System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a user's current status and goals; means for generating a competitor image based on the input data; means for providing the user with the generated competitor image data; means for receiving the user's progress data and comparing it to the competitor image's progress; means for updating the competitor image based on the comparison results; and means for re-providing the user with the updated competitor image information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional goal achievement systems and self-development tools have the problem that it is difficult for users to grasp their own progress and maintain sustained motivation. In particular, when achieving long-term goals, it is common for users to give up midway. This invention aims to solve these problems by enabling users to grasp their own progress in real time and providing a rival image that will help them continue to grow. [Means for solving the problem]

[0005] The goal achievement support system of the present invention solves the problems by using the following means. It includes means for inputting the user's current situation and goals, and means for generating a rival image based on the input data. It also includes means for providing the user with data on the generated rival image, and means for receiving the user's progress data and comparing it with the progress of the rival image. It also includes means for updating the rival image based on the comparison results, and means for re-offering the updated rival image information to the user. It also includes means for breaking down the goal into small steps and reflecting that information in the rival image, and means for simulating the progress of the rival image and visualizing it for the user, thereby enabling the user to specifically recognize their own progress and maintain sustainable motivation.

[0006] "User" means an individual or organization that uses the system.

[0007] "Current status" refers to data entered by the user, such as current skill level, experience, and activity status.

[0008] A "goal" refers to a specific outcome or state that a user wants to achieve in the future.

[0009] "Input means" refers to the interface or function that allows the user to provide data to the system.

[0010] A "rival image" is a model of a virtual competitor that is generated to support the user in achieving their goals.

[0011] "Means of generation" refers to the functions and algorithms that create new rival images within the system.

[0012] The "means for providing" refers to an interface or system function for presenting the generated rival image data to the user.

[0013] "Progress data" is data that records the user's daily activities and achievements.

[0014] The "means of receiving and comparing" is a function for collecting the user's progress data and comparing it with the progress of rival images.

[0015] "Means for updating" refers to the function of changing the settings and data of rival images based on the comparison results.

[0016] "Means to break down goals into small steps" is a function that divides a user's big goals into smaller goals that are easier to achieve.

[0017] "Visualization means" is a function that visually presents the progress of rivals and the process of achieving goals to users. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[0040] Program processing flow and specific examples

[0041] The system consists of the following main processing steps:

[0042] 1. Initial Setup

[0043] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[0044] 2. Providing a competitive image

[0045] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[0046] 3. Breakdown of rival's goals and progress simulation

[0047] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0048] 4. Tracking growth

[0049] The user enters their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[0050] Specific examples

[0051] For example, let's say a user aims to study for two hours per day.

[0052] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[0053] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[0054] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[0055] 4. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[0056] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user accesses the system and creates an account. The user enters their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day).

[0060] Step 2:

[0061] The terminal transmits the user's input data to the server.

[0062] Step 3:

[0063] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[0064] Step 4:

[0065] The server sends the generated rival image data to the terminal, which displays it to the user. The user checks the rival image.

[0066] Step 5:

[0067] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[0068] Step 6:

[0069] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[0070] Step 7:

[0071] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[0072] Step 8:

[0073] The terminal transmits the progress data to the server.

[0074] Step 9:

[0075] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[0076] Step 10:

[0077] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[0078] Step 11:

[0079] The server transmits the updated rival image information to the terminal, which displays it to the user.

[0080] Through these steps, users can constantly check their progress, compete against their best rivals, and continuously grow towards their goals.

[0081] Example 1

[0082] 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."

[0083] In order for users to effectively progress towards achieving their goals and maintain their motivation, they need to accurately understand their own status and set specific, realistic goals and the process to achieve them.However, there is a general lack of guidelines for achieving goals and means to maintain motivation, which means that many users give up midway.

[0084] 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.

[0085] In this invention, the server includes means for inputting the user's current situation and goals, a generation AI model means for generating a rival image based on the input data, means for providing the user with data on the generated rival image, means for receiving the user's progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, and means for re-providing the user with information on the updated rival image. This enables the user to constantly check their own progress and compete against appropriate rival images that are updated in real time.

[0086] "Current status of user" is data indicating the user's current status, skill level, activity status, and the like.

[0087] A "goal" is an indicator that indicates a specific result or status that a user is trying to achieve.

[0088] A "rival image" is an image of a virtual competitor in achieving a goal, generated by an AI model to increase user motivation.

[0089] A "generative AI model" is an artificial intelligence algorithm that generates an optimal image of a rival based on input data.

[0090] "Progress data" is data that indicates how much progress a user is making toward achieving their daily activities and goals.

[0091] "Means of breaking down goals into smaller steps" is a system function that performs the process of breaking down large goals into smaller, incremental goals that are easier to achieve.

[0092] "Progress simulation" is a process for predicting the progress of a rival image and visualizing that progress.

[0093] The "means for updating the rival image based on the comparison result" is a system function that compares the user's progress data with the rival image data and changes the state of the rival image based on the result.

[0094] A "re-presentation" is a process for presenting updated information to the user again.

[0095] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[0096] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, it uses AI algorithms such as Python's Scikit-learn or TENSORFLOW (registered trademark). The data on the generated rival profile is sent from the server to the device, which then displays it to the user.

[0097] For example, if a user aims to study for two hours a day, they would input that their current study time is 30 minutes a day. The device sends this information to the server, which then uses this information to generate a rival image that studies for one hour a day. The generative AI model would be given a prompt like this:

[0098] Example prompt: "If the user currently studies 30 minutes per day and their goal is 2 hours per day, please generate an appropriate rival profile for them."

[0099] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0100] Users enter their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[0101] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Step 1: Account Creation and Initial Settings

[0104] A user accesses the system's website or application and clicks the "Create an account" button. The user enters their name, email address, and password into the registration form and presses the "Register" button. The device sends this data to the server, which stores it in a database and creates an account. In this case, the input is the user's name, email address, and password, and the output is the user account information stored on the server.

[0105] Step 2: Enter your goals and current situation

[0106] After logging in, the user enters their current situation and goals. For example, if they currently study for 30 minutes a day and aim to study for 2 hours a day, they enter that information into their device. The device then sends this information to the server. The input is the user's current situation and goals, and the output is the input data saved on the server.

[0107] Step 3: Generate a rival profile

[0108] The data received by the server is analyzed using a generative AI model implemented in Python, such as Scikit-learn or TensorFlow. The server generates an optimal rival profile based on the user's current situation and goals. For example, a rival profile with "one hour of study time per day" is generated. The input is the user's current situation and goals, and the output is the generated rival profile data.

[0109] Step 4: Provide a picture of your competitors

[0110] The server sends the generated rival image data to the terminal. The terminal displays the received rival image data to the user. The user checks the rival image displayed on the screen. The input is the rival image data, and the output is the displayed rival image.

[0111] Step 5: Goal decomposition and progress simulation

[0112] The server breaks down the user's goal into small steps. For example, it might break down the goal into steps, such as increasing study time by 10 minutes each week. It then simulates the progress of the rival image and sends the results to the device. The device then displays the received data to the user. The input is the user's goal and rival image data, and the output is the broken down goal and the simulation results.

[0113] Step 6: Tracking growth

[0114] The user inputs their daily progress. For example, they might input "I studied for 40 minutes today." The device sends this data to the server. The server compares the received progress data with the rival image. Based on the comparison results, the rival image is updated. The server sends the updated rival image information back to the device, which displays it to the user. The input is the user's daily progress data, and the output is the updated rival image.

[0115] In this way, each processing step works together to form a system that helps users achieve their goals.

[0116] (Application example 1)

[0117] 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."

[0118] While existing support systems for users to grow toward their goals are effective in motivating users and managing their progress, they have not been applied to improving the work efficiency of robots in factory environments. Furthermore, the lack of a mechanism to improve efficiency by encouraging competition between robots makes it difficult to maximize overall production efficiency.

[0119] 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.

[0120] In this invention, the server includes means for inputting a user's current situation and goal, means for generating a rival image based on the input data, means for providing data on the generated rival image to the user, means for receiving user progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for inputting a current work efficiency and a target efficiency of a robot in a factory environment, means for generating a rival robot image based on the robot's work efficiency, means for providing data on the generated rival robot image to a display device of the robot, and means for tracking the user's growth and providing feedback. This makes it possible to effectively improve the work efficiency of robots in factories.

[0121] A "user" is someone who uses this system to grow toward their own goals.

[0122] The "current situation" is data indicating the current state of the user or robot, such as their current skills, work efficiency, or activity level.

[0123] A "goal" is a future skill, work efficiency, activity level, or the like that the user or robot aims to achieve.

[0124] A "rival figure" is a virtual competitor that the system generates to help the user or robot develop a competitive spirit.

[0125] "Progress data" refers to data such as the activities and work efficiency of a user or robot performed within a specific period of time.

[0126] "Factory environment" refers to the working environment in the production facility where the robot is located.

[0127] "Work efficiency" is an indicator of how much work a robot can accomplish within a specific time period.

[0128] A "display device" is a device used by a system or robot to visually present data or information to a user.

[0129] MODE FOR CARRYING OUT THE INVENTION

[0130] The present invention is a system that supports users or robots to efficiently grow toward their own goals. In particular, the present invention focuses on improving the work efficiency of robots in a factory environment.

[0131] Initial Setup

[0132] During the initial setup of the system, the user (robot manager) inputs the current and target work efficiency of the robots in the factory. The input is done via a terminal, which then sends this data to a cloud server. The cloud server then uses an AI algorithm (e.g., TensorFlow) to generate an optimal rival image for each robot based on the user's current situation and goals.

[0133] Providing a rival image

[0134] The generated rival image data is then provided to the robot's display device via a terminal. A simple computing device such as a Raspberry Pi can be used for the display device, which visually displays details of the rival robot image and the difference in its current work efficiency. This information allows the robot manager to recognize the rival image as a specific competitor.

[0135] Goal decomposition and progress simulation

[0136] The server breaks down the target efficiency into small steps and reflects them in the image of the rival. Using an AI model (e.g., TensorFlow), it simulates the robot's progress and visualizes the process. This simulation data is also sent to the robot's display device via a terminal, allowing the specific work improvement process to be understood.

[0137] Tracking growth

[0138] The robot's operational data is sent to a cloud server in real time. Data collected through IoT devices is analyzed on the server side, and progress is compared with the rival image. The server updates the rival image based on the comparison results and provides real-time feedback. For example, if the robot completes a task within a set time, the reference time of the rival image is automatically adjusted. The updated rival image information is then displayed on the robot's display device via the four terminals.

[0139] Specific examples

[0140] For example, let's say a factory robot "A" is operating at 1 minute / part, and the robot's goal is 0.8 minutes / part.

[0141] 1. Initial setting: Enter the current work efficiency of robot "A" as 1 minute / part and the target work efficiency as 0.8 minutes / part.

[0142] 2. Generation of rival robot images: The server generates rival robot images that work at 0.9 minutes per part.

[0143] 3. Provide: The display shows "Rival robot is working at 0.9 minutes per part."

[0144] 4. Target decomposition: AI generates steps to improve efficiency by 2% each month.

[0145] 5. Tracking: Robot “A’s” progress (e.g., current progress: 0.95 min / part) is collected in real time and compared with its rivals.

[0146] 6. Feedback: "Good progress, current time 0.93 minutes / part" appears on the display.

[0147] Example prompt for the generative AI model: "Generate the optimal image of a rival robot based on the user's current work efficiency and target efficiency."

[0148] This invention can effectively improve the work efficiency of robots in a factory environment, and can utilize the competitive spirit between robots to promote sustainable growth.

[0149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0150] Step 1:

[0151] The user (robot administrator) uses a terminal to input the robot's current work efficiency and target efficiency. For example, the current work efficiency of robot "A" is 1 minute / part, and the target efficiency is 0.8 minutes / part. The input data is sent from the terminal to the cloud server.

[0152] Step 2:

[0153] The server uses a generative AI model (e.g., TensorFlow) to generate an optimal rival robot image based on the received data on current work efficiency and target efficiency. For example, it generates an image of a rival robot that progresses from 1 minute / part to 0.9 minutes / part. The data on this rival robot image is stored on the server.

[0154] Step 3:

[0155] The server provides the generated rival robot image data to the robot's display device via the terminal. The terminal receives this data and displays "rival robot is working at 0.9 minutes per part" on the robot's display. This allows the user to visually recognize the rival robot image.

[0156] Step 4:

[0157] The server breaks down the target efficiency into small steps and reflects that information in the rival robot's profile. For example, an AI model could be used to generate steps to improve efficiency by 2% each month, and this information would be added to the rival robot's profile. This data on the broken down goals and steps is stored on the server.

[0158] Step 5:

[0159] The server simulates progress toward the target efficiency and provides the results to the robot's display device via the terminal. For example, it simulates "steps to achieve a 2% efficiency improvement each month" and displays the simulation data on the display. This allows the user to understand the specific action plan.

[0160] Step 6:

[0161] The robot's actual operating data is sent in real time to a cloud server via an IoT device. For example, data indicating the current work efficiency is 0.95 minutes per part is sent. The server receives this data and compares it with the progress of rival robots.

[0162] Step 7:

[0163] The server updates the rival robot image based on the progress comparison results. For example, if a robot is working at 0.95 minutes per part, the rival image standard is automatically adjusted to 0.88 minutes per part. The updated data is saved on the server.

[0164] Step 8:

[0165] The server then provides the updated rival robot image data to the robot's display device via the terminal. The display shows, "The rival robot's new standard is 0.88 minutes / part," allowing the user to check the latest information in real time.

[0166] 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.

[0167] The present invention is a system that allows users to effectively grow toward achieving their goals. Specifically, it generates rival images based on the user's current situation and goals and provides them to the user. It also collects and compares the user's progress data in real time and updates the rival images. However, the present invention also includes an emotion engine that recognizes the user's emotions and adjusts the content of the rival images provided based on those emotions, making it easier to maintain the user's motivation.

[0168] Program processing flow and specific examples

[0169] The system consists of the following main processing steps:

[0170] 1. Initial Setup

[0171] The user accesses the system and creates an account. The user inputs their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day). The device sends the user's input data to the server. The server receives this data and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[0172] 2. Providing a competitive image

[0173] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[0174] 3. Breakdown of rival's goals and progress simulation

[0175] The server breaks down the user's goal into small steps and reflects that information in the rival image (e.g., increase study time by 10 minutes each week). It also simulates the progress of the rival image and visualizes the path forward. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0176] 4. Introducing the Emotion Engine

[0177] The server collects and analyzes the user's emotional data. The emotion engine quantifies the user's emotional state based on facial expression recognition, self-reported data, and other emotional data. This data is used to adjust the rival's progress simulation and target steps. For example, if the user is feeling stressed, the rival's progress pace may be slowed down slightly.

[0178] 5. Tracking growth

[0179] The user enters their daily activities and progress (e.g., studied for 40 minutes today) into the system. The device sends this data to the server, which then compares the progress of the rival image with the user's growth based on the progress data received. Based on the comparison results, the server updates the rival image in real time. For example, if the user studies more than planned, it will slightly increase the rival image's study time. This updated rival image information is sent back to the device and displayed to the user.

[0180] Specific examples

[0181] For example, let's say a user aims to study for two hours per day.

[0182] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[0183] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[0184] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[0185] 4. Introducing an Emotion Engine: Analyze the user's emotional state and adjust the progress and goal steps of the rival figures based on that data. For example, if the user is feeling stressed, slow down the pace of the rival figure's progress.

[0186] 5. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[0187] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, by introducing an emotion engine, it reduces the psychological burden on users and achieves more effective motivation management.

[0188] The processing flow will be explained below.

[0189] Step 1:

[0190] The user accesses the system and creates an account. Then, the user enters their current situation (e.g., currently studying for 30 minutes a day) and their desired goal (e.g., studying for 2 hours a day).

[0191] Step 2:

[0192] The terminal transmits the user's input data to the server.

