System

Abstract

A system is provided.SOLUTION: A system comprising: input means for a user to set a goal; means for inputting current context data of the user; means for comparing the goal and the current context data of the user to calculate a difference; means for generating a concrete action plan based on the difference; means for the user to record an implementation status of the action plan; means for following up progress based on the implementation status; and means for modifying the action plan according to the progress.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many people face various mental health issues, career challenges, and health management problems. Effective and continuous support is needed to resolve these issues and achieve individual goals. However, current systems struggle to provide customized support tailored to the needs of individual users, resulting in a lack of persistent support for goal achievement. The objective of this invention is to provide a system that effectively supports goal achievement by proposing optimal action plans for the goals set by the user and following up on their implementation. [Means for solving the problem]

[0005] The present invention provides a system that includes an input means for a user to set goals and a means for inputting the user's current situation data. It also provides a means for comparing these data and calculating the difference between the goal and the current situation, and generates a specific action plan based on this difference. It also includes a means for the user to record the implementation status of the action plan and a means for following up on progress based on the implementation status. Furthermore, it provides a system that includes a means for modifying the action plan according to progress, thereby supporting the user in taking continuous and effective action toward achieving their goals.

[0006] "User" refers to any individual or entity that uses the System.

[0007] A "goal" is a specific state or result that a user is trying to achieve.

[0008] "Input means" refers to a device or interface that allows a user to provide information such as goals and current status data to the system.

[0009] "Current status data" is specific data that indicates the user's current situation, and includes, for example, weight, lifestyle habits, and work progress.

[0010] "Difference" refers to the gap that exists between a user's goal and their current situation.

[0011] An "action plan" is a plan that lists specific actions or steps that a user must take to achieve the goals they have set.

[0012] "Recording means" refers to a device or interface that allows a user to enter and store details of actions taken based on an action plan into the system.

[0013] "Follow-up measures" refer to the system's functions to monitor the user's progress and provide necessary support and advice to help them achieve their goals.

[0014] "Corrective measures" refers to the system's ability to adjust the action plan based on the user's progress and suggest a new plan that is best suited to achieving the goal. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention relates to an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to support goal achievement. This system works in conjunction with a server and a terminal, providing effective feedback to the user.

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

[0038] 1. Goal Setting

[0039] The terminal provides an interface for the user to set goals.

[0040] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[0041] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[0042] 2. Current situation analysis and understanding of differences

[0043] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[0044] Users input data about their current weight, daily diet, and exercise habits.

[0045] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[0046] 3. Develop an action plan

[0047] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[0048] The device displays this plan of action to the user, who can then fine-tune it as needed.

[0049] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[0050] 4. Implementing and following up on the action plan

[0051] Users record their daily meals and exercise based on an action plan.

[0052] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[0053] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[0054] 5. Feedback on results and revision of plans

[0055] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[0056] The server suggests next steps or additional advice and sends that information to the device.

[0057] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0058] Specific examples

[0059] For example, if a user's goal is to "lose 5kg," the steps would be as follows:

[0060] 1. Goal Setting

[0061] Users enter "lose 5kg" into the device to set their goal.

[0062] The server stores the goals in a database.

[0063] 2. Current situation analysis and understanding of differences

[0064] The user inputs their current weight (e.g., 75 kg).

[0065] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0066] 3. Develop an action plan

[0067] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[0068] The user reviews it and finalizes the plan on the device.

[0069] 4. Implementing and following up on the action plan

[0070] Users enter their daily diet and exercise habits into the device.

[0071] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0072] 5. Feedback on results and revision of plans

[0073] The server will evaluate progress after one month and suggest next steps to take.

[0074] The user sets up a new action plan and sends it back to the server, updating the system.

[0075] In this way, the system of the present invention effectively assists users in achieving their goals.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The device displays a goal entry form for the user, where the user enters a specific goal, such as "lose 5 kg."

[0079] Step 2:

[0080] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[0081] Step 3:

[0082] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[0083] Step 4:

[0084] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[0085] Step 5:

[0086] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[0087] Step 6:

[0088] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[0089] Step 7:

[0090] The device sends the determined action plan to the server, which stores the action plan in a database.

[0091] Step 8:

[0092] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[0093] Step 9:

[0094] The server analyzes the user's progress based on the transmitted data, periodically generates progress reports, and sends them to the device.

[0095] Step 10:

[0096] The device displays a progress report to the user, and the server summarises the user's progress data after one month to evaluate the achievement.

[0097] Step 11:

[0098] The server generates next steps and additional advice based on the progress achieved, and sends it to the device, which displays it to the user.

[0099] Step 12:

[0100] The user sets a new action plan based on the feedback and submits it again to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[0101] Through the above processing steps, a system that effectively supports the user in achieving their goals is realized.

[0102] Example 1

[0103] 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."

[0104] Conventional goal achievement support systems have not adequately integrated the analysis of the user's current situation, the creation of an action plan, and follow-up, resulting in a lack of support for users to consistently work toward their goals. In particular, it has been difficult to automate and continuously support the entire process from inputting current situation data to achieving the goal.

[0105] 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.

[0106] In this invention, the server includes input means for the user to set a goal, input means for inputting the user's current situation data, calculation means for comparing the user's goal with the current situation data and calculating the difference, generation means for generating a specific action plan based on the difference, recording means for the user to record the implementation status of the action plan, follow-up means for following up on progress based on the implementation status, correction means for correcting the action plan in accordance with the progress, display means for displaying the generated action plan to the user, and storage means for saving the data input by the user in a database. This allows the user to continue making consistent efforts toward their goal, and provides effective support for goal achievement.

[0107] "User" refers to an individual who uses this system to achieve a goal.

[0108] "Server" refers to the computer system that processes and stores user data and manages the entire system.

[0109] "Input means" refers to the interface through which the user inputs goals and current situation data into the system.

[0110] "Calculation means" refers to a processing device that has the function of comparing the user's goal with the current situation data and calculating the difference.

[0111] "Generation means" refers to a function that automatically generates a specific action plan based on the difference.

[0112] "Recording means" refers to an interface that allows a user to record the implementation status of an action plan in the system.

[0113] "Follow-up measures" refers to the ability to track progress and provide feedback to users based on recorded implementation.

[0114] "Correction measures" refers to the function of automatically correcting the action plan based on the results of the follow-up.

[0115] "Display means" refers to an interface for visually displaying the generated action plan to the user.

[0116] "Storage means" refers to the function for storing data entered by the user and the generated action plan in a database.

[0117] This invention is an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to help achieve the goal. This system works in conjunction with the server and the terminal, providing effective feedback to the user.

[0118] The system operates using the following hardware and software:

[0119] Hardware: User devices (smartphones and tablets), servers

[0120] software:

[0121] Server side: Python, Flask, MySQL, Pandas

[0122] Device side: Dedicated application (e.g., health management app)

[0123] System Operation

[0124] 1. Goal Setting

[0125] The terminal provides an interface for the user to set goals.

[0126] The user enters the goal they want to achieve (e.g., lose 5 kg) into the device's input screen.

[0127] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[0128] 2. Current situation analysis and understanding of differences

[0129] The terminal provides an interface for the user to input current data (e.g., weight, diet, and exercise habits).

[0130] Users input data about their current weight, daily diet, and exercise habits.

[0131] These data are sent from the terminal to a server, which stores them in a database and calculates the difference from the target.

[0132] 3. Develop an action plan

[0133] The server generates a specific action plan based on the difference between the user's goal and current situation (e.g., reducing 500 kcal per day and exercising three times a week).

[0134] The device displays this plan of action to the user, who can then fine-tune it as needed.

[0135] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[0136] 4. Implementing and following up on the action plan

[0137] Users record their daily meals and exercise based on an action plan.

[0138] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[0139] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[0140] 5. Feedback on results and revision of plans

[0141] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[0142] The server suggests next steps or additional advice and sends this information to the terminal.

[0143] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0144] Specific examples

[0145] For example, if a user's goal is to "lose 5kg," they might take the following steps:

[0146] 1. Goal Setting

[0147] Users enter "lose 5kg" into the device to set their goal.

[0148] The server stores the goals in a database.

[0149] 2. Current situation analysis and understanding of differences

[0150] The user inputs their current weight (e.g., 75 kg).

[0151] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0152] 3. Develop an action plan

[0153] The server generates a specific action plan to achieve the goal (e.g., reduce 500 kcal per day and exercise three times a week).

[0154] The user reviews it and finalizes the plan on the device.

[0155] 4. Implementing and following up on the action plan

[0156] Users enter their daily diet and exercise habits into the device.

[0157] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0158] 5. Feedback on results and revision of plans

[0159] The server will evaluate progress after one month and suggest next steps to take.

[0160] The user sets up a new action plan and sends it back to the server, updating the system.

[0161] Prompt Sentence Examples

[0162] "I'd like you to set a goal and create an action plan to achieve it. First, please enter your current weight and your goal weight. For example, current weight 75kg, goal weight 70kg."

[0163] "Next, enter your daily calorie intake and exercise frequency. Example: Lose 500 calories per day, exercise three times per week."

[0164] "It records your daily progress (food and exercise) and tracks your weight changes. It provides feedback based on your results and suggests new action plans."

[0165] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0166] Step 1:

[0167] goal setting

[0168] The terminal provides an input interface for the user to set goals.

[0169] Users input the goal they want to achieve (e.g., lose 5 kg).

[0170] Input: A goal entered by the user (e.g., weight 70 kg).

[0171] Data processing: The terminal formats the input target data and converts it into a form that can be sent to the server.

[0172] The device sends the entered goal to the server via REST API.

[0173] The server stores the received target data in a database.

[0174] Output: The status that the target data has been saved to the database.

[0175] Specific behavior:

[0176] The user launches the dedicated application and goes to the "Goal Setting" screen.

[0177] Enter "lose 5kg" and press the send button.

[0178] The terminal converts the input data into JSON format and sends it to the server.

[0179] The server receives the data and stores it in a MySQL database.

[0180] Step 2:

[0181] Current situation analysis and understanding of differences

[0182] The terminal provides an interface for the user to input current status data.

[0183] Users input their current weight, daily diet, and exercise habits.

[0184] Input: User-entered current weight (e.g., 75 kg), diet, and exercise habits.

[0185] Data processing: The terminal collects the current data entered and converts it into a form that can be sent to the server.

[0186] The terminal sends the entered data to the server via REST API.

[0187] The server stores the data in a database and calculates the difference between the target and the current situation.

[0188] Output: Difference data (e.g., 5 kg difference).

[0189] Specific behavior:

[0190] The user goes to the "Current Status Input" screen and enters their current weight (75 kg), dietary and exercise information.

[0191] The device converts the data into JSON format and sends it to the server.

[0192] The server receives the data and calculates the difference (5kg) from the target weight using a Python script.

[0193] Step 3:

[0194] Developing an action plan

[0195] The server generates an action plan based on the current data and the goals.

[0196] Input: Difference data (e.g., difference of 5 kg).

[0197] Data processing: The server generates a specific action plan using a Python script based on the differential data.

[0198] The server sends the generated action plan to the terminal in JSON format.

[0199] The device displays the action plan to the user and provides an interface for the user to modify it as needed.

[0200] The user finalizes the action plan and sends it from the device to the server.

[0201] The server stores the plan in a database.

[0202] Output: Status that the action plan data has been saved to the database.

[0203] Specific behavior:

[0204] Based on the differential data received, the server generates an action plan to reduce 500 kcal per day and exercise three times a week.

[0205] The generated plan is sent to the terminal in JSON format.

[0206] The user reviews the plan, makes any necessary adjustments, and presses the confirm button.

[0207] The terminal sends the final action plan to the server, which stores it in a database.

[0208] Step 4:

[0209] Implementing and following up on action plans

[0210] Users record their daily meals and exercise based on an action plan.

[0211] Input: Daily diet and exercise data entered by the user.

[0212] Data processing: The terminal aggregates the recorded data and converts it into a form that can be sent to the server.

[0213] The device sends the recorded data to the server via a REST API.

[0214] The server stores the data in a database and analyzes the progress.

[0215] The server periodically generates progress reports and sends them to the terminal.

[0216] The terminal displays progress reports to the user.

[0217] Output: Progress report data is sent to the user.

[0218] Specific behavior:

[0219] The user enters their daily food and exercise records into the device app.

[0220] Sends input data to the server in JSON format.

[0221] The server stores the received data in a database and analyzes the progress using Python's Pandas.

[0222] The server generates a progress report in JSON format and sends it to the device.

[0223] The terminal displays progress reports to the user.

[0224] Step 5:

[0225] Feedback on results and revision of plans

[0226] The server periodically compiles the user's progress data and evaluates their achievement.

[0227] Input: Accumulated progress data.

[0228] Data processing: The server aggregates the progress data and evaluates the achievement status using a Python script.

[0229] The server suggests next steps or additional advice and sends this information to the device in JSON format.

[0230] The device displays feedback to the user.

[0231] The user sets up a new action plan and sends it from the device to the server.

[0232] The server updates the plan to the database.

[0233] Output: Status that the new action plan data has been saved to the database.

[0234] Specific behavior:

[0235] The server will summarize the progress data after one month and evaluate the achievement.

[0236] Generate next steps or new advice and send it to the device.

[0237] The user receives feedback, sets a new plan of action, and hits submit.

[0238] The server stores the new action plan in the database and updates the system.

[0239] (Application example 1)

[0240] 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."

[0241] Conventional factory robot management systems have the problem of making it difficult to generate specific action plans to achieve production goals and to follow up on progress. In addition, there is no way to suggest next steps using generative AI models or prompts, so managers lack the support they need to carry out effective production activities.

[0242] 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.

[0243] In this invention, the server includes a generative AI model for generating an action plan based on difference data between the target and the current situation, a means for setting prompt sentences for inputting data according to the purpose into the generative AI model, and a means for summarizing progress data for each specific period and proposing the next step to be taken. This enables managers to generate specific action plans for effectively achieving the production targets of factory robots and provide feedback according to progress.

[0244] "Goal setting" is the process by which users input specific numbers or states they want to achieve.

[0245] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates an action plan based on data tailored to a specific purpose.

[0246] A "prompt" is an instruction to provide appropriate data input for a generative AI model, and is used to optimize the output of the generative AI model.

[0247] "Means" is a general term for methods and devices for performing a specific function.

[0248] "Differential data" is a number or piece of information that indicates the difference between the user's goal and the current situation data.

[0249] An "action plan" is a plan that specifies the specific actions and steps needed to achieve a specific goal.

[0250] "Follow-up" is the process of checking whether the action plan is being implemented and tracking progress.

[0251] "Feedback" refers to advice and evaluation provided based on progress and implementation results.

[0252] "Progress data" is data that indicates the activities and production volume that the user has carried out based on the action plan.

[0253] "At specific intervals" refers to a pre-set time interval (e.g., one day, one week, one month).

[0254] A "factory robot" refers to a mechanical device used in a factory to automate production activities.

[0255] A "server" is a computer system that centrally stores data, performs calculations, and operates generative AI models.

[0256] A system including "means" refers to the entire system that combines individual methods or functions for performing each function.

[0257] This invention applies to an AI personal coaching system that supports users in achieving their goals. This system aims to optimize production activities and is particularly effective in managing robots operated in factories.

[0258] System Configuration

[0259] This system operates in collaboration between a server and terminals (smartphones, head-mounted displays, robots). The server runs on a cloud service (e.g., Amazon Web Services, Google Cloud Platform) and operates generative AI models (e.g., TensorFlow, PyTorch). The terminals are factory robots (e.g., FANUC, ABB), as well as smartphones and head-mounted displays (e.g., Microsoft HoloLens) used by managers.

[0260] goal setting

[0261] The user (factory manager) is provided with an interface to set production targets via a smartphone or head-mounted display. For example, they can input a specific target, such as "daily production target 50 units." This data is sent to a server and stored in a database.

[0262] Current situation analysis and understanding of differences

[0263] The robot sends its current production volume and operation data to the server. The server stores this data in a database and calculates the difference between the current volume and the target volume. For example, if 30 units are currently being produced, the difference (20 units) from the target volume (50 units) is calculated.

[0264] Developing an action plan

[0265] The server uses a generative AI model to generate an action plan based on differential data between the current situation and the goal. The server inputs appropriate data using prompt statements, and the AI ​​model outputs the optimal action plan. For example, the generated action plan might be, "Recommend the production of an additional 20 units during the shift." This plan is displayed on a smartphone, head-mounted display, or robot.

[0266] Implementing and following up on action plans

[0267] The robot carries out production activities based on the generated action plan. The terminal periodically collects progress data and sends it to the server. The server analyzes the progress data and periodically generates progress reports. These reports are sent to the terminal and displayed to the user.

[0268] Feedback on results and revision of plans

[0269] At regular intervals (e.g., daily), the server summarizes the progress data and suggests next steps or improvements. It uses an AI model to generate the next action plan and sends that feedback to the device. The user then sets a new action plan based on this feedback and sends it back to the server to update the system.

[0270] Specific examples

[0271] For example, here is a prompt for a generative AI model:

[0272] "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next action plan (e.g., recommend producing an additional 20 units during this shift)."

[0273] In this way, the system of the present invention can provide specific support for effectively achieving production goals of factory robots.

[0274] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0275] Step 1:

[0276] The user sets a goal using a smartphone or head-mounted display. At this time, the user inputs a specific goal, such as "daily production target 50 units." The input data is sent from the device to a server, which stores the goal in a database. The specific operation is to input the goal into the goal setting interface.

[0277] Step 2:

[0278] The robot measures the current production volume and sends that data to the server. For example, data such as "current production volume: 30 units" is sent. The server stores the received data in a database and calculates the difference between the target value (e.g., 20 units). Specifically, the robot collects sensor data and sends the data through an interface.

[0279] Step 3:

[0280] The server sends a prompt to the generative AI model based on the difference data between the target and current situation. The prompt text is entered as "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next necessary action plan." The AI ​​model generates an action plan based on this prompt and outputs specific instructions (e.g., "Recommend the production of an additional 20 units during this shift"). Specifically, the data transmission process and the AI ​​generation process are carried out.

[0281] Step 4:

[0282] The generated action plan is sent to the terminal and displayed on the robot's and the administrator's smartphone or head-mounted display. The user can review the action plan and make any necessary adjustments. Specifically, data is sent and displayed via an API.

[0283] Step 5:

[0284] The robot carries out production activities according to the generated action plan. For example, it operates according to the instruction "produce an additional 20 units." Progress data is collected periodically and sent to the server. This data is stored in a database and used for subsequent processing. Specific operations are performed by the robot's control system.

[0285] Step 6:

[0286] The server periodically analyzes the progress data and generates a progress report. The generated progress report is sent to the device and displayed to the user. The user can use this information to determine next steps and areas for improvement. Specifically, the server aggregates and analyzes the data and generates a report.

[0287] Step 7:

[0288] After a certain period of time (e.g., daily), the server summarizes all progress data and suggests the next steps to take. It uses the generative AI model again to generate a new action plan. For example, it might suggest "produce 15 more units in the next shift." The user sets a new action plan based on the feedback and sends it back to the server. Specifically, the data is re-analyzed and a new action plan is generated.

