System

Abstract

An object of a system according to an embodiment is to improve an employment environment of a disabled person and provide an equal opportunity to work.SOLUTION: A system according to an embodiment includes a generation AI, a speech recognition unit, a speech synthesis unit, a reminding unit, and a solution proposing unit. Generative AI uses generative AI to mimic human behavior, reactions, and expertise. The voice recognition unit provides visual information by voice. The speech synthesis unit converts voice into text. The reminding unit reminds a task. The solution proposal unit proposes an appropriate solution.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 leaves room for improvement in terms of improving the employment environment for people with disabilities and providing equal employment opportunities.

[0005] The system according to the embodiment aims to improve the employment environment for people with disabilities and provide equal employment opportunities. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a speech recognition unit, a speech synthesis unit, a reminder unit, and a solution proposal unit. The generation AI uses the generation AI to mimic human behavior, reactions, and expertise. The speech recognition unit provides visual information by speech. The speech synthesis unit converts speech into text. The reminder unit reminds the user of a task. The solution proposal unit proposes an appropriate solution. [Effects of the Invention]

[0007] The system according to the embodiment can improve the employment environment for people with disabilities and provide equal employment opportunities. [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) The digital cloning system according to an embodiment of the present invention complements the abilities and needs of people with disabilities and ensures an equal employment environment. This system uses generative AI to mimic human behavior, reactions, and expertise. This enables the digital cloning system to promote voluntary social participation and economic independence for people with disabilities.

[0029] A digital clone system according to an embodiment includes a generation AI, a voice recognition unit, a voice synthesis unit, a text conversion unit, a reminder unit, and a solution proposal unit. The generation AI mimics human behavior, reactions, and expertise. For example, the generation AI is customized according to the specific needs and abilities of individuals with disabilities. For visually impaired individuals, the generation AI provides visual information as audio using voice recognition and voice synthesis technology. For hearing impaired individuals, the generation AI converts audio into text and displays it in real time. The voice recognition unit provides visual information as audio. For example, the voice recognition unit analyzes visual information and outputs it as audio. The voice synthesis unit converts audio into text. For example, the voice synthesis unit analyzes audio data and converts it into text data. The text conversion unit converts audio into text. For example, the text conversion unit analyzes audio data and converts it into text data. The reminder unit reminds individuals with disabilities of tasks. For example, the reminder unit reminds individuals with disabilities of tasks to be performed and provides necessary information. The solution proposal unit proposes appropriate solutions. For example, when a person with a disability faces difficulties, the solution proposal unit proposes appropriate solutions. This allows the digital clone system to complement the abilities and needs of people with disabilities and ensure an equal working environment. For example, a visually impaired person can use a digital clone to receive visual information as audio and smoothly carry out their work. Similarly, a hearing impaired person can use a digital clone to receive audio information as text and smoothly communicate.

[0030] The voice recognition unit can provide visual information by voice. For example, the voice recognition unit analyzes visual information and outputs it by voice. For example, the voice recognition unit uses image recognition technology to provide visual information by voice. Also, the voice recognition unit uses natural language processing technology to provide visual information by voice. This allows visually impaired people to obtain visual information by voice.

[0031] The speech synthesis unit can convert speech into text. For example, the speech synthesis unit analyzes speech data and converts it into text data. For example, the speech synthesis unit converts speech into text using speech recognition technology. The speech synthesis unit also converts speech data into text in real time. This allows hearing-impaired people to obtain speech information in text form.

[0032] The reminding unit can remind a person with a disability of a task. The reminding unit, for example, reminds the person with a disability of a task to be performed and provides necessary information. For example, the reminding unit reminds the person with a disability of a task using a calendar function. The reminding unit also reminds the person with a disability of a task using a notification function. This allows the person with a disability to remember to perform the task.

[0033] The solution proposal unit can propose an appropriate solution. For example, when a person with a disability faces a difficulty, the solution proposal unit proposes an appropriate solution. For example, the solution proposal unit proposes a solution using a problem-solving algorithm. Also, the solution proposal unit proposes a solution using a generative AI. In this way, when a person with a disability faces a difficulty, an appropriate solution can be obtained.