[0193] Step 3:

[0194] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[0195] Step 4:

[0196] The server sends the generated rival image data to the terminal, which displays it to the user, who can then check the rival image.

[0197] Step 5:

[0198] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[0199] Step 6:

[0200] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[0201] Step 7:

[0202] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[0203] Step 8:

[0204] The terminal transmits the progress data to the server.

[0205] Step 9:

[0206] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[0207] Step 10:

[0208] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[0209] Step 11:

[0210] The server transmits the updated rival image information to the terminal, which displays it to the user.

[0211] Step 12:

[0212] The user uses the emotion engine to input their emotional state into the system (e.g., the user inputs that they are "stressed" about studying today).

[0213] Step 13:

[0214] The device sends emotional data to a server, which analyzes it and quantifies the user's stress level.

[0215] Step 14:

[0216] The server adjusts the progress simulation of the rival image taking into account the emotional data (e.g., if the user is feeling stressed, the pace of the rival image's progress is slowed down).

[0217] Step 15:

[0218] The server sends the adjusted rival image information to the terminal, which then displays it to the user, showing the adjusted progress and the rival image's new step plan.

[0219] Through these steps, users can constantly monitor their progress and emotional state, compete against their best rivals, and grow efficiently towards their goals.

[0220] Example 2

[0221] 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."

[0222] Conventional goal achievement systems are limited to comparing a user's progress with that of competitors, and lack dynamic adjustments that take into account the user's emotional state and motivation, making it difficult for users to continuously pursue their goals. The present invention aims to make it easier for users to maintain their motivation and promote sustainable growth by adding a function that analyzes the user's emotional data and adjusts the progress and goal steps of competitors based on that data.

[0223] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's current situation and goal, a means for generating a competitive object image based on the input data, a means for providing the generated competitive object image data to the user, a means for receiving the user's progress data and comparing it with the progress of the competitive object image, a means for updating the competitive object image based on the comparison results, a means for re-providing the updated competitive object image information to the user, and a means for collecting and analyzing the user's emotional data and adjusting the progress of the competitive object image and goal steps. This enables dynamic progress adjustment based on the user's emotional state, making it easier to maintain the user's motivation and promoting sustainable growth for goal achievement.

[0224] A "User" is an individual who creates an account to use the system and record their progress toward achieving goals.

[0225] "Current status" refers to data on the progress and status that the user has achieved at the present time.

[0226] A "goal" is a quantitative or qualitative achievement criterion that a user hopes to achieve in the future.

[0227] "Means for input" refers to the interface or input form that allows a user to input data into the system.

[0228] A "competitive figure" is a fictional competitor pitted against the user, created to help the user achieve their goals.

[0229] "Generative means" refers to the algorithms and processes used to create a competitive profile based on the user's current situation and goals.

[0230] "Means for providing" refers to the interface and display functions for presenting the generated data of the competitive image to the user.

[0231] "Progress data" is a record of specific activities and results toward achieving a goal that is entered by the user daily or periodically.

[0232] "Means of comparison" refers to the algorithms or processes used to compare a user's progress data with the progress of a competitive image and assess any discrepancies.

[0233] "Means for updating" refers to algorithms or processes for modifying competitive profile data or goal steps based on the results of the comparison.

[0234] "Means for re-presentation" refers to an interface or display function for presenting the updated competitive profile information to the user again.

[0235] "Emotional data" refers to data that expresses a user's emotional state and is collected through facial expression recognition, self-reporting, or other emotion measurement methods.

[0236] "Means of collection" refers to the sensors and interfaces required to collect user emotional data.

[0237] "Means for analyzing" refers to algorithms or processes for processing collected emotional data to quantitatively assess the user's emotional state.

[0238] "Adjustment" refers to algorithms or processes for dynamically changing the pace of progress or target steps of a competitive profile based on emotional data.

[0239]

[0240] This invention is a system that allows users to effectively grow toward achieving their goals. The program processing of this system is explained in detail below. The system promotes user growth by inputting the user's current situation and goals, generating and providing rival images, and comparing progress data. It also analyzes the user's emotional data to dynamically adjust the progress of the rival image and goal steps.

[0241] Initial Setup

[0242] Users access the system, create an account, and enter their current situation (e.g., 30 minutes of study time per day) and goal (e.g., 2 hours of study time per day) into their device. The device then sends this data to the server. Based on the received data, the server uses an AI algorithm (e.g., TensorFlow or PyTorch) to generate a competitive model. For example, a competitive model with 1 hour of study time per day is generated.

[0243] Providing a rival image

[0244] The generated data of the competitor image is sent from the server to the terminal, which displays it to the user, who then recognizes the competitor image as a specific competitor for achieving his or her own goal.

[0245] Goal decomposition and progress simulation

[0246] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects this information in the competitive profile. It also simulates the progress of the competitive profile and calculates the path. The device displays this information to the user, allowing them to understand the specific growth process.

[0247] Introducing the Emotion Engine

[0248] The server collects and analyzes the user's emotional data (e.g., facial expression recognition and self-reported data). An emotion engine (e.g., Microsoft® Azure® Cognitive Services) quantifies the user's emotional state. This data is used to adjust the progress simulation and target steps of the competition. For example, if the user is feeling stressed, the pace of progress toward the competition may be slowed down.

[0249] Tracking growth

[0250] Users enter their daily activities and progress (e.g., "I studied for 40 minutes today") into the system, and the device sends this to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image in real time. The updated information is sent to the device and redisplayed to the user.

[0251] Specific examples

[0252] For example, let's say a user aims to study for two hours per day.

[0253] 1. Initial setup: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a competitive model with 1 hour of study time per day.

[0254] 2. Providing a picture of the rival: The device displays information about the competitor to the user, who then checks the information.

[0255] 3. Goal decomposition and simulation: The server decomposes the goal into a set of goals, for example, increasing study time by 10 minutes per week, and reflects this in the competitive image. The terminal displays the progress simulation to the user.

[0256] 4. Introducing an emotion engine: The server analyzes the user's emotional state and adjusts the progress and goal steps of the competition based on that data. If the user is feeling stressed, the pace of progress will be slowed down.

[0257] 5. Progress Tracking: The user types in "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress, updates the competitive picture, and the device displays the updates back to the user.

[0258] Example prompts for generative AI models

[0259] By inputting the following prompt sentence into the generative AI model, we can obtain an image of the competition target and a simulation of its progress.

[0260] Enter user data and goals in the goal achievement system:

[0261] Current study time: 30 minutes

[0262] Target study time: 2 hours

[0263] Weekly increase: 10 minutes

[0264] Simulation of the generated competitive image and goal decomposition:

[0265] generate_rival_and_simulation: true

[0266] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, the introduction of an emotion engine reduces the psychological burden on users and enables more effective motivation management.

[0267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0268]

[0269] Step 1:

[0270] A user accesses the system and creates an account. The user enters their name, email address, and password into the system's input form and clicks the register button. The terminal receives this input data and sends it to the server. This action causes the server to save the new user account in the database, and the user is notified that their account has been created.

[0271] Step 2:

[0272] The user inputs their current situation (e.g., 30 minutes of study time per day) and their goal (e.g., 2 hours of study time per day). The device enters this data into the system's input form and sends it to the server by clicking the send button. The server analyzes the received data and generates a competitive image using an AI algorithm. The data for the generated competitive image is calculated by the server and sent to the device.

[0273] Step 3:

[0274] The server sends the generated data of the competitor image to the terminal. The terminal receives this data and displays it to the user. Specifically, the generated data is displayed on the user interface, and the user recognizes it as a competitor. At this point, the user can confirm the existence of a specific competitor (e.g., someone who studies for one hour per day) that is aimed at their goal.

[0275] Step 4:

[0276] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects it in the image of the competitor. The server uses this information to simulate the progress of the competitor and sends the results to the device. The device then displays the received simulation data to the user, visualizing the progress in concrete terms (graphs and charts).

[0277] Step 5:

[0278] The server collects the user's emotional data (e.g., facial expression recognition and self-reported data) and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state (e.g., stress level) is quantified. This analysis data is processed by the server, and the progress pace and target steps of the competition object are adjusted as necessary. For example, if the user is feeling high stress, the progress pace is adjusted to slow down.

[0279] Step 6:

[0280] The user enters their daily activities and progress (e.g., "I studied for 40 minutes today") into the system. The device receives this data through an input form and sends it to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image as appropriate and sends this updated information to the device. The device then displays the updated content to the user, allowing them to check their progress in real time.

[0281] (Application example 2)

[0282] 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."

[0283] Conventional production efficiency improvement systems for factory robots have the problem of being difficult to adjust in real time, taking into account the robot's own production capacity and emotional state. As a result, maximizing production efficiency may not be fully achieved due to workload or stress levels.

[0284] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the user's current situation and goal; means for generating a rival image based on the input data; means for providing the user with data on the generated rival image; means for receiving the user's progress data and comparing it with the progress of the rival image; means for updating the rival image based on the comparison results; means for re-providing the user with information on the updated rival image; means for evaluating the robot's production capability; means for analyzing the robot's emotional state; and means for adjusting the progress of the rival image based on the user's emotional state and production status. This enables optimal production management that takes into account the user's workload and stress level while maximizing the robot's production efficiency.

[0285] A "user" is an individual or robot operator who utilizes the system to achieve a goal.

[0286] The "current situation" refers to the current environment or state in which the user or robot finds themselves in order to achieve their goal.

[0287] A "goal" is a specific numerical value or state that a user aims to achieve.

[0288] A "rival" is a virtual entity that a user views as a competitor in order to achieve a goal.

[0289] "Input data" refers to data about the current situation and goals that a user provides to the system.

[0290] "Means for generation" refers to the algorithms and functions used to generate a rival profile based on input data.

[0291] "Means for providing" refers to an interface or function for displaying the generated rival image data to the user.

[0292] "Progress data" refers to data that indicates a user's progress toward achieving a goal.

[0293] "Means of comparison" refers to algorithms or functions that compare a user's progress data with the progress of a rival image.

[0294] "Means for updating" refers to the algorithms and functions used to update rival image data based on the comparison results.

[0295] The "means for re-provision" refers to an interface or function for displaying the updated rival image data to the user again.

[0296] "Production capacity" refers to the amount of production a robot can achieve in a given time.

[0297] "Emotional state" refers to the psychological and physical state of a robot that is analyzed based on the robot's motion data and sensor information.

[0298] "Adjustment" refers to algorithms or functions for adjusting the progress of rival figures based on their emotional state and production status.

[0299] This invention provides a system for continuously improving the production efficiency of a factory robot, which includes means for inputting a user's current situation and goals, means for generating a rival image, means for providing data on the rival image to the user, means for receiving progress data from the user and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for evaluating the production capability of the robot, means for analyzing the emotional state of the robot, and means for adjusting the progress of the rival image.

[0300] Explanation of program processing

[0301] 1. Initial Setup

[0302] The user inputs the robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). These data are sent from the client terminal to the server.

[0303] The server uses AI algorithms (e.g., Scikit-Learn, TensorFlow) to generate the optimal "rival robot," which is set midway between the current production capacity and the target production capacity (e.g., 15 units per hour).

[0304] 2. Providing a competitive image

[0305] The rival image data generated by the server is sent to the terminal and displayed on the factory robot's display.

[0306] Factory robots recognize their rivals as competitors based on this image.

[0307] 3. Goal decomposition and progress simulation

[0308] The server breaks down the goal into small steps (e.g., "increase production by 2 units each week").

[0309] The progress of rival robots is simulated and displayed on the terminal, and the robot uses this progress information to understand the specific improvement process.

[0310] 4. Introducing the Emotion Engine

[0311] The server collects and analyzes the robot's operational data (battery status, operational load, etc.) and evaluates its emotional state.

[0312] The emotion engine classifies the emotional state as "stressed" or "comfortable," and adjusts the progress of the rival image accordingly (e.g., slowing down the pace of progress if the rival image is in a stressful state).

[0313] 5. Tracking growth

[0314] The robot inputs daily production performance data and sends it to the server.

[0315] The server compares the progress data and updates the rival's image, which is then sent back to the device for display.

[0316] Examples of concrete examples and prompts

[0317] Example: A factory robot currently has a production capacity of 10 units per hour and has a target production of 20 units per hour. The system creates a rival robot that produces 15 units per hour and sets a resolution target of "increase production by 2 units per week."

[0318] Input prompt for generative AI model:

[0319] "Current production capacity: 10 units / hour, target: 20 units / hour, increase by 2 units per week"

[0320] This allows for optimal production management that takes into account work load and stress levels while maximizing the production efficiency of the robot.

[0321] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0322] Program processing steps

[0323] Step 1: Initial Setup

[0324] The user inputs the factory robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). This data is sent from the terminal to the server.

[0325] Input: Current production capacity, target production capacity

[0326] Data processing: The server receives the data entered by the user and formats it as input data for the AI ​​algorithm.

[0327] Output: Formatted data

[0328] Step 2: Generate a rival profile

[0329] The server uses an AI algorithm based on the data it receives about current production capacity and target production capacity to generate the optimal "rival robot."

[0330] Input: Formatted data

[0331] Data calculation: Calculate the average of the current production capacity and the target production capacity, or an intermediate value using a specific algorithm.

[0332] Output: The production capacity of the spawned rival robot (e.g. 15 units per hour).

[0333] Step 3: Provide a picture of your competitors

[0334] The server generates rival image data, which is then sent to a terminal and displayed on the factory robot's display. The robot then recognizes the rival as a competitor based on this image data.

[0335] Input: Production capacity value of the generated rival robot

[0336] Data processing: Format the rival image data into the appropriate format and send it to the device.

[0337] Output: Information about the rival image displayed on the device

[0338] Step 4: Goal decomposition and progress simulation

[0339] The server breaks down the goal into small steps (e.g., "increase production by two units each week") and simulates the progress of rival robots, displaying it on the terminal.

[0340] Input: Target production capacity, Current production capacity

[0341] Data calculation: Calculate the steps to achieve the goal on a weekly basis and generate simulation data.

[0342] Output: Simulation data, progress information displayed on the terminal

[0343] Step 5: Analyzing the robot's emotional state

[0344] The server collects the robot's operational data (battery status, operational load, etc.) and uses this data to analyze its emotional state, which is then classified as "stressed" or "comfortable."

[0345] Input: Operating data (battery status, operating load, etc.)

[0346] Data calculation: The emotion engine analyzes behavior data and converts it into a quantitative emotional state.

[0347] Output: Emotional state (e.g., stress, comfort)

[0348] Step 6: Adjusting the progress of your rival's image based on their emotional state

[0349] The server adjusts the progress of the rival statue based on the analyzed emotional state, for example, if the emotional state is "stressed," the server slows down the pace of the rival statue's progress.

[0350] Input: Emotional state, simulation data

[0351] Data calculation: An algorithm is applied that adjusts the pace of progress depending on the emotional state.

[0352] Output: Adjusted simulation data

[0353] Step 7: Tracking growth

[0354] The factory robot inputs daily production performance data and sends it from the terminal to the server. The server analyzes the progress data and updates the image of the rival. This updated information is then sent back to the terminal and displayed.

[0355] Input: Daily production data

[0356] Data calculation: Compare progress data with rival image data and update the rival image as needed.

[0357] Output: Updated rival image information, updated content displayed on the device

[0358] The above processing steps enable optimal production management that takes into account work load and stress levels while maximizing the production efficiency of factory robots.

[0359] 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.

[0360] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0361] 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.

[0362] [Second embodiment]

[0363] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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).

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0374] 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."

[0375] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[0376] Program processing flow and specific examples

[0377] The system consists of the following main processing steps:

[0378] 1. Initial Setup

[0379] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[0380] 2. Providing a competitive image

[0381] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[0382] 3. Breakdown of rival's goals and progress simulation

[0383] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0384] 4. Tracking growth

[0385] The user enters their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[0386] Specific examples

[0387] For example, let's say a user aims to study for two hours per day.

