] Points awarded

[1928] After verifying the validity of the coupon, the server adds points to the user's account. For example, if a user uses a "salad coupon," 5 points are added. The point addition involves updating the user's account.

[1929] Input: Coupon validity check result

[1930] Output: Points are credited to the user account.

[1931] Step 12:

[1932] Input of user emotion data

[1933] The user inputs their emotional state using an input form on the device. For example, they answer "yes" to the question "Are you feeling stressed today?"

[1934] Input: User's emotional state input

[1935] Output: Emotional state data ready to send

[1936] Step 13:

[1937] Sending emotional data

[1938] The device sends the emotion data entered by the user to the server in JSON format.

[1939] Input: Emotion data entered by the user

[1940] Output: Emotion data is sent to the server in JSON format.

[1941] Step 14:

[1942] Emotional Data Analysis

[1943] The server analyzes the received emotion data and evaluates the user's current emotional state. The analysis uses natural language processing and machine learning models. For example, if a user inputs "stress," the server recognizes the user as being in a high-stress state.

[1944] Input: Received emotion data

[1945] Output: Emotion evaluation result

[1946] Step 15:

[1947] Emotion-based suggestion optimization

[1948] The server optimizes health suggestions and special coupons based on emotional data. For example, if a user is under high stress, it will suggest relaxing suggestions. Machine learning algorithms are used for optimization.

[1949] Input: Emotion evaluation result

[1950] Output: Optimized health recommendations and coupon information

[1951] Step 16:

[1952] Emotion-based notifications

[1953] The server transmits the optimized proposal and the special coupon to the terminal, which notifies the user of the same.

[1954] Input: Optimized health recommendations and coupon information

[1955] Output: Offer and coupon information is sent to the terminal and notified to the user

[1956] (Application example 2)

[1957] 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."

[1958] Conventional dietary management systems can record a user's dietary data and suggest healthy meals, but they have difficulty making personalized suggestions that take into account the user's emotional state. Furthermore, there is a lack of effective health management methods, particularly for specific occupational environments such as factory workers. This has led to issues such as not being able to adequately maintain user motivation or reduce stress.

[1959] 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.

[1960] In this invention, the server includes a means for inputting and analyzing the user's emotional state and optimizing healthy meal suggestions and special coupons based on the input, a means for combining the analysis results with the emotional state to make health management suggestions for factory workers, and a means for analyzing the accumulated data, thereby enabling personalized health management suggestions according to the user's emotional state and work environment.

[1961] "User" refers to an individual or worker who uses the system of the present invention.

[1962] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[1963] "Input form" refers to the screen displayed on the terminal for the user to input their meal details and emotional state.

[1964] "Device" means a smartphone, tablet, personal computer, or other digital device.

[1965] "Server" refers to a central system for receiving, storing, and analyzing data submitted by users.

[1966] "Database" refers to a storage system for storing accumulated dietary data and analysis results.

[1967] "Healthy meal suggestions" refer to meal suggestions that are effective for maintaining health and dieting, based on the user's dietary data and emotional state.

[1968] "Discount Coupon" means a coupon for applying a discount to a proposed meal.

[1969] "Special coupons" refer to special coupons provided to users to reduce stress and increase motivation.

[1970] "Emotional state" refers to the emotional or mental state a user is experiencing.

[1971] "Personalized suggestions" refers to health suggestions or coupon offers that are tailored based on an individual user's specific situation or emotional state.

[1972] "Factory workers" refers to people who work in factories and who require health care in a specific occupational environment.

[1973] The system of the present invention supports users in dieting and health management by recording and analyzing their dietary habits and suggesting healthy meals. Furthermore, by combining it with an emotion engine, it is possible to maintain the user's motivation.

[1974] Hardware and Software Configuration

[1975] The system is configured so that users can input data using devices such as smartphones, tablets, and personal computers, and the server analyzes the data. Communication between the server and devices and data storage are performed using an internet connection and a database management system.

[1976] Recording user's dietary data

[1977] The user inputs the details of their meal and their emotional state via an input form displayed on the device. The input form includes fields for the type of meal (breakfast, lunch, dinner, snack, etc.), the details of the meal, the amount of intake, and store information (such as the name of the restaurant or convenience store). The user can also input their emotional state. For example, options such as "I feel stressed today" and "I'm in a happy mood" are provided.

[1978] Data transmission and storage

[1979] The device converts the entered dietary data and emotional state into JSON format and sends it to the server using a POST request. The server then stores the received data in a database. This data includes the user ID, meal content, intake amount, time, store information, emotional state, etc.

[1980] Analysis of dietary data

[1981] The server periodically queries the database for dietary data and retrieves data accumulated over a certain period of time. For example, it retrieves a meal record for the past month. Based on this retrieved data, it extracts the user's meal frequency, preferences, and restaurant preferences. It also analyzes the user's emotional state and detects signs of stress.

[1982] Healthy meal suggestions and coupons

[1983] The server then suggests healthy meals suitable for the user based on the analysis results and generates discount coupons for the suggested meals. The generated coupon information is sent to the device in JSON format and displayed on the device as a coupon list.

[1984] Coupon use and points

[1985] The user presents and uses the coupon at the participating store. The terminal (store cash register or QR code reader) sends the coupon code to the server to check its validity. After the server checks the validity of the coupon, points are added to the user's account.

[1986] Optimizing recommendations with an emotion engine

[1987] The server analyzes the input emotional data and optimizes healthy meal suggestions and special coupons based on the user's current emotional state. For example, if the user is feeling stressed, it will suggest relaxing meals and coupons.

[1988] Specific examples

[1989] For example, if a user with user ID 123 ate "chicken salad" for lunch and entered their emotional state as "feeling stressed," the server would generate healthy meal suggestions and relaxation coupons based on this data. An example of a prompt sentence entered in the system is as follows:

[1990] User ID: 123

[1991] Meal Type: Lunch

[1992] Meal: Chicken salad

[1993] Quantity: 1

[1994] Store name: Convenience store A

[1995] Emotional state: Stressed

[1996] Based on this prompt, appropriate suggestions can be made to the user.

