System

Abstract

A system is provided.SOLUTION: A system, comprising: means for responding to health consultations using a generative model; means for aggregating group purchasing offers and agreements; means for managing and sharing progress information; and means for creating and coordinating collaborative schedules and dietary plans.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many people are interested in health management, but do not know how to do it, making it difficult to manage their health effectively. Furthermore, maintaining motivation through individual health management is difficult, and purchasing expensive health-related products often becomes a financial burden. Furthermore, it is difficult to receive appropriate health information and advice individually and efficiently achieve health goals through group cooperation. Given this background, a platform that integrates health management and group purchasing support through communication applications such as LINE is needed. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for responding to health consultations using a generative model, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans. By providing a user interface via a communication application, and further including a means for sending responses based on the generative model and a communication means, users can efficiently carry out everything from individual health consultations to group purchasing necessary to achieve health goals as a group. Furthermore, the means for aggregating group purchasing proposals and agreements includes a database for storing and providing product information and a communication means for transmitting the product information to group members, thereby supporting economical group purchasing. This allows users to integrate health management and economical purchasing, providing a new social health experience.

[0006] A "generative model" refers to an artificial intelligence algorithm that automatically generates responses or advice based on user input.

[0007] "Means for responding to health consultations" refers to the function of receiving health questions and consultations from users and providing appropriate advice using a generative model.

[0008] "Means for aggregating group purchase proposals and consents" refers to the function of proposing a joint purchase within a group for a specific product and collecting consent from members.

[0009] "Means for managing and sharing progress information" refers to the function for recording progress information toward a user's health goals and sharing it with group members.

[0010] "Means for creating and coordinating collaborative schedules and meal plans" refers to the ability to automatically generate and, if necessary, adjust health-related schedules and meal plans to be shared among group members.

[0011] "Communications Application" means a software platform that enables communication between users.

[0012] "Means for providing a user interface" refers to the function of providing a screen and operation methods for users to interact with the system.

[0013] "Communication means" refers to the technology and protocols used to send and receive user data between servers.

[0014] "Database for storing and providing product information" refers to a system for storing detailed information about products that are the subject of joint purchases and providing that information when needed. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention provides a system that utilizes generative models to provide health consultations, aggregate group purchasing proposals and agreements, manage and share progress information, and create and adjust joint schedules and meal plans, to support users in efficiently managing their health and making economical purchases.

[0037] A natural language description of the program's processing

[0038] How to respond to health consultations

[0039] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[0040] 2. Terminal: Sends the user's message to the server.

[0041] 3. Server: Receives messages and passes them to the generative model.

[0042] 4. Generative model: Analyzes the message content and generates optimal advice for the user.

[0043] 5. Server: Sends the generated advice to the user via LINE message.

[0044] 6. Terminal: Display an advisory message to the user.

[0045] Aggregation of group buying proposals and consents

[0046] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[0047] 2. Terminal: Sends product information and suggestion messages to the server.

[0048] 3. Server: Sends a proposal message to all group members.

[0049] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[0050] 5. Terminal: Sends the response to the server.

[0051] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[0052] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[0053] 8. Terminal: Display a notification to the user that the order has been completed.

[0054] Managing and sharing progress information

[0055] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[0056] 2. Device: Sends progress data to the server.

[0057] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[0058] 4. Server: Aggregates progress information for the entire group and generates motivational messages.

[0059] 5. Server: Sends the generated message to all group members.

[0060] 6. Terminal: Receives and displays messages.

[0061] Creating and coordinating joint schedules and meal plans

[0062] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[0063] 2. Terminal: Sends input data to the server.

[0064] 3. Server: Passes the received data to the generative model.

[0065] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[0066] 5. Server: Shares the generated plan within the group.

[0067] 6. User (group member): Sends feedback on the proposed plan.

[0068] 7. Device: Sends feedback to the server.

[0069] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[0070] 9. Server: Sends the finalized plan to all members.

[0071] 10. Terminal: Receives the final plan and displays it to the user.

[0072] Specific examples

[0073] For example, if User A recently feels fatigued and sends a health consultation on LINE, the generative model will suggest taking vitamin D. Next, User A suggests purchasing protein with the group, and the group members agree on LINE, and the server coordinates the purchase. Furthermore, when User A records his daily jogging progress on LINE, the server aggregates the progress information of the entire group and sends encouraging messages. Finally, the group collaboratively creates a weekly meal plan, and everyone provides feedback to provide an optimized plan. In this way, this system is a multi-functional platform that supports users' health management and economical purchases.

[0074] The processing flow will be explained below.

[0075] How to respond to health consultations

[0076] Step 1:

[0077] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[0078] Step 2:

[0079] The device sends the user's message to the server.

[0080] Step 3:

[0081] The server passes the received message to the generative model.

[0082] Step 4:

[0083] A generative model analyzes the message content and generates optimal advice for the user.

[0084] Step 5:

[0085] The server sends the generated advice to the user via LINE message.

[0086] Step 6:

[0087] The device displays an advisory message to the user.

[0088] Aggregation of group buying proposals and consents

[0089] Step 1:

[0090] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[0091] Step 2:

[0092] The terminal transmits product information and a proposal message to the server.

[0093] Step 3:

[0094] The server sends a proposal message to all group members.

[0095] Step 4:

[0096] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[0097] Step 5:

[0098] The device sends a response to the server.

[0099] Step 6:

[0100] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[0101] Step 7:

[0102] The server will send a notification of order completion and the expected arrival date of the product to all members.

[0103] Step 8:

[0104] The terminal will display a notification to the user that the order is complete.

[0105] Managing and sharing progress information

[0106] Step 1:

[0107] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[0108] Step 2:

[0109] The device sends progress data to the server.

[0110] Step 3:

[0111] The server stores the progress data in a database and updates the progress of the entire group.

[0112] Step 4:

[0113] The server aggregates progress information for the entire group and generates motivational messages.

[0114] Step 5:

[0115] The server generates a message and sends it to all group members.

[0116] Step 6:

[0117] The terminal receives and displays the message.

[0118] Creating and coordinating joint schedules and meal plans

[0119] Step 1:

[0120] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[0121] Step 2:

[0122] The terminal sends the input data to the server.

[0123] Step 3:

[0124] The server passes the received data to the generative model.

[0125] Step 4:

[0126] The generative model generates optimal schedules and meal plans based on input data.

[0127] Step 5:

[0128] The server shares the generated plan within the group.

[0129] Step 6:

[0130] Users (group members) submit feedback on the proposed plan.

[0131] Step 7:

[0132] The device sends the feedback to the server.

[0133] Step 8:

[0134] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[0135] Step 9:

[0136] The server sends the finalized plan to all members.

[0137] Step 10:

[0138] The device receives the final plan and displays it to the user.

[0139] Example 1

[0140] 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."

[0141] In today's world, there is a growing need for efficient health management and financial purchasing. However, there are few systems that provide multiple functions, such as health consultations, group purchasing, progress information management, and collaborative schedule creation, in an integrated manner. In particular, there is a need for technology that can quickly provide optimal advice to users. There is also a lack of tools that allow information to be shared among groups and make decision-making easier. The development of a comprehensive system that can address these complex needs is required.

[0142] 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.

[0143] In this invention, the server includes means for responding to health consultations using generative models, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, and means for creating and adjusting joint schedules and meal plans, thereby enabling users to efficiently manage their health and make economical purchases, and share and adjust progress information and joint plans within the group.

[0144] A "generative model" is an artificial intelligence algorithm that analyzes natural language messages from users and generates appropriate responses or advice.

[0145] The "means for responding to health consultations" is the part of the system that receives health-related questions from users and provides appropriate responses to those questions.

[0146] "Means for aggregating group purchase proposals and consents" refers to the part of the system that, when a user wishes to jointly purchase a particular product as a group, notifies other members of the group of the proposal and aggregates the responses of consent or denial.

[0147] "Means for managing and sharing progress information" refers to the system part that records progress information such as the user's training and diet, and shares that information within the group.

[0148] "Means for creating and adjusting collaborative schedules and meal plans" means the portion of the system that allows multiple users to create schedules and meal plans for collaborative use and adjust the contents of those schedules and meal plans as needed.

[0149] A "communications application" is a software product that allows a user to access a system and send or receive information.

[0150] "User interface" refers to the means, such as screens and operating methods, that a user uses to interact with a system.

[0151] "Communication means" means hardware and software functions for transmitting and receiving data.

[0152] A "database that stores and provides product information" is a data management system that stores information on candidate products for purchase and provides that information when needed.

[0153] The present invention is a multi-functional system that supports users in efficiently managing their health and making economical purchases. The system includes a means for utilizing generative models to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans. Specific embodiments and usage examples are described below.

[0154] How to respond to health consultations

[0155] User: Send a health consultation message using the LINE app. For example, send a message saying, "I've been feeling tired lately and I'd like some advice."

[0156] Device: Sends the user's message to the server via the LINE API.

[0157] Server: Receives messages and passes them to the generative model. It analyzes the messages and converts them into an appropriate format. The generative model uses a natural language processing model such as GPT-3.

[0158] Generative model: Analyzes the content of received messages and generates optimal advice for the user. For example, it generates advice such as "Try increasing your intake of vitamin D."

[0159] Server: Sends the generated advice to the user via LINE message.

[0160] Terminal: Receives advice messages and displays them to the user.

[0161] Aggregation of group buying proposals and consents

[0162] User: Selects a product that the group wants to purchase and sends a suggestion message. For example, the user sends a message saying, "Would you like to buy this protein with everyone?"

[0163] Device: Product information and suggestion messages are sent to the server via the LINE API.

[0164] Server: Distributes proposal messages to all group members. Uses a database to store and provide product information.

[0165] User (group member): Receives the proposal message and sends a response of acceptance or rejection through the LINE app.

[0166] Device: Sends the response to the server using the LINE API.

[0167] Server: Aggregates responses from all members and, if there is a majority agreement, aggregates the order.

[0168] Server: Sends notification of order completion and estimated delivery date to all members.

[0169] Terminal: Receives notification of order completion and displays it to the user.

[0170] Managing and sharing progress information

[0171] User: Records progress of regular training and meals on LINE. For example, sends a message saying "I jogged 5km today."

[0172] Device: Progress data is sent to the server via the LINE API.

[0173] Server: Stores progress data in a database and updates the progress of the entire group.

[0174] Server: Aggregates progress information for the entire group and generates motivational messages, such as "Everyone is doing great! Keep it up!"

[0175] Server: Sends the generated message to all group members.

[0176] Terminal: Receives and displays messages.

[0177] Creating and coordinating joint schedules and meal plans

[0178] User: Enter details of the health plan they want to collaborate on. For example, they can send a "Weekly Meal Plan Suggestion."

[0179] Device: Sends input data to the server via the LINE API.

[0180] Server: Passes the received data to the generative model.

[0181] Generative models: Generate optimal schedules and meal plans based on input data.

[0182] Server: Shares the generated plan within the group.

[0183] User (group member): Sends feedback on the proposed plan.

[0184] Device: Sends feedback to the server using the LINE API.

[0185] Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[0186] Server: Sends the finalized plan to all members.

[0187] Terminal: Receives the final plan and displays it to the user.

[0188] In this way, users can efficiently manage their health and make economical purchases, and share and coordinate progress information and joint plans within the group.

[0189] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0190] Flow of processing for responding to health consultations

[0191] Step 1:

[0192] The user uses the LINE app to enter and send a health consultation message.

[0193] Specific actions: The user enters "I've been feeling tired lately and would like some advice" into the text box of the LINE app and presses the send button.

[0194] Input: The health consultation message entered by the user.

[0195] Output: The message is sent to the LINE server.

[0196] Step 2:

[0197] The device sends the user's message to the server using the LINE API.

[0198] Specific operation: The device receives the message via the LINE API and sends it to the server.

[0199] Input: Health consultation message from user.

[0200] Output: The message is sent to the server.

[0201] Step 3:

[0202] The server receives the message and passes it to the generative model.

[0203] Specific behavior: The server parses the message, converts it into an appropriate format, and passes it to the generative model.

[0204] Input: Health consultation message sent from the device.

[0205] Output: Data converted into a format that can be input to a generative model.

[0206] Step 4:

[0207] The generative model analyzes the message content and generates optimal advice for the user.

[0208] What it does: A generative model (e.g., GPT-3) analyzes the message and generates advice like, "Try increasing your vitamin D intake."

[0209] Input: Message data passed to the generative model by the server.

[0210] Output: The generated advice.

[0211] Step 5:

[0212] The server sends the generated advice to the user via a LINE message.

[0213] Specific behavior: The server uses the LINE API to generate an advice message and send it to the user.

[0214] Input: The advice generated by the generative model.

[0215] Output: Advisory message sent to the user's terminal.

[0216] Step 6:

[0217] The terminal receives the advice message and displays it to the user.

[0218] Specific operation: The user's device receives a LINE message and displays the advice.

[0219] Input: The advice message sent by the server.

[0220] Output: The advice message will be displayed in the chat window of the LINE app.

[0221] Group Buy Proposal and Consent Aggregation Process

[0222] Step 1:

[0223] A user selects a product they wish to purchase within the group, enters a suggestion message, and sends it.

[0224] Specific operation: The user enters "Would you like to buy this protein for everyone?" in the text box of the LINE app, attaches a product link and image, and presses the send button.

[0225] Input: The purchase offer message entered by the user.

[0226] Output: The message is sent to the LINE server.

[0227] Step 2:

[0228] The device sends the user's suggestion message to the server using the LINE API.

[0229] Specific operation: The device receives the message via the LINE API and sends it to the server.

[0230] Input: Purchase suggestion message from the user.

[0231] Output: The message is sent to the server.

[0232] Step 3:

[0233] The server broadcasts the proposal message to all group members.

[0234] Specific operation: The server receives the proposal message, distributes it to all members in the group, and saves the proposal in the database.

[0235] Input: The user's suggested message.

[0236] Output: The proposal message that will be delivered to group members.

[0237] Step 4:

[0238] Group members receive the proposal message, enter a response of agreement or rejection, and submit it.

[0239] Specific actions: Group members enter a response such as "I'll participate" or "I'll pass this time" through the LINE app and press the send button.

[0240] Input: Group member responses.

[0241] Output: A response is sent to the LINE server.

[0242] Step 5:

[0243] The device sends the group member's response to the server using the LINE API.

[0244] Specific operation: The group member's device receives the response via the LINE API and sends it to the server.

[0245] Input: Responses from group members.

[0246] Output: The response is sent to the server.

[0247] Step 6:

[0248] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order data.

[0249] Specific operation: The server aggregates the responses from all members, compiles the order data based on the majority vote, and saves the order details in the database.

[0250] Input: Group member responses.

[0251] Output: Aggregated order data.

[0252] Step 7:

[0253] The server will send a notification of order completion and the expected arrival date of the product to all members.

[0254] Specific operation: The server uses the LINE API to send a notification of order completion and details to all members.

[0255] Input: Order completion information and estimated delivery date.

[0256] Output: Notifications delivered to group members.

[0257] Step 8:

[0258] The terminal receives the notification of order completion and displays it to the user.

[0259] Specific behavior: The user's device receives a LINE message and displays details of the completed order.

[0260] Input: Order completion notification sent from the server.

[0261] Output: A notification that the order is complete will be displayed in the chat window of the LINE app.

[0262] keyword:

[0263] Generative AI model, prompt sentence

[0264] (Application example 1)

[0265] 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."

[0266] While traditional health management and financial purchasing support systems offer individual functions, few offer a multi-functional platform that integrates them. As a result, users are forced to use multiple applications, reducing utilization efficiency. It has also been difficult to provide integrated functions such as health consultations, group purchasing suggestions, progress management, and meal plan creation.

[0267] 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.

[0268] In this invention, the server includes a means for responding to health consultations using a generative model, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and adjusting joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing, thereby enabling users to efficiently manage their health and financial activities on a single platform.

[0269] A "generative model" is an algorithm that analyzes input data from a user and generates optimal responses and suggestions.

[0270] "Health consultation" is a function that allows users to input information about their daily health problems and physical condition, and provides advice and solutions to those problems.

[0271] "Group purchase" is a function that aggregates proposals and agreements for multiple users to jointly purchase products.

[0272] "Progress information" is data that records, manages, and shares a user's daily activities and goal achievement status.

[0273] "Joint Schedule" is a function that allows multiple users to create and adjust schedules for joint activities.

[0274] A "meal plan" is a plan that suggests optimal meal plans based on a user's health goals and needs.

[0275] "Health management" is the process of monitoring a user's health status and providing appropriate advice and suggestions.

[0276] "Economic Purchasing" is the process of providing information and suggestions to help users effectively purchase products.

[0277] An "application" is a software program that a user utilizes to perform a particular function.

[0278] A "server" is a computer system that receives and processes data and provides services to users.

[0279] The present invention is a multi-functional system that enables users to efficiently manage their health and financial activities on a single platform. The system includes a means for responding to health consultations using generative models, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and coordinating joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing.

[0280] Hardware and software used

[0281] This system uses the following hardware and software:

[0282] Hardware:

[0283] Smartphone (iOS or Android)

[0284] Smart Glasses

[0285] Server PC

[0286] software:

[0287] "Health Concierge" app (iOS / Android app)

[0288] OpenAI GPT-4

[0289] Database (e.g. AWS DynamoDB)

[0290] In an implementation of the present invention, a user accesses an application via a smartphone or smart glasses. The user's input data is analyzed by a generative model (e.g., OpenAI GPT-4) to generate appropriate responses or suggestions. The generated responses or suggestions are then sent to the user via a communication medium.

[0291] Examples of concrete examples and prompts

[0292] Health consultation example:

[0293] When a user uses their smartphone to type, "I've been feeling tired lately and I'd like some advice," this message is sent to the server. The server passes the message to a generative model (OpenAI GPT-4) and generates appropriate health advice. The generated advice is sent to the user, allowing them to receive specific measures for improvement.

[0294] Group Buying Example:

[0295] When a user uses the app to suggest to the group, "Shall we all buy this protein?", this information is sent to the server and all group members are notified. All members send responses of either agree or decline, and the results are tallied by the server. If a majority vote agrees, the server processes the order and a notification of order completion is sent to all members.

[0296] Progress information management example:

[0297] When a user logs in to the app using their smartphone, "I jogged 5km today," this information is sent to the server and stored in a database. The server then aggregates the progress of the entire group and generates a motivational message, such as "The whole group has completed 50km this week! Let's do our best!", which is then sent to all group members.

[0298] Collaborative planning example:

[0299] The user proposes a weekly meal plan in the app, entering specific details such as "fish on Mondays and salad on Wednesdays." This information is sent to the server, which passes it through a generative model to generate the optimal plan. The generated plan is shared with the group, and feedback is sent. This feedback is aggregated on the server, and the final plan is adjusted and re-notified to all members.

[0300] Examples of prompts:

[0301] "I've been feeling tired lately and I'd like some advice. Can you tell me how to take vitamins or other ways to improve my condition?"

[0302] "Wouldn't we all like to buy this protein?"

[0303] I jogged 5km today.

[0304] "I'm proposing a meal plan with fish on Mondays and salads on Wednesdays. I'd love your feedback."

[0305] Thus, the present invention provides a multifunctional system that efficiently supports the user's health management and economic activities.

[0306] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0307] Health consultation function processing steps

[0308] Step 1:

[0309] User: Logs into the health concierge app using a smartphone or smart glasses and enters a health consultation prompt, for example, "I've been feeling tired lately and would like some advice."

[0310] Input: Health consultation message from user

[0311] Output: Health consultation message sent

[0312] Step 2:

[0313] Terminal: Sends the health consultation message entered by the user to the server.

[0314] Input: Health consultation message

[0315] Output: Message sent to the server

[0316] Step 3:

[0317] Server: Passes received messages to a generative model (e.g., OpenAI GPT-4).

[0318] Input: User message

[0319] Output: Messages passed to the generative model

[0320] Step 4:

[0321] Generative model: Analyzes the content of the user's message and generates optimal health advice. The generative model refers to a large amount of data based on the input consultation content and makes optimal suggestions.

[0322] Input: User's health consultation message

[0323] Output: Health advice

[0324] Step 5:

[0325] Server: Receives the generated advice and sends it to the user device.

[0326] Enter: Health Advice

[0327] Output: Advice sent to the user's terminal

[0328] Step 6:

[0329] Terminal: Display health advice messages to the user.

[0330] Input: Advice received from the server

[0331] Output: Displayed health advice

[0332] Group Buying Feature Processing Steps

[0333] Step 1:

[0334] User: Enter the product information and message you want to share with the app. For example, you could say, "Let's all buy this protein together."

[0335] Input: Product information and suggestion message

[0336] Output: Sent product information and suggestion message

[0337] Step 2:

[0338] Terminal: Sends product information and suggestion messages to the server.

[0339] Input: Product information and suggestion message

[0340] Output: Proposal message sent to the server

[0341] Step 3:

[0342] Server: Sends the proposal message to all group members.

[0343] Input: Proposal message

[0344] Output: Message sent to group members

[0345] Step 4:

[0346] User (group member): Receives the proposal message and selects a response of either accept or reject in the app.

[0347] Input: Proposal message

[0348] Output: Agree or decline response

[0349] Step 5:

[0350] Terminal: Sends the response to the server.

[0351] Input: Response

[0352] Output: The response sent to the server

[0353] Step 6:

[0354] Server: Aggregates responses from all members, and if a majority vote agrees, proceeds with the order.

[0355] Input: Responses of all members

[0356] Output: Checkout completed

[0357] Step 7:

[0358] Server: Notify all members of order completion and estimated arrival of the product.

[0359] Input: Order Checkout Information

[0360] Output: Order completion notification and estimated delivery date

[0361] Step 8:

[0362] Terminal: Display a notification to the user that the order has been completed.

[0363] Input: Notification

[0364] Output: Displayed order completion notification

[0365] Progress information management function processing steps

[0366] Step 1:

[0367] User: Records daily training progress in the app, for example, "Today I jogged 5km."

[0368] Input: Progress information

[0369] Output: Progress information sent

[0370] Step 2:

[0371] Device: Sends progress data to the server.

[0372] Input: Progress data

[0373] Output: Data sent to the server

[0374] Step 3:

[0375] Server: Stores progress data in a database and updates progress for the entire group.