[0289] Step 8:

[0290] The user checks the updated action plan and starts operating the robot again based on it. This cycle is repeated, providing continuous support for achieving production targets. Specifically, the process begins again with goal setting as a loop.

[0291] 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.

[0292] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. This system performs each step of the user's goal setting, current situation analysis, action plan formulation and execution, and progress follow-up. It can also recognize the user's emotions and adjust its behavior based on those emotions. This system operates in cooperation with a server, a terminal, and an emotion engine.

[0293] A natural language description of the program's operation

[0294] 1. Goal Setting

[0295] The terminal provides an interface for the user to set goals.

[0296] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[0297] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[0298] 2. Current situation analysis and understanding of differences

[0299] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[0300] Users input data about their current weight, daily diet, and exercise habits.

[0301] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[0302] 3. Develop an action plan

[0303] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[0304] The device displays this plan of action to the user, who can then fine-tune it as needed.

[0305] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[0306] 4. Implementing and following up on the action plan

[0307] Users record their daily meals and exercise based on an action plan.

[0308] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[0309] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[0310] 5. Feedback on results and revision of plans

[0311] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[0312] The server suggests next steps or additional advice and sends that information to the device.

[0313] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0314] Incorporating an emotion engine

[0315] 1. Emotion recognition

[0316] The device provides an interface for collecting emotions through facial recognition and voice analysis of the user.

[0317] When a user uses the device, it scans the user's facial expressions and tone of voice and sends that data to the emotion engine.

[0318] 2. Emotion analysis

[0319] The emotion engine analyzes emotion data in real time to determine the user's current emotional state.

[0320] The emotion analysis results are sent to the server and stored in the user's database.

[0321] 3. Emotion-based adaptive feedback

[0322] The server tailors action plans and advice based on the user's emotional data, for example providing motivational support if the user is feeling down.

[0323] The device displays tailored feedback and action plans to the user, who then decides on their next move.

[0324] Specific examples

[0325] For example, if a user has a goal of "lose 5kg" and is recording their daily progress, the following steps might be added:

[0326] 1. Goal Setting

[0327] Users enter "lose 5kg" into the device to set their goal.

[0328] The server stores the goals in a database.

[0329] 2. Current situation analysis and understanding of differences

[0330] The user inputs their current weight (e.g., 75 kg).

[0331] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0332] 3. Emotion recognition

[0333] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[0334] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[0335] 4. Develop an action plan

[0336] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[0337] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[0338] 5. Implementing and following up on the action plan

[0339] Users enter their daily diet and exercise habits into the device.

[0340] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0341] It also provides feedback based on the user's emotional state.

[0342] 6. Feedback on results and revision of plans

[0343] The server will assess progress every month and generate next steps or additional advice.

[0344] The plan is readjusted depending on the user's emotional state.

[0345] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[0346] The processing flow will be explained below.

[0347] Step 1:

[0348] The device displays a goal entry form for the user, allowing them to enter a specific goal, such as "lose 5 kg of weight."

[0349] Step 2:

[0350] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[0351] Step 3:

[0352] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[0353] Step 4:

[0354] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[0355] Step 5:

[0356] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[0357] Step 6:

[0358] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[0359] Step 7:

[0360] The device sends the determined action plan to the server, which stores the action plan in a database.

[0361] Step 8:

[0362] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[0363] Step 9:

[0364] The device provides an interface for recognizing the user's emotions. When the user inputs data, the device performs facial recognition and voice analysis to obtain the user's emotional data.

[0365] Step 10:

[0366] The acquired emotion data is sent from the device to the emotion engine, which analyzes the emotion data in real time and identifies the user's emotional state.

[0367] Step 11:

[0368] The emotion engine sends the analyzed emotion results to a server and stores them in the user's database, where the server uses this information to adjust its action plan and feedback.

[0369] Step 12:

[0370] The server periodically generates a progress report based on the user's progress data and emotion data, and sends the report to the device, which then displays the progress report to the user.

[0371] Step 13:

[0372] Every certain period (e.g., one month), the server aggregates the user's progress and emotional data, evaluates their progress, and generates next steps and additional advice, which are then sent to the device.

[0373] Step 14:

[0374] The user then develops a new plan of action based on the feedback and submits it back to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[0375] Through this series of steps, a system that effectively supports users in achieving their goals is realized. The introduction of an emotion engine provides adaptive feedback according to the user's emotional state, achieving more precise support.

[0376] Example 2

[0377] 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."

[0378] Conventional user goal achievement support systems support users in achieving their goals, but they lack feedback that takes into account the user's emotional state. As a result, users' motivation may decrease and stress may increase. Furthermore, the adjustments to the action plan required for goal achievement do not reflect the user's emotional state, so they often fail to provide effective support. Therefore, a system with an adaptive feedback function that takes into account the user's emotional state is needed to effectively support users in achieving their goals.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0380] In this invention, the server includes means for comparing the user's goal with current situation data and calculating the difference, means for generating a specific action plan based on the difference, recognition means for collecting the user's emotional data, analysis means for analyzing the collected emotional data, and adaptive feedback means for adjusting the action plan based on the emotional data, thereby enabling adjustment of the action plan taking the user's emotional state into consideration and more effective support for goal achievement.

[0381] "User" refers to the individual who sets goals, enters data, reviews feedback, etc.

[0382] "Input means" refers to the device or software that provides the interface through which the user inputs goals, current situation data, etc. into the system.

[0383] "Current status data" refers to information such as the user's current weight, lifestyle, and health condition.

[0384] "Comparison method" refers to a program or algorithm that compares the user's goals with current situation data and calculates the difference.

[0385] An "action plan" refers to a plan of specific actions required to achieve the goals set by the user.

[0386] "Action plan generation means" refers to a program or algorithm for creating an appropriate action plan for a user based on differential data.

[0387] "Implementation status" refers to the actions and results that users have actually taken based on their action plan.

[0388] "Follow-up tools" refers to programs or devices that track progress based on the user's implementation and provide advice or feedback as appropriate.

[0389] "Corrective measures" refer to programs or algorithms that review and make necessary corrections to a user's action plan based on their progress data and emotional state.

[0390] "Recognition methods" refers to facial recognition and voice analysis technologies used to collect user emotional data.

[0391] "Analysis Means" refers to a process or program for analyzing the emotional data collected by the Recognition Means and identifying the user's emotional state.

[0392] "Adaptive feedback means" refers to programs or algorithms that adjust action plans and feedback based on analyzed emotional data to provide appropriate assistance to users.

[0393] This invention is an AI personal coaching system that supports users in achieving their goals and operates in combination with an emotion engine. This system supports users through each step of goal setting, current situation analysis, action plan formulation and execution, and progress follow-up, and also has the ability to recognize the user's emotions and adjust its behavior based on those emotions. Specifically, this system operates in cooperation with a server, a terminal, and an emotion engine.

[0394] First, the device provides an interface for users to set goals. Users use this interface to input the goals they want to achieve. For example, if a user sets a goal of "lose 5 kg of weight," this goal is sent from the device to the server, which then stores it in a database.

[0395] Next, the device provides the user with an interface for inputting data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. This data is sent from the device to a server, which stores it in a database and calculates the difference between the user's current status and their goal.

[0396] The server generates a specific action plan based on the difference between the goal and the current situation. For example, if the device suggests reducing 500 kcal per day and exercising three times a week, the device will display this action plan to the user. The user can fine-tune the action plan as needed, and the final action plan will be sent from the device to the server, which will then store it in a database.

[0397] The user records their daily meals and exercise based on the action plan, and the device sends the recorded data to the server, which stores the data in a database and analyzes the progress. Periodically, the server generates a progress report and sends it to the device for display to the user.

[0398] At regular intervals, the server summarizes the user's progress data, evaluates their progress, suggests next steps and additional advice, and sends this information to the device. The user then sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0399] The system is equipped with an emotion engine that recognizes the user's emotional state and provides adaptive feedback based on it. Specifically, the device provides an interface that collects emotions through facial recognition and voice analysis. When the user uses the device, the device scans the user's facial expressions and tone of voice and sends the data to the emotion engine. The emotion engine analyzes the emotion data in real time and determines the user's current emotional state. The emotion analysis results are sent to a server and stored in the user's database.

[0400] The server adjusts the action plan and advice based on the user's emotional data. For example, if the user is feeling depressed, it provides motivational support. The device displays the adjusted feedback and action plan to the user, who then decides on their next course of action.

[0401] Specific examples

[0402] For example, if a user sets a goal of "lose 5kg" and tracks their progress daily, the following steps might be added:

[0403] 1. Goal Setting

[0404] Users enter "lose 5kg" into the device to set their goal.

[0405] The server stores the goals in a database.

[0406] 2. Current situation analysis and understanding of differences

[0407] The user inputs their current weight (e.g., 75 kg).

[0408] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0409] 3. Emotion recognition

[0410] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[0411] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[0412] 4. Develop an action plan

[0413] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[0414] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[0415] 5. Implementing and following up on the action plan

[0416] Users enter their daily diet and exercise habits into the device.

[0417] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0418] It also provides feedback based on the user's emotional state.

[0419] 6. Feedback on results and revision of plans

[0420] The server will assess progress every month and generate next steps or additional advice.

[0421] The plan is readjusted depending on the user's emotional state.

[0422] Prompt Sentence Examples

[0423] Use the following prompt for the generative AI model:

[0424] “Design a system to help users achieve their goals. This system should allow users to set goals, analyze their current situation, create and execute an action plan, follow up on progress, recognize emotions and reflect on that information.”

[0425] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[0426] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0427] Step 1: Goal Setting

[0428] Input: Goal data entered by the user

[0429] Processing: The terminal displays an interface for the user to set a goal. The user inputs the goal they want to achieve. For example, they input the goal "to lose 5 kg."

[0430] Output: The target data is generated and sent from the terminal to the server, which stores it in a database.

[0431] Step 2: Enter current data

[0432] Input: Current weight and lifestyle data entered by the user

[0433] Processing: The device displays an interface for the user to input data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. For example, the user inputs that their current weight is 75 kg and that they exercise twice a week.

[0434] Output: Current status data is generated and sent from the terminal to the server. The server stores this data in a database and calculates the difference from the target.

[0435] Step 3: Calculate the difference

[0436] Input: Goal data and current status data stored on the server

[0437] Processing: The server compares the user's goal with the current data and calculates the difference. For example, it calculates the difference of 5 kg between the goal weight of 70 kg and the current weight of 75 kg.

[0438] Output: The differential data is generated and stored on the server.

[0439] Step 4: Generate an action plan

[0440] Input: Differential data stored on the server

[0441] Processing: The server generates a specific action plan based on the difference data, for example, recommending a reduction of 500 kcal per day and exercising three times a week.

[0442] Output: An action plan is generated and sent to the device, where the user can review it and fine-tune it if necessary.

[0443] Step 5: Adjust your action plan

[0444] Input: User feedback

[0445] Processing: The device displays the generated action plan to the user. The user can fine-tune the action plan as needed. For example, they can make adjustments such as "reduce 600 kcal instead of 500 kcal."

[0446] Output: An adjusted action plan is generated, sent from the device to the server, and stored in a database.

[0447] Step 6: Enter your daily record

[0448] Input: Daily food and exercise data entered by the user

[0449] Process: The user enters their daily diet and exercise information into the device. For example, they record what they ate for breakfast and how much exercise they did that day.

[0450] Output: Daily data is generated and sent from the device to the server, which stores it in a database and analyzes the progress.

[0451] Step 7: Generate progress reports

[0452] Input: Daily data stored on the server

[0453] Processing: The server periodically analyzes the daily data and generates progress reports, e.g., graphing weekly weight loss and calorie consumption trends.

[0454] Output: A progress report is generated, sent to the terminal, and displayed to the user.

[0455] Step 8: Collect emotion data

[0456] Input: User's facial recognition and voice analysis data

[0457] Processing: The device collects emotions through facial recognition and voice analysis of the user, for example, using the camera to collect facial expression data and the microphone to analyze tone of voice.

[0458] Output: Emotion data is generated and sent from the device to the server, where it is analyzed by the emotion engine.

[0459] Step 9: Analyze the sentiment data

[0460] Input: Collected emotion data

[0461] Processing: The emotion engine analyzes the emotion data in real time and determines the user's emotional state. For example, it outputs an analysis result such as "The current emotional state is 'anxious.'"

[0462] Output: The analysis results are generated, sent to the server, and stored in a database.

[0463] Step 10: Providing adaptive feedback

[0464] Input: Parsed emotion data

[0465] Processing: The server adjusts the action plan or advice based on the emotional data, for example, providing motivational support if the user is feeling down.

[0466] Output: The adjusted feedback is generated, sent to the device, and displayed to the user.

[0467] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[0468] (Application example 2)

[0469] 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."

[0470] Conventional personal coaching systems often lack the ability to provide dynamic feedback that takes into account a user's emotional state when helping them achieve their goals. As a result, users' motivation can easily drop, making it difficult to achieve their goals. In particular, when it comes to lifestyle and health management, users' emotional fluctuations have a significant impact on their behavior, so systems that ignore this have difficulty providing effective support.

[0471] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0472] In this invention, the server includes input means for the user to set goals, means for inputting the user's current situation data, means for comparing the user's goals with the current situation data and calculating the difference, means for generating a specific action plan based on the difference, means for the user to record the implementation status of the action plan, means for following up on progress based on the implementation status, means for recognizing and analyzing the user's emotions, means for adjusting the action plan and feedback based on the user's emotional state, and means for modifying the action plan in accordance with progress. This enables dynamic feedback and adjustment of the action plan in response to the user's emotional state, thereby enabling effective goal achievement support.

[0473] "User" refers to an individual who uses the system to achieve a goal.

[0474] "Goal" means a specific result or outcome that the user wants to achieve.

[0475] "Input means" refers to an interface or device that allows a user to input data or information into a system.

[0476] "Current status data" is information about the user's current condition and lifestyle.

[0477] "Difference" refers to the difference or gap between the user's goal and the current situation data.

[0478] An "action plan" is a set of specific actions or steps that a user must take to achieve a goal.

[0479] "Implementation status" refers to the actions that a user actually takes based on the action plan and the results of those actions.

[0480] "Follow-up measures" are measures used to monitor the implementation of the user's action plan and evaluate progress.

[0481] "Means of recognizing emotions" refers to technologies and devices that can read a user's emotional state from their facial expressions, voice, etc.

[0482] "Means for analyzing emotions" means processing techniques for analyzing recognized emotion data and identifying the user's current emotional state.

[0483] "Feedback" refers to information and advice about what actions users should take next and what they should pay attention to.

[0484] "Adjusting means" is a means for dynamically changing action plans or feedback based on the user's emotional state.

[0485] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. As a specific application example, this system is applied to a food delivery health management app.

[0486] The program of this system operates using the following hardware and software.

[0487] Hardware used

[0488] Smartphone: Provides the user interface and allows data entry and display.

[0489] Laptop or desktop PC: Used for application development and testing.

[0490] Software used

[0491] Python 3: Used as the primary programming language for the application.

[0492] Tkinter library: Used as a GUI library for building user interfaces.

[0493] Emotion recognition library: A library for recognizing a user's emotional state by analyzing their facial expressions and voice.

[0494] Diet planner library: A library for generating customized meal plans based on your goal weight and emotional state.

[0495] System Functions and Processes

[0496] 1. Goal setting: The server allows the user to input their goal weight through the application, which is then sent to the server and stored in the database.

[0497] 2. Emotion Recognition: When a user uses the application, the device will scan the user's emotional state through facial recognition and voice analysis. This emotional data will be analyzed by the emotion engine to identify the user's emotional state. The emotional data will be sent to the server and stored in the database.

[0498] 3. Action plan generation: The server generates a specific meal plan based on the user's target weight and emotional state. The generated meal plan is sent to the device and displayed to the user.

[0499] 4. Recording of activity status: Users enter their daily diet and exercise records into the application, which are then sent to the server and stored in a database.

[0500] 5. Progress Follow-up: The server analyzes the user's progress based on their diet and exercise data, and generates regular progress reports that also take into account the user's emotional state.

[0501] 6. Result feedback: The server summarizes the progress data and emotional state, and suggests next steps or additional advice. This information is displayed on the device and fed back to the user.

[0502] Specific use cases

[0503] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, the system first scans the user's face and voice to analyze their emotional state. It then generates and presents a customized meal plan to help them reach their goal. If the user feels anxious or fatigued, it also provides advice on relaxation and motivation.

[0504] Generative AI model prompt example

[0505] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[0506] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0507] Step 1:

[0508] The user inputs the target weight using the terminal. The target data is input by inputting the target weight into the user interface provided by the terminal. This data is sent from the terminal to the server and stored in the database. Input data: target weight, Output data: target weight stored in the database

[0509] Step 2:

[0510] The user inputs data related to their current weight and lifestyle habits. The device sends this input data to the server, where it is stored in a database. The server compares the target and current status data and calculates the difference. Input data: current weight and lifestyle habit data, Output data: difference between the target and current status

[0511] Step 3:

[0512] The device uses an emotion engine to recognize the user's face and analyze their voice, collecting emotional data. The collected emotional data is sent to a server where it is analyzed. Input data: User's facial expressions and voice data, Output data: Analyzed emotional state

[0513] Step 4:

[0514] The server generates a specific action plan based on the user's goal, current situation data, and emotional state. This action plan is constructed as, for example, an appropriate diet or exercise plan. The generated action plan is sent to the terminal and displayed to the user. Input data: goal, current situation data, emotional state, Output data: action plan

[0515] Step 5:

[0516] The user inputs daily records of their diet and exercise into the terminal. The terminal sends this recorded data to the server and stores it in a database. Input data: Daily diet and exercise data, Output data: Status data stored in the database

[0517] Step 6:

[0518] The server analyzes the progress based on the implementation status data stored in the database. It periodically generates progress reports, sends them to the terminal, and displays them to the user. This progress report is created taking into account the user's emotional state. Input data: implementation status data, Output data: progress report

[0519] Step 7:

[0520] The server compiles the user's progress data and emotional state, and suggests the next steps to take or additional advice. These suggestions are displayed on the device and fed back to the user. Input data: progress data and emotional state, Output data: next steps to take or additional advice

[0521] Specific examples

[0522] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, step 1 is to enter the goal weight, and step 2 is to enter the current weight. Step 3 is emotion recognition and the emotional state is analyzed. Step 4 is to generate a specific meal plan, and step 5 is for the user to record their daily meals and exercise. Step 6 is then generated by the server, and step 7 is to provide next steps and further advice.

[0523] Generative AI model prompt example

[0524] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[0525] 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.

[0526] 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.

[0527] 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.

[0528] [Second embodiment]

[0529] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0530] 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.

[0531] 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).

[0532] 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.

[0533] 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.

[0534] 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).

[0535] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0536] 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.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] 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."

[0541] This invention relates to an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to support goal achievement. This system works in conjunction with a server and a terminal, providing effective feedback to the user.

[0542] A natural language description of the program's operation

[0543] 1. Goal Setting

[0544] The terminal provides an interface for the user to set goals.

[0545] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[0546] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[0547] 2. Current situation analysis and understanding of differences

[0548] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[0549] Users input data about their current weight, daily diet, and exercise habits.

[0550] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[0551] 3. Develop an action plan

[0552] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[0553] The device displays this plan of action to the user, who can then fine-tune it as needed.

