System

Abstract

An object of a system according to an embodiment is to increase the continuity of rehabilitation and prevent social isolation.SOLUTION: A system according to an embodiment includes a rehabilitation plan generation unit, an interactive practice unit, a gamification unit, a progress tracking unit, a community unit, and an operability improvement unit. The rehabilitation plan generation unit generates a personalized rehabilitation plan using a AI technique. The interactive training unit performs rehabilitation training through an interaction with the user based on the rehabilitation plan generated by the rehabilitation plan generation unit. The gamification unit makes it possible to pleasantly continue the rehabilitation training performed by the interactive training unit. The progress tracking unit tracks the progress of the rehabilitation performed by the gamification unit. The community unit shares the progress of the rehabilitation tracked by the progress tracking unit among the users and provides support and communication. The operability improvement unit realizes the support and interaction provided by the community unit with intuitive operability and multilingual compatibility.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 technology has faced challenges such as difficulty in maintaining continuity in rehabilitation and motivation, and a lack of support to prevent social isolation.

[0005] The system according to the embodiment aims to improve the continuity of rehabilitation and prevent social isolation. [Means for solving the problem]

[0006] The system according to the embodiment includes a rehabilitation plan generation unit, an interactive practice unit, a gamification unit, a progress tracking unit, a community unit, and an operability improvement unit. The rehabilitation plan generation unit generates an individualized rehabilitation plan using AI technology. The interactive practice unit conducts rehabilitation practice through dialogue with the user based on the rehabilitation plan generated by the rehabilitation plan generation unit. The gamification unit enables the rehabilitation practice conducted by the interactive practice unit to be enjoyable and continuous. The progress tracking unit tracks the rehabilitation progress conducted by the gamification unit. The community unit allows users to share the rehabilitation progress tracked by the progress tracking unit with each other, providing support and interaction. The operability improvement unit realizes the support and interaction provided by the community unit with intuitive operability and multilingual support. [Effects of the Invention]

[0007] The system according to the embodiment can improve the continuity of rehabilitation and prevent social isolation. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A rehabilitation platform according to an embodiment of the present invention is a system that utilizes AI technology to provide a personalized rehabilitation plan and interactive practice to improve a user's communication skills, thereby improving the user's communication skills and enabling them to continue rehabilitation in an enjoyable manner.

[0029] A rehabilitation platform according to an embodiment includes a rehabilitation plan generation unit, an interactive practice unit, a gamification unit, a progress tracking unit, a community unit, and an operability improvement unit. The rehabilitation plan generation unit generates an individualized rehabilitation plan using AI technology. For example, the generation AI creates a rehabilitation plan based on assessment data of the user's language ability and speech function. The generation AI can also analyze the user's past rehabilitation data and propose a rehabilitation plan that takes long-term progress into consideration. The generation AI can also collect data on the user's lifestyle habits and daily activities and adjust the rehabilitation plan based on that data. The interactive practice unit conducts rehabilitation practice through dialogue with the user based on the rehabilitation plan generated by the rehabilitation plan generation unit. For example, the generation AI poses questions to the user, and the user responds to the questions. The generation AI can also analyze the user's speech, point out pronunciation and grammatical errors in real time, and suggest corrections. The generation AI can also learn the user's dialogue history and provide practice tailored to the user's individual dialogue style. The gamification unit enables the user to continue the rehabilitation practice conducted by the interactive practice unit in an enjoyable manner. For example, a system can be introduced in which points are accumulated each time a rehabilitation exercise is completed, and a reward is given when a certain number of points are reached. It is also possible to introduce game elements that allow users to explore a virtual world according to the progress of their rehabilitation exercise. Furthermore, a system can be provided in which users can develop a customizable avatar based on the results of their rehabilitation exercise. The progress tracking unit tracks the rehabilitation progress made by the gamification unit. For example, it can record how much practice the user has performed and what results they have achieved, and visually display this in graphs and charts. It is also possible to record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data. Furthermore, it is also possible to introduce an algorithm that analyzes the user's rehabilitation progress and predicts future progress. The community unit allows users to share the rehabilitation progress tracked by the progress tracking unit with each other, providing support and interaction.For example, a bulletin board for sharing rehabilitation progress and results, or a chat room for users with the same goals to encourage each other, can be provided. Events can also be held periodically where users compete against each other on their rehabilitation progress. Furthermore, online seminars can be provided where users can present their rehabilitation results. The operability improvement unit realizes the support and interaction provided by the community unit with intuitive operability and multilingual support. For example, large buttons and a simple menu structure can be used to make the platform easy to use, even for elderly people and those unfamiliar with technology. A function for automatically switching the interface depending on the language selected by the user can also be provided. Furthermore, a help function can be provided to provide real-time support when users have difficulty operating the platform. This allows the rehabilitation platform according to the embodiment to improve users' communication skills and make rehabilitation enjoyable and continuous. For example, users can check their progress and maintain motivation throughout rehabilitation. Furthermore, the community function allows users to interact with other users and prevent social isolation. Furthermore, the intuitive operability and multilingual support make the platform easy to use, even for elderly people and those unfamiliar with technology.

[0030] The rehabilitation plan generation unit can analyze the user's past rehabilitation data and propose a rehabilitation plan that takes long-term progress into consideration. In the rehabilitation plan generation unit, for example, the generation AI collects the user's past rehabilitation data and performs data analysis. For example, it identifies the user's progress pattern based on past practice content and results, and creates a long-term rehabilitation plan. The generation AI proposes a rehabilitation plan that takes long-term progress into consideration based on the user's past rehabilitation data. For example, it analyzes the results of the user's past rehabilitation and adjusts future rehabilitation plans. This makes it possible to propose a rehabilitation plan that takes long-term progress into consideration based on the user's past rehabilitation data.