[0376] Input: Received progress data

[0377] Output: Updated progress information

[0378] Step 4:

[0379] Server: Aggregates progress information for the entire group and generates motivational messages.

[0380] Input: Aggregated progress data

[0381] Output: Motivation message

[0382] Step 5:

[0383] Server: Sends the generated message to all group members.

[0384] Input: Motivational message

[0385] Output: The message sent.

[0386] Step 6:

[0387] Terminal: Receives and displays messages.

[0388] Input: Message from the server

[0389] Output: The displayed message

[0390] Collaborative Planning Feature Processing Steps

[0391] Step 1:

[0392] User: Enter details of the health plan they want to collaborate on in the app, for example, suggesting a weekly meal plan.

[0393] Enter: Health plan details

[0394] Output: Health plan information submitted

[0395] Step 2:

[0396] Terminal: Sends input data to the server.

[0397] Input: Health plan data

[0398] Output: Data sent to the server

[0399] Step 3:

[0400] Server: Passes the received data to the generative model.

[0401] Input: Health plan data

[0402] Output: The data passed to the generative model

[0403] Step 4:

[0404] Generative models: Generate optimal schedules and meal plans based on input data.

[0405] Input: Health plan data

[0406] Output: Generated plan

[0407] Step 5:

[0408] Server: Shares the generated plan within the group.

[0409] Input: Generated plan

[0410] Output: Shared plans

[0411] Step 6:

[0412] User (group member): Send feedback on the proposed plan.

[0413] Input: Plan

[0414] Output: Feedback

[0415] Step 7:

[0416] Device: Sends feedback to the server.

[0417] Input: Feedback

[0418] Output: Feedback sent to the server

[0419] Step 8:

[0420] Server: Aggregates feedback and generates the final plan.

[0421] Input: Feedback

[0422] Output: Final plan

[0423] Step 9:

[0424] Server: Notify all members of the finalized plan.

[0425] Input: Final Plan

[0426] Output: Final plan reported

[0427] Step 10:

[0428] Terminal: Receives the final plan and displays it to the user.

[0429] Input: Final Plan

[0430] Output: Final plan displayed

[0431] 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.

[0432] The present invention provides a system including a means for utilizing a generative model to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans, as well as an emotion engine that recognizes user emotions. This system is intended to support users in efficiently managing their health and making economical purchases.

[0433] A natural language description of the program's processing

[0434] Enhancing health consultations with an emotion engine

[0435] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[0436] 2. Terminal: Sends the user's message to the server.

[0437] 3. Server: Passes the received message to the emotion engine to recognize the user's emotion.

[0438] 4. Emotion engine: Analyzes message content and user emotion data and provides it to the generative model.

[0439] 5. Generative models: Generate optimal advice for users by taking into account emotional data (e.g., suggesting relaxation techniques if emotions indicate fatigue or stress).

[0440] 6. Server: Sends the generated advice to the user via LINE message.

[0441] 7. Terminal: Display an advisory message to the user.

[0442] Aggregation of group buying proposals and consents

[0443] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[0444] 2. Terminal: Sends product information and suggestion messages to the server.

[0445] 3. Server: Sends a proposal message to all group members.

[0446] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[0447] 5. Terminal: Sends the response to the server.

[0448] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[0449] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[0450] 8. Terminal: Display a notification to the user that the order has been completed.

[0451] Managing and sharing progress information

[0452] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[0453] 2. Device: Sends progress data to the server.

[0454] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[0455] 4. Server: Aggregates progress information for the entire group.

[0456] 5. Emotion Engine: Analyzes the user's emotional data based on progress data and generates motivational messages.

[0457] 6. Server: Sends the generated message to all group members.

[0458] 7. Terminal: Receives and displays messages.

[0459] Creating and coordinating joint schedules and meal plans

[0460] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[0461] 2. Terminal: Sends input data to the server.

[0462] 3. Server: Passes the received data to the generative model.

[0463] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[0464] 5. Server: Shares the generated plan within the group.

[0465] 6. User (group member): Sends feedback on the proposed plan.

[0466] 7. Device: Sends feedback to the server.

[0467] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[0468] 9. Server: Sends the finalized plan to all members.

[0469] 10. Terminal: Receives the final plan and displays it to the user.

[0470] Specific examples

[0471] For example, if User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine will read the stress and fatigue from User A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements. Next, if User A suggests purchasing protein with the group, the group members will indicate high agreement through the emotion engine, and the server will coordinate the purchase. When User A records his daily jogging progress, the emotion engine will generate positive feedback on his achievements, increasing the motivation of the entire group. Finally, the entire group will jointly create a meal plan, and the emotion engine will provide the optimal plan based on each member's preferences and emotions. In this way, this system utilizes users' emotional data to support more personalized health management and economical purchasing.

[0472] The processing flow will be explained below.

[0473] Enhancing health consultations with an emotion engine

[0474] Step 1:

[0475] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[0476] Step 2:

[0477] The device sends the user's message to the server.

[0478] Step 3:

[0479] The server passes the received message to the emotion engine, which begins analyzing it to recognize the user's emotions.

[0480] Step 4:

[0481] The emotion engine analyzes the message content and the user's emotional data (e.g., fatigue, stress level).

[0482] Step 5:

[0483] The emotion engine provides the analyzed emotion data to the generative model.

[0484] Step 6:

[0485] The generative model takes into account emotional data to generate optimal advice for the user (e.g., suggesting relaxation methods if emotions indicate fatigue or stress).

[0486] Step 7:

[0487] The server sends the generated advice to the user via LINE message.

[0488] Step 8:

[0489] The device displays an advisory message to the user.

[0490] Aggregation of group buying proposals and consents

[0491] Step 1:

[0492] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[0493] Step 2:

[0494] The terminal transmits product information and a proposal message to the server.

[0495] Step 3:

[0496] The server sends a proposal message to all group members.

[0497] Step 4:

[0498] Users (group members) receive the proposal message and send a response of agreement or rejection, including their emotions.

[0499] Step 5:

[0500] The device sends a response to the server.

[0501] Step 6:

[0502] The sentiment engine analyzes the emotional data of the responses and assesses the level of agreement across the group.

[0503] Step 7:

[0504] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[0505] Step 8:

[0506] The server will send a notification of order completion and the expected arrival date of the product to all members.

[0507] Step 9:

[0508] The terminal will display a notification to the user that the order is complete.

[0509] Managing and sharing progress information

[0510] Step 1:

[0511] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[0512] Step 2:

[0513] The device sends progress data to the server.

[0514] Step 3:

[0515] The server stores the progress data in a database and updates the progress of the entire group.

[0516] Step 4:

[0517] The server aggregates progress information for the entire group.

[0518] Step 5:

[0519] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages (e.g., encouraging messages that evoke positive emotions).

[0520] Step 6:

[0521] The server generates a message and sends it to all group members.

[0522] Step 7:

[0523] The terminal receives and displays the message.

[0524] Creating and coordinating joint schedules and meal plans

[0525] Step 1:

[0526] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[0527] Step 2:

[0528] The terminal sends the input data to the server.

[0529] Step 3:

[0530] The server passes the received data to the generative model.

[0531] Step 4:

[0532] The generative model generates optimal schedules and meal plans based on input data.

[0533] Step 5:

[0534] The server shares the generated plan within the group.

[0535] Step 6:

[0536] Users (group members) submit feedback on the proposed plan along with emotional data.

[0537] Step 7:

[0538] The device sends the feedback to the server.

[0539] Step 8:

[0540] The emotion engine analyzes the emotional data of the feedback and evaluates the opinions of the entire group.

[0541] Step 9:

[0542] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[0543] Step 10:

[0544] The server sends the finalized plan to all members.

[0545] Step 11:

[0546] The device receives the final plan and displays it to the user.

[0547] Example 2

[0548] 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."

[0549] Conventional health management systems and group purchasing systems have difficulty responding to individual users' emotions, and tend to provide uniform advice and suggestions. Furthermore, group schedule coordination and progress management are cumbersome, making it difficult to adjust to each member's opinions and circumstances. This has led to issues such as reduced user satisfaction and reduced utilization efficiency.

[0550] 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.

[0551] In this invention, the server includes a means for responding to health consultations using a generative model, an emotion engine that analyzes a user's emotion data and provides it to the generative model, and a means for generating and transmitting advice that takes the emotion data into consideration. This enables personalized advice and suggestions based on the user's emotions. The server also includes a means for aggregating group purchase proposals and agreements, a means for managing and sharing progress information, and a means for creating and coordinating joint schedules and meal plans. This facilitates collaboration and progress management within a group, improving user satisfaction and usage efficiency.

[0552] A "generative model" is an algorithm that uses artificial intelligence to generate appropriate responses or suggestions based on input data.

[0553] The "emotion engine" is a function that analyzes emotions from user messages and data and provides that information to other system components.

[0554] "Means for responding to health consultations" is a function that provides appropriate advice in response to health-related questions and consultations from users.

[0555] The "means for generating and sending advice" is a function for sending advice created by the generative model based on emotion data to the user.

[0556] "Means for aggregating proposals and consents for group purchases" is a function that aggregates and unifies proposals and consents when multiple users jointly purchase products.

[0557] "Means for managing and sharing progress information" refers to a function for managing a user's training or activity progress information and sharing it with other users.

[0558] "Means for creating and adjusting shared schedules and meal plans" refers to a function that allows multiple users to create schedules and meal plans for joint use and adjust them as needed.

[0559] A "communications application" is software for sending and receiving messages over the Internet.

[0560] "Communication means" refers to the hardware and network infrastructure for transmitting and receiving data.

[0561] A "database" is an information system that can efficiently store large amounts of information and retrieve it as needed.

[0562] The present invention is a system for supporting users in efficient health management and economical purchasing, which mainly consists of a generative model, an emotion engine, a communication application via a user interface, a communication means, and a database.

[0563] 1. Means of responding to health consultations

[0564] The user sends a health consultation message to the server via a communication application such as the LINE app. For example, the message could be, "I've been feeling tired lately and I'd like some advice." The device then sends this message to the server via the Internet.

[0565] The server passes the received message to the emotion engine to recognize the user's emotion. The emotion engine analyzes the message content and the user's emotion data and provides that data to the generative model. The generative model takes the emotion data into account and generates optimal advice for the user. For example, if the emotion engine recognizes fatigue or stress, it will generate suggestions for relaxation methods or vitamin supplements.

[0566] The server sends the generated advice to the user via a LINE message, and the device displays the advice message to the user.

[0567] 2. Aggregation of group purchase proposals and agreements

[0568] A user sends a message suggesting a product they would like to purchase within the group. For example, a message like "Would you like to buy this protein with everyone?" The device sends this message along with the product information to the server.

[0569] The server sends a proposal message to all group members, who then respond by agreeing or rejecting the request. The device then sends these responses back to the server. The server then aggregates the responses from all members and, if the majority agrees, consolidates the orders. Notifications of order completion and estimated arrival times are sent to all members, and the devices display these.

[0570] 3. Managing and sharing progress information

[0571] Users record their progress with their regular training and meals on LINE. For example, they could write a message like, "Today I jogged 5km." The device sends this progress data to the server, which then stores it in a database and updates the progress of the entire group.

[0572] The server aggregates the progress information of the entire group, and the emotion engine analyzes the user's emotional data based on the progress data. The generated positive feedback messages are sent from the server to all group members and displayed on their devices.

[0573] 4. Creating and coordinating joint schedules and meal plans

[0574] The user inputs details of the health plan they want to implement together. For example, they might input "Suggested weekly meal plan." The device then sends this input data to the server.

[0575] The server passes the received data to a generative model, which generates an optimal schedule and meal plan based on the input data. The generated plan is shared with the group from the server and receives feedback from group members. The server aggregates the feedback and generates a final plan with any necessary adjustments. The finalized plan is sent to all members and displayed on their devices.

[0576] Specific examples

[0577] For example, if user A complains on LINE that they have been feeling fatigued lately, the emotion engine will read the stress and fatigue from user A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements.

[0578] Next, when user A suggests that the group purchase protein, the group members express high agreement through the emotion engine, and the server consolidates the purchase.

[0579] When User A records their daily jogging progress, the emotion engine generates positive feedback on their achievements, increasing the motivation of the entire group.

[0580] Finally, the entire group collaborates to create a meal plan, and the emotion engine provides the optimal plan based on each member's preferences and emotions. In this way, the system utilizes users' emotion data to support more personalized health management and economical purchasing.

[0581] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0582] Health consultation processing steps

[0583] Step 1:

[0584] The user opens the LINE app, enters a health consultation message, and sends it.

[0585] Input: User message (e.g. "I've been feeling tired lately and I'd like some advice").

[0586] Output: A request to send a message from the user's device to the server.

[0587] Step 2:

[0588] The terminal sends the user's message to the server.

[0589] Input: The user's message.

[0590] Output: Sending data to the server (API request).

[0591] Step 3:

[0592] The server passes the received message to the emotion engine to recognize the user's emotion.

[0593] Input: Received message data.

[0594] Output: Sentiment analysis request to be passed to the sentiment engine.

[0595] Step 4:

[0596] An emotion engine analyzes message content and user emotion data.

[0597] Input: Message data and existing user emotion data.

[0598] Data processing: Recognize emotions (e.g., stress, fatigue, etc.) using text analysis algorithms.

[0599] Output: User emotion data.

[0600] Step 5:

[0601] The emotion engine provides emotion data to the generative model.

[0602] Input: Parsed emotion data.

[0603] Output: Sending data to the generative model.

[0604] Step 6:

[0605] The generative model takes into account emotional data to generate optimal advice for the user.

[0606] Input: Emotion data.

[0607] Data computation: Using AI models such as deep learning to generate advice (e.g., relaxation suggestions).

[0608] Output: The generated advice.

[0609] Step 7:

[0610] The server sends the generated advice to the user via a LINE message.

[0611] Input: Advice provided by the generative model.

[0612] Output: Sends a message to the user via the LINE API.

[0613] Step 8:

[0614] The terminal receives the advice message and displays it to the user.

[0615] Input: The advice message sent by the server.

[0616] Output: Display advice to the user.

[0617] Group Buying Process Steps

[0618] Step 1:

[0619] Send messages suggesting products that users might want to purchase within the group.

[0620] Input: User's suggested message (e.g., "Would we all like to buy this protein?").

[0621] Output: A request to send a message from the user's device to the server.

[0622] Step 2:

[0623] The terminal transmits product information and a proposal message to the server.

[0624] Input: Proposal message and product information.

[0625] Output: Sending data to the server (API request).

[0626] Step 3:

[0627] The server sends a proposal message to all group members.

[0628] Input: Product information and suggestion message.

[0629] Output: Notification to group members.

[0630] Step 4:

[0631] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[0632] Input: The proposal message.

[0633] Output: An accept or reject response.

[0634] Step 5:

[0635] The device sends a response to the server.

[0636] Input: Agree or decline response.

[0637] Output: Sending the response to the server.

[0638] Step 6:

[0639] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[0640] Input: Group member responses.

[0641] Data processing: Response aggregation.

[0642] Output: Order management information.

[0643] Step 7:

[0644] The server will send a notification of order completion and the expected arrival date of the product to all members.

[0645] Input: Order management information.

[0646] Output: Sends order completion notification.

[0647] Step 8:

[0648] The terminal displays a notification to the user that the order is complete.

[0649] Input: Notification message from the server.

[0650] Output: Display a notification to the user.

[0651] Steps for managing and sharing progress information

[0652] Step 1:

[0653] Users record their regular training and dietary progress on LINE.

[0654] Input: Progress message (e.g. "I jogged 5km today").

[0655] Output: A request to send a message from the user's device to the server.

[0656] Step 2:

[0657] The device sends progress data to the server.

[0658] Input: A progress message.

[0659] Output: Sending data to the server (API request).

[0660] Step 3:

[0661] The server stores the progress data in a database.

[0662] Input: Progress data.

[0663] Output: Save data to a database.

[0664] Step 4:

[0665] The server updates the progress of the entire group.

[0666] Input: Progress data from the database.

[0667] Data processing: Aggregation of progress data.

[0668] Output: Group progress.

[0669] Step 5:

[0670] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages.

[0671] Input: Progress data.

[0672] Data processing: text analysis and sentiment analysis.

[0673] Output: Motivational messages.

[0674] Step 6:

[0675] The server generates a message and sends it to all group members.

[0676] Input: Motivational message.

[0677] Output: Notification to group members.

[0678] Step 7:

[0679] The terminal receives and displays the message.

[0680] Input: Notification message from the server.

[0681] Output: Display a message to the user.

[0682] Process steps for creating and coordinating joint schedules and meal plans

[0683] Step 1:

[0684] The user enters details of the health plan they wish to collaborate on.

[0685] Input: Health plan suggestion message (e.g., weekly meal plan suggestion).

[0686] Output: A request to send a message from the user's device to the server.

[0687] Step 2:

[0688] The terminal sends the input data to the server.

[0689] Input: Health plan details.

[0690] Output: Sending data to the server (API request).

[0691] Step 3:

[0692] The server passes the received data to the generative model.

[0693] Input: Health plan details data.

[0694] Output: Sending data to the generative model.

[0695] Step 4:

[0696] The generative model generates optimal schedules and meal plans based on input data.

[0697] Input: Health plan data.

[0698] Data calculation: Optimization based on user sentiment data and preferences.

[0699] Output: Generated schedule and meal plan.

[0700] Step 5:

[0701] The server shares the generated plan within the group.

[0702] Input: The plan received from the generative model.

[0703] Output: Notification to group.

[0704] Step 6:

[0705] Users (group members) submit feedback on the proposed plan.

[0706] Input: Proposed plan.

[0707] Output: The feedback message.

[0708] Step 7:

[0709] The device sends the feedback to the server.

[0710] Input: The feedback message.

[0711] Output: Sending data to the server (API request).

[0712] Step 8:

[0713] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[0714] Input: Feedback data.

[0715] Data processing: Feedback aggregation and optimization.

[0716] Output: The finalized plan.

[0717] Step 9:

[0718] The server sends the finalized plan to all members.

[0719] Enter: Final plan.

[0720] Output: Notification to group members.

[0721] Step 10:

[0722] The terminal receives the final plan and displays it to the user.

[0723] Input: The final plan sent by the server.

[0724] Output: Display the final plan to the user.

[0725] (Application example 2)

[0726] 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."

[0727] Conventional health consultation and group purchasing systems simply process data without considering the user's feelings, which means that the advice and suggestions they provide do not adequately meet the user's needs. Furthermore, when it comes to managing and sharing progress information, they lack a mechanism for maintaining motivation, making it difficult to improve overall user satisfaction.

[0728] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for responding to health consultations using a generative model, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, means for creating and adjusting joint schedules and meal plans, means for generating optimal advice for health consultations and group purchasing based on the user's emotional data using an emotion engine that recognizes the user's emotions, and means for sharing the generated plans and advice within the group and aggregating feedback. This makes it possible to support the user's health management and financial purchasing activities in a more personalized manner, thereby improving user satisfaction and motivation.

[0729] A "generative model" refers to an algorithm or system that generates a specific output based on input data.

[0730] "Health consultation" refers to the act of a user consulting an expert or the system about their health condition or concerns and receiving advice.

[0731] "Group buying" refers to multiple users jointly purchasing goods or services in bulk.

[0732] "Consent aggregation" refers to the process of gathering consent from multiple participants to reach a single decision.

[0733] "Progress information" is data or information that indicates the progress a user is making toward a task or goal.

[0734] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from user text and behavior.

[0735] A "collaborative schedule" refers to a schedule that multiple users share and have a common activity schedule.

[0736] "Meal Plan" means a plan of what a User should eat over a specific period of time.

[0737] A "server" refers to a computer system that provides services and data to clients over a network.

[0738] "Aggregating feedback" refers to collecting and summarizing ratings and opinions provided by multiple users.

[0739] The present invention is a system for efficiently providing users with health consultations, group purchasing, progress tracking, and collaborative scheduling. The system utilizes a generative model and emotion engine to take into account users' emotion data and provide personalized advice and suggestions.

[0740] Hardware and software used

[0741] Hardware:

[0742] Smartphone

[0743] Smart Glasses

[0744] software:

[0745] Messaging apps for communication (e.g., LINE)

[0746] Emotion Engine (Emotion Recognition API)

[0747] Generative models (e.g., OpenAI GPT-3)

[0748] Schedule management app

[0749] Health management app

[0750] System configuration for implementing the program

[0751] Health consultation function

[0752] A user sends a health-related question using a messaging app. For example, the user sends a message saying, "I've been feeling tired lately. Can you give me some advice?" This message is first analyzed by the emotion engine, which recognizes the user's emotion data (e.g., feeling tired). This emotion data is then provided to a generative model, which generates optimal advice (e.g., relaxation techniques or suggested supplements). Finally, the generated advice is sent to the user.

[0753] Group purchase feature

[0754] A user uses a messaging app to propose a product purchase. For example, they send a proposal saying, "Shall we all buy this protein?" This proposal is forwarded to all group members via the server. Each member sends a response of either agreeing or declining, and these responses are collected and aggregated. If the majority agrees, the purchase is carried out collectively and the result is notified to all members.

[0755] Health Progress Management

[0756] Users record their daily training and dietary progress in a health management app. This data is sent to a server and analyzed using an emotion engine. Based on the analyzed data, a generative model generates motivational messages that are shared within the group. For example, if a user records "I jogged 5km today," the system recognizes a "sense of accomplishment" and generates positive feedback.

[0757] Creating a joint schedule and meal plan

[0758] A user enters details of a health plan into a schedule management app. For example, they enter a weekly meal plan, and the generative model generates an optimal plan. This plan is shared with the group and refined based on feedback from members. The final plan is sent to all members and implemented accordingly.

[0759] Specific examples

[0760] When User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine recognizes the "feeling of fatigue," and the generative model suggests relaxation methods and vitamin supplements.

[0761] Example prompt sentence:

[0762] I've been feeling tired lately, what relaxation methods or supplements would be good?

[0763] (Emotional data): Fatigue

[0764] When User B suggests on LINE, "Let's all buy this protein together," the group members agree and the purchase is carried out all at once.

[0765] Example prompt sentence:

[0766] Would you like everyone to buy this protein?

[0767] (Consent list): [User C: Agree, User D: Agree, User E: Agree]

[0768] When user C records in a health management app that he or she "jogged 5km today," the emotion engine recognizes the "sense of accomplishment" and the generative model generates positive feedback.