[1997] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1998] Step 1:

[1999] Data Entry

[2000] The user inputs information such as meal details, intake amount, restaurant information, and emotional state into an input form on the device. The input data includes specific information such as "Lunch," "Chicken Salad," "Quantity: 1," "Convenience Store A," and "Feeling Stressed." This information is transmitted from the device as dietary and emotional data.

[2001] Step 2:

[2002] Data transmission

[2003] The device converts the entered dietary and emotional data into JSON format and sends it to the server as a POST request. Examples of data sent include the following JSON format data:

[2004] json

[2005] {

[2006] "user_id": "123",

[2007] "meal_type": "Lunch",

[2008] "meal_content": "Chicken Salad",

[2009] "quantity": "1",

[2010] "store_name": "Convenience store A",

[2011] "emotion_state": "stressed"

[2012] }

[2013] The server receives this and proceeds to the next step.

[2014] Step 3:

[2015] Data storage

[2016] The server analyzes the received JSON data and breaks it down into items such as user ID, meal content, intake amount, time, store information, and emotional state. The broken down data is stored in a database, which accumulates data for subsequent data analysis and health recommendations.

[2017] Step 4:

[2018] Data Query and Analysis

[2019] The server periodically queries the database for accumulated dietary and emotional data, obtaining data from a certain period of time in the past. For example, it obtains data from the past month's worth of food records and emotional state. The obtained data is processed by an analysis engine to extract the user's eating habits, preferences, restaurant habits, and signs of stress.

[2020] Step 5:

[2021] Generate healthy eating suggestions

[2022] Based on the analysis results, the server will suggest healthy meals suitable for the user. For example, it will generate specific suggestions such as "We recommend herbal tea with a relaxing effect for user 123." This information includes food names, quantities, recommended stores, and even discount coupons.

[2023] Step 6:

[2024] Generate and send coupons

[2025] Taking into account the analysis results and the user's emotional state, the server generates discount coupons and special offers for healthy eating suggestions. The generated coupon information is then packaged in JSON format and sent to the device.

[2026] Step 7:

[2027] Coupon and Offer Notifications

[2028] The device notifies the user of the offer information and coupons received from the server. The user can check and use the received coupons. The notifications are displayed as push notifications or in-app notifications.

[2029] Step 8:

[2030] Coupon usage and confirmation

[2031] The user presents the coupon at a participating store to use it. The store's terminal (e.g., QR code reader or cash register) sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server records the successful use of the coupon in the database.

[2032] Step 9:

[2033] Points awarded

[2034] After verifying the validity of the coupon, the server will add points to the user's account. This completes the coupon usage and provides an incentive to the user. For example, an operation such as "add 5 points to user 123" is executed.

[2035] 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.

[2036] 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.

[2037] 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.

[2038] [Fourth embodiment]

[2039] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2040] 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.

[2041] 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).

[2042] 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.

[2043] 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.

[2044] 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).

[2045] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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.

[2051] 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."

[2052] The system of the present invention supports a user's diet and health management by recording and analyzing the user's eating habits and suggesting healthy meals. Specific embodiments will be described below.

[2053] Recording user's dietary data

[2054] 1. Display the meal entry form

[2055] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (convenience store or restaurant name).

[2056] 2. Enter your meal data

[2057] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[2058] Data transmission and storage

[2059] 1. Data transmission

[2060] The device packages the entered meal data and sends it to the server using a POST request in JSON format.

[2061] 2. Data storage

[2062] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[2063] Analysis of dietary data

[2064] 1. Periodic database queries

[2065] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[2066] 2. Data Analysis

[2067] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[2068] Healthy meal suggestions and coupons

[2069] 1. Health suggestion generation

[2070] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[2071] 2. Coupon Notification

[2072] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[2073] Coupon use and points

[2074] 1. Using coupons

[2075] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[2076] 2. Check the coupon

[2077] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[2078] 3. Points Award

[2079] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[2080] Special coupon information

[2081] 1. Stress indicator detection

[2082] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[2083] 2. Selection of special coupons

[2084] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[2085] 3. Notification of coupons

[2086] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[2087] In this way, the system of the present invention records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which the user can enjoyably and continuously manage their health, thereby eliminating the stress and difficulty of continuing with conventional methods.

[2088] The processing flow will be explained below.

[2089] Recording user's dietary data

[2090] Step 1:

[2091] The terminal displays a meal entry form.

[2092] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[2093] Step 2:

[2094] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[2095] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[2096] Step 3:

[2097] The device packages the entered meal data in JSON format and sends it to the server.

[2098] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[2099] Data transmission and storage

[2100] Step 4:

[2101] The server analyzes the received data and stores it in a database.

[2102] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[2103] Analysis of dietary data

[2104] Step 5:

[2105] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[2106] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[2107] Step 6:

[2108] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[2109] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[2110] Healthy meal suggestions and coupons

[2111] Step 7:

[2112] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[2113] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[2114] Step 8:

[2115] The server sends the generated proposals and coupon information in JSON format to the terminal.

[2116] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[2117] Coupon use and points

[2118] Step 9:

[2119] The user presents the coupon at the store to use it.

[2120] Specific action: Present the barcode or coupon code in the app at the store.

[2121] Step 10:

[2122] The terminal (store terminal) sends the coupon code to the server and checks its validity.

[2123] Specific behavior: Enter a coupon code and send a validation request to the server.

[2124] Step 11:

[2125] The server checks the validity of the coupon and stores the usage history in a database.

[2126] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[2127] Step 12:

[2128] The server verifies that the coupon has been used and credits the points to the user's account.

[2129] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[2130] Special coupon information

[2131] Step 13:

[2132] The server analyzes the user's dietary data and detects signs of stress.

[2133] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[2134] Step 14:

[2135] The server selects special coupons for stress reduction.