[0031] The rehabilitation plan generation unit can collect data on the user's lifestyle habits and daily activities and adjust the rehabilitation plan based on that. In the rehabilitation plan generation unit, for example, the generation AI collects the user's lifestyle habits data and reflects it in the rehabilitation plan. For example, it analyzes the user's sleep patterns and eating habits and suggests the optimal rehabilitation time. The generation AI collects the user's daily activity data and adjusts the rehabilitation plan based on that. For example, it analyzes the user's number of steps and activity time and adjusts the rehabilitation menu. This allows the rehabilitation plan to be adjusted based on the user's lifestyle habits and daily activity data.

[0032] The rehabilitation plan generation unit can incorporate feedback from the user's family and caregivers and reflect it in the rehabilitation plan. In the rehabilitation plan generation unit, for example, the generation AI collects feedback from the user's family and caregivers and reflects it in the rehabilitation plan. For example, the rehabilitation menu is adjusted based on changes in the user's behavior and emotions provided by the family. The generation AI adjusts the rehabilitation plan based on feedback from the family and caregivers. For example, it reflects the user's progress and challenges observed by the family. This makes it possible to adjust the rehabilitation plan based on feedback from the family and caregivers.

[0033] The interactive practice unit can analyze the user's speech content, point out pronunciation and grammatical errors in real time, and make correction suggestions. In the interactive practice unit, for example, the generation AI analyzes the user's speech content in real time and points out pronunciation and grammatical errors. For example, it analyzes audio data of words pronounced by the user and presents the correct pronunciation. The generation AI points out the user's grammatical errors and makes correction suggestions. For example, if the user uses incorrect grammar, it presents the correct grammar. This makes it possible to analyze the user's speech content, point out pronunciation and grammatical errors in real time, and make correction suggestions.

[0034] The interactive practice unit can learn the user's dialogue history and provide practice tailored to the individual dialogue style. In the interactive practice unit, for example, the generation AI collects the user's dialogue history and provides practice tailored to the individual dialogue style. For example, it analyzes the content of the user's past dialogues and creates an appropriate practice menu. The generation AI provides practice tailored to the user's dialogue style. For example, it adjusts the practice based on the user's preferred dialogue format and topic. This makes it possible to learn the user's dialogue history and provide practice tailored to the individual dialogue style.

[0035] The interactive practice unit can set different scenarios and situations, allowing the user to practice in a variety of communication situations. For example, the generation AI sets different scenarios and situations, allowing the user to practice in a variety of communication situations. For example, different scenarios such as everyday conversations and business situations are provided. The generation AI adjusts the scenario so that the user can practice in a variety of communication situations. For example, if the user wishes to practice in a specific scenario, practice based on that scenario is provided. This allows the user to practice in a variety of communication situations.

[0036] The interactive practice unit can generate a virtual character as a conversation partner for the user, providing more realistic conversation practice. In the interactive practice unit, for example, a generation AI generates a virtual character and sets it as the user's conversation partner. For example, it generates a character of a friend or family member and provides conversation practice. The generation AI generates a virtual character as the user's conversation partner. For example, if the user wishes to have a conversation with a specific character, it generates that character and provides conversation practice. This allows more realistic conversation practice to be provided to the user.

[0037] The gamification unit can introduce game elements that allow the user to explore the virtual world according to the progress of rehabilitation practice. The gamification unit, for example, introduces game elements that allow the user to explore the virtual world according to the progress of rehabilitation practice. For example, a mechanism is provided in which a new area is unlocked each time a practice is completed. The generation AI adjusts the virtual world according to the user's progress. For example, if the user wants to explore a specific area, the generation AI unlocks that area. This allows the introduction of game elements that allow the user to explore the virtual world according to the progress of rehabilitation practice.

[0038] The gamification unit can provide a mechanism for a user to grow a customizable avatar based on the results of rehabilitation practice. The gamification unit can provide a mechanism for a user to grow a customizable avatar based on the results of rehabilitation practice, for example. For example, the avatar's appearance and abilities can be customized each time a practice is completed. The generation AI adjusts the avatar based on the user's results. For example, if the user achieves a specific result, the avatar grows in accordance with that result. This can provide a mechanism for a user to grow a customizable avatar based on the results of rehabilitation practice.

[0039] The gamification unit can introduce a ranking system in which users compete with other users based on the results of their rehabilitation practice. The gamification unit, for example, introduces a ranking system in which users compete with other users based on the results of their rehabilitation practice. For example, the results of practice are scored and a ranking is displayed. The generation AI adjusts the ranking based on the user's results. For example, if a user achieves a specific result, the ranking is updated according to that result. This makes it possible to introduce a ranking system in which users compete with other users based on the results of their rehabilitation practice.

[0040] The gamification unit can provide a mechanism that allows the user to earn virtual rewards according to the progress of rehabilitation practice. The gamification unit provides a mechanism that allows the user to earn virtual rewards according to the progress of rehabilitation practice, for example. For example, each time a practice is completed, virtual coins or items can be earned. The generation AI adjusts the reward according to the user's progress. For example, if the user achieves a specific progress, a reward is provided according to that progress. This makes it possible to provide a mechanism that allows the user to earn virtual rewards according to the progress of rehabilitation practice.

[0041] The progress tracking unit can record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data. The progress tracking unit can, for example, record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data. For example, it can display the results of practice in graphs and charts. The generation AI can analyze the user's progress and visualize the progress by comparing it with past data. For example, it can predict future progress based on the results of the user's past rehabilitation. This makes it possible to record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data.

[0042] The progress tracking unit can introduce an algorithm that analyzes the user's rehabilitation progress and predicts future progress. The progress tracking unit, for example, analyzes the user's rehabilitation progress and introduces an algorithm that predicts future progress. For example, future progress is predicted based on past data. The generation AI analyzes the user's progress and predicts future progress. For example, future progress is predicted based on the results of rehabilitation the user has undergone in the past. This makes it possible to introduce an algorithm that analyzes the user's rehabilitation progress and predicts future progress.