[0034] The digital clone can learn the user's behavioral patterns and prepare support in advance based on predicted behavior. For example, the digital clone can learn the user's behavioral patterns and prepare support in advance based on predicted behavior. For example, if the user has the habit of checking email every morning, a summary of the email can be prepared in advance. The digital clone can also learn the user's behavioral patterns and provide necessary information based on predicted behavior. For example, if the user is going to attend a meeting, materials for the meeting can be prepared in advance. This allows support to be prepared in advance based on the user's behavioral patterns.

[0035] The digital clone can continuously update the user's expertise and provide support based on the latest information and technology. The digital clone can, for example, continuously update the user's expertise and provide support based on the latest information and technology. For example, it can automatically collect the latest industry news and technology trends and provide them to the user. The digital clone can also continuously update the user's expertise and provide necessary information. For example, it can provide resources for learning new technologies and methods. This allows the user's expertise to be updated based on the latest information.

[0036] A digital clone can accommodate different languages ​​and cultures, enabling international support for people with disabilities. For example, a digital clone can be made multilingual to provide support for users who speak different languages. For example, it can support multiple languages ​​such as English, French, and Chinese. A digital clone can also accommodate different cultures and provide support that takes cultural backgrounds into consideration. For example, it can provide business etiquette and communication methods in different cultures. This makes it possible to accommodate different languages ​​and cultures and provide international support for people with disabilities.

[0037] Digital clones can also be applied to support within the home, assisting with daily life. Digital clones can be applied to support within the home, assisting with daily life, for example, by managing household schedules and creating shopping lists. Digital clones can also support communication within the home, for example, by supporting the sending and receiving of messages between family members. This allows for support in daily life within the home.

[0038] The digital clone can monitor the user's work progress in real time and automatically adjust tasks as needed. The digital clone can, for example, monitor the user's work progress in real time and automatically adjust tasks as needed. For example, it can change the priority of tasks so that important tasks are done first. The digital clone can also monitor the user's work progress and send reminders according to the progress. For example, it can remind the user of tasks with an approaching deadline. This makes it possible to monitor the user's work progress in real time and automatically adjust tasks.

[0039] The digital clone can analyze the user's work environment and propose the optimal work environment. The digital clone, for example, analyzes the user's work environment and proposes the optimal work environment. For example, it makes suggestions to adjust the lighting, temperature, and sound environment. The digital clone also analyzes the user's work environment and proposes ways to improve work efficiency. For example, it makes suggestions to optimize the layout of the work space. In this way, the digital clone can analyze the user's work environment and propose the optimal work environment.

[0040] Digital clones can be introduced into educational institutions to provide learning support for students with disabilities. Digital clones can be introduced into educational institutions to provide learning support for students with disabilities. For example, digital clones can provide audio versions of textbook content to visually impaired students. Digital clones can also convert lesson content into text in real time and provide it to hearing impaired students. Furthermore, digital clones can provide simple instructions and guides to students with intellectual disabilities to support their learning progress. This allows educational institutions to provide learning support for students with disabilities.

[0041] Digital clones can be introduced into medical settings to support the rehabilitation of people with disabilities. Digital clones can be introduced into medical settings to support the rehabilitation of people with disabilities. For example, they can monitor the progress of rehabilitation and suggest appropriate exercises. Digital clones can also customize rehabilitation programs to provide optimal rehabilitation for individual people with disabilities. Furthermore, digital clones can evaluate the results of rehabilitation and adjust the programs as necessary. This makes it possible to support the rehabilitation of people with disabilities in medical settings.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The digital clone system can also monitor the user's health and provide appropriate health management advice. For example, it can measure the user's heart rate and blood pressure in real time and, if abnormalities are detected, encourage the user to consult a medical institution. It can also analyze the user's diet and exercise records to suggest a balanced diet and appropriate exercise plan. It can also monitor the user's sleep patterns and provide advice on how to get quality sleep. This allows for comprehensive management of the user's health and supports their health maintenance.

[0044] The digital clone system can analyze a user's learning style and suggest the most suitable learning method. For example, a user who prefers visual learning can be provided with learning materials that make extensive use of diagrams and graphs. A user who prefers auditory learning can be provided with learning materials that include audio commentary. Furthermore, a user who prefers hands-on learning can be suggested interactive simulations and experiments. This makes it possible to provide effective learning support tailored to each user's learning style.