[0769] Example prompt sentence:

[0770] I jogged 5km today.

[0771] (Emotional data): Sense of accomplishment

[0772] When User D enters a weekly meal plan into a schedule management app, the generative model generates an optimal plan, which is then shared with the group to gather feedback and be refined.

[0773] Example prompt sentence:

[0774] A weekly meal plan is provided.

[0775] (Feedback): [User E: Favorite food, User F: Disliked food]

[0776] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0777] Step 1:

[0778] A user sends a health consultation message using a messaging app. The input is the specific consultation content entered by the user (e.g., "I've been feeling tired lately. Can you give me some advice?"). The output is the message being sent to a server via the messaging app.

[0779] Step 2:

[0780] The terminal sends the user's message to the server. The input is the user's message received by the communication application. The output is the message sent to the server and received by the server.

[0781] Step 3:

[0782] The server passes the received message to the emotion engine. The input is the message received from the user. The output is the message and user data for the emotion engine to analyze.

[0783] Step 4:

[0784] The emotion engine analyzes the message content and the user's emotion data. The input is the received message and user information. The output is the analyzed emotion data (e.g., tiredness, stress). This data is output as numerical data by using a specific algorithm to detect emotions from the text.

[0785] Step 5:

[0786] The emotion engine provides emotion data to the generative model. The input is the emotion data obtained by analysis. The output is the emotion data passed to the generative model.

[0787] Step 6:

[0788] The generative model generates optimal advice for the user based on the emotional data. The input is the emotional data provided by the emotion engine. The output is the generated advice (e.g., relaxation techniques, vitamin supplement suggestions).

[0789] Step 7:

[0790] The server sends the generated advice to the user. The input is the advice generated by the generative model. The output is the advice sent to the user via a communication application.

[0791] Step 8:

[0792] The terminal receives the advice message and displays it to the user. The input is the advice message sent from the server. The output is a form in which the user can check the advice in the communication application.

[0793] Step 9:

[0794] A messaging app is used to suggest products that users would like to purchase as a group. The input is information about the product that the user wants to purchase and a suggestion message (e.g., "Shall we all buy this protein?"). The output is a suggestion message that is sent to group members.

[0795] Step 10:

[0796] Group members send responses to the proposal message, either agreeing or rejecting it. The input is the responses from group members. The output is the form in which these responses are sent to the server.

[0797] Step 11:

[0798] The server aggregates the responses of all members and, if there is a majority of agreement, consolidates the order. The input is the response data from the group members. The output is the execution of the order and a notification of the order completion.

[0799] Step 12:

[0800] The server sends a notification of order completion and the expected arrival date of the product to all members. The input is the order completion data. The output is the notification sent to group members.

[0801] Step 13:

[0802] The terminal receives the order completion notification and displays it to the user. The input is the order completion notification sent from the server. The output is a form in which the user can confirm the order completion notification.

[0803] Step 14:

[0804] A user records their daily training and diet progress in a health management app. The input is the training and diet data recorded by the user (e.g., "I jogged 5km today"). The output is the progress data sent to the server.

[0805] Step 15:

[0806] The server saves the progress data to a database and updates the progress status of the entire group. The input is the progress data received from the user. The output is the updated progress information in the database.

[0807] Step 16:

[0808] The server passes the progress data to the emotion engine, which analyzes the user's emotion data. The input is the progress data. The output is the analyzed emotion data.

[0809] Step 17:

[0810] The emotion engine generates motivational messages based on the user's emotion data. The inputs are progress data and emotion data. The output is the generated motivational message.

[0811] Step 18:

[0812] The server sends the generated motivation message to all group members. The input is the generated motivation message. The output is the form in which the message is sent to group members.

[0813] Step 19:

[0814] The terminal receives and displays messages. The input is the motivation message sent from the server. The output is a form in which the user can confirm the message.

[0815] Step 20:

[0816] A user enters details of a joint health plan into a scheduling app. The input is the plan data entered by the user (e.g., a weekly meal plan). The output is the plan data sent to a server.

[0817] Step 21:

[0818] The server passes the received plan data to the generative model. The input is the received plan data. The output is the plan data passed to the generative model.

[0819] Step 22:

[0820] The generative model generates an optimal schedule or meal plan based on the plan data. The input is the received plan data. The output is the generated optimal plan.

[0821] Step 23:

[0822] The server shares the generated plan with the group members. The input is the generated plan. The output is the form in which the plan is sent to the group members.

[0823] Step 24:

[0824] Group members submit feedback on the proposed plan. The input is the proposed plan and the members' feedback data. The output is the feedback sent to the server.

[0825] Step 25:

[0826] The server aggregates the feedback and generates a final plan with any necessary adjustments. The input is the feedback data. The output is the adjusted final plan.

[0827] Step 26:

[0828] The server sends the finalized plan to all members. The input is the final plan. The output is the final plan sent to all members.

[0829] Step 27:

[0830] The terminal receives the final plan and displays it to the user. The input is the final plan data. The output is a format that allows the user to check the final plan.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] [Second embodiment]

[0835] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0836] 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.

[0837] 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).

[0838] 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.

[0839] 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.

[0840] 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).

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] The present invention provides a system that utilizes generative models to provide health consultations, aggregate group purchasing proposals and agreements, manage and share progress information, and create and adjust joint schedules and meal plans, to support users in efficiently managing their health and making economical purchases.

[0848] A natural language description of the program's processing

[0849] How to respond to health consultations

[0850] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[0851] 2. Terminal: Sends the user's message to the server.

[0852] 3. Server: Receives messages and passes them to the generative model.

[0853] 4. Generative model: Analyzes the message content and generates optimal advice for the user.

[0854] 5. Server: Sends the generated advice to the user via LINE message.

[0855] 6. Terminal: Display an advisory message to the user.

[0856] Aggregation of group buying proposals and consents

[0857] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[0858] 2. Terminal: Sends product information and suggestion messages to the server.

[0859] 3. Server: Sends a proposal message to all group members.

[0860] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[0861] 5. Terminal: Sends the response to the server.

[0862] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[0863] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[0864] 8. Terminal: Display a notification to the user that the order has been completed.

[0865] Managing and sharing progress information

[0866] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[0867] 2. Device: Sends progress data to the server.

[0868] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[0869] 4. Server: Aggregates progress information for the entire group and generates motivational messages.

[0870] 5. Server: Sends the generated message to all group members.

[0871] 6. Terminal: Receives and displays messages.

[0872] Creating and coordinating joint schedules and meal plans

[0873] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[0874] 2. Terminal: Sends input data to the server.

[0875] 3. Server: Passes the received data to the generative model.

[0876] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[0877] 5. Server: Shares the generated plan within the group.

[0878] 6. User (group member): Sends feedback on the proposed plan.

[0879] 7. Device: Sends feedback to the server.

[0880] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[0881] 9. Server: Sends the finalized plan to all members.

[0882] 10. Terminal: Receives the final plan and displays it to the user.

[0883] Specific examples

[0884] For example, if User A recently feels fatigued and sends a health consultation on LINE, the generative model will suggest taking vitamin D. Next, User A suggests purchasing protein with the group, and the group members agree on LINE, and the server coordinates the purchase. Furthermore, when User A records his daily jogging progress on LINE, the server aggregates the progress information of the entire group and sends encouraging messages. Finally, the group collaboratively creates a weekly meal plan, and everyone provides feedback to provide an optimized plan. In this way, this system is a multi-functional platform that supports users' health management and economical purchases.

[0885] The processing flow will be explained below.

[0886] How to respond to health consultations

[0887] Step 1:

[0888] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[0889] Step 2:

[0890] The device sends the user's message to the server.

[0891] Step 3:

[0892] The server passes the received message to the generative model.

[0893] Step 4:

[0894] A generative model analyzes the message content and generates optimal advice for the user.

[0895] Step 5:

[0896] The server sends the generated advice to the user via LINE message.

[0897] Step 6:

[0898] The device displays an advisory message to the user.

[0899] Aggregation of group buying proposals and consents

[0900] Step 1:

[0901] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[0902] Step 2:

[0903] The terminal transmits product information and a proposal message to the server.

[0904] Step 3:

[0905] The server sends a proposal message to all group members.

[0906] Step 4:

[0907] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[0908] Step 5:

[0909] The device sends a response to the server.

[0910] Step 6:

[0911] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[0912] Step 7:

[0913] The server will send a notification of order completion and the expected arrival date of the product to all members.

[0914] Step 8:

[0915] The terminal will display a notification to the user that the order is complete.

[0916] Managing and sharing progress information

[0917] Step 1:

[0918] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[0919] Step 2:

[0920] The device sends progress data to the server.

[0921] Step 3:

[0922] The server stores the progress data in a database and updates the progress of the entire group.

[0923] Step 4:

[0924] The server aggregates progress information for the entire group and generates motivational messages.

[0925] Step 5:

[0926] The server generates a message and sends it to all group members.

[0927] Step 6:

[0928] The terminal receives and displays the message.

[0929] Creating and coordinating joint schedules and meal plans

[0930] Step 1:

[0931] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[0932] Step 2:

[0933] The terminal sends the input data to the server.

[0934] Step 3:

[0935] The server passes the received data to the generative model.

[0936] Step 4:

[0937] The generative model generates optimal schedules and meal plans based on input data.

[0938] Step 5:

[0939] The server shares the generated plan within the group.

[0940] Step 6:

[0941] Users (group members) submit feedback on the proposed plan.

[0942] Step 7:

[0943] The device sends the feedback to the server.

[0944] Step 8:

[0945] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[0946] Step 9:

[0947] The server sends the finalized plan to all members.

[0948] Step 10:

[0949] The device receives the final plan and displays it to the user.

[0950] Example 1

[0951] 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."

[0952] In today's world, there is a growing need for efficient health management and financial purchasing. However, there are few systems that provide multiple functions, such as health consultations, group purchasing, progress information management, and collaborative schedule creation, in an integrated manner. In particular, there is a need for technology that can quickly provide optimal advice to users. There is also a lack of tools that allow information to be shared among groups and make decision-making easier. The development of a comprehensive system that can address these complex needs is required.

[0953] 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.

[0954] In this invention, the server includes means for responding to health consultations using generative models, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, and means for creating and adjusting joint schedules and meal plans, thereby enabling users to efficiently manage their health and make economical purchases, and share and adjust progress information and joint plans within the group.

[0955] A "generative model" is an artificial intelligence algorithm that analyzes natural language messages from users and generates appropriate responses or advice.

[0956] The "means for responding to health consultations" is the part of the system that receives health-related questions from users and provides appropriate responses to those questions.

[0957] "Means for aggregating group purchase proposals and consents" refers to the part of the system that, when a user wishes to jointly purchase a particular product as a group, notifies other members of the group of the proposal and aggregates the responses of consent or denial.

[0958] "Means for managing and sharing progress information" refers to the system part that records progress information such as the user's training and diet, and shares that information within the group.

[0959] "Means for creating and adjusting collaborative schedules and meal plans" means the portion of the system that allows multiple users to create schedules and meal plans for collaborative use and adjust the contents of those schedules and meal plans as needed.

[0960] A "communications application" is a software product that allows a user to access a system and send or receive information.

[0961] "User interface" refers to the means, such as screens and operating methods, that a user uses to interact with a system.

[0962] "Communication means" means hardware and software functions for transmitting and receiving data.

[0963] A "database that stores and provides product information" is a data management system that stores information on candidate products for purchase and provides that information when needed.

[0964] The present invention is a multi-functional system that supports users in efficiently managing their health and making economical purchases. The system includes a means for utilizing generative models to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans. Specific embodiments and usage examples are described below.

[0965] How to respond to health consultations

[0966] User: Send a health consultation message using the LINE app. For example, send a message saying, "I've been feeling tired lately and I'd like some advice."

[0967] Device: Sends the user's message to the server via the LINE API.

[0968] Server: Receives messages and passes them to the generative model. It analyzes the messages and converts them into an appropriate format. The generative model uses a natural language processing model such as GPT-3.

[0969] Generative model: Analyzes the content of received messages and generates optimal advice for the user. For example, it generates advice such as "Try increasing your intake of vitamin D."

[0970] Server: Sends the generated advice to the user via LINE message.

[0971] Terminal: Receives advice messages and displays them to the user.

[0972] Aggregation of group buying proposals and consents

[0973] User: Selects a product that the group wants to purchase and sends a suggestion message. For example, the user sends a message saying, "Would you like to buy this protein with everyone?"

[0974] Device: Product information and suggestion messages are sent to the server via the LINE API.

[0975] Server: Distributes proposal messages to all group members. Uses a database to store and provide product information.

[0976] User (group member): Receives the proposal message and sends a response of acceptance or rejection through the LINE app.

[0977] Device: Sends the response to the server using the LINE API.

[0978] Server: Aggregates responses from all members and, if there is a majority agreement, aggregates the order.

[0979] Server: Sends notification of order completion and estimated delivery date to all members.

[0980] Terminal: Receives notification of order completion and displays it to the user.

[0981] Managing and sharing progress information

[0982] User: Records progress of regular training and meals on LINE. For example, sends a message saying "I jogged 5km today."

[0983] Device: Progress data is sent to the server via the LINE API.

[0984] Server: Stores progress data in a database and updates the progress of the entire group.

[0985] Server: Aggregates progress information for the entire group and generates motivational messages, such as "Everyone is doing great! Keep it up!"

[0986] Server: Sends the generated message to all group members.

[0987] Terminal: Receives and displays messages.

[0988] Creating and coordinating joint schedules and meal plans

[0989] User: Enter details of the health plan they want to collaborate on. For example, they can send a "Weekly Meal Plan Suggestion."

[0990] Device: Sends input data to the server via the LINE API.

[0991] Server: Passes the received data to the generative model.

[0992] Generative models: Generate optimal schedules and meal plans based on input data.

[0993] Server: Shares the generated plan within the group.

[0994] User (group member): Sends feedback on the proposed plan.

[0995] Device: Sends feedback to the server using the LINE API.

[0996] Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[0997] Server: Sends the finalized plan to all members.

[0998] Terminal: Receives the final plan and displays it to the user.

[0999] In this way, users can efficiently manage their health and make economical purchases, and share and coordinate progress information and joint plans within the group.

[1000] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1001] Flow of processing for responding to health consultations

[1002] Step 1:

[1003] The user uses the LINE app to enter and send a health consultation message.

[1004] Specific actions: The user enters "I've been feeling tired lately and would like some advice" into the text box of the LINE app and presses the send button.

[1005] Input: The health consultation message entered by the user.

[1006] Output: The message is sent to the LINE server.

[1007] Step 2:

[1008] The device sends the user's message to the server using the LINE API.

[1009] Specific operation: The device receives the message via the LINE API and sends it to the server.

[1010] Input: Health consultation message from user.

[1011] Output: The message is sent to the server.

[1012] Step 3:

[1013] The server receives the message and passes it to the generative model.

[1014] Specific behavior: The server parses the message, converts it into an appropriate format, and passes it to the generative model.

[1015] Input: Health consultation message sent from the device.

[1016] Output: Data converted into a format that can be input to a generative model.

[1017] Step 4:

[1018] The generative model analyzes the message content and generates optimal advice for the user.

[1019] What it does: A generative model (e.g., GPT-3) analyzes the message and generates advice like, "Try increasing your vitamin D intake."

[1020] Input: Message data passed to the generative model by the server.

[1021] Output: The generated advice.

[1022] Step 5:

[1023] The server sends the generated advice to the user via a LINE message.

[1024] Specific behavior: The server uses the LINE API to generate an advice message and send it to the user.

[1025] Input: The advice generated by the generative model.

[1026] Output: Advisory message sent to the user's terminal.

[1027] Step 6:

[1028] The terminal receives the advice message and displays it to the user.

[1029] Specific operation: The user's device receives a LINE message and displays the advice.

[1030] Input: The advice message sent by the server.

[1031] Output: The advice message will be displayed in the chat window of the LINE app.

[1032] Group Buy Proposal and Consent Aggregation Process

[1033] Step 1:

[1034] A user selects a product they wish to purchase within the group, enters a suggestion message, and sends it.

[1035] Specific operation: The user enters "Would you like to buy this protein for everyone?" in the text box of the LINE app, attaches a product link and image, and presses the send button.

[1036] Input: The purchase offer message entered by the user.

[1037] Output: The message is sent to the LINE server.

[1038] Step 2:

[1039] The device sends the user's suggestion message to the server using the LINE API.

[1040] Specific operation: The device receives the message via the LINE API and sends it to the server.

[1041] Input: Purchase suggestion message from the user.

[1042] Output: The message is sent to the server.

[1043] Step 3:

[1044] The server broadcasts the proposal message to all group members.

[1045] Specific operation: The server receives the proposal message, distributes it to all members in the group, and saves the proposal in the database.

[1046] Input: The user's suggested message.

[1047] Output: The proposal message that will be delivered to group members.

[1048] Step 4:

[1049] Group members receive the proposal message, enter a response of agreement or rejection, and submit it.

[1050] Specific actions: Group members enter a response such as "I'll participate" or "I'll pass this time" through the LINE app and press the send button.

[1051] Input: Group member responses.

[1052] Output: A response is sent to the LINE server.

[1053] Step 5:

[1054] The device sends the group member's response to the server using the LINE API.

[1055] Specific operation: The group member's device receives the response via the LINE API and sends it to the server.

[1056] Input: Responses from group members.

[1057] Output: The response is sent to the server.

[1058] Step 6:

[1059] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order data.

[1060] Specific operation: The server aggregates the responses from all members, compiles the order data based on the majority vote, and saves the order details in the database.

[1061] Input: Group member responses.

[1062] Output: Aggregated order data.

[1063] Step 7:

[1064] The server will send a notification of order completion and the expected arrival date of the product to all members.

[1065] Specific operation: The server uses the LINE API to send a notification of order completion and details to all members.

[1066] Input: Order completion information and estimated delivery date.

[1067] Output: Notifications delivered to group members.

[1068] Step 8:

[1069] The terminal receives the notification of order completion and displays it to the user.

[1070] Specific behavior: The user's device receives a LINE message and displays details of the completed order.

[1071] Input: Order completion notification sent from the server.

[1072] Output: A notification that the order is complete will be displayed in the chat window of the LINE app.

[1073] keyword:

[1074] Generative AI model, prompt sentence

[1075] (Application example 1)

[1076] 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."

[1077] While traditional health management and financial purchasing support systems offer individual functions, few offer a multi-functional platform that integrates them. As a result, users are forced to use multiple applications, reducing utilization efficiency. It has also been difficult to provide integrated functions such as health consultations, group purchasing suggestions, progress management, and meal plan creation.

[1078] 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.

[1079] In this invention, the server includes a means for responding to health consultations using a generative model, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and adjusting joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing, thereby enabling users to efficiently manage their health and financial activities on a single platform.

[1080] A "generative model" is an algorithm that analyzes input data from a user and generates optimal responses and suggestions.

[1081] "Health consultation" is a function that allows users to input information about their daily health problems and physical condition, and provides advice and solutions to those problems.

[1082] "Group purchase" is a function that aggregates proposals and agreements for multiple users to jointly purchase products.

[1083] "Progress information" is data that records, manages, and shares a user's daily activities and goal achievement status.

[1084] "Joint Schedule" is a function that allows multiple users to create and adjust schedules for joint activities.

[1085] A "meal plan" is a plan that suggests optimal meal plans based on a user's health goals and needs.

[1086] "Health management" is the process of monitoring a user's health status and providing appropriate advice and suggestions.

[1087] "Economic Purchasing" is the process of providing information and suggestions to help users effectively purchase products.

[1088] An "application" is a software program that a user utilizes to perform a particular function.

[1089] A "server" is a computer system that receives and processes data and provides services to users.

[1090] The present invention is a multi-functional system that enables users to efficiently manage their health and financial activities on a single platform. The system includes a means for responding to health consultations using generative models, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and coordinating joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing.

[1091] Hardware and software used

[1092] This system uses the following hardware and software:

[1093] Hardware:

[1094] Smartphone (iOS or Android)

[1095] Smart Glasses

[1096] Server PC

[1097] software:

[1098] "Health Concierge" app (iOS / Android app)

[1099] OpenAI GPT-4

[1100] Database (e.g. AWS DynamoDB)

[1101] In an implementation of the present invention, a user accesses an application via a smartphone or smart glasses. The user's input data is analyzed by a generative model (e.g., OpenAI GPT-4) to generate appropriate responses or suggestions. The generated responses or suggestions are then sent to the user via a communication medium.

[1102] Examples of concrete examples and prompts

[1103] Health consultation example:

[1104] When a user uses their smartphone to type, "I've been feeling tired lately and I'd like some advice," this message is sent to the server. The server passes the message to a generative model (OpenAI GPT-4) and generates appropriate health advice. The generated advice is sent to the user, allowing them to receive specific measures for improvement.

[1105] Group Buying Example:

[1106] When a user uses the app to suggest to the group, "Shall we all buy this protein?", this information is sent to the server and all group members are notified. All members send responses of either agree or decline, and the results are tallied by the server. If a majority vote agrees, the server processes the order and a notification of order completion is sent to all members.

[1107] Progress information management example:

[1108] When a user logs in to the app using their smartphone, "I jogged 5km today," this information is sent to the server and stored in a database. The server then aggregates the progress of the entire group and generates a motivational message, such as "The whole group has completed 50km this week! Let's do our best!", which is then sent to all group members.

[1109] Collaborative planning example:

[1110] The user proposes a weekly meal plan in the app, entering specific details such as "fish on Mondays and salad on Wednesdays." This information is sent to the server, which passes it through a generative model to generate the optimal plan. The generated plan is shared with the group, and feedback is sent. This feedback is aggregated on the server, and the final plan is adjusted and re-notified to all members.

[1111] Examples of prompts:

[1112] "I've been feeling tired lately and I'd like some advice. Can you tell me how to take vitamins or other ways to improve my condition?"

[1113] "Wouldn't we all like to buy this protein?"

[1114] I jogged 5km today.

[1115] "I'm proposing a meal plan with fish on Mondays and salads on Wednesdays. I'd love your feedback."

[1116] Thus, the present invention provides a multifunctional system that efficiently supports the user's health management and economic activities.