[0043] The progress tracking unit can provide a mechanism for sharing the user's rehabilitation progress with other users and encouraging each other. The progress tracking unit, for example, provides a mechanism for sharing the user's rehabilitation progress with other users and encouraging each other. For example, it provides a bulletin board or chat room for sharing progress. The generation AI provides a mechanism for sharing the user's progress and encouraging each other. For example, the user posts progress and receives comments and supportive messages from other users. This can provide a mechanism for sharing the user's rehabilitation progress with other users and encouraging each other.

[0044] The progress tracking unit can share the user's rehabilitation progress with family and caregivers, making it easier to receive support. The progress tracking unit, for example, provides a mechanism for sharing the user's rehabilitation progress with family and caregivers, making it easier to receive support. For example, sharing progress makes it easier to receive support from family and caregivers. The generation AI shares the user's progress with family and caregivers, making it easier to receive support. For example, the user shares progress and receives feedback from family and caregivers. This allows the user's rehabilitation progress to be shared with family and caregivers, making it easier to receive support.

[0045] The community unit can provide a dedicated bulletin board where users can share their rehabilitation progress and results with each other. The community unit, for example, provides a dedicated bulletin board where users can share their rehabilitation progress and results with each other. For example, users can post their progress and receive comments and messages of support from other users. The generation AI provides a bulletin board where users can share their progress and results. For example, users can post their progress and receive feedback from other users. This makes it possible to provide a dedicated bulletin board where users can share their rehabilitation progress and results with each other.

[0046] The community unit can provide a chat room where users can interact with each other in real time. The community unit, for example, provides a chat room where users can interact with each other in real time. For example, users can discuss rehabilitation progress and results in real time. The generation AI provides a chat room where users can interact with each other in real time. For example, users can interact in real time in the chat room and discuss rehabilitation progress and results. This provides a chat room where users can interact with each other in real time.

[0047] The community unit can periodically hold events where users compete against each other to see who is the most advanced in their rehabilitation. The community unit, for example, periodically holds events where users compete against each other to see who is the most advanced in their rehabilitation. For example, a special reward is provided to the user who makes the most progress within a specific period of time. The generation AI adjusts the events based on the user's progress. For example, if a user achieves a specific progress, an event is held according to that progress. In this way, it is possible to periodically hold events where users compete against each other to see who is the most advanced in their rehabilitation.

[0048] The community unit can provide online seminars where users can share their rehabilitation results with each other. The community unit, for example, provides online seminars where users can share their rehabilitation results with each other. For example, online seminars can be held regularly to provide a forum for users to share their results. The generation AI adjusts the online seminars based on the user's results. For example, if a user achieves a specific result, an online seminar can be held in accordance with that result. This makes it possible to provide online seminars where users can share their rehabilitation results with each other.

[0049] The operability improvement unit can introduce an algorithm that analyzes the user's operation history and suggests the optimal operation method. The operability improvement unit, for example, introduces an algorithm that analyzes the user's operation history and suggests the optimal operation method. For example, the unit suggests the optimal operation method for the user based on the past operation history. The generation AI adjusts the operation method based on the user's operation history. For example, if the user has difficulty with a particular operation, the generation AI makes suggestions to simplify that operation. This makes it possible to introduce an algorithm that analyzes the user's operation history and suggests the optimal operation method.

[0050] The operability improvement unit can provide a function of automatically switching the interface depending on the language selected by the user for multilingual support. The operability improvement unit can provide a function of automatically switching the interface depending on the language selected by the user for multilingual support. For example, when the user selects a language, the interface automatically switches to that language. The generation AI adjusts the interface based on the user's language settings. For example, when the user selects a specific language, the generation AI provides an interface corresponding to that language. This makes it possible to provide a function of automatically switching the interface depending on the language selected by the user.

[0051] The operability improvement unit can provide a help function that allows the user to receive support in real time when the user has difficulty operating the device. The operability improvement unit, for example, provides a help function that allows the user to receive support in real time when the user has difficulty operating the device. For example, it makes it possible to use a chatbot or online support. The generation AI adjusts the help function based on the user's operation. For example, if the user has difficulty with a particular operation, it provides support for that operation. This makes it possible to provide a help function that allows the user to receive support in real time when the user has difficulty operating the device.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The rehabilitation plan generation unit can customize a rehabilitation plan taking into account the user's hobbies and interests. For example, if the user is interested in music, a rehabilitation menu incorporating music can be suggested. If the user is interested in sports, rehabilitation exercises related to sports can be offered. Furthermore, if the user is interested in art, rehabilitation activities through art can be suggested. This makes it possible to provide a rehabilitation plan that reflects the user's hobbies and interests.

[0054] The rehabilitation plan generation unit can monitor the user's physical condition in real time and adjust the rehabilitation plan based on that. For example, it can monitor the user's heart rate and blood pressure and suggest appropriate exercise intensity. It can also detect the user's muscle movements with a sensor and provide an optimal rehabilitation menu. It can also measure the user's fatigue level and suggest rest periods. This makes it possible to provide a rehabilitation plan that suits the user's physical condition.