[2136] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[2137] Step 15:

[2138] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[2139] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[2140] Through the above processing steps, the system of the present invention can support the user's eating habits in an enjoyable and healthy way.

[2141] Example 1

[2142] 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."

[2143] In today's busy lifestyles, it is difficult for users to maintain a healthy diet. In particular, recording and managing dietary habits, choosing healthy meals, and maintaining motivation are major challenges. Increased stress can also lead to unhealthy eating habits. Conventional methods are difficult to efficiently solve these challenges, and effective means for users to continuously manage their health are needed.

[2144] 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.

[2145] In this invention, the server includes: means for having the user input dietary details via an input form displayed on a terminal for recording the user's dietary data; means for transmitting the recorded dietary data to the server; means for storing the transmitted data in a database and analyzing the data accumulated over a certain period of time; means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals; means for confirming that the coupons have been used and awarding points to the user based on that; means for detecting signs of stress in the user and providing special coupons for stress reduction; means for having the user input dietary data including the type, content, amount of intake, and store information of the meals; and means for the server to confirm the validity of the coupons. This makes it easier for users to manage their health in an enjoyable and continuous manner, and also reduces unhealthy food choices due to stress, enabling overall health maintenance.

[2146] A "terminal" is a communication device used by a user, and includes a smartphone, tablet, personal computer, etc.

[2147] An "input form" refers to an interface that is displayed on the terminal screen and allows the user to input data such as meal contents.

[2148] "Dietary data" refers to data including information such as the type, content, and amount of food consumed by the user, as well as store information.

[2149] "Server" refers to a central processing unit for receiving, storing, and analyzing data sent by users.

[2150] "Database" refers to an information management system that manages dietary data stored on a server and accumulates it over a certain period of time.

[2151] "Analysis" refers to the process of evaluating the dietary data stored in the database and extracting information such as the user's meal frequency, preferences, and tendencies toward the stores they frequent.

[2152] "Health suggestions" refer to content that recommends healthy eating to the user based on the analysis results.

[2153] "Discount Coupon" refers to an electronic or paper coupon that a user can use to receive a discount.

[2154] "Points" refer to incentives given to users when they use coupons, and are electronic points that can be later exchanged for rewards.

[2155] A "special complimentary coupon" refers to a coupon for receiving special discounts or benefits provided to reduce the user's stress.

[2156] "Validating a coupon" refers to the process by which the server verifies whether a coupon submitted by a user is valid.

[2157] The system of the present invention supports a user's diet and health management by recording and analyzing the user's dietary data and suggesting healthy meals. Specific embodiments will be described below.

[2158] Recording user's dietary data

[2159] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter the details of their meal. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount consumed, and store information (the name of the convenience store or restaurant). The user enters the details of the meal they actually ate and presses the "Submit" button to send the data. For example, if the user ate "chicken salad" for lunch, they enter that information and submit it.

[2160] Data transmission and storage

[2161] The device packages the entered meal data and sends it to the server. This sends JSON format data via a POST request. The server analyzes the received data and stores it in a database. The stored data includes the user ID, meal content, intake amount, time, store information, etc.

[2162] Analysis of dietary data

[2163] The server periodically queries the database for food data and retrieves data accumulated over a certain period of time. For example, it retrieves food records from the past month. The server analyzes the retrieved data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[2164] Healthy meal suggestions and coupons

[2165] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it generates a suggestion such as "User A can use a 20 yen discount coupon for a salad today." The server sends the generated suggestion and coupon information to the device in JSON format. The device receives this and displays it to the user as a list of available coupons.

[2166] Coupon use and points

[2167] The user presents and uses a coupon at a participating store. For example, a user uses a "20 yen off salad coupon" at a convenience store. The store's cash register, QR code reader, or other terminal sends the coupon code to the server and checks its validity. After checking the validity of the coupon, the server adds points to the user's account. For example, a user who uses a salad coupon receives 5 points.

[2168] Special coupon information

[2169] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. The server selects special coupons to reduce stress. For example, if the user has a sweet tooth, it selects a "50% off dessert coupon." The server sends the selected special coupon in JSON format to the device, which notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain motivation.

[2170] Examples of concrete examples and prompts

[2171] Specific examples

[2172] User A ate "toast and eggs" for breakfast, "chicken salad" for lunch, and "grilled fish" for dinner. The server analyzes this and recognizes the pattern that "he eats salad at least three times a week." As a result, the server offers a 20 yen discount coupon for salad today to help him enjoy it even more.

[2173] Prompt Sentence Examples

[2174] "Please explain the process of providing healthy meal suggestions and a 20 yen discount coupon for salad in JSON format to User A, who eats salad at least three times a week."

[2175] In this way, this system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[2176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2177] Step 1:

[2178] The terminal displays an input form in which the user can input the meal details.

[2179] Input: User's diet, type of food, intake amount, store information, etc.

[2180] Output: Meal data entered by the user

[2181] Specific operation: An input form is displayed on the screen of a smartphone or PC, and the user enters the meal details in the designated fields.

[2182] Step 2:

[2183] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data.

[2184] Input: User-entered meal data

[2185] Output: Meal data when the send button was pressed

[2186] Specific actions: The user enters something like "toast and eggs" for breakfast and presses the "Submit" button.

[2187] Step 3:

[2188] The terminal packages the input meal data and sends it to the server.

[2189] Input: Meal data when the submit button is pressed

[2190] Output: Data packaged in JSON format, request sent to server

[2191] Specific operation: The device converts the meal data into JSON format and sends a POST request to the server.

[2192] Step 4:

[2193] The server analyzes the received data and stores it in a database.

[2194] Input: JSON formatted meal data

[2195] Output: Saved data (user ID, meal contents, intake amount, time, store information, etc.)

[2196] Specific operation: The server parses the received data, executes SQL queries and saves them to the database.

[2197] Step 5:

[2198] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[2199] Input: Query from the database

[2200] Output: Food record for the past month

[2201] Specific operation: Every day at 2:00 AM, the database is accessed and an SQL query such as "SELECT FROM Meal Record WHERE Date >= DATE_SUB(CURDATE(), INTERVAL 1 MONTH)" is executed.