[1117] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1118] Health consultation function processing steps

[1119] Step 1:

[1120] User: Logs into the health concierge app using a smartphone or smart glasses and enters a health consultation prompt, for example, "I've been feeling tired lately and would like some advice."

[1121] Input: Health consultation message from user

[1122] Output: Health consultation message sent

[1123] Step 2:

[1124] Terminal: Sends the health consultation message entered by the user to the server.

[1125] Input: Health consultation message

[1126] Output: Message sent to the server

[1127] Step 3:

[1128] Server: Passes received messages to a generative model (e.g., OpenAI GPT-4).

[1129] Input: User message

[1130] Output: Messages passed to the generative model

[1131] Step 4:

[1132] Generative model: Analyzes the content of the user's message and generates optimal health advice. The generative model refers to a large amount of data based on the input consultation content and makes optimal suggestions.

[1133] Input: User's health consultation message

[1134] Output: Health advice

[1135] Step 5:

[1136] Server: Receives the generated advice and sends it to the user device.

[1137] Enter: Health Advice

[1138] Output: Advice sent to the user's terminal

[1139] Step 6:

[1140] Terminal: Display health advice messages to the user.

[1141] Input: Advice received from the server

[1142] Output: Displayed health advice

[1143] Group Buying Feature Processing Steps

[1144] Step 1:

[1145] User: Enter the product information and message you want to share with the app. For example, you could say, "Let's all buy this protein together."

[1146] Input: Product information and suggestion message

[1147] Output: Sent product information and suggestion message

[1148] Step 2:

[1149] Terminal: Sends product information and suggestion messages to the server.

[1150] Input: Product information and suggestion message

[1151] Output: Proposal message sent to the server

[1152] Step 3:

[1153] Server: Sends the proposal message to all group members.

[1154] Input: Proposal message

[1155] Output: Message sent to group members

[1156] Step 4:

[1157] User (group member): Receives the proposal message and selects a response of either accept or reject in the app.

[1158] Input: Proposal message

[1159] Output: Agree or decline response

[1160] Step 5:

[1161] Terminal: Sends the response to the server.

[1162] Input: Response

[1163] Output: The response sent to the server

[1164] Step 6:

[1165] Server: Aggregates responses from all members, and if a majority vote agrees, proceeds with the order.

[1166] Input: Responses of all members

[1167] Output: Checkout completed

[1168] Step 7:

[1169] Server: Notify all members of order completion and estimated arrival of the product.

[1170] Input: Order Checkout Information

[1171] Output: Order completion notification and estimated delivery date

[1172] Step 8:

[1173] Terminal: Display a notification to the user that the order has been completed.

[1174] Input: Notification

[1175] Output: Displayed order completion notification

[1176] Progress information management function processing steps

[1177] Step 1:

[1178] User: Records daily training progress in the app, for example, "Today I jogged 5km."

[1179] Input: Progress information

[1180] Output: Progress information sent

[1181] Step 2:

[1182] Device: Sends progress data to the server.

[1183] Input: Progress data

[1184] Output: Data sent to the server

[1185] Step 3:

[1186] Server: Stores progress data in a database and updates progress for the entire group.

[1187] Input: Received progress data

[1188] Output: Updated progress information

[1189] Step 4:

[1190] Server: Aggregates progress information for the entire group and generates motivational messages.

[1191] Input: Aggregated progress data

[1192] Output: Motivation message

[1193] Step 5:

[1194] Server: Sends the generated message to all group members.

[1195] Input: Motivational message

[1196] Output: The message sent.

[1197] Step 6:

[1198] Terminal: Receives and displays messages.

[1199] Input: Message from the server

[1200] Output: The displayed message

[1201] Collaborative Planning Feature Processing Steps

[1202] Step 1:

[1203] User: Enter details of the health plan they want to collaborate on in the app, for example, suggesting a weekly meal plan.

[1204] Enter: Health plan details

[1205] Output: Health plan information submitted

[1206] Step 2:

[1207] Terminal: Sends input data to the server.

[1208] Input: Health plan data

[1209] Output: Data sent to the server

[1210] Step 3:

[1211] Server: Passes the received data to the generative model.

[1212] Input: Health plan data

[1213] Output: The data passed to the generative model

[1214] Step 4:

[1215] Generative models: Generate optimal schedules and meal plans based on input data.

[1216] Input: Health plan data

[1217] Output: Generated plan

[1218] Step 5:

[1219] Server: Shares the generated plan within the group.

[1220] Input: Generated plan

[1221] Output: Shared plans

[1222] Step 6:

[1223] User (group member): Send feedback on the proposed plan.

[1224] Input: Plan

[1225] Output: Feedback

[1226] Step 7:

[1227] Device: Sends feedback to the server.

[1228] Input: Feedback

[1229] Output: Feedback sent to the server

[1230] Step 8:

[1231] Server: Aggregates feedback and generates the final plan.

[1232] Input: Feedback

[1233] Output: Final plan

[1234] Step 9:

[1235] Server: Notify all members of the finalized plan.

[1236] Input: Final Plan

[1237] Output: Final plan reported

[1238] Step 10:

[1239] Terminal: Receives the final plan and displays it to the user.

[1240] Input: Final Plan

[1241] Output: Final plan displayed

[1242] 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.

[1243] The present invention provides a system including a means for utilizing a generative model to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans, as well as an emotion engine that recognizes user emotions. This system is intended to support users in efficiently managing their health and making economical purchases.

[1244] A natural language description of the program's processing

[1245] Enhancing health consultations with an emotion engine

[1246] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[1247] 2. Terminal: Sends the user's message to the server.

[1248] 3. Server: Passes the received message to the emotion engine to recognize the user's emotion.

[1249] 4. Emotion engine: Analyzes message content and user emotion data and provides it to the generative model.

[1250] 5. Generative models: Generate optimal advice for users by taking into account emotional data (e.g., suggesting relaxation techniques if emotions indicate fatigue or stress).

[1251] 6. Server: Sends the generated advice to the user via LINE message.

[1252] 7. Terminal: Display an advisory message to the user.

[1253] Aggregation of group buying proposals and consents

[1254] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[1255] 2. Terminal: Sends product information and suggestion messages to the server.

[1256] 3. Server: Sends a proposal message to all group members.

[1257] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[1258] 5. Terminal: Sends the response to the server.

[1259] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[1260] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[1261] 8. Terminal: Display a notification to the user that the order has been completed.

[1262] Managing and sharing progress information

[1263] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[1264] 2. Device: Sends progress data to the server.

[1265] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[1266] 4. Server: Aggregates progress information for the entire group.

[1267] 5. Emotion Engine: Analyzes the user's emotional data based on progress data and generates motivational messages.

[1268] 6. Server: Sends the generated message to all group members.

[1269] 7. Terminal: Receives and displays messages.

[1270] Creating and coordinating joint schedules and meal plans

[1271] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[1272] 2. Terminal: Sends input data to the server.

[1273] 3. Server: Passes the received data to the generative model.

[1274] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[1275] 5. Server: Shares the generated plan within the group.

[1276] 6. User (group member): Sends feedback on the proposed plan.

[1277] 7. Device: Sends feedback to the server.

[1278] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[1279] 9. Server: Sends the finalized plan to all members.

[1280] 10. Terminal: Receives the final plan and displays it to the user.

[1281] Specific examples

[1282] For example, if User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine will read the stress and fatigue from User A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements. Next, if User A suggests purchasing protein with the group, the group members will indicate high agreement through the emotion engine, and the server will coordinate the purchase. When User A records his daily jogging progress, the emotion engine will generate positive feedback on his achievements, increasing the motivation of the entire group. Finally, the entire group will jointly create a meal plan, and the emotion engine will provide the optimal plan based on each member's preferences and emotions. In this way, this system utilizes users' emotional data to support more personalized health management and economical purchasing.

[1283] The processing flow will be explained below.

[1284] Enhancing health consultations with an emotion engine

[1285] Step 1:

[1286] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[1287] Step 2:

[1288] The device sends the user's message to the server.

[1289] Step 3:

[1290] The server passes the received message to the emotion engine, which begins analyzing it to recognize the user's emotions.

[1291] Step 4:

[1292] The emotion engine analyzes the message content and the user's emotional data (e.g., fatigue, stress level).

[1293] Step 5:

[1294] The emotion engine provides the analyzed emotion data to the generative model.

[1295] Step 6:

[1296] The generative model takes into account emotional data to generate optimal advice for the user (e.g., suggesting relaxation methods if emotions indicate fatigue or stress).

[1297] Step 7:

[1298] The server sends the generated advice to the user via LINE message.

[1299] Step 8:

[1300] The device displays an advisory message to the user.

[1301] Aggregation of group buying proposals and consents

[1302] Step 1:

[1303] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[1304] Step 2:

[1305] The terminal transmits product information and a proposal message to the server.

[1306] Step 3:

[1307] The server sends a proposal message to all group members.

[1308] Step 4:

[1309] Users (group members) receive the proposal message and send a response of agreement or rejection, including their emotions.

[1310] Step 5:

[1311] The device sends a response to the server.

[1312] Step 6:

[1313] The sentiment engine analyzes the emotional data of the responses and assesses the level of agreement across the group.

[1314] Step 7:

[1315] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[1316] Step 8:

[1317] The server will send a notification of order completion and the expected arrival date of the product to all members.

[1318] Step 9:

[1319] The terminal will display a notification to the user that the order is complete.

[1320] Managing and sharing progress information

[1321] Step 1:

[1322] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[1323] Step 2:

[1324] The device sends progress data to the server.

[1325] Step 3:

[1326] The server stores the progress data in a database and updates the progress of the entire group.

[1327] Step 4:

[1328] The server aggregates progress information for the entire group.

[1329] Step 5:

[1330] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages (e.g., encouraging messages that evoke positive emotions).

[1331] Step 6:

[1332] The server generates a message and sends it to all group members.

[1333] Step 7:

[1334] The terminal receives and displays the message.

[1335] Creating and coordinating joint schedules and meal plans

[1336] Step 1:

[1337] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[1338] Step 2:

[1339] The terminal sends the input data to the server.

[1340] Step 3:

[1341] The server passes the received data to the generative model.

[1342] Step 4:

[1343] The generative model generates optimal schedules and meal plans based on input data.

[1344] Step 5:

[1345] The server shares the generated plan within the group.

[1346] Step 6:

[1347] Users (group members) submit feedback on the proposed plan along with emotional data.

[1348] Step 7:

[1349] The device sends the feedback to the server.

[1350] Step 8:

[1351] The emotion engine analyzes the emotional data of the feedback and evaluates the opinions of the entire group.

[1352] Step 9:

[1353] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[1354] Step 10:

[1355] The server sends the finalized plan to all members.

[1356] Step 11:

[1357] The device receives the final plan and displays it to the user.

[1358] Example 2

[1359] 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."

[1360] Conventional health management systems and group purchasing systems have difficulty responding to individual users' emotions, and tend to provide uniform advice and suggestions. Furthermore, group schedule coordination and progress management are cumbersome, making it difficult to adjust to each member's opinions and circumstances. This has led to issues such as reduced user satisfaction and reduced utilization efficiency.

[1361] 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.

[1362] In this invention, the server includes a means for responding to health consultations using a generative model, an emotion engine that analyzes a user's emotion data and provides it to the generative model, and a means for generating and transmitting advice that takes the emotion data into consideration. This enables personalized advice and suggestions based on the user's emotions. The server also includes a means for aggregating group purchase proposals and agreements, a means for managing and sharing progress information, and a means for creating and coordinating joint schedules and meal plans. This facilitates collaboration and progress management within a group, improving user satisfaction and usage efficiency.

[1363] A "generative model" is an algorithm that uses artificial intelligence to generate appropriate responses or suggestions based on input data.

[1364] The "emotion engine" is a function that analyzes emotions from user messages and data and provides that information to other system components.

[1365] "Means for responding to health consultations" is a function that provides appropriate advice in response to health-related questions and consultations from users.

[1366] The "means for generating and sending advice" is a function for sending advice created by the generative model based on emotion data to the user.

[1367] "Means for aggregating proposals and consents for group purchases" is a function that aggregates and unifies proposals and consents when multiple users jointly purchase products.

[1368] "Means for managing and sharing progress information" refers to a function for managing a user's training or activity progress information and sharing it with other users.

[1369] "Means for creating and adjusting shared schedules and meal plans" refers to a function that allows multiple users to create schedules and meal plans for joint use and adjust them as needed.

[1370] A "communications application" is software for sending and receiving messages over the Internet.

[1371] "Communication means" refers to the hardware and network infrastructure for transmitting and receiving data.

[1372] A "database" is an information system that can efficiently store large amounts of information and retrieve it as needed.

[1373] The present invention is a system for supporting users in efficient health management and economical purchasing, which mainly consists of a generative model, an emotion engine, a communication application via a user interface, a communication means, and a database.

[1374] 1. Means of responding to health consultations

[1375] The user sends a health consultation message to the server via a communication application such as the LINE app. For example, the message could be, "I've been feeling tired lately and I'd like some advice." The device then sends this message to the server via the Internet.

[1376] The server passes the received message to the emotion engine to recognize the user's emotion. The emotion engine analyzes the message content and the user's emotion data and provides that data to the generative model. The generative model takes the emotion data into account and generates optimal advice for the user. For example, if the emotion engine recognizes fatigue or stress, it will generate suggestions for relaxation methods or vitamin supplements.

[1377] The server sends the generated advice to the user via a LINE message, and the device displays the advice message to the user.

[1378] 2. Aggregation of group purchase proposals and agreements

[1379] A user sends a message suggesting a product they would like to purchase within the group. For example, a message like "Would you like to buy this protein with everyone?" The device sends this message along with the product information to the server.

[1380] The server sends a proposal message to all group members, who then respond by agreeing or rejecting the request. The device then sends these responses back to the server. The server then aggregates the responses from all members and, if the majority agrees, consolidates the orders. Notifications of order completion and estimated arrival times are sent to all members, and the devices display these.

[1381] 3. Managing and sharing progress information

[1382] Users record their progress with their regular training and meals on LINE. For example, they could write a message like, "Today I jogged 5km." The device sends this progress data to the server, which then stores it in a database and updates the progress of the entire group.

[1383] The server aggregates the progress information of the entire group, and the emotion engine analyzes the user's emotional data based on the progress data. The generated positive feedback messages are sent from the server to all group members and displayed on their devices.

[1384] 4. Creating and coordinating joint schedules and meal plans

[1385] The user inputs details of the health plan they want to implement together. For example, they might input "Suggested weekly meal plan." The device then sends this input data to the server.

[1386] The server passes the received data to a generative model, which generates an optimal schedule and meal plan based on the input data. The generated plan is shared with the group from the server and receives feedback from group members. The server aggregates the feedback and generates a final plan with any necessary adjustments. The finalized plan is sent to all members and displayed on their devices.

[1387] Specific examples

[1388] For example, if user A complains on LINE that they have been feeling fatigued lately, the emotion engine will read the stress and fatigue from user A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements.

[1389] Next, when user A suggests that the group purchase protein, the group members express high agreement through the emotion engine, and the server consolidates the purchase.

[1390] When User A records their daily jogging progress, the emotion engine generates positive feedback on their achievements, increasing the motivation of the entire group.

[1391] Finally, the entire group collaborates to create a meal plan, and the emotion engine provides the optimal plan based on each member's preferences and emotions. In this way, the system utilizes users' emotion data to support more personalized health management and economical purchasing.

[1392] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1393] Health consultation processing steps

[1394] Step 1:

[1395] The user opens the LINE app, enters a health consultation message, and sends it.

[1396] Input: User message (e.g. "I've been feeling tired lately and I'd like some advice").

[1397] Output: A request to send a message from the user's device to the server.

[1398] Step 2:

[1399] The terminal sends the user's message to the server.

[1400] Input: The user's message.

[1401] Output: Sending data to the server (API request).

[1402] Step 3:

[1403] The server passes the received message to the emotion engine to recognize the user's emotion.

[1404] Input: Received message data.

[1405] Output: Sentiment analysis request to be passed to the sentiment engine.

[1406] Step 4:

[1407] An emotion engine analyzes message content and user emotion data.

[1408] Input: Message data and existing user emotion data.

[1409] Data processing: Recognize emotions (e.g., stress, fatigue, etc.) using text analysis algorithms.

[1410] Output: User emotion data.

[1411] Step 5:

[1412] The emotion engine provides emotion data to the generative model.

[1413] Input: Parsed emotion data.

[1414] Output: Sending data to the generative model.

[1415] Step 6:

[1416] The generative model takes into account emotional data to generate optimal advice for the user.

[1417] Input: Emotion data.

[1418] Data computation: Using AI models such as deep learning to generate advice (e.g., relaxation suggestions).

[1419] Output: The generated advice.

[1420] Step 7:

[1421] The server sends the generated advice to the user via a LINE message.

[1422] Input: Advice provided by the generative model.

[1423] Output: Sends a message to the user via the LINE API.

[1424] Step 8:

[1425] The terminal receives the advice message and displays it to the user.

[1426] Input: The advice message sent by the server.

[1427] Output: Display advice to the user.

[1428] Group Buying Process Steps

[1429] Step 1:

[1430] Send messages suggesting products that users might want to purchase within the group.

[1431] Input: User's suggested message (e.g., "Would we all like to buy this protein?").

[1432] Output: A request to send a message from the user's device to the server.

[1433] Step 2:

[1434] The terminal transmits product information and a proposal message to the server.

[1435] Input: Proposal message and product information.

[1436] Output: Sending data to the server (API request).

[1437] Step 3:

[1438] The server sends a proposal message to all group members.

[1439] Input: Product information and suggestion message.

[1440] Output: Notification to group members.

[1441] Step 4:

[1442] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[1443] Input: The proposal message.

[1444] Output: An accept or reject response.

[1445] Step 5:

[1446] The device sends a response to the server.

[1447] Input: Agree or decline response.

[1448] Output: Sending the response to the server.

[1449] Step 6:

[1450] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[1451] Input: Group member responses.

[1452] Data processing: Response aggregation.

[1453] Output: Order management information.

[1454] Step 7:

[1455] The server will send a notification of order completion and the expected arrival date of the product to all members.

[1456] Input: Order management information.

[1457] Output: Sends order completion notification.

[1458] Step 8:

[1459] The terminal displays a notification to the user that the order is complete.

[1460] Input: Notification message from the server.

[1461] Output: Display a notification to the user.

[1462] Steps for managing and sharing progress information

[1463] Step 1:

[1464] Users record their regular training and dietary progress on LINE.

[1465] Input: Progress message (e.g. "I jogged 5km today").

[1466] Output: A request to send a message from the user's device to the server.

[1467] Step 2:

[1468] The device sends progress data to the server.

[1469] Input: A progress message.

[1470] Output: Sending data to the server (API request).

[1471] Step 3:

[1472] The server stores the progress data in a database.

[1473] Input: Progress data.

[1474] Output: Save data to a database.

[1475] Step 4:

[1476] The server updates the progress of the entire group.

[1477] Input: Progress data from the database.

[1478] Data processing: Aggregation of progress data.

[1479] Output: Group progress.

[1480] Step 5:

[1481] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages.

[1482] Input: Progress data.

[1483] Data processing: text analysis and sentiment analysis.

[1484] Output: Motivational messages.

[1485] Step 6:

[1486] The server generates a message and sends it to all group members.

[1487] Input: Motivational message.

[1488] Output: Notification to group members.

[1489] Step 7:

[1490] The terminal receives and displays the message.

[1491] Input: Notification message from the server.

[1492] Output: Display a message to the user.

[1493] Process steps for creating and coordinating joint schedules and meal plans

[1494] Step 1:

[1495] The user enters details of the health plan they wish to collaborate on.

[1496] Input: Health plan suggestion message (e.g., weekly meal plan suggestion).

[1497] Output: A request to send a message from the user's device to the server.

[1498] Step 2:

[1499] The terminal sends the input data to the server.

[1500] Input: Health plan details.

[1501] Output: Sending data to the server (API request).

[1502] Step 3:

[1503] The server passes the received data to the generative model.

[1504] Input: Health plan details data.

[1505] Output: Sending data to the generative model.

[1506] Step 4:

[1507] The generative model generates optimal schedules and meal plans based on input data.

[1508] Input: Health plan data.

[1509] Data calculation: Optimization based on user sentiment data and preferences.

[1510] Output: Generated schedule and meal plan.

[1511] Step 5:

[1512] The server shares the generated plan within the group.

[1513] Input: The plan received from the generative model.

[1514] Output: Notification to group.

[1515] Step 6:

[1516] Users (group members) submit feedback on the proposed plan.

[1517] Input: Proposed plan.

[1518] Output: The feedback message.

[1519] Step 7:

[1520] The device sends the feedback to the server.

[1521] Input: The feedback message.

[1522] Output: Sending data to the server (API request).

[1523] Step 8:

[1524] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[1525] Input: Feedback data.

[1526] Data processing: Feedback aggregation and optimization.

[1527] Output: The finalized plan.

[1528] Step 9:

[1529] The server sends the finalized plan to all members.

[1530] Enter: Final plan.

[1531] Output: Notification to group members.

[1532] Step 10:

[1533] The terminal receives the final plan and displays it to the user.

[1534] Input: The final plan sent by the server.

[1535] Output: Display the final plan to the user.

[1536] (Application example 2)

[1537] 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."

[1538] Conventional health consultation and group purchasing systems simply process data without considering the user's feelings, which means that the advice and suggestions they provide do not adequately meet the user's needs. Furthermore, when it comes to managing and sharing progress information, they lack a mechanism for maintaining motivation, making it difficult to improve overall user satisfaction.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for responding to health consultations using a generative model, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, means for creating and adjusting joint schedules and meal plans, means for generating optimal advice for health consultations and group purchasing based on the user's emotional data using an emotion engine that recognizes the user's emotions, and means for sharing the generated plans and advice within the group and aggregating feedback. This makes it possible to support the user's health management and financial purchasing activities in a more personalized manner, thereby improving user satisfaction and motivation.

[1540] A "generative model" refers to an algorithm or system that generates a specific output based on input data.

[1541] "Health consultation" refers to the act of a user consulting an expert or the system about their health condition or concerns and receiving advice.

[1542] "Group buying" refers to multiple users jointly purchasing goods or services in bulk.

[1543] "Consent aggregation" refers to the process of gathering consent from multiple participants to reach a single decision.