[0055] The rehabilitation plan generation unit can adjust the rehabilitation plan taking into account the user's social environment. For example, if the user lives with their family, the unit can suggest a rehabilitation menu to be carried out in cooperation with the family. If the user lives alone, the unit can also provide rehabilitation activities to reduce feelings of loneliness. Furthermore, if the user wishes to undergo rehabilitation at work, the unit can create a rehabilitation plan suited to the work environment. This makes it possible to provide a rehabilitation plan that suits the user's social environment.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The rehabilitation plan generation unit uses AI technology to generate an individualized rehabilitation plan. For example, the generation AI creates a rehabilitation plan based on evaluation data of the user's language ability and speech function. The generation AI can also analyze the user's past rehabilitation data and propose a rehabilitation plan that takes long-term progress into consideration. Furthermore, the generation AI can collect data on the user's lifestyle habits and daily activities and adjust the rehabilitation plan based on that data. Step 2: The interactive practice unit conducts rehabilitation practice through dialogue with the user based on the rehabilitation plan generated by the rehabilitation plan generation unit. For example, the generation AI may ask the user questions, and the user may respond to the questions to progress the practice. The generation AI can also analyze the user's speech, point out pronunciation and grammar errors in real time, and suggest corrections. Furthermore, the generation AI can learn the user's dialogue history and provide practice tailored to the user's individual dialogue style. Step 3: The gamification unit makes the rehabilitation exercises performed by the interactive exercise unit fun and continuous. For example, a system can be introduced in which points are accumulated each time a rehabilitation exercise is completed, and rewards are given when a certain number of points are reached. It is also possible to introduce game elements that allow users to explore a virtual world according to the progress of their rehabilitation exercises. Furthermore, it is also possible to provide a system that allows users to develop a customizable avatar based on the results of their rehabilitation exercises. Step 4: The progress tracking unit tracks the rehabilitation progress made by the gamification unit. For example, it records how much practice the user has done and what results they have achieved, and visually displays this in graphs and charts. It can also record the user's rehabilitation progress in detail and visualize it by comparing it with past data. It can also introduce algorithms that analyze the user's rehabilitation progress and predict future progress. Step 5: The community section allows users to share their rehabilitation progress tracked by the progress tracking section, and provides support and interaction. For example, it can provide a bulletin board where users can share their rehabilitation progress and results, or a chat room where users with the same goals can encourage each other. It can also periodically hold events where users compete against each other on their rehabilitation progress. It can also provide online seminars where users can present their rehabilitation results to each other. Step 6: The Usability Improvement Department makes the support and interaction provided by the Community Department intuitive and multilingual. For example, it can use large buttons and a simple menu structure to make it easy to use even for elderly people and those who are not familiar with technology. It can also provide a function that automatically switches the interface depending on the language selected by the user. It can also provide a help function that allows users to receive real-time support when they have trouble operating the device.

[0058] (Example 2) A rehabilitation platform according to an embodiment of the present invention is a system that utilizes AI technology to provide a personalized rehabilitation plan and interactive practice to improve a user's communication skills, thereby improving the user's communication skills and enabling them to continue rehabilitation in an enjoyable manner.

[0059] A rehabilitation platform according to an embodiment includes a rehabilitation plan generation unit, an interactive practice unit, a gamification unit, a progress tracking unit, a community unit, and an operability improvement unit. The rehabilitation plan generation unit generates an individualized rehabilitation plan using AI technology. For example, the generation AI creates a rehabilitation plan based on assessment data of the user's language ability and speech function. The generation AI can also analyze the user's past rehabilitation data and propose a rehabilitation plan that takes long-term progress into consideration. The generation AI can also collect data on the user's lifestyle habits and daily activities and adjust the rehabilitation plan based on that data. The interactive practice unit conducts rehabilitation practice through dialogue with the user based on the rehabilitation plan generated by the rehabilitation plan generation unit. For example, the generation AI poses questions to the user, and the user responds to the questions. The generation AI can also analyze the user's speech, point out pronunciation and grammatical errors in real time, and suggest corrections. The generation AI can also learn the user's dialogue history and provide practice tailored to the user's individual dialogue style. The gamification unit enables the user to continue the rehabilitation practice conducted by the interactive practice unit in an enjoyable manner. For example, a system can be introduced in which points are accumulated each time a rehabilitation exercise is completed, and a reward is given when a certain number of points are reached. It is also possible to introduce game elements that allow users to explore a virtual world according to the progress of their rehabilitation exercise. Furthermore, a system can be provided in which users can develop a customizable avatar based on the results of their rehabilitation exercise. The progress tracking unit tracks the rehabilitation progress made by the gamification unit. For example, it can record how much practice the user has performed and what results they have achieved, and visually display this in graphs and charts. It is also possible to record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data. Furthermore, it is also possible to introduce an algorithm that analyzes the user's rehabilitation progress and predicts future progress. The community unit allows users to share the rehabilitation progress tracked by the progress tracking unit with each other, providing support and interaction.For example, a bulletin board for sharing rehabilitation progress and results, or a chat room for users with the same goals to encourage each other, can be provided. Events can also be held periodically where users compete against each other on their rehabilitation progress. Furthermore, online seminars can be provided where users can present their rehabilitation results. The operability improvement unit realizes the support and interaction provided by the community unit with intuitive operability and multilingual support. For example, large buttons and a simple menu structure can be used to make the platform easy to use, even for elderly people and those unfamiliar with technology. A function for automatically switching the interface depending on the language selected by the user can also be provided. Furthermore, a help function can be provided to provide real-time support when users have difficulty operating the platform. This allows the rehabilitation platform according to the embodiment to improve users' communication skills and make rehabilitation enjoyable and continuous. For example, users can check their progress and maintain motivation throughout rehabilitation. Furthermore, the community function allows users to interact with other users and prevent social isolation. Furthermore, the intuitive operability and multilingual support make the platform easy to use, even for elderly people and those unfamiliar with technology.

[0060] The rehabilitation plan generation unit can analyze the user's past rehabilitation data and propose a rehabilitation plan that takes long-term progress into consideration. In the rehabilitation plan generation unit, for example, the generation AI collects the user's past rehabilitation data and performs data analysis. For example, it identifies the user's progress pattern based on past practice content and results, and creates a long-term rehabilitation plan. The generation AI proposes a rehabilitation plan that takes long-term progress into consideration based on the user's past rehabilitation data. For example, it analyzes the results of the user's past rehabilitation and adjusts future rehabilitation plans. This makes it possible to propose a rehabilitation plan that takes long-term progress into consideration based on the user's past rehabilitation data.