[2202] Step 6:

[2203] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[2204] Input: Food record for the past month

[2205] Output: Analysis results (e.g., "User A eats salad at least three times a week" and "User B eats fast food at least twice a week")

[2206] What it does: Analyzes data and extracts patterns and trends using statistical methods and machine learning models.

[2207] Step 7:

[2208] The server then suggests healthy meals suitable for the user based on the analysis results.

[2209] Input: Analysis results

[2210] Output: Suggested healthy meals and corresponding discount coupons

[2211] Specific operation: Generate specific suggestions such as "User A can use a coupon for 20 yen off a salad today" and create coupon data.

[2212] Step 8:

[2213] The server sends the generated proposals and coupon information in JSON format to the terminal.

[2214] Input: Suggested healthy meals and corresponding discount coupons

[2215] Output: The list of offers and coupons displayed on the terminal.

[2216] Specific operation: The offer content and coupon information are converted into JSON format and sent to the device, which receives it and displays it to the user.

[2217] Step 9:

[2218] The user presents and uses the coupon at the target store.

[2219] Input: Coupon information displayed on the device

[2220] Output: Record of coupon usage at store

[2221] Specific operation: The user presents the "20 yen off salad coupon" and uses it at the store.

[2222] Step 10:

[2223] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[2224] Input: Coupon code, User ID

[2225] Output: Coupon validity check result

[2226] Specific operation: The device sends the coupon code and user ID to the server and checks the expiration date and whether it has been used.

[2227] Step 11:

[2228] The server will credit the points to the user's account after verifying the validity of the coupon.

[2229] Input: Coupon validity check result

[2230] Output: Points awarded to the user account

[2231] Specific operation: The server checks the validity of the coupon and sends the data to the points system to add points to the user's account.

[2232] Step 12:

[2233] The server analyzes the user's dietary data and detects signs of stress.

[2234] Input: Past meal data

[2235] Output: Stress symptom analysis results

[2236] Specific behavior: Analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods to detect signs of stress.

[2237] Step 13:

[2238] The server selects special coupons for stress reduction.

[2239] Input: Stress symptom analysis results

[2240] Output: Selected special coupon (e.g. "50% off dessert coupon")

[2241] Specific operation: Based on the analysis results, coupons that are likely to be preferred by the user are selected.

[2242] Step 14:

[2243] The server sends the selected special discount coupon in JSON format to the terminal, and the terminal notifies the user.

[2244] Input: Selected special coupon

[2245] Output: Coupon notification on the terminal

[2246] Specific operation: The special discount coupon information is converted into JSON format and sent to the terminal, which then notifies the user.

[2247] This system records and analyzes the user's eating habits, suggests healthy meals, and provides coupons, providing an environment where users can enjoy continuous health management.

[2248] (Application example 1)

[2249] 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."

[2250] In today's world, maintaining a healthy diet is a major challenge, especially for urban residents and young people who lead busy lives. While there are a wide variety of food options available, there are few systems that can help users choose appropriate meals or nutritionally balanced meals. There is also a need for a system that can increase users' interest and motivation by providing relevant content and discount coupons in a timely manner.

[2251] 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.

[2252] In this invention, the server includes a means for analyzing the accumulated data and delivering content related to healthy meal suggestions, a means for delivering coupons for specific ingredients and dishes, and a means for detecting signs of stress in the user. This allows users to record their own eating habits and receive personalized healthy meal suggestions and useful information based on the records. Furthermore, by appropriately using coupons, users can enjoy economic benefits and promote healthy choices.

[2253] A "terminal" is an electronic device that a user uses to input dietary data and receive meal suggestions and coupons.

[2254] The "input form" is a data input screen displayed on the terminal for the user to record the details of their meal.

[2255] The "server" is a central management device that receives, stores, and analyzes the user's dietary data and generates suggestions and notifications.

[2256] "Database" refers to a data repository within the system that stores dietary data submitted by users.

[2257] The "analysis results" are information about the user's eating habits and health condition obtained by the server analyzing data acquired from the database.

[2258] "Healthy meal suggestions" are specific meal suggestions to the user to maintain good health based on the analysis results of the server.

[2259] "Discount Coupon" means an electronic coupon containing a discount offer that can be used when purchasing a suggested meal or ingredient.

[2260] "Points" are incentives given to users for using coupons or taking specific actions.

[2261] "Signs of stress" are possible signs of mental stress detected from fluctuations in the user's dietary data or changes in specific patterns.

[2262] A "special coupon" is a coupon that provides discounts and special benefits to reduce the user's stress.

[2263] "Related content" includes videos, articles, recipes, and other informational materials related to healthy eating.

[2264] The system of the present invention assists the user in managing his or her health by recording and analyzing the user's eating habits and suggesting healthy meals. Specific embodiments will be described below.

[2265] Recording user's dietary data

[2266] 1. Display the meal entry form

[2267] Users record their meal details through an application installed on a device such as a smartphone. A meal entry form is displayed on the device, and users enter information such as the type of meal (breakfast, lunch, dinner, snack, etc.), meal details, intake amount, and restaurant information.

[2268] 2. Enter your meal data

[2269] The user enters the details of their meal into the input form and presses the "Submit" button to send the data. For example, if a user ate "chicken salad" for lunch, they enter that information and submit it.

[2270] Data transmission and storage

[2271] 1. Data transmission

[2272] The device packages the entered meal data in JSON format and sends it to the server via an HTTP POST request.

[2273] 2. Data storage

[2274] The server analyzes the received data and stores it in a database, including the user ID, meal details, intake amount, time, and store information.

[2275] Analysis of dietary data

[2276] 1. Periodic database queries

[2277] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[2278] 2. Data Analysis

[2279] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[2280] Healthy eating suggestions and content distribution

[2281] 1. Health suggestion generation

[2282] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[2283] 2. Content Delivery

[2284] Deliver videos and articles related to the suggestions. For example, if a user likes salads, provide new salad recipe videos and health articles.