[1544] "Progress information" is data or information that indicates the progress a user is making toward a task or goal.

[1545] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from user text and behavior.

[1546] A "collaborative schedule" refers to a schedule that multiple users share and have a common activity schedule.

[1547] "Meal Plan" means a plan of what a User should eat over a specific period of time.

[1548] A "server" refers to a computer system that provides services and data to clients over a network.

[1549] "Aggregating feedback" refers to collecting and summarizing ratings and opinions provided by multiple users.

[1550] The present invention is a system for efficiently providing users with health consultations, group purchasing, progress tracking, and collaborative scheduling. The system utilizes a generative model and emotion engine to take into account users' emotion data and provide personalized advice and suggestions.

[1551] Hardware and software used

[1552] Hardware:

[1553] Smartphone

[1554] Smart Glasses

[1555] software:

[1556] Messaging apps for communication (e.g., LINE)

[1557] Emotion Engine (Emotion Recognition API)

[1558] Generative models (e.g., OpenAI GPT-3)

[1559] Schedule management app

[1560] Health management app

[1561] System configuration for implementing the program

[1562] Health consultation function

[1563] A user sends a health-related question using a messaging app. For example, the user sends a message saying, "I've been feeling tired lately. Can you give me some advice?" This message is first analyzed by the emotion engine, which recognizes the user's emotion data (e.g., feeling tired). This emotion data is then provided to a generative model, which generates optimal advice (e.g., relaxation techniques or suggested supplements). Finally, the generated advice is sent to the user.

[1564] Group purchase feature

[1565] A user uses a messaging app to propose a product purchase. For example, they send a proposal saying, "Shall we all buy this protein?" This proposal is forwarded to all group members via the server. Each member sends a response of either agreeing or declining, and these responses are collected and aggregated. If the majority agrees, the purchase is carried out collectively and the result is notified to all members.

[1566] Health Progress Management

[1567] Users record their daily training and dietary progress in a health management app. This data is sent to a server and analyzed using an emotion engine. Based on the analyzed data, a generative model generates motivational messages that are shared within the group. For example, if a user records "I jogged 5km today," the system recognizes a "sense of accomplishment" and generates positive feedback.

[1568] Creating a joint schedule and meal plan

[1569] A user enters details of a health plan into a schedule management app. For example, they enter a weekly meal plan, and the generative model generates an optimal plan. This plan is shared with the group and refined based on feedback from members. The final plan is sent to all members and implemented accordingly.

[1570] Specific examples

[1571] When User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine recognizes the "feeling of fatigue," and the generative model suggests relaxation methods and vitamin supplements.

[1572] Example prompt sentence:

[1573] I've been feeling tired lately, what relaxation methods or supplements would be good?

[1574] (Emotional data): Fatigue

[1575] When User B suggests on LINE, "Let's all buy this protein together," the group members agree and the purchase is carried out all at once.

[1576] Example prompt sentence:

[1577] Would you like everyone to buy this protein?

[1578] (Consent list): [User C: Agree, User D: Agree, User E: Agree]

[1579] When user C records in a health management app that he or she "jogged 5km today," the emotion engine recognizes the "sense of accomplishment" and the generative model generates positive feedback.

[1580] Example prompt sentence:

[1581] I jogged 5km today.

[1582] (Emotional data): Sense of accomplishment

[1583] When User D enters a weekly meal plan into a schedule management app, the generative model generates an optimal plan, which is then shared with the group to gather feedback and be refined.

[1584] Example prompt sentence:

[1585] A weekly meal plan is provided.

[1586] (Feedback): [User E: Favorite food, User F: Disliked food]

[1587] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1588] Step 1:

[1589] A user sends a health consultation message using a messaging app. The input is the specific consultation content entered by the user (e.g., "I've been feeling tired lately. Can you give me some advice?"). The output is the message being sent to a server via the messaging app.

[1590] Step 2:

[1591] The terminal sends the user's message to the server. The input is the user's message received by the communication application. The output is the message sent to the server and received by the server.

[1592] Step 3:

[1593] The server passes the received message to the emotion engine. The input is the message received from the user. The output is the message and user data for the emotion engine to analyze.

[1594] Step 4:

[1595] The emotion engine analyzes the message content and the user's emotion data. The input is the received message and user information. The output is the analyzed emotion data (e.g., tiredness, stress). This data is output as numerical data by using a specific algorithm to detect emotions from the text.

[1596] Step 5:

[1597] The emotion engine provides emotion data to the generative model. The input is the emotion data obtained by analysis. The output is the emotion data passed to the generative model.

[1598] Step 6:

[1599] The generative model generates optimal advice for the user based on the emotional data. The input is the emotional data provided by the emotion engine. The output is the generated advice (e.g., relaxation techniques, vitamin supplement suggestions).

[1600] Step 7:

[1601] The server sends the generated advice to the user. The input is the advice generated by the generative model. The output is the advice sent to the user via a communication application.

[1602] Step 8:

[1603] The terminal receives the advice message and displays it to the user. The input is the advice message sent from the server. The output is a form in which the user can check the advice in the communication application.

[1604] Step 9:

[1605] A messaging app is used to suggest products that users would like to purchase as a group. The input is information about the product that the user wants to purchase and a suggestion message (e.g., "Shall we all buy this protein?"). The output is a suggestion message that is sent to group members.

[1606] Step 10:

[1607] Group members send responses to the proposal message, either agreeing or rejecting it. The input is the responses from group members. The output is the form in which these responses are sent to the server.

[1608] Step 11:

[1609] The server aggregates the responses of all members and, if there is a majority of agreement, consolidates the order. The input is the response data from the group members. The output is the execution of the order and a notification of the order completion.

[1610] Step 12:

[1611] The server sends a notification of order completion and the expected arrival date of the product to all members. The input is the order completion data. The output is the notification sent to group members.

[1612] Step 13:

[1613] The terminal receives the order completion notification and displays it to the user. The input is the order completion notification sent from the server. The output is a form in which the user can confirm the order completion notification.

[1614] Step 14:

[1615] A user records their daily training and diet progress in a health management app. The input is the training and diet data recorded by the user (e.g., "I jogged 5km today"). The output is the progress data sent to the server.

[1616] Step 15:

[1617] The server saves the progress data to a database and updates the progress status of the entire group. The input is the progress data received from the user. The output is the updated progress information in the database.

[1618] Step 16:

[1619] The server passes the progress data to the emotion engine, which analyzes the user's emotion data. The input is the progress data. The output is the analyzed emotion data.

[1620] Step 17:

[1621] The emotion engine generates motivational messages based on the user's emotion data. The inputs are progress data and emotion data. The output is the generated motivational message.

[1622] Step 18:

[1623] The server sends the generated motivation message to all group members. The input is the generated motivation message. The output is the form in which the message is sent to group members.

[1624] Step 19:

[1625] The terminal receives and displays messages. The input is the motivation message sent from the server. The output is a form in which the user can confirm the message.

[1626] Step 20:

[1627] A user enters details of a joint health plan into a scheduling app. The input is the plan data entered by the user (e.g., a weekly meal plan). The output is the plan data sent to a server.

[1628] Step 21:

[1629] The server passes the received plan data to the generative model. The input is the received plan data. The output is the plan data passed to the generative model.

[1630] Step 22:

[1631] The generative model generates an optimal schedule or meal plan based on the plan data. The input is the received plan data. The output is the generated optimal plan.

[1632] Step 23:

[1633] The server shares the generated plan with the group members. The input is the generated plan. The output is the form in which the plan is sent to the group members.

[1634] Step 24:

[1635] Group members submit feedback on the proposed plan. The input is the proposed plan and the members' feedback data. The output is the feedback sent to the server.

[1636] Step 25:

[1637] The server aggregates the feedback and generates a final plan with any necessary adjustments. The input is the feedback data. The output is the adjusted final plan.

[1638] Step 26:

[1639] The server sends the finalized plan to all members. The input is the final plan. The output is the final plan sent to all members.

[1640] Step 27:

[1641] The terminal receives the final plan and displays it to the user. The input is the final plan data. The output is a format that allows the user to check the final plan.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] [Third embodiment]

[1646] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1647] 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.

[1648] 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).

[1649] 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.

[1650] 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.

[1651] 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).

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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."

[1658] The present invention provides a system that utilizes generative models to provide health consultations, aggregate group purchasing proposals and agreements, manage and share progress information, and create and adjust joint schedules and meal plans, to support users in efficiently managing their health and making economical purchases.

[1659] A natural language description of the program's processing

[1660] How to respond to health consultations

[1661] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[1662] 2. Terminal: Sends the user's message to the server.

[1663] 3. Server: Receives messages and passes them to the generative model.

[1664] 4. Generative model: Analyzes the message content and generates optimal advice for the user.

[1665] 5. Server: Sends the generated advice to the user via LINE message.

[1666] 6. Terminal: Display an advisory message to the user.

[1667] Aggregation of group buying proposals and consents

[1668] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[1669] 2. Terminal: Sends product information and suggestion messages to the server.

[1670] 3. Server: Sends a proposal message to all group members.

[1671] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[1672] 5. Terminal: Sends the response to the server.

[1673] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[1674] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[1675] 8. Terminal: Display a notification to the user that the order has been completed.

[1676] Managing and sharing progress information

[1677] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[1678] 2. Device: Sends progress data to the server.

[1679] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[1680] 4. Server: Aggregates progress information for the entire group and generates motivational messages.

[1681] 5. Server: Sends the generated message to all group members.

[1682] 6. Terminal: Receives and displays messages.

[1683] Creating and coordinating joint schedules and meal plans

[1684] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[1685] 2. Terminal: Sends input data to the server.

[1686] 3. Server: Passes the received data to the generative model.

[1687] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[1688] 5. Server: Shares the generated plan within the group.

[1689] 6. User (group member): Sends feedback on the proposed plan.

[1690] 7. Device: Sends feedback to the server.

[1691] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[1692] 9. Server: Sends the finalized plan to all members.

[1693] 10. Terminal: Receives the final plan and displays it to the user.

[1694] Specific examples

[1695] For example, if User A recently feels fatigued and sends a health consultation on LINE, the generative model will suggest taking vitamin D. Next, User A suggests purchasing protein with the group, and the group members agree on LINE, and the server coordinates the purchase. Furthermore, when User A records his daily jogging progress on LINE, the server aggregates the progress information of the entire group and sends encouraging messages. Finally, the group collaboratively creates a weekly meal plan, and everyone provides feedback to provide an optimized plan. In this way, this system is a multi-functional platform that supports users' health management and economical purchases.

[1696] The processing flow will be explained below.

[1697] How to respond to health consultations

[1698] Step 1:

[1699] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[1700] Step 2:

[1701] The device sends the user's message to the server.

[1702] Step 3:

[1703] The server passes the received message to the generative model.

[1704] Step 4:

[1705] A generative model analyzes the message content and generates optimal advice for the user.

[1706] Step 5:

[1707] The server sends the generated advice to the user via LINE message.

[1708] Step 6:

[1709] The device displays an advisory message to the user.

[1710] Aggregation of group buying proposals and consents

[1711] Step 1:

[1712] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[1713] Step 2:

[1714] The terminal transmits product information and a proposal message to the server.

[1715] Step 3:

[1716] The server sends a proposal message to all group members.

[1717] Step 4:

[1718] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[1719] Step 5:

[1720] The device sends a response to the server.

[1721] Step 6:

[1722] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[1723] Step 7:

[1724] The server will send a notification of order completion and the expected arrival date of the product to all members.

[1725] Step 8:

[1726] The terminal will display a notification to the user that the order is complete.

[1727] Managing and sharing progress information

[1728] Step 1:

[1729] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[1730] Step 2:

[1731] The device sends progress data to the server.

[1732] Step 3:

[1733] The server stores the progress data in a database and updates the progress of the entire group.

[1734] Step 4:

[1735] The server aggregates progress information for the entire group and generates motivational messages.

[1736] Step 5:

[1737] The server generates a message and sends it to all group members.

[1738] Step 6:

[1739] The terminal receives and displays the message.

[1740] Creating and coordinating joint schedules and meal plans

[1741] Step 1:

[1742] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[1743] Step 2:

[1744] The terminal sends the input data to the server.

[1745] Step 3:

[1746] The server passes the received data to the generative model.

[1747] Step 4:

[1748] The generative model generates optimal schedules and meal plans based on input data.

[1749] Step 5:

[1750] The server shares the generated plan within the group.

[1751] Step 6:

[1752] Users (group members) submit feedback on the proposed plan.

[1753] Step 7:

[1754] The device sends the feedback to the server.

[1755] Step 8:

[1756] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[1757] Step 9:

[1758] The server sends the finalized plan to all members.

[1759] Step 10:

[1760] The device receives the final plan and displays it to the user.

[1761] Example 1

[1762] 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."

[1763] In today's world, there is a growing need for efficient health management and financial purchasing. However, there are few systems that provide multiple functions, such as health consultations, group purchasing, progress information management, and collaborative schedule creation, in an integrated manner. In particular, there is a need for technology that can quickly provide optimal advice to users. There is also a lack of tools that allow information to be shared among groups and make decision-making easier. The development of a comprehensive system that can address these complex needs is required.

[1764] 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.

[1765] In this invention, the server includes means for responding to health consultations using generative models, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, and means for creating and adjusting joint schedules and meal plans, thereby enabling users to efficiently manage their health and make economical purchases, and share and adjust progress information and joint plans within the group.

[1766] A "generative model" is an artificial intelligence algorithm that analyzes natural language messages from users and generates appropriate responses or advice.

[1767] The "means for responding to health consultations" is the part of the system that receives health-related questions from users and provides appropriate responses to those questions.

[1768] "Means for aggregating group purchase proposals and consents" refers to the part of the system that, when a user wishes to jointly purchase a particular product as a group, notifies other members of the group of the proposal and aggregates the responses of consent or denial.

[1769] "Means for managing and sharing progress information" refers to the system part that records progress information such as the user's training and diet, and shares that information within the group.

[1770] "Means for creating and adjusting collaborative schedules and meal plans" means the portion of the system that allows multiple users to create schedules and meal plans for collaborative use and adjust the contents of those schedules and meal plans as needed.

[1771] A "communications application" is a software product that allows a user to access a system and send or receive information.

[1772] "User interface" refers to the means, such as screens and operating methods, that a user uses to interact with a system.

[1773] "Communication means" means hardware and software functions for transmitting and receiving data.

[1774] A "database that stores and provides product information" is a data management system that stores information on candidate products for purchase and provides that information when needed.

[1775] The present invention is a multi-functional system that supports users in efficiently managing their health and making economical purchases. The system includes a means for utilizing generative models to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans. Specific embodiments and usage examples are described below.

[1776] How to respond to health consultations

[1777] User: Send a health consultation message using the LINE app. For example, send a message saying, "I've been feeling tired lately and I'd like some advice."

[1778] Device: Sends the user's message to the server via the LINE API.

[1779] Server: Receives messages and passes them to the generative model. It analyzes the messages and converts them into an appropriate format. The generative model uses a natural language processing model such as GPT-3.

[1780] Generative model: Analyzes the content of received messages and generates optimal advice for the user. For example, it generates advice such as "Try increasing your intake of vitamin D."

[1781] Server: Sends the generated advice to the user via LINE message.

[1782] Terminal: Receives advice messages and displays them to the user.

[1783] Aggregation of group buying proposals and consents

[1784] User: Selects a product that the group wants to purchase and sends a suggestion message. For example, the user sends a message saying, "Would you like to buy this protein with everyone?"

[1785] Device: Product information and suggestion messages are sent to the server via the LINE API.

[1786] Server: Distributes proposal messages to all group members. Uses a database to store and provide product information.

[1787] User (group member): Receives the proposal message and sends a response of acceptance or rejection through the LINE app.

[1788] Device: Sends the response to the server using the LINE API.

[1789] Server: Aggregates responses from all members and, if there is a majority agreement, aggregates the order.

[1790] Server: Sends notification of order completion and estimated delivery date to all members.

[1791] Terminal: Receives notification of order completion and displays it to the user.

[1792] Managing and sharing progress information

[1793] User: Records progress of regular training and meals on LINE. For example, sends a message saying "I jogged 5km today."

[1794] Device: Progress data is sent to the server via the LINE API.

[1795] Server: Stores progress data in a database and updates the progress of the entire group.

[1796] Server: Aggregates progress information for the entire group and generates motivational messages, such as "Everyone is doing great! Keep it up!"

[1797] Server: Sends the generated message to all group members.

[1798] Terminal: Receives and displays messages.

[1799] Creating and coordinating joint schedules and meal plans

[1800] User: Enter details of the health plan they want to collaborate on. For example, they can send a "Weekly Meal Plan Suggestion."

[1801] Device: Sends input data to the server via the LINE API.

[1802] Server: Passes the received data to the generative model.

[1803] Generative models: Generate optimal schedules and meal plans based on input data.

[1804] Server: Shares the generated plan within the group.

[1805] User (group member): Sends feedback on the proposed plan.

[1806] Device: Sends feedback to the server using the LINE API.

[1807] Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[1808] Server: Sends the finalized plan to all members.

[1809] Terminal: Receives the final plan and displays it to the user.

[1810] In this way, users can efficiently manage their health and make economical purchases, and share and coordinate progress information and joint plans within the group.

[1811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1812] Flow of processing for responding to health consultations

[1813] Step 1:

[1814] The user uses the LINE app to enter and send a health consultation message.

[1815] Specific actions: The user enters "I've been feeling tired lately and would like some advice" into the text box of the LINE app and presses the send button.

[1816] Input: The health consultation message entered by the user.

[1817] Output: The message is sent to the LINE server.

[1818] Step 2:

[1819] The device sends the user's message to the server using the LINE API.

[1820] Specific operation: The device receives the message via the LINE API and sends it to the server.

[1821] Input: Health consultation message from user.

[1822] Output: The message is sent to the server.

[1823] Step 3:

[1824] The server receives the message and passes it to the generative model.

[1825] Specific behavior: The server parses the message, converts it into an appropriate format, and passes it to the generative model.

[1826] Input: Health consultation message sent from the device.

[1827] Output: Data converted into a format that can be input to a generative model.

[1828] Step 4:

[1829] The generative model analyzes the message content and generates optimal advice for the user.

[1830] What it does: A generative model (e.g., GPT-3) analyzes the message and generates advice like, "Try increasing your vitamin D intake."

[1831] Input: Message data passed to the generative model by the server.

[1832] Output: The generated advice.

[1833] Step 5:

[1834] The server sends the generated advice to the user via a LINE message.

[1835] Specific behavior: The server uses the LINE API to generate an advice message and send it to the user.

[1836] Input: The advice generated by the generative model.

[1837] Output: Advisory message sent to the user's terminal.

[1838] Step 6:

[1839] The terminal receives the advice message and displays it to the user.

[1840] Specific operation: The user's device receives a LINE message and displays the advice.

[1841] Input: The advice message sent by the server.

[1842] Output: The advice message will be displayed in the chat window of the LINE app.

[1843] Group Buy Proposal and Consent Aggregation Process

[1844] Step 1:

[1845] A user selects a product they wish to purchase within the group, enters a suggestion message, and sends it.

[1846] Specific operation: The user enters "Would you like to buy this protein for everyone?" in the text box of the LINE app, attaches a product link and image, and presses the send button.

[1847] Input: The purchase offer message entered by the user.

[1848] Output: The message is sent to the LINE server.

[1849] Step 2:

[1850] The device sends the user's suggestion message to the server using the LINE API.

[1851] Specific operation: The device receives the message via the LINE API and sends it to the server.

[1852] Input: Purchase suggestion message from the user.

[1853] Output: The message is sent to the server.

[1854] Step 3:

[1855] The server broadcasts the proposal message to all group members.

[1856] Specific operation: The server receives the proposal message, distributes it to all members in the group, and saves the proposal in the database.

[1857] Input: The user's suggested message.

[1858] Output: The proposal message that will be delivered to group members.

[1859] Step 4:

[1860] Group members receive the proposal message, enter a response of agreement or rejection, and submit it.

[1861] Specific actions: Group members enter a response such as "I'll participate" or "I'll pass this time" through the LINE app and press the send button.

[1862] Input: Group member responses.

[1863] Output: A response is sent to the LINE server.

[1864] Step 5:

[1865] The device sends the group member's response to the server using the LINE API.

[1866] Specific operation: The group member's device receives the response via the LINE API and sends it to the server.

[1867] Input: Responses from group members.

[1868] Output: The response is sent to the server.

[1869] Step 6:

[1870] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order data.

[1871] Specific operation: The server aggregates the responses from all members, compiles the order data based on the majority vote, and saves the order details in the database.

[1872] Input: Group member responses.

[1873] Output: Aggregated order data.

[1874] Step 7:

[1875] The server will send a notification of order completion and the expected arrival date of the product to all members.

[1876] Specific operation: The server uses the LINE API to send a notification of order completion and details to all members.

[1877] Input: Order completion information and estimated delivery date.

[1878] Output: Notifications delivered to group members.

[1879] Step 8:

[1880] The terminal receives the notification of order completion and displays it to the user.

[1881] Specific behavior: The user's device receives a LINE message and displays details of the completed order.

[1882] Input: Order completion notification sent from the server.

[1883] Output: A notification that the order is complete will be displayed in the chat window of the LINE app.

[1884] keyword:

[1885] Generative AI model, prompt sentence

[1886] (Application example 1)

[1887] 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."

[1888] While traditional health management and financial purchasing support systems offer individual functions, few offer a multi-functional platform that integrates them. As a result, users are forced to use multiple applications, reducing utilization efficiency. It has also been difficult to provide integrated functions such as health consultations, group purchasing suggestions, progress management, and meal plan creation.

[1889] 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.

[1890] In this invention, the server includes a means for responding to health consultations using a generative model, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and adjusting joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing, thereby enabling users to efficiently manage their health and financial activities on a single platform.

[1891] A "generative model" is an algorithm that analyzes input data from a user and generates optimal responses and suggestions.

[1892] "Health consultation" is a function that allows users to input information about their daily health problems and physical condition, and provides advice and solutions to those problems.

[1893] "Group purchase" is a function that aggregates proposals and agreements for multiple users to jointly purchase products.

[1894] "Progress information" is data that records, manages, and shares a user's daily activities and goal achievement status.