[0061] The rehabilitation plan generation unit can collect data on the user's lifestyle habits and daily activities and adjust the rehabilitation plan based on that. In the rehabilitation plan generation unit, for example, the generation AI collects the user's lifestyle habits data and reflects it in the rehabilitation plan. For example, it analyzes the user's sleep patterns and eating habits and suggests the optimal rehabilitation time. The generation AI collects the user's daily activity data and adjusts the rehabilitation plan based on that. For example, it analyzes the user's number of steps and activity time and adjusts the rehabilitation menu. This allows the rehabilitation plan to be adjusted based on the user's lifestyle habits and daily activity data.

[0062] The rehabilitation plan generation unit can use the emotion estimation function to analyze the user's emotional state in real time and dynamically change the rehabilitation plan according to the emotion. The rehabilitation plan generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI dynamically changes the rehabilitation plan according to the user's emotional state. For example, if the user is feeling stressed, it adjusts the rehabilitation menu and suggests relaxation exercises. This allows the rehabilitation plan to be dynamically changed according to the user's emotional state.

[0063] The rehabilitation plan generation unit can incorporate feedback from the user's family and caregivers and reflect it in the rehabilitation plan. In the rehabilitation plan generation unit, for example, the generation AI collects feedback from the user's family and caregivers and reflects it in the rehabilitation plan. For example, the rehabilitation menu is adjusted based on changes in the user's behavior and emotions provided by the family. The generation AI adjusts the rehabilitation plan based on feedback from the family and caregivers. For example, it reflects the user's progress and challenges observed by the family. This makes it possible to adjust the rehabilitation plan based on feedback from the family and caregivers.

[0064] The rehabilitation plan generation unit uses the emotion estimation function to analyze how the user feels about the rehabilitation plan and can make suggestions to elicit positive emotions. The rehabilitation plan generation unit, for example, uses the emotion estimation function to analyze how the user feels about the rehabilitation plan. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI analyzes the user's emotions and makes suggestions to elicit positive emotions. For example, if the user feels anxious about rehabilitation, it displays an encouraging message. This makes it possible to analyze the user's emotions and make suggestions to elicit positive emotions.

[0065] The interactive practice unit can analyze the user's speech content, point out pronunciation and grammatical errors in real time, and make correction suggestions. In the interactive practice unit, for example, the generation AI analyzes the user's speech content in real time and points out pronunciation and grammatical errors. For example, it analyzes audio data of words pronounced by the user and presents the correct pronunciation. The generation AI points out the user's grammatical errors and makes correction suggestions. For example, if the user uses incorrect grammar, it presents the correct grammar. This makes it possible to analyze the user's speech content, point out pronunciation and grammatical errors in real time, and make correction suggestions.

[0066] The interactive practice unit can learn the user's dialogue history and provide practice tailored to the individual dialogue style. In the interactive practice unit, for example, the generation AI collects the user's dialogue history and provides practice tailored to the individual dialogue style. For example, it analyzes the content of the user's past dialogues and creates an appropriate practice menu. The generation AI provides practice tailored to the user's dialogue style. For example, it adjusts the practice based on the user's preferred dialogue format and topic. This makes it possible to learn the user's dialogue history and provide practice tailored to the individual dialogue style.

[0067] The interactive practice unit uses the emotion estimation function to generate dialogue content according to the user's emotional state, thereby maximizing the effectiveness of rehabilitation. The interactive practice unit, for example, uses the emotion estimation function to generate dialogue content according to the user's emotional state. For example, if the user is relaxed, dialogue content with a relaxing atmosphere is provided. The generation AI adjusts the dialogue content according to the user's emotional state. For example, if the user is feeling stressed, dialogue content that helps the user to relax is provided. This allows dialogue content according to the user's emotional state to be generated, thereby maximizing the effectiveness of rehabilitation.

[0068] The interactive practice unit can set different scenarios and situations, allowing the user to practice in a variety of communication situations. For example, the generation AI sets different scenarios and situations, allowing the user to practice in a variety of communication situations. For example, different scenarios such as everyday conversations and business situations are provided. The generation AI adjusts the scenario so that the user can practice in a variety of communication situations. For example, if the user wishes to practice in a specific scenario, practice based on that scenario is provided. This allows the user to practice in a variety of communication situations.

[0069] The interactive practice unit can generate a virtual character as a conversation partner for the user, providing more realistic conversation practice. In the interactive practice unit, for example, a generation AI generates a virtual character and sets it as the user's conversation partner. For example, it generates a character of a friend or family member and provides conversation practice. The generation AI generates a virtual character as the user's conversation partner. For example, if the user wishes to have a conversation with a specific character, it generates that character and provides conversation practice. This allows more realistic conversation practice to be provided to the user.

[0070] The gamification unit can introduce game elements that allow the user to explore the virtual world according to the progress of rehabilitation practice. The gamification unit, for example, introduces game elements that allow the user to explore the virtual world according to the progress of rehabilitation practice. For example, a mechanism is provided in which a new area is unlocked each time a practice is completed. The generation AI adjusts the virtual world according to the user's progress. For example, if the user wants to explore a specific area, the generation AI unlocks that area. This allows the introduction of game elements that allow the user to explore the virtual world according to the progress of rehabilitation practice.

[0071] The gamification unit can provide a mechanism for a user to grow a customizable avatar based on the results of rehabilitation practice. The gamification unit can provide a mechanism for a user to grow a customizable avatar based on the results of rehabilitation practice, for example. For example, the avatar's appearance and abilities can be customized each time a practice is completed. The generation AI adjusts the avatar based on the user's results. For example, if the user achieves a specific result, the avatar grows in accordance with that result. This can provide a mechanism for a user to grow a customizable avatar based on the results of rehabilitation practice.