[2285] Offering coupons and awarding points

[2286] 1. Coupon Notification

[2287] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[2288] 2. Using coupons

[2289] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[2290] 3. Points Award

[2291] After the server verifies the validity of the coupon, it will add points to the user's account. For example, if a user uses a salad coupon, they will receive 5 points.

[2292] Special coupon information

[2293] 1. Stress indicator detection

[2294] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[2295] 2. Selection of special coupons

[2296] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[2297] 3. Notification of coupons

[2298] The server sends the selected special coupon in JSON format to the device, and the device notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[2299] This provides an environment in which users can enjoyably and continuously manage their health, eliminating the stress and difficulty of continuing with conventional methods.

[2300] Hardware and Software Used

[2301] Smartphone: iOS or Android device

[2302] Server: A remote server (AWS, Google Cloud, etc.) that analyzes data and distributes coupons

[2303] Database: MySQL or PostgreSQL

[2304] Communication protocol: HTTP / HTTPS, JSON format data

[2305] Examples of prompt statements

[2306] Example prompts to input to a generative AI model:

[2307] A user inputs "I had a salad for lunch" into a smartphone app. Based on this information, the server should perform the following analysis and generate healthy meal suggestions and coupons accordingly.

[2308] Input data:

[2309] User ID: 12345

[2310] Meal: Chicken salad

[2311] Serving size: 200g

[2312] Store name: Salad Stop

[2313] Example output:

[2314] Healthy Eating Suggestion: This Week's New Salad Recipe

[2315] Proposal content: Video link, related article URL

[2316] Coupon: 20 yen off salad coupon

[2317] In this way, the system of the present invention records and analyzes the user's dietary habits and delivers healthy meal suggestions and related content, making it easier for users to make healthy choices and manage their health sustainably.

[2318] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2319] Step 1:

[2320] The user enters the details of their meal into the device. The user opens the application installed on their smartphone and enters the type of meal (e.g., breakfast, lunch, dinner, snack), meal details, intake amount, and store information into the meal entry form. Once the input is complete, the user presses the "Submit" button. The input data includes the user ID, meal details, intake amount, and store information.

[2321] Input: Meal details, intake amount, store information

[2322] Output: Input food data

[2323] Step 2:

[2324] The device packages the entered food data in JSON format and sends it to the server via an HTTP POST request, where it is processed in real time and delivered to the server.

[2325] Input: JSON formatted meal data

[2326] Output: Data sent to the server

[2327] Step 3:

[2328] The server analyzes the received meal data and saves it in a database. The server converts the received data into a specific format and stores the user ID, meal details, intake amount, store information, etc. in the database.

[2329] Input: Data sent to the server

[2330] Output: Data stored in the database

[2331] Step 4:

[2332] The server periodically queries the database for food data, retrieving data accumulated over a period of time, for example, the past month's food record.

[2333] Input: Query dietary data from the database

[2334] Output: Food record for the past month

[2335] Step 5:

[2336] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it can recognize patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[2337] Input: Food record for the past month

[2338] Output: User's meal frequency, preferences, and store preferences

[2339] Step 6:

[2340] The server generates healthy eating suggestions based on the analysis results and delivers videos and articles related to the suggestions. For example, if a user likes salads, it will provide new salad recipe videos and health-related articles.

[2341] Input: User's meal frequency, preferences, and store preferences

[2342] Output: Healthy eating suggestions and related content

[2343] Step 7:

[2344] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupons list.

[2345] Input: Healthy eating suggestions and coupon information

[2346] Output: Coupon sent to the terminal

[2347] Step 8:

[2348] The user presents and uses the coupon at a participating store. For example, they use a "20 yen off salad coupon" at a convenience store. The store's cash register or QR code reader sends the coupon code to the server and verifies its validity.

[2349] Input: Coupon presented at the participating store

[2350] Output: Coupon validity check

[2351] Step 9:

[2352] After the server verifies the validity of the coupon, it will add points to the user's account. For example, if a user uses a salad coupon, they will receive 5 points.

[2353] Input: Check coupon validity

[2354] Output: Points awarded to user account

[2355] Step 10:

[2356] The server analyzes the user's dietary data to detect signs of stress. For example, it analyzes patterns such as a decrease in dietary diversity or an increase in high-calorie foods. Based on the analysis results, it selects special coupons for stress reduction and sends them to the device in JSON format.

[2357] Input: Analysis results of dietary data

[2358] Output: Select and send special coupons

[2359] Step 11:

[2360] The terminal displays the special coupon received from the server and notifies the user. The user can use the special coupon to enjoy an appropriate cheat day and maintain motivation.

[2361] Input: Special coupon information

[2362] Output: Special coupon notification displayed on the terminal

[2363] In this way, the system of the invention records and analyzes the user's eating habits, and delivers healthy meal suggestions and related content. Users can also use coupons and points to enjoyably manage their health on an ongoing basis.

[2364] 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.

[2365] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and suggesting healthy meals, and by combining it with an emotion engine, it can more effectively maintain the motivation of users. Specific embodiments are described below.

[2366] Recording user's dietary data

[2367] 1. Display the meal entry form

[2368] The device (smartphone, tablet, PC, etc.) displays an input form where the user can enter their meal details. This form includes fields such as the type of meal (breakfast, lunch, dinner, snack, etc.), the meal details, the amount of food consumed, and store information (convenience store or restaurant name).

[2369] 2. Enter your meal data

[2370] The user inputs the details of the meal they actually ate and presses the "Send" button to send the data. For example, if the user ate "chicken salad" for lunch, they input that information and send it.

[2371] Data transmission and storage

[2372] 1. Data transmission

[2373] The device packages the entered meal data in JSON format and sends it to the server using a POST request.

[2374] 2. Data storage

[2375] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, and store information.

