System

Abstract

A system is provided.SOLUTION: A system comprising: means for generating a personalized plan by a generation AI based on a goal set by a user; means for obtaining schedule information of the user and finding an optimal time; and means for generating a dashboard and an escalation message for visualizing a progress status based on an analysis result.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] Even if users set goals such as exercise or language learning, it is difficult to continue working on them, and maintaining motivation is a challenge. Specifically, there is a lack of methods to create appropriate plans, incorporate them into schedules, monitor progress, and provide encouragement, which often leads users to give up midway. [Means for solving the problem]

[0005] This invention combines a means for a generation AI to generate an individual plan based on the goals set by the user, a means for acquiring the user's schedule information and finding the optimal time, and a means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results.Furthermore, by adding a means for setting reminders in conjunction with a calendar application and a means for the generation AI to automatically improve and update the plan, we provide a system that makes it easier for users to consistently work on their goals and enables continuous motivation to be maintained.

[0006] "Generative AI" refers to artificial intelligence that automatically generates optimal action plans based on the user's goals and schedule information.

[0007] "Individualized Plan" refers to a specific action plan customized to a user's capabilities, schedule, and goals.

[0008] "Schedule information" refers to calendar information including the user's schedule and daily time allocation.

[0009] The "optimal time" refers to a time period that is suitable for efficiently achieving the goals set by the user.

[0010] "Progress data" refers to data that records the results and progress of activities performed by a user in the form of numbers, logs, etc.

[0011] "Monitoring" refers to the act of regularly observing a user's activity and collecting it as data.

[0012] A "dashboard" refers to a visual display screen that allows users to see their progress and results at a glance.

[0013] An "encouragement message" refers to a message that encourages or advises a user to maintain motivation to achieve their goal.

[0014] "Reminder" refers to a function that notifies or warns the user so that they do not forget the plans they have set.

[0015] A "calendar application" refers to software that manages a user's schedule and visually displays appointments.

[0016] "Auto-improvement" refers to the process by which the generative AI optimizes and updates the plan based on the user's progress and feedback. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system that helps users form habits by using generative AI to create individual plans tailored to the user's goals and supporting their execution. Furthermore, this invention also includes functions for managing the user's schedule and monitoring progress, with the aim of providing sustained motivation.

[0039] Program processing overview

[0040] A specific embodiment of this system performs the following operations.

[0041] User registration and goal setting

[0042] 1. The user sets a goal for exercise or studying and enters it into the device. For example, the user sets a goal of "running five times a month."

[0043] 2. The device formats the user input data in JSON format and sends it to the server, including information such as the goal type and number of times.

[0044] 3. The server analyzes the received data and requests the AI ​​to generate an appropriate plan. The AI ​​then generates the optimal running plan, taking into account the user's current abilities and time constraints.

[0045] 4. The generation AI generates a plan and sends it back to the server, including specific dates and time slots.

[0046] 5. The server sends the generated plan to the terminal and displays it to the user on the terminal, who then plans his or her activities accordingly.

[0047] Plan execution and reminders

[0048] 1. The server retrieves the user's calendar and schedule information and finds the best time to schedule the plan. For example, a "Running" plan will start at 6:00 AM on Mondays, Wednesdays, and Fridays.

[0049] 2. The server generates the optimal reminder settings and connects to the calendar application to set reminders. The reminders notify the user, helping them execute their plan.

[0050] 3. The device will notify the user of the reminder at the specified time, so that the user will not forget to perform the activity.

[0051] Progress monitoring and advice

[0052] 1. After performing an activity, the user enters progress data into the device, for example, "Completed a 5km run in 30 minutes."

[0053] 2. The device formats the progress data and sends it to the server.

[0054] 3. The server monitors the received progress data and stores it in a database. The progress is periodically analyzed and a report is generated by the generation AI.

[0055] 4. The Generative AI generates an encouragement message for the user based on their progress, such as "Great! You achieved your goal! Try going a little further next time!"

[0056] 5. The server sends the report and message to the terminal, where it is displayed to the user, motivating the user to take further action.

[0057] 6. If necessary, the user requests a change to the plan, for example, "I want to change the distance to 7 kilometers."

[0058] 7. The server receives the change request and regenerates the plan using the generation AI.

[0059] 8. Once the new plan is generated, the server sends it to the device and updates the calendar and reminder settings.

[0060] In this way, a system that supports users in forming habits is realized. This system flexibly responds to users' goals and provides continuous motivation, allowing them to continue their exercise or study efforts.

[0061] The processing flow will be explained below.

[0062] User registration and goal setting

[0063] Step 1:

[0064] The user inputs the target information into the terminal.

[0065] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[0066] Step 2:

[0067] The terminal receives user input, formats the data, and sends it to the server.

[0068] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[0069] Send data to the server.

[0070] Step 3:

[0071] The server analyzes the received user data and passes it to the generation AI.

[0072] Generate a request to the generation AI: generate_plan("running", 3, 5)

[0073] Step 4:

[0074] The generative AI generates a specific plan based on the user's goals.

[0075] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[0076] Step 5:

[0077] The server receives the plan data returned from the generation AI and sends it to the terminal.

[0078] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[0079] Step 6:

[0080] The terminal displays the generated plan to the user.

[0081] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[0082] Plan execution and reminders

[0083] Step 1:

[0084] The server retrieves the user's schedule data and finds the best time.

[0085] Retrieves calendar information from the database.

[0086] Step 2:

[0087] The server generates reminder settings based on the measurement results.

[0088] Reminder settings: {"date": "Monday", "time": "5:50"}

[0089] Step 3:

[0090] The server connects the reminder data to the calendar app.

[0091] Call the Calendar API to add a reminder.

[0092] Step 4:

[0093] The device will display a reminder notification to the user at the specified time.

[0094] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[0095] Progress monitoring and advice

[0096] Step 1:

[0097] After exercising, the user inputs progress data into the terminal.

[0098] Example: "Distance run: 5km, time taken: 30 minutes"

[0099] Step 2:

[0100] The terminal formats the user input data and sends it to the server.

[0101] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[0102] Step 3:

[0103] The server stores the user's progress data in a database and analyzes it.

[0104] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[0105] Step 4:

[0106] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[0107] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[0108] Step 5:

[0109] The server receives reports and messages from the generated AI and sends them to the terminal.

[0110] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[0111] Step 6:

[0112] The terminal displays progress reports and enquiries to the user.

[0113] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[0114] Example 1

[0115] 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."

[0116] Many people today struggle with goal setting and achievement. Maintaining consistent daily habits, such as exercise and studying, requires individual planning, schedule management, and progress monitoring. However, performing these tasks individually can be burdensome and challenging, making it difficult to maintain motivation. It's also difficult to track progress in real time and receive appropriate feedback based on that progress. Furthermore, the lack of reminder settings and automatic plan updates is also problematic.

[0117] 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.

[0118] In this invention, the server includes: a means for generating an individual plan using a generation AI based on goals set by the user; a means for acquiring the user's schedule information and finding the optimal time; a visualization means for monitoring the user's progress data and visualizing the progress based on the analysis results, and a means for generating engraving messages; a means for formatting goal data entered by the user into a terminal in JSON format and sending it to the server; and a means for receiving the generated plan and displaying it on the terminal. This makes it possible to provide consistent support for the user's goal achievement, from planning to execution, progress management, and motivation maintenance.

[0119] "User" refers to an individual or end user who uses the System to set and achieve goals.

[0120] A "goal" refers to a specific outcome or purpose that a user aims to achieve in relation to sports, studies, or other activities.

[0121] "Generative AI" refers to artificial intelligence algorithms that automatically create plans based on a user's goals and circumstances.

[0122] "Plan" refers to a specific action plan created by generative AI for a user to achieve their goal.

[0123] "Schedule information" refers to data related to schedule and time management, such as a user's calendar and appointment times.

[0124] The "optimal time" refers to the most suitable time to perform an action for a set goal based on the user's schedule information.

[0125] "Progress data" refers to data that shows the record and results of activities that a user has performed toward a goal.

[0126] "Visualization means" refers to a method for displaying progress data in the form of diagrams, graphs, etc., to help the user understand the progress status.

[0127] "Encouragement messages" refer to messages created by generative AI to encourage and motivate users.

[0128] "Device" refers to the device (e.g., smartphone or tablet) that a user uses to access the system, enter goals, and record progress.

[0129] "JSON format" refers to a text-based data interchange format for representing data in a concise and structured form.

[0130] A "server" is the central computer of the system, and refers to a device that analyzes data, generates plans, monitors progress, etc.

[0131] This invention is a system for supporting users in achieving their goals. Specifically, it utilizes a generative AI model to generate personalized plans, manage schedules, monitor progress, and provide encouragement messages.

[0132] 1. The user sets a goal

[0133] Users input their goals, such as exercise or study goals, into the device. A dedicated input application is installed on the device, and the user inputs information such as the type of goal, duration, and frequency. For example, "My goal is to run five times a month."

[0134] 2. Formatting and sending data

[0135] The device formats the goal data collected from the user into JSON format and sends it to the server. As a specific example, the following data is generated:

[0136] json

[0137] {

[0138] "goal_type": "running",

[0139] "frequency": 5,

[0140] "duration_in_months": 1

[0141] }

[0142] This data is sent to the server via an HTTP request.

[0143] 3. Plan Generation

[0144] The server analyzes the received data and requests the generation AI to generate a plan. For example, OpenAI GPT-4 is used as the generation AI. Examples of prompt sentences include the following:

[0145] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[0146] The generation AI generates a personalized plan based on the request and returns it to the server, including specific dates and time slots. For example,

[0147] json

[0148] {

[0149] "plan": [

[0150] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[0151] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[0152] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[0153] ]

[0154] }

[0155] Something like this.

[0156] 4. View your plan

[0157] The server transmits the generated plan to the terminal, which then displays the plan to the user, allowing the user to plan their daily activities.

[0158] 5. Schedule management and reminder settings

[0159] The server retrieves the user's calendar information and finds the time to schedule the generated plan. It also works with a calendar application (e.g., Google Calendar) to set reminders, helping the user remember to execute the plan.

[0160] 6. Progress Monitoring

[0161] After completing each activity, the user enters progress data into the device. For example, "I completed a 5-kilometer run in 30 minutes." The device formats this data in JSON format and sends it to the server. The server stores the received progress data in a database and periodically analyzes it.

[0162] 7. Generating Encalagement Messages

[0163] The generation AI generates an encouragement message to motivate the user based on the progress data. For example, a message such as "Great! You've achieved your goal. Next time, try to go a little further!" is generated and sent from the server to the device. The device then displays this message to the user.

[0164] 8. Regenerating and Updating Plans

[0165] When a user wants to set a new goal or modify an existing one, they input a request into their device. The server receives the request and asks the generation AI to regenerate the plan. The new plan is sent to the device, and it updates the calendar and reminders as needed.

[0166] In this way, a system that continuously supports users in achieving their goals is realized. This system consistently supports users from goal setting to progress management, and can provide continuous motivation.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1: Enter user goals

[0169] The user inputs goals such as exercise or study into the device. Using a dedicated application, the user inputs information such as the type of goal (e.g., "running"), frequency (e.g., "five times per month"), and duration (e.g., "for one month"). This input information is treated as data required for the next processing step.

[0170] input:

[0171] User goal information (goal type, frequency, duration)

[0172] output:

[0173] Formatting data (stored on the device)

[0174] Step 2: Convert data format and send

[0175] The device converts the entered user goal information into JSON format, for example, "goal_type: running, frequency: 5, duration_in_months: 1". The converted data is sent to the server via an HTTP request.

[0176] input:

[0177] User goal information

[0178] output:

[0179] JSON format data (sent to server)

[0180] Step 3: Data reception and analysis

[0181] The server parses the received JSON data using Python or Node.js, extracting the type of goal, frequency, duration, etc. from the JSON data and storing them in memory.

[0182] input:

[0183] Goal data in JSON format

[0184] output:

[0185] Parsed target data (in memory)

[0186] Step 4: Plan Generation Request

[0187] The server requests the AI ​​to generate a plan based on the analyzed data. The AI ​​is prompted with the following prompt:

[0188] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[0189] This prompt is sent to the generation AI, which then initiates plan generation.

[0190] input:

[0191] Analyzed target data

[0192] output:

[0193] Prompt text (sent to the generation AI)

[0194] Step 5: Plan Generation and Reception

[0195] Based on the request, the AI ​​generates a personalized plan that fits the user's schedule. The plan, including specific dates and time slots, is generated and returned to the server in JSON format. For example,

[0196] json

[0197] {

[0198] "plan": [

[0199] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[0200] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[0201] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[0202] ]

[0203] }

[0204] input:

[0205] Prompt statement

[0206] output:

[0207] Generated plan (JSON format, returned to server)

[0208] Step 6: Submit and view your plan

[0209] The server receives the plan returned by the generation AI and sends it to the device, which displays the plan to the user, allowing the user to plan their daily activities based on the plan.

[0210] input:

[0211] Generated plan (JSON format)

[0212] output:

[0213] Plan to display to user (sent to device)

[0214] Step 7: Schedule

[0215] The server retrieves the user's calendar information and finds the best time to schedule the generated plan, using the Google Calendar API or other calendar application APIs.

[0216] input:

[0217] Generated Plan

[0218] User's calendar information

[0219] output:

[0220] Scheduled plans

[0221] Step 8: Set reminders

[0222] The server uses a calendar application to set reminders so that the user remembers to follow through on their plans. For example, a reminder is set for each day of a run, telling the user, "It's time to go for a run."

[0223] input:

[0224] Scheduled plans

[0225] output:

[0226] Set reminders (reflected in the calendar)

[0227] Step 9: Reminders

[0228] The device will send reminders to the user at the specified time, helping the user remember to perform the activity. For example, a push notification will be sent saying, "Start your 5km run now."

[0229] input:

[0230] Set reminders

[0231] output:

[0232] Reminders to users

[0233] Step 10: Enter progress data

[0234] After completing an activity, the user enters progress data into the device, for example, "I completed a 5-kilometer run in 30 minutes." This progress data is then saved on the device.

[0235] input:

[0236] Completed activity information

[0237] output:

[0238] Progress data (saved on the device)

[0239] Step 11: Format and send data

[0240] The device will format the progress data in JSON format and send it to the server. For example, the following data will be generated:

[0241] json

[0242] {

[0243] "goal_type": "running",

[0244] "progress": "5km running completed in 30 minutes",

[0245] "date": "2023-10-01"

[0246] }

[0247] This data is sent to the server via an HTTP request.

[0248] input:

[0249] Progress Data

[0250] output:

[0251] Progress data in JSON format (sent to the server)

[0252] Step 12: Saving and analyzing progress data

[0253] The server stores the received progress data in a database and periodically analyzes it. This analysis allows the user's progress to be understood and used as data for the next step.

[0254] input:

[0255] Progress data in JSON format

[0256] output:

[0257] Saved Progress Data

[0258] Analysis results (in memory)

[0259] Step 13: Generate Encalagement Messages

[0260] Based on the analysis of the progress data, the AI ​​generates motivational messages to the user, such as "Great! You've achieved your goal. Next time, try going a little further!"

[0261] input:

[0262] Analysis results

[0263] output:

[0264] Encapsulation message (sent back to server)

[0265] Step 14: Sending and Viewing Messages

[0266] The server sends the generated encouraging message to the terminal, which displays it to the user, providing motivation for the next activity.

[0267] input:

[0268] Encouragement Message

[0269] output:

[0270] Message to be displayed to the user (sent to the terminal)

[0271] Step 15: Regenerate and update the plan

[0272] If the user requests a change to the plan as needed, for example, by entering "I want to change the distance to 7 kilometers" into the device, the server receives this request and regenerates the plan using the generation AI. The new plan is then sent to the device and notified to the user.

[0273] input:

[0274] Plan change request

[0275] output:

[0276] Generated new plan (sent to device)

[0277] The above is a specific program processing flow divided into processing steps.

[0278] (Application example 1)

[0279] 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."

[0280] It is difficult for users with health and fitness goals to consistently maintain a healthy diet. In particular, creating a healthy meal plan, selecting appropriate meals based on that plan, and arranging timely delivery can be a heavy burden for users. Furthermore, in today's busy society, it is difficult to effectively monitor a user's progress toward achieving their health goals and provide ongoing appropriate advice as needed. There is a need for a system that solves these challenges and helps users make healthy eating a habit.

[0281] 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.

[0282] In this invention, the server includes: means for generating an individual plan using a generation AI based on goals set by the user; means for acquiring the user's schedule information and finding the optimal time; means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results; means for generating a meal plan according to the goals and establishing a connection with a food delivery service; and means for setting meal reminders and arranging delivery based on the meal plan. This enables the user to receive continuous support for achieving their health goals and to eat healthy meals at appropriate times.

[0283] "Means for a generative AI to generate an individual plan based on the goals set by the user" refers to a technology in which a generative AI creates a specific action plan for achieving a goal based on the health or fitness goals entered by the user.

[0284] The "means for obtaining the user's schedule information and finding the optimal time" is a technology that analyzes the user's calendar and schedule information to find the optimal time period for carrying out activities to achieve a goal.

[0285] "Means for generating dashboards and encouraging messages that monitor user progress data and visualize progress based on the analysis results" refers to technology that collects and analyzes data on activities performed by users, visually displays the results, and generates messages that motivate users.

[0286] "Means for generating meal plans according to goals and establishing collaboration with food delivery services" refers to a technology in which a generation AI creates a meal plan based on the user's health goals, and then collaborates with a food delivery service to provide appropriate meals based on that plan.

[0287] "Means for setting meal reminders and arranging delivery based on a meal plan" refers to technology that sets reminders based on a generated meal plan and arranges for meals to be delivered at a specified time through a food delivery service.

[0288] The configuration of a system for implementing this invention will be described below. The system operates in cooperation with a user terminal, a server, a generative AI model, a database, and an API of a food delivery service.

[0289] The server provides a means for the generative AI to generate an individualized plan based on the health goals set by the user. It receives the health goals sent from the user's device, converts them into JSON format, and inputs them into the generative AI model. The generative AI model generates an appropriate meal plan based on the received data and sends it back to the server. The meal plan includes specific menus and meal timings.

[0290] The server also obtains the user's schedule information and provides a means to find the optimal time. The server works in conjunction with a calendar application to analyze the user's schedule information and identify the optimal time period for executing the meal plan. It then sets a reminder based on the optimal time and notifies the user's device.

[0291] Furthermore, the server monitors the user's progress data and provides a means to generate a dashboard that visualizes progress and encouragement messages based on the analysis results. When the user reports the meal details they have completed from their device, the progress data is sent to the server. The server integrates this data and analyzes it using generative AI. Based on the results, it generates scored progress and praise messages, etc., to provide feedback to the user.

[0292] It also generates a meal plan tailored to your goals and provides a way to connect with food delivery services, which then send the meal plan as an order request through the food delivery service's API to order the appropriate menu, which then automatically arranges for the meal to be delivered to the user.

[0293] Finally, the system also provides a means to set meal reminders and arrange for delivery based on the meal plan. Based on the plan created by the generative AI, the server sets reminders and notifies the user at the specified time. It also arranges for the specified meal to be delivered at the appropriate time through the delivery service's API.

[0294] For example, if a user sets a goal of "dieting," the AI ​​will generate a healthy meal plan that takes into account calorie restriction and nutritional balance. The user will be notified of plans such as a "low-calorie smoothie" at 8 a.m. on Monday and a "salad bowl" at noon on Wednesday.

[0295] Below is an example of a prompt sentence.

[0296] Example prompt sentence:

[0297] Generate optimal meal plans for your following health goals:

[0298] Health Goal: Diet

[0299] User ID: 12345

[0300] Current weight: 70kg

[0301] Target weight: 65kg

[0302] Food preference: Vegetarian

[0303] Allergens: nuts

[0304] Weekly calorie goal: 1500kcal

[0305] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0306] Step 1:

[0307] Enter the goal set by the user.

[0308] Users launch the application and enter their health and fitness goals, including goal details, duration, current weight, target weight, food preferences, allergy information, etc. The entered data is formatted in JSON format and sent to the server.

[0309] Step 2:

[0310] The server requests the generative AI model to generate a plan.

[0311] The server analyzes the received user's goals and related data and requests the generative AI model to generate a plan. The generative AI model creates a detailed meal plan based on the goal data and sends it back to the server. This process involves data processing and calculations that allow the AI ​​to convert the input data (user's goals and preferences) into an appropriate plan.

[0312] Step 3:

[0313] The server saves the generated plan in the database and notifies the user.

[0314] The meal plan returned by the generative AI model is stored in a database on the server, and the saved plan is sent to the user's device, where the specific meal contents and timing are displayed to the user.

[0315] Step 4:

[0316] The server obtains the user's schedule information and identifies the best time.

[0317] The server interacts with the calendar application to retrieve the user's schedule information, which is then parsed to identify the optimal time based on the generated meal plan. This process uses the schedule data as input data and determines the optimal time based on that.

[0318] Step 5:

[0319] The server sets a reminder and notifies the user terminal.

[0320] The server sets a reminder based on the optimal time determined, and the reminder information is sent to the user's terminal and notified to the user. The notified reminder serves as a support for the user to remember to carry out the meal plan.

[0321] Step 6:

[0322] The server sends a request to a food delivery service.