[0388] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[0389] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[0390] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[0391] 4. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[0392] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] The user accesses the system and creates an account. The user enters their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day).

[0396] Step 2:

[0397] The terminal transmits the user's input data to the server.

[0398] Step 3:

[0399] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[0400] Step 4:

[0401] The server sends the generated rival image data to the terminal, which displays it to the user. The user checks the rival image.

[0402] Step 5:

[0403] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[0404] Step 6:

[0405] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[0406] Step 7:

[0407] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[0408] Step 8:

[0409] The terminal transmits the progress data to the server.

[0410] Step 9:

[0411] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[0412] Step 10:

[0413] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[0414] Step 11:

[0415] The server transmits the updated rival image information to the terminal, which displays it to the user.

[0416] Through these steps, users can constantly check their progress, compete against their best rivals, and continuously grow towards their goals.

[0417] Example 1

[0418] 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."

[0419] In order for users to effectively progress towards achieving their goals and maintain their motivation, they need to accurately understand their own status and set specific, realistic goals and the process to achieve them.However, there is a general lack of guidelines for achieving goals and means to maintain motivation, which means that many users give up midway.

[0420] 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.

[0421] In this invention, the server includes means for inputting the user's current situation and goals, a generation AI model means for generating a rival image based on the input data, means for providing the user with data on the generated rival image, means for receiving the user's progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, and means for re-providing the user with information on the updated rival image. This enables the user to constantly check their own progress and compete against appropriate rival images that are updated in real time.

[0422] "Current status of user" is data indicating the user's current status, skill level, activity status, and the like.

[0423] A "goal" is an indicator that indicates a specific result or status that a user is trying to achieve.

[0424] A "rival image" is an image of a virtual competitor in achieving a goal, generated by an AI model to increase user motivation.

[0425] A "generative AI model" is an artificial intelligence algorithm that generates an optimal image of a rival based on input data.

[0426] "Progress data" is data that indicates how much progress a user is making toward achieving their daily activities and goals.

[0427] "Means of breaking down goals into smaller steps" is a system function that performs the process of breaking down large goals into smaller, incremental goals that are easier to achieve.

[0428] "Progress simulation" is a process for predicting the progress of a rival image and visualizing that progress.

[0429] The "means for updating the rival image based on the comparison result" is a system function that compares the user's progress data with the rival image data and changes the state of the rival image based on the result.

[0430] A "re-presentation" is a process for presenting updated information to the user again.

[0431] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[0432] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, AI algorithms such as Python's Scikit-learn or TensorFlow are used. The data on the generated rival profile is sent from the server to the device, which then displays it to the user.

[0433] For example, if a user aims to study for two hours a day, they would input that their current study time is 30 minutes a day. The device sends this information to the server, which then uses this information to generate a rival image that studies for one hour a day. The generative AI model would be given a prompt like this:

[0434] Example prompt: "If the user currently studies 30 minutes per day and their goal is 2 hours per day, please generate an appropriate rival profile for them."

[0435] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0436] Users enter their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[0437] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[0438] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0439] Step 1: Account Creation and Initial Settings

[0440] A user accesses the system's website or application and clicks the "Create an account" button. The user enters their name, email address, and password into the registration form and presses the "Register" button. The device sends this data to the server, which stores it in a database and creates an account. In this case, the input is the user's name, email address, and password, and the output is the user account information stored on the server.

[0441] Step 2: Enter your goals and current situation

[0442] After logging in, the user enters their current situation and goals. For example, if they currently study for 30 minutes a day and aim to study for 2 hours a day, they enter that information into their device. The device then sends this information to the server. The input is the user's current situation and goals, and the output is the input data saved on the server.

[0443] Step 3: Generate a rival profile

[0444] The data received by the server is analyzed using a generative AI model implemented in Python, such as Scikit-learn or TensorFlow. The server generates an optimal rival profile based on the user's current situation and goals. For example, a rival profile with "one hour of study time per day" is generated. The input is the user's current situation and goals, and the output is the generated rival profile data.

[0445] Step 4: Provide a picture of your competitors

[0446] The server sends the generated rival image data to the terminal. The terminal displays the received rival image data to the user. The user checks the rival image displayed on the screen. The input is the rival image data, and the output is the displayed rival image.

[0447] Step 5: Goal decomposition and progress simulation

[0448] The server breaks down the user's goal into small steps. For example, it might break down the goal into steps, such as increasing study time by 10 minutes each week. It then simulates the progress of the rival image and sends the results to the device. The device then displays the received data to the user. The input is the user's goal and rival image data, and the output is the broken down goal and the simulation results.

[0449] Step 6: Tracking growth

[0450] The user inputs their daily progress. For example, they might input "I studied for 40 minutes today." The device sends this data to the server. The server compares the received progress data with the rival image. Based on the comparison results, the rival image is updated. The server sends the updated rival image information back to the device, which displays it to the user. The input is the user's daily progress data, and the output is the updated rival image.

[0451] In this way, each processing step works together to form a system that helps users achieve their goals.

[0452] (Application example 1)

[0453] 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."

[0454] While existing support systems for users to grow toward their goals are effective in motivating users and managing their progress, they have not been applied to improving the work efficiency of robots in factory environments. Furthermore, the lack of a mechanism to improve efficiency by encouraging competition between robots makes it difficult to maximize overall production efficiency.

[0455] 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.

[0456] In this invention, the server includes means for inputting a user's current situation and goal, means for generating a rival image based on the input data, means for providing data on the generated rival image to the user, means for receiving user progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for inputting a current work efficiency and a target efficiency of a robot in a factory environment, means for generating a rival robot image based on the robot's work efficiency, means for providing data on the generated rival robot image to a display device of the robot, and means for tracking the user's growth and providing feedback. This makes it possible to effectively improve the work efficiency of robots in factories.

[0457] A "user" is someone who uses this system to grow toward their own goals.

[0458] The "current situation" is data indicating the current state of the user or robot, such as their current skills, work efficiency, or activity level.

[0459] A "goal" is a future skill, work efficiency, activity level, or the like that the user or robot aims to achieve.

[0460] A "rival figure" is a virtual competitor that the system generates to help the user or robot develop a competitive spirit.

[0461] "Progress data" refers to data such as the activities and work efficiency of a user or robot performed within a specific period of time.

[0462] "Factory environment" refers to the working environment in the production facility where the robot is located.

[0463] "Work efficiency" is an indicator of how much work a robot can accomplish within a specific time period.

[0464] A "display device" is a device used by a system or robot to visually present data or information to a user.

[0465] MODE FOR CARRYING OUT THE INVENTION

[0466] The present invention is a system that supports users or robots to efficiently grow toward their own goals. In particular, the present invention focuses on improving the work efficiency of robots in a factory environment.

[0467] Initial Setup

[0468] During the initial setup of the system, the user (robot manager) inputs the current and target work efficiency of the robots in the factory. The input is done via a terminal, which then sends this data to a cloud server. The cloud server then uses an AI algorithm (e.g., TensorFlow) to generate an optimal rival image for each robot based on the user's current situation and goals.

[0469] Providing a rival image

[0470] The generated rival image data is then provided to the robot's display device via a terminal. A simple computing device such as a Raspberry Pi can be used for the display device, which visually displays details of the rival robot image and the difference in its current work efficiency. This information allows the robot manager to recognize the rival image as a specific competitor.

[0471] Goal decomposition and progress simulation

[0472] The server breaks down the target efficiency into small steps and reflects them in the image of the rival. Using an AI model (e.g., TensorFlow), it simulates the robot's progress and visualizes the process. This simulation data is also sent to the robot's display device via a terminal, allowing the specific work improvement process to be understood.

[0473] Tracking growth

[0474] The robot's operational data is sent to a cloud server in real time. Data collected through IoT devices is analyzed on the server side, and progress is compared with the rival image. The server updates the rival image based on the comparison results and provides real-time feedback. For example, if the robot completes a task within a set time, the reference time of the rival image is automatically adjusted. The updated rival image information is then displayed on the robot's display device via the four terminals.

[0475] Specific examples

[0476] For example, let's say a factory robot "A" is operating at 1 minute / part, and the robot's goal is 0.8 minutes / part.

[0477] 1. Initial setting: Enter the current work efficiency of robot "A" as 1 minute / part and the target work efficiency as 0.8 minutes / part.

[0478] 2. Generation of rival robot images: The server generates rival robot images that work at 0.9 minutes per part.

[0479] 3. Provide: The display shows "Rival robot is working at 0.9 minutes per part."

[0480] 4. Target decomposition: AI generates steps to improve efficiency by 2% each month.

[0481] 5. Tracking: Robot “A’s” progress (e.g., current progress: 0.95 min / part) is collected in real time and compared with its rivals.

[0482] 6. Feedback: "Good progress, current time 0.93 minutes / part" appears on the display.

[0483] Example prompt for the generative AI model: "Generate the optimal image of a rival robot based on the user's current work efficiency and target efficiency."

[0484] This invention can effectively improve the work efficiency of robots in a factory environment, and can utilize the competitive spirit between robots to promote sustainable growth.

[0485] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0486] Step 1:

[0487] The user (robot administrator) uses a terminal to input the robot's current work efficiency and target efficiency. For example, the current work efficiency of robot "A" is 1 minute / part, and the target efficiency is 0.8 minutes / part. The input data is sent from the terminal to the cloud server.

[0488] Step 2:

[0489] The server uses a generative AI model (e.g., TensorFlow) to generate an optimal rival robot image based on the received data on current work efficiency and target efficiency. For example, it generates an image of a rival robot that progresses from 1 minute / part to 0.9 minutes / part. The data on this rival robot image is stored on the server.

[0490] Step 3:

[0491] The server provides the generated rival robot image data to the robot's display device via the terminal. The terminal receives this data and displays "rival robot is working at 0.9 minutes per part" on the robot's display. This allows the user to visually recognize the rival robot image.

[0492] Step 4:

[0493] The server breaks down the target efficiency into small steps and reflects that information in the rival robot's profile. For example, an AI model could be used to generate steps to improve efficiency by 2% each month, and this information would be added to the rival robot's profile. This data on the broken down goals and steps is stored on the server.

[0494] Step 5:

[0495] The server simulates progress toward the target efficiency and provides the results to the robot's display device via the terminal. For example, it simulates "steps to achieve a 2% efficiency improvement each month" and displays the simulation data on the display. This allows the user to understand the specific action plan.

[0496] Step 6:

[0497] The robot's actual operating data is sent in real time to a cloud server via an IoT device. For example, data indicating the current work efficiency is 0.95 minutes per part is sent. The server receives this data and compares it with the progress of rival robots.

[0498] Step 7:

[0499] The server updates the rival robot image based on the progress comparison results. For example, if a robot is working at 0.95 minutes per part, the rival image standard is automatically adjusted to 0.88 minutes per part. The updated data is saved on the server.

[0500] Step 8:

[0501] The server then provides the updated rival robot image data to the robot's display device via the terminal. The display shows, "The rival robot's new standard is 0.88 minutes / part," allowing the user to check the latest information in real time.

[0502] 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.

[0503] The present invention is a system that allows users to effectively grow toward achieving their goals. Specifically, it generates rival images based on the user's current situation and goals and provides them to the user. It also collects and compares the user's progress data in real time and updates the rival images. However, the present invention also includes an emotion engine that recognizes the user's emotions and adjusts the content of the rival images provided based on those emotions, making it easier to maintain the user's motivation.

[0504] Program processing flow and specific examples

[0505] The system consists of the following main processing steps:

[0506] 1. Initial Setup

[0507] The user accesses the system and creates an account. The user inputs their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day). The device sends the user's input data to the server. The server receives this data and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[0508] 2. Providing a competitive image

[0509] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[0510] 3. Breakdown of rival's goals and progress simulation

[0511] The server breaks down the user's goal into small steps and reflects that information in the rival image (e.g., increase study time by 10 minutes each week). It also simulates the progress of the rival image and visualizes the path forward. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0512] 4. Introducing the Emotion Engine

[0513] The server collects and analyzes the user's emotional data. The emotion engine quantifies the user's emotional state based on facial expression recognition, self-reported data, and other emotional data. This data is used to adjust the rival's progress simulation and target steps. For example, if the user is feeling stressed, the rival's progress pace may be slowed down slightly.

[0514] 5. Tracking growth

[0515] The user enters their daily activities and progress (e.g., studied for 40 minutes today) into the system. The device sends this data to the server, which then compares the progress of the rival image with the user's growth based on the progress data received. Based on the comparison results, the server updates the rival image in real time. For example, if the user studies more than planned, it will slightly increase the rival image's study time. This updated rival image information is sent back to the device and displayed to the user.

[0516] Specific examples

[0517] For example, let's say a user aims to study for two hours per day.

[0518] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[0519] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[0520] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[0521] 4. Introducing an Emotion Engine: Analyze the user's emotional state and adjust the progress and goal steps of the rival figures based on that data. For example, if the user is feeling stressed, slow down the pace of the rival figure's progress.

[0522] 5. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[0523] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, by introducing an emotion engine, it reduces the psychological burden on users and achieves more effective motivation management.

[0524] The processing flow will be explained below.

[0525] Step 1:

[0526] The user accesses the system and creates an account. Then, the user enters their current situation (e.g., currently studying for 30 minutes a day) and their desired goal (e.g., studying for 2 hours a day).

[0527] Step 2:

[0528] The terminal transmits the user's input data to the server.

[0529] Step 3:

[0530] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[0531] Step 4:

[0532] The server sends the generated rival image data to the terminal, which displays it to the user, who can then check the rival image.

[0533] Step 5:

[0534] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[0535] Step 6:

[0536] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[0537] Step 7:

[0538] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[0539] Step 8:

[0540] The terminal transmits the progress data to the server.

[0541] Step 9:

[0542] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[0543] Step 10:

[0544] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[0545] Step 11:

[0546] The server transmits the updated rival image information to the terminal, which displays it to the user.

[0547] Step 12:

[0548] The user uses the emotion engine to input their emotional state into the system (e.g., the user inputs that they are "stressed" about studying today).

[0549] Step 13:

[0550] The device sends emotional data to a server, which analyzes it and quantifies the user's stress level.

[0551] Step 14:

[0552] The server adjusts the progress simulation of the rival image taking into account the emotional data (e.g., if the user is feeling stressed, the pace of the rival image's progress is slowed down).

[0553] Step 15:

[0554] The server sends the adjusted rival image information to the terminal, which then displays it to the user, showing the adjusted progress and the rival image's new step plan.

[0555] Through these steps, users can constantly monitor their progress and emotional state, compete against their best rivals, and grow efficiently towards their goals.

[0556] Example 2

[0557] 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."

[0558] Conventional goal achievement systems are limited to comparing a user's progress with that of competitors, and lack dynamic adjustments that take into account the user's emotional state and motivation, making it difficult for users to continuously pursue their goals. The present invention aims to make it easier for users to maintain their motivation and promote sustainable growth by adding a function that analyzes the user's emotional data and adjusts the progress and goal steps of competitors based on that data.

[0559] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's current situation and goal, a means for generating a competitive object image based on the input data, a means for providing the generated competitive object image data to the user, a means for receiving the user's progress data and comparing it with the progress of the competitive object image, a means for updating the competitive object image based on the comparison results, a means for re-providing the updated competitive object image information to the user, and a means for collecting and analyzing the user's emotional data and adjusting the progress of the competitive object image and goal steps. This enables dynamic progress adjustment based on the user's emotional state, making it easier to maintain the user's motivation and promoting sustainable growth for goal achievement.

[0560] A "User" is an individual who creates an account to use the system and record their progress toward achieving goals.

[0561] "Current status" refers to data on the progress and status that the user has achieved at the present time.

[0562] A "goal" is a quantitative or qualitative achievement criterion that a user hopes to achieve in the future.

[0563] "Means for input" refers to the interface or input form that allows a user to input data into the system.

[0564] A "competitive figure" is a fictional competitor pitted against the user, created to help the user achieve their goals.