[0045] The digital clone system can learn a user's hobbies and interests and suggest personalized leisure activities. For example, if a user enjoys outdoor activities, it can suggest nearby hiking trails and campsites. If a user likes reading, it can recommend books that match their interests. If a user enjoys cooking, it can provide information on new recipes and cooking classes. This allows it to suggest leisure activities based on the user's hobbies and interests and support them in spending their leisure time more fulfillingly.

[0046] The digital clone system can manage users' schedules and support efficient time management. For example, it can automatically organize users' schedules and prioritize important tasks. It can also set reminders based on the user's schedule and notify them of tasks that are likely to be forgotten. It can also suggest ways to free up space in the user's schedule and help them avoid overly busy schedules. This allows users to manage their time efficiently and reduce stress.

[0047] The digital clone system can support communication in the workplace and improve teamwork. For example, it can analyze the user's communication style and suggest effective communication methods. It can also monitor the progress of meetings and projects at the user's workplace and provide appropriate feedback. It can also provide advice to smooth interpersonal relationships at the user's workplace and improve teamwork. This can support communication in the workplace and enable efficient work execution.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: Generative AI mimics human behavior, reactions, and expertise. For example, generative AI is customized to the specific needs and abilities of individuals with disabilities. For the visually impaired, generative AI uses speech recognition and speech synthesis technology to provide visual information as audio. For the hearing impaired, generative AI converts speech to text and displays it in real time. Step 2: The voice recognition unit provides the visual information by voice. For example, the voice recognition unit analyzes the visual information and outputs it by voice. Step 3: The speech synthesis unit converts the speech into text. For example, the speech synthesis unit analyzes the speech data and converts it into text data. Step 4: The reminder unit reminds the person with a disability of the task. For example, the reminder unit reminds the person with a disability of the task to be performed and provides necessary information. Step 5: The solution suggestion unit proposes an appropriate solution. For example, when a person with a disability faces difficulties, the solution suggestion unit proposes an appropriate solution.

[0050] (Example 2) The digital cloning system according to an embodiment of the present invention complements the abilities and needs of people with disabilities and ensures an equal employment environment. This system uses generative AI to mimic human behavior, reactions, and expertise. This enables the digital cloning system to promote voluntary social participation and economic independence for people with disabilities.

[0051] A digital clone system according to an embodiment includes a generation AI, a voice recognition unit, a voice synthesis unit, a text conversion unit, a reminder unit, and a solution proposal unit. The generation AI mimics human behavior, reactions, and expertise. For example, the generation AI is customized according to the specific needs and abilities of individuals with disabilities. For visually impaired individuals, the generation AI provides visual information as audio using voice recognition and voice synthesis technology. For hearing impaired individuals, the generation AI converts audio into text and displays it in real time. The voice recognition unit provides visual information as audio. For example, the voice recognition unit analyzes visual information and outputs it as audio. The voice synthesis unit converts audio into text. For example, the voice synthesis unit analyzes audio data and converts it into text data. The text conversion unit converts audio into text. For example, the text conversion unit analyzes audio data and converts it into text data. The reminder unit reminds individuals with disabilities of tasks. For example, the reminder unit reminds individuals with disabilities of tasks to be performed and provides necessary information. The solution proposal unit proposes appropriate solutions. For example, when a person with a disability faces difficulties, the solution proposal unit proposes appropriate solutions. This allows the digital clone system to complement the abilities and needs of people with disabilities and ensure an equal working environment. For example, a visually impaired person can use a digital clone to receive visual information as audio and smoothly carry out their work. Similarly, a hearing impaired person can use a digital clone to receive audio information as text and smoothly communicate.

[0052] The voice recognition unit can provide visual information by voice. For example, the voice recognition unit analyzes visual information and outputs it by voice. For example, the voice recognition unit uses image recognition technology to provide visual information by voice. Also, the voice recognition unit uses natural language processing technology to provide visual information by voice. This allows visually impaired people to obtain visual information by voice.