[1895] "Joint Schedule" is a function that allows multiple users to create and adjust schedules for joint activities.

[1896] A "meal plan" is a plan that suggests optimal meal plans based on a user's health goals and needs.

[1897] "Health management" is the process of monitoring a user's health status and providing appropriate advice and suggestions.

[1898] "Economic Purchasing" is the process of providing information and suggestions to help users effectively purchase products.

[1899] An "application" is a software program that a user utilizes to perform a particular function.

[1900] A "server" is a computer system that receives and processes data and provides services to users.

[1901] The present invention is a multi-functional system that enables users to efficiently manage their health and financial activities on a single platform. The system includes a means for responding to health consultations using generative models, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and coordinating joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing.

[1902] Hardware and software used

[1903] This system uses the following hardware and software:

[1904] Hardware:

[1905] Smartphone (iOS or Android)

[1906] Smart Glasses

[1907] Server PC

[1908] software:

[1909] "Health Concierge" app (iOS / Android app)

[1910] OpenAI GPT-4

[1911] Database (e.g. AWS DynamoDB)

[1912] In an implementation of the present invention, a user accesses an application via a smartphone or smart glasses. The user's input data is analyzed by a generative model (e.g., OpenAI GPT-4) to generate appropriate responses or suggestions. The generated responses or suggestions are then sent to the user via a communication medium.

[1913] Examples of concrete examples and prompts

[1914] Health consultation example:

[1915] When a user uses their smartphone to type, "I've been feeling tired lately and I'd like some advice," this message is sent to the server. The server passes the message to a generative model (OpenAI GPT-4) and generates appropriate health advice. The generated advice is sent to the user, allowing them to receive specific measures for improvement.

[1916] Group Buying Example:

[1917] When a user uses the app to suggest to the group, "Shall we all buy this protein?", this information is sent to the server and all group members are notified. All members send responses of either agree or decline, and the results are tallied by the server. If a majority vote agrees, the server processes the order and a notification of order completion is sent to all members.

[1918] Progress information management example:

[1919] When a user logs in to the app using their smartphone, "I jogged 5km today," this information is sent to the server and stored in a database. The server then aggregates the progress of the entire group and generates a motivational message, such as "The whole group has completed 50km this week! Let's do our best!", which is then sent to all group members.

[1920] Collaborative planning example:

[1921] The user proposes a weekly meal plan in the app, entering specific details such as "fish on Mondays and salad on Wednesdays." This information is sent to the server, which passes it through a generative model to generate the optimal plan. The generated plan is shared with the group, and feedback is sent. This feedback is aggregated on the server, and the final plan is adjusted and re-notified to all members.

[1922] Examples of prompts:

[1923] "I've been feeling tired lately and I'd like some advice. Can you tell me how to take vitamins or other ways to improve my condition?"

[1924] "Wouldn't we all like to buy this protein?"

[1925] I jogged 5km today.

[1926] "I'm proposing a meal plan with fish on Mondays and salads on Wednesdays. I'd love your feedback."

[1927] Thus, the present invention provides a multifunctional system that efficiently supports the user's health management and economic activities.

[1928] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1929] Health consultation function processing steps

[1930] Step 1:

[1931] User: Logs into the health concierge app using a smartphone or smart glasses and enters a health consultation prompt, for example, "I've been feeling tired lately and would like some advice."

[1932] Input: Health consultation message from user

[1933] Output: Health consultation message sent

[1934] Step 2:

[1935] Terminal: Sends the health consultation message entered by the user to the server.

[1936] Input: Health consultation message

[1937] Output: Message sent to the server

[1938] Step 3:

[1939] Server: Passes received messages to a generative model (e.g., OpenAI GPT-4).

[1940] Input: User message

[1941] Output: Messages passed to the generative model

[1942] Step 4:

[1943] Generative model: Analyzes the content of the user's message and generates optimal health advice. The generative model refers to a large amount of data based on the input consultation content and makes optimal suggestions.

[1944] Input: User's health consultation message

[1945] Output: Health advice

[1946] Step 5:

[1947] Server: Receives the generated advice and sends it to the user device.

[1948] Enter: Health Advice

[1949] Output: Advice sent to the user's terminal

[1950] Step 6:

[1951] Terminal: Display health advice messages to the user.

[1952] Input: Advice received from the server

[1953] Output: Displayed health advice

[1954] Group Buying Feature Processing Steps

[1955] Step 1:

[1956] User: Enter the product information and message you want to share with the app. For example, you could say, "Let's all buy this protein together."

[1957] Input: Product information and suggestion message

[1958] Output: Sent product information and suggestion message

[1959] Step 2:

[1960] Terminal: Sends product information and suggestion messages to the server.

[1961] Input: Product information and suggestion message

[1962] Output: Proposal message sent to the server

[1963] Step 3:

[1964] Server: Sends the proposal message to all group members.

[1965] Input: Proposal message

[1966] Output: Message sent to group members

[1967] Step 4:

[1968] User (group member): Receives the proposal message and selects a response of either accept or reject in the app.

[1969] Input: Proposal message

[1970] Output: Agree or decline response

[1971] Step 5:

[1972] Terminal: Sends the response to the server.

[1973] Input: Response

[1974] Output: The response sent to the server

[1975] Step 6:

[1976] Server: Aggregates responses from all members, and if a majority vote agrees, proceeds with the order.

[1977] Input: Responses of all members

[1978] Output: Checkout completed

[1979] Step 7:

[1980] Server: Notify all members of order completion and estimated arrival of the product.

[1981] Input: Order Checkout Information

[1982] Output: Order completion notification and estimated delivery date

[1983] Step 8:

[1984] Terminal: Display a notification to the user that the order has been completed.

[1985] Input: Notification

[1986] Output: Displayed order completion notification

[1987] Progress information management function processing steps

[1988] Step 1:

[1989] User: Records daily training progress in the app, for example, "Today I jogged 5km."

[1990] Input: Progress information

[1991] Output: Progress information sent

[1992] Step 2:

[1993] Device: Sends progress data to the server.

[1994] Input: Progress data

[1995] Output: Data sent to the server

[1996] Step 3:

[1997] Server: Stores progress data in a database and updates progress for the entire group.

[1998] Input: Received progress data

[1999] Output: Updated progress information

[2000] Step 4:

[2001] Server: Aggregates progress information for the entire group and generates motivational messages.

[2002] Input: Aggregated progress data

[2003] Output: Motivation message

[2004] Step 5:

[2005] Server: Sends the generated message to all group members.

[2006] Input: Motivational message

[2007] Output: The message sent.

[2008] Step 6:

[2009] Terminal: Receives and displays messages.

[2010] Input: Message from the server

[2011] Output: The displayed message

[2012] Collaborative Planning Feature Processing Steps

[2013] Step 1:

[2014] User: Enter details of the health plan they want to collaborate on in the app, for example, suggesting a weekly meal plan.

[2015] Enter: Health plan details

[2016] Output: Health plan information submitted

[2017] Step 2:

[2018] Terminal: Sends input data to the server.

[2019] Input: Health plan data

[2020] Output: Data sent to the server

[2021] Step 3:

[2022] Server: Passes the received data to the generative model.

[2023] Input: Health plan data

[2024] Output: The data passed to the generative model

[2025] Step 4:

[2026] Generative models: Generate optimal schedules and meal plans based on input data.

[2027] Input: Health plan data

[2028] Output: Generated plan

[2029] Step 5:

[2030] Server: Shares the generated plan within the group.

[2031] Input: Generated plan

[2032] Output: Shared plans

[2033] Step 6:

[2034] User (group member): Send feedback on the proposed plan.

[2035] Input: Plan

[2036] Output: Feedback

[2037] Step 7:

[2038] Device: Sends feedback to the server.

[2039] Input: Feedback

[2040] Output: Feedback sent to the server

[2041] Step 8:

[2042] Server: Aggregates feedback and generates the final plan.

[2043] Input: Feedback

[2044] Output: Final plan

[2045] Step 9:

[2046] Server: Notify all members of the finalized plan.

[2047] Input: Final Plan

[2048] Output: Final plan reported

[2049] Step 10:

[2050] Terminal: Receives the final plan and displays it to the user.

[2051] Input: Final Plan

[2052] Output: Final plan displayed

[2053] 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.

[2054] The present invention provides a system including a means for utilizing a generative model to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans, as well as an emotion engine that recognizes user emotions. This system is intended to support users in efficiently managing their health and making economical purchases.

[2055] A natural language description of the program's processing

[2056] Enhancing health consultations with an emotion engine

[2057] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[2058] 2. Terminal: Sends the user's message to the server.

[2059] 3. Server: Passes the received message to the emotion engine to recognize the user's emotion.

[2060] 4. Emotion engine: Analyzes message content and user emotion data and provides it to the generative model.

[2061] 5. Generative models: Generate optimal advice for users by taking into account emotional data (e.g., suggesting relaxation techniques if emotions indicate fatigue or stress).

[2062] 6. Server: Sends the generated advice to the user via LINE message.

[2063] 7. Terminal: Display an advisory message to the user.

[2064] Aggregation of group buying proposals and consents

[2065] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[2066] 2. Terminal: Sends product information and suggestion messages to the server.

[2067] 3. Server: Sends a proposal message to all group members.

[2068] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[2069] 5. Terminal: Sends the response to the server.

[2070] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[2071] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[2072] 8. Terminal: Display a notification to the user that the order has been completed.

[2073] Managing and sharing progress information

[2074] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[2075] 2. Device: Sends progress data to the server.

[2076] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[2077] 4. Server: Aggregates progress information for the entire group.

[2078] 5. Emotion Engine: Analyzes the user's emotional data based on progress data and generates motivational messages.

[2079] 6. Server: Sends the generated message to all group members.

[2080] 7. Terminal: Receives and displays messages.

[2081] Creating and coordinating joint schedules and meal plans

[2082] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[2083] 2. Terminal: Sends input data to the server.

[2084] 3. Server: Passes the received data to the generative model.

[2085] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[2086] 5. Server: Shares the generated plan within the group.

[2087] 6. User (group member): Sends feedback on the proposed plan.

[2088] 7. Device: Sends feedback to the server.

[2089] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[2090] 9. Server: Sends the finalized plan to all members.

[2091] 10. Terminal: Receives the final plan and displays it to the user.

[2092] Specific examples

[2093] For example, if User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine will read the stress and fatigue from User A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements. Next, if User A suggests purchasing protein with the group, the group members will indicate high agreement through the emotion engine, and the server will coordinate the purchase. When User A records his daily jogging progress, the emotion engine will generate positive feedback on his achievements, increasing the motivation of the entire group. Finally, the entire group will jointly create a meal plan, and the emotion engine will provide the optimal plan based on each member's preferences and emotions. In this way, this system utilizes users' emotional data to support more personalized health management and economical purchasing.

[2094] The processing flow will be explained below.

[2095] Enhancing health consultations with an emotion engine

[2096] Step 1:

[2097] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[2098] Step 2:

[2099] The device sends the user's message to the server.

[2100] Step 3:

[2101] The server passes the received message to the emotion engine, which begins analyzing it to recognize the user's emotions.

[2102] Step 4:

[2103] The emotion engine analyzes the message content and the user's emotional data (e.g., fatigue, stress level).

[2104] Step 5:

[2105] The emotion engine provides the analyzed emotion data to the generative model.

[2106] Step 6:

[2107] The generative model takes into account emotional data to generate optimal advice for the user (e.g., suggesting relaxation methods if emotions indicate fatigue or stress).

[2108] Step 7:

[2109] The server sends the generated advice to the user via LINE message.

[2110] Step 8:

[2111] The device displays an advisory message to the user.

[2112] Aggregation of group buying proposals and consents

[2113] Step 1:

[2114] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[2115] Step 2:

[2116] The terminal transmits product information and a proposal message to the server.

[2117] Step 3:

[2118] The server sends a proposal message to all group members.

[2119] Step 4:

[2120] Users (group members) receive the proposal message and send a response of agreement or rejection, including their emotions.

[2121] Step 5:

[2122] The device sends a response to the server.

[2123] Step 6:

[2124] The sentiment engine analyzes the emotional data of the responses and assesses the level of agreement across the group.

[2125] Step 7:

[2126] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[2127] Step 8:

[2128] The server will send a notification of order completion and the expected arrival date of the product to all members.

[2129] Step 9:

[2130] The terminal will display a notification to the user that the order is complete.

[2131] Managing and sharing progress information

[2132] Step 1:

[2133] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[2134] Step 2:

[2135] The device sends progress data to the server.

[2136] Step 3:

[2137] The server stores the progress data in a database and updates the progress of the entire group.

[2138] Step 4:

[2139] The server aggregates progress information for the entire group.

[2140] Step 5:

[2141] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages (e.g., encouraging messages that evoke positive emotions).

[2142] Step 6:

[2143] The server generates a message and sends it to all group members.

[2144] Step 7:

[2145] The terminal receives and displays the message.

[2146] Creating and coordinating joint schedules and meal plans

[2147] Step 1:

[2148] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[2149] Step 2:

[2150] The terminal sends the input data to the server.

[2151] Step 3:

[2152] The server passes the received data to the generative model.

[2153] Step 4:

[2154] The generative model generates optimal schedules and meal plans based on input data.

[2155] Step 5:

[2156] The server shares the generated plan within the group.

[2157] Step 6:

[2158] Users (group members) submit feedback on the proposed plan along with emotional data.

[2159] Step 7:

[2160] The device sends the feedback to the server.

[2161] Step 8:

[2162] The emotion engine analyzes the emotional data of the feedback and evaluates the opinions of the entire group.

[2163] Step 9:

[2164] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[2165] Step 10:

[2166] The server sends the finalized plan to all members.

[2167] Step 11:

[2168] The device receives the final plan and displays it to the user.

[2169] Example 2

[2170] 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."

[2171] Conventional health management systems and group purchasing systems have difficulty responding to individual users' emotions, and tend to provide uniform advice and suggestions. Furthermore, group schedule coordination and progress management are cumbersome, making it difficult to adjust to each member's opinions and circumstances. This has led to issues such as reduced user satisfaction and reduced utilization efficiency.

[2172] 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.

[2173] In this invention, the server includes a means for responding to health consultations using a generative model, an emotion engine that analyzes a user's emotion data and provides it to the generative model, and a means for generating and transmitting advice that takes the emotion data into consideration. This enables personalized advice and suggestions based on the user's emotions. The server also includes a means for aggregating group purchase proposals and agreements, a means for managing and sharing progress information, and a means for creating and coordinating joint schedules and meal plans. This facilitates collaboration and progress management within a group, improving user satisfaction and usage efficiency.

[2174] A "generative model" is an algorithm that uses artificial intelligence to generate appropriate responses or suggestions based on input data.

[2175] The "emotion engine" is a function that analyzes emotions from user messages and data and provides that information to other system components.

[2176] "Means for responding to health consultations" is a function that provides appropriate advice in response to health-related questions and consultations from users.

[2177] The "means for generating and sending advice" is a function for sending advice created by the generative model based on emotion data to the user.

[2178] "Means for aggregating proposals and consents for group purchases" is a function that aggregates and unifies proposals and consents when multiple users jointly purchase products.

[2179] "Means for managing and sharing progress information" refers to a function for managing a user's training or activity progress information and sharing it with other users.

[2180] "Means for creating and adjusting shared schedules and meal plans" refers to a function that allows multiple users to create schedules and meal plans for joint use and adjust them as needed.

[2181] A "communications application" is software for sending and receiving messages over the Internet.

[2182] "Communication means" refers to the hardware and network infrastructure for transmitting and receiving data.

[2183] A "database" is an information system that can efficiently store large amounts of information and retrieve it as needed.

[2184] The present invention is a system for supporting users in efficient health management and economical purchasing, which mainly consists of a generative model, an emotion engine, a communication application via a user interface, a communication means, and a database.

[2185] 1. Means of responding to health consultations

[2186] The user sends a health consultation message to the server via a communication application such as the LINE app. For example, the message could be, "I've been feeling tired lately and I'd like some advice." The device then sends this message to the server via the Internet.

[2187] The server passes the received message to the emotion engine to recognize the user's emotion. The emotion engine analyzes the message content and the user's emotion data and provides that data to the generative model. The generative model takes the emotion data into account and generates optimal advice for the user. For example, if the emotion engine recognizes fatigue or stress, it will generate suggestions for relaxation methods or vitamin supplements.

[2188] The server sends the generated advice to the user via a LINE message, and the device displays the advice message to the user.

[2189] 2. Aggregation of group purchase proposals and agreements

[2190] A user sends a message suggesting a product they would like to purchase within the group. For example, a message like "Would you like to buy this protein with everyone?" The device sends this message along with the product information to the server.

[2191] The server sends a proposal message to all group members, who then respond by agreeing or rejecting the request. The device then sends these responses back to the server. The server then aggregates the responses from all members and, if the majority agrees, consolidates the orders. Notifications of order completion and estimated arrival times are sent to all members, and the devices display these.

[2192] 3. Managing and sharing progress information

[2193] Users record their progress with their regular training and meals on LINE. For example, they could write a message like, "Today I jogged 5km." The device sends this progress data to the server, which then stores it in a database and updates the progress of the entire group.

[2194] The server aggregates the progress information of the entire group, and the emotion engine analyzes the user's emotional data based on the progress data. The generated positive feedback messages are sent from the server to all group members and displayed on their devices.

[2195] 4. Creating and coordinating joint schedules and meal plans

[2196] The user inputs details of the health plan they want to implement together. For example, they might input "Suggested weekly meal plan." The device then sends this input data to the server.

[2197] The server passes the received data to a generative model, which generates an optimal schedule and meal plan based on the input data. The generated plan is shared with the group from the server and receives feedback from group members. The server aggregates the feedback and generates a final plan with any necessary adjustments. The finalized plan is sent to all members and displayed on their devices.

[2198] Specific examples

[2199] For example, if user A complains on LINE that they have been feeling fatigued lately, the emotion engine will read the stress and fatigue from user A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements.

[2200] Next, when user A suggests that the group purchase protein, the group members express high agreement through the emotion engine, and the server consolidates the purchase.

[2201] When User A records their daily jogging progress, the emotion engine generates positive feedback on their achievements, increasing the motivation of the entire group.

[2202] Finally, the entire group collaborates to create a meal plan, and the emotion engine provides the optimal plan based on each member's preferences and emotions. In this way, the system utilizes users' emotion data to support more personalized health management and economical purchasing.

[2203] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2204] Health consultation processing steps

[2205] Step 1:

[2206] The user opens the LINE app, enters a health consultation message, and sends it.

[2207] Input: User message (e.g. "I've been feeling tired lately and I'd like some advice").

[2208] Output: A request to send a message from the user's device to the server.

[2209] Step 2:

[2210] The terminal sends the user's message to the server.

[2211] Input: The user's message.

[2212] Output: Sending data to the server (API request).

[2213] Step 3:

[2214] The server passes the received message to the emotion engine to recognize the user's emotion.

[2215] Input: Received message data.

[2216] Output: Sentiment analysis request to be passed to the sentiment engine.

[2217] Step 4:

[2218] An emotion engine analyzes message content and user emotion data.

[2219] Input: Message data and existing user emotion data.

[2220] Data processing: Recognize emotions (e.g., stress, fatigue, etc.) using text analysis algorithms.

[2221] Output: User emotion data.

[2222] Step 5:

[2223] The emotion engine provides emotion data to the generative model.

[2224] Input: Parsed emotion data.

[2225] Output: Sending data to the generative model.

[2226] Step 6:

[2227] The generative model takes into account emotional data to generate optimal advice for the user.

[2228] Input: Emotion data.

[2229] Data computation: Using AI models such as deep learning to generate advice (e.g., relaxation suggestions).

[2230] Output: The generated advice.

[2231] Step 7:

[2232] The server sends the generated advice to the user via a LINE message.

[2233] Input: Advice provided by the generative model.

[2234] Output: Sends a message to the user via the LINE API.

[2235] Step 8:

[2236] The terminal receives the advice message and displays it to the user.

[2237] Input: The advice message sent by the server.

[2238] Output: Display advice to the user.

[2239] Group Buying Process Steps

[2240] Step 1:

[2241] Send messages suggesting products that users might want to purchase within the group.

[2242] Input: User's suggested message (e.g., "Would we all like to buy this protein?").

[2243] Output: A request to send a message from the user's device to the server.

[2244] Step 2:

[2245] The terminal transmits product information and a proposal message to the server.

[2246] Input: Proposal message and product information.

[2247] Output: Sending data to the server (API request).

[2248] Step 3:

[2249] The server sends a proposal message to all group members.

[2250] Input: Product information and suggestion message.

[2251] Output: Notification to group members.

[2252] Step 4:

[2253] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[2254] Input: The proposal message.

[2255] Output: An accept or reject response.

[2256] Step 5:

[2257] The device sends a response to the server.

[2258] Input: Agree or decline response.

[2259] Output: Sending the response to the server.

[2260] Step 6:

[2261] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[2262] Input: Group member responses.

[2263] Data processing: Response aggregation.

[2264] Output: Order management information.

[2265] Step 7:

[2266] The server will send a notification of order completion and the expected arrival date of the product to all members.

[2267] Input: Order management information.

[2268] Output: Sends order completion notification.

[2269] Step 8:

[2270] The terminal displays a notification to the user that the order is complete.

[2271] Input: Notification message from the server.

[2272] Output: Display a notification to the user.

[2273] Steps for managing and sharing progress information

[2274] Step 1:

[2275] Users record their regular training and dietary progress on LINE.

[2276] Input: Progress message (e.g. "I jogged 5km today").

[2277] Output: A request to send a message from the user's device to the server.

[2278] Step 2:

[2279] The device sends progress data to the server.

[2280] Input: A progress message.

[2281] Output: Sending data to the server (API request).

[2282] Step 3:

[2283] The server stores the progress data in a database.

[2284] Input: Progress data.

[2285] Output: Save data to a database.

[2286] Step 4:

[2287] The server updates the progress of the entire group.

[2288] Input: Progress data from the database.

[2289] Data processing: Aggregation of progress data.

[2290] Output: Group progress.

[2291] Step 5:

[2292] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages.

[2293] Input: Progress data.

[2294] Data processing: text analysis and sentiment analysis.

[2295] Output: Motivational messages.

[2296] Step 6:

[2297] The server generates a message and sends it to all group members.