[0323] Based on the generated meal plan, the server sends a request to the food delivery service's API. The request includes the specific meal contents and delivery time, and the meal is then delivered to the user. In this step, the generated plan is used as input data and an API request is made to arrange delivery.

[0324] Step 7:

[0325] The user records their meal contents and sends progress data to the server.

[0326] After the user eats a meal, they record the details of their meal in the application. The recorded progress data is formatted in JSON format and sent to the server.

[0327] Step 8:

[0328] The server analyzes the progress data and generates advice based on a generative AI model.

[0329] The server analyzes the received progress data and generates advice messages based on the generation AI. In this process, the progress data is used as input and the generation AI generates appropriate feedback and advice.

[0330] Step 9:

[0331] The server transmits the generated advice to the user terminal and provides feedback.

[0332] The generated advice is sent from the server to the user's device and displayed to the user, allowing the user to understand their own progress and maintain motivation for the next step.

[0333] 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.

[0334] This invention is a system that generates an optimal plan based on the user's set goals and supports their execution. In particular, it uses a generative AI to create an individual plan, finds an appropriate time using the user's schedule information, and monitors the user's progress. It also combines an emotion engine that recognizes the user's emotions to provide encouragement messages according to the user's emotional state.

[0335] Program processing overview

[0336] User registration and goal setting

[0337] First, the user sets goals for exercise or studying and enters them into the device. For example, the user sets specific goals such as "running," "three times a week," and "5 kilometers per session."

[0338] The device formats the data entered by the user and sends it to the server. The data is formatted in JSON format and includes information such as the type and number of goals. The server analyzes the received data and asks the generation AI to generate a plan. The generation AI generates the optimal plan, taking into account the user's current abilities and time constraints. The plan generated by the generation AI is sent back to the server, and then sent from the server to the device. The device displays the generated plan to the user.

[0339] Plan execution and reminders

[0340] The server retrieves the user's calendar and schedule information and finds the optimal time to incorporate the plan into the schedule. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings based on those times. The server then connects the reminder data to the calendar app and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[0341] Progress monitoring and advice

[0342] After the user finishes an activity, they input progress data into the device. For example, they might input "distance run: 5 km, time taken: 30 minutes." The device formats the input data and sends it to the server. The server stores the progress data in a database and analyzes it. Based on the analysis results, the generation AI generates a progress dashboard and encouraging messages. The server sends reports and messages from the generation AI to the device, which displays them to the user.

[0343] Collaboration with emotion engine

[0344] This system can also be combined with an emotion engine that recognizes the user's emotions from facial expressions, voice, text input, etc., and sends the data to the server.

[0345] Based on the emotional data recognized by the emotion engine, the generation AI adjusts the encouragement message. For example, if the user is feeling stressed, the generation AI generates a message such as "Try some light exercise to refresh yourself." The server sends the generated encouragement message to the device, which then displays it to the user. Emotional data is also used to generate or improve plans. For example, if the user is feeling tired, the generation AI can adjust the plan to reduce the load of the next activity.

[0346] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation. The system responds flexibly to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[0347] The processing flow will be explained below.

[0348] User registration and goal setting

[0349] Step 1:

[0350] The user inputs the target information into the terminal.

[0351] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[0352] Step 2:

[0353] The terminal receives user input, formats the data, and sends it to the server.

[0354] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[0355] Send data to the server.

[0356] Step 3:

[0357] The server analyzes the received user data and passes it to the generation AI.

[0358] Generate a request to the generation AI: generate_plan("running", 3, 5)

[0359] Step 4:

[0360] The generative AI generates a specific plan based on the user's goals.

[0361] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[0362] Step 5:

[0363] The server receives the plan data returned from the generation AI and sends it to the terminal.

[0364] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[0365] Step 6:

[0366] The terminal displays the generated plan to the user.

[0367] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[0368] Plan execution and reminders

[0369] Step 1:

[0370] The server retrieves the user's schedule data and finds the best time.

[0371] Retrieves calendar information from the database.

[0372] Step 2:

[0373] The server generates reminder settings based on the measurement results.

[0374] Reminder settings: {"date": "Monday", "time": "5:50"}

[0375] Step 3:

[0376] The server connects the reminder data to the calendar app.

[0377] Call the Calendar API to add a reminder.

[0378] Step 4:

[0379] The device will display a reminder notification to the user at the specified time.

[0380] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[0381] Progress monitoring and advice

[0382] Step 1:

[0383] After exercising, the user inputs progress data into the terminal.

[0384] Example: "Distance run: 5km, time taken: 30 minutes"

[0385] Step 2:

[0386] The terminal formats the user input data and sends it to the server.

[0387] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[0388] Step 3:

[0389] The server stores the user's progress data in a database and analyzes it.

[0390] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[0391] Step 4:

[0392] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[0393] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[0394] Step 5:

[0395] The server receives reports and messages from the generated AI and sends them to the terminal.

[0396] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[0397] Step 6:

[0398] The terminal displays progress reports and enquiries to the user.

[0399] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[0400] Collaboration with emotion engine

[0401] Step 1:

[0402] The user provides input for emotion recognition.

[0403] For example, after exercising, the user may input feedback such as "I'm a little tired from running today."

[0404] Step 2:

[0405] The device formats the emotion data and sends it to the server.

[0406] Emotion data format: { "emotional_state": "Tired"}

[0407] Step 3:

[0408] The server receives the emotion data and sends it to the emotion engine.

[0409] Pass data to the emotion engine: analyze_emotion("tired")

[0410] Step 4:

[0411] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[0412] Analysis result: { "emotion": "fatigue", "level": "high"}

[0413] Step 5:

[0414] Based on the results of the emotion engine, the server instructs the generation AI to adjust the encouraging message.

[0415] Instructions to the generating AI: generate_message("Fatigue", "High")

[0416] Step 6:

[0417] The generative AI generates encouraging messages based on emotional state.

[0418] Generated message: "Get some light exercise to refresh yourself."

[0419] Step 7:

[0420] The server sends the generated enqueuing message to the terminal.

[0421] Send data: { "message": "Let's do some light exercise to refresh ourselves"}

[0422] Step 8:

[0423] The terminal displays the enlargement message to the user.

[0424] The screen will display a message saying, "Let's incorporate some light exercise to refresh ourselves." Emotional data will also be used to generate and improve plans. For example, if the user is feeling tired, the generation AI will adjust the plan to reduce the load of the next activity.

[0425] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation.The system flexibly responds to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[0426] Example 2

[0427] 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."

[0428] While traditional goal management systems can monitor users' progress and provide personalized plans, they struggle to adapt flexibly to the user's emotional state. They also lack the ability to automatically improve and update the generated plans, limiting their ability to continuously support user motivation. Furthermore, reminder functions are not fully integrated, making it difficult to smoothly manage users' schedules.

[0429] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the generative AI model to generate an individual plan based on the goal set by the user, a means for acquiring the user's schedule information and finding the optimal time, and a means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results. This not only provides an individual plan for achieving the user's goal, but also makes it possible to provide encouraging messages that adapt to the user's emotional state. In addition, the user's schedule is smoothly managed through the reminder function, and the generative AI model automatically improves and updates the plan, enabling continuous motivation support.

[0430] A "generative AI model" refers to an artificial intelligence that generates a personalized plan based on the goals set by the user.

[0431] A "goal" is an indicator that indicates a specific activity or result that a user sets to achieve.

[0432] "Schedule information" refers to data used for a user's activity schedule and time management.

[0433] "Progress data" is information that indicates a user's actual progress and achievements toward a goal.

[0434] A "dashboard" is an interface that visually displays a user's progress, making it easier to understand.

[0435] "Encouragement messages" are messages provided to increase motivation depending on the user's progress and emotional state.

[0436] "Emotion data" is information about the user's emotional state estimated based on facial expressions, voice, text input, and the like.

[0437] "Automatic plan improvement and updating" refers to the process in which the generative AI model optimizes existing plans and generates new plans based on the user's progress data and emotional data.

[0438] A "calendar application" refers to software that manages a user's schedule information and adjusts reminders and appointments.

[0439] The present invention is a system for generating an optimal plan based on a goal set by a user and supporting the execution of that plan. This system is realized mainly by three entities: a server, a terminal, and a user. Specific embodiments of the system are described in detail below.

[0440] User registration and goal setting

[0441] First, the user sets a goal for exercise or studying and enters that information into the device. For example, specific goals such as "running," "three times a week," and "5 kilometers each time" are entered into the device's application. The device then formats this information into JSON format and sends it to the server. The server analyzes the received data and requests the generative AI model to generate a plan. At this time, it generates a prompt like the following:

[0442] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[0443] The generative AI model generates an optimal plan based on this prompt and sends it back to the server, which then sends the plan to the device, which displays it to the user.

[0444] Plan execution and reminders

[0445] The server retrieves the user's calendar and schedule information and finds the optimal time based on the generated plan. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings for those times. The server then connects the reminder data to the calendar application and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[0446] Progress monitoring and advice

[0447] After the user finishes an activity, they enter progress data into the device. For example, they enter data such as "distance run: 5 km, time required: 30 minutes." The device formats this data into JSON format and sends it to the server. The server stores the progress data in a database and performs analysis. Based on the analysis results, the generative AI model generates a progress dashboard and enumeration messages. The server sends the generated reports and messages to the device, which then displays them to the user.

[0448] Collaboration with emotion engine

[0449] This system can also be combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. and sends the data to the server. For example, if the user is feeling stressed, the emotion engine will send the data tagged with "stress." Based on this emotion data, the generative AI model adjusts the encouragement message. For example, if the user is feeling stressed, a message such as "Try some light exercise to refresh yourself" is generated. The server then sends the generated encouragement message to the device, which then displays it to the user. Emotion data is also used to generate and improve plans.

[0450] In this way, the system provides flexible and effective planning and motivational maintenance functions to help users achieve their goals. Plan generation and automatic improvement using a generative AI model, reminder setting, progress monitoring, and even integration with an emotion engine can support users' sustained motivation.

[0451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0452] Step 1:

[0453] User sets goals

[0454] Users input their exercise or study goals into the device application, such as "running," "three times a week," and "5 kilometers per session."

[0455] (Input): Goal data set by the user (e.g., "running," "three times a week," "5 kilometers per session").

[0456] (Output): The target data entered into the terminal.

[0457] (Specific action): The user enters their goal into the app's input form and clicks the "Save" button.

[0458] Step 2:

[0459] The device formats the data and sends it to the server

[0460] The terminal formats the target data entered by the user into JSON format and sends it to the server.

[0461] (Input): The target data entered into the terminal.

[0462] (Output): The goal data formatted in JSON.

[0463] (Specific operation): The terminal program formats the target data in JSON format as shown below and sends it to the server via an HTTP POST request.

[0464] json

[0465] {

[0466] "goal_type": "running",

[0467] "frequency_per_week": 3,

[0468] "distance_per_session": 5

[0469] }

[0470] Step 3:

[0471] The server requests the generative AI model to generate a plan.

[0472] The server analyzes the received data and generates a prompt statement that requests the generative AI model to generate a plan.

[0473] (Input): The target data formatted in JSON.

[0474] (Output): The prompt sent to the generative AI model.

[0475] (Specific action): The server generates a prompt like this:

[0476] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[0477] This prompt is then sent to a generative AI model and a plan is received.

[0478] Step 4:

[0479] The server sends the generated plan to the device.

[0480] The generated plan is returned to the server, and then transmitted from the server to the terminal.

[0481] (Input): The plan received from the generative AI model.

[0482] (Output): Plan data sent to the device.

[0483] (Specific operation): The server returns the plan as an HTTP response, and the device analyzes and displays the received data.

[0484] Step 5:

[0485] The device displays the plan to the user.

[0486] The terminal displays the generated plan to the user.

[0487] (Input): Plan data received from the server.

[0488] (Output): The plan that is displayed to the user.

[0489] (Specific behavior): The device app displays plans in a list format and adds "Next" and "Details" buttons.

[0490] Step 6:

[0491] The server retrieves the user's schedule information.

[0492] The server retrieves the user's calendar and schedule information.

[0493] (Input): Schedule information from a calendar application.

[0494] (Output): Schedule information stored on the server.

[0495] (Specific operation): The server retrieves schedule data from third-party services such as Google Calendar via API.

[0496] Step 7:

[0497] The server generates the reminder settings

[0498] The server generates reminder settings based on the acquired schedule information.

[0499] (Input): User's schedule information and generated plan.

[0500] (Output): Reminder setting data.

[0501] (Specific behavior): The server generates reminder settings in JSON format based on the schedule information and plan:

[0502] json

[0503] {

[0504] "reminder_time": "2023-10-02T06:00:00",

[0505] "reminder_message": "It's time for a run"

[0506] }

[0507] Step 8:

[0508] The device displays a reminder notification to the user.

[0509] Your device will display notifications at the specified time based on your reminder settings.

[0510] (Input): Reminder setting data.

[0511] (Output): Reminder notification to the user.

[0512] (Specific behavior): The device will pop up a reminder notification and provide a "Confirm" or "Snooze" button.

[0513] Step 9:

[0514] The user enters progress data

[0515] After completing an activity, the user inputs progress data into the terminal.

[0516] (Input): Progress data entered by the user (e.g., "Distance run: 5 km, Time taken: 30 mins").

[0517] (Output): Progress data entered into the terminal.

[0518] (Specific action): The user enters data into a dedicated input form and clicks the "Submit" button.

[0519] Step 10:

[0520] The device formats the progress data and sends it to the server.

[0521] The device formats the entered progress data into JSON format and sends it to the server.

[0522] (Input): Progress data entered by the user.

[0523] (Output): Progress data in JSON format.

[0524] (Specific operation): The device formats the progress data as follows and sends it to the server:

[0525] json

[0526] {

[0527] "distance_ran": 5,

[0528] "time_taken": 30

[0529] }

[0530] Step 11:

[0531] The server saves the progress data in a database and performs analysis.

[0532] The server stores the progress data in a database and performs analysis.

[0533] (Input): Progress data in JSON format.

[0534] (Output): Analysis result data.

[0535] (Specific behavior): The server executes a database query, stores progress data, and then launches an analysis script to process the data.

[0536] Step 12:

[0537] Generative AI models generate dashboards and enlargement messages

[0538] The generative AI model generates progress dashboards and enlargement messages based on the analysis results.

[0539] (Input): Analysis result data.

[0540] (Output): Dashboard data and enumeration messages.

[0541] (Specific behavior): The generative AI model generates feedback on progress and creates messages to motivate the user.

[0542] Step 13:

[0543] The server sends reports and messages to the terminal, which displays them

[0544] The server sends reports and enlargement messages from the generative AI model to the terminal, which displays them to the user.

[0545] (Input): Dashboard data and enumeration messages.

[0546] (Output): Reports and messages displayed to the user.

[0547] (Specific behavior): The device displays a dashboard or message on the screen and provides "Next step" or "View details" buttons.

[0548] Step 14:

[0549] Emotion engine recognizes user emotions

[0550] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, and sends that data to the server.

[0551] (Input): User facial expression, voice, and text input data.

[0552] (Output): Emotion data.

[0553] (Specific operation): The emotion engine analyzes data in real time and generates emotion data (e.g., stress).

[0554] Step 15:

[0555] Generative AI models tailor encouraging messages based on emotional data

[0556] The generative AI model tailors appropriate encouraging messages based on emotional data.

[0557] (Input): Emotion data.

[0558] (Output): The adjusted encausation message.

[0559] (Specific operation): The generative AI model takes emotion data as input and generates messages such as the following:

[0560] If the user is feeling stressed, the message is "Try some light exercise to refresh yourself."

[0561] Step 16:

[0562] The server sends a message to the terminal, which displays it to the user.

[0563] The server sends the generated encumbrance message to the terminal, which displays it to the user.

[0564] (Input): The adjusted encausation message.

[0565] (Output): The enumeration message that is displayed to the user.

[0566] (Specific behavior): The device displays the message as a notification and provides a "View details" or "OK" button.

[0567] In this way, the system generates a plan based on the user's goals and helps maintain the user's motivation through schedule management, progress monitoring, and providing emotionally appropriate encouragement messages.

[0568] (Application example 2)

[0569] 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."

[0570] In conventional security services, managing security guards' work schedules and patrol routes is complicated, and there are a lack of appropriate measures to maintain the guards' motivation and mental health. This makes it difficult for them to work efficiently and safely, and leads to the accumulation of stress and fatigue.

[0571] 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 the generation AI to generate an individual plan based on goals set by the user; means for acquiring the user's date and time information and finding the optimal time; means for monitoring the user's progress data and generating a display device and encouraging messages that visualize the progress based on the analysis results; means including an emotion analysis device that recognizes the user's emotional state; and means for the generation AI to adjust the encouraging messages based on data from the emotion analysis device. This improves the efficiency of schedule management for security guards and enables appropriate work plans and emotional care tailored to each individual guard.

[0572] "Generative AI" is a system that uses artificial intelligence technology to generate optimal plans according to the goals set by the user.

[0573] A "personalized plan" is a personalized execution plan that takes into account each user's goals, schedule, and emotional state.

[0574] "Date and time information" is data from a user's schedule or calendar that is used to optimize the timing of activities.

[0575] "Progress data" refers to data that indicates the actual progress of a user as they execute a plan, including, for example, the tasks completed and the time required.

[0576] A "display device" is a device that visually displays progress and encouraging messages to a user, such as smart glasses or a smartphone.

[0577] An "encouraging message" is a message of encouragement or advice provided to increase the user's motivation.

[0578] An "emotion analysis device" is a device that recognizes a user's emotional state and responds or adjusts appropriately. It includes facial expression analysis cameras and voice analysis devices.

[0579] A "calendar application" is software that manages a user's schedule and has the function of setting reminders and sending notifications.

[0580] "Automatic improvement" is the process by which the generative AI dynamically modifies the plan based on the user's progress data and emotional state, updating it to a more effective plan.

[0581] The system for implementing this invention consists of the following steps: First, a user uses smart glasses to register and set goals. The user enters specific exercise or study goals, and the information is formatted and sent to the server. The data format used to format the information is JSON.

[0582] The server analyzes the received data and generates an individual plan using a generative AI model. The generative AI model takes into account the user's current abilities and time constraints to construct an optimal plan. The generated plan is then sent from the server to the smart glasses and displayed to the user.

[0583] The server also retrieves the user's date and time information (schedule and calendar data) to find the optimal time to incorporate the plan into the schedule. For example, optimizing security guard patrol times and break times. This data is integrated with the calendar application and set as a reminder. The smart glasses will display the reminder at the specified time to help the user execute the plan.

[0584] After the user executes the plan, progress data is input into the smart glasses. For example, "Time to complete the tour route: 30 minutes." This data is formatted and sent back to the server. The server stores the progress data in a database, and the generative AI model analyzes it. Based on the analysis results, a progress display and encouraging messages are generated and sent to the smart glasses.

[0585] Furthermore, the emotion analyzer monitors the user's emotional state. Using a facial expression camera and a voice analyzer, the emotion analyzer sends data to the server if the user feels stressed or tired. Based on the emotional data, the generative AI model adjusts the encouraging messages and generates custom messages such as "Take a short break" or "Do some light exercise to change your mood." These are then displayed on the smart glasses.

[0586] As a concrete example, consider a security guard who sets the goal of "patrol routes three times a day, breaks every two hours." The generative AI model uses this information to create a personalized work plan. If the emotion analyzer detects "high stress," the generative AI model automatically suggests increasing break time, displaying the message, "Take a five-minute break in a relaxing place."

[0587] An example prompt is:

[0588] User ID: Security Guard 123

[0589] Current Ability: Intermediate

[0590] Time constraints:

[0591] Start time: 8:00

[0592] End time: 18:00

[0593] the goal:

[0594] Patrol frequency: 3 times a day

[0595] Break interval: Every 2 hours

[0596] In this way, it is possible to realize a system that improves both the work efficiency and mental health of security guards.

[0597] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0598] Step 1:

[0599] The user sets goals using the smart glasses. The user uses the input interface of the smart glasses to input goals, such as "Patrol route: 3 times a day, Break: every 2 hours." This data is formatted in JSON format and sent to the server. The input is specific goal data, and the output is formatted JSON data.

[0600] Step 2:

[0601] The server parses the received goal data. The server parses the JSON format goal data and generates an individual plan based on the generative AI model, taking into account the user's current abilities and time constraints. The input is the JSON data to be parsed, and the output is the generated plan data.

[0602] Step 3:

[0603] The server sends the generated plan to the smart glasses, which the terminal displays to the user. The terminal visually displays the received plan data and guides the user to the next step. The input is the plan data sent from the server, and the output is the plan displayed on the smart glasses.

[0604] Step 4:

[0605] The server obtains the user's date and time information and incorporates the plan into the schedule. The server works with the calendar application to find the optimal patrol and break times and set reminders. The input is the user's schedule data, and the output is the set reminder data.

[0606] Step 5:

[0607] When the reminder time arrives, the smart glasses notify the user. The device displays the reminder at the set time and prompts the user to carry out the plan. The input is the reminder data, and the output is the notification displayed on the smart glasses.

[0608] Step 6:

[0609] The user executes the plan and inputs the progress data into the smart glasses. The terminal formats the progress data received from the user and sends it to the server. The input is the progress data, and the output is the formatted progress data.

[0610] Step 7:

[0611] The server analyzes the received progress data. The server stores the progress data in a database, and the generative AI model analyzes the progress and generates a display and encouragement message. The input is the received progress data, and the output is the encouragement message and progress data.