[0053] The speech synthesis unit can convert speech into text. For example, the speech synthesis unit analyzes speech data and converts it into text data. For example, the speech synthesis unit converts speech into text using speech recognition technology. The speech synthesis unit also converts speech data into text in real time. This allows hearing-impaired people to obtain speech information in text form.

[0054] The reminding unit can remind a person with a disability of a task. The reminding unit, for example, reminds the person with a disability of a task to be performed and provides necessary information. For example, the reminding unit reminds the person with a disability of a task using a calendar function. The reminding unit also reminds the person with a disability of a task using a notification function. This allows the person with a disability to remember to perform the task.

[0055] The solution proposal unit can propose an appropriate solution. For example, when a person with a disability faces a difficulty, the solution proposal unit proposes an appropriate solution. For example, the solution proposal unit proposes a solution using a problem-solving algorithm. Also, the solution proposal unit proposes a solution using a generative AI. In this way, when a person with a disability faces a difficulty, an appropriate solution can be obtained.

[0056] The digital clone has an emotion estimation function, which can provide feedback and support according to the user's emotional state. For example, the digital clone uses the emotion estimation function to analyze emotions from the user's facial expressions and voice in real time. For example, if the user is feeling stressed, the digital clone can provide advice on how to relax. The digital clone also uses the emotion estimation function to monitor the user's emotional state and provide appropriate feedback. For example, if the user is tired, the digital clone can provide advice to take a break. This makes it possible to provide appropriate support according to the user's emotional state.

[0057] The digital clone can learn the user's behavioral patterns and prepare support in advance based on predicted behavior. For example, the digital clone can learn the user's behavioral patterns and prepare support in advance based on predicted behavior. For example, if the user has the habit of checking email every morning, a summary of the email can be prepared in advance. The digital clone can also learn the user's behavioral patterns and provide necessary information based on predicted behavior. For example, if the user is going to attend a meeting, materials for the meeting can be prepared in advance. This allows support to be prepared in advance based on the user's behavioral patterns.

[0058] The digital clone can continuously update the user's expertise and provide support based on the latest information and technology. The digital clone can, for example, continuously update the user's expertise and provide support based on the latest information and technology. For example, it can automatically collect the latest industry news and technology trends and provide them to the user. The digital clone can also continuously update the user's expertise and provide necessary information. For example, it can provide resources for learning new technologies and methods. This allows the user's expertise to be updated based on the latest information.

[0059] A digital clone can accommodate different languages ​​and cultures, enabling international support for people with disabilities. For example, a digital clone can be made multilingual to provide support for users who speak different languages. For example, it can support multiple languages ​​such as English, French, and Chinese. A digital clone can also accommodate different cultures and provide support that takes cultural backgrounds into consideration. For example, it can provide business etiquette and communication methods in different cultures. This makes it possible to accommodate different languages ​​and cultures and provide international support for people with disabilities.

[0060] Digital clones can also be applied to support within the home, assisting with daily life. Digital clones can be applied to support within the home, assisting with daily life, for example, by managing household schedules and creating shopping lists. Digital clones can also support communication within the home, for example, by supporting the sending and receiving of messages between family members. This allows for support in daily life within the home.

[0061] The digital clone is equipped with an emotion estimation function and can make suggestions to facilitate smooth communication within the home. For example, the digital clone is equipped with an emotion estimation function and can make suggestions to facilitate smooth communication within the home. For example, it can analyze the emotional state of family members and suggest appropriate communication methods. The digital clone can also use the emotion estimation function to make suggestions to reduce stress within the home. For example, it can suggest activities to help people relax. This makes it possible to provide suggestions to facilitate smooth communication within the home.

[0062] The digital clone can monitor the user's work progress in real time and automatically adjust tasks as needed. The digital clone can, for example, monitor the user's work progress in real time and automatically adjust tasks as needed. For example, it can change the priority of tasks so that important tasks are done first. The digital clone can also monitor the user's work progress and send reminders according to the progress. For example, it can remind the user of tasks with an approaching deadline. This makes it possible to monitor the user's work progress in real time and automatically adjust tasks.