[2298] Input: Motivational message.

[2299] Output: Notification to group members.

[2300] Step 7:

[2301] The terminal receives and displays the message.

[2302] Input: Notification message from the server.

[2303] Output: Display a message to the user.

[2304] Process steps for creating and coordinating joint schedules and meal plans

[2305] Step 1:

[2306] The user enters details of the health plan they wish to collaborate on.

[2307] Input: Health plan suggestion message (e.g., weekly meal plan suggestion).

[2308] Output: A request to send a message from the user's device to the server.

[2309] Step 2:

[2310] The terminal sends the input data to the server.

[2311] Input: Health plan details.

[2312] Output: Sending data to the server (API request).

[2313] Step 3:

[2314] The server passes the received data to the generative model.

[2315] Input: Health plan details data.

[2316] Output: Sending data to the generative model.

[2317] Step 4:

[2318] The generative model generates optimal schedules and meal plans based on input data.

[2319] Input: Health plan data.

[2320] Data calculation: Optimization based on user sentiment data and preferences.

[2321] Output: Generated schedule and meal plan.

[2322] Step 5:

[2323] The server shares the generated plan within the group.

[2324] Input: The plan received from the generative model.

[2325] Output: Notification to group.

[2326] Step 6:

[2327] Users (group members) submit feedback on the proposed plan.

[2328] Input: Proposed plan.

[2329] Output: The feedback message.

[2330] Step 7:

[2331] The device sends the feedback to the server.

[2332] Input: The feedback message.

[2333] Output: Sending data to the server (API request).

[2334] Step 8:

[2335] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[2336] Input: Feedback data.

[2337] Data processing: Feedback aggregation and optimization.

[2338] Output: The finalized plan.

[2339] Step 9:

[2340] The server sends the finalized plan to all members.

[2341] Enter: Final plan.

[2342] Output: Notification to group members.

[2343] Step 10:

[2344] The terminal receives the final plan and displays it to the user.

[2345] Input: The final plan sent by the server.

[2346] Output: Display the final plan to the user.

[2347] (Application example 2)

[2348] 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."

[2349] Conventional health consultation and group purchasing systems simply process data without considering the user's feelings, which means that the advice and suggestions they provide do not adequately meet the user's needs. Furthermore, when it comes to managing and sharing progress information, they lack a mechanism for maintaining motivation, making it difficult to improve overall user satisfaction.

[2350] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for responding to health consultations using a generative model, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, means for creating and adjusting joint schedules and meal plans, means for generating optimal advice for health consultations and group purchasing based on the user's emotional data using an emotion engine that recognizes the user's emotions, and means for sharing the generated plans and advice within the group and aggregating feedback. This makes it possible to support the user's health management and financial purchasing activities in a more personalized manner, thereby improving user satisfaction and motivation.

[2351] A "generative model" refers to an algorithm or system that generates a specific output based on input data.

[2352] "Health consultation" refers to the act of a user consulting an expert or the system about their health condition or concerns and receiving advice.

[2353] "Group buying" refers to multiple users jointly purchasing goods or services in bulk.

[2354] "Consent aggregation" refers to the process of gathering consent from multiple participants to reach a single decision.

[2355] "Progress information" is data or information that indicates the progress a user is making toward a task or goal.

[2356] An "emotion engine" refers to an algorithm or system that analyzes and recognizes emotions from user text and behavior.

[2357] A "collaborative schedule" refers to a schedule that multiple users share and have a common activity schedule.

[2358] "Meal Plan" means a plan of what a User should eat over a specific period of time.

[2359] A "server" refers to a computer system that provides services and data to clients over a network.

[2360] "Aggregating feedback" refers to collecting and summarizing ratings and opinions provided by multiple users.

[2361] The present invention is a system for efficiently providing users with health consultations, group purchasing, progress tracking, and collaborative scheduling. The system utilizes a generative model and emotion engine to take into account users' emotion data and provide personalized advice and suggestions.

[2362] Hardware and software used

[2363] Hardware:

[2364] Smartphone

[2365] Smart Glasses

[2366] software:

[2367] Messaging apps for communication (e.g., LINE)

[2368] Emotion Engine (Emotion Recognition API)

[2369] Generative models (e.g., OpenAI GPT-3)

[2370] Schedule management app

[2371] Health management app

[2372] System configuration for implementing the program

[2373] Health consultation function

[2374] A user sends a health-related question using a messaging app. For example, the user sends a message saying, "I've been feeling tired lately. Can you give me some advice?" This message is first analyzed by the emotion engine, which recognizes the user's emotion data (e.g., feeling tired). This emotion data is then provided to a generative model, which generates optimal advice (e.g., relaxation techniques or suggested supplements). Finally, the generated advice is sent to the user.

[2375] Group purchase feature

[2376] A user uses a messaging app to propose a product purchase. For example, they send a proposal saying, "Shall we all buy this protein?" This proposal is forwarded to all group members via the server. Each member sends a response of either agreeing or declining, and these responses are collected and aggregated. If the majority agrees, the purchase is carried out collectively and the result is notified to all members.

[2377] Health Progress Management

[2378] Users record their daily training and dietary progress in a health management app. This data is sent to a server and analyzed using an emotion engine. Based on the analyzed data, a generative model generates motivational messages that are shared within the group. For example, if a user records "I jogged 5km today," the system recognizes a "sense of accomplishment" and generates positive feedback.

[2379] Creating a joint schedule and meal plan

[2380] A user enters details of a health plan into a schedule management app. For example, they enter a weekly meal plan, and the generative model generates an optimal plan. This plan is shared with the group and refined based on feedback from members. The final plan is sent to all members and implemented accordingly.

[2381] Specific examples

[2382] When User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine recognizes the "feeling of fatigue," and the generative model suggests relaxation methods and vitamin supplements.

[2383] Example prompt sentence:

[2384] I've been feeling tired lately, what relaxation methods or supplements would be good?

[2385] (Emotional data): Fatigue

[2386] When User B suggests on LINE, "Let's all buy this protein together," the group members agree and the purchase is carried out all at once.

[2387] Example prompt sentence:

[2388] Would you like everyone to buy this protein?

[2389] (Consent list): [User C: Agree, User D: Agree, User E: Agree]

[2390] When user C records in a health management app that he or she "jogged 5km today," the emotion engine recognizes the "sense of accomplishment" and the generative model generates positive feedback.

[2391] Example prompt sentence:

[2392] I jogged 5km today.

[2393] (Emotional data): Sense of accomplishment

[2394] When User D enters a weekly meal plan into a schedule management app, the generative model generates an optimal plan, which is then shared with the group to gather feedback and be refined.

[2395] Example prompt sentence:

[2396] A weekly meal plan is provided.

[2397] (Feedback): [User E: Favorite food, User F: Disliked food]

[2398] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2399] Step 1:

[2400] A user sends a health consultation message using a messaging app. The input is the specific consultation content entered by the user (e.g., "I've been feeling tired lately. Can you give me some advice?"). The output is the message being sent to a server via the messaging app.

[2401] Step 2:

[2402] The terminal sends the user's message to the server. The input is the user's message received by the communication application. The output is the message sent to the server and received by the server.

[2403] Step 3:

[2404] The server passes the received message to the emotion engine. The input is the message received from the user. The output is the message and user data for the emotion engine to analyze.

[2405] Step 4:

[2406] The emotion engine analyzes the message content and the user's emotion data. The input is the received message and user information. The output is the analyzed emotion data (e.g., tiredness, stress). This data is output as numerical data by using a specific algorithm to detect emotions from the text.

[2407] Step 5:

[2408] The emotion engine provides emotion data to the generative model. The input is the emotion data obtained by analysis. The output is the emotion data passed to the generative model.

[2409] Step 6:

[2410] The generative model generates optimal advice for the user based on the emotional data. The input is the emotional data provided by the emotion engine. The output is the generated advice (e.g., relaxation techniques, vitamin supplement suggestions).

[2411] Step 7:

[2412] The server sends the generated advice to the user. The input is the advice generated by the generative model. The output is the advice sent to the user via a communication application.

[2413] Step 8:

[2414] The terminal receives the advice message and displays it to the user. The input is the advice message sent from the server. The output is a form in which the user can check the advice in the communication application.

[2415] Step 9:

[2416] A messaging app is used to suggest products that users would like to purchase as a group. The input is information about the product that the user wants to purchase and a suggestion message (e.g., "Shall we all buy this protein?"). The output is a suggestion message that is sent to group members.

[2417] Step 10:

[2418] Group members send responses to the proposal message, either agreeing or rejecting it. The input is the responses from group members. The output is the form in which these responses are sent to the server.

[2419] Step 11:

[2420] The server aggregates the responses of all members and, if there is a majority of agreement, consolidates the order. The input is the response data from the group members. The output is the execution of the order and a notification of the order completion.

[2421] Step 12:

[2422] The server sends a notification of order completion and the expected arrival date of the product to all members. The input is the order completion data. The output is the notification sent to group members.

[2423] Step 13:

[2424] The terminal receives the order completion notification and displays it to the user. The input is the order completion notification sent from the server. The output is a form in which the user can confirm the order completion notification.

[2425] Step 14:

[2426] A user records their daily training and diet progress in a health management app. The input is the training and diet data recorded by the user (e.g., "I jogged 5km today"). The output is the progress data sent to the server.

[2427] Step 15:

[2428] The server saves the progress data to a database and updates the progress status of the entire group. The input is the progress data received from the user. The output is the updated progress information in the database.

[2429] Step 16:

[2430] The server passes the progress data to the emotion engine, which analyzes the user's emotion data. The input is the progress data. The output is the analyzed emotion data.

[2431] Step 17:

[2432] The emotion engine generates motivational messages based on the user's emotion data. The inputs are progress data and emotion data. The output is the generated motivational message.

[2433] Step 18:

[2434] The server sends the generated motivation message to all group members. The input is the generated motivation message. The output is the form in which the message is sent to group members.

[2435] Step 19:

[2436] The terminal receives and displays messages. The input is the motivation message sent from the server. The output is a form in which the user can confirm the message.

[2437] Step 20:

[2438] A user enters details of a joint health plan into a scheduling app. The input is the plan data entered by the user (e.g., a weekly meal plan). The output is the plan data sent to a server.

[2439] Step 21:

[2440] The server passes the received plan data to the generative model. The input is the received plan data. The output is the plan data passed to the generative model.

[2441] Step 22:

[2442] The generative model generates an optimal schedule or meal plan based on the plan data. The input is the received plan data. The output is the generated optimal plan.

[2443] Step 23:

[2444] The server shares the generated plan with the group members. The input is the generated plan. The output is the form in which the plan is sent to the group members.

[2445] Step 24:

[2446] Group members submit feedback on the proposed plan. The input is the proposed plan and the members' feedback data. The output is the feedback sent to the server.

[2447] Step 25:

[2448] The server aggregates the feedback and generates a final plan with any necessary adjustments. The input is the feedback data. The output is the adjusted final plan.

[2449] Step 26:

[2450] The server sends the finalized plan to all members. The input is the final plan. The output is the final plan sent to all members.

[2451] Step 27:

[2452] The terminal receives the final plan and displays it to the user. The input is the final plan data. The output is a format that allows the user to check the final plan.

[2453] 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.

[2454] 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.

[2455] 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.

[2456] [Fourth embodiment]

[2457] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2458] 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.

[2459] 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).

[2460] 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.

[2461] 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.

[2462] 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).

[2463] 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.

[2464] 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.

[2465] 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.

[2466] 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.

[2467] 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.

[2468] 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.

[2469] 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."

[2470] The present invention provides a system that utilizes generative models to provide health consultations, aggregate group purchasing proposals and agreements, manage and share progress information, and create and adjust joint schedules and meal plans, to support users in efficiently managing their health and making economical purchases.

[2471] A natural language description of the program's processing

[2472] How to respond to health consultations

[2473] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[2474] 2. Terminal: Sends the user's message to the server.

[2475] 3. Server: Receives messages and passes them to the generative model.

[2476] 4. Generative model: Analyzes the message content and generates optimal advice for the user.

[2477] 5. Server: Sends the generated advice to the user via LINE message.

[2478] 6. Terminal: Display an advisory message to the user.

[2479] Aggregation of group buying proposals and consents

[2480] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[2481] 2. Terminal: Sends product information and suggestion messages to the server.

[2482] 3. Server: Sends a proposal message to all group members.

[2483] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[2484] 5. Terminal: Sends the response to the server.

[2485] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[2486] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[2487] 8. Terminal: Display a notification to the user that the order has been completed.

[2488] Managing and sharing progress information

[2489] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[2490] 2. Device: Sends progress data to the server.

[2491] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[2492] 4. Server: Aggregates progress information for the entire group and generates motivational messages.

[2493] 5. Server: Sends the generated message to all group members.

[2494] 6. Terminal: Receives and displays messages.

[2495] Creating and coordinating joint schedules and meal plans

[2496] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[2497] 2. Terminal: Sends input data to the server.

[2498] 3. Server: Passes the received data to the generative model.

[2499] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[2500] 5. Server: Shares the generated plan within the group.

[2501] 6. User (group member): Sends feedback on the proposed plan.

[2502] 7. Device: Sends feedback to the server.

[2503] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[2504] 9. Server: Sends the finalized plan to all members.

[2505] 10. Terminal: Receives the final plan and displays it to the user.

[2506] Specific examples

[2507] For example, if User A recently feels fatigued and sends a health consultation on LINE, the generative model will suggest taking vitamin D. Next, User A suggests purchasing protein with the group, and the group members agree on LINE, and the server coordinates the purchase. Furthermore, when User A records his daily jogging progress on LINE, the server aggregates the progress information of the entire group and sends encouraging messages. Finally, the group collaboratively creates a weekly meal plan, and everyone provides feedback to provide an optimized plan. In this way, this system is a multi-functional platform that supports users' health management and economical purchases.

[2508] The processing flow will be explained below.

[2509] How to respond to health consultations

[2510] Step 1:

[2511] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[2512] Step 2:

[2513] The device sends the user's message to the server.

[2514] Step 3:

[2515] The server passes the received message to the generative model.

[2516] Step 4:

[2517] A generative model analyzes the message content and generates optimal advice for the user.

[2518] Step 5:

[2519] The server sends the generated advice to the user via LINE message.

[2520] Step 6:

[2521] The device displays an advisory message to the user.

[2522] Aggregation of group buying proposals and consents

[2523] Step 1:

[2524] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[2525] Step 2:

[2526] The terminal transmits product information and a proposal message to the server.

[2527] Step 3:

[2528] The server sends a proposal message to all group members.

[2529] Step 4:

[2530] A user (group member) receives the proposal message and sends a response of either accepting or rejecting it.

[2531] Step 5:

[2532] The device sends a response to the server.

[2533] Step 6:

[2534] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[2535] Step 7:

[2536] The server will send a notification of order completion and the expected arrival date of the product to all members.

[2537] Step 8:

[2538] The terminal will display a notification to the user that the order is complete.

[2539] Managing and sharing progress information

[2540] Step 1:

[2541] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[2542] Step 2:

[2543] The device sends progress data to the server.

[2544] Step 3:

[2545] The server stores the progress data in a database and updates the progress of the entire group.

[2546] Step 4:

[2547] The server aggregates progress information for the entire group and generates motivational messages.

[2548] Step 5:

[2549] The server generates a message and sends it to all group members.

[2550] Step 6:

[2551] The terminal receives and displays the message.

[2552] Creating and coordinating joint schedules and meal plans

[2553] Step 1:

[2554] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[2555] Step 2:

[2556] The terminal sends the input data to the server.

[2557] Step 3:

[2558] The server passes the received data to the generative model.

[2559] Step 4:

[2560] The generative model generates optimal schedules and meal plans based on input data.

[2561] Step 5:

[2562] The server shares the generated plan within the group.

[2563] Step 6:

[2564] Users (group members) submit feedback on the proposed plan.

[2565] Step 7:

[2566] The device sends the feedback to the server.

[2567] Step 8:

[2568] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[2569] Step 9:

[2570] The server sends the finalized plan to all members.

[2571] Step 10:

[2572] The device receives the final plan and displays it to the user.

[2573] Example 1

[2574] 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."

[2575] In today's world, there is a growing need for efficient health management and financial purchasing. However, there are few systems that provide multiple functions, such as health consultations, group purchasing, progress information management, and collaborative schedule creation, in an integrated manner. In particular, there is a need for technology that can quickly provide optimal advice to users. There is also a lack of tools that allow information to be shared among groups and make decision-making easier. The development of a comprehensive system that can address these complex needs is required.

[2576] 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.

[2577] In this invention, the server includes means for responding to health consultations using generative models, means for aggregating group purchasing proposals and agreements, means for managing and sharing progress information, and means for creating and adjusting joint schedules and meal plans, thereby enabling users to efficiently manage their health and make economical purchases, and share and adjust progress information and joint plans within the group.

[2578] A "generative model" is an artificial intelligence algorithm that analyzes natural language messages from users and generates appropriate responses or advice.

[2579] The "means for responding to health consultations" is the part of the system that receives health-related questions from users and provides appropriate responses to those questions.

[2580] "Means for aggregating group purchase proposals and consents" refers to the part of the system that, when a user wishes to jointly purchase a particular product as a group, notifies other members of the group of the proposal and aggregates the responses of consent or denial.

[2581] "Means for managing and sharing progress information" refers to the system part that records progress information such as the user's training and diet, and shares that information within the group.

[2582] "Means for creating and adjusting collaborative schedules and meal plans" means the portion of the system that allows multiple users to create schedules and meal plans for collaborative use and adjust the contents of those schedules and meal plans as needed.

[2583] A "communications application" is a software product that allows a user to access a system and send or receive information.

[2584] "User interface" refers to the means, such as screens and operating methods, that a user uses to interact with a system.

[2585] "Communication means" means hardware and software functions for transmitting and receiving data.

[2586] A "database that stores and provides product information" is a data management system that stores information on candidate products for purchase and provides that information when needed.

[2587] The present invention is a multi-functional system that supports users in efficiently managing their health and making economical purchases. The system includes a means for utilizing generative models to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans. Specific embodiments and usage examples are described below.

[2588] How to respond to health consultations

[2589] User: Send a health consultation message using the LINE app. For example, send a message saying, "I've been feeling tired lately and I'd like some advice."

[2590] Device: Sends the user's message to the server via the LINE API.

[2591] Server: Receives messages and passes them to the generative model. It analyzes the messages and converts them into an appropriate format. The generative model uses a natural language processing model such as GPT-3.

[2592] Generative model: Analyzes the content of received messages and generates optimal advice for the user. For example, it generates advice such as "Try increasing your intake of vitamin D."

[2593] Server: Sends the generated advice to the user via LINE message.

[2594] Terminal: Receives advice messages and displays them to the user.

[2595] Aggregation of group buying proposals and consents

[2596] User: Selects a product that the group wants to purchase and sends a suggestion message. For example, the user sends a message saying, "Would you like to buy this protein with everyone?"

[2597] Device: Product information and suggestion messages are sent to the server via the LINE API.

[2598] Server: Distributes proposal messages to all group members. Uses a database to store and provide product information.

[2599] User (group member): Receives the proposal message and sends a response of acceptance or rejection through the LINE app.

[2600] Device: Sends the response to the server using the LINE API.

[2601] Server: Aggregates responses from all members and, if there is a majority agreement, aggregates the order.

[2602] Server: Sends notification of order completion and estimated delivery date to all members.

[2603] Terminal: Receives notification of order completion and displays it to the user.

[2604] Managing and sharing progress information

[2605] User: Records progress of regular training and meals on LINE. For example, sends a message saying "I jogged 5km today."

[2606] Device: Progress data is sent to the server via the LINE API.

[2607] Server: Stores progress data in a database and updates the progress of the entire group.

[2608] Server: Aggregates progress information for the entire group and generates motivational messages, such as "Everyone is doing great! Keep it up!"

[2609] Server: Sends the generated message to all group members.

[2610] Terminal: Receives and displays messages.

[2611] Creating and coordinating joint schedules and meal plans

[2612] User: Enter details of the health plan they want to collaborate on. For example, they can send a "Weekly Meal Plan Suggestion."

[2613] Device: Sends input data to the server via the LINE API.

[2614] Server: Passes the received data to the generative model.

[2615] Generative models: Generate optimal schedules and meal plans based on input data.

[2616] Server: Shares the generated plan within the group.

[2617] User (group member): Sends feedback on the proposed plan.

[2618] Device: Sends feedback to the server using the LINE API.

[2619] Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[2620] Server: Sends the finalized plan to all members.

[2621] Terminal: Receives the final plan and displays it to the user.

[2622] In this way, users can efficiently manage their health and make economical purchases, and share and coordinate progress information and joint plans within the group.

[2623] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2624] Flow of processing for responding to health consultations

[2625] Step 1:

[2626] The user uses the LINE app to enter and send a health consultation message.

[2627] Specific actions: The user enters "I've been feeling tired lately and would like some advice" into the text box of the LINE app and presses the send button.

[2628] Input: The health consultation message entered by the user.

[2629] Output: The message is sent to the LINE server.

[2630] Step 2:

[2631] The device sends the user's message to the server using the LINE API.

[2632] Specific operation: The device receives the message via the LINE API and sends it to the server.

[2633] Input: Health consultation message from user.

[2634] Output: The message is sent to the server.

[2635] Step 3:

[2636] The server receives the message and passes it to the generative model.

[2637] Specific behavior: The server parses the message, converts it into an appropriate format, and passes it to the generative model.

[2638] Input: Health consultation message sent from the device.

[2639] Output: Data converted into a format that can be input to a generative model.

[2640] Step 4:

[2641] The generative model analyzes the message content and generates optimal advice for the user.

[2642] What it does: A generative model (e.g., GPT-3) analyzes the message and generates advice like, "Try increasing your vitamin D intake."

[2643] Input: Message data passed to the generative model by the server.

[2644] Output: The generated advice.

[2645] Step 5:

[2646] The server sends the generated advice to the user via a LINE message.

[2647] Specific behavior: The server uses the LINE API to generate an advice message and send it to the user.

[2648] Input: The advice generated by the generative model.

[2649] Output: Advisory message sent to the user's terminal.

[2650] Step 6:

[2651] The terminal receives the advice message and displays it to the user.

[2652] Specific operation: The user's device receives a LINE message and displays the advice.