[0554] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[0555] 4. Implementing and following up on the action plan

[0556] Users record their daily meals and exercise based on an action plan.

[0557] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[0558] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[0559] 5. Feedback on results and revision of plans

[0560] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[0561] The server suggests next steps or additional advice and sends that information to the device.

[0562] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0563] Specific examples

[0564] For example, if a user's goal is to "lose 5kg," the steps would be as follows:

[0565] 1. Goal Setting

[0566] Users enter "lose 5kg" into the device to set their goal.

[0567] The server stores the goals in a database.

[0568] 2. Current situation analysis and understanding of differences

[0569] The user inputs their current weight (e.g., 75 kg).

[0570] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0571] 3. Develop an action plan

[0572] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[0573] The user reviews it and finalizes the plan on the device.

[0574] 4. Implementing and following up on the action plan

[0575] Users enter their daily diet and exercise habits into the device.

[0576] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0577] 5. Feedback on results and revision of plans

[0578] The server will evaluate progress after one month and suggest next steps to take.

[0579] The user sets up a new action plan and sends it back to the server, updating the system.

[0580] In this way, the system of the present invention effectively assists users in achieving their goals.

[0581] The processing flow will be explained below.

[0582] Step 1:

[0583] The device displays a goal entry form for the user, where the user enters a specific goal, such as "lose 5 kg."

[0584] Step 2:

[0585] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[0586] Step 3:

[0587] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[0588] Step 4:

[0589] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[0590] Step 5:

[0591] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[0592] Step 6:

[0593] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[0594] Step 7:

[0595] The device sends the determined action plan to the server, which stores the action plan in a database.

[0596] Step 8:

[0597] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[0598] Step 9:

[0599] The server analyzes the user's progress based on the transmitted data, periodically generates progress reports, and sends them to the device.

[0600] Step 10:

[0601] The device displays a progress report to the user, and the server summarises the user's progress data after one month to evaluate the achievement.

[0602] Step 11:

[0603] The server generates next steps and additional advice based on the progress achieved, and sends it to the device, which displays it to the user.

[0604] Step 12:

[0605] The user sets a new action plan based on the feedback and submits it again to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[0606] Through the above processing steps, a system that effectively supports the user in achieving their goals is realized.

[0607] Example 1

[0608] 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."

[0609] Conventional goal achievement support systems have not adequately integrated the analysis of the user's current situation, the creation of an action plan, and follow-up, resulting in a lack of support for users to consistently work toward their goals. In particular, it has been difficult to automate and continuously support the entire process from inputting current situation data to achieving the goal.

[0610] 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.

[0611] In this invention, the server includes input means for the user to set a goal, input means for inputting the user's current situation data, calculation means for comparing the user's goal with the current situation data and calculating the difference, generation means for generating a specific action plan based on the difference, recording means for the user to record the implementation status of the action plan, follow-up means for following up on progress based on the implementation status, correction means for correcting the action plan in accordance with the progress, display means for displaying the generated action plan to the user, and storage means for saving the data input by the user in a database. This allows the user to continue making consistent efforts toward their goal, and provides effective support for goal achievement.

[0612] "User" refers to an individual who uses this system to achieve a goal.

[0613] "Server" refers to the computer system that processes and stores user data and manages the entire system.

[0614] "Input means" refers to the interface through which the user inputs goals and current situation data into the system.

[0615] "Calculation means" refers to a processing device that has the function of comparing the user's goal with the current situation data and calculating the difference.

[0616] "Generation means" refers to a function that automatically generates a specific action plan based on the difference.

[0617] "Recording means" refers to an interface that allows a user to record the implementation status of an action plan in the system.

[0618] "Follow-up measures" refers to the ability to track progress and provide feedback to users based on recorded implementation.

[0619] "Correction measures" refers to the function of automatically correcting the action plan based on the results of the follow-up.

[0620] "Display means" refers to an interface for visually displaying the generated action plan to the user.

[0621] "Storage means" refers to the function for storing data entered by the user and the generated action plan in a database.

[0622] This invention is an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to help achieve the goal. This system works in conjunction with the server and the terminal, providing effective feedback to the user.

[0623] The system operates using the following hardware and software:

[0624] Hardware: User devices (smartphones and tablets), servers

[0625] software:

[0626] Server side: Python, Flask, MySQL, Pandas

[0627] Device side: Dedicated application (e.g., health management app)

[0628] System Operation

[0629] 1. Goal Setting

[0630] The terminal provides an interface for the user to set goals.

[0631] The user enters the goal they want to achieve (e.g., lose 5 kg) into the device's input screen.

[0632] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[0633] 2. Current situation analysis and understanding of differences

[0634] The terminal provides an interface for the user to input current data (e.g., weight, diet, and exercise habits).

[0635] Users input data about their current weight, daily diet, and exercise habits.

[0636] These data are sent from the terminal to a server, which stores them in a database and calculates the difference from the target.

[0637] 3. Develop an action plan

[0638] The server generates a specific action plan based on the difference between the user's goal and current situation (e.g., reducing 500 kcal per day and exercising three times a week).

[0639] The device displays this plan of action to the user, who can then fine-tune it as needed.

[0640] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[0641] 4. Implementing and following up on the action plan

[0642] Users record their daily meals and exercise based on an action plan.

[0643] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[0644] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[0645] 5. Feedback on results and revision of plans

[0646] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[0647] The server suggests next steps or additional advice and sends this information to the terminal.

[0648] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0649] Specific examples

[0650] For example, if a user's goal is to "lose 5kg," they might take the following steps:

[0651] 1. Goal Setting

[0652] Users enter "lose 5kg" into the device to set their goal.

[0653] The server stores the goals in a database.

[0654] 2. Current situation analysis and understanding of differences

[0655] The user inputs their current weight (e.g., 75 kg).

[0656] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0657] 3. Develop an action plan

[0658] The server generates a specific action plan to achieve the goal (e.g., reduce 500 kcal per day and exercise three times a week).

[0659] The user reviews it and finalizes the plan on the device.

[0660] 4. Implementing and following up on the action plan

[0661] Users enter their daily diet and exercise habits into the device.

[0662] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0663] 5. Feedback on results and revision of plans

[0664] The server will evaluate progress after one month and suggest next steps to take.

[0665] The user sets up a new action plan and sends it back to the server, updating the system.

[0666] Prompt Sentence Examples

[0667] "I'd like you to set a goal and create an action plan to achieve it. First, please enter your current weight and your goal weight. For example, current weight 75kg, goal weight 70kg."

[0668] "Next, enter your daily calorie intake and exercise frequency. Example: Lose 500 calories per day, exercise three times per week."

[0669] "It records your daily progress (food and exercise) and tracks your weight changes. It provides feedback based on your results and suggests new action plans."

[0670] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0671] Step 1:

[0672] goal setting

[0673] The terminal provides an input interface for the user to set goals.

[0674] Users input the goal they want to achieve (e.g., lose 5 kg).

[0675] Input: A goal entered by the user (e.g., weight 70 kg).

[0676] Data processing: The terminal formats the input target data and converts it into a form that can be sent to the server.

[0677] The device sends the entered goal to the server via REST API.

[0678] The server stores the received target data in a database.

[0679] Output: The status that the target data has been saved to the database.

[0680] Specific behavior:

[0681] The user launches the dedicated application and goes to the "Goal Setting" screen.

[0682] Enter "lose 5kg" and press the send button.

[0683] The terminal converts the input data into JSON format and sends it to the server.

[0684] The server receives the data and stores it in a MySQL database.

[0685] Step 2:

[0686] Current situation analysis and understanding of differences

[0687] The terminal provides an interface for the user to input current status data.

[0688] Users input their current weight, daily diet, and exercise habits.

[0689] Input: User-entered current weight (e.g., 75 kg), diet, and exercise habits.

[0690] Data processing: The terminal collects the current data entered and converts it into a form that can be sent to the server.

[0691] The terminal sends the entered data to the server via REST API.

[0692] The server stores the data in a database and calculates the difference between the target and the current situation.

[0693] Output: Difference data (e.g., 5 kg difference).

[0694] Specific behavior:

[0695] The user goes to the "Current Status Input" screen and enters their current weight (75 kg), dietary and exercise information.

[0696] The device converts the data into JSON format and sends it to the server.

[0697] The server receives the data and calculates the difference (5kg) from the target weight using a Python script.

[0698] Step 3:

[0699] Developing an action plan

[0700] The server generates an action plan based on the current data and the goals.

[0701] Input: Difference data (e.g., difference of 5 kg).

[0702] Data processing: The server generates a specific action plan using a Python script based on the differential data.

[0703] The server sends the generated action plan to the terminal in JSON format.

[0704] The device displays the action plan to the user and provides an interface for the user to modify it as needed.

[0705] The user finalizes the action plan and sends it from the device to the server.

[0706] The server stores the plan in a database.

[0707] Output: Status that the action plan data has been saved to the database.

[0708] Specific behavior:

[0709] Based on the differential data received, the server generates an action plan to reduce 500 kcal per day and exercise three times a week.

[0710] The generated plan is sent to the terminal in JSON format.

[0711] The user reviews the plan, makes any necessary adjustments, and presses the confirm button.

[0712] The terminal sends the final action plan to the server, which stores it in a database.

[0713] Step 4:

[0714] Implementing and following up on action plans

[0715] Users record their daily meals and exercise based on an action plan.

[0716] Input: Daily diet and exercise data entered by the user.

[0717] Data processing: The terminal aggregates the recorded data and converts it into a form that can be sent to the server.

[0718] The device sends the recorded data to the server via a REST API.

[0719] The server stores the data in a database and analyzes the progress.

[0720] The server periodically generates progress reports and sends them to the terminal.

[0721] The terminal displays progress reports to the user.

[0722] Output: Progress report data is sent to the user.

[0723] Specific behavior:

[0724] The user enters their daily food and exercise records into the device app.

[0725] Sends input data to the server in JSON format.

[0726] The server stores the received data in a database and analyzes the progress using Python's Pandas.

[0727] The server generates a progress report in JSON format and sends it to the device.

[0728] The terminal displays progress reports to the user.

[0729] Step 5:

[0730] Feedback on results and revision of plans

[0731] The server periodically compiles the user's progress data and evaluates their achievement.

[0732] Input: Accumulated progress data.

[0733] Data processing: The server aggregates the progress data and evaluates the achievement status using a Python script.

[0734] The server suggests next steps or additional advice and sends this information to the device in JSON format.

[0735] The device displays feedback to the user.

[0736] The user sets up a new action plan and sends it from the device to the server.

[0737] The server updates the plan to the database.

[0738] Output: Status that the new action plan data has been saved to the database.

[0739] Specific behavior:

[0740] The server will summarize the progress data after one month and evaluate the achievement.

[0741] Generate next steps or new advice and send it to the device.

[0742] The user receives feedback, sets a new plan of action, and hits submit.

[0743] The server stores the new action plan in the database and updates the system.

[0744] (Application example 1)

[0745] 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."

[0746] Conventional factory robot management systems have the problem of making it difficult to generate specific action plans to achieve production goals and to follow up on progress. In addition, there is no way to suggest next steps using generative AI models or prompts, so managers lack the support they need to carry out effective production activities.

[0747] 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.

[0748] In this invention, the server includes a generative AI model for generating an action plan based on difference data between the target and the current situation, a means for setting prompt sentences for inputting data according to the purpose into the generative AI model, and a means for summarizing progress data for each specific period and proposing the next step to be taken. This enables managers to generate specific action plans for effectively achieving the production targets of factory robots and provide feedback according to progress.

[0749] "Goal setting" is the process by which users input specific numbers or states they want to achieve.

[0750] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates an action plan based on data tailored to a specific purpose.

[0751] A "prompt" is an instruction to provide appropriate data input for a generative AI model, and is used to optimize the output of the generative AI model.

[0752] "Means" is a general term for methods and devices for performing a specific function.

[0753] "Differential data" is a number or piece of information that indicates the difference between the user's goal and the current situation data.

[0754] An "action plan" is a plan that specifies the specific actions and steps needed to achieve a specific goal.

[0755] "Follow-up" is the process of checking whether the action plan is being implemented and tracking progress.

[0756] "Feedback" refers to advice and evaluation provided based on progress and implementation results.

[0757] "Progress data" is data that indicates the activities and production volume that the user has carried out based on the action plan.

[0758] "At specific intervals" refers to a pre-set time interval (e.g., one day, one week, one month).

[0759] A "factory robot" refers to a mechanical device used in a factory to automate production activities.

[0760] A "server" is a computer system that centrally stores data, performs calculations, and operates generative AI models.

[0761] A system including "means" refers to the entire system that combines individual methods or functions for performing each function.

[0762] This invention applies to an AI personal coaching system that supports users in achieving their goals. This system aims to optimize production activities and is particularly effective in managing robots operated in factories.

[0763] System Configuration

[0764] This system operates in collaboration between a server and terminals (smartphones, head-mounted displays, robots). The server runs on a cloud service (e.g., Amazon Web Services, Google Cloud Platform) and operates generative AI models (e.g., TensorFlow, PyTorch). The terminals are factory robots (e.g., FANUC, ABB), as well as smartphones and head-mounted displays (e.g., Microsoft HoloLens) used by managers.

[0765] goal setting

[0766] The user (factory manager) is provided with an interface to set production targets via a smartphone or head-mounted display. For example, they can input a specific target, such as "daily production target 50 units." This data is sent to a server and stored in a database.

[0767] Current situation analysis and understanding of differences

[0768] The robot sends its current production volume and operation data to the server. The server stores this data in a database and calculates the difference between the current volume and the target volume. For example, if 30 units are currently being produced, the difference (20 units) from the target volume (50 units) is calculated.

[0769] Developing an action plan

[0770] The server uses a generative AI model to generate an action plan based on differential data between the current situation and the goal. The server inputs appropriate data using prompt statements, and the AI ​​model outputs the optimal action plan. For example, the generated action plan might be, "Recommend the production of an additional 20 units during the shift." This plan is displayed on a smartphone, head-mounted display, or robot.

[0771] Implementing and following up on action plans

[0772] The robot carries out production activities based on the generated action plan. The terminal periodically collects progress data and sends it to the server. The server analyzes the progress data and periodically generates progress reports. These reports are sent to the terminal and displayed to the user.

[0773] Feedback on results and revision of plans

[0774] At regular intervals (e.g., daily), the server summarizes the progress data and suggests next steps or improvements. It uses an AI model to generate the next action plan and sends that feedback to the device. The user then sets a new action plan based on this feedback and sends it back to the server to update the system.

[0775] Specific examples

[0776] For example, here is a prompt for a generative AI model:

[0777] "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next action plan (e.g., recommend producing an additional 20 units during this shift)."

[0778] In this way, the system of the present invention can provide specific support for effectively achieving production goals of factory robots.

[0779] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0780] Step 1:

[0781] The user sets a goal using a smartphone or head-mounted display. At this time, the user inputs a specific goal, such as "daily production target 50 units." The input data is sent from the device to a server, which stores the goal in a database. The specific operation is to input the goal into the goal setting interface.

[0782] Step 2:

[0783] The robot measures the current production volume and sends that data to the server. For example, data such as "current production volume: 30 units" is sent. The server stores the received data in a database and calculates the difference between the target value (e.g., 20 units). Specifically, the robot collects sensor data and sends the data through an interface.

[0784] Step 3:

[0785] The server sends a prompt to the generative AI model based on the difference data between the target and current situation. The prompt text is entered as "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next necessary action plan." The AI ​​model generates an action plan based on this prompt and outputs specific instructions (e.g., "Recommend the production of an additional 20 units during this shift"). Specifically, the data transmission process and the AI ​​generation process are carried out.

[0786] Step 4:

[0787] The generated action plan is sent to the terminal and displayed on the robot's and the administrator's smartphone or head-mounted display. The user can review the action plan and make any necessary adjustments. Specifically, data is sent and displayed via an API.

[0788] Step 5:

[0789] The robot carries out production activities according to the generated action plan. For example, it operates according to the instruction "produce an additional 20 units." Progress data is collected periodically and sent to the server. This data is stored in a database and used for subsequent processing. Specific operations are performed by the robot's control system.

[0790] Step 6:

[0791] The server periodically analyzes the progress data and generates a progress report. The generated progress report is sent to the device and displayed to the user. The user can use this information to determine next steps and areas for improvement. Specifically, the server aggregates and analyzes the data and generates a report.

[0792] Step 7:

[0793] After a certain period of time (e.g., daily), the server summarizes all progress data and suggests the next steps to take. It uses the generative AI model again to generate a new action plan. For example, it might suggest "produce 15 more units in the next shift." The user sets a new action plan based on the feedback and sends it back to the server. Specifically, the data is re-analyzed and a new action plan is generated.

[0794] Step 8:

[0795] The user checks the updated action plan and starts operating the robot again based on it. This cycle is repeated, providing continuous support for achieving production targets. Specifically, the process begins again with goal setting as a loop.

[0796] 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.

[0797] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. This system performs each step of the user's goal setting, current situation analysis, action plan formulation and execution, and progress follow-up. It can also recognize the user's emotions and adjust its behavior based on those emotions. This system operates in cooperation with a server, a terminal, and an emotion engine.

[0798] A natural language description of the program's operation

[0799] 1. Goal Setting

[0800] The terminal provides an interface for the user to set goals.

[0801] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[0802] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[0803] 2. Current situation analysis and understanding of differences

[0804] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[0805] Users input data about their current weight, daily diet, and exercise habits.

[0806] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[0807] 3. Develop an action plan

[0808] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[0809] The device displays this plan of action to the user, who can then fine-tune it as needed.

[0810] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[0811] 4. Implementing and following up on the action plan

[0812] Users record their daily meals and exercise based on an action plan.

[0813] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[0814] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[0815] 5. Feedback on results and revision of plans

[0816] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[0817] The server suggests next steps or additional advice and sends that information to the device.

[0818] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0819] Incorporating an emotion engine

[0820] 1. Emotion recognition

[0821] The device provides an interface for collecting emotions through facial recognition and voice analysis of the user.

[0822] When a user uses the device, it scans the user's facial expressions and tone of voice and sends that data to the emotion engine.

[0823] 2. Emotion analysis

[0824] The emotion engine analyzes emotion data in real time to determine the user's current emotional state.

[0825] The emotion analysis results are sent to the server and stored in the user's database.

[0826] 3. Emotion-based adaptive feedback

[0827] The server tailors action plans and advice based on the user's emotional data, for example providing motivational support if the user is feeling down.

[0828] The device displays tailored feedback and action plans to the user, who then decides on their next move.

[0829] Specific examples

[0830] For example, if a user has a goal of "lose 5kg" and is recording their daily progress, the following steps might be added:

[0831] 1. Goal Setting

[0832] Users enter "lose 5kg" into the device to set their goal.

[0833] The server stores the goals in a database.

[0834] 2. Current situation analysis and understanding of differences

[0835] The user inputs their current weight (e.g., 75 kg).

[0836] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0837] 3. Emotion recognition

[0838] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[0839] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[0840] 4. Develop an action plan

[0841] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[0842] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[0843] 5. Implementing and following up on the action plan

[0844] Users enter their daily diet and exercise habits into the device.