[2376] Analysis of dietary data

[2377] 1. Periodic database queries

[2378] The server periodically queries the database for food data and retrieves data accumulated over a period of time, for example, the past month's food record.

[2379] 2. Data Analysis

[2380] The server analyzes the acquired data and extracts the user's meal frequency, preferences, and restaurant preferences. For example, it recognizes patterns such as "User A eats salad at least three times a week" and "User B eats fast food at least twice a week."

[2381] Healthy meal suggestions and coupons

[2382] 1. Health suggestion generation

[2383] The server then suggests healthy meals suitable for the user based on the analysis results. For example, it might suggest, "User A can use a coupon for 20 yen off a salad today."

[2384] 2. Coupon Notification

[2385] The server sends the generated offers and coupon information in JSON format to the device, where the coupons are displayed in the device's available coupon list.

[2386] Coupon use and points

[2387] 1. Using coupons

[2388] The user presents and uses the coupon at the applicable store. For example, they can use a "20 yen off salad coupon" at a convenience store.

[2389] 2. Check the coupon

[2390] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[2391] 3. Points Award

[2392] The server will then verify the validity of the coupon and add points to the user's account, for example, adding 5 points to a user who used a salad coupon.

[2393] Special coupon information

[2394] 1. Stress indicator detection

[2395] The server analyzes the user's dietary data to detect signs of stress, such as patterns of decreased dietary variety or increased consumption of high-calorie foods.

[2396] 2. Selection of special coupons

[2397] The server selects special coupons to reduce stress. For example, if the user likes sweets, it selects a "50% off dessert coupon."

[2398] 3. Notification of coupons

[2399] The server sends the selected special coupon in JSON format to the device, which then notifies the user. The user can then use the coupon to enjoy an appropriate cheat day and maintain their motivation.

[2400] Implementing the Emotion Engine

[2401] 1. Entering emotion data

[2402] Users can input their emotional state within the app, choosing from options such as "I'm feeling stressed today" or "I'm in a happy mood."

[2403] 2. Emotion Data Analysis

[2404] The server analyzes the input emotion data and evaluates the user's current emotional state.

[2405] 3. Emotion-based recommendation optimization

[2406] The server optimizes healthy meal suggestions and special coupons based on the user's emotional state: for example, if the user is feeling stressed, it will suggest more relaxing meals and coupons.

[2407] 4. Emotion-based notifications

[2408] The server then sends the optimized recommendations to the device, which then notifies the user. For example, a user who is feeling stressed can be offered a discount coupon for relaxing herbal tea.

[2409] In this way, the system of the present invention records and analyzes the user's eating habits, and by suggesting healthy meals and providing coupons, provides an environment where users can enjoy managing their health. In addition, by combining it with an emotion engine, it is possible to make more personalized suggestions according to the user's emotional state, contributing to maintaining motivation and reducing stress.

[2410] The processing flow will be explained below.

[2411] Processing of systems with emotion engines

[2412] Recording user's dietary data

[2413] Step 1:

[2414] The terminal displays a meal entry form.

[2415] Specific operation: The device screen displays fields for entering the type of meal, meal contents, intake amount, and store information.

[2416] Step 2:

[2417] The user inputs the details of the meal they actually ate and presses the "Submit" button.

[2418] Specific operation: If the user had "chicken salad" for lunch, enter that information.

[2419] Step 3:

[2420] The device packages the entered meal data in JSON format and sends it to the server.

[2421] Specific operation: The input data is packaged in JSON format and sent to the server via a POST request.

[2422] Data transmission and storage

[2423] Step 4:

[2424] The server analyzes the received data and stores it in a database.

[2425] Specific operation: Parse the received JSON data and store the user ID, meal details, intake amount, time, and store information in the database.

[2426] Entering emotion data

[2427] Step 5:

[2428] The terminal displays an emotion input form.

[2429] Specific behavior: Display options on the device screen to select an emotional state (e.g., "I feel stressed today" or "I feel happy").

[2430] Step 6:

[2431] The user inputs their emotional state and presses the "send" button.

[2432] Specific operation: The user selects today's emotional state and presses the send button.

[2433] Step 7:

[2434] The device packages the input emotion data in JSON format and sends it to the server.

[2435] Specific operation: Emotion data is packaged in JSON format and sent to the server via a POST request.

[2436] Emotional Data Analysis

[2437] Step 8:

[2438] The server analyzes the received emotional data and evaluates the current emotional state.

[2439] Specific operation: Emotional data is passed through an analysis algorithm to assess whether the user is stressed or relaxed.

[2440] Analysis of dietary data

[2441] Step 9:

[2442] The server periodically queries the database for meal data and retrieves data accumulated over a period of time.

[2443] What it does: Runs an SQL query to retrieve food data for a specific period (e.g., the past month).

[2444] Step 10:

[2445] The server analyzes the acquired data and extracts the user's eating frequency, preferences, and tendencies of the restaurants they frequent.

[2446] Specific operation: Using analytical algorithms, the user's eating patterns and preferences are statistically analyzed.

[2447] Healthy meal suggestions and coupons

[2448] Step 11:

[2449] The server generates healthy meal suggestions suitable for the user based on the analysis results.

[2450] Specific behavior: Based on user data, it selects healthy meal options and discount coupons and generates suggestion messages.

[2451] Step 12:

[2452] The server optimizes the suggestions taking into account the emotional state.

[2453] What it does: If the user is feeling stressed, it will adjust its suggestions to suggest relaxing meals or coupons.

[2454] Step 13:

[2455] The server sends the generated proposals and coupon information in JSON format to the terminal.

[2456] Specific operation: The proposal message and coupon data are packaged in JSON format and sent to the device.

[2457] Coupon use and points

[2458] Step 14:

[2459] The user presents the coupon at the store to use it.

[2460] Specific action: Present the barcode or coupon code in the app at the store.

[2461] Step 15:

[2462] The terminal (store cash register or QR code reader) sends the coupon code to the server and checks its validity.

[2463] Specific behavior: Enter a coupon code and send a validation request to the server.