[0072] The gamification unit uses the emotion estimation function to dynamically adjust game elements according to the user's emotional state, thereby maintaining motivation. The gamification unit, for example, uses the emotion estimation function to dynamically adjust game elements according to the user's emotional state. For example, if the user is feeling stressed, the difficulty of the game is lowered. The generation AI adjusts game elements according to the user's emotional state. For example, if the user is relaxed, it provides game elements that help the user relax. This allows the game elements to be dynamically adjusted according to the user's emotional state, thereby maintaining motivation.

[0073] The gamification unit can introduce a ranking system in which users compete with other users based on the results of their rehabilitation practice. The gamification unit, for example, introduces a ranking system in which users compete with other users based on the results of their rehabilitation practice. For example, the results of practice are scored and a ranking is displayed. The generation AI adjusts the ranking based on the user's results. For example, if a user achieves a specific result, the ranking is updated according to that result. This makes it possible to introduce a ranking system in which users compete with other users based on the results of their rehabilitation practice.

[0074] The gamification unit can provide a mechanism that allows the user to earn virtual rewards according to the progress of rehabilitation practice. The gamification unit provides a mechanism that allows the user to earn virtual rewards according to the progress of rehabilitation practice, for example. For example, each time a practice is completed, virtual coins or items can be earned. The generation AI adjusts the reward according to the user's progress. For example, if the user achieves a specific progress, a reward is provided according to that progress. This makes it possible to provide a mechanism that allows the user to earn virtual rewards according to the progress of rehabilitation practice.

[0075] The progress tracking unit can record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data. The progress tracking unit can, for example, record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data. For example, it can display the results of practice in graphs and charts. The generation AI can analyze the user's progress and visualize the progress by comparing it with past data. For example, it can predict future progress based on the results of the user's past rehabilitation. This makes it possible to record the user's rehabilitation progress in detail and visualize the progress by comparing it with past data.

[0076] The progress tracking unit can introduce an algorithm that analyzes the user's rehabilitation progress and predicts future progress. The progress tracking unit, for example, analyzes the user's rehabilitation progress and introduces an algorithm that predicts future progress. For example, future progress is predicted based on past data. The generation AI analyzes the user's progress and predicts future progress. For example, future progress is predicted based on the results of rehabilitation the user has undergone in the past. This makes it possible to introduce an algorithm that analyzes the user's rehabilitation progress and predicts future progress.

[0077] The progress tracking unit uses the emotion estimation function to provide progress feedback according to the user's emotional state, thereby maintaining motivation. The progress tracking unit, for example, uses the emotion estimation function to provide progress feedback according to the user's emotional state. For example, if the user is feeling positive, an encouraging message is displayed. The generation AI adjusts the feedback according to the user's emotional state. For example, if the user is feeling stressed, relaxation feedback is provided. In this way, progress feedback according to the user's emotional state is provided, thereby maintaining motivation.

[0078] The progress tracking unit can provide a mechanism for sharing the user's rehabilitation progress with other users and encouraging each other. The progress tracking unit, for example, provides a mechanism for sharing the user's rehabilitation progress with other users and encouraging each other. For example, it provides a bulletin board or chat room for sharing progress. The generation AI provides a mechanism for sharing the user's progress and encouraging each other. For example, the user posts progress and receives comments and supportive messages from other users. This can provide a mechanism for sharing the user's rehabilitation progress with other users and encouraging each other.

[0079] The progress tracking unit can share the user's rehabilitation progress with family and caregivers, making it easier to receive support. The progress tracking unit, for example, provides a mechanism for sharing the user's rehabilitation progress with family and caregivers, making it easier to receive support. For example, sharing progress makes it easier to receive support from family and caregivers. The generation AI shares the user's progress with family and caregivers, making it easier to receive support. For example, the user shares progress and receives feedback from family and caregivers. This allows the user's rehabilitation progress to be shared with family and caregivers, making it easier to receive support.

[0080] The community unit can provide a dedicated bulletin board where users can share their rehabilitation progress and results with each other. The community unit, for example, provides a dedicated bulletin board where users can share their rehabilitation progress and results with each other. For example, users can post their progress and receive comments and messages of support from other users. The generation AI provides a bulletin board where users can share their progress and results. For example, users can post their progress and receive feedback from other users. This makes it possible to provide a dedicated bulletin board where users can share their rehabilitation progress and results with each other.

[0081] The community unit can provide a chat room where users can interact with each other in real time. The community unit, for example, provides a chat room where users can interact with each other in real time. For example, users can discuss rehabilitation progress and results in real time. The generation AI provides a chat room where users can interact with each other in real time. For example, users can interact in real time in the chat room and discuss rehabilitation progress and results. This provides a chat room where users can interact with each other in real time.

[0082] The community unit uses the emotion estimation function to suggest community activities according to the user's emotional state, thereby preventing social isolation. The community unit, for example, uses the emotion estimation function to suggest community activities according to the user's emotional state. For example, if the user is feeling lonely, it suggests social events. The generation AI adjusts community activities based on the user's emotional state. For example, if the user is feeling positive, it suggests positive social events. This makes it possible to suggest community activities according to the user's emotional state and prevent social isolation.

[0083] The community unit can periodically hold events where users compete against each other to see who is the most advanced in their rehabilitation. The community unit, for example, periodically holds events where users compete against each other to see who is the most advanced in their rehabilitation. For example, a special reward is provided to the user who makes the most progress within a specific period of time. The generation AI adjusts the events based on the user's progress. For example, if a user achieves a specific progress, an event is held according to that progress. In this way, it is possible to periodically hold events where users compete against each other to see who is the most advanced in their rehabilitation.