[0565] "Generative means" refers to the algorithms and processes used to create a competitive profile based on the user's current situation and goals.

[0566] "Means for providing" refers to the interface and display functions for presenting the generated data of the competitive image to the user.

[0567] "Progress data" is a record of specific activities and results toward achieving a goal that is entered by the user daily or periodically.

[0568] "Means of comparison" refers to the algorithms or processes used to compare a user's progress data with the progress of a competitive image and assess any discrepancies.

[0569] "Means for updating" refers to algorithms or processes for modifying competitive profile data or goal steps based on the results of the comparison.

[0570] "Means for re-presentation" refers to an interface or display function for presenting the updated competitive profile information to the user again.

[0571] "Emotional data" refers to data that expresses a user's emotional state and is collected through facial expression recognition, self-reporting, or other emotion measurement methods.

[0572] "Means of collection" refers to the sensors and interfaces required to collect user emotional data.

[0573] "Means for analyzing" refers to algorithms or processes for processing collected emotional data to quantitatively assess the user's emotional state.

[0574] "Adjustment" refers to algorithms or processes for dynamically changing the pace of progress or target steps of a competitive profile based on emotional data.

[0575]

[0576] This invention is a system that allows users to effectively grow toward achieving their goals. The program processing of this system is explained in detail below. The system promotes user growth by inputting the user's current situation and goals, generating and providing rival images, and comparing progress data. It also analyzes the user's emotional data to dynamically adjust the progress of the rival image and goal steps.

[0577] Initial Setup

[0578] Users access the system, create an account, and enter their current situation (e.g., 30 minutes of study time per day) and goal (e.g., 2 hours of study time per day) into their device. The device then sends this data to the server. Based on the received data, the server uses an AI algorithm (e.g., TensorFlow or PyTorch) to generate a competitive model. For example, a competitive model with 1 hour of study time per day is generated.

[0579] Providing a rival image

[0580] The generated data of the competitor image is sent from the server to the terminal, which displays it to the user, who then recognizes the competitor image as a specific competitor for achieving his or her own goal.

[0581] Goal decomposition and progress simulation

[0582] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects this information in the competitive profile. It also simulates the progress of the competitive profile and calculates the path. The device displays this information to the user, allowing them to understand the specific growth process.

[0583] Introducing the Emotion Engine

[0584] The server collects and analyzes the user's emotional data (e.g., facial expression recognition and self-reported data). An emotion engine (e.g., Microsoft's Azure Cognitive Services) quantifies the user's emotional state. This data is used to adjust the progress simulation and target steps of the competition. For example, if the user is feeling stressed, the pace of progress toward the competition may be slowed down.

[0585] Tracking growth

[0586] Users enter their daily activities and progress (e.g., "I studied for 40 minutes today") into the system, and the device sends this to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image in real time. The updated information is sent to the device and redisplayed to the user.

[0587] Specific examples

[0588] For example, let's say a user aims to study for two hours per day.

[0589] 1. Initial setup: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a competitive model with 1 hour of study time per day.

[0590] 2. Providing a picture of the rival: The device displays information about the competitor to the user, who then checks the information.

[0591] 3. Goal decomposition and simulation: The server decomposes the goal into a set of goals, for example, increasing study time by 10 minutes per week, and reflects this in the competitive image. The terminal displays the progress simulation to the user.

[0592] 4. Introducing an emotion engine: The server analyzes the user's emotional state and adjusts the progress and goal steps of the competition based on that data. If the user is feeling stressed, the pace of progress will be slowed down.

[0593] 5. Progress Tracking: The user types in "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress, updates the competitive picture, and the device displays the updates back to the user.

[0594] Example prompts for generative AI models

[0595] By inputting the following prompt sentence into the generative AI model, we can obtain an image of the competition target and a simulation of its progress.

[0596] Enter user data and goals in the goal achievement system:

[0597] Current study time: 30 minutes

[0598] Target study time: 2 hours

[0599] Weekly increase: 10 minutes

[0600] Simulation of the generated competitive image and goal decomposition:

[0601] generate_rival_and_simulation: true

[0602] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, the introduction of an emotion engine reduces the psychological burden on users and enables more effective motivation management.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604]

[0605] Step 1:

[0606] A user accesses the system and creates an account. The user enters their name, email address, and password into the system's input form and clicks the register button. The terminal receives this input data and sends it to the server. This action causes the server to save the new user account in the database, and the user is notified that their account has been created.

[0607] Step 2:

[0608] The user inputs their current situation (e.g., 30 minutes of study time per day) and their goal (e.g., 2 hours of study time per day). The device enters this data into the system's input form and sends it to the server by clicking the send button. The server analyzes the received data and generates a competitive image using an AI algorithm. The data for the generated competitive image is calculated by the server and sent to the device.

[0609] Step 3:

[0610] The server sends the generated data of the competitor image to the terminal. The terminal receives this data and displays it to the user. Specifically, the generated data is displayed on the user interface, and the user recognizes it as a competitor. At this point, the user can confirm the existence of a specific competitor (e.g., someone who studies for one hour per day) that is aimed at their goal.

[0611] Step 4:

[0612] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects it in the image of the competitor. The server uses this information to simulate the progress of the competitor and sends the results to the device. The device then displays the received simulation data to the user, visualizing the progress in concrete terms (graphs and charts).

[0613] Step 5:

[0614] The server collects the user's emotional data (e.g., facial expression recognition and self-reported data) and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state (e.g., stress level) is quantified. This analysis data is processed by the server, and the progress pace and target steps of the competition object are adjusted as necessary. For example, if the user is feeling high stress, the progress pace is adjusted to slow down.

[0615] Step 6:

[0616] The user enters their daily activities and progress (e.g., "I studied for 40 minutes today") into the system. The device receives this data through an input form and sends it to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image as appropriate and sends this updated information to the device. The device then displays the updated content to the user, allowing them to check their progress in real time.

[0617] (Application example 2)

[0618] 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."

[0619] Conventional production efficiency improvement systems for factory robots have the problem of being difficult to adjust in real time, taking into account the robot's own production capacity and emotional state. As a result, maximizing production efficiency may not be fully achieved due to workload or stress levels.

[0620] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the user's current situation and goal; means for generating a rival image based on the input data; means for providing the user with data on the generated rival image; means for receiving the user's progress data and comparing it with the progress of the rival image; means for updating the rival image based on the comparison results; means for re-providing the user with information on the updated rival image; means for evaluating the robot's production capability; means for analyzing the robot's emotional state; and means for adjusting the progress of the rival image based on the user's emotional state and production status. This enables optimal production management that takes into account the user's workload and stress level while maximizing the robot's production efficiency.

[0621] A "user" is an individual or robot operator who utilizes the system to achieve a goal.

[0622] The "current situation" refers to the current environment or state in which the user or robot finds themselves in order to achieve their goal.

[0623] A "goal" is a specific numerical value or state that a user aims to achieve.

[0624] A "rival" is a virtual entity that a user views as a competitor in order to achieve a goal.

[0625] "Input data" refers to data about the current situation and goals that a user provides to the system.

[0626] "Means for generation" refers to the algorithms and functions used to generate a rival profile based on input data.

[0627] "Means for providing" refers to an interface or function for displaying the generated rival image data to the user.

[0628] "Progress data" refers to data that indicates a user's progress toward achieving a goal.

[0629] "Means of comparison" refers to algorithms or functions that compare a user's progress data with the progress of a rival image.

[0630] "Means for updating" refers to the algorithms and functions used to update rival image data based on the comparison results.

[0631] The "means for re-provision" refers to an interface or function for displaying the updated rival image data to the user again.

[0632] "Production capacity" refers to the amount of production a robot can achieve in a given time.

[0633] "Emotional state" refers to the psychological and physical state of a robot that is analyzed based on the robot's motion data and sensor information.

[0634] "Adjustment" refers to algorithms or functions for adjusting the progress of rival figures based on their emotional state and production status.

[0635] This invention provides a system for continuously improving the production efficiency of a factory robot, which includes means for inputting a user's current situation and goals, means for generating a rival image, means for providing data on the rival image to the user, means for receiving progress data from the user and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for evaluating the production capability of the robot, means for analyzing the emotional state of the robot, and means for adjusting the progress of the rival image.

[0636] Explanation of program processing

[0637] 1. Initial Setup

[0638] The user inputs the robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). These data are sent from the client terminal to the server.

[0639] The server uses AI algorithms (e.g., Scikit-Learn, TensorFlow) to generate the optimal "rival robot," which is set midway between the current production capacity and the target production capacity (e.g., 15 units per hour).

[0640] 2. Providing a competitive image

[0641] The rival image data generated by the server is sent to the terminal and displayed on the factory robot's display.

[0642] Factory robots recognize their rivals as competitors based on this image.

[0643] 3. Goal decomposition and progress simulation

[0644] The server breaks down the goal into small steps (e.g., "increase production by 2 units each week").

[0645] The progress of rival robots is simulated and displayed on the terminal, and the robot uses this progress information to understand the specific improvement process.

[0646] 4. Introducing the Emotion Engine

[0647] The server collects and analyzes the robot's operational data (battery status, operational load, etc.) and evaluates its emotional state.

[0648] The emotion engine classifies the emotional state as "stressed" or "comfortable," and adjusts the progress of the rival image accordingly (e.g., slowing down the pace of progress if the rival image is in a stressful state).

[0649] 5. Tracking growth

[0650] The robot inputs daily production performance data and sends it to the server.

[0651] The server compares the progress data and updates the rival's image, which is then sent back to the device for display.

[0652] Examples of concrete examples and prompts

[0653] Example: A factory robot currently has a production capacity of 10 units per hour and has a target production of 20 units per hour. The system creates a rival robot that produces 15 units per hour and sets a resolution target of "increase production by 2 units per week."

[0654] Input prompt for generative AI model:

[0655] "Current production capacity: 10 units / hour, target: 20 units / hour, increase by 2 units per week"

[0656] This allows for optimal production management that takes into account work load and stress levels while maximizing the production efficiency of the robot.

[0657] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0658] Program processing steps

[0659] Step 1: Initial Setup

[0660] The user inputs the factory robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). This data is sent from the terminal to the server.

[0661] Input: Current production capacity, target production capacity

[0662] Data processing: The server receives the data entered by the user and formats it as input data for the AI ​​algorithm.

[0663] Output: Formatted data

[0664] Step 2: Generate a rival profile

[0665] The server uses an AI algorithm based on the data it receives about current production capacity and target production capacity to generate the optimal "rival robot."

[0666] Input: Formatted data

[0667] Data calculation: Calculate the average of the current production capacity and the target production capacity, or an intermediate value using a specific algorithm.

[0668] Output: The production capacity of the spawned rival robot (e.g. 15 units per hour).

[0669] Step 3: Provide a picture of your competitors

[0670] The server generates rival image data, which is then sent to a terminal and displayed on the factory robot's display. The robot then recognizes the rival as a competitor based on this image data.

[0671] Input: Production capacity value of the generated rival robot

[0672] Data processing: Format the rival image data into the appropriate format and send it to the device.

[0673] Output: Information about the rival image displayed on the device

[0674] Step 4: Goal decomposition and progress simulation

[0675] The server breaks down the goal into small steps (e.g., "increase production by two units each week") and simulates the progress of rival robots, displaying it on the terminal.

[0676] Input: Target production capacity, Current production capacity

[0677] Data calculation: Calculate the steps to achieve the goal on a weekly basis and generate simulation data.

[0678] Output: Simulation data, progress information displayed on the terminal

[0679] Step 5: Analyzing the robot's emotional state

[0680] The server collects the robot's operational data (battery status, operational load, etc.) and uses this data to analyze its emotional state, which is then classified as "stressed" or "comfortable."

[0681] Input: Operating data (battery status, operating load, etc.)

[0682] Data calculation: The emotion engine analyzes behavior data and converts it into a quantitative emotional state.

[0683] Output: Emotional state (e.g., stress, comfort)

[0684] Step 6: Adjusting the progress of your rival's image based on their emotional state

[0685] The server adjusts the progress of the rival statue based on the analyzed emotional state, for example, if the emotional state is "stressed," the server slows down the pace of the rival statue's progress.

[0686] Input: Emotional state, simulation data

[0687] Data calculation: An algorithm is applied that adjusts the pace of progress depending on the emotional state.

[0688] Output: Adjusted simulation data

[0689] Step 7: Tracking growth

[0690] The factory robot inputs daily production performance data and sends it from the terminal to the server. The server analyzes the progress data and updates the image of the rival. This updated information is then sent back to the terminal and displayed.

[0691] Input: Daily production data

[0692] Data calculation: Compare progress data with rival image data and update the rival image as needed.

[0693] Output: Updated rival image information, updated content displayed on the device

[0694] The above processing steps enable optimal production management that takes into account work load and stress levels while maximizing the production efficiency of factory robots.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] [Third embodiment]

[0699] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0700] 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.

[0701] 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).

[0702] 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.

[0703] 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.

[0704] 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).

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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."

[0711] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[0712] Program processing flow and specific examples

[0713] The system consists of the following main processing steps:

[0714] 1. Initial Setup

[0715] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[0716] 2. Providing a competitive image

[0717] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[0718] 3. Breakdown of rival's goals and progress simulation

[0719] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0720] 4. Tracking growth

[0721] The user enters their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[0722] Specific examples

[0723] For example, let's say a user aims to study for two hours per day.

[0724] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[0725] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[0726] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[0727] 4. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[0728] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[0729] The processing flow will be explained below.

[0730] Step 1:

[0731] The user accesses the system and creates an account. The user enters their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day).

[0732] Step 2:

[0733] The terminal transmits the user's input data to the server.

[0734] Step 3:

[0735] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[0736] Step 4:

[0737] The server sends the generated rival image data to the terminal, which displays it to the user. The user checks the rival image.

[0738] Step 5:

[0739] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[0740] Step 6:

[0741] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[0742] Step 7:

[0743] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[0744] Step 8:

[0745] The terminal transmits the progress data to the server.

[0746] Step 9:

[0747] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[0748] Step 10:

[0749] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[0750] Step 11:

[0751] The server transmits the updated rival image information to the terminal, which displays it to the user.

[0752] Through these steps, users can constantly check their progress, compete against their best rivals, and continuously grow towards their goals.

[0753] Example 1

[0754] 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."

[0755] In order for users to effectively progress towards achieving their goals and maintain their motivation, they need to accurately understand their own status and set specific, realistic goals and the process to achieve them.However, there is a general lack of guidelines for achieving goals and means to maintain motivation, which means that many users give up midway.

[0756] 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.

[0757] In this invention, the server includes means for inputting the user's current situation and goals, a generation AI model means for generating a rival image based on the input data, means for providing the user with data on the generated rival image, means for receiving the user's progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, and means for re-providing the user with information on the updated rival image. This enables the user to constantly check their own progress and compete against appropriate rival images that are updated in real time.

[0758] "Current status of user" is data indicating the user's current status, skill level, activity status, and the like.

[0759] A "goal" is an indicator that indicates a specific result or status that a user is trying to achieve.

[0760] A "rival image" is an image of a virtual competitor in achieving a goal, generated by an AI model to increase user motivation.

[0761] A "generative AI model" is an artificial intelligence algorithm that generates an optimal image of a rival based on input data.

[0762] "Progress data" is data that indicates how much progress a user is making toward achieving their daily activities and goals.

[0763] "Means of breaking down goals into smaller steps" is a system function that performs the process of breaking down large goals into smaller, incremental goals that are easier to achieve.

[0764] "Progress simulation" is a process for predicting the progress of a rival image and visualizing that progress.

[0765] The "means for updating the rival image based on the comparison result" is a system function that compares the user's progress data with the rival image data and changes the state of the rival image based on the result.

[0766] A "re-presentation" is a process for presenting updated information to the user again.

[0767] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[0768] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, AI algorithms such as Python's Scikit-learn or TensorFlow are used. The data on the generated rival profile is sent from the server to the device, which then displays it to the user.