[0063] The digital clone can analyze the user's work environment and propose the optimal work environment. The digital clone, for example, analyzes the user's work environment and proposes the optimal work environment. For example, it makes suggestions to adjust the lighting, temperature, and sound environment. The digital clone also analyzes the user's work environment and proposes ways to improve work efficiency. For example, it makes suggestions to optimize the layout of the work space. In this way, the digital clone can analyze the user's work environment and propose the optimal work environment.

[0064] The digital clone can monitor the user's emotional state and provide advice to reduce stress and fatigue. For example, the digital clone can monitor the user's emotional state and provide advice to reduce stress and fatigue. For example, it can suggest breathing techniques or meditation to help relax. The digital clone can also monitor the user's emotional state and suggest appropriate times to take a break. For example, it can provide advice to encourage a break after working for a long period of time. In this way, the digital clone can monitor the user's emotional state and provide advice to reduce stress and fatigue.

[0065] Digital clones can be introduced into educational institutions to provide learning support for students with disabilities. Digital clones can be introduced into educational institutions to provide learning support for students with disabilities. For example, digital clones can provide audio versions of textbook content to visually impaired students. Digital clones can also convert lesson content into text in real time and provide it to hearing impaired students. Furthermore, digital clones can provide simple instructions and guides to students with intellectual disabilities to support their learning progress. This allows educational institutions to provide learning support for students with disabilities.

[0066] Digital clones can be introduced into medical settings to support the rehabilitation of people with disabilities. Digital clones can be introduced into medical settings to support the rehabilitation of people with disabilities. For example, they can monitor the progress of rehabilitation and suggest appropriate exercises. Digital clones can also customize rehabilitation programs to provide optimal rehabilitation for individual people with disabilities. Furthermore, digital clones can evaluate the results of rehabilitation and adjust the programs as necessary. This makes it possible to support the rehabilitation of people with disabilities in medical settings.

[0067] Equipped with an emotion estimation function, the digital clone can grasp the emotional state of students in educational settings and provide appropriate learning support. For example, if a student is feeling stressed, the digital clone can suggest activities to help them relax. The digital clone can also use the emotion estimation function to monitor the student's emotional state and provide support according to their learning progress. For example, if a student is struggling to understand, the digital clone can provide additional learning materials or explanations. This makes it possible to grasp the emotional state of students in educational settings and provide appropriate learning support.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The digital clone system can also monitor the user's health and provide appropriate health management advice. For example, it can measure the user's heart rate and blood pressure in real time and, if abnormalities are detected, encourage the user to consult a medical institution. It can also analyze the user's diet and exercise records to suggest a balanced diet and appropriate exercise plan. It can also monitor the user's sleep patterns and provide advice on how to get quality sleep. This allows for comprehensive management of the user's health and supports their health maintenance.

[0070] The digital clone system can estimate a user's emotional state and provide music and entertainment based on the user's emotions. For example, if the user is feeling stressed, it can play relaxing music, and if the user is happy, it can provide upbeat music. It can also recommend movies and TV shows based on the user's emotional state. Furthermore, if the user is feeling down, it can provide encouraging messages and positive content. This allows the system to provide entertainment tailored to the user's emotional state and improve their mood.

[0071] The digital clone system can analyze a user's learning style and suggest the most suitable learning method. For example, a user who prefers visual learning can be provided with learning materials that make extensive use of diagrams and graphs. A user who prefers auditory learning can be provided with learning materials that include audio commentary. Furthermore, a user who prefers hands-on learning can be suggested interactive simulations and experiments. This makes it possible to provide effective learning support tailored to each user's learning style.

[0072] The digital clone system can estimate the user's emotional state and provide communication advice based on the emotion. For example, if the user is angry, it can provide advice on how to stay calm, and if the user is sad, it can suggest words of comfort. If the user is tense, it can teach breathing or meditation techniques to help them relax. Furthermore, if the user is emotionally exhausted, it can provide advice encouraging them to take a break. This can support appropriate communication according to the user's emotional state.

[0073] The digital clone system can learn a user's hobbies and interests and suggest personalized leisure activities. For example, if a user enjoys outdoor activities, it can suggest nearby hiking trails and campsites. If a user likes reading, it can recommend books that match their interests. If a user enjoys cooking, it can provide information on new recipes and cooking classes. This allows it to suggest leisure activities based on the user's hobbies and interests and support them in spending their leisure time more fulfillingly.