[0845] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0846] It also provides feedback based on the user's emotional state.

[0847] 6. Feedback on results and revision of plans

[0848] The server will assess progress every month and generate next steps or additional advice.

[0849] The plan is readjusted depending on the user's emotional state.

[0850] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[0851] The processing flow will be explained below.

[0852] Step 1:

[0853] The device displays a goal entry form for the user, allowing them to enter a specific goal, such as "lose 5 kg of weight."

[0854] Step 2:

[0855] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[0856] Step 3:

[0857] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[0858] Step 4:

[0859] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[0860] Step 5:

[0861] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[0862] Step 6:

[0863] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[0864] Step 7:

[0865] The device sends the determined action plan to the server, which stores the action plan in a database.

[0866] Step 8:

[0867] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[0868] Step 9:

[0869] The device provides an interface for recognizing the user's emotions. When the user inputs data, the device performs facial recognition and voice analysis to obtain the user's emotional data.

[0870] Step 10:

[0871] The acquired emotion data is sent from the device to the emotion engine, which analyzes the emotion data in real time and identifies the user's emotional state.

[0872] Step 11:

[0873] The emotion engine sends the analyzed emotion results to a server and stores them in the user's database, where the server uses this information to adjust its action plan and feedback.

[0874] Step 12:

[0875] The server periodically generates a progress report based on the user's progress data and emotion data, and sends the report to the device, which then displays the progress report to the user.

[0876] Step 13:

[0877] Every certain period (e.g., one month), the server aggregates the user's progress and emotional data, evaluates their progress, and generates next steps and additional advice, which are then sent to the device.

[0878] Step 14:

[0879] The user then develops a new plan of action based on the feedback and submits it back to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[0880] Through this series of steps, a system that effectively supports users in achieving their goals is realized. The introduction of an emotion engine provides adaptive feedback according to the user's emotional state, achieving more precise support.

[0881] Example 2

[0882] 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."

[0883] Conventional user goal achievement support systems support users in achieving their goals, but they lack feedback that takes into account the user's emotional state. As a result, users' motivation may decrease and stress may increase. Furthermore, the adjustments to the action plan required for goal achievement do not reflect the user's emotional state, so they often fail to provide effective support. Therefore, a system with an adaptive feedback function that takes into account the user's emotional state is needed to effectively support users in achieving their goals.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0885] In this invention, the server includes means for comparing the user's goal with current situation data and calculating the difference, means for generating a specific action plan based on the difference, recognition means for collecting the user's emotional data, analysis means for analyzing the collected emotional data, and adaptive feedback means for adjusting the action plan based on the emotional data, thereby enabling adjustment of the action plan taking the user's emotional state into consideration and more effective support for goal achievement.

[0886] "User" refers to the individual who sets goals, enters data, reviews feedback, etc.

[0887] "Input means" refers to the device or software that provides the interface through which the user inputs goals, current situation data, etc. into the system.

[0888] "Current status data" refers to information such as the user's current weight, lifestyle, and health condition.

[0889] "Comparison method" refers to a program or algorithm that compares the user's goals with current situation data and calculates the difference.

[0890] An "action plan" refers to a plan of specific actions required to achieve the goals set by the user.

[0891] "Action plan generation means" refers to a program or algorithm for creating an appropriate action plan for a user based on differential data.

[0892] "Implementation status" refers to the actions and results that users have actually taken based on their action plan.

[0893] "Follow-up tools" refers to programs or devices that track progress based on the user's implementation and provide advice or feedback as appropriate.

[0894] "Corrective measures" refer to programs or algorithms that review and make necessary corrections to a user's action plan based on their progress data and emotional state.

[0895] "Recognition methods" refers to facial recognition and voice analysis technologies used to collect user emotional data.

[0896] "Analysis Means" refers to a process or program for analyzing the emotional data collected by the Recognition Means and identifying the user's emotional state.

[0897] "Adaptive feedback means" refers to programs or algorithms that adjust action plans and feedback based on analyzed emotional data to provide appropriate assistance to users.

[0898] This invention is an AI personal coaching system that supports users in achieving their goals and operates in combination with an emotion engine. This system supports users through each step of goal setting, current situation analysis, action plan formulation and execution, and progress follow-up, and also has the ability to recognize the user's emotions and adjust its behavior based on those emotions. Specifically, this system operates in cooperation with a server, a terminal, and an emotion engine.

[0899] First, the device provides an interface for users to set goals. Users use this interface to input the goals they want to achieve. For example, if a user sets a goal of "lose 5 kg of weight," this goal is sent from the device to the server, which then stores it in a database.

[0900] Next, the device provides the user with an interface for inputting data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. This data is sent from the device to a server, which stores it in a database and calculates the difference between the user's current status and their goal.

[0901] The server generates a specific action plan based on the difference between the goal and the current situation. For example, if the device suggests reducing 500 kcal per day and exercising three times a week, the device will display this action plan to the user. The user can fine-tune the action plan as needed, and the final action plan will be sent from the device to the server, which will then store it in a database.

[0902] The user records their daily meals and exercise based on the action plan, and the device sends the recorded data to the server, which stores the data in a database and analyzes the progress. Periodically, the server generates a progress report and sends it to the device for display to the user.

[0903] At regular intervals, the server summarizes the user's progress data, evaluates their progress, suggests next steps and additional advice, and sends this information to the device. The user then sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[0904] The system is equipped with an emotion engine that recognizes the user's emotional state and provides adaptive feedback based on it. Specifically, the device provides an interface that collects emotions through facial recognition and voice analysis. When the user uses the device, the device scans the user's facial expressions and tone of voice and sends the data to the emotion engine. The emotion engine analyzes the emotion data in real time and determines the user's current emotional state. The emotion analysis results are sent to a server and stored in the user's database.

[0905] The server adjusts the action plan and advice based on the user's emotional data. For example, if the user is feeling depressed, it provides motivational support. The device displays the adjusted feedback and action plan to the user, who then decides on their next course of action.

[0906] Specific examples

[0907] For example, if a user sets a goal of "lose 5kg" and tracks their progress daily, the following steps might be added:

[0908] 1. Goal Setting

[0909] Users enter "lose 5kg" into the device to set their goal.

[0910] The server stores the goals in a database.

[0911] 2. Current situation analysis and understanding of differences

[0912] The user inputs their current weight (e.g., 75 kg).

[0913] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[0914] 3. Emotion recognition

[0915] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[0916] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[0917] 4. Develop an action plan

[0918] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[0919] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[0920] 5. Implementing and following up on the action plan

[0921] Users enter their daily diet and exercise habits into the device.

[0922] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[0923] It also provides feedback based on the user's emotional state.

[0924] 6. Feedback on results and revision of plans

[0925] The server will assess progress every month and generate next steps or additional advice.

[0926] The plan is readjusted depending on the user's emotional state.

[0927] Prompt Sentence Examples

[0928] Use the following prompt for the generative AI model:

[0929] “Design a system to help users achieve their goals. This system should allow users to set goals, analyze their current situation, create and execute an action plan, follow up on progress, recognize emotions and reflect on that information.”

[0930] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[0931] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0932] Step 1: Goal Setting

[0933] Input: Goal data entered by the user

[0934] Processing: The terminal displays an interface for the user to set a goal. The user inputs the goal they want to achieve. For example, they input the goal "to lose 5 kg."

[0935] Output: The target data is generated and sent from the terminal to the server, which stores it in a database.

[0936] Step 2: Enter current data

[0937] Input: Current weight and lifestyle data entered by the user

[0938] Processing: The device displays an interface for the user to input data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. For example, the user inputs that their current weight is 75 kg and that they exercise twice a week.

[0939] Output: Current status data is generated and sent from the terminal to the server. The server stores this data in a database and calculates the difference from the target.

[0940] Step 3: Calculate the difference

[0941] Input: Goal data and current status data stored on the server

[0942] Processing: The server compares the user's goal with the current data and calculates the difference. For example, it calculates the difference of 5 kg between the goal weight of 70 kg and the current weight of 75 kg.

[0943] Output: The differential data is generated and stored on the server.

[0944] Step 4: Generate an action plan

[0945] Input: Differential data stored on the server

[0946] Processing: The server generates a specific action plan based on the difference data, for example, recommending a reduction of 500 kcal per day and exercising three times a week.

[0947] Output: An action plan is generated and sent to the device, where the user can review it and fine-tune it if necessary.

[0948] Step 5: Adjust your action plan

[0949] Input: User feedback

[0950] Processing: The device displays the generated action plan to the user. The user can fine-tune the action plan as needed. For example, "reduce 600 kcal instead of 500 kcal."

[0951] Output: An adjusted action plan is generated, sent from the device to the server, and stored in a database.

[0952] Step 6: Enter your daily records

[0953] Input: Daily food and exercise data entered by the user

[0954] Process: The user enters their daily diet and exercise information into the device. For example, they record what they ate for breakfast and how much exercise they did that day.

[0955] Output: Daily data is generated and sent from the device to the server, which stores it in a database and analyzes the progress.

[0956] Step 7: Generate a progress report

[0957] Input: Daily data stored on the server

[0958] Processing: The server periodically analyzes the daily data and generates progress reports, e.g., graphing weekly weight loss and calorie consumption trends.

[0959] Output: A progress report is generated, sent to the terminal, and displayed to the user.

[0960] Step 8: Collect emotion data

[0961] Input: User's facial recognition and voice analysis data

[0962] Processing: The device collects emotions through facial recognition and voice analysis of the user, for example, using the camera to collect facial expression data and the microphone to analyze tone of voice.

[0963] Output: Emotion data is generated and sent from the device to the server, where it is analyzed by the emotion engine.

[0964] Step 9: Analyze the sentiment data

[0965] Input: Collected emotion data

[0966] Processing: The emotion engine analyzes the emotion data in real time and determines the user's emotional state. For example, it outputs an analysis result such as "The current emotional state is 'anxious.'"

[0967] Output: The analysis results are generated, sent to the server, and stored in a database.

[0968] Step 10: Providing adaptive feedback

[0969] Input: Parsed emotion data

[0970] Processing: The server adjusts the action plan or advice based on the emotion data, for example, providing motivational support if the user is feeling down.

[0971] Output: The adjusted feedback is generated, sent to the device, and displayed to the user.

[0972] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[0973] (Application example 2)

[0974] 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."

[0975] Conventional personal coaching systems often lack the ability to provide dynamic feedback that takes into account a user's emotional state when helping them achieve their goals. As a result, users' motivation can easily drop, making it difficult to achieve their goals. In particular, when it comes to lifestyle and health management, users' emotional fluctuations have a significant impact on their behavior, so systems that ignore this have difficulty providing effective support.

[0976] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0977] In this invention, the server includes input means for the user to set goals, means for inputting the user's current situation data, means for comparing the user's goals with the current situation data and calculating the difference, means for generating a specific action plan based on the difference, means for the user to record the implementation status of the action plan, means for following up on progress based on the implementation status, means for recognizing and analyzing the user's emotions, means for adjusting the action plan and feedback based on the user's emotional state, and means for modifying the action plan in accordance with progress. This enables dynamic feedback and adjustment of the action plan in response to the user's emotional state, thereby enabling effective goal achievement support.

[0978] "User" refers to an individual who uses the system to achieve a goal.

[0979] "Goal" means a specific result or outcome that the user wants to achieve.

[0980] "Input means" refers to an interface or device that allows a user to input data or information into a system.

[0981] "Current status data" is information about the user's current condition and lifestyle.

[0982] "Difference" refers to the difference or gap between the user's goal and the current situation data.

[0983] An "action plan" is a set of specific actions or steps that a user must take to achieve a goal.

[0984] "Implementation status" refers to the actions that a user actually takes based on the action plan and the results of those actions.

[0985] "Follow-up measures" are measures used to monitor the implementation of the user's action plan and evaluate progress.

[0986] "Means of recognizing emotions" refers to technologies and devices that can read a user's emotional state from their facial expressions, voice, etc.

[0987] "Means for analyzing emotions" means processing techniques for analyzing recognized emotion data and identifying the user's current emotional state.

[0988] "Feedback" refers to information and advice about what actions users should take next and what they should pay attention to.

[0989] "Adjusting means" is a means for dynamically changing action plans or feedback based on the user's emotional state.

[0990] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. As a specific application example, this system is applied to a food delivery health management app.

[0991] The program of this system operates using the following hardware and software.

[0992] Hardware used

[0993] Smartphone: Provides the user interface and allows data entry and display.

[0994] Laptop or desktop PC: Used for application development and testing.

[0995] Software used

[0996] Python 3: Used as the primary programming language for the application.

[0997] Tkinter library: Used as a GUI library for building user interfaces.

[0998] Emotion recognition library: A library for recognizing a user's emotional state by analyzing their facial expressions and voice.

[0999] Diet planner library: A library for generating customized meal plans based on your goal weight and emotional state.

[1000] System Functions and Processes

[1001] 1. Goal setting: The server allows the user to input their goal weight through the application, which is then sent to the server and stored in the database.

[1002] 2. Emotion Recognition: When a user uses the application, the device will scan the user's emotional state through facial recognition and voice analysis. This emotional data will be analyzed by the emotion engine to identify the user's emotional state. The emotional data will be sent to the server and stored in the database.

[1003] 3. Action plan generation: The server generates a specific meal plan based on the user's target weight and emotional state. The generated meal plan is sent to the device and displayed to the user.

[1004] 4. Recording of activity status: Users enter their daily diet and exercise records into the application, which are then sent to the server and stored in a database.

[1005] 5. Progress Follow-up: The server analyzes the user's progress based on their diet and exercise data, and generates regular progress reports that also take into account the user's emotional state.

[1006] 6. Result feedback: The server summarizes the progress data and emotional state, and suggests next steps or additional advice. This information is displayed on the device and fed back to the user.

[1007] Specific use cases

[1008] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, the system first scans the user's face and voice to analyze their emotional state. It then generates and presents a customized meal plan to help them reach their goal. If the user feels anxious or fatigued, it also provides advice on relaxation and motivation.

[1009] Generative AI model prompt example

[1010] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[1011] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1012] Step 1:

[1013] The user inputs the target weight using the terminal. The target data is input by inputting the target weight into the user interface provided by the terminal. This data is sent from the terminal to the server and stored in the database. Input data: target weight, Output data: target weight stored in the database

[1014] Step 2:

[1015] The user inputs data related to their current weight and lifestyle habits. The device sends this input data to the server, where it is stored in a database. The server compares the target and current status data and calculates the difference. Input data: current weight and lifestyle habit data, Output data: difference between the target and current status

[1016] Step 3:

[1017] The device uses an emotion engine to recognize the user's face and analyze their voice, collecting emotional data. The collected emotional data is sent to a server where it is analyzed. Input data: User's facial expressions and voice data, Output data: Analyzed emotional state

[1018] Step 4:

[1019] The server generates a specific action plan based on the user's goal, current situation data, and emotional state. This action plan is constructed as, for example, an appropriate diet or exercise plan. The generated action plan is sent to the terminal and displayed to the user. Input data: goal, current situation data, emotional state, Output data: action plan

[1020] Step 5:

[1021] The user inputs daily records of their diet and exercise into the terminal. The terminal sends this recorded data to the server and stores it in a database. Input data: Daily diet and exercise data, Output data: Status data stored in the database

[1022] Step 6:

[1023] The server analyzes the progress based on the implementation status data stored in the database. It periodically generates progress reports, sends them to the terminal, and displays them to the user. This progress report is created taking into account the user's emotional state. Input data: implementation status data, Output data: progress report

[1024] Step 7:

[1025] The server compiles the user's progress data and emotional state, and suggests the next steps to take or additional advice. These suggestions are displayed on the device and fed back to the user. Input data: progress data and emotional state, Output data: next steps to take or additional advice

[1026] Specific examples

[1027] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, step 1 is to enter the goal weight, and step 2 is to enter the current weight. Step 3 is emotion recognition and the emotional state is analyzed. Step 4 is to generate a specific meal plan, and step 5 is for the user to record their daily meals and exercise. Step 6 is then generated by the server, and step 7 is to provide next steps and further advice.

[1028] Generative AI model prompt example

[1029] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[1030] 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.

[1031] 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.

[1032] 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.

[1033] [Third embodiment]

[1034] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1035] 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.

[1036] 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).

[1037] 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.

[1038] 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.

[1039] 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).

[1040] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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."

[1046] This invention relates to an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to support goal achievement. This system works in conjunction with a server and a terminal, providing effective feedback to the user.

[1047] A natural language description of the program's operation

[1048] 1. Goal Setting

[1049] The terminal provides an interface for the user to set goals.

[1050] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[1051] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[1052] 2. Current situation analysis and understanding of differences

[1053] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[1054] Users input data about their current weight, daily diet, and exercise habits.

[1055] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[1056] 3. Develop an action plan

[1057] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[1058] The device displays this plan of action to the user, who can then fine-tune it as needed.

[1059] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[1060] 4. Implementing and following up on the action plan

[1061] Users record their daily meals and exercise based on an action plan.

[1062] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[1063] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[1064] 5. Feedback on results and revision of plans

[1065] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[1066] The server suggests next steps or additional advice and sends that information to the device.

[1067] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1068] Specific examples

[1069] For example, if a user's goal is to "lose 5kg," the steps would be as follows:

[1070] 1. Goal Setting

[1071] Users enter "lose 5kg" into the device to set their goal.

[1072] The server stores the goals in a database.

[1073] 2. Current situation analysis and understanding of differences

[1074] The user inputs their current weight (e.g., 75 kg).

[1075] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1076] 3. Develop an action plan

[1077] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[1078] The user reviews it and finalizes the plan on the device.

[1079] 4. Implementing and following up on the action plan

[1080] Users enter their daily diet and exercise habits into the device.

[1081] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1082] 5. Feedback on results and revision of plans

[1083] The server will evaluate progress after one month and suggest next steps to take.

[1084] The user sets up a new action plan and sends it back to the server, updating the system.

[1085] In this way, the system of the present invention effectively assists users in achieving their goals.

[1086] The processing flow will be explained below.

[1087] Step 1:

[1088] The device displays a goal entry form for the user, where the user enters a specific goal, such as "lose 5 kg."

[1089] Step 2:

[1090] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[1091] Step 3:

[1092] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[1093] Step 4:

[1094] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[1095] Step 5:

[1096] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[1097] Step 6:

[1098] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[1099] Step 7:

[1100] The device sends the determined action plan to the server, which stores the action plan in a database.

[1101] Step 8:

[1102] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[1103] Step 9:

[1104] The server analyzes the user's progress based on the transmitted data, periodically generates progress reports, and sends them to the device.

[1105] Step 10:

[1106] The device displays a progress report to the user, and the server summarises the user's progress data after one month to evaluate the achievement.

[1107] Step 11:

[1108] The server generates next steps and additional advice based on the progress achieved, and sends it to the device, which displays it to the user.

[1109] Step 12:

[1110] The user sets a new action plan based on the feedback and submits it again to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[1111] Through the above processing steps, a system that effectively supports the user in achieving their goals is realized.

[1112] Example 1

[1113] 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."

[1114] Conventional goal achievement support systems have not adequately integrated the analysis of the user's current situation, the creation of an action plan, and follow-up, resulting in a lack of support for users to consistently work toward their goals. In particular, it has been difficult to automate and continuously support the entire process from inputting current situation data to achieving the goal.