[0769] For example, if a user aims to study for two hours a day, they would input that their current study time is 30 minutes a day. The device sends this information to the server, which then uses this information to generate a rival image that studies for one hour a day. The generative AI model would be given a prompt like this:

[0770] Example prompt: "If the user currently studies 30 minutes per day and their goal is 2 hours per day, please generate an appropriate rival profile for them."

[0771] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0772] Users enter their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[0773] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[0774] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0775] Step 1: Account Creation and Initial Settings

[0776] A user accesses the system's website or application and clicks the "Create an account" button. The user enters their name, email address, and password into the registration form and presses the "Register" button. The device sends this data to the server, which stores it in a database and creates an account. In this case, the input is the user's name, email address, and password, and the output is the user account information stored on the server.

[0777] Step 2: Enter your goals and current situation

[0778] After logging in, the user enters their current situation and goals. For example, if they currently study for 30 minutes a day and aim to study for 2 hours a day, they enter that information into their device. The device then sends this information to the server. The input is the user's current situation and goals, and the output is the input data saved on the server.

[0779] Step 3: Generate a rival profile

[0780] The data received by the server is analyzed using a generative AI model implemented in Python, such as Scikit-learn or TensorFlow. The server generates an optimal rival profile based on the user's current situation and goals. For example, a rival profile with "one hour of study time per day" is generated. The input is the user's current situation and goals, and the output is the generated rival profile data.

[0781] Step 4: Provide a picture of your competitors

[0782] The server sends the generated rival image data to the terminal. The terminal displays the received rival image data to the user. The user checks the rival image displayed on the screen. The input is the rival image data, and the output is the displayed rival image.

[0783] Step 5: Goal decomposition and progress simulation

[0784] The server breaks down the user's goal into small steps. For example, it might break down the goal into steps, such as increasing study time by 10 minutes each week. It then simulates the progress of the rival image and sends the results to the device. The device then displays the received data to the user. The input is the user's goal and rival image data, and the output is the broken down goal and the simulation results.

[0785] Step 6: Tracking growth

[0786] The user inputs their daily progress. For example, they might input "I studied for 40 minutes today." The device sends this data to the server. The server compares the received progress data with the rival image. Based on the comparison results, the rival image is updated. The server sends the updated rival image information back to the device, which displays it to the user. The input is the user's daily progress data, and the output is the updated rival image.

[0787] In this way, each processing step works together to form a system that helps users achieve their goals.

[0788] (Application example 1)

[0789] 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."

[0790] While existing support systems for users to grow toward their goals are effective in motivating users and managing their progress, they have not been applied to improving the work efficiency of robots in factory environments. Furthermore, the lack of a mechanism to improve efficiency by encouraging competition between robots makes it difficult to maximize overall production efficiency.

[0791] 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.

[0792] In this invention, the server includes means for inputting a user's current situation and goal, means for generating a rival image based on the input data, means for providing data on the generated rival image to the user, means for receiving user progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for inputting a current work efficiency and a target efficiency of a robot in a factory environment, means for generating a rival robot image based on the robot's work efficiency, means for providing data on the generated rival robot image to a display device of the robot, and means for tracking the user's growth and providing feedback. This makes it possible to effectively improve the work efficiency of robots in factories.

[0793] A "user" is someone who uses this system to grow toward their own goals.

[0794] The "current situation" is data indicating the current state of the user or robot, such as their current skills, work efficiency, or activity level.

[0795] A "goal" is a future skill, work efficiency, activity level, or the like that the user or robot aims to achieve.

[0796] A "rival figure" is a virtual competitor that the system generates to help the user or robot develop a competitive spirit.

[0797] "Progress data" refers to data such as the activities and work efficiency of a user or robot performed within a specific period of time.

[0798] "Factory environment" refers to the working environment in the production facility where the robot is located.

[0799] "Work efficiency" is an indicator of how much work a robot can accomplish within a specific time period.

[0800] A "display device" is a device used by a system or robot to visually present data or information to a user.

[0801] MODE FOR CARRYING OUT THE INVENTION

[0802] The present invention is a system that supports users or robots to efficiently grow toward their own goals. In particular, the present invention focuses on improving the work efficiency of robots in a factory environment.

[0803] Initial Setup

[0804] During the initial setup of the system, the user (robot manager) inputs the current and target work efficiency of the robots in the factory. The input is done via a terminal, which then sends this data to a cloud server. The cloud server then uses an AI algorithm (e.g., TensorFlow) to generate an optimal rival image for each robot based on the user's current situation and goals.

[0805] Providing a rival image

[0806] The generated rival image data is then provided to the robot's display device via a terminal. A simple computing device such as a Raspberry Pi can be used for the display device, which visually displays details of the rival robot image and the difference in its current work efficiency. This information allows the robot manager to recognize the rival image as a specific competitor.

[0807] Goal decomposition and progress simulation

[0808] The server breaks down the target efficiency into small steps and reflects them in the image of the rival. Using an AI model (e.g., TensorFlow), it simulates the robot's progress and visualizes the process. This simulation data is also sent to the robot's display device via a terminal, allowing the specific work improvement process to be understood.

[0809] Tracking growth

[0810] The robot's operational data is sent to a cloud server in real time. Data collected through IoT devices is analyzed on the server side, and progress is compared with the rival image. The server updates the rival image based on the comparison results and provides real-time feedback. For example, if the robot completes a task within a set time, the reference time of the rival image is automatically adjusted. The updated rival image information is then displayed on the robot's display device via the four terminals.

[0811] Specific examples

[0812] For example, let's say a factory robot "A" is operating at 1 minute / part, and the robot's goal is 0.8 minutes / part.

[0813] 1. Initial setting: Enter the current work efficiency of robot "A" as 1 minute / part and the target work efficiency as 0.8 minutes / part.

[0814] 2. Generation of rival robot images: The server generates rival robot images that work at 0.9 minutes per part.

[0815] 3. Provide: The display shows "Rival robot is working at 0.9 minutes per part."

[0816] 4. Target decomposition: AI generates steps to improve efficiency by 2% each month.

[0817] 5. Tracking: Robot “A’s” progress (e.g., current progress: 0.95 min / part) is collected in real time and compared with its rivals.

[0818] 6. Feedback: "Good progress, current time 0.93 minutes / part" appears on the display.

[0819] Example prompt for the generative AI model: "Generate the optimal image of a rival robot based on the user's current work efficiency and target efficiency."

[0820] This invention can effectively improve the work efficiency of robots in a factory environment, and can utilize the competitive spirit between robots to promote sustainable growth.

[0821] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0822] Step 1:

[0823] The user (robot administrator) uses a terminal to input the robot's current work efficiency and target efficiency. For example, the current work efficiency of robot "A" is 1 minute / part, and the target efficiency is 0.8 minutes / part. The input data is sent from the terminal to the cloud server.

[0824] Step 2:

[0825] The server uses a generative AI model (e.g., TensorFlow) to generate an optimal rival robot image based on the received data on current work efficiency and target efficiency. For example, it generates an image of a rival robot that progresses from 1 minute / part to 0.9 minutes / part. The data on this rival robot image is stored on the server.

[0826] Step 3:

[0827] The server provides the generated rival robot image data to the robot's display device via the terminal. The terminal receives this data and displays "rival robot is working at 0.9 minutes per part" on the robot's display. This allows the user to visually recognize the rival robot image.

[0828] Step 4:

[0829] The server breaks down the target efficiency into small steps and reflects that information in the rival robot's profile. For example, an AI model could be used to generate steps to improve efficiency by 2% each month, and this information would be added to the rival robot's profile. This data on the broken down goals and steps is stored on the server.

[0830] Step 5:

[0831] The server simulates progress toward the target efficiency and provides the results to the robot's display device via the terminal. For example, it simulates "steps to achieve a 2% efficiency improvement each month" and displays the simulation data on the display. This allows the user to understand the specific action plan.

[0832] Step 6:

[0833] The robot's actual operating data is sent in real time to a cloud server via an IoT device. For example, data indicating the current work efficiency is 0.95 minutes per part is sent. The server receives this data and compares it with the progress of rival robots.

[0834] Step 7:

[0835] The server updates the rival robot image based on the progress comparison results. For example, if a robot is working at 0.95 minutes per part, the rival image standard is automatically adjusted to 0.88 minutes per part. The updated data is saved on the server.

[0836] Step 8:

[0837] The server then provides the updated rival robot image data to the robot's display device via the terminal. The display shows, "The rival robot's new standard is 0.88 minutes / part," allowing the user to check the latest information in real time.

[0838] 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.

[0839] The present invention is a system that allows users to effectively grow toward achieving their goals. Specifically, it generates rival images based on the user's current situation and goals and provides them to the user. It also collects and compares the user's progress data in real time and updates the rival images. However, the present invention also includes an emotion engine that recognizes the user's emotions and adjusts the content of the rival images provided based on those emotions, making it easier to maintain the user's motivation.

[0840] Program processing flow and specific examples

[0841] The system consists of the following main processing steps:

[0842] 1. Initial Setup

[0843] The user accesses the system and creates an account. The user inputs their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day). The device sends the user's input data to the server. The server receives this data and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[0844] 2. Providing a competitive image

[0845] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[0846] 3. Breakdown of rival's goals and progress simulation

[0847] The server breaks down the user's goal into small steps and reflects that information in the rival image (e.g., increase study time by 10 minutes each week). It also simulates the progress of the rival image and visualizes the path forward. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[0848] 4. Introducing the Emotion Engine

[0849] The server collects and analyzes the user's emotional data. The emotion engine quantifies the user's emotional state based on facial expression recognition, self-reported data, and other emotional data. This data is used to adjust the rival's progress simulation and target steps. For example, if the user is feeling stressed, the rival's progress pace may be slowed down slightly.

[0850] 5. Tracking growth

[0851] The user enters their daily activities and progress (e.g., studied for 40 minutes today) into the system. The device sends this data to the server, which then compares the progress of the rival image with the user's growth based on the progress data received. Based on the comparison results, the server updates the rival image in real time. For example, if the user studies more than planned, it will slightly increase the rival image's study time. This updated rival image information is sent back to the device and displayed to the user.

[0852] Specific examples

[0853] For example, let's say a user aims to study for two hours per day.

[0854] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[0855] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[0856] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[0857] 4. Introducing an Emotion Engine: Analyze the user's emotional state and adjust the progress and goal steps of the rival figures based on that data. For example, if the user is feeling stressed, slow down the pace of the rival figure's progress.

[0858] 5. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[0859] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, by introducing an emotion engine, it reduces the psychological burden on users and achieves more effective motivation management.

[0860] The processing flow will be explained below.

[0861] Step 1:

[0862] The user accesses the system and creates an account. Then, the user enters their current situation (e.g., currently studying for 30 minutes a day) and their desired goal (e.g., studying for 2 hours a day).

[0863] Step 2:

[0864] The terminal transmits the user's input data to the server.

[0865] Step 3:

[0866] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[0867] Step 4:

[0868] The server sends the generated rival image data to the terminal, which displays it to the user, who can then check the rival image.

[0869] Step 5:

[0870] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[0871] Step 6:

[0872] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[0873] Step 7:

[0874] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[0875] Step 8:

[0876] The terminal transmits the progress data to the server.

[0877] Step 9:

[0878] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[0879] Step 10:

[0880] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[0881] Step 11:

[0882] The server transmits the updated rival image information to the terminal, which displays it to the user.

[0883] Step 12:

[0884] The user uses the emotion engine to input their emotional state into the system (e.g., the user inputs that they are "stressed" about studying today).

[0885] Step 13:

[0886] The device sends emotional data to a server, which analyzes it and quantifies the user's stress level.

[0887] Step 14:

[0888] The server adjusts the progress simulation of the rival image taking into account the emotional data (e.g., if the user is feeling stressed, the pace of the rival image's progress is slowed down).

[0889] Step 15:

[0890] The server sends the adjusted rival image information to the terminal, which then displays it to the user, showing the adjusted progress and the rival image's new step plan.

[0891] Through these steps, users can constantly monitor their progress and emotional state, compete against their best rivals, and grow efficiently towards their goals.

[0892] Example 2

[0893] 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."

[0894] Conventional goal achievement systems are limited to comparing a user's progress with that of competitors, and lack dynamic adjustments that take into account the user's emotional state and motivation, making it difficult for users to continuously pursue their goals. The present invention aims to make it easier for users to maintain their motivation and promote sustainable growth by adding a function that analyzes the user's emotional data and adjusts the progress and goal steps of competitors based on that data.

[0895] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's current situation and goal, a means for generating a competitive object image based on the input data, a means for providing the generated competitive object image data to the user, a means for receiving the user's progress data and comparing it with the progress of the competitive object image, a means for updating the competitive object image based on the comparison results, a means for re-providing the updated competitive object image information to the user, and a means for collecting and analyzing the user's emotional data and adjusting the progress of the competitive object image and goal steps. This enables dynamic progress adjustment based on the user's emotional state, making it easier to maintain the user's motivation and promoting sustainable growth for goal achievement.

[0896] A "User" is an individual who creates an account to use the system and record their progress toward achieving goals.

[0897] "Current status" refers to data on the progress and status that the user has achieved at the present time.

[0898] A "goal" is a quantitative or qualitative achievement criterion that a user hopes to achieve in the future.

[0899] "Means for input" refers to the interface or input form that allows a user to input data into the system.

[0900] A "competitive figure" is a fictional competitor pitted against the user, created to help the user achieve their goals.

[0901] "Generative means" refers to the algorithms and processes used to create a competitive profile based on the user's current situation and goals.

[0902] "Means for providing" refers to the interface and display functions for presenting the generated data of the competitive image to the user.

[0903] "Progress data" is a record of specific activities and results toward achieving a goal that is entered by the user daily or periodically.

[0904] "Means of comparison" refers to the algorithms or processes used to compare a user's progress data with the progress of a competitive image and assess any discrepancies.

[0905] "Means for updating" refers to algorithms or processes for modifying competitive profile data or goal steps based on the results of the comparison.

[0906] "Means for re-presentation" refers to an interface or display function for presenting the updated competitive profile information to the user again.

[0907] "Emotional data" refers to data that expresses a user's emotional state and is collected through facial expression recognition, self-reporting, or other emotion measurement methods.

[0908] "Means of collection" refers to the sensors and interfaces required to collect user emotional data.

[0909] "Means for analyzing" refers to algorithms or processes for processing collected emotional data to quantitatively assess the user's emotional state.

[0910] "Adjustment" refers to algorithms or processes for dynamically changing the pace of progress or target steps of a competitive profile based on emotional data.

[0911]

[0912] This invention is a system that allows users to effectively grow toward achieving their goals. The program processing of this system is explained in detail below. The system promotes user growth by inputting the user's current situation and goals, generating and providing rival images, and comparing progress data. It also analyzes the user's emotional data to dynamically adjust the progress of the rival image and goal steps.

[0913] Initial Setup

[0914] Users access the system, create an account, and enter their current situation (e.g., 30 minutes of study time per day) and goal (e.g., 2 hours of study time per day) into their device. The device then sends this data to the server. Based on the received data, the server uses an AI algorithm (e.g., TensorFlow or PyTorch) to generate a competitive model. For example, a competitive model with 1 hour of study time per day is generated.

[0915] Providing a rival image

[0916] The generated data of the competitor image is sent from the server to the terminal, which displays it to the user, who then recognizes the competitor image as a specific competitor for achieving his or her own goal.

[0917] Goal decomposition and progress simulation

[0918] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects this information in the competitive profile. It also simulates the progress of the competitive profile and calculates the path. The device displays this information to the user, allowing them to understand the specific growth process.

[0919] Introducing the Emotion Engine

[0920] The server collects and analyzes the user's emotional data (e.g., facial expression recognition and self-reported data). An emotion engine (e.g., Microsoft's Azure Cognitive Services) quantifies the user's emotional state. This data is used to adjust the progress simulation and target steps of the competition. For example, if the user is feeling stressed, the pace of progress toward the competition may be slowed down.