[0612] Step 8:

[0613] The server receives data from the emotion analyzer, and the generative AI model adjusts the encouraging message. The emotion analyzer determines the user's emotions from facial expressions and voice and sends that data to the server. The server analyzes the data and adjusts the encouraging message using the generative AI model. The input is emotional data, and the output is the adjusted encouraging message.

[0614] Step 9:

[0615] The smart glasses display the tailored encouraging message to the user. The terminal visually displays the message sent from the server to motivate the user. The input is the encouraging message sent from the server, and the output is the message displayed on the smart glasses.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] [Second embodiment]

[0620] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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).

[0626] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] In the smart glasses 214, 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.

[0631] 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."

[0632] This invention relates to a system that helps users form habits by using generative AI to create individual plans tailored to the user's goals and supporting their execution. Furthermore, this invention also includes functions for managing the user's schedule and monitoring progress, with the aim of providing sustained motivation.

[0633] Program processing overview

[0634] A specific embodiment of this system performs the following operations.

[0635] User registration and goal setting

[0636] 1. The user sets a goal for exercise or studying and enters it into the device. For example, the user sets a goal of "running five times a month."

[0637] 2. The device formats the user input data in JSON format and sends it to the server, including information such as the goal type and number of times.

[0638] 3. The server analyzes the received data and requests the AI ​​to generate an appropriate plan. The AI ​​then generates the optimal running plan, taking into account the user's current abilities and time constraints.

[0639] 4. The generation AI generates a plan and sends it back to the server, including specific dates and time slots.

[0640] 5. The server sends the generated plan to the terminal and displays it to the user on the terminal, who then plans his or her activities accordingly.

[0641] Plan execution and reminders

[0642] 1. The server retrieves the user's calendar and schedule information and finds the best time to schedule the plan. For example, a "Running" plan will start at 6:00 AM on Mondays, Wednesdays, and Fridays.

[0643] 2. The server generates the optimal reminder settings and connects to the calendar application to set reminders. The reminders notify the user, helping them execute their plan.

[0644] 3. The device will notify the user of the reminder at the specified time, so that the user will not forget to perform the activity.

[0645] Progress monitoring and advice

[0646] 1. After performing an activity, the user enters progress data into the device, for example, "Completed a 5km run in 30 minutes."

[0647] 2. The device formats the progress data and sends it to the server.

[0648] 3. The server monitors the received progress data and stores it in a database. The progress is periodically analyzed and a report is generated by the generation AI.

[0649] 4. The Generative AI generates an encouragement message for the user based on their progress, such as "Great! You achieved your goal! Try going a little further next time!"

[0650] 5. The server sends the report and message to the terminal, where it is displayed to the user, motivating the user to take further action.

[0651] 6. If necessary, the user requests a change to the plan, for example, "I want to change the distance to 7 kilometers."

[0652] 7. The server receives the change request and regenerates the plan using the generation AI.

[0653] 8. Once the new plan is generated, the server sends it to the device and updates the calendar and reminder settings.

[0654] In this way, a system that supports users in forming habits is realized. This system flexibly responds to users' goals and provides continuous motivation, allowing them to continue their exercise or study efforts.

[0655] The processing flow will be explained below.

[0656] User registration and goal setting

[0657] Step 1:

[0658] The user inputs the target information into the terminal.

[0659] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[0660] Step 2:

[0661] The terminal receives user input, formats the data, and sends it to the server.

[0662] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[0663] Send data to the server.

[0664] Step 3:

[0665] The server analyzes the received user data and passes it to the generation AI.

[0666] Generate a request to the generation AI: generate_plan("running", 3, 5)

[0667] Step 4:

[0668] The generative AI generates a specific plan based on the user's goals.

[0669] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[0670] Step 5:

[0671] The server receives the plan data returned from the generation AI and sends it to the terminal.

[0672] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[0673] Step 6:

[0674] The terminal displays the generated plan to the user.

[0675] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[0676] Plan execution and reminders

[0677] Step 1:

[0678] The server retrieves the user's schedule data and finds the best time.

[0679] Retrieves calendar information from the database.

[0680] Step 2:

[0681] The server generates reminder settings based on the measurement results.

[0682] Reminder settings: {"date": "Monday", "time": "5:50"}

[0683] Step 3:

[0684] The server connects the reminder data to the calendar app.

[0685] Call the Calendar API to add a reminder.

[0686] Step 4:

[0687] The device will display a reminder notification to the user at the specified time.

[0688] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[0689] Progress monitoring and advice

[0690] Step 1:

[0691] After exercising, the user inputs progress data into the terminal.

[0692] Example: "Distance run: 5km, time taken: 30 minutes"

[0693] Step 2:

[0694] The terminal formats the user input data and sends it to the server.

[0695] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[0696] Step 3:

[0697] The server stores the user's progress data in a database and analyzes it.

[0698] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[0699] Step 4:

[0700] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[0701] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[0702] Step 5:

[0703] The server receives reports and messages from the generated AI and sends them to the terminal.

[0704] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[0705] Step 6:

[0706] The terminal displays progress reports and enquiries to the user.

[0707] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[0708] Example 1

[0709] 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."

[0710] Many people today struggle with goal setting and achievement. Maintaining consistent daily habits, such as exercise and studying, requires individual planning, schedule management, and progress monitoring. However, performing these tasks individually can be burdensome and challenging, making it difficult to maintain motivation. It's also difficult to track progress in real time and receive appropriate feedback based on that progress. Furthermore, the lack of reminder settings and automatic plan updates is also problematic.

[0711] 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.

[0712] In this invention, the server includes: a means for generating an individual plan using a generation AI based on goals set by the user; a means for acquiring the user's schedule information and finding the optimal time; a visualization means for monitoring the user's progress data and visualizing the progress based on the analysis results, and a means for generating engraving messages; a means for formatting goal data entered by the user into a terminal in JSON format and sending it to the server; and a means for receiving the generated plan and displaying it on the terminal. This makes it possible to provide consistent support for the user's goal achievement, from planning to execution, progress management, and motivation maintenance.

[0713] "User" refers to an individual or end user who uses the System to set and achieve goals.

[0714] A "goal" refers to a specific outcome or purpose that a user aims to achieve in relation to sports, studies, or other activities.

[0715] "Generative AI" refers to artificial intelligence algorithms that automatically create plans based on a user's goals and circumstances.

[0716] "Plan" refers to a specific action plan created by generative AI for a user to achieve their goal.

[0717] "Schedule information" refers to data related to schedule and time management, such as a user's calendar and appointment times.

[0718] The "optimal time" refers to the most suitable time to perform an action for a set goal based on the user's schedule information.

[0719] "Progress data" refers to data that shows the record and results of activities that a user has performed toward a goal.

[0720] "Visualization means" refers to a method for displaying progress data in the form of diagrams, graphs, etc., to help the user understand the progress status.

[0721] "Encouragement messages" refer to messages created by generative AI to encourage and motivate users.

[0722] "Device" refers to the device (e.g., smartphone or tablet) that a user uses to access the system, enter goals, and record progress.

[0723] "JSON format" refers to a text-based data interchange format for representing data in a concise and structured form.

[0724] A "server" is the central computer of the system, and refers to a device that analyzes data, generates plans, monitors progress, etc.

[0725] This invention is a system for supporting users in achieving their goals. Specifically, it utilizes a generative AI model to generate personalized plans, manage schedules, monitor progress, and provide encouragement messages.

[0726] 1. The user sets a goal

[0727] Users input their goals, such as exercise or study goals, into the device. A dedicated input application is installed on the device, and the user inputs information such as the type of goal, duration, and frequency. For example, "My goal is to run five times a month."

[0728] 2. Formatting and sending data

[0729] The device formats the goal data collected from the user into JSON format and sends it to the server. As a specific example, the following data is generated:

[0730] json

[0731] {

[0732] "goal_type": "running",

[0733] "frequency": 5,

[0734] "duration_in_months": 1

[0735] }

[0736] This data is sent to the server via an HTTP request.

[0737] 3. Plan Generation

[0738] The server analyzes the received data and requests the generation AI to generate a plan. For example, OpenAI GPT-4 is used as the generation AI. Examples of prompt sentences include the following:

[0739] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[0740] The generation AI generates a personalized plan based on the request and returns it to the server, including specific dates and time slots. For example,

[0741] json

[0742] {

[0743] "plan": [

[0744] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[0745] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[0746] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[0747] ]

[0748] }

[0749] Something like this.

[0750] 4. View your plan

[0751] The server transmits the generated plan to the terminal, which then displays the plan to the user, allowing the user to plan their daily activities.

[0752] 5. Schedule management and reminder settings

[0753] The server retrieves the user's calendar information and finds the time to schedule the generated plan. It also works with a calendar application (e.g., Google Calendar) to set reminders, helping the user remember to execute the plan.

[0754] 6. Progress Monitoring

[0755] After completing each activity, the user enters progress data into the device. For example, "I completed a 5-kilometer run in 30 minutes." The device formats this data in JSON format and sends it to the server. The server stores the received progress data in a database and periodically analyzes it.

[0756] 7. Generating Encalagement Messages

[0757] The generation AI generates an encouragement message to motivate the user based on the progress data. For example, a message such as "Great! You've achieved your goal. Next time, try to go a little further!" is generated and sent from the server to the device. The device then displays this message to the user.

[0758] 8. Regenerating and Updating Plans

[0759] When a user wants to set a new goal or modify an existing one, they input a request into their device. The server receives the request and asks the generation AI to regenerate the plan. The new plan is sent to the device, and it updates the calendar and reminders as needed.

[0760] In this way, a system that continuously supports users in achieving their goals is realized. This system consistently supports users from goal setting to progress management, and can provide continuous motivation.

[0761] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0762] Step 1: Enter user goals

[0763] The user inputs goals such as exercise or study into the device. Using a dedicated application, the user inputs information such as the type of goal (e.g., "running"), frequency (e.g., "five times per month"), and duration (e.g., "for one month"). This input information is treated as data required for the next processing step.

[0764] input:

[0765] User goal information (goal type, frequency, duration)

[0766] output:

[0767] Formatting data (stored on the device)

[0768] Step 2: Convert data format and send

[0769] The device converts the entered user goal information into JSON format, for example, "goal_type: running, frequency: 5, duration_in_months: 1". The converted data is sent to the server via an HTTP request.

[0770] input:

[0771] User goal information

[0772] output:

[0773] JSON format data (sent to server)

[0774] Step 3: Data reception and analysis

[0775] The server parses the received JSON data using Python or Node.js, extracting the type of goal, frequency, duration, etc. from the JSON data and storing them in memory.

[0776] input:

[0777] Goal data in JSON format

[0778] output:

[0779] Parsed target data (in memory)

[0780] Step 4: Plan Generation Request

[0781] The server requests the AI ​​to generate a plan based on the analyzed data. The AI ​​is prompted with the following prompt:

[0782] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[0783] This prompt is sent to the generation AI, which then initiates plan generation.

[0784] input:

[0785] Analyzed target data

[0786] output:

[0787] Prompt text (sent to the generation AI)

[0788] Step 5: Plan Generation and Reception

[0789] Based on the request, the AI ​​generates a personalized plan that fits the user's schedule. The plan, including specific dates and time slots, is generated and returned to the server in JSON format. For example,

[0790] json

[0791] {

[0792] "plan": [

[0793] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[0794] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[0795] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[0796] ]

[0797] }

[0798] input:

[0799] Prompt statement

[0800] output:

[0801] Generated plan (JSON format, returned to server)

[0802] Step 6: Submit and view your plan

[0803] The server receives the plan returned by the generation AI and sends it to the device, which displays the plan to the user, allowing the user to plan their daily activities based on the plan.

[0804] input:

[0805] Generated plan (JSON format)

[0806] output:

[0807] Plan to display to user (sent to device)

[0808] Step 7: Schedule

[0809] The server retrieves the user's calendar information and finds the best time to schedule the generated plan, using the Google Calendar API or other calendar application APIs.

[0810] input:

[0811] Generated Plan

[0812] User's calendar information

[0813] output:

[0814] Scheduled plans

[0815] Step 8: Set reminders

[0816] The server uses a calendar application to set reminders so that the user remembers to follow through on their plans. For example, a reminder is set for each day of a run, telling the user, "It's time to go for a run."

[0817] input:

[0818] Scheduled plans

[0819] output:

[0820] Set reminders (reflected in the calendar)

[0821] Step 9: Reminders

[0822] The device will send reminders to the user at the specified time, helping the user remember to perform the activity. For example, a push notification will be sent saying, "Start your 5km run now."

[0823] input:

[0824] Set reminders

[0825] output:

[0826] Reminders to users

[0827] Step 10: Enter progress data

[0828] After completing an activity, the user enters progress data into the device, for example, "I completed a 5-kilometer run in 30 minutes." This progress data is then saved on the device.

[0829] input:

[0830] Completed activity information

[0831] output:

[0832] Progress data (saved on the device)

[0833] Step 11: Format and send data

[0834] The device will format the progress data in JSON format and send it to the server. For example, the following data will be generated:

[0835] json

[0836] {

[0837] "goal_type": "running",

[0838] "progress": "5km running completed in 30 minutes",

[0839] "date": "2023-10-01"

[0840] }

[0841] This data is sent to the server via an HTTP request.

[0842] input:

[0843] Progress Data

[0844] output:

[0845] Progress data in JSON format (sent to the server)

[0846] Step 12: Saving and analyzing progress data

[0847] The server stores the received progress data in a database and periodically analyzes it. This analysis allows the user's progress to be understood and used as data for the next step.

[0848] input:

[0849] Progress data in JSON format

[0850] output:

[0851] Saved Progress Data

[0852] Analysis results (in memory)

[0853] Step 13: Generate Encalagement Messages

[0854] Based on the analysis of the progress data, the AI ​​generates motivational messages to the user, such as "Great! You've achieved your goal. Next time, try going a little further!"

[0855] input:

[0856] Analysis results

[0857] output:

[0858] Encapsulation message (sent back to server)

[0859] Step 14: Sending and Viewing Messages

[0860] The server sends the generated encouraging message to the terminal, which displays it to the user, providing motivation for the next activity.

[0861] input:

[0862] Encouragement Message

[0863] output:

[0864] Message to be displayed to the user (sent to the terminal)

[0865] Step 15: Regenerate and update the plan

[0866] If the user requests a change to the plan as needed, for example, by entering "I want to change the distance to 7 kilometers" into the device, the server receives this request and regenerates the plan using the generation AI. The new plan is then sent to the device and notified to the user.

[0867] input:

[0868] Plan change request

[0869] output:

[0870] Generated new plan (sent to device)

[0871] The above is a specific program processing flow divided into processing steps.

[0872] (Application example 1)

[0873] 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."

[0874] It is difficult for users with health and fitness goals to consistently maintain a healthy diet. In particular, creating a healthy meal plan, selecting appropriate meals based on that plan, and arranging timely delivery can be a heavy burden for users. Furthermore, in today's busy society, it is difficult to effectively monitor a user's progress toward achieving their health goals and provide ongoing appropriate advice as needed. There is a need for a system that solves these challenges and helps users make healthy eating a habit.

[0875] 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.

[0876] In this invention, the server includes: means for generating an individual plan using a generation AI based on goals set by the user; means for acquiring the user's schedule information and finding the optimal time; means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results; means for generating a meal plan according to the goals and establishing a connection with a food delivery service; and means for setting meal reminders and arranging delivery based on the meal plan. This enables the user to receive continuous support for achieving their health goals and to eat healthy meals at appropriate times.

[0877] "Means for a generative AI to generate an individual plan based on the goals set by the user" refers to a technology in which a generative AI creates a specific action plan for achieving a goal based on the health or fitness goals entered by the user.

[0878] The "means for obtaining the user's schedule information and finding the optimal time" is a technology that analyzes the user's calendar and schedule information to find the optimal time period for carrying out activities to achieve a goal.

[0879] "Means for generating dashboards and encouraging messages that monitor user progress data and visualize progress based on the analysis results" refers to technology that collects and analyzes data on activities performed by users, visually displays the results, and generates messages that motivate users.

[0880] "Means for generating meal plans according to goals and establishing collaboration with food delivery services" refers to a technology in which a generation AI creates a meal plan based on the user's health goals, and then collaborates with a food delivery service to provide appropriate meals based on that plan.

[0881] "Means for setting meal reminders and arranging delivery based on a meal plan" refers to technology that sets reminders based on a generated meal plan and arranges for meals to be delivered at a specified time through a food delivery service.

[0882] The configuration of a system for implementing this invention will be described below. The system operates in cooperation with a user terminal, a server, a generative AI model, a database, and an API of a food delivery service.

[0883] The server provides a means for the generative AI to generate an individualized plan based on the health goals set by the user. It receives the health goals sent from the user's device, converts them into JSON format, and inputs them into the generative AI model. The generative AI model generates an appropriate meal plan based on the received data and sends it back to the server. The meal plan includes specific menus and meal timings.

[0884] The server also obtains the user's schedule information and provides a means to find the optimal time. The server works in conjunction with a calendar application to analyze the user's schedule information and identify the optimal time period for executing the meal plan. It then sets a reminder based on the optimal time and notifies the user's device.

[0885] Furthermore, the server monitors the user's progress data and provides a means to generate a dashboard that visualizes progress and encouragement messages based on the analysis results. When the user reports the meal details they have completed from their device, the progress data is sent to the server. The server integrates this data and analyzes it using generative AI. Based on the results, it generates scored progress and praise messages, etc., to provide feedback to the user.

[0886] It also generates a meal plan tailored to your goals and provides a way to connect with food delivery services, which then send the meal plan as an order request through the food delivery service's API to order the appropriate menu, which then automatically arranges for the meal to be delivered to the user.

[0887] Finally, the system also provides a means to set meal reminders and arrange for delivery based on the meal plan. Based on the plan created by the generative AI, the server sets reminders and notifies the user at the specified time. It also arranges for the specified meal to be delivered at the appropriate time through the delivery service's API.

[0888] For example, if a user sets a goal of "dieting," the AI ​​will generate a healthy meal plan that takes into account calorie restriction and nutritional balance. The user will be notified of plans such as a "low-calorie smoothie" at 8 a.m. on Monday and a "salad bowl" at noon on Wednesday.

[0889] Below is an example of a prompt sentence.

[0890] Example prompt sentence:

[0891] Generate optimal meal plans for your following health goals:

[0892] Health Goal: Diet

[0893] User ID: 12345

[0894] Current weight: 70kg

[0895] Target weight: 65kg

[0896] Food preference: Vegetarian

[0897] Allergens: nuts

[0898] Weekly calorie goal: 1500kcal

[0899] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0900] Step 1:

[0901] Enter the goal set by the user.

[0902] Users launch the application and enter their health and fitness goals, including goal details, duration, current weight, target weight, food preferences, allergy information, etc. The entered data is formatted in JSON format and sent to the server.

[0903] Step 2:

[0904] The server requests the generative AI model to generate a plan.

[0905] The server analyzes the received user's goals and related data and requests the generative AI model to generate a plan. The generative AI model creates a detailed meal plan based on the goal data and sends it back to the server. This process involves data processing and calculations that allow the AI ​​to convert the input data (user's goals and preferences) into an appropriate plan.

[0906] Step 3:

[0907] The server saves the generated plan in the database and notifies the user.

[0908] The meal plan returned by the generative AI model is stored in a database on the server, and the saved plan is sent to the user's device, where the specific meal contents and timing are displayed to the user.

[0909] Step 4:

[0910] The server obtains the user's schedule information and identifies the best time.

[0911] The server interacts with the calendar application to retrieve the user's schedule information, which is then parsed to identify the optimal time based on the generated meal plan. This process uses the schedule data as input data and determines the optimal time based on that.

[0912] Step 5:

[0913] The server sets a reminder and notifies the user terminal.

[0914] The server sets a reminder based on the optimal time determined, and the reminder information is sent to the user's terminal and notified to the user. The notified reminder serves as a support for the user to remember to carry out the meal plan.

[0915] Step 6:

[0916] The server sends a request to a food delivery service.

[0917] Based on the generated meal plan, the server sends a request to the food delivery service's API. The request includes the specific meal contents and delivery time, and the meal is then delivered to the user. In this step, the generated plan is used as input data and an API request is made to arrange delivery.

[0918] Step 7:

[0919] The user records their meal contents and sends progress data to the server.

[0920] After the user eats a meal, they record the details of their meal in the application. The recorded progress data is formatted in JSON format and sent to the server.

[0921] Step 8:

[0922] The server analyzes the progress data and generates advice based on a generative AI model.

[0923] The server analyzes the received progress data and generates advice messages based on the generation AI. In this process, the progress data is used as input and the generation AI generates appropriate feedback and advice.

[0924] Step 9:

[0925] The server transmits the generated advice to the user terminal and provides feedback.

[0926] The generated advice is sent from the server to the user's device and displayed to the user, allowing the user to understand their own progress and maintain motivation for the next step.

[0927] 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.

[0928] This invention is a system that generates an optimal plan based on the user's set goals and supports their execution. In particular, it uses a generative AI to create an individual plan, finds an appropriate time using the user's schedule information, and monitors the user's progress. It also combines an emotion engine that recognizes the user's emotions to provide encouragement messages according to the user's emotional state.