[1115] 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.

[1116] In this invention, the server includes input means for the user to set a goal, input means for inputting the user's current situation data, calculation means for comparing the user's goal with the current situation data and calculating the difference, generation means for generating a specific action plan based on the difference, recording means for the user to record the implementation status of the action plan, follow-up means for following up on progress based on the implementation status, correction means for correcting the action plan in accordance with the progress, display means for displaying the generated action plan to the user, and storage means for saving the data input by the user in a database. This allows the user to continue making consistent efforts toward their goal, and provides effective support for goal achievement.

[1117] "User" refers to an individual who uses this system to achieve a goal.

[1118] "Server" refers to the computer system that processes and stores user data and manages the entire system.

[1119] "Input means" refers to the interface through which the user inputs goals and current situation data into the system.

[1120] "Calculation means" refers to a processing device that has the function of comparing the user's goal with the current situation data and calculating the difference.

[1121] "Generation means" refers to a function that automatically generates a specific action plan based on the difference.

[1122] "Recording means" refers to an interface that allows a user to record the implementation status of an action plan in the system.

[1123] "Follow-up measures" refers to the ability to track progress and provide feedback to users based on recorded implementation.

[1124] "Correction measures" refers to the function of automatically correcting the action plan based on the results of the follow-up.

[1125] "Display means" refers to an interface for visually displaying the generated action plan to the user.

[1126] "Storage means" refers to the function for storing data entered by the user and the generated action plan in a database.

[1127] This invention is an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to help achieve the goal. This system works in conjunction with the server and the terminal, providing effective feedback to the user.

[1128] The system operates using the following hardware and software:

[1129] Hardware: User devices (smartphones and tablets), servers

[1130] software:

[1131] Server side: Python, Flask, MySQL, Pandas

[1132] Device side: Dedicated application (e.g., health management app)

[1133] System Operation

[1134] 1. Goal Setting

[1135] The terminal provides an interface for the user to set goals.

[1136] The user enters the goal they want to achieve (e.g., lose 5 kg) into the device's input screen.

[1137] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[1138] 2. Current situation analysis and understanding of differences

[1139] The terminal provides an interface for the user to input current data (e.g., weight, diet, and exercise habits).

[1140] Users input data about their current weight, daily diet, and exercise habits.

[1141] These data are sent from the terminal to a server, which stores them in a database and calculates the difference from the target.

[1142] 3. Develop an action plan

[1143] The server generates a specific action plan based on the difference between the user's goal and current situation (e.g., reducing 500 kcal per day and exercising three times a week).

[1144] The device displays this plan of action to the user, who can then fine-tune it as needed.

[1145] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[1146] 4. Implementing and following up on the action plan

[1147] Users record their daily meals and exercise based on an action plan.

[1148] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[1149] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[1150] 5. Feedback on results and revision of plans

[1151] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[1152] The server suggests next steps or additional advice and sends this information to the terminal.

[1153] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1154] Specific examples

[1155] For example, if a user's goal is to "lose 5kg," they might take the following steps:

[1156] 1. Goal Setting

[1157] Users enter "lose 5kg" into the device to set their goal.

[1158] The server stores the goals in a database.

[1159] 2. Current situation analysis and understanding of differences

[1160] The user inputs their current weight (e.g., 75 kg).

[1161] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1162] 3. Develop an action plan

[1163] The server generates a specific action plan to achieve the goal (e.g., reduce 500 kcal per day and exercise three times a week).

[1164] The user reviews it and finalizes the plan on the device.

[1165] 4. Implementing and following up on the action plan

[1166] Users enter their daily diet and exercise habits into the device.

[1167] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1168] 5. Feedback on results and revision of plans

[1169] The server will evaluate progress after one month and suggest next steps to take.

[1170] The user sets up a new action plan and sends it back to the server, updating the system.

[1171] Prompt Sentence Examples

[1172] "I'd like you to set a goal and create an action plan to achieve it. First, please enter your current weight and your goal weight. For example, current weight 75kg, goal weight 70kg."

[1173] "Next, enter your daily calorie intake and exercise frequency. Example: Lose 500 calories per day, exercise three times per week."

[1174] "It records your daily progress (food and exercise) and tracks your weight changes. It provides feedback based on your results and suggests new action plans."

[1175] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] goal setting

[1178] The terminal provides an input interface for the user to set goals.

[1179] Users input the goal they want to achieve (e.g., lose 5 kg).

[1180] Input: A goal entered by the user (e.g., weight 70 kg).

[1181] Data processing: The terminal formats the input target data and converts it into a form that can be sent to the server.

[1182] The device sends the entered goal to the server via REST API.

[1183] The server stores the received target data in a database.

[1184] Output: The status that the target data has been saved to the database.

[1185] Specific behavior:

[1186] The user launches the dedicated application and goes to the "Goal Setting" screen.

[1187] Enter "lose 5kg" and press the send button.

[1188] The terminal converts the input data into JSON format and sends it to the server.

[1189] The server receives the data and stores it in a MySQL database.

[1190] Step 2:

[1191] Current situation analysis and understanding of differences

[1192] The terminal provides an interface for the user to input current status data.

[1193] Users input their current weight, daily diet, and exercise habits.

[1194] Input: User-entered current weight (e.g., 75 kg), diet, and exercise habits.

[1195] Data processing: The terminal collects the current data entered and converts it into a form that can be sent to the server.

[1196] The terminal sends the entered data to the server via REST API.

[1197] The server stores the data in a database and calculates the difference between the target and the current situation.

[1198] Output: Difference data (e.g., 5 kg difference).

[1199] Specific behavior:

[1200] The user goes to the "Current Status Input" screen and enters their current weight (75 kg), dietary and exercise information.

[1201] The device converts the data into JSON format and sends it to the server.

[1202] The server receives the data and calculates the difference (5kg) from the target weight using a Python script.

[1203] Step 3:

[1204] Developing an action plan

[1205] The server generates an action plan based on the current data and the goals.

[1206] Input: Difference data (e.g., difference of 5 kg).

[1207] Data processing: The server generates a specific action plan using a Python script based on the differential data.

[1208] The server sends the generated action plan to the terminal in JSON format.

[1209] The device displays the action plan to the user and provides an interface for the user to modify it as needed.

[1210] The user finalizes the action plan and sends it from the device to the server.

[1211] The server stores the plan in a database.

[1212] Output: Status that the action plan data has been saved to the database.

[1213] Specific behavior:

[1214] Based on the differential data received, the server generates an action plan to reduce 500 kcal per day and exercise three times a week.

[1215] The generated plan is sent to the terminal in JSON format.

[1216] The user reviews the plan, makes any necessary adjustments, and presses the confirm button.

[1217] The terminal sends the final action plan to the server, which stores it in a database.

[1218] Step 4:

[1219] Implementing and following up on action plans

[1220] Users record their daily meals and exercise based on an action plan.

[1221] Input: Daily diet and exercise data entered by the user.

[1222] Data processing: The terminal aggregates the recorded data and converts it into a form that can be sent to the server.

[1223] The device sends the recorded data to the server via a REST API.

[1224] The server stores the data in a database and analyzes the progress.

[1225] The server periodically generates progress reports and sends them to the terminal.

[1226] The terminal displays progress reports to the user.

[1227] Output: Progress report data is sent to the user.

[1228] Specific behavior:

[1229] The user enters their daily food and exercise records into the device app.

[1230] Sends input data to the server in JSON format.

[1231] The server stores the received data in a database and analyzes the progress using Python's Pandas.

[1232] The server generates a progress report in JSON format and sends it to the device.

[1233] The terminal displays progress reports to the user.

[1234] Step 5:

[1235] Feedback on results and revision of plans

[1236] The server periodically compiles the user's progress data and evaluates their achievement.

[1237] Input: Accumulated progress data.

[1238] Data processing: The server aggregates the progress data and evaluates the achievement status using a Python script.

[1239] The server suggests next steps or additional advice and sends this information to the device in JSON format.

[1240] The device displays feedback to the user.

[1241] The user sets up a new action plan and sends it from the device to the server.

[1242] The server updates the plan to the database.

[1243] Output: Status that the new action plan data has been saved to the database.

[1244] Specific behavior:

[1245] The server will summarize the progress data after one month and evaluate the achievement.

[1246] Generate next steps or new advice and send it to the device.

[1247] The user receives feedback, sets a new plan of action, and hits submit.

[1248] The server stores the new action plan in the database and updates the system.

[1249] (Application example 1)

[1250] 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."

[1251] Conventional factory robot management systems have the problem of making it difficult to generate specific action plans to achieve production goals and to follow up on progress. In addition, there is no way to suggest next steps using generative AI models or prompts, so managers lack the support they need to carry out effective production activities.

[1252] 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.

[1253] In this invention, the server includes a generative AI model for generating an action plan based on difference data between the target and the current situation, a means for setting prompt sentences for inputting data according to the purpose into the generative AI model, and a means for summarizing progress data for each specific period and proposing the next step to be taken. This enables managers to generate specific action plans for effectively achieving the production targets of factory robots and provide feedback according to progress.

[1254] "Goal setting" is the process by which users input specific numbers or states they want to achieve.

[1255] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates an action plan based on data tailored to a specific purpose.

[1256] A "prompt" is an instruction to provide appropriate data input for a generative AI model, and is used to optimize the output of the generative AI model.

[1257] "Means" is a general term for methods and devices for performing a specific function.

[1258] "Differential data" is a number or piece of information that indicates the difference between the user's goal and the current situation data.

[1259] An "action plan" is a plan that specifies the specific actions and steps needed to achieve a specific goal.

[1260] "Follow-up" is the process of checking whether the action plan is being implemented and tracking progress.

[1261] "Feedback" refers to advice and evaluation provided based on progress and implementation results.

[1262] "Progress data" is data that indicates the activities and production volume that the user has carried out based on the action plan.

[1263] "At specific intervals" refers to a pre-set time interval (e.g., one day, one week, one month).

[1264] A "factory robot" refers to a mechanical device used in a factory to automate production activities.

[1265] A "server" is a computer system that centrally stores data, performs calculations, and operates generative AI models.

[1266] A system including "means" refers to the entire system that combines individual methods or functions for performing each function.

[1267] This invention applies to an AI personal coaching system that supports users in achieving their goals. This system aims to optimize production activities and is particularly effective in managing robots operated in factories.

[1268] System Configuration

[1269] This system operates in collaboration between a server and terminals (smartphones, head-mounted displays, robots). The server runs on a cloud service (e.g., Amazon Web Services, Google Cloud Platform) and operates generative AI models (e.g., TensorFlow, PyTorch). The terminals are factory robots (e.g., FANUC, ABB), as well as smartphones and head-mounted displays (e.g., Microsoft HoloLens) used by managers.

[1270] goal setting

[1271] The user (factory manager) is provided with an interface to set production targets via a smartphone or head-mounted display. For example, they can input a specific target, such as "daily production target 50 units." This data is sent to a server and stored in a database.

[1272] Current situation analysis and understanding of differences

[1273] The robot sends its current production volume and operation data to the server. The server stores this data in a database and calculates the difference between the current volume and the target volume. For example, if 30 units are currently being produced, the difference (20 units) from the target volume (50 units) is calculated.

[1274] Developing an action plan

[1275] The server uses a generative AI model to generate an action plan based on differential data between the current situation and the goal. The server inputs appropriate data using prompt statements, and the AI ​​model outputs the optimal action plan. For example, the generated action plan might be, "Recommend the production of an additional 20 units during the shift." This plan is displayed on a smartphone, head-mounted display, or robot.

[1276] Implementing and following up on action plans

[1277] The robot carries out production activities based on the generated action plan. The terminal periodically collects progress data and sends it to the server. The server analyzes the progress data and periodically generates progress reports. These reports are sent to the terminal and displayed to the user.

[1278] Feedback on results and revision of plans

[1279] At regular intervals (e.g., daily), the server summarizes the progress data and suggests next steps or improvements. It uses an AI model to generate the next action plan and sends that feedback to the device. The user then sets a new action plan based on this feedback and sends it back to the server to update the system.

[1280] Specific examples

[1281] For example, here is a prompt for a generative AI model:

[1282] "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next action plan (e.g., recommend producing an additional 20 units during this shift)."

[1283] In this way, the system of the present invention can provide specific support for effectively achieving production goals of factory robots.

[1284] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1285] Step 1:

[1286] The user sets a goal using a smartphone or head-mounted display. At this time, the user inputs a specific goal, such as "daily production target 50 units." The input data is sent from the device to a server, which stores the goal in a database. The specific operation is to input the goal into the goal setting interface.

[1287] Step 2:

[1288] The robot measures the current production volume and sends that data to the server. For example, data such as "current production volume: 30 units" is sent. The server stores the received data in a database and calculates the difference between the target value (e.g., 20 units). Specifically, the robot collects sensor data and sends the data through an interface.

[1289] Step 3:

[1290] The server sends a prompt to the generative AI model based on the difference data between the target and current situation. The prompt text is entered as "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next necessary action plan." The AI ​​model generates an action plan based on this prompt and outputs specific instructions (e.g., "Recommend the production of an additional 20 units during this shift"). Specifically, the data transmission process and the AI ​​generation process are carried out.

[1291] Step 4:

[1292] The generated action plan is sent to the terminal and displayed on the robot's and the administrator's smartphone or head-mounted display. The user can review the action plan and make any necessary adjustments. Specifically, data is sent and displayed via an API.

[1293] Step 5:

[1294] The robot carries out production activities according to the generated action plan. For example, it operates according to the instruction "produce an additional 20 units." Progress data is collected periodically and sent to the server. This data is stored in a database and used for subsequent processing. Specific operations are performed by the robot's control system.

[1295] Step 6:

[1296] The server periodically analyzes the progress data and generates a progress report. The generated progress report is sent to the device and displayed to the user. The user can use this information to determine next steps and areas for improvement. Specifically, the server aggregates and analyzes the data and generates a report.

[1297] Step 7:

[1298] After a certain period of time (e.g., daily), the server summarizes all progress data and suggests the next steps to take. It uses the generative AI model again to generate a new action plan. For example, it might suggest "produce 15 more units in the next shift." The user sets a new action plan based on the feedback and sends it back to the server. Specifically, the data is re-analyzed and a new action plan is generated.

[1299] Step 8:

[1300] The user checks the updated action plan and starts operating the robot again based on it. This cycle is repeated, providing continuous support for achieving production targets. Specifically, the process begins again with goal setting as a loop.

[1301] 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.

[1302] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. This system performs each step of the user's goal setting, current situation analysis, action plan formulation and execution, and progress follow-up. It can also recognize the user's emotions and adjust its behavior based on those emotions. This system operates in cooperation with a server, a terminal, and an emotion engine.

[1303] A natural language description of the program's operation

[1304] 1. Goal Setting

[1305] The terminal provides an interface for the user to set goals.

[1306] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[1307] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[1308] 2. Current situation analysis and understanding of differences

[1309] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[1310] Users input data about their current weight, daily diet, and exercise habits.

[1311] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[1312] 3. Develop an action plan

[1313] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[1314] The device displays this plan of action to the user, who can then fine-tune it as needed.

[1315] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[1316] 4. Implementing and following up on the action plan

[1317] Users record their daily meals and exercise based on an action plan.

[1318] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[1319] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[1320] 5. Feedback on results and revision of plans

[1321] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[1322] The server suggests next steps or additional advice and sends that information to the device.

[1323] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1324] Incorporating an emotion engine

[1325] 1. Emotion recognition

[1326] The device provides an interface for collecting emotions through facial recognition and voice analysis of the user.

[1327] When a user uses the device, it scans the user's facial expressions and tone of voice and sends that data to the emotion engine.

[1328] 2. Emotion analysis

[1329] The emotion engine analyzes emotion data in real time to determine the user's current emotional state.

[1330] The emotion analysis results are sent to the server and stored in the user's database.

[1331] 3. Emotion-based adaptive feedback

[1332] The server tailors action plans and advice based on the user's emotional data, for example providing motivational support if the user is feeling down.

[1333] The device displays tailored feedback and action plans to the user, who then decides on their next move.

[1334] Specific examples

[1335] For example, if a user has a goal of "lose 5kg" and is recording their daily progress, the following steps might be added:

[1336] 1. Goal Setting

[1337] Users enter "lose 5kg" into the device to set their goal.

[1338] The server stores the goals in a database.

[1339] 2. Current situation analysis and understanding of differences

[1340] The user inputs their current weight (e.g., 75 kg).

[1341] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1342] 3. Emotion recognition

[1343] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[1344] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[1345] 4. Develop an action plan

[1346] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[1347] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[1348] 5. Implementing and following up on the action plan

[1349] Users enter their daily diet and exercise habits into the device.

[1350] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1351] It also provides feedback based on the user's emotional state.

[1352] 6. Feedback on results and revision of plans

[1353] The server will assess progress every month and generate next steps or additional advice.

[1354] The plan is readjusted depending on the user's emotional state.

[1355] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[1356] The processing flow will be explained below.

[1357] Step 1:

[1358] The device displays a goal entry form for the user, allowing them to enter a specific goal, such as "lose 5 kg of weight."

[1359] Step 2:

[1360] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[1361] Step 3:

[1362] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[1363] Step 4:

[1364] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[1365] Step 5:

[1366] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[1367] Step 6:

[1368] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[1369] Step 7:

[1370] The device sends the determined action plan to the server, which stores the action plan in a database.

[1371] Step 8:

[1372] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[1373] Step 9:

[1374] The device provides an interface for recognizing the user's emotions. When the user inputs data, the device performs facial recognition and voice analysis to obtain the user's emotional data.

[1375] Step 10:

[1376] The acquired emotion data is sent from the device to the emotion engine, which analyzes the emotion data in real time and identifies the user's emotional state.

[1377] Step 11:

[1378] The emotion engine sends the analyzed emotion results to a server and stores them in the user's database, where the server uses this information to adjust its action plan and feedback.

[1379] Step 12:

[1380] The server periodically generates a progress report based on the user's progress data and emotion data, and sends the report to the device, which then displays the progress report to the user.

[1381] Step 13:

[1382] Every certain period (e.g., one month), the server aggregates the user's progress and emotional data, evaluates their progress, and generates next steps and additional advice, which are then sent to the device.

[1383] Step 14:

[1384] The user then develops a new plan of action based on the feedback and submits it back to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[1385] Through this series of steps, a system that effectively supports users in achieving their goals is realized. The introduction of an emotion engine provides adaptive feedback according to the user's emotional state, achieving more precise support.

[1386] Example 2

[1387] 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."

[1388] Conventional user goal achievement support systems support users in achieving their goals, but they lack feedback that takes into account the user's emotional state. As a result, users' motivation may decrease and stress may increase. Furthermore, the adjustments to the action plan required for goal achievement do not reflect the user's emotional state, so they often fail to provide effective support. Therefore, a system with an adaptive feedback function that takes into account the user's emotional state is needed to effectively support users in achieving their goals.

[1389] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1390] In this invention, the server includes means for comparing the user's goal with current situation data and calculating the difference, means for generating a specific action plan based on the difference, recognition means for collecting the user's emotional data, analysis means for analyzing the collected emotional data, and adaptive feedback means for adjusting the action plan based on the emotional data, thereby enabling adjustment of the action plan taking the user's emotional state into consideration and more effective support for goal achievement.