[0074] The digital clone system can estimate the user's emotional state and provide feedback based on that emotion. For example, if the user feels a sense of accomplishment, it can provide feedback encouraging them to try harder. If the user feels frustrated, it can provide words of encouragement. If the user feels anxious, it can provide information to reassure them. Furthermore, if the user feels joy, it can suggest ways to share that joy. This allows the system to provide appropriate feedback according to the user's emotional state and maintain their motivation.

[0075] The digital clone system can manage users' schedules and support efficient time management. For example, it can automatically organize users' schedules and prioritize important tasks. It can also set reminders based on the user's schedule and notify them of tasks that are likely to be forgotten. It can also suggest ways to free up space in the user's schedule and help them avoid overly busy schedules. This allows users to manage their time efficiently and reduce stress.

[0076] The digital clone system can estimate the user's emotional state and suggest relaxation methods based on the user's emotions. For example, if the user is feeling stressed, it can suggest yoga or meditation to help them relax. If the user is tired, it can suggest a short walk or stretching to refresh them. Furthermore, if the user is emotionally unstable, it can suggest deep breathing or playing relaxation music to help them regain a sense of stability. This allows the system to provide relaxation methods tailored to the user's emotional state and support their physical and mental health.

[0077] The digital clone system can support communication in the workplace and improve teamwork. For example, it can analyze the user's communication style and suggest effective communication methods. It can also monitor the progress of meetings and projects at the user's workplace and provide appropriate feedback. It can also provide advice to smooth interpersonal relationships at the user's workplace and improve teamwork. This can support communication in the workplace and enable efficient work execution.

[0078] The digital clone system can estimate a user's emotional state and provide a personalized learning plan based on their emotions. For example, if a user feels motivated to learn, it can provide challenging tasks, and if the user feels anxious about learning, it can suggest a plan to start with basic content. If the user is tired, it can suggest a short, effective learning method. It can also provide learning resources based on the user's areas of interest to increase their motivation to learn. This allows it to provide a personalized learning plan based on the user's emotional state and support effective learning.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: Generative AI mimics human behavior, reactions, and expertise. For example, generative AI is customized to the specific needs and abilities of individuals with disabilities. For the visually impaired, generative AI uses speech recognition and speech synthesis technology to provide visual information as audio. For the hearing impaired, generative AI converts speech to text and displays it in real time. Step 2: The voice recognition unit provides the visual information by voice. For example, the voice recognition unit analyzes the visual information and outputs it by voice. Step 3: The speech synthesis unit converts the speech into text. For example, the speech synthesis unit analyzes the speech data and converts it into text data. Step 4: The reminder unit reminds the person with a disability of the task. For example, the reminder unit reminds the person with a disability of the task to be performed and provides necessary information. Step 5: The solution suggestion unit proposes an appropriate solution. For example, when a person with a disability faces difficulties, the solution suggestion unit proposes an appropriate solution.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. Generative AI uses generative AI to mimic human behavior, reactions, and expertise. a voice recognition unit that provides visual information by voice; a speech synthesis unit that converts speech into text; A reminder section that reminds you of tasks, a solution proposal unit that proposes an appropriate solution; A system characterized by:

2. The voice recognition unit Providing the visual information by voice 2. The system of claim 1.

3. The speech synthesis unit Convert the speech to text 2. The system of claim 1.

4. Digital clones are Responding to different languages ​​and cultures, Enabling international support for people with disabilities 2. The system of claim 1.

5. Digital clones are Monitor users' work progress in real time, Automatically adjust tasks as needed 2. The system of claim 1.

6. Digital clones are It has been introduced into educational institutions, Providing learning support for students with disabilities 2. The system of claim 1.

7. Digital clones are Equipped with emotion estimation function, The emotion estimation function is Providing feedback and support based on the user's emotional state 2. The system of claim 1.

8. Digital clones are Equipped with emotion estimation function, Understanding students' emotional states in educational settings Providing appropriate learning support 2. The system of claim 1.

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