[0929] Program processing overview

[0930] User registration and goal setting

[0931] First, the user sets goals for exercise or studying and enters them into the device. For example, the user sets specific goals such as "running," "three times a week," and "5 kilometers per session."

[0932] The device formats the data entered by the user and sends it to the server. The data is formatted in JSON format and includes information such as the type and number of goals. The server analyzes the received data and asks the generation AI to generate a plan. The generation AI generates the optimal plan, taking into account the user's current abilities and time constraints. The plan generated by the generation AI is sent back to the server, and then sent from the server to the device. The device displays the generated plan to the user.

[0933] Plan execution and reminders

[0934] The server retrieves the user's calendar and schedule information and finds the optimal time to incorporate the plan into the schedule. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings based on those times. The server then connects the reminder data to the calendar app and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[0935] Progress monitoring and advice

[0936] After the user finishes an activity, they input progress data into the device. For example, they might input "distance run: 5 km, time taken: 30 minutes." The device formats the input data and sends it to the server. The server stores the progress data in a database and analyzes it. Based on the analysis results, the generation AI generates a progress dashboard and encouraging messages. The server sends reports and messages from the generation AI to the device, which displays them to the user.

[0937] Collaboration with emotion engine

[0938] This system can also be combined with an emotion engine that recognizes the user's emotions from facial expressions, voice, text input, etc., and sends the data to the server.

[0939] Based on the emotional data recognized by the emotion engine, the generation AI adjusts the encouragement message. For example, if the user is feeling stressed, the generation AI generates a message such as "Try some light exercise to refresh yourself." The server sends the generated encouragement message to the device, which then displays it to the user. Emotional data is also used to generate or improve plans. For example, if the user is feeling tired, the generation AI can adjust the plan to reduce the load of the next activity.

[0940] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation. The system responds flexibly to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[0941] The processing flow will be explained below.

[0942] User registration and goal setting

[0943] Step 1:

[0944] The user inputs the target information into the terminal.

[0945] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[0946] Step 2:

[0947] The terminal receives user input, formats the data, and sends it to the server.

[0948] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[0949] Send data to the server.

[0950] Step 3:

[0951] The server analyzes the received user data and passes it to the generation AI.

[0952] Generate a request to the generation AI: generate_plan("running", 3, 5)

[0953] Step 4:

[0954] The generative AI generates a specific plan based on the user's goals.

[0955] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[0956] Step 5:

[0957] The server receives the plan data returned from the generation AI and sends it to the terminal.

[0958] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[0959] Step 6:

[0960] The terminal displays the generated plan to the user.

[0961] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[0962] Plan execution and reminders

[0963] Step 1:

[0964] The server retrieves the user's schedule data and finds the best time.

[0965] Retrieves calendar information from the database.

[0966] Step 2:

[0967] The server generates reminder settings based on the measurement results.

[0968] Reminder settings: {"date": "Monday", "time": "5:50"}

[0969] Step 3:

[0970] The server connects the reminder data to the calendar app.

[0971] Call the Calendar API to add a reminder.

[0972] Step 4:

[0973] The device will display a reminder notification to the user at the specified time.

[0974] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[0975] Progress monitoring and advice

[0976] Step 1:

[0977] After exercising, the user inputs progress data into the terminal.

[0978] Example: "Distance run: 5km, time taken: 30 minutes"

[0979] Step 2:

[0980] The terminal formats the user input data and sends it to the server.

[0981] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[0982] Step 3:

[0983] The server stores the user's progress data in a database and analyzes it.

[0984] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[0985] Step 4:

[0986] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[0987] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[0988] Step 5:

[0989] The server receives reports and messages from the generated AI and sends them to the terminal.

[0990] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[0991] Step 6:

[0992] The terminal displays progress reports and enquiries to the user.

[0993] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[0994] Collaboration with emotion engine

[0995] Step 1:

[0996] The user provides input for emotion recognition.

[0997] For example, after exercising, the user may input feedback such as "I'm a little tired from running today."

[0998] Step 2:

[0999] The device formats the emotion data and sends it to the server.

[1000] Emotion data format: { "emotional_state": "Tired"}

[1001] Step 3:

[1002] The server receives the emotion data and sends it to the emotion engine.

[1003] Pass data to the emotion engine: analyze_emotion("tired")

[1004] Step 4:

[1005] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[1006] Analysis result: { "emotion": "fatigue", "level": "high"}

[1007] Step 5:

[1008] Based on the results of the emotion engine, the server instructs the generation AI to adjust the encouraging message.

[1009] Instructions to the generating AI: generate_message("Fatigue", "High")

[1010] Step 6:

[1011] The generative AI generates encouraging messages based on emotional state.

[1012] Generated message: "Get some light exercise to refresh yourself."

[1013] Step 7:

[1014] The server sends the generated enqueuing message to the terminal.

[1015] Send data: { "message": "Let's do some light exercise to refresh ourselves"}

[1016] Step 8:

[1017] The terminal displays the enlargement message to the user.

[1018] The screen will display a message saying, "Let's incorporate some light exercise to refresh ourselves." Emotional data will also be used to generate and improve plans. For example, if the user is feeling tired, the generation AI will adjust the plan to reduce the load of the next activity.

[1019] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation.The system flexibly responds to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[1020] Example 2

[1021] 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."

[1022] While traditional goal management systems can monitor users' progress and provide personalized plans, they struggle to adapt flexibly to the user's emotional state. They also lack the ability to automatically improve and update the generated plans, limiting their ability to continuously support user motivation. Furthermore, reminder functions are not fully integrated, making it difficult to smoothly manage users' schedules.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the generative AI model to generate an individual plan based on the goal set by the user, a means for acquiring the user's schedule information and finding the optimal time, and a means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results. This not only provides an individual plan for achieving the user's goal, but also makes it possible to provide encouraging messages that adapt to the user's emotional state. In addition, the user's schedule is smoothly managed through the reminder function, and the generative AI model automatically improves and updates the plan, enabling continuous motivation support.

[1024] A "generative AI model" refers to an artificial intelligence that generates a personalized plan based on the goals set by the user.

[1025] A "goal" is an indicator that indicates a specific activity or result that a user sets to achieve.

[1026] "Schedule information" refers to data used for a user's activity schedule and time management.

[1027] "Progress data" is information that indicates a user's actual progress and achievements toward a goal.

[1028] A "dashboard" is an interface that visually displays a user's progress, making it easier to understand.

[1029] "Encouragement messages" are messages provided to increase motivation depending on the user's progress and emotional state.

[1030] "Emotion data" is information about the user's emotional state estimated based on facial expressions, voice, text input, and the like.

[1031] "Automatic plan improvement and updating" refers to the process in which the generative AI model optimizes existing plans and generates new plans based on the user's progress data and emotional data.

[1032] A "calendar application" refers to software that manages a user's schedule information and adjusts reminders and appointments.

[1033] The present invention is a system for generating an optimal plan based on a goal set by a user and supporting the execution of that plan. This system is realized mainly by three entities: a server, a terminal, and a user. Specific embodiments of the system are described in detail below.

[1034] User registration and goal setting

[1035] First, the user sets a goal for exercise or studying and enters that information into the device. For example, specific goals such as "running," "three times a week," and "5 kilometers each time" are entered into the device's application. The device then formats this information into JSON format and sends it to the server. The server analyzes the received data and requests the generative AI model to generate a plan. At this time, it generates a prompt like the following:

[1036] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[1037] The generative AI model generates an optimal plan based on this prompt and sends it back to the server, which then sends the plan to the device, which displays it to the user.

[1038] Plan execution and reminders

[1039] The server retrieves the user's calendar and schedule information and finds the optimal time based on the generated plan. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings for those times. The server then connects the reminder data to the calendar application and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[1040] Progress monitoring and advice

[1041] After the user finishes an activity, they enter progress data into the device. For example, they enter data such as "distance run: 5 km, time required: 30 minutes." The device formats this data into JSON format and sends it to the server. The server stores the progress data in a database and performs analysis. Based on the analysis results, the generative AI model generates a progress dashboard and enumeration messages. The server sends the generated reports and messages to the device, which then displays them to the user.

[1042] Collaboration with emotion engine

[1043] This system can also be combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. and sends the data to the server. For example, if the user is feeling stressed, the emotion engine will send the data tagged with "stress." Based on this emotion data, the generative AI model adjusts the encouragement message. For example, if the user is feeling stressed, a message such as "Try some light exercise to refresh yourself" is generated. The server then sends the generated encouragement message to the device, which then displays it to the user. Emotion data is also used to generate and improve plans.

[1044] In this way, the system provides flexible and effective planning and motivational maintenance functions to help users achieve their goals. Plan generation and automatic improvement using a generative AI model, reminder setting, progress monitoring, and even integration with an emotion engine can support users' sustained motivation.

[1045] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1046] Step 1:

[1047] User sets goals

[1048] Users input their exercise or study goals into the device application, such as "running," "three times a week," and "5 kilometers per session."

[1049] (Input): Goal data set by the user (e.g., "running," "three times a week," "5 kilometers per session").

[1050] (Output): The target data entered into the terminal.

[1051] (Specific action): The user enters their goal into the app's input form and clicks the "Save" button.

[1052] Step 2:

[1053] The device formats the data and sends it to the server

[1054] The terminal formats the target data entered by the user into JSON format and sends it to the server.

[1055] (Input): The target data entered into the terminal.

[1056] (Output): The goal data formatted in JSON.

[1057] (Specific operation): The terminal program formats the target data in JSON format as shown below and sends it to the server via an HTTP POST request.

[1058] json

[1059] {

[1060] "goal_type": "running",

[1061] "frequency_per_week": 3,

[1062] "distance_per_session": 5

[1063] }

[1064] Step 3:

[1065] The server requests the generative AI model to generate a plan.

[1066] The server analyzes the received data and generates a prompt statement that requests the generative AI model to generate a plan.

[1067] (Input): The target data formatted in JSON.

[1068] (Output): The prompt sent to the generative AI model.

[1069] (Specific action): The server generates a prompt like this:

[1070] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[1071] This prompt is then sent to a generative AI model and a plan is received.

[1072] Step 4:

[1073] The server sends the generated plan to the device.

[1074] The generated plan is returned to the server, and then transmitted from the server to the terminal.

[1075] (Input): The plan received from the generative AI model.

[1076] (Output): Plan data sent to the device.

[1077] (Specific operation): The server returns the plan as an HTTP response, and the device analyzes and displays the received data.

[1078] Step 5:

[1079] The device displays the plan to the user.

[1080] The terminal displays the generated plan to the user.

[1081] (Input): Plan data received from the server.

[1082] (Output): The plan that is displayed to the user.

[1083] (Specific behavior): The device app displays plans in a list format and adds "Next" and "Details" buttons.

[1084] Step 6:

[1085] The server retrieves the user's schedule information.

[1086] The server retrieves the user's calendar and schedule information.

[1087] (Input): Schedule information from a calendar application.

[1088] (Output): Schedule information stored on the server.

[1089] (Specific operation): The server retrieves schedule data from third-party services such as Google Calendar via API.

[1090] Step 7:

[1091] The server generates the reminder settings

[1092] The server generates reminder settings based on the acquired schedule information.

[1093] (Input): User's schedule information and generated plan.

[1094] (Output): Reminder setting data.

[1095] (Specific behavior): The server generates reminder settings in JSON format based on the schedule information and plan:

[1096] json

[1097] {

[1098] "reminder_time": "2023-10-02T06:00:00",

[1099] "reminder_message": "It's time for a run"

[1100] }

[1101] Step 8:

[1102] The device displays a reminder notification to the user.

[1103] Your device will display notifications at the specified time based on your reminder settings.

[1104] (Input): Reminder setting data.

[1105] (Output): Reminder notification to the user.

[1106] (Specific behavior): The device will pop up a reminder notification and provide a "Confirm" or "Snooze" button.

[1107] Step 9:

[1108] The user enters progress data

[1109] After completing an activity, the user inputs progress data into the terminal.

[1110] (Input): Progress data entered by the user (e.g., "Distance run: 5 km, Time taken: 30 mins").

[1111] (Output): Progress data entered into the terminal.

[1112] (Specific action): The user enters data into a dedicated input form and clicks the "Submit" button.

[1113] Step 10:

[1114] The device formats the progress data and sends it to the server.

[1115] The device formats the entered progress data into JSON format and sends it to the server.

[1116] (Input): Progress data entered by the user.

[1117] (Output): Progress data in JSON format.

[1118] (Specific operation): The device formats the progress data as follows and sends it to the server:

[1119] json

[1120] {

[1121] "distance_ran": 5,

[1122] "time_taken": 30

[1123] }

[1124] Step 11:

[1125] The server saves the progress data in a database and performs analysis.

[1126] The server stores the progress data in a database and performs analysis.

[1127] (Input): Progress data in JSON format.

[1128] (Output): Analysis result data.

[1129] (Specific behavior): The server executes a database query, stores progress data, and then launches an analysis script to process the data.

[1130] Step 12:

[1131] Generative AI models generate dashboards and enlargement messages

[1132] The generative AI model generates progress dashboards and enlargement messages based on the analysis results.

[1133] (Input): Analysis result data.

[1134] (Output): Dashboard data and enumeration messages.

[1135] (Specific behavior): The generative AI model generates feedback on progress and creates messages to motivate the user.

[1136] Step 13:

[1137] The server sends reports and messages to the terminal, which displays them

[1138] The server sends reports and enlargement messages from the generative AI model to the terminal, which displays them to the user.

[1139] (Input): Dashboard data and enumeration messages.

[1140] (Output): Reports and messages displayed to the user.

[1141] (Specific behavior): The device displays a dashboard or message on the screen and provides "Next step" or "View details" buttons.

[1142] Step 14:

[1143] Emotion engine recognizes user emotions

[1144] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, and sends that data to the server.

[1145] (Input): User facial expression, voice, and text input data.

[1146] (Output): Emotion data.

[1147] (Specific operation): The emotion engine analyzes data in real time and generates emotion data (e.g., stress).

[1148] Step 15:

[1149] Generative AI models tailor encouraging messages based on emotional data

[1150] The generative AI model tailors appropriate encouraging messages based on emotional data.

[1151] (Input): Emotion data.

[1152] (Output): The adjusted encausation message.

[1153] (Specific operation): The generative AI model takes emotion data as input and generates messages such as the following:

[1154] If the user is feeling stressed, the message is "Try some light exercise to refresh yourself."

[1155] Step 16:

[1156] The server sends a message to the terminal, which displays it to the user.

[1157] The server sends the generated encumbrance message to the terminal, which displays it to the user.

[1158] (Input): The adjusted encausation message.

[1159] (Output): The enumeration message that is displayed to the user.

[1160] (Specific behavior): The device displays the message as a notification and provides a "View details" or "OK" button.

[1161] In this way, the system generates a plan based on the user's goals and helps maintain the user's motivation through schedule management, progress monitoring, and providing emotionally appropriate encouragement messages.

[1162] (Application example 2)

[1163] 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."

[1164] In conventional security services, managing security guards' work schedules and patrol routes is complicated, and there are a lack of appropriate measures to maintain the guards' motivation and mental health. This makes it difficult for them to work efficiently and safely, and leads to the accumulation of stress and fatigue.

[1165] 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 the generation AI to generate an individual plan based on goals set by the user; means for acquiring the user's date and time information and finding the optimal time; means for monitoring the user's progress data and generating a display device and encouraging messages that visualize the progress based on the analysis results; means including an emotion analysis device that recognizes the user's emotional state; and means for the generation AI to adjust the encouraging messages based on data from the emotion analysis device. This improves the efficiency of schedule management for security guards and enables appropriate work plans and emotional care tailored to each individual guard.

[1166] "Generative AI" is a system that uses artificial intelligence technology to generate optimal plans according to the goals set by the user.

[1167] A "personalized plan" is a personalized execution plan that takes into account each user's goals, schedule, and emotional state.

[1168] "Date and time information" is data from a user's schedule or calendar that is used to optimize the timing of activities.

[1169] "Progress data" refers to data that indicates the actual progress of a user as they execute a plan, including, for example, the tasks completed and the time required.

[1170] A "display device" is a device that visually displays progress and encouraging messages to a user, such as smart glasses or a smartphone.

[1171] An "encouraging message" is a message of encouragement or advice provided to increase the user's motivation.

[1172] An "emotion analysis device" is a device that recognizes a user's emotional state and responds or adjusts appropriately. It includes facial expression analysis cameras and voice analysis devices.

[1173] A "calendar application" is software that manages a user's schedule and has the function of setting reminders and sending notifications.

[1174] "Automatic improvement" is the process by which the generative AI dynamically modifies the plan based on the user's progress data and emotional state, updating it to a more effective plan.

[1175] The system for implementing this invention consists of the following steps: First, a user uses smart glasses to register and set goals. The user enters specific exercise or study goals, and the information is formatted and sent to the server. The data format used to format the information is JSON.

[1176] The server analyzes the received data and generates an individual plan using a generative AI model. The generative AI model takes into account the user's current abilities and time constraints to construct an optimal plan. The generated plan is then sent from the server to the smart glasses and displayed to the user.

[1177] The server also retrieves the user's date and time information (schedule and calendar data) to find the optimal time to incorporate the plan into the schedule. For example, optimizing security guard patrol times and break times. This data is integrated with the calendar application and set as a reminder. The smart glasses will display the reminder at the specified time to help the user execute the plan.

[1178] After the user executes the plan, progress data is input into the smart glasses. For example, "Time to complete the tour route: 30 minutes." This data is formatted and sent back to the server. The server stores the progress data in a database, and the generative AI model analyzes it. Based on the analysis results, a progress display and encouraging messages are generated and sent to the smart glasses.

[1179] Furthermore, the emotion analyzer monitors the user's emotional state. Using a facial expression camera and a voice analyzer, the emotion analyzer sends data to the server if the user feels stressed or tired. Based on the emotional data, the generative AI model adjusts the encouraging messages and generates custom messages such as "Take a short break" or "Do some light exercise to change your mood." These are then displayed on the smart glasses.

[1180] As a concrete example, consider a security guard who sets the goal of "patrol routes three times a day, breaks every two hours." The generative AI model uses this information to create a personalized work plan. If the emotion analyzer detects "high stress," the generative AI model automatically suggests increasing break time, displaying the message, "Take a five-minute break in a relaxing place."

[1181] An example prompt is:

[1182] User ID: Security Guard 123

[1183] Current Ability: Intermediate

[1184] Time constraints:

[1185] Start time: 8:00

[1186] End time: 18:00

[1187] the goal:

[1188] Patrol frequency: 3 times a day

[1189] Break interval: Every 2 hours

[1190] In this way, it is possible to realize a system that improves both the work efficiency and mental health of security guards.

[1191] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1192] Step 1:

[1193] The user sets goals using the smart glasses. The user uses the input interface of the smart glasses to input goals, such as "Patrol route: 3 times a day, Break: every 2 hours." This data is formatted in JSON format and sent to the server. The input is specific goal data, and the output is formatted JSON data.

[1194] Step 2:

[1195] The server parses the received goal data. The server parses the JSON format goal data and generates an individual plan based on the generative AI model, taking into account the user's current abilities and time constraints. The input is the JSON data to be parsed, and the output is the generated plan data.

[1196] Step 3:

[1197] The server sends the generated plan to the smart glasses, which the terminal displays to the user. The terminal visually displays the received plan data and guides the user to the next step. The input is the plan data sent from the server, and the output is the plan displayed on the smart glasses.

[1198] Step 4:

[1199] The server obtains the user's date and time information and incorporates the plan into the schedule. The server works with the calendar application to find the optimal patrol and break times and set reminders. The input is the user's schedule data, and the output is the set reminder data.

[1200] Step 5:

[1201] When the reminder time arrives, the smart glasses notify the user. The device displays the reminder at the set time and prompts the user to carry out the plan. The input is the reminder data, and the output is the notification displayed on the smart glasses.

[1202] Step 6:

[1203] The user executes the plan and inputs the progress data into the smart glasses. The terminal formats the progress data received from the user and sends it to the server. The input is the progress data, and the output is the formatted progress data.

[1204] Step 7:

[1205] The server analyzes the received progress data. The server stores the progress data in a database, and the generative AI model analyzes the progress and generates a display and encouragement message. The input is the received progress data, and the output is the encouragement message and progress data.

[1206] Step 8:

[1207] The server receives data from the emotion analyzer, and the generative AI model adjusts the encouraging message. The emotion analyzer determines the user's emotions from facial expressions and voice and sends that data to the server. The server analyzes the data and adjusts the encouraging message using the generative AI model. The input is emotional data, and the output is the adjusted encouraging message.

[1208] Step 9:

[1209] The smart glasses display the tailored encouraging message to the user. The terminal visually displays the message sent from the server to motivate the user. The input is the encouraging message sent from the server, and the output is the message displayed on the smart glasses.

[1210] 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.

[1211] 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.

[1212] 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.

[1213] [Third embodiment]

[1214] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1215] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1216] 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).

[1217] 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.

[1218] 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.

[1219] 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).

[1220] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1221] 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.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] 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."

[1226] This invention relates to a system that helps users form habits by using generative AI to create individual plans tailored to the user's goals and supporting their execution. Furthermore, this invention also includes functions for managing the user's schedule and monitoring progress, with the aim of providing sustained motivation.

[1227] Program processing overview

[1228] A specific embodiment of this system performs the following operations.