[1391] "User" refers to the individual who sets goals, enters data, reviews feedback, etc.

[1392] "Input means" refers to the device or software that provides the interface through which the user inputs goals, current situation data, etc. into the system.

[1393] "Current status data" refers to information such as the user's current weight, lifestyle, and health condition.

[1394] "Comparison method" refers to a program or algorithm that compares the user's goals with current situation data and calculates the difference.

[1395] An "action plan" refers to a plan of specific actions required to achieve the goals set by the user.

[1396] "Action plan generation means" refers to a program or algorithm for creating an appropriate action plan for a user based on differential data.

[1397] "Implementation status" refers to the actions and results that users have actually taken based on their action plan.

[1398] "Follow-up tools" refers to programs or devices that track progress based on the user's implementation and provide advice or feedback as appropriate.

[1399] "Corrective measures" refer to programs or algorithms that review and make necessary corrections to a user's action plan based on their progress data and emotional state.

[1400] "Recognition methods" refers to facial recognition and voice analysis technologies used to collect user emotional data.

[1401] "Analysis Means" refers to a process or program for analyzing the emotional data collected by the Recognition Means and identifying the user's emotional state.

[1402] "Adaptive feedback means" refers to programs or algorithms that adjust action plans and feedback based on analyzed emotional data to provide appropriate assistance to users.

[1403] This invention is an AI personal coaching system that supports users in achieving their goals and operates in combination with an emotion engine. This system supports users through each step of goal setting, current situation analysis, action plan formulation and execution, and progress follow-up, and also has the ability to recognize the user's emotions and adjust its behavior based on those emotions. Specifically, this system operates in cooperation with a server, a terminal, and an emotion engine.

[1404] First, the device provides an interface for users to set goals. Users use this interface to input the goals they want to achieve. For example, if a user sets a goal of "lose 5 kg of weight," this goal is sent from the device to the server, which then stores it in a database.

[1405] Next, the device provides the user with an interface for inputting data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. This data is sent from the device to a server, which stores it in a database and calculates the difference between the user's current status and their goal.

[1406] The server generates a specific action plan based on the difference between the goal and the current situation. For example, if the device suggests reducing 500 kcal per day and exercising three times a week, the device will display this action plan to the user. The user can fine-tune the action plan as needed, and the final action plan will be sent from the device to the server, which will then store it in a database.

[1407] The user records their daily meals and exercise based on the action plan, and the device sends the recorded data to the server, which stores the data in a database and analyzes the progress. Periodically, the server generates a progress report and sends it to the device for display to the user.

[1408] At regular intervals, the server summarizes the user's progress data, evaluates their progress, suggests next steps and additional advice, and sends this information to the device. The user then sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1409] The system is equipped with an emotion engine that recognizes the user's emotional state and provides adaptive feedback based on it. Specifically, the device provides an interface that collects emotions through facial recognition and voice analysis. When the user uses the device, the device scans the user's facial expressions and tone of voice and sends the data to the emotion engine. The emotion engine analyzes the emotion data in real time and determines the user's current emotional state. The emotion analysis results are sent to a server and stored in the user's database.

[1410] The server adjusts the action plan and advice based on the user's emotional data. For example, if the user is feeling depressed, it provides motivational support. The device displays the adjusted feedback and action plan to the user, who then decides on their next course of action.

[1411] Specific examples

[1412] For example, if a user sets a goal of "lose 5kg" and tracks their progress daily, the following steps might be added:

[1413] 1. Goal Setting

[1414] Users enter "lose 5kg" into the device to set their goal.

[1415] The server stores the goals in a database.

[1416] 2. Current situation analysis and understanding of differences

[1417] The user inputs their current weight (e.g., 75 kg).

[1418] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1419] 3. Emotion recognition

[1420] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[1421] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[1422] 4. Develop an action plan

[1423] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[1424] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[1425] 5. Implementing and following up on the action plan

[1426] Users enter their daily diet and exercise habits into the device.

[1427] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1428] It also provides feedback based on the user's emotional state.

[1429] 6. Feedback on results and revision of plans

[1430] The server will assess progress every month and generate next steps or additional advice.

[1431] The plan is readjusted depending on the user's emotional state.

[1432] Prompt Sentence Examples

[1433] Use the following prompt for the generative AI model:

[1434] “Design a system to help users achieve their goals. This system should allow users to set goals, analyze their current situation, create and execute an action plan, follow up on progress, recognize emotions and reflect on that information.”

[1435] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[1436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1437] Step 1: Goal Setting

[1438] Input: Goal data entered by the user

[1439] Processing: The terminal displays an interface for the user to set a goal. The user inputs the goal they want to achieve. For example, they input the goal "to lose 5 kg."

[1440] Output: The target data is generated and sent from the terminal to the server, which stores it in a database.

[1441] Step 2: Enter current data

[1442] Input: Current weight and lifestyle data entered by the user

[1443] Processing: The device displays an interface for the user to input data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. For example, the user inputs that their current weight is 75 kg and that they exercise twice a week.

[1444] Output: Current status data is generated and sent from the terminal to the server. The server stores this data in a database and calculates the difference from the target.

[1445] Step 3: Calculate the difference

[1446] Input: Goal data and current status data stored on the server

[1447] Processing: The server compares the user's goal with the current data and calculates the difference. For example, it calculates the difference of 5 kg between the goal weight of 70 kg and the current weight of 75 kg.

[1448] Output: The differential data is generated and stored on the server.

[1449] Step 4: Generate an action plan

[1450] Input: Differential data stored on the server

[1451] Processing: The server generates a specific action plan based on the difference data, for example, recommending a reduction of 500 kcal per day and exercising three times a week.

[1452] Output: An action plan is generated and sent to the device, where the user can review it and fine-tune it if necessary.

[1453] Step 5: Adjust your action plan

[1454] Input: User feedback

[1455] Processing: The device displays the generated action plan to the user. The user can fine-tune the action plan as needed. For example, they can make adjustments such as "reduce 600 kcal instead of 500 kcal."

[1456] Output: An adjusted action plan is generated, sent from the device to the server, and stored in a database.

[1457] Step 6: Enter your daily record

[1458] Input: Daily food and exercise data entered by the user

[1459] Process: The user enters their daily diet and exercise information into the device. For example, they record what they ate for breakfast and how much exercise they did that day.

[1460] Output: Daily data is generated and sent from the device to the server, which stores it in a database and analyzes the progress.

[1461] Step 7: Generate progress reports

[1462] Input: Daily data stored on the server

[1463] Processing: The server periodically analyzes the daily data and generates progress reports, e.g., graphing weekly weight loss and calorie consumption trends.

[1464] Output: A progress report is generated, sent to the terminal, and displayed to the user.

[1465] Step 8: Collect emotion data

[1466] Input: User's facial recognition and voice analysis data

[1467] Processing: The device collects emotions through facial recognition and voice analysis of the user, for example, using the camera to collect facial expression data and the microphone to analyze tone of voice.

[1468] Output: Emotion data is generated and sent from the device to the server, where it is analyzed by the emotion engine.

[1469] Step 9: Analyze the sentiment data

[1470] Input: Collected emotion data

[1471] Processing: The emotion engine analyzes the emotion data in real time and determines the user's emotional state. For example, it outputs an analysis result such as "The current emotional state is 'anxious.'"

[1472] Output: The analysis results are generated, sent to the server, and stored in a database.

[1473] Step 10: Providing adaptive feedback

[1474] Input: Parsed emotion data

[1475] Processing: The server adjusts the action plan or advice based on the emotion data, for example, providing motivational support if the user is feeling down.

[1476] Output: The adjusted feedback is generated, sent to the device, and displayed to the user.

[1477] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[1478] (Application example 2)

[1479] 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."

[1480] Conventional personal coaching systems often lack the ability to provide dynamic feedback that takes into account a user's emotional state when helping them achieve their goals. As a result, users' motivation can easily drop, making it difficult to achieve their goals. In particular, when it comes to lifestyle and health management, users' emotional fluctuations have a significant impact on their behavior, so systems that ignore this have difficulty providing effective support.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1482] In this invention, the server includes input means for the user to set goals, means for inputting the user's current situation data, means for comparing the user's goals with the current situation data and calculating the difference, means for generating a specific action plan based on the difference, means for the user to record the implementation status of the action plan, means for following up on progress based on the implementation status, means for recognizing and analyzing the user's emotions, means for adjusting the action plan and feedback based on the user's emotional state, and means for modifying the action plan in accordance with progress. This enables dynamic feedback and adjustment of the action plan in response to the user's emotional state, thereby enabling effective goal achievement support.

[1483] "User" refers to an individual who uses the system to achieve a goal.

[1484] "Goal" means a specific result or outcome that the user wants to achieve.

[1485] "Input means" refers to an interface or device that allows a user to input data or information into a system.

[1486] "Current status data" is information about the user's current condition and lifestyle.

[1487] "Difference" refers to the difference or gap between the user's goal and the current situation data.

[1488] An "action plan" is a set of specific actions or steps that a user must take to achieve a goal.

[1489] "Implementation status" refers to the actions that a user actually takes based on the action plan and the results of those actions.

[1490] "Follow-up measures" are measures used to monitor the implementation of the user's action plan and evaluate progress.

[1491] "Means of recognizing emotions" refers to technologies and devices that can read a user's emotional state from their facial expressions, voice, etc.

[1492] "Means for analyzing emotions" means processing techniques for analyzing recognized emotion data and identifying the user's current emotional state.

[1493] "Feedback" refers to information and advice about what actions users should take next and what they should pay attention to.

[1494] "Adjusting means" is a means for dynamically changing action plans or feedback based on the user's emotional state.

[1495] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. As a specific application example, this system is applied to a food delivery health management app.

[1496] The program of this system operates using the following hardware and software.

[1497] Hardware used

[1498] Smartphone: Provides the user interface and allows data entry and display.

[1499] Laptop or desktop PC: Used for application development and testing.

[1500] Software used

[1501] Python 3: Used as the primary programming language for the application.

[1502] Tkinter library: Used as a GUI library for building user interfaces.

[1503] Emotion recognition library: A library for recognizing a user's emotional state by analyzing their facial expressions and voice.

[1504] Diet planner library: A library for generating customized meal plans based on your goal weight and emotional state.

[1505] System Functions and Processes

[1506] 1. Goal setting: The server allows the user to input their goal weight through the application, which is then sent to the server and stored in the database.

[1507] 2. Emotion Recognition: When a user uses the application, the device will scan the user's emotional state through facial recognition and voice analysis. This emotional data will be analyzed by the emotion engine to identify the user's emotional state. The emotional data will be sent to the server and stored in the database.

[1508] 3. Action plan generation: The server generates a specific meal plan based on the user's target weight and emotional state. The generated meal plan is sent to the device and displayed to the user.

[1509] 4. Recording of activity status: Users enter their daily diet and exercise records into the application, which are then sent to the server and stored in a database.

[1510] 5. Progress Follow-up: The server analyzes the user's progress based on their diet and exercise data, and generates regular progress reports that also take into account the user's emotional state.

[1511] 6. Result feedback: The server summarizes the progress data and emotional state, and suggests next steps or additional advice. This information is displayed on the device and fed back to the user.

[1512] Specific use cases

[1513] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, the system first scans the user's face and voice to analyze their emotional state. It then generates and presents a customized meal plan to help them reach their goal. If the user feels anxious or fatigued, it also provides advice on relaxation and motivation.

[1514] Generative AI model prompt example

[1515] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[1516] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1517] Step 1:

[1518] The user inputs the target weight using the terminal. The target data is input by inputting the target weight into the user interface provided by the terminal. This data is sent from the terminal to the server and stored in the database. Input data: target weight, Output data: target weight stored in the database

[1519] Step 2:

[1520] The user inputs data related to their current weight and lifestyle habits. The device sends this input data to the server, where it is stored in a database. The server compares the target and current status data and calculates the difference. Input data: current weight and lifestyle habit data, Output data: difference between the target and current status

[1521] Step 3:

[1522] The device uses an emotion engine to recognize the user's face and analyze their voice, collecting emotional data. The collected emotional data is sent to a server where it is analyzed. Input data: User's facial expressions and voice data, Output data: Analyzed emotional state

[1523] Step 4:

[1524] The server generates a specific action plan based on the user's goal, current situation data, and emotional state. This action plan is constructed as, for example, an appropriate diet or exercise plan. The generated action plan is sent to the terminal and displayed to the user. Input data: goal, current situation data, emotional state, Output data: action plan

[1525] Step 5:

[1526] The user inputs daily records of their diet and exercise into the terminal. The terminal sends this recorded data to the server and stores it in a database. Input data: Daily diet and exercise data, Output data: Status data stored in the database

[1527] Step 6:

[1528] The server analyzes the progress based on the implementation status data stored in the database. It periodically generates progress reports, sends them to the terminal, and displays them to the user. This progress report is created taking into account the user's emotional state. Input data: implementation status data, Output data: progress report

[1529] Step 7:

[1530] The server compiles the user's progress data and emotional state, and suggests the next steps to take or additional advice. These suggestions are displayed on the device and fed back to the user. Input data: progress data and emotional state, Output data: next steps to take or additional advice

[1531] Specific examples

[1532] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, step 1 is to enter the goal weight, and step 2 is to enter the current weight. Step 3 is emotion recognition and the emotional state is analyzed. Step 4 is to generate a specific meal plan, and step 5 is for the user to record their daily meals and exercise. Step 6 is then generated by the server, and step 7 is to provide next steps and further advice.

[1533] Generative AI model prompt example

[1534] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[1535] 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.

[1536] 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.

[1537] 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.

[1538] [Fourth embodiment]

[1539] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1540] 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.

[1541] 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).

[1542] 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.

[1543] 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.

[1544] 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).

[1545] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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.

[1551] 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."

[1552] This invention relates to an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to support goal achievement. This system works in conjunction with a server and a terminal, providing effective feedback to the user.

[1553] A natural language description of the program's operation

[1554] 1. Goal Setting

[1555] The terminal provides an interface for the user to set goals.

[1556] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[1557] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[1558] 2. Current situation analysis and understanding of differences

[1559] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[1560] Users input data about their current weight, daily diet, and exercise habits.

[1561] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[1562] 3. Develop an action plan

[1563] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[1564] The device displays this plan of action to the user, who can then fine-tune it as needed.

[1565] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[1566] 4. Implementing and following up on the action plan

[1567] Users record their daily meals and exercise based on an action plan.

[1568] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[1569] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[1570] 5. Feedback on results and revision of plans

[1571] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[1572] The server suggests next steps or additional advice and sends that information to the device.

[1573] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1574] Specific examples

[1575] For example, if a user's goal is to "lose 5kg," the steps would be as follows:

[1576] 1. Goal Setting

[1577] Users enter "lose 5kg" into the device to set their goal.

[1578] The server stores the goals in a database.

[1579] 2. Current situation analysis and understanding of differences

[1580] The user inputs their current weight (e.g., 75 kg).

[1581] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1582] 3. Develop an action plan

[1583] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[1584] The user reviews it and finalizes the plan on the device.

[1585] 4. Implementing and following up on the action plan

[1586] Users enter their daily diet and exercise habits into the device.

[1587] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1588] 5. Feedback on results and revision of plans

[1589] The server will evaluate progress after one month and suggest next steps to take.

[1590] The user sets up a new action plan and sends it back to the server, updating the system.

[1591] In this way, the system of the present invention effectively assists users in achieving their goals.

[1592] The processing flow will be explained below.

[1593] Step 1:

[1594] The device displays a goal entry form for the user, where the user enters a specific goal, such as "lose 5 kg."

[1595] Step 2:

[1596] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[1597] Step 3:

[1598] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[1599] Step 4:

[1600] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[1601] Step 5:

[1602] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[1603] Step 6:

[1604] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[1605] Step 7:

[1606] The device sends the determined action plan to the server, which stores the action plan in a database.

[1607] Step 8:

[1608] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[1609] Step 9:

[1610] The server analyzes the user's progress based on the transmitted data, periodically generates progress reports, and sends them to the device.

[1611] Step 10:

[1612] The device displays a progress report to the user, and the server summarises the user's progress data after one month to evaluate the achievement.

[1613] Step 11:

[1614] The server generates next steps and additional advice based on the progress achieved, and sends it to the device, which displays it to the user.

[1615] Step 12:

[1616] The user sets a new action plan based on the feedback and submits it again to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[1617] Through the above processing steps, a system that effectively supports the user in achieving their goals is realized.

[1618] Example 1

[1619] 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."

[1620] Conventional goal achievement support systems have not adequately integrated the analysis of the user's current situation, the creation of an action plan, and follow-up, resulting in a lack of support for users to consistently work toward their goals. In particular, it has been difficult to automate and continuously support the entire process from inputting current situation data to achieving the goal.

[1621] 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.

[1622] In this invention, the server includes input means for the user to set a goal, input means for inputting the user's current situation data, calculation means for comparing the user's goal with the current situation data and calculating the difference, generation means for generating a specific action plan based on the difference, recording means for the user to record the implementation status of the action plan, follow-up means for following up on progress based on the implementation status, correction means for correcting the action plan in accordance with the progress, display means for displaying the generated action plan to the user, and storage means for saving the data input by the user in a database. This allows the user to continue making consistent efforts toward their goal, and provides effective support for goal achievement.

[1623] "User" refers to an individual who uses this system to achieve a goal.

[1624] "Server" refers to the computer system that processes and stores user data and manages the entire system.

[1625] "Input means" refers to the interface through which the user inputs goals and current situation data into the system.

[1626] "Calculation means" refers to a processing device that has the function of comparing the user's goal with the current situation data and calculating the difference.

[1627] "Generation means" refers to a function that automatically generates a specific action plan based on the difference.

[1628] "Recording means" refers to an interface that allows a user to record the implementation status of an action plan in the system.

[1629] "Follow-up measures" refers to the ability to track progress and provide feedback to users based on recorded implementation.

[1630] "Correction measures" refers to the function of automatically correcting the action plan based on the results of the follow-up.

[1631] "Display means" refers to an interface for visually displaying the generated action plan to the user.

[1632] "Storage means" refers to the function for storing data entered by the user and the generated action plan in a database.

[1633] This invention is an AI personal coaching system that supports users in achieving their goals. This system analyzes the current situation, formulates a specific action plan, and follows up on the progress of the goal set by the user to help achieve the goal. This system works in conjunction with the server and the terminal, providing effective feedback to the user.

[1634] The system operates using the following hardware and software:

[1635] Hardware: User devices (smartphones and tablets), servers

[1636] software:

[1637] Server side: Python, Flask, MySQL, Pandas

[1638] Device side: Dedicated application (e.g., health management app)

[1639] System Operation

[1640] 1. Goal Setting

[1641] The terminal provides an interface for the user to set goals.

[1642] The user enters the goal they want to achieve (e.g., lose 5 kg) into the device's input screen.

[1643] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[1644] 2. Current situation analysis and understanding of differences

[1645] The terminal provides an interface for the user to input current data (e.g., weight, diet, and exercise habits).

[1646] Users input data about their current weight, daily diet, and exercise habits.