[0084] The community unit can provide online seminars where users can share their rehabilitation results with each other. The community unit, for example, provides online seminars where users can share their rehabilitation results with each other. For example, online seminars can be held regularly to provide a forum for users to share their results. The generation AI adjusts the online seminars based on the user's results. For example, if a user achieves a specific result, an online seminar can be held in accordance with that result. This makes it possible to provide online seminars where users can share their rehabilitation results with each other.

[0085] The community unit uses the emotion estimation function to analyze the emotional state of the user when participating in community activities and can make suggestions to elicit positive emotions. The community unit, for example, uses the emotion estimation function to analyze the emotional state of the user when participating in community activities. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI makes suggestions to elicit positive emotions based on the user's emotional state. For example, if the user is feeling anxious, it suggests activities that will help them relax. This makes it possible to analyze the emotional state of the user when participating in community activities and make suggestions to elicit positive emotions.

[0086] The operability improvement unit can introduce an algorithm that analyzes the user's operation history and suggests the optimal operation method. The operability improvement unit, for example, introduces an algorithm that analyzes the user's operation history and suggests the optimal operation method. For example, the unit suggests the optimal operation method for the user based on the past operation history. The generation AI adjusts the operation method based on the user's operation history. For example, if the user has difficulty with a particular operation, the generation AI makes suggestions to simplify that operation. This makes it possible to introduce an algorithm that analyzes the user's operation history and suggests the optimal operation method.

[0087] The operability improvement unit can use the emotion estimation function to provide an interface for reducing the stress felt by the user during operation. The operability improvement unit, for example, uses the emotion estimation function to provide an interface for reducing the stress felt by the user during operation. For example, the operability improvement unit analyzes the user's facial expressions and voice and provides feedback for reducing stress. The generation AI adjusts the interface based on the user's emotional state. For example, if the user is feeling stressed, the generation AI provides an interface that allows the user to relax. This makes it possible to provide an interface for reducing the stress felt by the user during operation.

[0088] The operability improvement unit can provide a function of automatically switching the interface depending on the language selected by the user for multilingual support. The operability improvement unit can provide a function of automatically switching the interface depending on the language selected by the user for multilingual support. For example, when the user selects a language, the interface automatically switches to that language. The generation AI adjusts the interface based on the user's language settings. For example, when the user selects a specific language, the generation AI provides an interface corresponding to that language. This makes it possible to provide a function of automatically switching the interface depending on the language selected by the user.

[0089] The operability improvement unit can provide a help function that allows the user to receive support in real time when the user has difficulty operating the device. The operability improvement unit, for example, provides a help function that allows the user to receive support in real time when the user has difficulty operating the device. For example, it makes it possible to use a chatbot or online support. The generation AI adjusts the help function based on the user's operation. For example, if the user has difficulty with a particular operation, it provides support for that operation. This makes it possible to provide a help function that allows the user to receive support in real time when the user has difficulty operating the device.

[0090] The operability improvement unit can use the emotion estimation function to provide support to reduce the anxiety the user feels during operation. The operability improvement unit can use the emotion estimation function to provide support to reduce the anxiety the user feels during operation. For example, the operability improvement unit analyzes the user's facial expressions and voice and provides feedback to reduce anxiety. The generation AI adjusts the support based on the user's emotional state. For example, if the user feels anxious, the generation AI provides support to help the user relax. This makes it possible to provide support to reduce the anxiety the user feels during operation.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The rehabilitation plan generation unit can customize a rehabilitation plan taking into account the user's hobbies and interests. For example, if the user is interested in music, a rehabilitation menu incorporating music can be suggested. If the user is interested in sports, rehabilitation exercises related to sports can be offered. Furthermore, if the user is interested in art, rehabilitation activities through art can be suggested. This makes it possible to provide a rehabilitation plan that reflects the user's hobbies and interests.

[0093] The rehabilitation plan generation unit can monitor the user's physical condition in real time and adjust the rehabilitation plan based on that. For example, it can monitor the user's heart rate and blood pressure and suggest appropriate exercise intensity. It can also detect the user's muscle movements with a sensor and provide an optimal rehabilitation menu. It can also measure the user's fatigue level and suggest rest periods. This makes it possible to provide a rehabilitation plan that suits the user's physical condition.

[0094] The rehabilitation plan generation unit can adjust the rehabilitation plan taking into account the user's social environment. For example, if the user lives with their family, the unit can suggest a rehabilitation menu to be carried out in cooperation with the family. If the user lives alone, the unit can also provide rehabilitation activities to reduce feelings of loneliness. Furthermore, if the user wishes to undergo rehabilitation at work, the unit can create a rehabilitation plan suited to the work environment. This makes it possible to provide a rehabilitation plan that suits the user's social environment.

[0095] The rehabilitation plan generation unit can estimate the user's emotional state and reflect it in the rehabilitation plan. For example, if the user feels anxious about rehabilitation, it can suggest relaxing exercises. If the user is losing motivation, it can also display encouraging messages. Furthermore, if the user is feeling positive, it can provide a challenging rehabilitation menu. This makes it possible to provide a rehabilitation plan that suits the user's emotional state.

[0096] The rehabilitation plan generation unit can estimate the user's emotional state and dynamically change the rehabilitation plan according to the emotion. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user is tired, it can also provide a light rehabilitation menu. Furthermore, if the user is feeling positive emotions, it can also provide a challenging rehabilitation menu. This makes it possible to dynamically change the rehabilitation plan according to the user's emotional state.

[0097] The rehabilitation plan generation unit can estimate the user's emotional state and make suggestions to bring out positive emotions. For example, if the user is feeling anxious about rehabilitation, an encouraging message can be displayed. Also, if the user is losing motivation, a rehabilitation menu that will provide a successful experience can be suggested. Furthermore, if the user is feeling positive emotions, a rehabilitation menu that encourages further challenges can be provided. In this way, the user's emotions can be analyzed and suggestions can be made to bring out positive emotions.