[1229] User registration and goal setting

[1230] 1. The user sets a goal for exercise or studying and enters it into the device. For example, the user sets a goal of "running five times a month."

[1231] 2. The device formats the user input data in JSON format and sends it to the server, including information such as the goal type and number of times.

[1232] 3. The server analyzes the received data and requests the AI ​​to generate an appropriate plan. The AI ​​then generates the optimal running plan, taking into account the user's current abilities and time constraints.

[1233] 4. The generation AI generates a plan and sends it back to the server, including specific dates and time slots.

[1234] 5. The server sends the generated plan to the terminal and displays it to the user on the terminal, who then plans his or her activities accordingly.

[1235] Plan execution and reminders

[1236] 1. The server retrieves the user's calendar and schedule information and finds the best time to schedule the plan. For example, a "Running" plan will start at 6:00 AM on Mondays, Wednesdays, and Fridays.

[1237] 2. The server generates the optimal reminder settings and connects to the calendar application to set reminders. The reminders notify the user, helping them execute their plan.

[1238] 3. The device will notify the user of the reminder at the specified time, so that the user will not forget to perform the activity.

[1239] Progress monitoring and advice

[1240] 1. After performing an activity, the user enters progress data into the device, for example, "Completed a 5km run in 30 minutes."

[1241] 2. The device formats the progress data and sends it to the server.

[1242] 3. The server monitors the received progress data and stores it in a database. The progress is periodically analyzed and a report is generated by the generation AI.

[1243] 4. The Generative AI generates an encouragement message for the user based on their progress, such as "Great! You achieved your goal! Try going a little further next time!"

[1244] 5. The server sends the report and message to the terminal, where it is displayed to the user, motivating the user to take further action.

[1245] 6. If necessary, the user requests a change to the plan, for example, "I want to change the distance to 7 kilometers."

[1246] 7. The server receives the change request and regenerates the plan using the generation AI.

[1247] 8. Once the new plan is generated, the server sends it to the device and updates the calendar and reminder settings.

[1248] In this way, a system that supports users in forming habits is realized. This system flexibly responds to users' goals and provides continuous motivation, allowing them to continue their exercise or study efforts.

[1249] The processing flow will be explained below.

[1250] User registration and goal setting

[1251] Step 1:

[1252] The user inputs the target information into the terminal.

[1253] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[1254] Step 2:

[1255] The terminal receives user input, formats the data, and sends it to the server.

[1256] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[1257] Send data to the server.

[1258] Step 3:

[1259] The server analyzes the received user data and passes it to the generation AI.

[1260] Generate a request to the generation AI: generate_plan("running", 3, 5)

[1261] Step 4:

[1262] The generative AI generates a specific plan based on the user's goals.

[1263] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[1264] Step 5:

[1265] The server receives the plan data returned from the generation AI and sends it to the terminal.

[1266] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[1267] Step 6:

[1268] The terminal displays the generated plan to the user.

[1269] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[1270] Plan execution and reminders

[1271] Step 1:

[1272] The server retrieves the user's schedule data and finds the best time.

[1273] Retrieves calendar information from the database.

[1274] Step 2:

[1275] The server generates reminder settings based on the measurement results.

[1276] Reminder settings: {"date": "Monday", "time": "5:50"}

[1277] Step 3:

[1278] The server connects the reminder data to the calendar app.

[1279] Call the Calendar API to add a reminder.

[1280] Step 4:

[1281] The device will display a reminder notification to the user at the specified time.

[1282] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[1283] Progress monitoring and advice

[1284] Step 1:

[1285] After exercising, the user inputs progress data into the terminal.

[1286] Example: "Distance run: 5km, time taken: 30 minutes"

[1287] Step 2:

[1288] The terminal formats the user input data and sends it to the server.

[1289] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[1290] Step 3:

[1291] The server stores the user's progress data in a database and analyzes it.

[1292] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[1293] Step 4:

[1294] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[1295] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[1296] Step 5:

[1297] The server receives reports and messages from the generated AI and sends them to the terminal.

[1298] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[1299] Step 6:

[1300] The terminal displays progress reports and enquiries to the user.

[1301] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[1302] Example 1

[1303] 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."

[1304] Many people today struggle with goal setting and achievement. Maintaining consistent daily habits, such as exercise and studying, requires individual planning, schedule management, and progress monitoring. However, performing these tasks individually can be burdensome and challenging, making it difficult to maintain motivation. It's also difficult to track progress in real time and receive appropriate feedback based on that progress. Furthermore, the lack of reminder settings and automatic plan updates is also problematic.

[1305] 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.

[1306] In this invention, the server includes: a means for generating an individual plan using a generation AI based on goals set by the user; a means for acquiring the user's schedule information and finding the optimal time; a visualization means for monitoring the user's progress data and visualizing the progress based on the analysis results, and a means for generating engraving messages; a means for formatting goal data entered by the user into a terminal in JSON format and sending it to the server; and a means for receiving the generated plan and displaying it on the terminal. This makes it possible to provide consistent support for the user's goal achievement, from planning to execution, progress management, and motivation maintenance.

[1307] "User" refers to an individual or end user who uses the System to set and achieve goals.

[1308] A "goal" refers to a specific outcome or purpose that a user aims to achieve in relation to sports, studies, or other activities.

[1309] "Generative AI" refers to artificial intelligence algorithms that automatically create plans based on a user's goals and circumstances.

[1310] "Plan" refers to a specific action plan created by generative AI for a user to achieve their goal.

[1311] "Schedule information" refers to data related to schedule and time management, such as a user's calendar and appointment times.

[1312] The "optimal time" refers to the most suitable time to perform an action for a set goal based on the user's schedule information.

[1313] "Progress data" refers to data that shows the record and results of activities that a user has performed toward a goal.

[1314] "Visualization means" refers to a method for displaying progress data in the form of diagrams, graphs, etc., to help the user understand the progress status.

[1315] "Encouragement messages" refer to messages created by generative AI to encourage and motivate users.

[1316] "Device" refers to the device (e.g., smartphone or tablet) that a user uses to access the system, enter goals, and record progress.

[1317] "JSON format" refers to a text-based data interchange format for representing data in a concise and structured form.

[1318] A "server" is the central computer of the system, and refers to a device that analyzes data, generates plans, monitors progress, etc.

[1319] This invention is a system for supporting users in achieving their goals. Specifically, it utilizes a generative AI model to generate personalized plans, manage schedules, monitor progress, and provide encouragement messages.

[1320] 1. The user sets a goal

[1321] Users input their goals, such as exercise or study goals, into the device. A dedicated input application is installed on the device, and the user inputs information such as the type of goal, duration, and frequency. For example, "My goal is to run five times a month."

[1322] 2. Formatting and sending data

[1323] The device formats the goal data collected from the user into JSON format and sends it to the server. As a specific example, the following data is generated:

[1324] json

[1325] {

[1326] "goal_type": "running",

[1327] "frequency": 5,

[1328] "duration_in_months": 1

[1329] }

[1330] This data is sent to the server via an HTTP request.

[1331] 3. Plan Generation

[1332] The server analyzes the received data and requests the generation AI to generate a plan. For example, OpenAI GPT-4 is used as the generation AI. Examples of prompt sentences include the following:

[1333] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[1334] The generation AI generates a personalized plan based on the request and returns it to the server, including specific dates and time slots. For example,

[1335] json

[1336] {

[1337] "plan": [

[1338] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[1339] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[1340] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[1341] ]

[1342] }

[1343] Something like this.

[1344] 4. View your plan

[1345] The server transmits the generated plan to the terminal, which then displays the plan to the user, allowing the user to plan their daily activities.

[1346] 5. Schedule management and reminder settings

[1347] The server retrieves the user's calendar information and finds the time to schedule the generated plan. It also works with a calendar application (e.g., Google Calendar) to set reminders, helping the user remember to execute the plan.

[1348] 6. Progress Monitoring

[1349] After completing each activity, the user enters progress data into the device. For example, "I completed a 5-kilometer run in 30 minutes." The device formats this data in JSON format and sends it to the server. The server stores the received progress data in a database and periodically analyzes it.

[1350] 7. Generating Encalagement Messages

[1351] The generation AI generates an encouragement message to motivate the user based on the progress data. For example, a message such as "Great! You've achieved your goal. Next time, try to go a little further!" is generated and sent from the server to the device. The device then displays this message to the user.

[1352] 8. Regenerating and Updating Plans

[1353] When a user wants to set a new goal or modify an existing one, they input a request into their device. The server receives the request and asks the generation AI to regenerate the plan. The new plan is sent to the device, and it updates the calendar and reminders as needed.

[1354] In this way, a system that continuously supports users in achieving their goals is realized. This system consistently supports users from goal setting to progress management, and can provide continuous motivation.

[1355] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1356] Step 1: Enter user goals

[1357] The user inputs goals such as exercise or study into the device. Using a dedicated application, the user inputs information such as the type of goal (e.g., "running"), frequency (e.g., "five times per month"), and duration (e.g., "for one month"). This input information is treated as data required for the next processing step.

[1358] input:

[1359] User goal information (goal type, frequency, duration)

[1360] output:

[1361] Formatting data (stored on the device)

[1362] Step 2: Convert data format and send

[1363] The device converts the entered user goal information into JSON format, for example, "goal_type: running, frequency: 5, duration_in_months: 1". The converted data is sent to the server via an HTTP request.

[1364] input:

[1365] User goal information

[1366] output:

[1367] JSON format data (sent to server)

[1368] Step 3: Data reception and analysis

[1369] The server parses the received JSON data using Python or Node.js, extracting the type of goal, frequency, duration, etc. from the JSON data and storing them in memory.

[1370] input:

[1371] Goal data in JSON format

[1372] output:

[1373] Parsed target data (in memory)

[1374] Step 4: Plan Generation Request

[1375] The server requests the AI ​​to generate a plan based on the analyzed data. The AI ​​is prompted with the following prompt:

[1376] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[1377] This prompt is sent to the generation AI, which then initiates plan generation.

[1378] input:

[1379] Analyzed target data

[1380] output:

[1381] Prompt text (sent to the generation AI)

[1382] Step 5: Plan Generation and Reception

[1383] Based on the request, the AI ​​generates a personalized plan that fits the user's schedule. The plan, including specific dates and time slots, is generated and returned to the server in JSON format. For example,

[1384] json

[1385] {

[1386] "plan": [

[1387] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[1388] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[1389] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[1390] ]

[1391] }

[1392] input:

[1393] Prompt statement

[1394] output:

[1395] Generated plan (JSON format, returned to server)

[1396] Step 6: Submit and view your plan

[1397] The server receives the plan returned by the generation AI and sends it to the device, which displays the plan to the user, allowing the user to plan their daily activities based on the plan.

[1398] input:

[1399] Generated plan (JSON format)

[1400] output:

[1401] Plan to display to user (sent to device)

[1402] Step 7: Schedule

[1403] The server retrieves the user's calendar information and finds the best time to schedule the generated plan, using the Google Calendar API or other calendar application APIs.

[1404] input:

[1405] Generated Plan

[1406] User's calendar information

[1407] output:

[1408] Scheduled plans

[1409] Step 8: Set reminders

[1410] The server uses a calendar application to set reminders so that the user remembers to follow through on their plans. For example, a reminder is set for each day of a run, telling the user, "It's time to go for a run."

[1411] input:

[1412] Scheduled plans

[1413] output:

[1414] Set reminders (reflected in the calendar)

[1415] Step 9: Reminders

[1416] The device will send reminders to the user at the specified time, helping the user remember to perform the activity. For example, a push notification will be sent saying, "Start your 5km run now."

[1417] input:

[1418] Set reminders

[1419] output:

[1420] Reminders to users

[1421] Step 10: Enter progress data

[1422] After completing an activity, the user enters progress data into the device, for example, "I completed a 5-kilometer run in 30 minutes." This progress data is then saved on the device.

[1423] input:

[1424] Completed activity information

[1425] output:

[1426] Progress data (saved on the device)

[1427] Step 11: Format and send data

[1428] The device will format the progress data in JSON format and send it to the server. For example, the following data will be generated:

[1429] json

[1430] {

[1431] "goal_type": "running",

[1432] "progress": "5km running completed in 30 minutes",

[1433] "date": "2023-10-01"

[1434] }

[1435] This data is sent to the server via an HTTP request.

[1436] input:

[1437] Progress Data

[1438] output:

[1439] Progress data in JSON format (sent to the server)

[1440] Step 12: Saving and analyzing progress data

[1441] The server stores the received progress data in a database and periodically analyzes it. This analysis allows the user's progress to be understood and used as data for the next step.

[1442] input:

[1443] Progress data in JSON format

[1444] output:

[1445] Saved Progress Data

[1446] Analysis results (in memory)

[1447] Step 13: Generate Encalagement Messages

[1448] Based on the analysis of the progress data, the AI ​​generates motivational messages to the user, such as "Great! You've achieved your goal. Next time, try going a little further!"

[1449] input:

[1450] Analysis results

[1451] output:

[1452] Encapsulation message (sent back to server)

[1453] Step 14: Sending and Viewing Messages

[1454] The server sends the generated encouraging message to the terminal, which displays it to the user, providing motivation for the next activity.

[1455] input:

[1456] Encouragement Message

[1457] output:

[1458] Message to be displayed to the user (sent to the terminal)

[1459] Step 15: Regenerate and update the plan

[1460] If the user requests a change to the plan as needed, for example, by entering "I want to change the distance to 7 kilometers" into the device, the server receives this request and regenerates the plan using the generation AI. The new plan is then sent to the device and notified to the user.

[1461] input:

[1462] Plan change request

[1463] output:

[1464] Generated new plan (sent to device)

[1465] The above is a specific program processing flow divided into processing steps.

[1466] (Application example 1)

[1467] 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."

[1468] It is difficult for users with health and fitness goals to consistently maintain a healthy diet. In particular, creating a healthy meal plan, selecting appropriate meals based on that plan, and arranging timely delivery can be a heavy burden for users. Furthermore, in today's busy society, it is difficult to effectively monitor a user's progress toward achieving their health goals and provide ongoing appropriate advice as needed. There is a need for a system that solves these challenges and helps users make healthy eating a habit.

[1469] 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.

[1470] In this invention, the server includes: means for generating an individual plan using a generation AI based on goals set by the user; means for acquiring the user's schedule information and finding the optimal time; means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results; means for generating a meal plan according to the goals and establishing a connection with a food delivery service; and means for setting meal reminders and arranging delivery based on the meal plan. This enables the user to receive continuous support for achieving their health goals and to eat healthy meals at appropriate times.

[1471] "Means for a generative AI to generate an individual plan based on the goals set by the user" refers to a technology in which a generative AI creates a specific action plan for achieving a goal based on the health or fitness goals entered by the user.

[1472] The "means for obtaining the user's schedule information and finding the optimal time" is a technology that analyzes the user's calendar and schedule information to find the optimal time period for carrying out activities to achieve a goal.

[1473] "Means for generating dashboards and encouraging messages that monitor user progress data and visualize progress based on the analysis results" refers to technology that collects and analyzes data on activities performed by users, visually displays the results, and generates messages that motivate users.

[1474] "Means for generating meal plans according to goals and establishing collaboration with food delivery services" refers to a technology in which a generation AI creates a meal plan based on the user's health goals, and then collaborates with a food delivery service to provide appropriate meals based on that plan.

[1475] "Means for setting meal reminders and arranging delivery based on a meal plan" refers to technology that sets reminders based on a generated meal plan and arranges for meals to be delivered at a specified time through a food delivery service.

[1476] The configuration of a system for implementing this invention will be described below. The system operates in cooperation with a user terminal, a server, a generative AI model, a database, and an API of a food delivery service.

[1477] The server provides a means for the generative AI to generate an individualized plan based on the health goals set by the user. It receives the health goals sent from the user's device, converts them into JSON format, and inputs them into the generative AI model. The generative AI model generates an appropriate meal plan based on the received data and sends it back to the server. The meal plan includes specific menus and meal timings.

[1478] The server also obtains the user's schedule information and provides a means to find the optimal time. The server works in conjunction with a calendar application to analyze the user's schedule information and identify the optimal time period for executing the meal plan. It then sets a reminder based on the optimal time and notifies the user's device.

[1479] Furthermore, the server monitors the user's progress data and provides a means to generate a dashboard that visualizes progress and encouragement messages based on the analysis results. When the user reports the meal details they have completed from their device, the progress data is sent to the server. The server integrates this data and analyzes it using generative AI. Based on the results, it generates scored progress and praise messages, etc., to provide feedback to the user.

[1480] It also generates a meal plan tailored to your goals and provides a way to connect with food delivery services, which then send the meal plan as an order request through the food delivery service's API to order the appropriate menu, which then automatically arranges for the meal to be delivered to the user.

[1481] Finally, the system also provides a means to set meal reminders and arrange for delivery based on the meal plan. Based on the plan created by the generative AI, the server sets reminders and notifies the user at the specified time. It also arranges for the specified meal to be delivered at the appropriate time through the delivery service's API.

[1482] For example, if a user sets a goal of "dieting," the AI ​​will generate a healthy meal plan that takes into account calorie restriction and nutritional balance. The user will be notified of plans such as a "low-calorie smoothie" at 8 a.m. on Monday and a "salad bowl" at noon on Wednesday.

[1483] Below is an example of a prompt sentence.

[1484] Example prompt sentence:

[1485] Generate optimal meal plans for your following health goals:

[1486] Health Goal: Diet

[1487] User ID: 12345

[1488] Current weight: 70kg

[1489] Target weight: 65kg

[1490] Food preference: Vegetarian

[1491] Allergens: nuts

[1492] Weekly calorie goal: 1500kcal

[1493] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1494] Step 1:

[1495] Enter the goal set by the user.

[1496] Users launch the application and enter their health and fitness goals, including goal details, duration, current weight, target weight, food preferences, allergy information, etc. The entered data is formatted in JSON format and sent to the server.

[1497] Step 2:

[1498] The server requests the generative AI model to generate a plan.

[1499] The server analyzes the received user's goals and related data and requests the generative AI model to generate a plan. The generative AI model creates a detailed meal plan based on the goal data and sends it back to the server. This process involves data processing and calculations that allow the AI ​​to convert the input data (user's goals and preferences) into an appropriate plan.

[1500] Step 3:

[1501] The server saves the generated plan in the database and notifies the user.

[1502] The meal plan returned by the generative AI model is stored in a database on the server, and the saved plan is sent to the user's device, where the specific meal contents and timing are displayed to the user.

[1503] Step 4:

[1504] The server obtains the user's schedule information and identifies the best time.

[1505] The server interacts with the calendar application to retrieve the user's schedule information, which is then parsed to identify the optimal time based on the generated meal plan. This process uses the schedule data as input data and determines the optimal time based on that.

[1506] Step 5:

[1507] The server sets a reminder and notifies the user terminal.

[1508] The server sets a reminder based on the optimal time determined, and the reminder information is sent to the user's terminal and notified to the user. The notified reminder serves as a support for the user to remember to carry out the meal plan.

[1509] Step 6:

[1510] The server sends a request to a food delivery service.

[1511] Based on the generated meal plan, the server sends a request to the food delivery service's API. The request includes the specific meal contents and delivery time, and the meal is then delivered to the user. In this step, the generated plan is used as input data and an API request is made to arrange delivery.

[1512] Step 7:

[1513] The user records their meal contents and sends progress data to the server.

[1514] After the user eats a meal, they record the details of their meal in the application. The recorded progress data is formatted in JSON format and sent to the server.

[1515] Step 8:

[1516] The server analyzes the progress data and generates advice based on a generative AI model.

[1517] The server analyzes the received progress data and generates advice messages based on the generation AI. In this process, the progress data is used as input and the generation AI generates appropriate feedback and advice.

[1518] Step 9:

[1519] The server transmits the generated advice to the user terminal and provides feedback.

[1520] The generated advice is sent from the server to the user's device and displayed to the user, allowing the user to understand their own progress and maintain motivation for the next step.

[1521] 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.

[1522] This invention is a system that generates an optimal plan based on the user's set goals and supports their execution. In particular, it uses a generative AI to create an individual plan, finds an appropriate time using the user's schedule information, and monitors the user's progress. It also combines an emotion engine that recognizes the user's emotions to provide encouragement messages according to the user's emotional state.

[1523] Program processing overview

[1524] User registration and goal setting

[1525] First, the user sets goals for exercise or studying and enters them into the device. For example, the user sets specific goals such as "running," "three times a week," and "5 kilometers per session."

[1526] The device formats the data entered by the user and sends it to the server. The data is formatted in JSON format and includes information such as the type and number of goals. The server analyzes the received data and asks the generation AI to generate a plan. The generation AI generates the optimal plan, taking into account the user's current abilities and time constraints. The plan generated by the generation AI is sent back to the server, and then sent from the server to the device. The device displays the generated plan to the user.

[1527] Plan execution and reminders

[1528] The server retrieves the user's calendar and schedule information and finds the optimal time to incorporate the plan into the schedule. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings based on those times. The server then connects the reminder data to the calendar app and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[1529] Progress monitoring and advice

[1530] After the user finishes an activity, they input progress data into the device. For example, they might input "distance run: 5 km, time taken: 30 minutes." The device formats the input data and sends it to the server. The server stores the progress data in a database and analyzes it. Based on the analysis results, the generation AI generates a progress dashboard and encouraging messages. The server sends reports and messages from the generation AI to the device, which displays them to the user.