[1647] These data are sent from the terminal to a server, which stores them in a database and calculates the difference from the target.

[1648] 3. Develop an action plan

[1649] The server generates a specific action plan based on the difference between the user's goal and current situation (e.g., reducing 500 kcal per day and exercising three times a week).

[1650] The device displays this plan of action to the user, who can then fine-tune it as needed.

[1651] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[1652] 4. Implementing and following up on the action plan

[1653] Users record their daily meals and exercise based on an action plan.

[1654] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[1655] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[1656] 5. Feedback on results and revision of plans

[1657] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[1658] The server suggests next steps or additional advice and sends this information to the terminal.

[1659] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1660] Specific examples

[1661] For example, if a user's goal is to "lose 5kg," they might take the following steps:

[1662] 1. Goal Setting

[1663] Users enter "lose 5kg" into the device to set their goal.

[1664] The server stores the goals in a database.

[1665] 2. Current situation analysis and understanding of differences

[1666] The user inputs their current weight (e.g., 75 kg).

[1667] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1668] 3. Develop an action plan

[1669] The server generates a specific action plan to achieve the goal (e.g., reduce 500 kcal per day and exercise three times a week).

[1670] The user reviews it and finalizes the plan on the device.

[1671] 4. Implementing and following up on the action plan

[1672] Users enter their daily diet and exercise habits into the device.

[1673] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1674] 5. Feedback on results and revision of plans

[1675] The server will evaluate progress after one month and suggest next steps to take.

[1676] The user sets up a new action plan and sends it back to the server, updating the system.

[1677] Prompt Sentence Examples

[1678] "I'd like you to set a goal and create an action plan to achieve it. First, please enter your current weight and your goal weight. For example, current weight 75kg, goal weight 70kg."

[1679] "Next, enter your daily calorie intake and exercise frequency. Example: Lose 500 calories per day, exercise three times per week."

[1680] "It records your daily progress (food and exercise) and tracks your weight changes. It provides feedback based on your results and suggests new action plans."

[1681] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1682] Step 1:

[1683] goal setting

[1684] The terminal provides an input interface for the user to set goals.

[1685] Users input the goal they want to achieve (e.g., lose 5 kg).

[1686] Input: A goal entered by the user (e.g., weight 70 kg).

[1687] Data processing: The terminal formats the input target data and converts it into a form that can be sent to the server.

[1688] The device sends the entered goal to the server via REST API.

[1689] The server stores the received target data in a database.

[1690] Output: The status that the target data has been saved to the database.

[1691] Specific behavior:

[1692] The user launches the dedicated application and goes to the "Goal Setting" screen.

[1693] Enter "lose 5kg" and press the send button.

[1694] The terminal converts the input data into JSON format and sends it to the server.

[1695] The server receives the data and stores it in a MySQL database.

[1696] Step 2:

[1697] Current situation analysis and understanding of differences

[1698] The terminal provides an interface for the user to input current status data.

[1699] Users input their current weight, daily diet, and exercise habits.

[1700] Input: User-entered current weight (e.g., 75 kg), diet, and exercise habits.

[1701] Data processing: The terminal collects the current data entered and converts it into a form that can be sent to the server.

[1702] The terminal sends the entered data to the server via REST API.

[1703] The server stores the data in a database and calculates the difference between the target and the current situation.

[1704] Output: Difference data (e.g., 5 kg difference).

[1705] Specific behavior:

[1706] The user goes to the "Current Status Input" screen and enters their current weight (75 kg), dietary and exercise information.

[1707] The device converts the data into JSON format and sends it to the server.

[1708] The server receives the data and calculates the difference (5kg) from the target weight using a Python script.

[1709] Step 3:

[1710] Developing an action plan

[1711] The server generates an action plan based on the current data and the goals.

[1712] Input: Difference data (e.g., difference of 5 kg).

[1713] Data processing: The server generates a specific action plan using a Python script based on the differential data.

[1714] The server sends the generated action plan to the terminal in JSON format.

[1715] The device displays the action plan to the user and provides an interface for the user to modify it as needed.

[1716] The user finalizes the action plan and sends it from the device to the server.

[1717] The server stores the plan in a database.

[1718] Output: Status that the action plan data has been saved to the database.

[1719] Specific behavior:

[1720] Based on the differential data received, the server generates an action plan to reduce 500 kcal per day and exercise three times a week.

[1721] The generated plan is sent to the terminal in JSON format.

[1722] The user reviews the plan, makes any necessary adjustments, and presses the confirm button.

[1723] The terminal sends the final action plan to the server, which stores it in a database.

[1724] Step 4:

[1725] Implementing and following up on action plans

[1726] Users record their daily meals and exercise based on an action plan.

[1727] Input: Daily diet and exercise data entered by the user.

[1728] Data processing: The terminal aggregates the recorded data and converts it into a form that can be sent to the server.

[1729] The device sends the recorded data to the server via a REST API.

[1730] The server stores the data in a database and analyzes the progress.

[1731] The server periodically generates progress reports and sends them to the terminal.

[1732] The terminal displays progress reports to the user.

[1733] Output: Progress report data is sent to the user.

[1734] Specific behavior:

[1735] The user enters their daily food and exercise records into the device app.

[1736] Sends input data to the server in JSON format.

[1737] The server stores the received data in a database and analyzes the progress using Python's Pandas.

[1738] The server generates a progress report in JSON format and sends it to the device.

[1739] The terminal displays progress reports to the user.

[1740] Step 5:

[1741] Feedback on results and revision of plans

[1742] The server periodically compiles the user's progress data and evaluates their achievement.

[1743] Input: Accumulated progress data.

[1744] Data processing: The server aggregates the progress data and evaluates the achievement status using a Python script.

[1745] The server suggests next steps or additional advice and sends this information to the device in JSON format.

[1746] The device displays feedback to the user.

[1747] The user sets up a new action plan and sends it from the device to the server.

[1748] The server updates the plan to the database.

[1749] Output: Status that the new action plan data has been saved to the database.

[1750] Specific behavior:

[1751] The server will summarize the progress data after one month and evaluate the achievement.

[1752] Generate next steps or new advice and send it to the device.

[1753] The user receives feedback, sets a new plan of action, and hits submit.

[1754] The server stores the new action plan in the database and updates the system.

[1755] (Application example 1)

[1756] 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."

[1757] Conventional factory robot management systems have the problem of making it difficult to generate specific action plans to achieve production goals and to follow up on progress. In addition, there is no way to suggest next steps using generative AI models or prompts, so managers lack the support they need to carry out effective production activities.

[1758] 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.

[1759] In this invention, the server includes a generative AI model for generating an action plan based on difference data between the target and the current situation, a means for setting prompt sentences for inputting data according to the purpose into the generative AI model, and a means for summarizing progress data for each specific period and proposing the next step to be taken. This enables managers to generate specific action plans for effectively achieving the production targets of factory robots and provide feedback according to progress.

[1760] "Goal setting" is the process by which users input specific numbers or states they want to achieve.

[1761] A "generative AI model" refers to an artificial intelligence algorithm that automatically generates an action plan based on data tailored to a specific purpose.

[1762] A "prompt" is an instruction to provide appropriate data input for a generative AI model, and is used to optimize the output of the generative AI model.

[1763] "Means" is a general term for methods and devices for performing a specific function.

[1764] "Differential data" is a number or piece of information that indicates the difference between the user's goal and the current situation data.

[1765] An "action plan" is a plan that specifies the specific actions and steps needed to achieve a specific goal.

[1766] "Follow-up" is the process of checking whether the action plan is being implemented and tracking progress.

[1767] "Feedback" refers to advice and evaluation provided based on progress and implementation results.

[1768] "Progress data" is data that indicates the activities and production volume that the user has carried out based on the action plan.

[1769] "At specific intervals" refers to a pre-set time interval (e.g., one day, one week, one month).

[1770] A "factory robot" refers to a mechanical device used in a factory to automate production activities.

[1771] A "server" is a computer system that centrally stores data, performs calculations, and operates generative AI models.

[1772] A system including "means" refers to the entire system that combines individual methods or functions for performing each function.

[1773] This invention applies to an AI personal coaching system that supports users in achieving their goals. This system aims to optimize production activities and is particularly effective in managing robots operated in factories.

[1774] System Configuration

[1775] This system operates in collaboration between a server and terminals (smartphones, head-mounted displays, robots). The server runs on a cloud service (e.g., Amazon Web Services, Google Cloud Platform) and operates generative AI models (e.g., TensorFlow, PyTorch). The terminals are factory robots (e.g., FANUC, ABB), as well as smartphones and head-mounted displays (e.g., Microsoft HoloLens) used by managers.

[1776] goal setting

[1777] The user (factory manager) is provided with an interface to set production targets via a smartphone or head-mounted display. For example, they can input a specific target, such as "daily production target 50 units." This data is sent to a server and stored in a database.

[1778] Current situation analysis and understanding of differences

[1779] The robot sends its current production volume and operation data to the server. The server stores this data in a database and calculates the difference between the current volume and the target volume. For example, if 30 units are currently being produced, the difference (20 units) from the target volume (50 units) is calculated.

[1780] Developing an action plan

[1781] The server uses a generative AI model to generate an action plan based on differential data between the current situation and the goal. The server inputs appropriate data using prompt statements, and the AI ​​model outputs the optimal action plan. For example, the generated action plan might be, "Recommend the production of an additional 20 units during the shift." This plan is displayed on a smartphone, head-mounted display, or robot.

[1782] Implementing and following up on action plans

[1783] The robot carries out production activities based on the generated action plan. The terminal periodically collects progress data and sends it to the server. The server analyzes the progress data and periodically generates progress reports. These reports are sent to the terminal and displayed to the user.

[1784] Feedback on results and revision of plans

[1785] At regular intervals (e.g., daily), the server summarizes the progress data and suggests next steps or improvements. It uses an AI model to generate the next action plan and sends that feedback to the device. The user then sets a new action plan based on this feedback and sends it back to the server to update the system.

[1786] Specific examples

[1787] For example, here is a prompt for a generative AI model:

[1788] "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next action plan (e.g., recommend producing an additional 20 units during this shift)."

[1789] In this way, the system of the present invention can provide specific support for effectively achieving production goals of factory robots.

[1790] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1791] Step 1:

[1792] The user sets a goal using a smartphone or head-mounted display. At this time, the user inputs a specific goal, such as "daily production target 50 units." The input data is sent from the device to a server, which stores the goal in a database. The specific operation is to input the goal into the goal setting interface.

[1793] Step 2:

[1794] The robot measures the current production volume and sends that data to the server. For example, data such as "current production volume: 30 units" is sent. The server stores the received data in a database and calculates the difference between the target value (e.g., 20 units). Specifically, the robot collects sensor data and sends the data through an interface.

[1795] Step 3:

[1796] The server sends a prompt to the generative AI model based on the difference data between the target and current situation. The prompt text is entered as "The factory robot is currently producing 30 units. The target is 50 units. Please suggest the next necessary action plan." The AI ​​model generates an action plan based on this prompt and outputs specific instructions (e.g., "Recommend the production of an additional 20 units during this shift"). Specifically, the data transmission process and the AI ​​generation process are carried out.

[1797] Step 4:

[1798] The generated action plan is sent to the terminal and displayed on the robot's and the administrator's smartphone or head-mounted display. The user can review the action plan and make any necessary adjustments. Specifically, data is sent and displayed via an API.

[1799] Step 5:

[1800] The robot carries out production activities according to the generated action plan. For example, it operates according to the instruction "produce an additional 20 units." Progress data is collected periodically and sent to the server. This data is stored in a database and used for subsequent processing. Specific operations are performed by the robot's control system.

[1801] Step 6:

[1802] The server periodically analyzes the progress data and generates a progress report. The generated progress report is sent to the device and displayed to the user. The user can use this information to determine next steps and areas for improvement. Specifically, the server aggregates and analyzes the data and generates a report.

[1803] Step 7:

[1804] After a certain period of time (e.g., daily), the server summarizes all progress data and suggests the next steps to take. It uses the generative AI model again to generate a new action plan. For example, it might suggest "produce 15 more units in the next shift." The user sets a new action plan based on the feedback and sends it back to the server. Specifically, the data is re-analyzed and a new action plan is generated.

[1805] Step 8:

[1806] The user checks the updated action plan and starts operating the robot again based on it. This cycle is repeated, providing continuous support for achieving production targets. Specifically, the process begins again with goal setting as a loop.

[1807] 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.

[1808] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. This system performs each step of the user's goal setting, current situation analysis, action plan formulation and execution, and progress follow-up. It can also recognize the user's emotions and adjust its behavior based on those emotions. This system operates in cooperation with a server, a terminal, and an emotion engine.

[1809] A natural language description of the program's operation

[1810] 1. Goal Setting

[1811] The terminal provides an interface for the user to set goals.

[1812] The user inputs the goal they want to achieve (e.g., lose 5 kg).

[1813] The entered goal is sent from the terminal to the server, which stores the goal in a database.

[1814] 2. Current situation analysis and understanding of differences

[1815] The terminal provides an interface for inputting data about current weight and lifestyle habits.

[1816] Users input data about their current weight, daily diet, and exercise habits.

[1817] These data are sent from the terminal to a server, which stores the data in a database and calculates the difference between the target and the current situation.

[1818] 3. Develop an action plan

[1819] The server generates a specific action plan (e.g., reducing 500 kcal per day and exercising three times a week) based on the difference between the user's goal and current situation.

[1820] The device displays this plan of action to the user, who can then fine-tune it as needed.

[1821] The final action plan is sent from the terminal to the server, which stores the plan in a database.

[1822] 4. Implementing and following up on the action plan

[1823] Users record their daily meals and exercise based on an action plan.

[1824] The device sends the recorded data to a server, which stores the data in a database and analyzes the progress.

[1825] Periodically, the server generates a progress report and sends it to the terminal for display to the user.

[1826] 5. Feedback on results and revision of plans

[1827] Every certain period (e.g., one month), the server aggregates the user's progress data and evaluates their achievement.

[1828] The server suggests next steps or additional advice and sends that information to the device.

[1829] The user sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1830] Incorporating an emotion engine

[1831] 1. Emotion recognition

[1832] The device provides an interface for collecting emotions through facial recognition and voice analysis of the user.

[1833] When a user uses the device, it scans the user's facial expressions and tone of voice and sends that data to the emotion engine.

[1834] 2. Emotion analysis

[1835] The emotion engine analyzes emotion data in real time to determine the user's current emotional state.

[1836] The emotion analysis results are sent to the server and stored in the user's database.

[1837] 3. Emotion-based adaptive feedback

[1838] The server tailors action plans and advice based on the user's emotional data, for example providing motivational support if the user is feeling down.

[1839] The device displays tailored feedback and action plans to the user, who then decides on their next move.

[1840] Specific examples

[1841] For example, if a user has a goal of "lose 5kg" and is recording their daily progress, the following steps might be added:

[1842] 1. Goal Setting

[1843] Users enter "lose 5kg" into the device to set their goal.

[1844] The server stores the goals in a database.

[1845] 2. Current situation analysis and understanding of differences

[1846] The user inputs their current weight (e.g., 75 kg).

[1847] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1848] 3. Emotion recognition

[1849] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[1850] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[1851] 4. Develop an action plan

[1852] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[1853] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[1854] 5. Implementing and following up on the action plan

[1855] Users enter their daily diet and exercise habits into the device.

[1856] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1857] It also provides feedback based on the user's emotional state.

[1858] 6. Feedback on results and revision of plans

[1859] The server will assess progress every month and generate next steps or additional advice.

[1860] The plan is readjusted depending on the user's emotional state.

[1861] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[1862] The processing flow will be explained below.

[1863] Step 1:

[1864] The device displays a goal entry form for the user, allowing them to enter a specific goal, such as "lose 5 kg of weight."

[1865] Step 2:

[1866] The terminal sends the goal data entered by the user to the server, which stores the goal data in a database.

[1867] Step 3:

[1868] The device displays a form for the user to input their current status (e.g., weight and lifestyle habits). The user inputs data on their current weight, diet, and exercise habits.

[1869] Step 4:

[1870] The device sends the user's current status data to the server, which stores the data in a database and calculates the difference between the target and the current status.

[1871] Step 5:

[1872] The server generates a specific action plan (e.g., reduce 500 kcal per day, exercise three times a week) based on the difference between the user's goal and their current situation. The generated action plan is sent to the device.

[1873] Step 6:

[1874] The device displays the action plan to the user, who can review it, make any necessary adjustments, and finalize the action plan.

[1875] Step 7:

[1876] The device sends the determined action plan to the server, which stores the action plan in a database.

[1877] Step 8:

[1878] To record daily meals and exercise, users enter data into an input form on the device, which then sends the data to a server, which stores it in a database.

[1879] Step 9:

[1880] The device provides an interface for recognizing the user's emotions. When the user inputs data, the device performs facial recognition and voice analysis to obtain the user's emotional data.

[1881] Step 10:

[1882] The acquired emotion data is sent from the device to the emotion engine, which analyzes the emotion data in real time and identifies the user's emotional state.

[1883] Step 11:

[1884] The emotion engine sends the analyzed emotion results to a server and stores them in the user's database, where the server uses this information to adjust its action plan and feedback.

[1885] Step 12:

[1886] The server periodically generates a progress report based on the user's progress data and emotion data, and sends the report to the device, which then displays the progress report to the user.

[1887] Step 13:

[1888] Every certain period (e.g., one month), the server aggregates the user's progress and emotional data, evaluates their progress, and generates next steps and additional advice, which are then sent to the device.

[1889] Step 14:

[1890] The user then develops a new plan of action based on the feedback and submits it back to the server, which updates the database with the new plan, and the cycle repeats as necessary.

[1891] Through this series of steps, a system that effectively supports users in achieving their goals is realized. The introduction of an emotion engine provides adaptive feedback according to the user's emotional state, achieving more precise support.

[1892] Example 2

[1893] 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."

[1894] Conventional user goal achievement support systems support users in achieving their goals, but they lack feedback that takes into account the user's emotional state. As a result, users' motivation may decrease and stress may increase. Furthermore, the adjustments to the action plan required for goal achievement do not reflect the user's emotional state, so they often fail to provide effective support. Therefore, a system with an adaptive feedback function that takes into account the user's emotional state is needed to effectively support users in achieving their goals.

[1895] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1896] In this invention, the server includes means for comparing the user's goal with current situation data and calculating the difference, means for generating a specific action plan based on the difference, recognition means for collecting the user's emotional data, analysis means for analyzing the collected emotional data, and adaptive feedback means for adjusting the action plan based on the emotional data, thereby enabling adjustment of the action plan taking the user's emotional state into consideration and more effective support for goal achievement.

[1897] "User" refers to the individual who sets goals, enters data, reviews feedback, etc.

[1898] "Input means" refers to the device or software that provides the interface through which the user inputs goals, current situation data, etc. into the system.

[1899] "Current status data" refers to information such as the user's current weight, lifestyle, and health condition.

[1900] "Comparison method" refers to a program or algorithm that compares the user's goals with current situation data and calculates the difference.

[1901] An "action plan" refers to a plan of specific actions required to achieve the goals set by the user.

[1902] "Action plan generation means" refers to a program or algorithm for creating an appropriate action plan for a user based on differential data.