[0098] The rehabilitation plan generation unit can estimate the user's emotional state and dynamically change the rehabilitation plan according to the emotion. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user is tired, it can also provide a light rehabilitation menu. Furthermore, if the user is feeling positive emotions, it can also provide a challenging rehabilitation menu. This makes it possible to dynamically change the rehabilitation plan according to the user's emotional state.

[0099] The rehabilitation plan generation unit can estimate the user's emotional state and dynamically change the rehabilitation plan according to the emotion. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user is tired, it can also provide a light rehabilitation menu. Furthermore, if the user is feeling positive emotions, it can also provide a challenging rehabilitation menu. This makes it possible to dynamically change the rehabilitation plan according to the user's emotional state.

[0100] The rehabilitation plan generation unit can estimate the user's emotional state and dynamically change the rehabilitation plan according to the emotion. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user is tired, it can also provide a light rehabilitation menu. Furthermore, if the user is feeling positive emotions, it can also provide a challenging rehabilitation menu. This makes it possible to dynamically change the rehabilitation plan according to the user's emotional state.

[0101] The rehabilitation plan generation unit can estimate the user's emotional state and dynamically change the rehabilitation plan according to the emotion. For example, if the user is feeling stressed, it can suggest relaxing exercises. If the user is tired, it can also provide a light rehabilitation menu. Furthermore, if the user is feeling positive emotions, it can also provide a challenging rehabilitation menu. This makes it possible to dynamically change the rehabilitation plan according to the user's emotional state.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The rehabilitation plan generation unit uses AI technology to generate an individualized rehabilitation plan. For example, the generation AI creates a rehabilitation plan based on evaluation data of the user's language ability and speech function. The generation AI can also analyze the user's past rehabilitation data and propose a rehabilitation plan that takes long-term progress into consideration. Furthermore, the generation AI can collect data on the user's lifestyle habits and daily activities and adjust the rehabilitation plan based on that data. Step 2: The interactive practice unit conducts rehabilitation practice through dialogue with the user based on the rehabilitation plan generated by the rehabilitation plan generation unit. For example, the generation AI may ask the user questions, and the user may respond to the questions to progress the practice. The generation AI can also analyze the user's speech, point out pronunciation and grammar errors in real time, and suggest corrections. Furthermore, the generation AI can learn the user's dialogue history and provide practice tailored to the user's individual dialogue style. Step 3: The gamification unit makes the rehabilitation exercises performed by the interactive exercise unit fun and continuous. For example, a system can be introduced in which points are accumulated each time a rehabilitation exercise is completed, and rewards are given when a certain number of points are reached. It is also possible to introduce game elements that allow users to explore a virtual world according to the progress of their rehabilitation exercises. Furthermore, it is also possible to provide a system that allows users to develop a customizable avatar based on the results of their rehabilitation exercises. Step 4: The progress tracking unit tracks the rehabilitation progress made by the gamification unit. For example, it records how much practice the user has done and what results they have achieved, and visually displays this in graphs and charts. It can also record the user's rehabilitation progress in detail and visualize it by comparing it with past data. It can also introduce algorithms that analyze the user's rehabilitation progress and predict future progress. Step 5: The community section allows users to share their rehabilitation progress tracked by the progress tracking section, and provides support and interaction. For example, it can provide a bulletin board where users can share their rehabilitation progress and results, or a chat room where users with the same goals can encourage each other. It can also periodically hold events where users compete against each other on their rehabilitation progress. It can also provide online seminars where users can present their rehabilitation results to each other. Step 6: The Usability Improvement Department makes the support and interaction provided by the Community Department intuitive and multilingual. For example, it can use large buttons and a simple menu structure to make it easy to use even for elderly people and those who are not familiar with technology. It can also provide a function that automatically switches the interface depending on the language selected by the user. It can also provide a help function that allows users to receive real-time support when they have trouble operating the device.

[0104] 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.

[0105] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] 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.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive 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.

[0145] 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.

[0146] 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.

[0147] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] 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.

[0154] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0155] 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.

[0156] 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).

[0157] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0165] The hardware resource that executes the specific process 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 process may be a single processor.

[0166] 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.

[0167] 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.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] 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.

[0170] 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. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a rehabilitation plan generation unit that generates an individualized rehabilitation plan using AI technology; an interactive practice unit that performs rehabilitation practice through dialogue with a user based on the rehabilitation plan generated by the rehabilitation plan generation unit; a gamification unit that enables the rehabilitation practice performed by the interactive practice unit to be continued in a fun manner; a progress tracking unit that tracks the progress of the rehabilitation performed by the gamification unit; a community unit that allows users to share the rehabilitation progress tracked by the progress tracking unit with each other and provides support and interaction; and an operability improvement unit that realizes the support and interaction provided by the community unit with intuitive operability and multilingual support. A system characterized by:

2. The rehabilitation plan generation unit Analyzing the user's emotional state in real time and dynamically changing the rehabilitation plan according to the emotion.

2. The system of claim 1.

3. The interactive practice section includes: Analyzes the user's speech, points out pronunciation and grammar errors in real time, and makes correction suggestions 2. The system of claim 1.

4. The gamification unit Dynamically adjust game elements according to the user's emotional state to maintain motivation 2. The system of claim 1.

5. The progress tracking unit Providing progress feedback according to the user's emotional state to maintain motivation 2. The system of claim 1.

6. The community section Suggesting community activities according to the user's emotional state to prevent social isolation 2. The system of claim 1.

7. The operability improvement unit includes: To provide an interface for reducing the stress felt by the user during operation 2. The system of claim 1.

8. The interactive practice section includes: Generate dialogue content according to the emotional state of the user to maximize the effect of the rehabilitation.

2. The system of claim 1.

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