[1531] Collaboration with emotion engine

[1532] This system can also be combined with an emotion engine that recognizes the user's emotions from facial expressions, voice, text input, etc., and sends the data to the server.

[1533] Based on the emotional data recognized by the emotion engine, the generation AI adjusts the encouragement message. For example, if the user is feeling stressed, the generation AI generates a message such as "Try some light exercise to refresh yourself." The server sends the generated encouragement message to the device, which then displays it to the user. Emotional data is also used to generate or improve plans. For example, if the user is feeling tired, the generation AI can adjust the plan to reduce the load of the next activity.

[1534] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation. The system responds flexibly to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[1535] The processing flow will be explained below.

[1536] User registration and goal setting

[1537] Step 1:

[1538] The user inputs the target information into the terminal.

[1539] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[1540] Step 2:

[1541] The terminal receives user input, formats the data, and sends it to the server.

[1542] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[1543] Send data to the server.

[1544] Step 3:

[1545] The server analyzes the received user data and passes it to the generation AI.

[1546] Generate a request to the generation AI: generate_plan("running", 3, 5)

[1547] Step 4:

[1548] The generative AI generates a specific plan based on the user's goals.

[1549] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[1550] Step 5:

[1551] The server receives the plan data returned from the generation AI and sends it to the terminal.

[1552] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[1553] Step 6:

[1554] The terminal displays the generated plan to the user.

[1555] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[1556] Plan execution and reminders

[1557] Step 1:

[1558] The server retrieves the user's schedule data and finds the best time.

[1559] Retrieves calendar information from the database.

[1560] Step 2:

[1561] The server generates reminder settings based on the measurement results.

[1562] Reminder settings: {"date": "Monday", "time": "5:50"}

[1563] Step 3:

[1564] The server connects the reminder data to the calendar app.

[1565] Call the Calendar API to add a reminder.

[1566] Step 4:

[1567] The device will display a reminder notification to the user at the specified time.

[1568] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[1569] Progress monitoring and advice

[1570] Step 1:

[1571] After exercising, the user inputs progress data into the terminal.

[1572] Example: "Distance run: 5km, time taken: 30 minutes"

[1573] Step 2:

[1574] The terminal formats the user input data and sends it to the server.

[1575] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[1576] Step 3:

[1577] The server stores the user's progress data in a database and analyzes it.

[1578] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[1579] Step 4:

[1580] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[1581] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[1582] Step 5:

[1583] The server receives reports and messages from the generated AI and sends them to the terminal.

[1584] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[1585] Step 6:

[1586] The terminal displays progress reports and enquiries to the user.

[1587] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[1588] Collaboration with emotion engine

[1589] Step 1:

[1590] The user provides input for emotion recognition.

[1591] For example, after exercising, the user may input feedback such as "I'm a little tired from running today."

[1592] Step 2:

[1593] The device formats the emotion data and sends it to the server.

[1594] Emotion data format: { "emotional_state": "Tired"}

[1595] Step 3:

[1596] The server receives the emotion data and sends it to the emotion engine.

[1597] Pass data to the emotion engine: analyze_emotion("tired")

[1598] Step 4:

[1599] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[1600] Analysis result: { "emotion": "fatigue", "level": "high"}

[1601] Step 5:

[1602] Based on the results of the emotion engine, the server instructs the generation AI to adjust the encouraging message.

[1603] Instructions to the generating AI: generate_message("Fatigue", "High")

[1604] Step 6:

[1605] The generative AI generates encouraging messages based on emotional state.

[1606] Generated message: "Get some light exercise to refresh yourself."

[1607] Step 7:

[1608] The server sends the generated enqueuing message to the terminal.

[1609] Send data: { "message": "Let's do some light exercise to refresh ourselves"}

[1610] Step 8:

[1611] The terminal displays the enlargement message to the user.

[1612] The screen will display a message saying, "Let's incorporate some light exercise to refresh ourselves." Emotional data will also be used to generate and improve plans. For example, if the user is feeling tired, the generation AI will adjust the plan to reduce the load of the next activity.

[1613] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation.The system flexibly responds to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[1614] Example 2

[1615] 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."

[1616] While traditional goal management systems can monitor users' progress and provide personalized plans, they struggle to adapt flexibly to the user's emotional state. They also lack the ability to automatically improve and update the generated plans, limiting their ability to continuously support user motivation. Furthermore, reminder functions are not fully integrated, making it difficult to smoothly manage users' schedules.

[1617] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the generative AI model to generate an individual plan based on the goal set by the user, a means for acquiring the user's schedule information and finding the optimal time, and a means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results. This not only provides an individual plan for achieving the user's goal, but also makes it possible to provide encouraging messages that adapt to the user's emotional state. In addition, the user's schedule is smoothly managed through the reminder function, and the generative AI model automatically improves and updates the plan, enabling continuous motivation support.

[1618] A "generative AI model" refers to an artificial intelligence that generates a personalized plan based on the goals set by the user.

[1619] A "goal" is an indicator that indicates a specific activity or result that a user sets to achieve.

[1620] "Schedule information" refers to data used for a user's activity schedule and time management.

[1621] "Progress data" is information that indicates a user's actual progress and achievements toward a goal.

[1622] A "dashboard" is an interface that visually displays a user's progress, making it easier to understand.

[1623] "Encouragement messages" are messages provided to increase motivation depending on the user's progress and emotional state.

[1624] "Emotion data" is information about the user's emotional state estimated based on facial expressions, voice, text input, and the like.

[1625] "Automatic plan improvement and updating" refers to the process in which the generative AI model optimizes existing plans and generates new plans based on the user's progress data and emotional data.

[1626] A "calendar application" refers to software that manages a user's schedule information and adjusts reminders and appointments.

[1627] The present invention is a system for generating an optimal plan based on a goal set by a user and supporting the execution of that plan. This system is realized mainly by three entities: a server, a terminal, and a user. Specific embodiments of the system are described in detail below.

[1628] User registration and goal setting

[1629] First, the user sets a goal for exercise or studying and enters that information into the device. For example, specific goals such as "running," "three times a week," and "5 kilometers each time" are entered into the device's application. The device then formats this information into JSON format and sends it to the server. The server analyzes the received data and requests the generative AI model to generate a plan. At this time, it generates a prompt like the following:

[1630] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[1631] The generative AI model generates an optimal plan based on this prompt and sends it back to the server, which then sends the plan to the device, which displays it to the user.

[1632] Plan execution and reminders

[1633] The server retrieves the user's calendar and schedule information and finds the optimal time based on the generated plan. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings for those times. The server then connects the reminder data to the calendar application and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[1634] Progress monitoring and advice

[1635] After the user finishes an activity, they enter progress data into the device. For example, they enter data such as "distance run: 5 km, time required: 30 minutes." The device formats this data into JSON format and sends it to the server. The server stores the progress data in a database and performs analysis. Based on the analysis results, the generative AI model generates a progress dashboard and enumeration messages. The server sends the generated reports and messages to the device, which then displays them to the user.

[1636] Collaboration with emotion engine

[1637] This system can also be combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. and sends the data to the server. For example, if the user is feeling stressed, the emotion engine will send the data tagged with "stress." Based on this emotion data, the generative AI model adjusts the encouragement message. For example, if the user is feeling stressed, a message such as "Try some light exercise to refresh yourself" is generated. The server then sends the generated encouragement message to the device, which then displays it to the user. Emotion data is also used to generate and improve plans.

[1638] In this way, the system provides flexible and effective planning and motivational maintenance functions to help users achieve their goals. Plan generation and automatic improvement using a generative AI model, reminder setting, progress monitoring, and even integration with an emotion engine can support users' sustained motivation.

[1639] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1640] Step 1:

[1641] User sets goals

[1642] Users input their exercise or study goals into the device application, such as "running," "three times a week," and "5 kilometers per session."

[1643] (Input): Goal data set by the user (e.g., "running," "three times a week," "5 kilometers per session").

[1644] (Output): The target data entered into the terminal.

[1645] (Specific action): The user enters their goal into the app's input form and clicks the "Save" button.

[1646] Step 2:

[1647] The device formats the data and sends it to the server

[1648] The terminal formats the target data entered by the user into JSON format and sends it to the server.

[1649] (Input): The target data entered into the terminal.

[1650] (Output): The goal data formatted in JSON.

[1651] (Specific operation): The terminal program formats the target data in JSON format as shown below and sends it to the server via an HTTP POST request.

[1652] json

[1653] {

[1654] "goal_type": "running",

[1655] "frequency_per_week": 3,

[1656] "distance_per_session": 5

[1657] }

[1658] Step 3:

[1659] The server requests the generative AI model to generate a plan.

[1660] The server analyzes the received data and generates a prompt statement that requests the generative AI model to generate a plan.

[1661] (Input): The target data formatted in JSON.

[1662] (Output): The prompt sent to the generative AI model.

[1663] (Specific action): The server generates a prompt like this:

[1664] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[1665] This prompt is then sent to a generative AI model and a plan is received.

[1666] Step 4:

[1667] The server sends the generated plan to the device.

[1668] The generated plan is returned to the server, and then transmitted from the server to the terminal.

[1669] (Input): The plan received from the generative AI model.

[1670] (Output): Plan data sent to the device.

[1671] (Specific operation): The server returns the plan as an HTTP response, and the device analyzes and displays the received data.

[1672] Step 5:

[1673] The device displays the plan to the user.

[1674] The terminal displays the generated plan to the user.

[1675] (Input): Plan data received from the server.

[1676] (Output): The plan that is displayed to the user.

[1677] (Specific behavior): The device app displays plans in a list format and adds "Next" and "Details" buttons.

[1678] Step 6:

[1679] The server retrieves the user's schedule information.

[1680] The server retrieves the user's calendar and schedule information.

[1681] (Input): Schedule information from a calendar application.

[1682] (Output): Schedule information stored on the server.

[1683] (Specific operation): The server retrieves schedule data from third-party services such as Google Calendar via API.

[1684] Step 7:

[1685] The server generates the reminder settings

[1686] The server generates reminder settings based on the acquired schedule information.

[1687] (Input): User's schedule information and generated plan.

[1688] (Output): Reminder setting data.

[1689] (Specific behavior): The server generates reminder settings in JSON format based on the schedule information and plan:

[1690] json

[1691] {

[1692] "reminder_time": "2023-10-02T06:00:00",

[1693] "reminder_message": "It's time for a run"

[1694] }

[1695] Step 8:

[1696] The device displays a reminder notification to the user.

[1697] Your device will display notifications at the specified time based on your reminder settings.

[1698] (Input): Reminder setting data.

[1699] (Output): Reminder notification to the user.

[1700] (Specific behavior): The device will pop up a reminder notification and provide a "Confirm" or "Snooze" button.

[1701] Step 9:

[1702] The user enters progress data

[1703] After completing an activity, the user inputs progress data into the terminal.

[1704] (Input): Progress data entered by the user (e.g., "Distance run: 5 km, Time taken: 30 mins").

[1705] (Output): Progress data entered into the terminal.

[1706] (Specific action): The user enters data into a dedicated input form and clicks the "Submit" button.

[1707] Step 10:

[1708] The device formats the progress data and sends it to the server.

[1709] The device formats the entered progress data into JSON format and sends it to the server.

[1710] (Input): Progress data entered by the user.

[1711] (Output): Progress data in JSON format.

[1712] (Specific operation): The device formats the progress data as follows and sends it to the server:

[1713] json

[1714] {

[1715] "distance_ran": 5,

[1716] "time_taken": 30

[1717] }

[1718] Step 11:

[1719] The server saves the progress data in a database and performs analysis.

[1720] The server stores the progress data in a database and performs analysis.

[1721] (Input): Progress data in JSON format.

[1722] (Output): Analysis result data.

[1723] (Specific behavior): The server executes a database query, stores progress data, and then launches an analysis script to process the data.

[1724] Step 12:

[1725] Generative AI models generate dashboards and enlargement messages

[1726] The generative AI model generates progress dashboards and enlargement messages based on the analysis results.

[1727] (Input): Analysis result data.

[1728] (Output): Dashboard data and enumeration messages.

[1729] (Specific behavior): The generative AI model generates feedback on progress and creates messages to motivate the user.

[1730] Step 13:

[1731] The server sends reports and messages to the terminal, which displays them

[1732] The server sends reports and enlargement messages from the generative AI model to the terminal, which displays them to the user.

[1733] (Input): Dashboard data and enumeration messages.

[1734] (Output): Reports and messages displayed to the user.

[1735] (Specific behavior): The device displays a dashboard or message on the screen and provides "Next step" or "View details" buttons.

[1736] Step 14:

[1737] Emotion engine recognizes user emotions

[1738] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, and sends that data to the server.

[1739] (Input): User facial expression, voice, and text input data.

[1740] (Output): Emotion data.

[1741] (Specific operation): The emotion engine analyzes data in real time and generates emotion data (e.g., stress).

[1742] Step 15:

[1743] Generative AI models tailor encouraging messages based on emotional data

[1744] The generative AI model tailors appropriate encouraging messages based on emotional data.

[1745] (Input): Emotion data.

[1746] (Output): The adjusted encausation message.

[1747] (Specific operation): The generative AI model takes emotion data as input and generates messages such as the following:

[1748] If the user is feeling stressed, the message is "Try some light exercise to refresh yourself."

[1749] Step 16:

[1750] The server sends a message to the terminal, which displays it to the user.

[1751] The server sends the generated encumbrance message to the terminal, which displays it to the user.

[1752] (Input): The adjusted encausation message.

[1753] (Output): The enumeration message that is displayed to the user.

[1754] (Specific behavior): The device displays the message as a notification and provides a "View details" or "OK" button.

[1755] In this way, the system generates a plan based on the user's goals and helps maintain the user's motivation through schedule management, progress monitoring, and providing emotionally appropriate encouragement messages.

[1756] (Application example 2)

[1757] 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."

[1758] In conventional security services, managing security guards' work schedules and patrol routes is complicated, and there are a lack of appropriate measures to maintain the guards' motivation and mental health. This makes it difficult for them to work efficiently and safely, and leads to the accumulation of stress and fatigue.

[1759] 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 the generation AI to generate an individual plan based on goals set by the user; means for acquiring the user's date and time information and finding the optimal time; means for monitoring the user's progress data and generating a display device and encouraging messages that visualize the progress based on the analysis results; means including an emotion analysis device that recognizes the user's emotional state; and means for the generation AI to adjust the encouraging messages based on data from the emotion analysis device. This improves the efficiency of schedule management for security guards and enables appropriate work plans and emotional care tailored to each individual guard.

[1760] "Generative AI" is a system that uses artificial intelligence technology to generate optimal plans according to the goals set by the user.

[1761] A "personalized plan" is a personalized execution plan that takes into account each user's goals, schedule, and emotional state.

[1762] "Date and time information" is data from a user's schedule or calendar that is used to optimize the timing of activities.

[1763] "Progress data" refers to data that indicates the actual progress of a user as they execute a plan, including, for example, the tasks completed and the time required.

[1764] A "display device" is a device that visually displays progress and encouraging messages to a user, such as smart glasses or a smartphone.

[1765] An "encouraging message" is a message of encouragement or advice provided to increase the user's motivation.

[1766] An "emotion analysis device" is a device that recognizes a user's emotional state and responds or adjusts appropriately. It includes facial expression analysis cameras and voice analysis devices.

[1767] A "calendar application" is software that manages a user's schedule and has the function of setting reminders and sending notifications.

[1768] "Automatic improvement" is the process by which the generative AI dynamically modifies the plan based on the user's progress data and emotional state, updating it to a more effective plan.

[1769] The system for implementing this invention consists of the following steps: First, a user uses smart glasses to register and set goals. The user enters specific exercise or study goals, and the information is formatted and sent to the server. The data format used to format the information is JSON.

[1770] The server analyzes the received data and generates an individual plan using a generative AI model. The generative AI model takes into account the user's current abilities and time constraints to construct an optimal plan. The generated plan is then sent from the server to the smart glasses and displayed to the user.

[1771] The server also retrieves the user's date and time information (schedule and calendar data) to find the optimal time to incorporate the plan into the schedule. For example, optimizing security guard patrol times and break times. This data is integrated with the calendar application and set as a reminder. The smart glasses will display the reminder at the specified time to help the user execute the plan.

[1772] After the user executes the plan, progress data is input into the smart glasses. For example, "Time to complete the tour route: 30 minutes." This data is formatted and sent back to the server. The server stores the progress data in a database, and the generative AI model analyzes it. Based on the analysis results, a progress display and encouraging messages are generated and sent to the smart glasses.

[1773] Furthermore, the emotion analyzer monitors the user's emotional state. Using a facial expression camera and a voice analyzer, the emotion analyzer sends data to the server if the user feels stressed or tired. Based on the emotional data, the generative AI model adjusts the encouraging messages and generates custom messages such as "Take a short break" or "Do some light exercise to change your mood." These are then displayed on the smart glasses.

[1774] As a concrete example, consider a security guard who sets the goal of "patrol routes three times a day, breaks every two hours." The generative AI model uses this information to create a personalized work plan. If the emotion analyzer detects "high stress," the generative AI model automatically suggests increasing break time, displaying the message, "Take a five-minute break in a relaxing place."

[1775] An example prompt is:

[1776] User ID: Security Guard 123

[1777] Current Ability: Intermediate

[1778] Time constraints:

[1779] Start time: 8:00

[1780] End time: 18:00

[1781] the goal:

[1782] Patrol frequency: 3 times a day

[1783] Break interval: Every 2 hours

[1784] In this way, it is possible to realize a system that improves both the work efficiency and mental health of security guards.

[1785] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1786] Step 1:

[1787] The user sets goals using the smart glasses. The user uses the input interface of the smart glasses to input goals, such as "Patrol route: 3 times a day, Break: every 2 hours." This data is formatted in JSON format and sent to the server. The input is specific goal data, and the output is formatted JSON data.

[1788] Step 2:

[1789] The server parses the received goal data. The server parses the JSON format goal data and generates an individual plan based on the generative AI model, taking into account the user's current abilities and time constraints. The input is the JSON data to be parsed, and the output is the generated plan data.

[1790] Step 3:

[1791] The server sends the generated plan to the smart glasses, which the terminal displays to the user. The terminal visually displays the received plan data and guides the user to the next step. The input is the plan data sent from the server, and the output is the plan displayed on the smart glasses.

[1792] Step 4:

[1793] The server obtains the user's date and time information and incorporates the plan into the schedule. The server works with the calendar application to find the optimal patrol and break times and set reminders. The input is the user's schedule data, and the output is the set reminder data.

[1794] Step 5:

[1795] When the reminder time arrives, the smart glasses notify the user. The device displays the reminder at the set time and prompts the user to carry out the plan. The input is the reminder data, and the output is the notification displayed on the smart glasses.

[1796] Step 6:

[1797] The user executes the plan and inputs the progress data into the smart glasses. The terminal formats the progress data received from the user and sends it to the server. The input is the progress data, and the output is the formatted progress data.

[1798] Step 7:

[1799] The server analyzes the received progress data. The server stores the progress data in a database, and the generative AI model analyzes the progress and generates a display and encouragement message. The input is the received progress data, and the output is the encouragement message and progress data.

[1800] Step 8:

[1801] The server receives data from the emotion analyzer, and the generative AI model adjusts the encouraging message. The emotion analyzer determines the user's emotions from facial expressions and voice and sends that data to the server. The server analyzes the data and adjusts the encouraging message using the generative AI model. The input is emotional data, and the output is the adjusted encouraging message.

[1802] Step 9:

[1803] The smart glasses display the tailored encouraging message to the user. The terminal visually displays the message sent from the server to motivate the user. The input is the encouraging message sent from the server, and the output is the message displayed on the smart glasses.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] [Fourth embodiment]

[1808] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1809] 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.

[1810] 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).

[1811] 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.

[1812] 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.

[1813] 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).

[1814] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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."

[1821] This invention relates to a system that helps users form habits by using generative AI to create individual plans tailored to the user's goals and supporting their execution. Furthermore, this invention also includes functions for managing the user's schedule and monitoring progress, with the aim of providing sustained motivation.

[1822] Program processing overview

[1823] A specific embodiment of this system performs the following operations.

[1824] User registration and goal setting

[1825] 1. The user sets a goal for exercise or studying and enters it into the device. For example, the user sets a goal of "running five times a month."

[1826] 2. The device formats the user input data in JSON format and sends it to the server, including information such as the goal type and number of times.

[1827] 3. The server analyzes the received data and requests the AI ​​to generate an appropriate plan. The AI ​​then generates the optimal running plan, taking into account the user's current abilities and time constraints.

[1828] 4. The generation AI generates a plan and sends it back to the server, including specific dates and time slots.

[1829] 5. The server sends the generated plan to the terminal and displays it to the user on the terminal, who then plans his or her activities accordingly.

[1830] Plan execution and reminders

[1831] 1. The server retrieves the user's calendar and schedule information and finds the best time to schedule the plan. For example, a "Running" plan will start at 6:00 AM on Mondays, Wednesdays, and Fridays.

[1832] 2. The server generates the optimal reminder settings and connects to the calendar application to set reminders. The reminders notify the user, helping them execute their plan.

[1833] 3. The device will notify the user of the reminder at the specified time, so that the user will not forget to perform the activity.

[1834] Progress monitoring and advice

[1835] 1. After performing an activity, the user enters progress data into the device, for example, "Completed a 5km run in 30 minutes."