[1903] "Implementation status" refers to the actions and results that users have actually taken based on their action plan.

[1904] "Follow-up tools" refers to programs or devices that track progress based on the user's implementation and provide advice or feedback as appropriate.

[1905] "Corrective measures" refer to programs or algorithms that review and make necessary corrections to a user's action plan based on their progress data and emotional state.

[1906] "Recognition methods" refers to facial recognition and voice analysis technologies used to collect user emotional data.

[1907] "Analysis Means" refers to a process or program for analyzing the emotional data collected by the Recognition Means and identifying the user's emotional state.

[1908] "Adaptive feedback means" refers to programs or algorithms that adjust action plans and feedback based on analyzed emotional data to provide appropriate assistance to users.

[1909] This invention is an AI personal coaching system that supports users in achieving their goals and operates in combination with an emotion engine. This system supports users through each step of goal setting, current situation analysis, action plan formulation and execution, and progress follow-up, and also has the ability to recognize the user's emotions and adjust its behavior based on those emotions. Specifically, this system operates in cooperation with a server, a terminal, and an emotion engine.

[1910] First, the device provides an interface for users to set goals. Users use this interface to input the goals they want to achieve. For example, if a user sets a goal of "lose 5 kg of weight," this goal is sent from the device to the server, which then stores it in a database.

[1911] Next, the device provides the user with an interface for inputting data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. This data is sent from the device to a server, which stores it in a database and calculates the difference between the user's current status and their goal.

[1912] The server generates a specific action plan based on the difference between the goal and the current situation. For example, if the device suggests reducing 500 kcal per day and exercising three times a week, the device will display this action plan to the user. The user can fine-tune the action plan as needed, and the final action plan will be sent from the device to the server, which will then store it in a database.

[1913] The user records their daily meals and exercise based on the action plan, and the device sends the recorded data to the server, which stores the data in a database and analyzes the progress. Periodically, the server generates a progress report and sends it to the device for display to the user.

[1914] At regular intervals, the server summarizes the user's progress data, evaluates their progress, suggests next steps and additional advice, and sends this information to the device. The user then sets a new action plan based on this feedback and sends it back to the server, updating the plan in the database.

[1915] The system is equipped with an emotion engine that recognizes the user's emotional state and provides adaptive feedback based on it. Specifically, the device provides an interface that collects emotions through facial recognition and voice analysis. When the user uses the device, the device scans the user's facial expressions and tone of voice and sends the data to the emotion engine. The emotion engine analyzes the emotion data in real time and determines the user's current emotional state. The emotion analysis results are sent to a server and stored in the user's database.

[1916] The server adjusts the action plan and advice based on the user's emotional data. For example, if the user is feeling depressed, it provides motivational support. The device displays the adjusted feedback and action plan to the user, who then decides on their next course of action.

[1917] Specific examples

[1918] For example, if a user sets a goal of "lose 5kg" and tracks their progress daily, the following steps might be added:

[1919] 1. Goal Setting

[1920] Users enter "lose 5kg" into the device to set their goal.

[1921] The server stores the goals in a database.

[1922] 2. Current situation analysis and understanding of differences

[1923] The user inputs their current weight (e.g., 75 kg).

[1924] The server calculates the difference (5kg) from the target (70kg) and analyzes the current situation.

[1925] 3. Emotion recognition

[1926] When a user enters their weight data into the device, it performs facial recognition and voice analysis to scan the user's current emotions.

[1927] This data is analyzed by an emotion engine to determine whether the user is in a state such as "anxious" or "highly motivated."

[1928] 4. Develop an action plan

[1929] The server generates a specific action plan to achieve the goal (lose 500 kcal per day and exercise three times a week).

[1930] If the user is feeling impatient, the plan also includes relaxation techniques and activities to build up small wins.

[1931] 5. Implementing and following up on the action plan

[1932] Users enter their daily diet and exercise habits into the device.

[1933] The server analyzes the data and periodically generates progress reports that are sent to the terminal.

[1934] It also provides feedback based on the user's emotional state.

[1935] 6. Feedback on results and revision of plans

[1936] The server will assess progress every month and generate next steps or additional advice.

[1937] The plan is readjusted depending on the user's emotional state.

[1938] Prompt Sentence Examples

[1939] Use the following prompt for the generative AI model:

[1940] “Design a system to help users achieve their goals. This system should allow users to set goals, analyze their current situation, create and execute an action plan, follow up on progress, recognize emotions and reflect on that information.”

[1941] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[1942] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1943] Step 1: Goal Setting

[1944] Input: Goal data entered by the user

[1945] Processing: The terminal displays an interface for the user to set a goal. The user inputs the goal they want to achieve. For example, they input the goal "to lose 5 kg."

[1946] Output: The target data is generated and sent from the terminal to the server, which stores it in a database.

[1947] Step 2: Enter current data

[1948] Input: Current weight and lifestyle data entered by the user

[1949] Processing: The device displays an interface for the user to input data about their current weight and lifestyle habits. The user inputs data about their current weight, daily diet, and exercise habits. For example, the user inputs that their current weight is 75 kg and that they exercise twice a week.

[1950] Output: Current status data is generated and sent from the terminal to the server. The server stores this data in a database and calculates the difference from the target.

[1951] Step 3: Calculate the difference

[1952] Input: Goal data and current status data stored on the server

[1953] Processing: The server compares the user's goal with the current data and calculates the difference. For example, it calculates the difference of 5 kg between the goal weight of 70 kg and the current weight of 75 kg.

[1954] Output: The differential data is generated and stored on the server.

[1955] Step 4: Generate an action plan

[1956] Input: Differential data stored on the server

[1957] Processing: The server generates a specific action plan based on the difference data, for example, recommending a reduction of 500 kcal per day and exercising three times a week.

[1958] Output: An action plan is generated and sent to the device, where the user can review it and fine-tune it if necessary.

[1959] Step 5: Adjust your action plan

[1960] Input: User feedback

[1961] Processing: The device displays the generated action plan to the user. The user can fine-tune the action plan as needed. For example, they can make adjustments such as "reduce 600 kcal instead of 500 kcal."

[1962] Output: An adjusted action plan is generated, sent from the device to the server, and stored in a database.

[1963] Step 6: Enter your daily record

[1964] Input: Daily food and exercise data entered by the user

[1965] Process: The user enters their daily diet and exercise information into the device. For example, they record what they ate for breakfast and how much exercise they did that day.

[1966] Output: Daily data is generated and sent from the device to the server, which stores it in a database and analyzes the progress.

[1967] Step 7: Generate progress reports

[1968] Input: Daily data stored on the server

[1969] Processing: The server periodically analyzes the daily data and generates progress reports, e.g., graphing weekly weight loss and calorie consumption trends.

[1970] Output: A progress report is generated, sent to the terminal, and displayed to the user.

[1971] Step 8: Collect emotion data

[1972] Input: User's facial recognition and voice analysis data

[1973] Processing: The device collects emotions through facial recognition and voice analysis of the user, for example, using the camera to collect facial expression data and the microphone to analyze tone of voice.

[1974] Output: Emotion data is generated and sent from the device to the server, where it is analyzed by the emotion engine.

[1975] Step 9: Analyze the sentiment data

[1976] Input: Collected emotion data

[1977] Processing: The emotion engine analyzes the emotion data in real time and determines the user's emotional state. For example, it outputs an analysis result such as "The current emotional state is 'anxious.'"

[1978] Output: The analysis results are generated, sent to the server, and stored in a database.

[1979] Step 10: Providing adaptive feedback

[1980] Input: Parsed emotion data

[1981] Processing: The server adjusts the action plan or advice based on the emotion data, for example, providing motivational support if the user is feeling down.

[1982] Output: The adjusted feedback is generated, sent to the device, and displayed to the user.

[1983] In this way, the system of the present invention effectively supports goal achievement while taking into account the user's emotional state.

[1984] (Application example 2)

[1985] 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."

[1986] Conventional personal coaching systems often lack the ability to provide dynamic feedback that takes into account a user's emotional state when helping them achieve their goals. As a result, users' motivation can easily drop, making it difficult to achieve their goals. In particular, when it comes to lifestyle and health management, users' emotional fluctuations have a significant impact on their behavior, so systems that ignore this have difficulty providing effective support.

[1987] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1988] In this invention, the server includes input means for the user to set goals, means for inputting the user's current situation data, means for comparing the user's goals with the current situation data and calculating the difference, means for generating a specific action plan based on the difference, means for the user to record the implementation status of the action plan, means for following up on progress based on the implementation status, means for recognizing and analyzing the user's emotions, means for adjusting the action plan and feedback based on the user's emotional state, and means for modifying the action plan in accordance with progress. This enables dynamic feedback and adjustment of the action plan in response to the user's emotional state, thereby enabling effective goal achievement support.

[1989] "User" refers to an individual who uses the system to achieve a goal.

[1990] "Goal" means a specific result or outcome that the user wants to achieve.

[1991] "Input means" refers to an interface or device that allows a user to input data or information into a system.

[1992] "Current status data" is information about the user's current condition and lifestyle.

[1993] "Difference" refers to the difference or gap between the user's goal and the current situation data.

[1994] An "action plan" is a set of specific actions or steps that a user must take to achieve a goal.

[1995] "Implementation status" refers to the actions that a user actually takes based on the action plan and the results of those actions.

[1996] "Follow-up measures" are measures used to monitor the implementation of the user's action plan and evaluate progress.

[1997] "Means of recognizing emotions" refers to technologies and devices that can read a user's emotional state from their facial expressions, voice, etc.

[1998] "Means for analyzing emotions" means processing techniques for analyzing recognized emotion data and identifying the user's current emotional state.

[1999] "Feedback" refers to information and advice about what actions users should take next and what they should pay attention to.

[2000] "Adjusting means" is a means for dynamically changing action plans or feedback based on the user's emotional state.

[2001] This invention combines an emotion engine with an AI personal coaching system that helps users achieve their goals. As a specific application example, this system is applied to a food delivery health management app.

[2002] The program of this system operates using the following hardware and software.

[2003] Hardware used

[2004] Smartphone: Provides the user interface and allows data entry and display.

[2005] Laptop or desktop PC: Used for application development and testing.

[2006] Software used

[2007] Python 3: Used as the primary programming language for the application.

[2008] Tkinter library: Used as a GUI library for building user interfaces.

[2009] Emotion recognition library: A library for recognizing a user's emotional state by analyzing their facial expressions and voice.

[2010] Diet planner library: A library for generating customized meal plans based on your goal weight and emotional state.

[2011] System Functions and Processes

[2012] 1. Goal setting: The server allows the user to input their goal weight through the application, which is then sent to the server and stored in the database.

[2013] 2. Emotion Recognition: When a user uses the application, the device will scan the user's emotional state through facial recognition and voice analysis. This emotional data will be analyzed by the emotion engine to identify the user's emotional state. The emotional data will be sent to the server and stored in the database.

[2014] 3. Action plan generation: The server generates a specific meal plan based on the user's target weight and emotional state. The generated meal plan is sent to the device and displayed to the user.

[2015] 4. Recording of activity status: Users enter their daily diet and exercise records into the application, which are then sent to the server and stored in a database.

[2016] 5. Progress Follow-up: The server analyzes the user's progress based on their diet and exercise data, and generates regular progress reports that also take into account the user's emotional state.

[2017] 6. Result feedback: The server summarizes the progress data and emotional state, and suggests next steps or additional advice. This information is displayed on the device and fed back to the user.

[2018] Specific use cases

[2019] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, the system first scans the user's face and voice to analyze their emotional state. It then generates and presents a customized meal plan to help them reach their goal. If the user feels anxious or fatigued, it also provides advice on relaxation and motivation.

[2020] Generative AI model prompt example

[2021] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[2022] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2023] Step 1:

[2024] The user inputs the target weight using the terminal. The target data is input by inputting the target weight into the user interface provided by the terminal. This data is sent from the terminal to the server and stored in the database. Input data: target weight, Output data: target weight stored in the database

[2025] Step 2:

[2026] The user inputs data related to their current weight and lifestyle habits. The device sends this input data to the server, where it is stored in a database. The server compares the target and current status data and calculates the difference. Input data: current weight and lifestyle habit data, Output data: difference between the target and current status

[2027] Step 3:

[2028] The device uses an emotion engine to recognize the user's face and analyze their voice, collecting emotional data. The collected emotional data is sent to a server where it is analyzed. Input data: User's facial expressions and voice data, Output data: Analyzed emotional state

[2029] Step 4:

[2030] The server generates a specific action plan based on the user's goal, current situation data, and emotional state. This action plan is constructed as, for example, an appropriate diet or exercise plan. The generated action plan is sent to the terminal and displayed to the user. Input data: goal, current situation data, emotional state, Output data: action plan

[2031] Step 5:

[2032] The user inputs daily records of their diet and exercise into the terminal. The terminal sends this recorded data to the server and stores it in a database. Input data: Daily diet and exercise data, Output data: Status data stored in the database

[2033] Step 6:

[2034] The server analyzes the progress based on the implementation status data stored in the database. It periodically generates progress reports, sends them to the terminal, and displays them to the user. This progress report is created taking into account the user's emotional state. Input data: implementation status data, Output data: progress report

[2035] Step 7:

[2036] The server compiles the user's progress data and emotional state, and suggests the next steps to take or additional advice. These suggestions are displayed on the device and fed back to the user. Input data: progress data and emotional state, Output data: next steps to take or additional advice

[2037] Specific examples

[2038] For example, suppose a user sets a goal of "reach 60kg" and currently weighs 70kg. When the user accesses the application, step 1 is to enter the goal weight, and step 2 is to enter the current weight. Step 3 is emotion recognition and the emotional state is analyzed. Step 4 is to generate a specific meal plan, and step 5 is for the user to record their daily meals and exercise. Step 6 is then generated by the server, and step 7 is to provide next steps and further advice.

[2039] Generative AI model prompt example

[2040] "User's goal weight is 60kg. Their current weight is 70kg and their emotional state is anxious. Generate a healthy eating plan that's optimal for this user. Please be specific about the recommended diet and exercise plan."

[2041] 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.

[2042] 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.

[2043] 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.

[2044] 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.

[2045] 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.

[2046] 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.

[2047] 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).

[2048] 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.

[2049] 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."

[2050] 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.

[2051] 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).

[2052] 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.

[2053] 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.

[2054] 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.

[2055] 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.

[2056] 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.

[2057] 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.

[2058] 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.

[2059] 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.

[2060] 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, to avoid confusion and 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.

[2061] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2062] The following is further disclosed regarding the above embodiment.

[2063] (Claim 1)

[2064] an input means for a user to set a goal;

[2065] means for inputting the user's current situation data;

[2066] a means for comparing the user's goals with current situation data and calculating the difference;

[2067] A means for generating a specific action plan based on the difference;

[2068] A means for users to record the implementation of their action plans;

[2069] A means of following up on progress based on implementation;

[2070] A system that includes a means to modify the action plan as progress is made.

[2071] (Claim 2)

[2072] 10. The system of claim 1, further comprising means for analyzing the user's current situation data.

[2073] (Claim 3)

[2074] 10. The system of claim 1, further comprising means for periodically assessing the user's progress and suggesting next steps to take.

[2075] "Example 1"

[2076] (Claim 1)

[2077] an input means for a user to set a goal;

[2078] input means for inputting the user's current situation data;

[2079] a calculation means for comparing the user's goal with the current situation data to calculate a difference;

[2080] A generating means for generating a specific action plan based on the difference;

[2081] a recording means for the user to record the implementation status of the action plan;

[2082] Follow-up measures to follow up on progress based on implementation status;

[2083] Corrective measures to modify the action plan as progress is made;

[2084] display means for displaying the generated action plan to a user;

[2085] The system includes a storage means for storing data entered by a user in a database.

[2086] (Claim 2)

[2087] 10. The system of claim 1, further comprising an analysis means for analyzing the user's current situation data.

[2088] (Claim 3)

[2089] 10. The system of claim 1, further comprising suggestion means for periodically assessing the user's progress and suggesting next steps to take.

[2090] "Application Example 1"

[2091] (Claim 1)

[2092] an input means for a user to set a goal;

[2093] means for inputting the user's current situation data;

[2094] a means for comparing the user's goals with current situation data and calculating the difference;

[2095] A means for generating a specific action plan based on the difference;

[2096] A means for users to record the implementation of their action plans;

[2097] A means of following up on progress based on implementation;

[2098] A means of modifying the action plan as progress is made;

[2099] A generative AI model for generating an action plan based on difference data between the goal and the current situation;

[2100] A means for setting a prompt sentence to input data according to a purpose into the generative AI model;

[2101] A means of summarizing progress data over a specific period of time and suggesting next steps to take;

[2102] A system including:

[2103] (Claim 2)

[2104] 10. The system of claim 1, further comprising: means for analyzing current status data of a user; and means for managing production target achievement status of a factory robot.

[2105] (Claim 3)

[2106] 10. The system of claim 1, further comprising: means for periodically assessing a user's progress and suggesting next steps to take; and means for generating a next action plan using a generative AI model.

[2107] "Example 2: Combining Emotion Engines"

[2108] (Claim 1)

[2109] an input means for a user to set a goal;

[2110] means for inputting the user's current situation data;

[2111] a means for comparing the user's goals with current situation data and calculating the difference;

[2112] A means for generating a specific action plan based on the difference;

[2113] A means for users to record the implementation of their action plans;

[2114] A means of following up on progress based on implementation;

[2115] A means of modifying the action plan as progress is made;

[2116] a recognition means for collecting user emotion data;

[2117] an analysis means for analyzing the collected emotion data;

[2118] A system including adaptive feedback means for adjusting an action plan based on emotional data.

[2119] (Claim 2)

[2120] 10. The system of claim 1, further comprising means for analyzing the user's current situation data.

[2121] (Claim 3)

[2122] 10. The system of claim 1, further comprising means for periodically assessing the user's progress and suggesting next steps to take.

[2123] "Application example 2 when combining emotion engines"

[2124] (Claim 1)

[2125] an input means for a user to set a goal;

[2126] means for inputting the user's current situation data;

[2127] a means for comparing the user's goals with current situation data and calculating the difference;

[2128] A means for generating a specific action plan based on the difference;

[2129] A means for users to record the implementation of their action plans;

[2130] A means of following up on progress based on implementation;

[2131] A means of recognizing and analyzing user emotions;

[2132] a means of adjusting action plans and feedback based on the user's emotional state;

[2133] A means to adjust the action plan as progress is made

[2134] A system including:

[2135] (Claim 2)

[2136] 10. The system of claim 1, further comprising means for analyzing the user's current situation data and emotion data.

[2137] (Claim 3)

[2138] 10. The system of claim 1, including means for periodically assessing the user's progress and emotional state and suggesting next steps to take. [Explanation of symbols]

[2139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a user to set a goal; means for inputting the user's current situation data; a means for comparing the user's goals with current situation data and calculating the difference; A means for generating a specific action plan based on the difference; A means for users to record the implementation of their action plans; A means of following up on progress based on implementation; A system that includes a means to modify the action plan as progress is made.

2. 10. The system of claim 1, further comprising means for analyzing the user's current situation data.

3. 10. The system of claim 1, further comprising means for periodically assessing the user's progress and suggesting next steps to take.

Images