[2464] Step 16:

[2465] The server checks the validity of the coupon and stores the usage history in a database.

[2466] Specific operation: Check the validity of the coupon and store the usage history data in the database.

[2467] Step 17:

[2468] The server verifies that the coupon has been used and credits the points to the user's account.

[2469] Specific operation: Increase the points corresponding to the user ID and update the new point balance in the database.

[2470] Special coupon information

[2471] Step 18:

[2472] The server analyzes the user's dietary data and detects signs of stress.

[2473] Specific operation: Stress indicators such as dietary imbalances and frequency fluctuations are calculated using an analytical algorithm.

[2474] Step 19:

[2475] The server selects special coupons for stress reduction.

[2476] Specific operation: Select an appropriate special coupon (e.g., a 50% off coupon for dessert) based on the user's preferences.

[2477] Step 20:

[2478] The server transmits the selected special coupon to the terminal, and the terminal notifies the user of it.

[2479] Specific operation: Coupon information is packaged in JSON format and sent to the device, which then displays a notification.

[2480] In this way, by combining an emotion engine, the system of the present invention supports the user's enjoyable and healthy eating habits and can make more personalized suggestions based on the user's emotional state, contributing to maintaining motivation and reducing stress.

[2481] Example 2

[2482] 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."

[2483] Conventional health management systems were able to collect and analyze users' dietary data, but they struggled to provide personalized recommendations that took into account the user's emotional state. This made it difficult to maintain user motivation over the long term, making effective health management difficult to achieve. Furthermore, they lacked a mechanism for early detection of stress symptoms and the provision of appropriate coupons and recommendations.

[2484] 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.

[2485] In this invention, the server is configured to record the user's dietary habit data.

[2486] A means for allowing a user to input meal details via an input form displayed on a terminal;

[2487] means for transmitting the recorded dietary habit data to a server;

[2488] A means for storing the transmitted data in a database and analyzing the data accumulated over a period of time;

[2489] A means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals;

[2490] a means for confirming that the coupon has been used and awarding points to the user based on the confirmation;

[2491] means for inputting the user's emotional state and transmitting the emotional data to a server;

[2492] A means of analyzing emotional data and optimizing healthy meal suggestions and special coupons based on the analysis results;

[2493] A means for notifying the user of sentiment-based optimized offers and special offers;

[2494] This will enable a comprehensive analysis of the user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons. This will help maintain user motivation and reduce stress, enabling long-term health management.

[2495] "User" refers to an individual who uses this system to input dietary data and emotional data.

[2496] "Terminal" refers to a device used by a user, such as a smartphone, tablet, or PC.

[2497] "Input form" refers to an on-screen form that allows the user to input dietary details and emotional state.

[2498] "Dietary data" refers to information such as the contents of meals consumed by the user, the amount consumed, meal times, and restaurant information.

[2499] "Server" refers to the computer on the system that receives transmitted data and stores and analyzes the data.

[2500] "Database" refers to a storage device for storing dietary data and emotional data.

[2501] "Analysis results" refers to information obtained by the server analyzing the accumulated data.

[2502] "Health Suggestions" refers to suggestions for healthy eating and behaviors provided to users based on the analysis results.

[2503] "Coupon" refers to an electronic voucher that offers discounts or special offers on specific meals or products.

[2504] "Points" refers to rewards credited to a user's account based on the use of a coupon.

[2505] "Emotional state" refers to the psychological state such as stress or mood felt by the user.

[2506] "Emotional data" refers to information about an emotional state entered by a user.

[2507] "Discount coupon" refers to an electronic coupon that comes with special discounts or benefits.

[2508] "Notification" refers to a message or alert sent from the server to the device.

[2509] The system of the present invention supports users in dieting and health management by recording and analyzing their eating habits and providing appropriate healthy meal suggestions and coupons. Furthermore, by combining it with an emotion engine, it is possible to more effectively maintain the user's motivation. Specific embodiments are described below.

[2510] Hardware and Software Use

[2511] Device: Use a device such as a smartphone, tablet, or PC.

[2512] Server: A computer on a network used to receive, analyze, and store data.

[2513] Database: Uses a storage device to store dietary and emotional data.

[2514] Data processing and calculation

[2515] Data collection:

[2516] The terminal collects user input data and receives dietary details and emotional state in an input form.

[2517] The user enters their dietary habits and emotional state into an input form and submits it.

[2518] Data transmission and storage:

[2519] The terminal sends the data entered by the user to the server in JSON format.

[2520] The server analyzes the received data and stores it in a database, including the user ID, meal contents, intake amount, time, store information, emotional state, etc.

[2521] Data analysis:

[2522] The server periodically queries and analyzes the data stored in the database, including the user's eating habits, preferences, restaurant preferences, and emotional state.

[2523] For example, if user A tends to eat salad more than three times a week, this pattern will be recognized.

[2524] Dining suggestions and coupons:

[2525] The server generates healthy meal suggestions based on the analysis results and provides discount coupons for the suggested meals.

[2526] The server sends the generated offer and coupon information to the terminal, which receives it and notifies the user.

[2527] Coupon use and points awarded:

[2528] The user presents the coupon and uses it at the target store.

[2529] The terminal (store device) sends the coupon code to the server and checks its validity.

[2530] The server awards points to the user based on valid coupons.

[2531] Detecting stress indicators and providing special coupons:

[2532] The server analyzes the user's dietary and emotional data to detect stress indicators. For example, if the user's intake of high-calorie foods increases, the server determines that the user is feeling stressed.

[2533] The server sends the selected special coupon to the terminal and notifies the user, helping them enjoy their cheat day.

[2534] Specific prompt examples

[2535] Food data entry prompt:

[2536] What did you have for lunch today? Please enter the exact menu item and portion size.

[2537] Example: Grilled Chicken Salad, 200g

[2538] Emotional state prompt:

[2539] Please tell us how you're feeling right now. Please choose from the options below.