[1836] 2. The device formats the progress data and sends it to the server.

[1837] 3. The server monitors the received progress data and stores it in a database. The progress is periodically analyzed and a report is generated by the generation AI.

[1838] 4. The Generative AI generates an encouragement message for the user based on their progress, such as "Great! You achieved your goal! Try going a little further next time!"

[1839] 5. The server sends the report and message to the terminal, where it is displayed to the user, motivating the user to take further action.

[1840] 6. If necessary, the user requests a change to the plan, for example, "I want to change the distance to 7 kilometers."

[1841] 7. The server receives the change request and regenerates the plan using the generation AI.

[1842] 8. Once the new plan is generated, the server sends it to the device and updates the calendar and reminder settings.

[1843] In this way, a system that supports users in forming habits is realized. This system flexibly responds to users' goals and provides continuous motivation, allowing them to continue their exercise or study efforts.

[1844] The processing flow will be explained below.

[1845] User registration and goal setting

[1846] Step 1:

[1847] The user inputs the target information into the terminal.

[1848] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[1849] Step 2:

[1850] The terminal receives user input, formats the data, and sends it to the server.

[1851] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[1852] Send data to the server.

[1853] Step 3:

[1854] The server analyzes the received user data and passes it to the generation AI.

[1855] Generate a request to the generation AI: generate_plan("running", 3, 5)

[1856] Step 4:

[1857] The generative AI generates a specific plan based on the user's goals.

[1858] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[1859] Step 5:

[1860] The server receives the plan data returned from the generation AI and sends it to the terminal.

[1861] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[1862] Step 6:

[1863] The terminal displays the generated plan to the user.

[1864] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[1865] Plan execution and reminders

[1866] Step 1:

[1867] The server retrieves the user's schedule data and finds the best time.

[1868] Retrieves calendar information from the database.

[1869] Step 2:

[1870] The server generates reminder settings based on the measurement results.

[1871] Reminder settings: {"date": "Monday", "time": "5:50"}

[1872] Step 3:

[1873] The server connects the reminder data to the calendar app.

[1874] Call the Calendar API to add a reminder.

[1875] Step 4:

[1876] The device will display a reminder notification to the user at the specified time.

[1877] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[1878] Progress monitoring and advice

[1879] Step 1:

[1880] After exercising, the user inputs progress data into the terminal.

[1881] Example: "Distance run: 5km, time taken: 30 minutes"

[1882] Step 2:

[1883] The terminal formats the user input data and sends it to the server.

[1884] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[1885] Step 3:

[1886] The server stores the user's progress data in a database and analyzes it.

[1887] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[1888] Step 4:

[1889] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[1890] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[1891] Step 5:

[1892] The server receives reports and messages from the generated AI and sends them to the terminal.

[1893] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[1894] Step 6:

[1895] The terminal displays progress reports and enquiries to the user.

[1896] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[1897] Example 1

[1898] 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."

[1899] Many people today struggle with goal setting and achievement. Maintaining consistent daily habits, such as exercise and studying, requires individual planning, schedule management, and progress monitoring. However, performing these tasks individually can be burdensome and challenging, making it difficult to maintain motivation. It's also difficult to track progress in real time and receive appropriate feedback based on that progress. Furthermore, the lack of reminder settings and automatic plan updates is also problematic.

[1900] 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.

[1901] In this invention, the server includes: a means for generating an individual plan using a generation AI based on goals set by the user; a means for acquiring the user's schedule information and finding the optimal time; a visualization means for monitoring the user's progress data and visualizing the progress based on the analysis results, and a means for generating engraving messages; a means for formatting goal data entered by the user into a terminal in JSON format and sending it to the server; and a means for receiving the generated plan and displaying it on the terminal. This makes it possible to provide consistent support for the user's goal achievement, from planning to execution, progress management, and motivation maintenance.

[1902] "User" refers to an individual or end user who uses the System to set and achieve goals.

[1903] A "goal" refers to a specific outcome or purpose that a user aims to achieve in relation to sports, studies, or other activities.

[1904] "Generative AI" refers to artificial intelligence algorithms that automatically create plans based on a user's goals and circumstances.

[1905] "Plan" refers to a specific action plan created by generative AI for a user to achieve their goal.

[1906] "Schedule information" refers to data related to schedule and time management, such as a user's calendar and appointment times.

[1907] The "optimal time" refers to the most suitable time to perform an action for a set goal based on the user's schedule information.

[1908] "Progress data" refers to data that shows the record and results of activities that a user has performed toward a goal.

[1909] "Visualization means" refers to a method for displaying progress data in the form of diagrams, graphs, etc., to help the user understand the progress status.

[1910] "Encouragement messages" refer to messages created by generative AI to encourage and motivate users.

[1911] "Device" refers to the device (e.g., smartphone or tablet) that a user uses to access the system, enter goals, and record progress.

[1912] "JSON format" refers to a text-based data interchange format for representing data in a concise and structured form.

[1913] A "server" is the central computer of the system, and refers to a device that analyzes data, generates plans, monitors progress, etc.

[1914] This invention is a system for supporting users in achieving their goals. Specifically, it utilizes a generative AI model to generate personalized plans, manage schedules, monitor progress, and provide encouragement messages.

[1915] 1. The user sets a goal

[1916] Users input their goals, such as exercise or study goals, into the device. A dedicated input application is installed on the device, and the user inputs information such as the type of goal, duration, and frequency. For example, "My goal is to run five times a month."

[1917] 2. Formatting and sending data

[1918] The device formats the goal data collected from the user into JSON format and sends it to the server. As a specific example, the following data is generated:

[1919] json

[1920] {

[1921] "goal_type": "running",

[1922] "frequency": 5,

[1923] "duration_in_months": 1

[1924] }

[1925] This data is sent to the server via an HTTP request.

[1926] 3. Plan Generation

[1927] The server analyzes the received data and requests the generation AI to generate a plan. For example, OpenAI GPT-4 is used as the generation AI. Examples of prompt sentences include the following:

[1928] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[1929] The generation AI generates a personalized plan based on the request and returns it to the server, including specific dates and time slots. For example,

[1930] json

[1931] {

[1932] "plan": [

[1933] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[1934] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[1935] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[1936] ]

[1937] }

[1938] Something like this.

[1939] 4. View your plan

[1940] The server transmits the generated plan to the terminal, which then displays the plan to the user, allowing the user to plan their daily activities.

[1941] 5. Schedule management and reminder settings

[1942] The server retrieves the user's calendar information and finds the time to schedule the generated plan. It also works with a calendar application (e.g., Google Calendar) to set reminders, helping the user remember to execute the plan.

[1943] 6. Progress Monitoring

[1944] After completing each activity, the user enters progress data into the device. For example, "I completed a 5-kilometer run in 30 minutes." The device formats this data in JSON format and sends it to the server. The server stores the received progress data in a database and periodically analyzes it.

[1945] 7. Generating Encalagement Messages

[1946] The generation AI generates an encouragement message to motivate the user based on the progress data. For example, a message such as "Great! You've achieved your goal. Next time, try to go a little further!" is generated and sent from the server to the device. The device then displays this message to the user.

[1947] 8. Regenerating and Updating Plans

[1948] When a user wants to set a new goal or modify an existing one, they input a request into their device. The server receives the request and asks the generation AI to regenerate the plan. The new plan is sent to the device, and it updates the calendar and reminders as needed.

[1949] In this way, a system that continuously supports users in achieving their goals is realized. This system consistently supports users from goal setting to progress management, and can provide continuous motivation.

[1950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1951] Step 1: Enter user goals

[1952] The user inputs goals such as exercise or study into the device. Using a dedicated application, the user inputs information such as the type of goal (e.g., "running"), frequency (e.g., "five times per month"), and duration (e.g., "for one month"). This input information is treated as data required for the next processing step.

[1953] input:

[1954] User goal information (goal type, frequency, duration)

[1955] output:

[1956] Formatting data (stored on the device)

[1957] Step 2: Convert data format and send

[1958] The device converts the entered user goal information into JSON format, for example, "goal_type: running, frequency: 5, duration_in_months: 1". The converted data is sent to the server via an HTTP request.

[1959] input:

[1960] User goal information

[1961] output:

[1962] JSON format data (sent to server)

[1963] Step 3: Data reception and analysis

[1964] The server parses the received JSON data using Python or Node.js, extracting the type of goal, frequency, duration, etc. from the JSON data and storing them in memory.

[1965] input:

[1966] Goal data in JSON format

[1967] output:

[1968] Parsed target data (in memory)

[1969] Step 4: Plan Generation Request

[1970] The server requests the AI ​​to generate a plan based on the analyzed data. The AI ​​is prompted with the following prompt:

[1971] "The user's exercise goal is to run five times a month. Generate an optimal running plan based on the user's current ability and schedule. Please include specific dates and times in the plan."

[1972] This prompt is sent to the generation AI, which then initiates plan generation.

[1973] input:

[1974] Analyzed target data

[1975] output:

[1976] Prompt text (sent to the generation AI)

[1977] Step 5: Plan Generation and Reception

[1978] Based on the request, the AI ​​generates a personalized plan that fits the user's schedule. The plan, including specific dates and time slots, is generated and returned to the server in JSON format. For example,

[1979] json

[1980] {

[1981] "plan": [

[1982] {"date": "2023-10-02", "time": "06:00", "activity": "5km running"},

[1983] {"date": "2023-10-04", "time": "06:00", "activity": "5km running"},

[1984] {"date": "2023-10-06", "time": "06:00", "activity": "5km running"}

[1985] ]

[1986] }

[1987] input:

[1988] Prompt statement

[1989] output:

[1990] Generated plan (JSON format, returned to server)

[1991] Step 6: Submit and view your plan

[1992] The server receives the plan returned by the generation AI and sends it to the device, which displays the plan to the user, allowing the user to plan their daily activities based on the plan.

[1993] input:

[1994] Generated plan (JSON format)

[1995] output:

[1996] Plan to display to user (sent to device)

[1997] Step 7: Schedule

[1998] The server retrieves the user's calendar information and finds the best time to schedule the generated plan, using the Google Calendar API or other calendar application APIs.

[1999] input:

[2000] Generated Plan

[2001] User's calendar information

[2002] output:

[2003] Scheduled plans

[2004] Step 8: Set reminders

[2005] The server uses a calendar application to set reminders so that the user remembers to follow through on their plans. For example, a reminder is set for each day of a run, telling the user, "It's time to go for a run."

[2006] input:

[2007] Scheduled plans

[2008] output:

[2009] Set reminders (reflected in the calendar)

[2010] Step 9: Reminders

[2011] The device will send reminders to the user at the specified time, helping the user remember to perform the activity. For example, a push notification will be sent saying, "Start your 5km run now."

[2012] input:

[2013] Set reminders

[2014] output:

[2015] Reminders to users

[2016] Step 10: Enter progress data

[2017] After completing an activity, the user enters progress data into the device, for example, "I completed a 5-kilometer run in 30 minutes." This progress data is then saved on the device.

[2018] input:

[2019] Completed activity information

[2020] output:

[2021] Progress data (saved on the device)

[2022] Step 11: Format and send data

[2023] The device will format the progress data in JSON format and send it to the server. For example, the following data will be generated:

[2024] json

[2025] {

[2026] "goal_type": "running",

[2027] "progress": "5km running completed in 30 minutes",

[2028] "date": "2023-10-01"

[2029] }

[2030] This data is sent to the server via an HTTP request.

[2031] input:

[2032] Progress Data

[2033] output:

[2034] Progress data in JSON format (sent to the server)

[2035] Step 12: Saving and analyzing progress data

[2036] The server stores the received progress data in a database and periodically analyzes it. This analysis allows the user's progress to be understood and used as data for the next step.

[2037] input:

[2038] Progress data in JSON format

[2039] output:

[2040] Saved Progress Data

[2041] Analysis results (in memory)

[2042] Step 13: Generate Encalagement Messages

[2043] Based on the analysis of the progress data, the AI ​​generates motivational messages to the user, such as "Great! You've achieved your goal. Next time, try going a little further!"

[2044] input:

[2045] Analysis results

[2046] output:

[2047] Encapsulation message (sent back to server)

[2048] Step 14: Sending and Viewing Messages

[2049] The server sends the generated encouraging message to the terminal, which displays it to the user, providing motivation for the next activity.

[2050] input:

[2051] Encouragement Message

[2052] output:

[2053] Message to be displayed to the user (sent to the terminal)

[2054] Step 15: Regenerate and update the plan

[2055] If the user requests a change to the plan as needed, for example, by entering "I want to change the distance to 7 kilometers" into the device, the server receives this request and regenerates the plan using the generation AI. The new plan is then sent to the device and notified to the user.

[2056] input:

[2057] Plan change request

[2058] output:

[2059] Generated new plan (sent to device)

[2060] The above is a specific program processing flow divided into processing steps.

[2061] (Application example 1)

[2062] 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."

[2063] It is difficult for users with health and fitness goals to consistently maintain a healthy diet. In particular, creating a healthy meal plan, selecting appropriate meals based on that plan, and arranging timely delivery can be a heavy burden for users. Furthermore, in today's busy society, it is difficult to effectively monitor a user's progress toward achieving their health goals and provide ongoing appropriate advice as needed. There is a need for a system that solves these challenges and helps users make healthy eating a habit.

[2064] 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.

[2065] In this invention, the server includes: means for generating an individual plan using a generation AI based on goals set by the user; means for acquiring the user's schedule information and finding the optimal time; means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results; means for generating a meal plan according to the goals and establishing a connection with a food delivery service; and means for setting meal reminders and arranging delivery based on the meal plan. This enables the user to receive continuous support for achieving their health goals and to eat healthy meals at appropriate times.

[2066] "Means for a generative AI to generate an individual plan based on the goals set by the user" refers to a technology in which a generative AI creates a specific action plan for achieving a goal based on the health or fitness goals entered by the user.

[2067] The "means for obtaining the user's schedule information and finding the optimal time" is a technology that analyzes the user's calendar and schedule information to find the optimal time period for carrying out activities to achieve a goal.

[2068] "Means for generating dashboards and encouraging messages that monitor user progress data and visualize progress based on the analysis results" refers to technology that collects and analyzes data on activities performed by users, visually displays the results, and generates messages that motivate users.

[2069] "Means for generating meal plans according to goals and establishing collaboration with food delivery services" refers to a technology in which a generation AI creates a meal plan based on the user's health goals, and then collaborates with a food delivery service to provide appropriate meals based on that plan.

[2070] "Means for setting meal reminders and arranging delivery based on a meal plan" refers to technology that sets reminders based on a generated meal plan and arranges for meals to be delivered at a specified time through a food delivery service.

[2071] The configuration of a system for implementing this invention will be described below. The system operates in cooperation with a user terminal, a server, a generative AI model, a database, and an API of a food delivery service.

[2072] The server provides a means for the generative AI to generate an individualized plan based on the health goals set by the user. It receives the health goals sent from the user's device, converts them into JSON format, and inputs them into the generative AI model. The generative AI model generates an appropriate meal plan based on the received data and sends it back to the server. The meal plan includes specific menus and meal timings.

[2073] The server also obtains the user's schedule information and provides a means to find the optimal time. The server works in conjunction with a calendar application to analyze the user's schedule information and identify the optimal time period for executing the meal plan. It then sets a reminder based on the optimal time and notifies the user's device.

[2074] Furthermore, the server monitors the user's progress data and provides a means to generate a dashboard that visualizes progress and encouragement messages based on the analysis results. When the user reports the meal details they have completed from their device, the progress data is sent to the server. The server integrates this data and analyzes it using generative AI. Based on the results, it generates scored progress and praise messages, etc., to provide feedback to the user.

[2075] It also generates a meal plan tailored to your goals and provides a way to connect with food delivery services, which then send the meal plan as an order request through the food delivery service's API to order the appropriate menu, which then automatically arranges for the meal to be delivered to the user.

[2076] Finally, the system also provides a means to set meal reminders and arrange for delivery based on the meal plan. Based on the plan created by the generative AI, the server sets reminders and notifies the user at the specified time. It also arranges for the specified meal to be delivered at the appropriate time through the delivery service's API.

[2077] For example, if a user sets a goal of "dieting," the AI ​​will generate a healthy meal plan that takes into account calorie restriction and nutritional balance. The user will be notified of plans such as a "low-calorie smoothie" at 8 a.m. on Monday and a "salad bowl" at noon on Wednesday.

[2078] Below is an example of a prompt sentence.

[2079] Example prompt sentence:

[2080] Generate optimal meal plans for your following health goals:

[2081] Health Goal: Diet

[2082] User ID: 12345

[2083] Current weight: 70kg

[2084] Target weight: 65kg

[2085] Food preference: Vegetarian

[2086] Allergens: nuts

[2087] Weekly calorie goal: 1500kcal

[2088] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2089] Step 1:

[2090] Enter the goal set by the user.

[2091] Users launch the application and enter their health and fitness goals, including goal details, duration, current weight, target weight, food preferences, allergy information, etc. The entered data is formatted in JSON format and sent to the server.

[2092] Step 2:

[2093] The server requests the generative AI model to generate a plan.

[2094] The server analyzes the received user's goals and related data and requests the generative AI model to generate a plan. The generative AI model creates a detailed meal plan based on the goal data and sends it back to the server. This process involves data processing and calculations that allow the AI ​​to convert the input data (user's goals and preferences) into an appropriate plan.

[2095] Step 3:

[2096] The server saves the generated plan in the database and notifies the user.

[2097] The meal plan returned by the generative AI model is stored in a database on the server, and the saved plan is sent to the user's device, where the specific meal contents and timing are displayed to the user.

[2098] Step 4:

[2099] The server obtains the user's schedule information and identifies the best time.

[2100] The server interacts with the calendar application to retrieve the user's schedule information, which is then parsed to identify the optimal time based on the generated meal plan. This process uses the schedule data as input data and determines the optimal time based on that.

[2101] Step 5:

[2102] The server sets a reminder and notifies the user terminal.

[2103] The server sets a reminder based on the optimal time determined, and the reminder information is sent to the user's terminal and notified to the user. The notified reminder serves as a support for the user to remember to carry out the meal plan.

[2104] Step 6:

[2105] The server sends a request to a food delivery service.

[2106] Based on the generated meal plan, the server sends a request to the food delivery service's API. The request includes the specific meal contents and delivery time, and the meal is then delivered to the user. In this step, the generated plan is used as input data and an API request is made to arrange delivery.

[2107] Step 7:

[2108] The user records their meal contents and sends progress data to the server.

[2109] After the user eats a meal, they record the details of their meal in the application. The recorded progress data is formatted in JSON format and sent to the server.

[2110] Step 8:

[2111] The server analyzes the progress data and generates advice based on a generative AI model.

[2112] The server analyzes the received progress data and generates advice messages based on the generation AI. In this process, the progress data is used as input and the generation AI generates appropriate feedback and advice.

[2113] Step 9:

[2114] The server transmits the generated advice to the user terminal and provides feedback.

[2115] The generated advice is sent from the server to the user's device and displayed to the user, allowing the user to understand their own progress and maintain motivation for the next step.

[2116] 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.

[2117] This invention is a system that generates an optimal plan based on the user's set goals and supports their execution. In particular, it uses a generative AI to create an individual plan, finds an appropriate time using the user's schedule information, and monitors the user's progress. It also combines an emotion engine that recognizes the user's emotions to provide encouragement messages according to the user's emotional state.

[2118] Program processing overview

[2119] User registration and goal setting

[2120] First, the user sets goals for exercise or studying and enters them into the device. For example, the user sets specific goals such as "running," "three times a week," and "5 kilometers per session."

[2121] The device formats the data entered by the user and sends it to the server. The data is formatted in JSON format and includes information such as the type and number of goals. The server analyzes the received data and asks the generation AI to generate a plan. The generation AI generates the optimal plan, taking into account the user's current abilities and time constraints. The plan generated by the generation AI is sent back to the server, and then sent from the server to the device. The device displays the generated plan to the user.

[2122] Plan execution and reminders

[2123] The server retrieves the user's calendar and schedule information and finds the optimal time to incorporate the plan into the schedule. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings based on those times. The server then connects the reminder data to the calendar app and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[2124] Progress monitoring and advice

[2125] After the user finishes an activity, they input progress data into the device. For example, they might input "distance run: 5 km, time taken: 30 minutes." The device formats the input data and sends it to the server. The server stores the progress data in a database and analyzes it. Based on the analysis results, the generation AI generates a progress dashboard and encouraging messages. The server sends reports and messages from the generation AI to the device, which displays them to the user.

[2126] Collaboration with emotion engine

[2127] This system can also be combined with an emotion engine that recognizes the user's emotions from facial expressions, voice, text input, etc., and sends the data to the server.

[2128] Based on the emotional data recognized by the emotion engine, the generation AI adjusts the encouragement message. For example, if the user is feeling stressed, the generation AI generates a message such as "Try some light exercise to refresh yourself." The server sends the generated encouragement message to the device, which then displays it to the user. Emotional data is also used to generate or improve plans. For example, if the user is feeling tired, the generation AI can adjust the plan to reduce the load of the next activity.

[2129] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation. The system responds flexibly to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[2130] The processing flow will be explained below.

[2131] User registration and goal setting

[2132] Step 1:

[2133] The user inputs the target information into the terminal.