[0921] Tracking growth

[0922] Users enter their daily activities and progress (e.g., "I studied for 40 minutes today") into the system, and the device sends this to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image in real time. The updated information is sent to the device and redisplayed to the user.

[0923] Specific examples

[0924] For example, let's say a user aims to study for two hours per day.

[0925] 1. Initial setup: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a competitive model with 1 hour of study time per day.

[0926] 2. Providing a picture of the rival: The device displays information about the competitor to the user, who then checks the information.

[0927] 3. Goal decomposition and simulation: The server decomposes the goal into a set of goals, for example, increasing study time by 10 minutes per week, and reflects this in the competitive image. The terminal displays the progress simulation to the user.

[0928] 4. Introducing an emotion engine: The server analyzes the user's emotional state and adjusts the progress and goal steps of the competition based on that data. If the user is feeling stressed, the pace of progress will be slowed down.

[0929] 5. Progress Tracking: The user types in "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress, updates the competitive picture, and the device displays the updates back to the user.

[0930] Example prompts for generative AI models

[0931] By inputting the following prompt sentence into the generative AI model, we can obtain an image of the competition target and a simulation of its progress.

[0932] Enter user data and goals in the goal achievement system:

[0933] Current study time: 30 minutes

[0934] Target study time: 2 hours

[0935] Weekly increase: 10 minutes

[0936] Simulation of the generated competitive image and goal decomposition:

[0937] generate_rival_and_simulation: true

[0938] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, the introduction of an emotion engine reduces the psychological burden on users and enables more effective motivation management.

[0939] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0940]

[0941] Step 1:

[0942] A user accesses the system and creates an account. The user enters their name, email address, and password into the system's input form and clicks the register button. The terminal receives this input data and sends it to the server. This action causes the server to save the new user account in the database, and the user is notified that their account has been created.

[0943] Step 2:

[0944] The user inputs their current situation (e.g., 30 minutes of study time per day) and their goal (e.g., 2 hours of study time per day). The device enters this data into the system's input form and sends it to the server by clicking the send button. The server analyzes the received data and generates a competitive image using an AI algorithm. The data for the generated competitive image is calculated by the server and sent to the device.

[0945] Step 3:

[0946] The server sends the generated data of the competitor image to the terminal. The terminal receives this data and displays it to the user. Specifically, the generated data is displayed on the user interface, and the user recognizes it as a competitor. At this point, the user can confirm the existence of a specific competitor (e.g., someone who studies for one hour per day) that is aimed at their goal.

[0947] Step 4:

[0948] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects it in the image of the competitor. The server uses this information to simulate the progress of the competitor and sends the results to the device. The device then displays the received simulation data to the user, visualizing the progress in concrete terms (graphs and charts).

[0949] Step 5:

[0950] The server collects the user's emotional data (e.g., facial expression recognition and self-reported data) and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state (e.g., stress level) is quantified. This analysis data is processed by the server, and the progress pace and target steps of the competition object are adjusted as necessary. For example, if the user is feeling high stress, the progress pace is adjusted to slow down.

[0951] Step 6:

[0952] The user enters their daily activities and progress (e.g., "I studied for 40 minutes today") into the system. The device receives this data through an input form and sends it to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image as appropriate and sends this updated information to the device. The device then displays the updated content to the user, allowing them to check their progress in real time.

[0953] (Application example 2)

[0954] 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."

[0955] Conventional production efficiency improvement systems for factory robots have the problem of being difficult to adjust in real time, taking into account the robot's own production capacity and emotional state. As a result, maximizing production efficiency may not be fully achieved due to workload or stress levels.

[0956] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the user's current situation and goal; means for generating a rival image based on the input data; means for providing the user with data on the generated rival image; means for receiving the user's progress data and comparing it with the progress of the rival image; means for updating the rival image based on the comparison results; means for re-providing the user with information on the updated rival image; means for evaluating the robot's production capability; means for analyzing the robot's emotional state; and means for adjusting the progress of the rival image based on the user's emotional state and production status. This enables optimal production management that takes into account the user's workload and stress level while maximizing the robot's production efficiency.

[0957] A "user" is an individual or robot operator who utilizes the system to achieve a goal.

[0958] The "current situation" refers to the current environment or state in which the user or robot finds themselves in order to achieve their goal.

[0959] A "goal" is a specific numerical value or state that a user aims to achieve.

[0960] A "rival" is a virtual entity that a user views as a competitor in order to achieve a goal.

[0961] "Input data" refers to data about the current situation and goals that a user provides to the system.

[0962] "Means for generation" refers to the algorithms and functions used to generate a rival profile based on input data.

[0963] "Means for providing" refers to an interface or function for displaying the generated rival image data to the user.

[0964] "Progress data" refers to data that indicates a user's progress toward achieving a goal.

[0965] "Means of comparison" refers to algorithms or functions that compare a user's progress data with the progress of a rival image.

[0966] "Means for updating" refers to the algorithms and functions used to update rival image data based on the comparison results.

[0967] The "means for re-provision" refers to an interface or function for displaying the updated rival image data to the user again.

[0968] "Production capacity" refers to the amount of production a robot can achieve in a given time.

[0969] "Emotional state" refers to the psychological and physical state of a robot that is analyzed based on the robot's motion data and sensor information.

[0970] "Adjustment" refers to algorithms or functions for adjusting the progress of rival figures based on their emotional state and production status.

[0971] This invention provides a system for continuously improving the production efficiency of a factory robot, which includes means for inputting a user's current situation and goals, means for generating a rival image, means for providing data on the rival image to the user, means for receiving progress data from the user and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for evaluating the production capability of the robot, means for analyzing the emotional state of the robot, and means for adjusting the progress of the rival image.

[0972] Explanation of program processing

[0973] 1. Initial Setup

[0974] The user inputs the robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). These data are sent from the client terminal to the server.

[0975] The server uses AI algorithms (e.g., Scikit-Learn, TensorFlow) to generate the optimal "rival robot," which is set midway between the current production capacity and the target production capacity (e.g., 15 units per hour).

[0976] 2. Providing a competitive image

[0977] The rival image data generated by the server is sent to the terminal and displayed on the factory robot's display.

[0978] Factory robots recognize their rivals as competitors based on this image.

[0979] 3. Goal decomposition and progress simulation

[0980] The server breaks down the goal into small steps (e.g., "increase production by 2 units each week").

[0981] The progress of rival robots is simulated and displayed on the terminal, and the robot uses this progress information to understand the specific improvement process.

[0982] 4. Introducing the Emotion Engine

[0983] The server collects and analyzes the robot's operational data (battery status, operational load, etc.) and evaluates its emotional state.

[0984] The emotion engine classifies the emotional state as "stressed" or "comfortable," and adjusts the progress of the rival image accordingly (e.g., slowing down the pace of progress if the rival image is in a stressful state).

[0985] 5. Tracking growth

[0986] The robot inputs daily production performance data and sends it to the server.

[0987] The server compares the progress data and updates the rival's image, which is then sent back to the device for display.

[0988] Examples of concrete examples and prompts

[0989] Example: A factory robot currently has a production capacity of 10 units per hour and has a target production of 20 units per hour. The system creates a rival robot that produces 15 units per hour and sets a resolution target of "increase production by 2 units per week."

[0990] Input prompt for generative AI model:

[0991] "Current production capacity: 10 units / hour, target: 20 units / hour, increase by 2 units per week"

[0992] This allows for optimal production management that takes into account work load and stress levels while maximizing the production efficiency of the robot.

[0993] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0994] Program processing steps

[0995] Step 1: Initial Setup

[0996] The user inputs the factory robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). This data is sent from the terminal to the server.

[0997] Input: Current production capacity, target production capacity

[0998] Data processing: The server receives the data entered by the user and formats it as input data for the AI ​​algorithm.

[0999] Output: Formatted data

[1000] Step 2: Generate a rival profile

[1001] The server uses an AI algorithm based on the data it receives about current production capacity and target production capacity to generate the optimal "rival robot."

[1002] Input: Formatted data

[1003] Data calculation: Calculate the average of the current production capacity and the target production capacity, or an intermediate value using a specific algorithm.

[1004] Output: The production capacity of the spawned rival robot (e.g. 15 units per hour).

[1005] Step 3: Provide a picture of your competitors

[1006] The server generates rival image data, which is then sent to a terminal and displayed on the factory robot's display. The robot then recognizes the rival as a competitor based on this image data.

[1007] Input: Production capacity value of the generated rival robot

[1008] Data processing: Format the rival image data into the appropriate format and send it to the device.

[1009] Output: Information about the rival image displayed on the device

[1010] Step 4: Goal decomposition and progress simulation

[1011] The server breaks down the goal into small steps (e.g., "increase production by two units each week") and simulates the progress of rival robots, displaying it on the terminal.

[1012] Input: Target production capacity, Current production capacity

[1013] Data calculation: Calculate the steps to achieve the goal on a weekly basis and generate simulation data.

[1014] Output: Simulation data, progress information displayed on the terminal

[1015] Step 5: Analyzing the robot's emotional state

[1016] The server collects the robot's operational data (battery status, operational load, etc.) and uses this data to analyze its emotional state, which is then classified as "stressed" or "comfortable."

[1017] Input: Operating data (battery status, operating load, etc.)

[1018] Data calculation: The emotion engine analyzes behavior data and converts it into a quantitative emotional state.

[1019] Output: Emotional state (e.g., stress, comfort)

[1020] Step 6: Adjusting the progress of your rival's image based on their emotional state

[1021] The server adjusts the progress of the rival statue based on the analyzed emotional state, for example, if the emotional state is "stressed," the server slows down the pace of the rival statue's progress.

[1022] Input: Emotional state, simulation data

[1023] Data calculation: An algorithm is applied that adjusts the pace of progress depending on the emotional state.

[1024] Output: Adjusted simulation data

[1025] Step 7: Tracking growth

[1026] The factory robot inputs daily production performance data and sends it from the terminal to the server. The server analyzes the progress data and updates the image of the rival. This updated information is then sent back to the terminal and displayed.

[1027] Input: Daily production data

[1028] Data calculation: Compare progress data with rival image data and update the rival image as needed.

[1029] Output: Updated rival image information, updated content displayed on the device

[1030] The above processing steps enable optimal production management that takes into account work load and stress levels while maximizing the production efficiency of factory robots.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] [Fourth embodiment]

[1035] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1036] 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.

[1037] 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).

[1038] 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.

[1039] 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.

[1040] 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).

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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."

[1048] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[1049] Program processing flow and specific examples

[1050] The system consists of the following main processing steps:

[1051] 1. Initial Setup

[1052] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[1053] 2. Providing a competitive image

[1054] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[1055] 3. Breakdown of rival's goals and progress simulation

[1056] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[1057] 4. Tracking growth

[1058] The user enters their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[1059] Specific examples

[1060] For example, let's say a user aims to study for two hours per day.

[1061] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[1062] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[1063] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[1064] 4. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[1065] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[1066] The processing flow will be explained below.

[1067] Step 1:

[1068] The user accesses the system and creates an account. The user enters their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day).

[1069] Step 2:

[1070] The terminal transmits the user's input data to the server.

[1071] Step 3:

[1072] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[1073] Step 4:

[1074] The server sends the generated rival image data to the terminal, which displays it to the user. The user checks the rival image.

[1075] Step 5:

[1076] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[1077] Step 6:

[1078] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[1079] Step 7:

[1080] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[1081] Step 8:

[1082] The terminal transmits the progress data to the server.

[1083] Step 9:

[1084] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[1085] Step 10:

[1086] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[1087] Step 11:

[1088] The server transmits the updated rival image information to the terminal, which displays it to the user.

[1089] Through these steps, users can constantly check their progress, compete against their best rivals, and continuously grow towards their goals.

[1090] Example 1

[1091] 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."

[1092] In order for users to effectively progress towards achieving their goals and maintain their motivation, they need to accurately understand their own status and set specific, realistic goals and the process to achieve them.However, there is a general lack of guidelines for achieving goals and means to maintain motivation, which means that many users give up midway.

[1093] 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.

[1094] In this invention, the server includes means for inputting the user's current situation and goals, a generation AI model means for generating a rival image based on the input data, means for providing the user with data on the generated rival image, means for receiving the user's progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, and means for re-providing the user with information on the updated rival image. This enables the user to constantly check their own progress and compete against appropriate rival images that are updated in real time.

[1095] "Current status of user" is data indicating the user's current status, skill level, activity status, and the like.

[1096] A "goal" is an indicator that indicates a specific result or status that a user is trying to achieve.

[1097] A "rival image" is an image of a virtual competitor in achieving a goal, generated by an AI model to increase user motivation.

[1098] A "generative AI model" is an artificial intelligence algorithm that generates an optimal image of a rival based on input data.

[1099] "Progress data" is data that indicates how much progress a user is making toward achieving their daily activities and goals.

[1100] "Means of breaking down goals into smaller steps" is a system function that performs the process of breaking down large goals into smaller, incremental goals that are easier to achieve.

[1101] "Progress simulation" is a process for predicting the progress of a rival image and visualizing that progress.

[1102] The "means for updating the rival image based on the comparison result" is a system function that compares the user's progress data with the rival image data and changes the state of the rival image based on the result.

[1103] A "re-presentation" is a process for presenting updated information to the user again.

[1104] The present invention is a system that supports users in effectively progressing toward their goals. This system generates a rival image based on the user's current situation and goals and provides it to the user. In addition, the system collects the user's progress data in real time and compares it with the rival image, making it easier for the user to maintain sustained motivation.

[1105] The user accesses the system and creates an account. Next, the user enters their current situation (for example, their current skill level and 30 minutes of study time per day) and their desired goal (for example, 2 hours of study time per day). The device sends this input data to the server. The server receives this and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, AI algorithms such as Python's Scikit-learn or TensorFlow are used. The data on the generated rival profile is sent from the server to the device, which then displays it to the user.

[1106] For example, if a user aims to study for two hours a day, they would input that their current study time is 30 minutes a day. The device sends this information to the server, which then uses this information to generate a rival image that studies for one hour a day. The generative AI model would be given a prompt like this:

[1107] Example prompt: "If the user currently studies 30 minutes per day and their goal is 2 hours per day, please generate an appropriate rival profile for them."

[1108] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects these in the rival image. It also simulates the progress of the rival image and visualizes the path. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[1109] Users enter their daily activities and progress (for example, "hours spent studying today") into the system. The device sends this to the server. Based on the received progress data, the server compares the user's growth with that of the rival image and presents it in a form that can be seen by the user. Based on the results of this comparison, the server updates the rival image in real time. For example, if the user studies more than planned, the study time of the rival image will be increased slightly. The updated rival image information is sent again to the device and displayed to the user.

[1110] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow towards their goals.

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] Step 1: Account Creation and Initial Settings

[1113] A user accesses the system's website or application and clicks the "Create an account" button. The user enters their name, email address, and password into the registration form and presses the "Register" button. The device sends this data to the server, which stores it in a database and creates an account. In this case, the input is the user's name, email address, and password, and the output is the user account information stored on the server.

[1114] Step 2: Enter your goals and current situation

[1115] After logging in, the user enters their current situation and goals. For example, if they currently study for 30 minutes a day and aim to study for 2 hours a day, they enter that information into their device. The device then sends this information to the server. The input is the user's current situation and goals, and the output is the input data saved on the server.

[1116] Step 3: Generate a rival profile

[1117] The data received by the server is analyzed using a generative AI model implemented in Python, such as Scikit-learn or TensorFlow. The server generates an optimal rival profile based on the user's current situation and goals. For example, a rival profile with "one hour of study time per day" is generated. The input is the user's current situation and goals, and the output is the generated rival profile data.

[1118] Step 4: Provide a picture of your competitors

[1119] The server sends the generated rival image data to the terminal. The terminal displays the received rival image data to the user. The user checks the rival image displayed on the screen. The input is the rival image data, and the output is the displayed rival image.