[2540] 1. Fun

[2541] 2. Normal

[2542] 3. You feel stressed

[2543] 4. Feeling depressed

[2544] Example

[2545] For example, if a user has "grilled chicken salad" for lunch, they enter and submit the details as "grilled chicken salad, 200g, salad shop ABC." The server analyzes the data and recognizes the user's eating habits. If the user tends to eat salad more than three times a week, the server will suggest a "50 yen discount coupon" for the next salad purchase and notify the user's device.

[2546] This will enable comprehensive analysis of a user's dietary and emotional data, making it possible to provide personalized health suggestions and coupons, which is expected to help maintain user motivation and reduce stress, enabling long-term health management.

[2547] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2548] Step 1:

[2549] Display of meal entry form

[2550] The terminal displays an input form that allows the user to enter meal details. The form includes radio buttons such as "Breakfast," "Lunch," "Dinner," and "Snack," as well as text input fields. The input form is displayed when the program is started or when a specific button is pressed.

[2551] Input: Launching a program or pressing a button

[2552] Output: Display of meal entry form

[2553] Step 2:

[2554] Entering meal data

[2555] The user inputs the meal details using the input form on the terminal and presses the "Submit" button to send the data. For example, the user inputs "Grilled chicken salad, 200g, salad shop ABC" for lunch.

[2556] Input: The meal details entered by the user in the input form

[2557] Output: Pressing the send button puts the data into a waiting state for transmission.

[2558] Step 3:

[2559] Data transmission

[2560] The device packages the meal data entered by the user into JSON format and sends it to the server using an HTTP POST request.

[2561] Input: User-entered meal data (e.g., Grilled Chicken Salad, 200g, Salad Shop ABC)

[2562] Output: Meal data is sent to the server in JSON format

[2563] Step 4:

[2564] Data storage

[2565] The server analyzes the received meal data and stores it in a database, including the user ID, meal content, intake amount, meal time, and restaurant information.

[2566] Input: Meal data sent in JSON format

[2567] Output: The data is saved correctly in the database

[2568] Step 5:

[2569] Periodic database queries

[2570] The server periodically queries the database to obtain meal data for a certain period of time, for example, to obtain meal data for the past month.

[2571] Input: A trigger that runs a periodic query from the database

[2572] Output: Meal data for a certain period of time is obtained.

[2573] Step 6:

[2574] Data analysis

[2575] The server analyzes the food data retrieved from the database to extract the user's meal frequency, preferences, and restaurant preferences. This analysis uses standard statistical analysis methods and machine learning algorithms. For example, the server can determine that User A eats salad at least three times a week.

[2576] Input: Acquired dietary data over a period of time

[2577] Output: Analysis results of each user's meal frequency, preferences, and store usage trends

[2578] Step 7:

[2579] Health suggestion generation

[2580] The server generates healthy meal suggestions based on the analysis results. For example, it might suggest to User A, "A 50 yen discount coupon for the next salad purchase." The suggestions are generated using pre-defined rules and machine learning models.

[2581] Input: Analysis results of dietary data

[2582] Output: Health recommendations and coupon information tailored to each individual user

[2583] Step 8:

[2584] Coupon Notifications

[2585] The server sends the generated offers and coupon information to the device, which receives it in JSON format and displays a notification to the user.

[2586] Input: Offer and coupon information

[2587] Output: Offer and coupon information is sent to the terminal and the user is notified

[2588] Step 9:

[2589] Use coupon

[2590] The user presents the coupon at the store. For example, the user uses a "20 yen off salad coupon" at a convenience store. The coupon is presented using a QR code or barcode.

[2591] Input: Present coupon

[2592] Output: A confirmation of coupon redemption is displayed to the user

[2593] Step 10:

[2594] Check coupons

[2595] The terminal (such as a store cash register or QR code reader) sends the coupon code to a server to verify its validity, which typically involves checking a database of coupons.

[2596] Enter: Coupon Code

[2597] Output: Coupon code validation result

[2598] Step 11:

[2599] Points awarded

[2600] After verifying the validity of the coupon, the server adds points to the user's account. For example, if a user uses a "salad coupon," 5 points are added. The point addition involves updating the user's account.

[2601] Input: Coupon validity check result

[2602] Output: Points are credited to the user account.

[2603] Step 12:

[2604] Input of user emotion data

[2605] The user inputs their emotional state using an input form on the device. For example, they answer "yes" to the question "Are you feeling stressed today?"

[2606] Input: User's emotional state input

[2607] Output: Emotional state data ready to send

[2608] Step 13:

[2609] Sending emotional data

[2610] The device sends the emotion data entered by the user to the server in JSON format.

[2611] Input: Emotion data entered by the user

[2612] Output: Emotion data is sent to the server in JSON format.

[2613] Step 14:

[2614] Emotional Data Analysis

[2615] The server analyzes the received emotion data and evaluates the user's current emotional state. The analysis uses natural language processing and machine learning models. For example, if a user inputs "stress," the server recognizes the user as being in a high-stress state.

[2616] Input: Received emotion data

[2617] Output: Emotion evaluation result

[2618] Step 15:

[2619] Emotion-based suggestion optimization

[2620] The server optimizes health suggestions and special coupons based on emotional data. Fo...

Claims

1. To record the user's dietary habits A means for allowing a user to input meal details via an input form displayed on a terminal; means for transmitting the recorded dietary habit data to a server; A means for storing the transmitted data in a database and analyzing the data accumulated over a period of time; A means for suggesting healthy meals based on the analysis results and providing discount coupons for the suggested meals; a means for confirming that the coupon has been used and awarding points to the user based on the confirmation; means for detecting a user's stress symptoms and providing a special coupon for stress reduction; A system including:

2. The system according to claim 1 , wherein the server further comprises means for extracting the user's eating frequency, preferences, and tendencies of the stores they frequent based on an analysis of the dietary habit data.

3. 2. The system according to claim 1, wherein the terminal further comprises means for displaying to the user a notice of offers and special coupons from the server.

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