[2134] Users enter their goals, such as "running," "three times a week," and "5 kilometers per session," into an input form on the device.

[2135] Step 2:

[2136] The terminal receives user input, formats the data, and sends it to the server.

[2137] Create JSON data: { "type": "running", "frequency": "3 times a week", "distance": 5}

[2138] Send data to the server.

[2139] Step 3:

[2140] The server analyzes the received user data and passes it to the generation AI.

[2141] Generate a request to the generation AI: generate_plan("running", 3, 5)

[2142] Step 4:

[2143] The generative AI generates a specific plan based on the user's goals.

[2144] Generated plan: ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]

[2145] Step 5:

[2146] The server receives the plan data returned from the generation AI and sends it to the terminal.

[2147] Data to send to the device: { "plan": ["Monday 6:00 - 7:00", "Wednesday 6:00 - 7:00", "Friday 6:00 - 7:00"]}

[2148] Step 6:

[2149] The terminal displays the generated plan to the user.

[2150] The screen will display "Your Running Plan: Monday 6:00 - 7:00, Wednesday 6:00 - 7:00, Friday 6:00 - 7:00."

[2151] Plan execution and reminders

[2152] Step 1:

[2153] The server retrieves the user's schedule data and finds the best time.

[2154] Retrieves calendar information from the database.

[2155] Step 2:

[2156] The server generates reminder settings based on the measurement results.

[2157] Reminder settings: {"date": "Monday", "time": "5:50"}

[2158] Step 3:

[2159] The server connects the reminder data to the calendar app.

[2160] Call the Calendar API to add a reminder.

[2161] Step 4:

[2162] The device will display a reminder notification to the user at the specified time.

[2163] Display a notification message: "It's time for a run. Starting Monday at 6:00."

[2164] Progress monitoring and advice

[2165] Step 1:

[2166] After exercising, the user inputs progress data into the terminal.

[2167] Example: "Distance run: 5km, time taken: 30 minutes"

[2168] Step 2:

[2169] The terminal formats the user input data and sends it to the server.

[2170] Data format: { "date": "2023-10-03", "distance": 5, "time": 30}

[2171] Step 3:

[2172] The server stores the user's progress data in a database and analyzes it.

[2173] SQL query to save the data: INSERT INTO progress (date, distance, time) VALUES ("2023-10-03", 5, 30)

[2174] Step 4:

[2175] The generative AI generates progress dashboards and enforcing messages based on the analysis results.

[2176] Generate a progress report and encouraging message: {"total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}

[2177] Step 5:

[2178] The server receives reports and messages from the generated AI and sends them to the terminal.

[2179] Send data: { "report": { "total_distance": 15, "total_time": 90, "message": "Great! You achieved your goal. Next time, try going a little further!"}}

[2180] Step 6:

[2181] The terminal displays progress reports and enquiries to the user.

[2182] The screen will display "Total distance: 15 km, Total time: 90 minutes. Great! You achieved your goal. Next time, try going a little further!"

[2183] Collaboration with emotion engine

[2184] Step 1:

[2185] The user provides input for emotion recognition.

[2186] For example, after exercising, the user may input feedback such as "I'm a little tired from running today."

[2187] Step 2:

[2188] The device formats the emotion data and sends it to the server.

[2189] Emotion data format: { "emotional_state": "Tired"}

[2190] Step 3:

[2191] The server receives the emotion data and sends it to the emotion engine.

[2192] Pass data to the emotion engine: analyze_emotion("tired")

[2193] Step 4:

[2194] The emotion engine analyzes the user's emotional state and sends the results back to the server.

[2195] Analysis result: { "emotion": "fatigue", "level": "high"}

[2196] Step 5:

[2197] Based on the results of the emotion engine, the server instructs the generation AI to adjust the encouraging message.

[2198] Instructions to the generating AI: generate_message("Fatigue", "High")

[2199] Step 6:

[2200] The generative AI generates encouraging messages based on emotional state.

[2201] Generated message: "Get some light exercise to refresh yourself."

[2202] Step 7:

[2203] The server sends the generated enqueuing message to the terminal.

[2204] Send data: { "message": "Let's do some light exercise to refresh ourselves"}

[2205] Step 8:

[2206] The terminal displays the enlargement message to the user.

[2207] The screen will display a message saying, "Let's incorporate some light exercise to refresh ourselves." Emotional data will also be used to generate and improve plans. For example, if the user is feeling tired, the generation AI will adjust the plan to reduce the load of the next activity.

[2208] In this way, it is possible to create and support plans that take the user's emotional state into account, and to more effectively support the user's motivation.The system flexibly responds to the goals set by the user and provides continuous motivation, allowing the user to continue their exercise or study efforts.

[2209] Example 2

[2210] 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."

[2211] While traditional goal management systems can monitor users' progress and provide personalized plans, they struggle to adapt flexibly to the user's emotional state. They also lack the ability to automatically improve and update the generated plans, limiting their ability to continuously support user motivation. Furthermore, reminder functions are not fully integrated, making it difficult to smoothly manage users' schedules.

[2212] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for the generative AI model to generate an individual plan based on the goal set by the user, a means for acquiring the user's schedule information and finding the optimal time, and a means for monitoring the user's progress data and generating a dashboard and encouraging messages that visualize the progress based on the analysis results. This not only provides an individual plan for achieving the user's goal, but also makes it possible to provide encouraging messages that adapt to the user's emotional state. In addition, the user's schedule is smoothly managed through the reminder function, and the generative AI model automatically improves and updates the plan, enabling continuous motivation support.

[2213] A "generative AI model" refers to an artificial intelligence that generates a personalized plan based on the goals set by the user.

[2214] A "goal" is an indicator that indicates a specific activity or result that a user sets to achieve.

[2215] "Schedule information" refers to data used for a user's activity schedule and time management.

[2216] "Progress data" is information that indicates a user's actual progress and achievements toward a goal.

[2217] A "dashboard" is an interface that visually displays a user's progress, making it easier to understand.

[2218] "Encouragement messages" are messages provided to increase motivation depending on the user's progress and emotional state.

[2219] "Emotion data" is information about the user's emotional state estimated based on facial expressions, voice, text input, and the like.

[2220] "Automatic plan improvement and updating" refers to the process in which the generative AI model optimizes existing plans and generates new plans based on the user's progress data and emotional data.

[2221] A "calendar application" refers to software that manages a user's schedule information and adjusts reminders and appointments.

[2222] The present invention is a system for generating an optimal plan based on a goal set by a user and supporting the execution of that plan. This system is realized mainly by three entities: a server, a terminal, and a user. Specific embodiments of the system are described in detail below.

[2223] User registration and goal setting

[2224] First, the user sets a goal for exercise or studying and enters that information into the device. For example, specific goals such as "running," "three times a week," and "5 kilometers each time" are entered into the device's application. The device then formats this information into JSON format and sends it to the server. The server analyzes the received data and requests the generative AI model to generate a plan. At this time, it generates a prompt like the following:

[2225] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[2226] The generative AI model generates an optimal plan based on this prompt and sends it back to the server, which then sends the plan to the device, which displays it to the user.

[2227] Plan execution and reminders

[2228] The server retrieves the user's calendar and schedule information and finds the optimal time based on the generated plan. For example, if a "running plan" starts at 6:00 a.m. on Mondays, Wednesdays, and Fridays, it generates reminder settings for those times. The server then connects the reminder data to the calendar application and sets the reminder. The device then displays a reminder notification to the user at the specified time to help them execute the plan.

[2229] Progress monitoring and advice

[2230] After the user finishes an activity, they enter progress data into the device. For example, they enter data such as "distance run: 5 km, time required: 30 minutes." The device formats this data into JSON format and sends it to the server. The server stores the progress data in a database and performs analysis. Based on the analysis results, the generative AI model generates a progress dashboard and enumeration messages. The server sends the generated reports and messages to the device, which then displays them to the user.

[2231] Collaboration with emotion engine

[2232] This system can also be combined with an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. and sends the data to the server. For example, if the user is feeling stressed, the emotion engine will send the data tagged with "stress." Based on this emotion data, the generative AI model adjusts the encouragement message. For example, if the user is feeling stressed, a message such as "Try some light exercise to refresh yourself" is generated. The server then sends the generated encouragement message to the device, which then displays it to the user. Emotion data is also used to generate and improve plans.

[2233] In this way, the system provides flexible and effective planning and motivational maintenance functions to help users achieve their goals. Plan generation and automatic improvement using a generative AI model, reminder setting, progress monitoring, and even integration with an emotion engine can support users' sustained motivation.

[2234] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2235] Step 1:

[2236] User sets goals

[2237] Users input their exercise or study goals into the device application, such as "running," "three times a week," and "5 kilometers per session."

[2238] (Input): Goal data set by the user (e.g., "running," "three times a week," "5 kilometers per session").

[2239] (Output): The target data entered into the terminal.

[2240] (Specific action): The user enters their goal into the app's input form and clicks the "Save" button.

[2241] Step 2:

[2242] The device formats the data and sends it to the server

[2243] The terminal formats the target data entered by the user into JSON format and sends it to the server.

[2244] (Input): The target data entered into the terminal.

[2245] (Output): The goal data formatted in JSON.

[2246] (Specific operation): The terminal program formats the target data in JSON format as shown below and sends it to the server via an HTTP POST request.

[2247] json

[2248] {

[2249] "goal_type": "running",

[2250] "frequency_per_week": 3,

[2251] "distance_per_session": 5

[2252] }

[2253] Step 3:

[2254] The server requests the generative AI model to generate a plan.

[2255] The server analyzes the received data and generates a prompt statement that requests the generative AI model to generate a plan.

[2256] (Input): The target data formatted in JSON.

[2257] (Output): The prompt sent to the generative AI model.

[2258] (Specific action): The server generates a prompt like this:

[2259] "User name: Yamada Taro, goal: running, 3 times a week, 5km per session, current ability: intermediate. Please generate the optimal training plan."

[2260] This prompt is then sent to a generative AI model and a plan is received.

[2261] Step 4:

[2262] The server sends the generated plan to the device.

[2263] The generated plan is returned to the server, and then transmitted from the server to the terminal.

[2264] (Input): The plan received from the generative AI model.

[2265] (Output): Plan data sent to the device.

[2266] (Specific operation): The server returns the plan as an HTTP response, and the device analyzes and displays the received data.

[2267] Step 5:

[2268] The device displays the plan to the user.

[2269] The terminal displays the generated plan to the user.

[2270] (Input): Plan data received from the server.

[2271] (Output): The plan that is displayed to the user.

[2272] (Specific behavior): The device app displays plans in a list format and adds "Next" and "Details" buttons.

[2273] Step 6:

[2274] The server retrieves the user's schedule information.

[2275] The server retrieves the user's calendar and schedule information.

[2276] (Input): Schedule information from a calendar application.

[2277] (Output): Schedule information stored on the server.

[2278] (Specific operation): The server retrieves schedule data from third-party services such as Google Calendar via API.

[2279] Step 7:

[2280] The server generates the reminder settings

[2281] The server generates reminder settings based on the acquired schedule information.

[2282] (Input): User's schedule information and generated plan.

[2283] (Output): Reminder setting data.

[2284] (Specific behavior): The server generates reminder settings in JSON format based on the schedule information and plan:

[2285] json

[2286] {

[2287] "reminder_time": "2023-10-02T06:00:00",

[2288] "reminder_message": "It's time for a run"

[2289] }

[2290] Step 8:

[2291] The device displays a reminder notification to the user.

[2292] Your device will display notifications at the specified time based on your reminder settings.

[2293] (Input): Reminder setting data.

[2294] (Output): Reminder notification to the user.

[2295] (Specific behavior): The device will pop up a reminder notification and provide a "Confirm" or "Snooze" button.

[2296] Step 9:

[2297] The user enters progress data

[2298] After completing an activity, the user inputs progress data into the terminal.

[2299] (Input): Progress data entered by the user (e.g., "Distance run: 5 km, Time taken: 30 mins").

[2300] (Output): Progress data entered into the terminal.

[2301] (Specific action): The user enters data into a dedicated input form and clicks the "Submit" button.

[2302] Step 10:

[2303] The device formats the progress data and sends it to the server.

[2304] The device formats the entered progress data into JSON format and sends it to the server.

[2305] (Input): Progress data entered by the user.

[2306] (Output): Progress data in JSON format.

[2307] (Specific operation): The device formats the progress data as follows and sends it to the server:

[2308] json

[2309] {

[2310] "distance_ran": 5,

[2311] "time_taken": 30

[2312] }

[2313] Step 11:

[2314] The server saves the progress data in a database and performs analysis.

[2315] The server stores the progress data in a database and performs analysis.

[2316] (Input): Progress data in JSON format.

[2317] (Output): Analysis result data.

[2318] (Specific behavior): The server executes a database query, stores progress data, and then launches an analysis script to process the data.

[2319] Step 12:

[2320] Generative AI models generate dashboards and enlargement messages

[2321] The generative AI model generates progress dashboards and enlargement messages based on the analysis results.

[2322] (Input): Analysis result data.

[2323] (Output): Dashboard data and enumeration messages.

[2324] (Specific behavior): The generative AI model generates feedback on progress and creates messages to motivate the user.

[2325] Step 13:

[2326] The server sends reports and messages to the terminal, which displays them

[2327] The server sends reports and enlargement messages from the generative AI model to the terminal, which displays them to the user.

[2328] (Input): Dashboard data and enumeration messages.

[2329] (Output): Reports and messages displayed to the user.

[2330] (Specific behavior): The device displays a dashboard or message on the screen and provides "Next step" or "View details" buttons.

[2331] Step 14:

[2332] Emotion engine recognizes user emotions

[2333] The emotion engine recognizes emotions from the user's facial expressions, voice, and text input, and sends that data to the server.

[2334] (Input): User facial expression, voice, and text input data.

[2335] (Output): Emotion data.

[2336] (Specific operation): The emotion engine analyzes data in real time and generates emotion data (e.g., stress).

[2337] Step 15:

[2338] Generative AI models tailor encouraging messages based on emotional data

[2339] The generative AI model tailors appropriate encouraging messages based on emotional data.

[2340] (Input): Emotion data.

[2341] (Output): The adjusted encausation message.

[2342] (Specific operation): The generative AI model takes emotion data as input and generates messages such as the following:

[2343] If the user is feeling stressed, the message is "Try some light exercise to refresh yourself."

[2344] Step 16:

[2345] The server sends a message to the terminal, which displays it to the user.

[2346] The server sends the generated encumbrance message to the terminal, which displays it to the user.

[2347] (Input): The adjusted encausation message.

[2348] (Output): The enumeration message that is displayed to the user.

[2349] (Specific behavior): The device displays the message as a notification and provides a "View details" or "OK" button.

[2350] In this way, the system generates a plan based on the user's goals and helps maintain the user's motivation through schedule management, progress monitoring, and providing emotionally appropriate encouragement messages.

[2351] (Application example 2)

[2352] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2353] In conventional security services, managing security guards' work schedules and patrol routes is complicated, and there are a lack of appropriate measures to maintain the guards' motivation and mental health. This makes it difficult for them to work efficiently and safely, and leads to the accumulation of stress and fatigue.

[2354] 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 the generation AI to generate an individual plan based on goals set by the user; means for acquiring the user's date and time information and finding the optimal time; means for monitoring the user's progress data and generating a display device and encouraging messages that visualize the progress based on the analysis results; means including an emotion analysis device that recognizes the user's emotional state; and means for the generation AI to adjust the encouraging messages based on data from the emotion analysis device. This improves the efficiency of schedule management for security guards and enables appropriate work plans and emotional care tailored to each individual guard.

[2355] "Generative AI" is a system that uses artificial intelligence technology to generate optimal plans according to the goals set by the user.

[2356] A "personalized plan" is a personalized execution plan that takes into account each user's goals, schedule, and emotional state.

[2357] "Date and time information" is data from a user's schedule or calendar that is used to optimize the timing of activities.

[2358] "Progress data" refers to data that indicates the actual progress of a user as they execute a plan, including, for example, the tasks completed and the time required.

[2359] A "display device" is a device that visually displays progress and encouraging messages to a user, such as smart glasses or a smartphone.

[2360] An "encouraging message" is a message of encouragement or advice provided to increase the user's motivation.

[2361] An "emotion analysis device" is a device that recognizes a user's emotional state and responds or adjusts appropriately. It includes facial expression analysis cameras and voice analysis devices.

[2362] A "calendar application" is software that manages a user's schedule and has the function of setting reminders and sending notifications.

[2363] "Automatic improvement" is the process by which the generative AI dynamically modifies the plan based on the user's progress data and emotional state, updating it to a more effective plan.

[2364] The system for implementing this invention consists of the following steps: First, a user uses smart glasses to register and set goals. The user enters specific exercise or study goals, and the information is formatted and sent to the server. The data format used to format the information is JSON.

[2365] The server analyzes the received data and generates an individual plan using a generative AI model. The generative AI model takes into account the user's current abilities and time constraints to construct an optimal plan. The generated plan is then sent from the server to the smart glasses and displayed to the user.

[2366] The server also retrieves the user's date and time information (schedule and calendar data) to find the optimal time to incorporate the plan into the schedule. For example, optimizing security guard patrol times and break times. This data is integrated with the calendar application and set as a reminder. The smart glasses will display the reminder at the specified time to help the user execute the plan.

[2367] After the user executes the plan, progress data is input into the smart glasses. For example, "Time to complete the tour route: 30 minutes." This data is formatted and sent back to the server. The server stores the progress data in a database, and the generative AI model analyzes it. Based on the analysis results, a progress display and encouraging messages are generated and sent to the smart glasses.

[2368] Furthermore, the emotion analyzer monitors the user's emotional state. Using a facial expression camera and a voice analyzer, the emotion analyzer sends data to the server if the user feels stressed or tired. Based on the emotional data, the generative AI model adjusts the encouraging messages and generates custom messages such as "Take a short break" or "Do some light exercise to change your mood." These are then displayed on the smart glasses.

[2369] As a concrete example, consider a security guard who sets the goal of "patrol routes three times a day, breaks every two hours." The generative AI model uses this information to create a personalized work plan. If the emotion analyzer detects "high stress," the generative AI model automatically suggests increasing break time, displaying the message, "Take a five-minute break in a relaxing place."

[2370] An example prompt is:

[2371] User ID: Security Guard 123

[2372] Current Ability: Intermediate

[2373] Time constraints:

[2374] Start time: 8:00

[2375] End time: 18:00

[2376] the goal:

[2377] Patrol frequency: 3 times a day

[2378] Break interval: Every 2 hours

[2379] In this way, it is possible to realize a system that improves both the work efficiency and mental health of security guards.

[2380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2381] Step 1:

[2382] The user sets goals using the smart glasses. The user uses the input interface of the smart glasses to input goals, such as "Patrol route: 3 times a day, Break: every 2 hours." This data is formatted in JSON format and sent to the server. The input is specific goal data, and the output is formatted JSON data.

[2383] Step 2:

[2384] The server parses the received goal data. The server parses the JSON format goal data and generates an individual plan based on the generative AI model, taking into account the user's current abilities and time constraints. The input is the JSON data to be parsed, and the output is the generated plan data.

[2385] Step 3:

[2386] The server sends the generated plan to the smart glasses, which the terminal displays to the user. The terminal visually displays the received plan data and guides the user to the next step. The input is the plan data sent from the server, and the output is the plan displayed on the smart glasses.

[2387] Step 4:

[2388] The server obtains the user's date and time information and incorporates the plan into the schedule. The server works with the calendar application to find the optimal patrol and break times and set reminders. The input is the user's schedule data, and the output is the set reminder data.

[2389] Step 5:

[2390] When the reminder time arrives, the smart glasses notify the user. The device displays the reminder at the set time and prompts the user to carry out the plan. The input is the reminder data, and the output is the notification displayed on the smart glasses.

[2391] Step 6:

[2392] The user executes the plan and inputs the progress data into the smart glasses. The terminal formats the progress data received from the user and sends it to the server. The input is the progress data, and the output is the formatted progress data.

[2393] Step 7:

[2394] The server analyzes the received progress data. The server stores the progress data in a database, and the generative AI model analyzes the progress and generates a display and encouragement message. The input is the received progress data, and the output is the encouragement message and progress data.

[2395] Step 8:

[2396] The server receives data from the emotion analyzer, and the generative AI model adjusts the encouraging message. The emotion analyzer determines the user's emotions from facial expressions and voice and sends that data to the server. The server analyzes the data and adjusts the encouraging message using the generative AI model. The input is emotional data, and the output is the adjusted encouraging message.

[2397] Step 9:

[2398] The smart glasses display the tailored encouraging message to the user. The terminal visually displays the message sent from the server to motivate the user. The input is the encouraging message sent from the server, and the output is the message displayed on the smart glasses.

[2399] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2400] 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.

[2401] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2402] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2403] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2404] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2405] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2406] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2407] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2408] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2409] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2410] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2411] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2412] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2413] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2414] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2415] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2416] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2417] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2418] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2419] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publ...

Claims

1. A means for the Generative AI to generate an individual plan based on the goals set by the user; A means of obtaining the user's schedule information and finding the best time; A means for monitoring the user's progress data and generating a dashboard and enumeration messages that visualize the progress based on the analysis results; A system including:

2. The system of claim 1 further comprising means for linking with a calendar application to set reminders.

3. The system of claim 1 further comprising means for automatically improving and updating the plan by the generating AI.

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