[1120] Step 5: Goal decomposition and progress simulation

[1121] The server breaks down the user's goal into small steps. For example, it might break down the goal into steps, such as increasing study time by 10 minutes each week. It then simulates the progress of the rival image and sends the results to the device. The device then displays the received data to the user. The input is the user's goal and rival image data, and the output is the broken down goal and the simulation results.

[1122] Step 6: Tracking growth

[1123] The user inputs their daily progress. For example, they might input "I studied for 40 minutes today." The device sends this data to the server. The server compares the received progress data with the rival image. Based on the comparison results, the rival image is updated. The server sends the updated rival image information back to the device, which displays it to the user. The input is the user's daily progress data, and the output is the updated rival image.

[1124] In this way, each processing step works together to form a system that helps users achieve their goals.

[1125] (Application example 1)

[1126] 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."

[1127] While existing support systems for users to grow toward their goals are effective in motivating users and managing their progress, they have not been applied to improving the work efficiency of robots in factory environments. Furthermore, the lack of a mechanism to improve efficiency by encouraging competition between robots makes it difficult to maximize overall production efficiency.

[1128] 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.

[1129] In this invention, the server includes means for inputting a user's current situation and goal, means for generating a rival image based on the input data, means for providing data on the generated rival image to the user, means for receiving user progress data and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for inputting a current work efficiency and a target efficiency of a robot in a factory environment, means for generating a rival robot image based on the robot's work efficiency, means for providing data on the generated rival robot image to a display device of the robot, and means for tracking the user's growth and providing feedback. This makes it possible to effectively improve the work efficiency of robots in factories.

[1130] A "user" is someone who uses this system to grow toward their own goals.

[1131] The "current situation" is data indicating the current state of the user or robot, such as their current skills, work efficiency, or activity level.

[1132] A "goal" is a future skill, work efficiency, activity level, or the like that the user or robot aims to achieve.

[1133] A "rival figure" is a virtual competitor that the system generates to help the user or robot develop a competitive spirit.

[1134] "Progress data" refers to data such as the activities and work efficiency of a user or robot performed within a specific period of time.

[1135] "Factory environment" refers to the working environment in the production facility where the robot is located.

[1136] "Work efficiency" is an indicator of how much work a robot can accomplish within a specific time period.

[1137] A "display device" is a device used by a system or robot to visually present data or information to a user.

[1138] MODE FOR CARRYING OUT THE INVENTION

[1139] The present invention is a system that supports users or robots to efficiently grow toward their own goals. In particular, the present invention focuses on improving the work efficiency of robots in a factory environment.

[1140] Initial Setup

[1141] During the initial setup of the system, the user (robot manager) inputs the current and target work efficiency of the robots in the factory. The input is done via a terminal, which then sends this data to a cloud server. The cloud server then uses an AI algorithm (e.g., TensorFlow) to generate an optimal rival image for each robot based on the user's current situation and goals.

[1142] Providing a rival image

[1143] The generated rival image data is then provided to the robot's display device via a terminal. A simple computing device such as a Raspberry Pi can be used for the display device, which visually displays details of the rival robot image and the difference in its current work efficiency. This information allows the robot manager to recognize the rival image as a specific competitor.

[1144] Goal decomposition and progress simulation

[1145] The server breaks down the target efficiency into small steps and reflects them in the image of the rival. Using an AI model (e.g., TensorFlow), it simulates the robot's progress and visualizes the process. This simulation data is also sent to the robot's display device via a terminal, allowing the specific work improvement process to be understood.

[1146] Tracking growth

[1147] The robot's operational data is sent to a cloud server in real time. Data collected through IoT devices is analyzed on the server side, and progress is compared with the rival image. The server updates the rival image based on the comparison results and provides real-time feedback. For example, if the robot completes a task within a set time, the reference time of the rival image is automatically adjusted. The updated rival image information is then displayed on the robot's display device via the four terminals.

[1148] Specific examples

[1149] For example, let's say a factory robot "A" is operating at 1 minute / part, and the robot's goal is 0.8 minutes / part.

[1150] 1. Initial setting: Enter the current work efficiency of robot "A" as 1 minute / part and the target work efficiency as 0.8 minutes / part.

[1151] 2. Generation of rival robot images: The server generates rival robot images that work at 0.9 minutes per part.

[1152] 3. Provide: The display shows "Rival robot is working at 0.9 minutes per part."

[1153] 4. Target decomposition: AI generates steps to improve efficiency by 2% each month.

[1154] 5. Tracking: Robot “A’s” progress (e.g., current progress: 0.95 min / part) is collected in real time and compared with its rivals.

[1155] 6. Feedback: "Good progress, current time 0.93 minutes / part" appears on the display.

[1156] Example prompt for the generative AI model: "Generate the optimal image of a rival robot based on the user's current work efficiency and target efficiency."

[1157] This invention can effectively improve the work efficiency of robots in a factory environment, and can utilize the competitive spirit between robots to promote sustainable growth.

[1158] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1159] Step 1:

[1160] The user (robot administrator) uses a terminal to input the robot's current work efficiency and target efficiency. For example, the current work efficiency of robot "A" is 1 minute / part, and the target efficiency is 0.8 minutes / part. The input data is sent from the terminal to the cloud server.

[1161] Step 2:

[1162] The server uses a generative AI model (e.g., TensorFlow) to generate an optimal rival robot image based on the received data on current work efficiency and target efficiency. For example, it generates an image of a rival robot that progresses from 1 minute / part to 0.9 minutes / part. The data on this rival robot image is stored on the server.

[1163] Step 3:

[1164] The server provides the generated rival robot image data to the robot's display device via the terminal. The terminal receives this data and displays "rival robot is working at 0.9 minutes per part" on the robot's display. This allows the user to visually recognize the rival robot image.

[1165] Step 4:

[1166] The server breaks down the target efficiency into small steps and reflects that information in the rival robot's profile. For example, an AI model could be used to generate steps to improve efficiency by 2% each month, and this information would be added to the rival robot's profile. This data on the broken down goals and steps is stored on the server.

[1167] Step 5:

[1168] The server simulates progress toward the target efficiency and provides the results to the robot's display device via the terminal. For example, it simulates "steps to achieve a 2% efficiency improvement each month" and displays the simulation data on the display. This allows the user to understand the specific action plan.

[1169] Step 6:

[1170] The robot's actual operating data is sent in real time to a cloud server via an IoT device. For example, data indicating the current work efficiency is 0.95 minutes per part is sent. The server receives this data and compares it with the progress of rival robots.

[1171] Step 7:

[1172] The server updates the rival robot image based on the progress comparison results. For example, if a robot is working at 0.95 minutes per part, the rival image standard is automatically adjusted to 0.88 minutes per part. The updated data is saved on the server.

[1173] Step 8:

[1174] The server then provides the updated rival robot image data to the robot's display device via the terminal. The display shows, "The rival robot's new standard is 0.88 minutes / part," allowing the user to check the latest information in real time.

[1175] 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.

[1176] The present invention is a system that allows users to effectively grow toward achieving their goals. Specifically, it generates rival images based on the user's current situation and goals and provides them to the user. It also collects and compares the user's progress data in real time and updates the rival images. However, the present invention also includes an emotion engine that recognizes the user's emotions and adjusts the content of the rival images provided based on those emotions, making it easier to maintain the user's motivation.

[1177] Program processing flow and specific examples

[1178] The system consists of the following main processing steps:

[1179] 1. Initial Setup

[1180] The user accesses the system and creates an account. The user inputs their current situation (e.g., 30 minutes of study time per day) and their desired goal (e.g., 2 hours of study time per day). The device sends the user's input data to the server. The server receives this data and uses an AI algorithm to generate a rival profile that is optimal for the user. For example, a rival profile that studies 1 hour per day is generated.

[1181] 2. Providing a competitive image

[1182] The server sends the generated rival image data to the terminal, which displays it to the user. The user recognizes the rival image as a specific opponent for achieving their own goal.

[1183] 3. Breakdown of rival's goals and progress simulation

[1184] The server breaks down the user's goal into small steps and reflects that information in the rival image (e.g., increase study time by 10 minutes each week). It also simulates the progress of the rival image and visualizes the path forward. The device displays this information to the user, allowing them to understand the specific process they should aim for.

[1185] 4. Introducing the Emotion Engine

[1186] The server collects and analyzes the user's emotional data. The emotion engine quantifies the user's emotional state based on facial expression recognition, self-reported data, and other emotional data. This data is used to adjust the rival's progress simulation and target steps. For example, if the user is feeling stressed, the rival's progress pace may be slowed down slightly.

[1187] 5. Tracking growth

[1188] The user enters their daily activities and progress (e.g., studied for 40 minutes today) into the system. The device sends this data to the server, which then compares the progress of the rival image with the user's growth based on the progress data received. Based on the comparison results, the server updates the rival image in real time. For example, if the user studies more than planned, it will slightly increase the rival image's study time. This updated rival image information is sent back to the device and displayed to the user.

[1189] Specific examples

[1190] For example, let's say a user aims to study for two hours per day.

[1191] 1. Initial setting: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a rival image with 1 hour of study time per day.

[1192] 2. Providing a rival profile: The device displays information about the rival profile to the user, who then confirms the information.

[1193] 3. Goal decomposition and simulation: The server breaks down the goal into, for example, "increase study time by 10 minutes each week" and reflects it in the rival image. The device displays the progress simulation to the user.

[1194] 4. Introducing an Emotion Engine: Analyze the user's emotional state and adjust the progress and goal steps of the rival figures based on that data. For example, if the user is feeling stressed, slow down the pace of the rival figure's progress.

[1195] 5. Progress Tracking: The user types, "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress and updates the competitor image. The device displays the updates back to the user.

[1196] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, by introducing an emotion engine, it reduces the psychological burden on users and achieves more effective motivation management.

[1197] The processing flow will be explained below.

[1198] Step 1:

[1199] The user accesses the system and creates an account. Then, the user enters their current situation (e.g., currently studying for 30 minutes a day) and their desired goal (e.g., studying for 2 hours a day).

[1200] Step 2:

[1201] The terminal transmits the user's input data to the server.

[1202] Step 3:

[1203] Based on the data received by the server, an AI algorithm is used to generate a rival profile that is optimal for the user. For example, it generates a rival profile that studies one hour per day.

[1204] Step 4:

[1205] The server sends the generated rival image data to the terminal, which displays it to the user, who can then check the rival image.

[1206] Step 5:

[1207] The server breaks down the user's goal into small steps (e.g., increasing study time by 10 minutes each week) and reflects that information in the rival profile.

[1208] Step 6:

[1209] The server simulates the progress of the rival image and clarifies the path the user should aim for. The terminal displays this progress simulation to the user.

[1210] Step 7:

[1211] The user enters their daily activities and progress into the system (e.g., they entered that they studied for 40 minutes today).

[1212] Step 8:

[1213] The terminal transmits the progress data to the server.

[1214] Step 9:

[1215] Based on the progress data received by the server, the progress of the rival image and the growth of the user are compared.

[1216] Step 10:

[1217] The server updates the rival image in real time based on the comparison results (e.g., if a user studies more than planned, the rival image's study time is increased slightly).

[1218] Step 11:

[1219] The server transmits the updated rival image information to the terminal, which displays it to the user.

[1220] Step 12:

[1221] The user uses the emotion engine to input their emotional state into the system (e.g., the user inputs that they are "stressed" about studying today).

[1222] Step 13:

[1223] The device sends emotional data to a server, which analyzes it and quantifies the user's stress level.

[1224] Step 14:

[1225] The server adjusts the progress simulation of the rival image taking into account the emotional data (e.g., if the user is feeling stressed, the pace of the rival image's progress is slowed down).

[1226] Step 15:

[1227] The server sends the adjusted rival image information to the terminal, which then displays it to the user, showing the adjusted progress and the rival image's new step plan.

[1228] Through these steps, users can constantly monitor their progress and emotional state, compete against their best rivals, and grow efficiently towards their goals.

[1229] Example 2

[1230] 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."

[1231] Conventional goal achievement systems are limited to comparing a user's progress with that of competitors, and lack dynamic adjustments that take into account the user's emotional state and motivation, making it difficult for users to continuously pursue their goals. The present invention aims to make it easier for users to maintain their motivation and promote sustainable growth by adding a function that analyzes the user's emotional data and adjusts the progress and goal steps of competitors based on that data.

[1232] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting the user's current situation and goal, a means for generating a competitive object image based on the input data, a means for providing the generated competitive object image data to the user, a means for receiving the user's progress data and comparing it with the progress of the competitive object image, a means for updating the competitive object image based on the comparison results, a means for re-providing the updated competitive object image information to the user, and a means for collecting and analyzing the user's emotional data and adjusting the progress of the competitive object image and goal steps. This enables dynamic progress adjustment based on the user's emotional state, making it easier to maintain the user's motivation and promoting sustainable growth for goal achievement.

[1233] A "User" is an individual who creates an account to use the system and record their progress toward achieving goals.

[1234] "Current status" refers to data on the progress and status that the user has achieved at the present time.

[1235] A "goal" is a quantitative or qualitative achievement criterion that a user hopes to achieve in the future.

[1236] "Means for input" refers to the interface or input form that allows a user to input data into the system.

[1237] A "competitive figure" is a fictional competitor pitted against the user, created to help the user achieve their goals.

[1238] "Generative means" refers to the algorithms and processes used to create a competitive profile based on the user's current situation and goals.

[1239] "Means for providing" refers to the interface and display functions for presenting the generated data of the competitive image to the user.

[1240] "Progress data" is a record of specific activities and results toward achieving a goal that is entered by the user daily or periodically.

[1241] "Means of comparison" refers to the algorithms or processes used to compare a user's progress data with the progress of a competitive image and assess any discrepancies.

[1242] "Means for updating" refers to algorithms or processes for modifying competitive profile data or goal steps based on the results of the comparison.

[1243] "Means for re-presentation" refers to an interface or display function for presenting the updated competitive profile information to the user again.

[1244] "Emotional data" refers to data that expresses a user's emotional state and is collected through facial expression recognition, self-reporting, or other emotion measurement methods.

[1245] "Means of collection" refers to the sensors and interfaces required to collect user emotional data.

[1246] "Means for analyzing" refers to algorithms or processes for processing collected emotional data to quantitatively assess the user's emotional state.

[1247] "Adjustment" refers to algorithms or processes for dynamically changing the pace of progress or target steps of a competitive profile based on emotional data.

[1248]

[1249] This invention is a system that allows users to effectively grow toward achieving their goals. The program processing of this system is explained in detail below. The system promotes user growth by inputting the user's current situation and goals, generating and providing rival images, and comparing progress data. It also analyzes the user's emotional data to dynamically adjust the progress of the rival image and goal steps.

[1250] Initial Setup

[1251] Users access the system, create an account, and enter their current situation (e.g., 30 minutes of study time per day) and goal (e.g., 2 hours of study time per day) into their device. The device then sends this data to the server. Based on the received data, the server uses an AI algorithm (e.g., TensorFlow or PyTorch) to generate a competitive model. For example, a competitive model with 1 hour of study time per day is generated.

[1252] Providing a rival image

[1253] The generated data of the competitor image is sent from the server to the terminal, which displays it to the user, who then recognizes the competitor image as a specific competitor for achieving his or her own goal.

[1254] Goal decomposition and progress simulation

[1255] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects this information in the competitive profile. It also simulates the progress of the competitive profile and calculates the path. The device displays this information to the user, allowing them to understand the specific growth process.

[1256] Introducing the Emotion Engine

[1257] The server collects and analyzes the user's emotional data (e.g., facial expression recognition and self-reported data). An emotion engine (e.g., Microsoft's Azure Cognitive Services) quantifies the user's emotional state. This data is used to adjust the progress simulation and target steps of the competition. For example, if the user is feeling stressed, the pace of progress toward the competition may be slowed down.