[2653] Input: The advice message sent by the server.

[2654] Output: The advice message will be displayed in the chat window of the LINE app.

[2655] Group Buy Proposal and Consent Aggregation Process

[2656] Step 1:

[2657] A user selects a product they wish to purchase within the group, enters a suggestion message, and sends it.

[2658] Specific operation: The user enters "Would you like to buy this protein for everyone?" in the text box of the LINE app, attaches a product link and image, and presses the send button.

[2659] Input: The purchase offer message entered by the user.

[2660] Output: The message is sent to the LINE server.

[2661] Step 2:

[2662] The device sends the user's suggestion message to the server using the LINE API.

[2663] Specific operation: The device receives the message via the LINE API and sends it to the server.

[2664] Input: Purchase suggestion message from the user.

[2665] Output: The message is sent to the server.

[2666] Step 3:

[2667] The server broadcasts the proposal message to all group members.

[2668] Specific operation: The server receives the proposal message, distributes it to all members in the group, and saves the proposal in the database.

[2669] Input: The user's suggested message.

[2670] Output: The proposal message that will be delivered to group members.

[2671] Step 4:

[2672] Group members receive the proposal message, enter a response of agreement or rejection, and submit it.

[2673] Specific actions: Group members enter a response such as "I'll participate" or "I'll pass this time" through the LINE app and press the send button.

[2674] Input: Group member responses.

[2675] Output: A response is sent to the LINE server.

[2676] Step 5:

[2677] The device sends the group member's response to the server using the LINE API.

[2678] Specific operation: The group member's device receives the response via the LINE API and sends it to the server.

[2679] Input: Responses from group members.

[2680] Output: The response is sent to the server.

[2681] Step 6:

[2682] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order data.

[2683] Specific operation: The server aggregates the responses from all members, compiles the order data based on the majority vote, and saves the order details in the database.

[2684] Input: Group member responses.

[2685] Output: Aggregated order data.

[2686] Step 7:

[2687] The server will send a notification of order completion and the expected arrival date of the product to all members.

[2688] Specific operation: The server uses the LINE API to send a notification of order completion and details to all members.

[2689] Input: Order completion information and estimated delivery date.

[2690] Output: Notifications delivered to group members.

[2691] Step 8:

[2692] The terminal receives the notification of order completion and displays it to the user.

[2693] Specific behavior: The user's device receives a LINE message and displays details of the completed order.

[2694] Input: Order completion notification sent from the server.

[2695] Output: A notification that the order is complete will be displayed in the chat window of the LINE app.

[2696] keyword:

[2697] Generative AI model, prompt sentence

[2698] (Application example 1)

[2699] 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."

[2700] While traditional health management and financial purchasing support systems offer individual functions, few offer a multi-functional platform that integrates them. As a result, users are forced to use multiple applications, reducing utilization efficiency. It has also been difficult to provide integrated functions such as health consultations, group purchasing suggestions, progress management, and meal plan creation.

[2701] 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.

[2702] In this invention, the server includes a means for responding to health consultations using a generative model, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and adjusting joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing, thereby enabling users to efficiently manage their health and financial activities on a single platform.

[2703] A "generative model" is an algorithm that analyzes input data from a user and generates optimal responses and suggestions.

[2704] "Health consultation" is a function that allows users to input information about their daily health problems and physical condition, and provides advice and solutions to those problems.

[2705] "Group purchase" is a function that aggregates proposals and agreements for multiple users to jointly purchase products.

[2706] "Progress information" is data that records, manages, and shares a user's daily activities and goal achievement status.

[2707] "Joint Schedule" is a function that allows multiple users to create and adjust schedules for joint activities.

[2708] A "meal plan" is a plan that suggests optimal meal plans based on a user's health goals and needs.

[2709] "Health management" is the process of monitoring a user's health status and providing appropriate advice and suggestions.

[2710] "Economic Purchasing" is the process of providing information and suggestions to help users effectively purchase products.

[2711] An "application" is a software program that a user utilizes to perform a particular function.

[2712] A "server" is a computer system that receives and processes data and provides services to users.

[2713] The present invention is a multi-functional system that enables users to efficiently manage their health and financial activities on a single platform. The system includes a means for responding to health consultations using generative models, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, a means for creating and coordinating joint schedules and meal plans, and a means for providing applications that support health management and financial purchasing.

[2714] Hardware and software used

[2715] This system uses the following hardware and software:

[2716] Hardware:

[2717] Smartphone (iOS or Android)

[2718] Smart Glasses

[2719] Server PC

[2720] software:

[2721] "Health Concierge" app (iOS / Android app)

[2722] OpenAI GPT-4

[2723] Database (e.g. AWS DynamoDB)

[2724] In an implementation of the present invention, a user accesses an application via a smartphone or smart glasses. The user's input data is analyzed by a generative model (e.g., OpenAI GPT-4) to generate appropriate responses or suggestions. The generated responses or suggestions are then sent to the user via a communication medium.

[2725] Examples of concrete examples and prompts

[2726] Health consultation example:

[2727] When a user uses their smartphone to type, "I've been feeling tired lately and I'd like some advice," this message is sent to the server. The server passes the message to a generative model (OpenAI GPT-4) and generates appropriate health advice. The generated advice is sent to the user, allowing them to receive specific measures for improvement.

[2728] Group Buying Example:

[2729] When a user uses the app to suggest to the group, "Shall we all buy this protein?", this information is sent to the server and all group members are notified. All members send responses of either agree or decline, and the results are tallied by the server. If a majority vote agrees, the server processes the order and a notification of order completion is sent to all members.

[2730] Progress information management example:

[2731] When a user logs in to the app using their smartphone, "I jogged 5km today," this information is sent to the server and stored in a database. The server then aggregates the progress of the entire group and generates a motivational message, such as "The whole group has completed 50km this week! Let's do our best!", which is then sent to all group members.

[2732] Collaborative planning example:

[2733] The user proposes a weekly meal plan in the app, entering specific details such as "fish on Mondays and salad on Wednesdays." This information is sent to the server, which passes it through a generative model to generate the optimal plan. The generated plan is shared with the group, and feedback is sent. This feedback is aggregated on the server, and the final plan is adjusted and re-notified to all members.

[2734] Examples of prompts:

[2735] "I've been feeling tired lately and I'd like some advice. Can you tell me how to take vitamins or other ways to improve my condition?"

[2736] "Wouldn't we all like to buy this protein?"

[2737] I jogged 5km today.

[2738] "I'm proposing a meal plan with fish on Mondays and salads on Wednesdays. I'd love your feedback."

[2739] Thus, the present invention provides a multifunctional system that efficiently supports the user's health management and economic activities.

[2740] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2741] Health consultation function processing steps

[2742] Step 1:

[2743] User: Logs into the health concierge app using a smartphone or smart glasses and enters a health consultation prompt, for example, "I've been feeling tired lately and would like some advice."

[2744] Input: Health consultation message from user

[2745] Output: Health consultation message sent

[2746] Step 2:

[2747] Terminal: Sends the health consultation message entered by the user to the server.

[2748] Input: Health consultation message

[2749] Output: Message sent to the server

[2750] Step 3:

[2751] Server: Passes received messages to a generative model (e.g., OpenAI GPT-4).

[2752] Input: User message

[2753] Output: Messages passed to the generative model

[2754] Step 4:

[2755] Generative model: Analyzes the content of the user's message and generates optimal health advice. The generative model refers to a large amount of data based on the input consultation content and makes optimal suggestions.

[2756] Input: User's health consultation message

[2757] Output: Health advice

[2758] Step 5:

[2759] Server: Receives the generated advice and sends it to the user device.

[2760] Enter: Health Advice

[2761] Output: Advice sent to the user's terminal

[2762] Step 6:

[2763] Terminal: Display health advice messages to the user.

[2764] Input: Advice received from the server

[2765] Output: Displayed health advice

[2766] Group Buying Feature Processing Steps

[2767] Step 1:

[2768] User: Enter the product information and message you want to share with the app. For example, you could say, "Let's all buy this protein together."

[2769] Input: Product information and suggestion message

[2770] Output: Sent product information and suggestion message

[2771] Step 2:

[2772] Terminal: Sends product information and suggestion messages to the server.

[2773] Input: Product information and suggestion message

[2774] Output: Proposal message sent to the server

[2775] Step 3:

[2776] Server: Sends the proposal message to all group members.

[2777] Input: Proposal message

[2778] Output: Message sent to group members

[2779] Step 4:

[2780] User (group member): Receives the proposal message and selects a response of either accept or reject in the app.

[2781] Input: Proposal message

[2782] Output: Agree or decline response

[2783] Step 5:

[2784] Terminal: Sends the response to the server.

[2785] Input: Response

[2786] Output: The response sent to the server

[2787] Step 6:

[2788] Server: Aggregates responses from all members, and if a majority vote agrees, proceeds with the order.

[2789] Input: Responses of all members

[2790] Output: Checkout completed

[2791] Step 7:

[2792] Server: Notify all members of order completion and estimated arrival of the product.

[2793] Input: Order Checkout Information

[2794] Output: Order completion notification and estimated delivery date

[2795] Step 8:

[2796] Terminal: Display a notification to the user that the order has been completed.

[2797] Input: Notification

[2798] Output: Displayed order completion notification

[2799] Progress information management function processing steps

[2800] Step 1:

[2801] User: Records daily training progress in the app, for example, "Today I jogged 5km."

[2802] Input: Progress information

[2803] Output: Progress information sent

[2804] Step 2:

[2805] Device: Sends progress data to the server.

[2806] Input: Progress data

[2807] Output: Data sent to the server

[2808] Step 3:

[2809] Server: Stores progress data in a database and updates progress for the entire group.

[2810] Input: Received progress data

[2811] Output: Updated progress information

[2812] Step 4:

[2813] Server: Aggregates progress information for the entire group and generates motivational messages.

[2814] Input: Aggregated progress data

[2815] Output: Motivation message

[2816] Step 5:

[2817] Server: Sends the generated message to all group members.

[2818] Input: Motivational message

[2819] Output: The message sent.

[2820] Step 6:

[2821] Terminal: Receives and displays messages.

[2822] Input: Message from the server

[2823] Output: The displayed message

[2824] Collaborative Planning Feature Processing Steps

[2825] Step 1:

[2826] User: Enter details of the health plan they want to collaborate on in the app, for example, suggesting a weekly meal plan.

[2827] Enter: Health plan details

[2828] Output: Health plan information submitted

[2829] Step 2:

[2830] Terminal: Sends input data to the server.

[2831] Input: Health plan data

[2832] Output: Data sent to the server

[2833] Step 3:

[2834] Server: Passes the received data to the generative model.

[2835] Input: Health plan data

[2836] Output: The data passed to the generative model

[2837] Step 4:

[2838] Generative models: Generate optimal schedules and meal plans based on input data.

[2839] Input: Health plan data

[2840] Output: Generated plan

[2841] Step 5:

[2842] Server: Shares the generated plan within the group.

[2843] Input: Generated plan

[2844] Output: Shared plans

[2845] Step 6:

[2846] User (group member): Send feedback on the proposed plan.

[2847] Input: Plan

[2848] Output: Feedback

[2849] Step 7:

[2850] Device: Sends feedback to the server.

[2851] Input: Feedback

[2852] Output: Feedback sent to the server

[2853] Step 8:

[2854] Server: Aggregates feedback and generates the final plan.

[2855] Input: Feedback

[2856] Output: Final plan

[2857] Step 9:

[2858] Server: Notify all members of the finalized plan.

[2859] Input: Final Plan

[2860] Output: Final plan reported

[2861] Step 10:

[2862] Terminal: Receives the final plan and displays it to the user.

[2863] Input: Final Plan

[2864] Output: Final plan displayed

[2865] 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.

[2866] The present invention provides a system including a means for utilizing a generative model to provide health consultations, a means for aggregating group purchasing proposals and agreements, a means for managing and sharing progress information, and a means for creating and adjusting joint schedules and meal plans, as well as an emotion engine that recognizes user emotions. This system is intended to support users in efficiently managing their health and making economical purchases.

[2867] A natural language description of the program's processing

[2868] Enhancing health consultations with an emotion engine

[2869] 1. User: Sends a health consultation message using the LINE app (e.g., "I've been feeling tired lately and I'd like some advice").

[2870] 2. Terminal: Sends the user's message to the server.

[2871] 3. Server: Passes the received message to the emotion engine to recognize the user's emotion.

[2872] 4. Emotion engine: Analyzes message content and user emotion data and provides it to the generative model.

[2873] 5. Generative models: Generate optimal advice for users by taking into account emotional data (e.g., suggesting relaxation techniques if emotions indicate fatigue or stress).

[2874] 6. Server: Sends the generated advice to the user via LINE message.

[2875] 7. Terminal: Display an advisory message to the user.

[2876] Aggregation of group buying proposals and consents

[2877] 1. User: Selects a product they would like to purchase within the group and sends a suggestion message (e.g., "Would you like to buy this protein for everyone?").

[2878] 2. Terminal: Sends product information and suggestion messages to the server.

[2879] 3. Server: Sends a proposal message to all group members.

[2880] 4. User (group member): receives the proposal message and sends a response of acceptance or rejection.

[2881] 5. Terminal: Sends the response to the server.

[2882] 6. Server: Aggregates the responses of all members and, if there is a majority agreement, consolidates the orders.

[2883] 7. Server: Sends notification of order completion and estimated delivery date to all members.

[2884] 8. Terminal: Display a notification to the user that the order has been completed.

[2885] Managing and sharing progress information

[2886] 1. User: Record your regular training and diet progress on LINE (e.g., "I jogged 5km today").

[2887] 2. Device: Sends progress data to the server.

[2888] 3. Server: Stores the progress data in a database and updates the progress of the entire group.

[2889] 4. Server: Aggregates progress information for the entire group.

[2890] 5. Emotion Engine: Analyzes the user's emotional data based on progress data and generates motivational messages.

[2891] 6. Server: Sends the generated message to all group members.

[2892] 7. Terminal: Receives and displays messages.

[2893] Creating and coordinating joint schedules and meal plans

[2894] 1. User: Enter details of the health plan they want to collaborate on (e.g., a suggested weekly meal plan).

[2895] 2. Terminal: Sends input data to the server.

[2896] 3. Server: Passes the received data to the generative model.

[2897] 4. Generative model: Generates optimal schedules and meal plans based on input data.

[2898] 5. Server: Shares the generated plan within the group.

[2899] 6. User (group member): Sends feedback on the proposed plan.

[2900] 7. Device: Sends feedback to the server.

[2901] 8. Server: Aggregates the feedback and generates the final plan with any necessary adjustments.

[2902] 9. Server: Sends the finalized plan to all members.

[2903] 10. Terminal: Receives the final plan and displays it to the user.

[2904] Specific examples

[2905] For example, if User A complains on LINE that "I can't seem to get rid of my fatigue lately," the emotion engine will read the stress and fatigue from User A's message and provide this emotional data to the generative model. Based on this, the generative model will suggest relaxation methods and vitamin supplements. Next, if User A suggests purchasing protein with the group, the group members will indicate high agreement through the emotion engine, and the server will coordinate the purchase. When User A records his daily jogging progress, the emotion engine will generate positive feedback on his achievements, increasing the motivation of the entire group. Finally, the entire group will jointly create a meal plan, and the emotion engine will provide the optimal plan based on each member's preferences and emotions. In this way, this system utilizes users' emotional data to support more personalized health management and economical purchasing.

[2906] The processing flow will be explained below.

[2907] Enhancing health consultations with an emotion engine

[2908] Step 1:

[2909] A user uses the LINE app to send a health consultation message such as, "I've been feeling tired lately and I'd like some advice."

[2910] Step 2:

[2911] The device sends the user's message to the server.

[2912] Step 3:

[2913] The server passes the received message to the emotion engine, which begins analyzing it to recognize the user's emotions.

[2914] Step 4:

[2915] The emotion engine analyzes the message content and the user's emotional data (e.g., fatigue, stress level).

[2916] Step 5:

[2917] The emotion engine provides the analyzed emotion data to the generative model.

[2918] Step 6:

[2919] The generative model takes into account emotional data to generate optimal advice for the user (e.g., suggesting relaxation methods if emotions indicate fatigue or stress).

[2920] Step 7:

[2921] The server sends the generated advice to the user via LINE message.

[2922] Step 8:

[2923] The device displays an advisory message to the user.

[2924] Aggregation of group buying proposals and consents

[2925] Step 1:

[2926] A user selects a product they wish to purchase within the group and sends a suggestion message saying, "Would you like to buy this protein with everyone?"

[2927] Step 2:

[2928] The terminal transmits product information and a proposal message to the server.

[2929] Step 3:

[2930] The server sends a proposal message to all group members.

[2931] Step 4:

[2932] Users (group members) receive the proposal message and send a response of agreement or rejection, including their emotions.

[2933] Step 5:

[2934] The device sends a response to the server.

[2935] Step 6:

[2936] The sentiment engine analyzes the emotional data of the responses and assesses the level of agreement across the group.

[2937] Step 7:

[2938] The server aggregates the responses of all members and, if there is a majority agreement, consolidates the order.

[2939] Step 8:

[2940] The server will send a notification of order completion and the expected arrival date of the product to all members.

[2941] Step 9:

[2942] The terminal will display a notification to the user that the order is complete.

[2943] Managing and sharing progress information

[2944] Step 1:

[2945] Users record their regular training and dietary progress on LINE (e.g., "I jogged 5km today").

[2946] Step 2:

[2947] The device sends progress data to the server.

[2948] Step 3:

[2949] The server stores the progress data in a database and updates the progress of the entire group.

[2950] Step 4:

[2951] The server aggregates progress information for the entire group.

[2952] Step 5:

[2953] The emotion engine analyzes the user's emotional data based on progress data and generates motivational messages (e.g., encouraging messages that evoke positive emotions).

[2954] Step 6:

[2955] The server generates a message and sends it to all group members.

[2956] Step 7:

[2957] The terminal receives and displays the message.

[2958] Creating and coordinating joint schedules and meal plans

[2959] Step 1:

[2960] Users enter details of the health plan they would like to collaborate on (e.g., a suggested weekly meal plan).

[2961] Step 2:

[2962] The terminal sends the input data to the server.

[2963] Step 3:

[2964] The server passes the received data to the generative model.

[2965] Step 4:

[2966] The generative model generates optimal schedules and meal plans based on input data.

[2967] Step 5:

[2968] The server shares the generated plan within the group.

[2969] Step 6:

[2970] Users (group members) submit feedback on the proposed plan along with emotional data.

[2971] Step 7:

[2972] The device sends the feedback to the server.

[2973] Step 8:

[2974] The emotion engine analyzes the emotional data of the feedback and evaluates the opinions of the entire group.

[2975] Step 9:

[2976] The server aggregates the feedback and generates a final plan with any necessary adjustments.

[2977] Step 10:

[2978] The server sends the finalized plan to all members.

[2979] Step 11:

[2980] The device receives the final plan and displays it to the user.

[2981] Example 2

[2982] 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."

[2983] Conventional health management systems and group purchasing systems have difficulty responding to individual users' emotions, and tend to provide uniform advice and suggestions. Furthermore, group schedule coordination and progress management are cumbersome, making it difficult to adjust to each member's opinions and circumstances. This has led to issues such as reduced user satisfaction and reduced utilization efficiency.

[2984] 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.

[2985] In this invention, the server includes a means for responding to health consultations using a generative model, an emotion engine that analyzes a user's emotion data and provides it to the generative model, and a means for generating and transmitting advice that takes the emotion data into consideration. This enables personalized advice and suggestions based on the user's emotions. The server also includes a means for aggregating group purchase proposals and agreements, a means for managing and sharing progress information, and a means for creating and coordinating joint schedules and meal plans. This facilitates collaboration and progress management within a group, improving user satisfaction and usage efficiency.

[2986] A "generative model" is an algorithm that uses artificial intelligence to generate appropriate responses or suggestions based on input data.

[2987] The "emotion engine" is a function that analyzes emotions from user messages and data and provides that information to other system components.

[2988] "Means for responding to health consultations" is a function that provides appropriate advice in response to health-related questions and consultations from users.

[2989] The "means for generating and sending advice" is a function for sending advice created by the generative model based on emotion data to the user.

[2990] "Means for aggregating proposals and consents for group purchases" is a function that aggregates and unifies proposals and consents when multiple users jointly purchase products.

[2991] "Means for managing and sharing progress information" refers to a function for managing a user's training or activity progress information and sharing it with other users.

[2992] "Means for creating and adjusting shared schedules and meal plans" refers to a function that allows multiple users to create schedules and meal plans for joint use and adjust them as needed.

[2993] A "communications application" is software for sending and receiving messages over the Internet.

[2994] "Communication means" refers to the hardware and network infrastructure for transmitting and receiving data.

[2995] A "database" is an information system that can efficiently store large amounts of information and retrieve it as needed.

[2996] The present invention is a system for supporting users in efficient health management and economical purchasing, which mainly consists of a generative model, an emotion engine, a communication application via a user interface, a communication means, and a database.

[2997] 1. Means of responding to health consultations

[2998] The user sends a health consultation message to the server via a communication application such as the LINE app. For example, the message could be, "I've been feeling tired lately and I'd like some advice." The device then sends this message to the server via the Internet.

[2999] The server passes the received message to the emotion engine to recognize the user's emotion. The emotion engine analyzes the message content and the user's emotion data and provides that data to the generative model. The generative model takes the emotion data into account and generates optimal advice for the user. For example, if the emotion engine recognizes fatigue or stress, it will generate suggestions for relaxation methods or vitamin supplements.

[3000] The server sends the generated advice to the user via a LINE message, and the device displays the advice message to the user.

[3001] 2. Aggregation of group purchase proposals and agreements

[3002] A user sends a message suggesting a product they would like to purchase within the group. For example, a message ...

Claims

1. a means for responding to health consultations using generative models; a means of aggregating group purchasing proposals and agreements; A means to manage and share progress information; a means of creating and coordinating joint schedules and meal plans; A system including:

2. means for providing a user interface via a communication application; means for transmitting a response based on the generative model; means of communication; The system of claim 1 further comprising:

3. The means for aggregating group purchase proposals and consents is a database for storing and providing product information; 2. The system according to claim 1, further comprising a communication means for transmitting said product information to group members.

Images