[1258] Tracking growth

[1259] Users enter their daily activities and progress (e.g., "I studied for 40 minutes today") into the system, and the device sends this to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image in real time. The updated information is sent to the device and redisplayed to the user.

[1260] Specific examples

[1261] For example, let's say a user aims to study for two hours per day.

[1262] 1. Initial setup: The user inputs their current study time (30 minutes per day) and goal (2 hours per day). Based on this, the server generates a competitive model with 1 hour of study time per day.

[1263] 2. Providing a picture of the rival: The device displays information about the competitor to the user, who then checks the information.

[1264] 3. Goal decomposition and simulation: The server decomposes the goal into a set of goals, for example, increasing study time by 10 minutes per week, and reflects this in the competitive image. The terminal displays the progress simulation to the user.

[1265] 4. Introducing an emotion engine: The server analyzes the user's emotional state and adjusts the progress and goal steps of the competition based on that data. If the user is feeling stressed, the pace of progress will be slowed down.

[1266] 5. Progress Tracking: The user types in "I studied for 40 minutes today," and the device sends this to the server. The server compares the progress, updates the competitive picture, and the device displays the updates back to the user.

[1267] Example prompts for generative AI models

[1268] By inputting the following prompt sentence into the generative AI model, we can obtain an image of the competition target and a simulation of its progress.

[1269] Enter user data and goals in the goal achievement system:

[1270] Current study time: 30 minutes

[1271] Target study time: 2 hours

[1272] Weekly increase: 10 minutes

[1273] Simulation of the generated competitive image and goal decomposition:

[1274] generate_rival_and_simulation: true

[1275] This system allows users to constantly check their progress and compete against appropriate rivals, enabling them to continuously grow toward their goals.In addition, the introduction of an emotion engine reduces the psychological burden on users and enables more effective motivation management.

[1276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1277]

[1278] Step 1:

[1279] A user accesses the system and creates an account. The user enters their name, email address, and password into the system's input form and clicks the register button. The terminal receives this input data and sends it to the server. This action causes the server to save the new user account in the database, and the user is notified that their account has been created.

[1280] Step 2:

[1281] The user inputs their current situation (e.g., 30 minutes of study time per day) and their goal (e.g., 2 hours of study time per day). The device enters this data into the system's input form and sends it to the server by clicking the send button. The server analyzes the received data and generates a competitive image using an AI algorithm. The data for the generated competitive image is calculated by the server and sent to the device.

[1282] Step 3:

[1283] The server sends the generated data of the competitor image to the terminal. The terminal receives this data and displays it to the user. Specifically, the generated data is displayed on the user interface, and the user recognizes it as a competitor. At this point, the user can confirm the existence of a specific competitor (e.g., someone who studies for one hour per day) that is aimed at their goal.

[1284] Step 4:

[1285] The server breaks down the user's goal into small steps (for example, increasing study time by 10 minutes each week) and reflects it in the image of the competitor. The server uses this information to simulate the progress of the competitor and sends the results to the device. The device then displays the received simulation data to the user, visualizing the progress in concrete terms (graphs and charts).

[1286] Step 5:

[1287] The server collects the user's emotional data (e.g., facial expression recognition and self-reported data) and analyzes it using an emotion engine. Based on the analysis results, the user's emotional state (e.g., stress level) is quantified. This analysis data is processed by the server, and the progress pace and target steps of the competition object are adjusted as necessary. For example, if the user is feeling high stress, the progress pace is adjusted to slow down.

[1288] Step 6:

[1289] The user enters their daily activities and progress (e.g., "I studied for 40 minutes today") into the system. The device receives this data through an input form and sends it to the server. Based on the received progress data, the server compares the progress of the competitive image with the user's growth. Based on the comparison results, the server updates the competitive image as appropriate and sends this updated information to the device. The device then displays the updated content to the user, allowing them to check their progress in real time.

[1290] (Application example 2)

[1291] 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."

[1292] Conventional production efficiency improvement systems for factory robots have the problem of being difficult to adjust in real time, taking into account the robot's own production capacity and emotional state. As a result, maximizing production efficiency may not be fully achieved due to workload or stress levels.

[1293] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for inputting the user's current situation and goal; means for generating a rival image based on the input data; means for providing the user with data on the generated rival image; means for receiving the user's progress data and comparing it with the progress of the rival image; means for updating the rival image based on the comparison results; means for re-providing the user with information on the updated rival image; means for evaluating the robot's production capability; means for analyzing the robot's emotional state; and means for adjusting the progress of the rival image based on the user's emotional state and production status. This enables optimal production management that takes into account the user's workload and stress level while maximizing the robot's production efficiency.

[1294] A "user" is an individual or robot operator who utilizes the system to achieve a goal.

[1295] The "current situation" refers to the current environment or state in which the user or robot finds themselves in order to achieve their goal.

[1296] A "goal" is a specific numerical value or state that a user aims to achieve.

[1297] A "rival" is a virtual entity that a user views as a competitor in order to achieve a goal.

[1298] "Input data" refers to data about the current situation and goals that a user provides to the system.

[1299] "Means for generation" refers to the algorithms and functions used to generate a rival profile based on input data.

[1300] "Means for providing" refers to an interface or function for displaying the generated rival image data to the user.

[1301] "Progress data" refers to data that indicates a user's progress toward achieving a goal.

[1302] "Means of comparison" refers to algorithms or functions that compare a user's progress data with the progress of a rival image.

[1303] "Means for updating" refers to the algorithms and functions used to update rival image data based on the comparison results.

[1304] The "means for re-provision" refers to an interface or function for displaying the updated rival image data to the user again.

[1305] "Production capacity" refers to the amount of production a robot can achieve in a given time.

[1306] "Emotional state" refers to the psychological and physical state of a robot that is analyzed based on the robot's motion data and sensor information.

[1307] "Adjustment" refers to algorithms or functions for adjusting the progress of rival figures based on their emotional state and production status.

[1308] This invention provides a system for continuously improving the production efficiency of a factory robot, which includes means for inputting a user's current situation and goals, means for generating a rival image, means for providing data on the rival image to the user, means for receiving progress data from the user and comparing it with the progress of the rival image, means for updating the rival image based on the comparison results, means for re-providing information on the updated rival image to the user, means for evaluating the production capability of the robot, means for analyzing the emotional state of the robot, and means for adjusting the progress of the rival image.

[1309] Explanation of program processing

[1310] 1. Initial Setup

[1311] The user inputs the robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). These data are sent from the client terminal to the server.

[1312] The server uses AI algorithms (e.g., Scikit-Learn, TensorFlow) to generate the optimal "rival robot," which is set midway between the current production capacity and the target production capacity (e.g., 15 units per hour).

[1313] 2. Providing a competitive image

[1314] The rival image data generated by the server is sent to the terminal and displayed on the factory robot's display.

[1315] Factory robots recognize their rivals as competitors based on this image.

[1316] 3. Goal decomposition and progress simulation

[1317] The server breaks down the goal into small steps (e.g., "increase production by 2 units each week").

[1318] The progress of rival robots is simulated and displayed on the terminal, and the robot uses this progress information to understand the specific improvement process.

[1319] 4. Introducing the Emotion Engine

[1320] The server collects and analyzes the robot's operational data (battery status, operational load, etc.) and evaluates its emotional state.

[1321] The emotion engine classifies the emotional state as "stressed" or "comfortable," and adjusts the progress of the rival image accordingly (e.g., slowing down the pace of progress if the rival image is in a stressful state).

[1322] 5. Tracking growth

[1323] The robot inputs daily production performance data and sends it to the server.

[1324] The server compares the progress data and updates the rival's image, which is then sent back to the device for display.

[1325] Examples of concrete examples and prompts

[1326] Example: A factory robot currently has a production capacity of 10 units per hour and has a target production of 20 units per hour. The system creates a rival robot that produces 15 units per hour and sets a resolution target of "increase production by 2 units per week."

[1327] Input prompt for generative AI model:

[1328] "Current production capacity: 10 units / hour, target: 20 units / hour, increase by 2 units per week"

[1329] This allows for optimal production management that takes into account work load and stress levels while maximizing the production efficiency of the robot.

[1330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1331] Program processing steps

[1332] Step 1: Initial Setup

[1333] The user inputs the factory robot's current production capacity (e.g., 10 units per hour) and target production capacity (e.g., 20 units per hour). This data is sent from the terminal to the server.

[1334] Input: Current production capacity, target production capacity

[1335] Data processing: The server receives the data entered by the user and formats it as input data for the AI ​​algorithm.

[1336] Output: Formatted data

[1337] Step 2: Generate a rival profile

[1338] The server uses an AI algorithm based on the data it receives about current production capacity and target production capacity to generate the optimal "rival robot."

[1339] Input: Formatted data

[1340] Data calculation: Calculate the average of the current production capacity and the target production capacity, or an intermediate value using a specific algorithm.

[1341] Output: The production capacity of the spawned rival robot (e.g. 15 units per hour).

[1342] Step 3: Provide a picture of your competitors

[1343] The server generates rival image data, which is then sent to a terminal and displayed on the factory robot's display. The robot then recognizes the rival as a competitor based on this image data.

[1344] Input: Production capacity value of the generated rival robot

[1345] Data processing: Format the rival image data into the appropriate format and send it to the device.

[1346] Output: Information about the rival image displayed on the device

[1347] Step 4: Goal decomposition and progress simulation

[1348] The server breaks down the goal into small steps (e.g., "increase production by two units each week") and simulates the progress of rival robots, displaying it on the terminal.

[1349] Input: Target production capacity, Current production capacity

[1350] Data calculation: Calculate the steps to achieve the goal on a weekly basis and generate simulation data.

[1351] Output: Simulation data, progress information displayed on the terminal

[1352] Step 5: Analyzing the robot's emotional state

[1353] The server collects the robot's operational data (battery status, operational load, etc.) and uses this data to analyze its emotional state, which is then classified as "stressed" or "comfortable."

[1354] Input: Operating data (battery status, operating load, etc.)

[1355] Data calculation: The emotion engine analyzes behavior data and converts it into a quantitative emotional state.

[1356] Output: Emotional state (e.g., stress, comfort)

[1357] Step 6: Adjusting the progress of your rival's image based on their emotional state

[1358] The server adjusts the progress of the rival statue based on the analyzed emotional state, for example, if the emotional state is "stressed," the server slows down the pace of the rival statue's progress.

[1359] Input: Emotional state, simulation data

[1360] Data calculation: An algorithm is applied that adjusts the pace of progress depending on the emotional state.

[1361] Output: Adjusted simulation data

[1362] Step 7: Tracking growth

[1363] The factory robot inputs daily production performance data and sends it from the terminal to the server. The server analyzes the progress data and updates the image of the rival. This updated information is then sent back to the terminal and displayed.

[1364] Input: Daily production data

[1365] Data calculation: Compare progress data with rival image data and update the rival image as needed.

[1366] Output: Updated rival image information, updated content displayed on the device

[1367] The above processing steps enable optimal production management that takes into account work load and stress levels while maximizing the production efficiency of factory robots.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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).

[1375] 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.

[1376] 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."

[1377] 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.

[1378] 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).

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] The following is further disclosed regarding the above embodiment.

[1390] (Claim 1)

[1391] a means for inputting the user's current situation and goals;

[1392] A means for generating a rival image based on input data;

[1393] a means for providing data of the generated rival image to a user;

[1394] a means for receiving the user's progress data and comparing it with the progress of a rival image;

[1395] A means for updating the rival image based on the comparison results;

[1396] A means for re-providing updated rival profile information to users;

[1397] A system including:

[1398] (Claim 2)

[1399] 10. The system of claim 1, further comprising means for breaking down a goal into small steps and reflecting that information in a rival image.

[1400] (Claim 3)

[1401] 2. The system according to claim 1, further comprising means for simulating the progress of the rival image and making it visible to the user.

[1402] "Example 1"

[1403] (Claim 1)

[1404] a means for inputting the user's current situation and goals;

[1405] A generating AI model means for generating a rival image based on input data;

[1406] a means for providing data of the generated rival image to a user;

[1407] a means for receiving the user's progress data and comparing it with the progress of a rival image;

[1408] A means for updating the rival image based on the comparison results;

[1409] A means for re-providing updated rival profile information to users;

[1410] A system including:

[1411] (Claim 2)

[1412] 2. The system of claim 1, further comprising means for breaking down input goals into small steps and reflecting the information in the rival image.

[1413] (Claim 3)

[1414] 2. The system according to claim 1, further comprising means for simulating the progress of the rival image and making it visible to the user.

[1415] "Application Example 1"

[1416] (Claim 1)

[1417] a means for inputting the user's current situation and goals;

[1418] A means for generating a rival image based on input data;

[1419] a means for providing data of the generated rival image to a user;

[1420] a means for receiving the user's progress data and comparing it with the progress of a rival image;

[1421] A means for updating the rival image based on the comparison results;

[1422] A means for re-providing updated rival profile information to users;

[1423] A means for inputting a current work efficiency and a target efficiency of a robot in a factory environment;

[1424] means for generating a rival robot image based on the work efficiency of the robot;

[1425] means for providing data of the generated rival robot image to a display device of the robot;

[1426] A means of tracking and providing feedback to users on their progress;

[1427] A system including:

[1428] (Claim 2)

[1429] 10. The system of claim 1, further comprising means for breaking down a goal into small steps and reflecting that information in a rival image.

[1430] (Claim 3)

[1431] 2. The system according to claim 1, further comprising means for simulating the progress of the rival image and making it visible to the user.

[1432] "Example 2: Combining Emotion Engines"

[1433] (Claim 1)

[1434] a means for inputting the user's current situation and goals;

[1435] A means for generating a competitive image based on input data;

[1436] A means for providing data of the generated competitive image to a user;

[1437] means for receiving the user's progress data and comparing it with the progress of a competitive image;

[1438] means for updating the competitive profile based on the comparison results;

[1439] means for providing the updated competitive profile information back to the user;

[1440] A means of collecting and analyzing user emotional data and adjusting the progress and target steps of the competitive image;

[1441] A system including:

[1442] (Claim 2)

[1443] 10. The system of claim 1, further comprising means for breaking down goals into small steps and incorporating that information into a competitive landscape.

[1444] (Claim 3)

[1445] 10. The system of claim 1, further comprising means for simulating and visualizing the progress of the competitive image to a user.

[1446] (Claim 4)

[1447] 10. The system of claim 1, further comprising means for analyzing the user's emotional data and adjusting the competitive image progress or goal steps based on the data.

[1448] "Application example 2 when combining emotion engines"

[1449] (Claim 1)

[1450] a means for inputting the user's current situation and goals;

[1451] A means for generating a rival image based on input data;

[1452] a means for providing data of the generated rival image to a user;

[1453] a means for receiving the user's progress data and comparing it with the progress of a rival image;

[1454] A means for updating the rival image based on the comparison results;

[1455] A means for re-providing updated rival profile information to users;

[1456] a means for assessing the production capacity of the robot;

[1457] means for analyzing the emotional state of the robot;

[1458] means for adjusting the progress of the rival image based on the user's emotional state and production status;

[1459] A system including:

[1460] (Claim 2)

[1461] 10. The system of claim 1, further comprising means for breaking down a goal into small steps and reflecting that information in a rival image.

[1462] (Claim 3)

[1463] 2. The system according to claim 1, further comprising means for simulating the progress of the rival image and making it visible to the user. [Explanation of symbols]

[1464] 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. a means for inputting the user's current situation and goals; A means for generating a rival image based on input data; a means for providing data of the generated rival image to a user; a means for receiving the user's progress data and comparing it with the progress of a rival image; A means for updating the rival image based on the comparison results; A means for re-providing updated rival profile information to users; A system including:

2. 2. The system of claim 1, further comprising means for breaking down a goal into small steps and reflecting that information in a rival image.

3. The system according to claim 1, further comprising means for simulating the progress of the rival image and making it visible to the user.

Images