System

Abstract

An object of a system according to an embodiment is to detect a sign of psychosis at an early stage through a daily chat and prompt an appropriate response.SOLUTION: A system according to an embodiment includes a generation AI, a conversation analysis unit, a sign detection unit, and a response promotion unit. The generated AI is exchanged by the user on a daily basis through the chat application using the generated AI. The conversation analysis unit analyzes the conversation content generated by the generation AI. The sign detection unit detects a sign of psychosis from the conversation content analyzed by the conversation analysis unit. The response prompt unit prompts an appropriate response based on the symptom of psychosis detected by the symptom detection unit.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 makes it difficult to detect signs of psychosis early, and there is a risk that the condition may worsen without the patient realizing it.

[0005] The system according to the embodiment aims to detect signs of mental illness early through everyday chats and encourage appropriate responses. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a conversation analysis unit, a symptom detection unit, and a response promotion unit. The generation AI handles daily interactions between users through a chat app using the generation AI. The conversation analysis unit analyzes the content of the conversation generated by the generation AI. The symptom detection unit detects symptoms of mental illness from the content of the conversation analyzed by the conversation analysis unit. The response promotion unit encourages appropriate responses based on the symptoms of mental illness detected by the symptom detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of mental illness early through everyday chats and encourage appropriate responses. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 mental illness detection system according to an embodiment of the present invention is a system that detects signs of mental illness in a user's daily interactions through a chat app that utilizes generative AI and encourages early resolution. As a result, the mental illness detection system can detect signs of mental illness early through the user's daily chats and encourage appropriate responses.

[0029] A mental illness detection system according to an embodiment includes a generation AI, a conversation analysis unit, a symptom detection unit, and a response promotion unit. The generation AI performs daily user interactions. For example, the generation AI asks the user a question such as, "How was your day today?" and advances the conversation based on the user's response. The generation AI also generates appropriate responses to the user's input, allowing for a natural conversation. The conversation analysis unit analyzes the conversation content generated by the generation AI. For example, the conversation analysis unit analyzes the conversation content using natural language processing technology to extract the user's emotional state and speech patterns. The conversation analysis unit can also perform emotion analysis to identify emotions contained in the user's speech. The symptom detection unit detects signs of mental illness from the conversation content analyzed by the conversation analysis unit. For example, the symptom detection unit detects signs of mental illness based on specific keywords or patterns of emotional fluctuations. The symptom detection unit can also analyze the frequency and patterns of the user's speech to detect abnormal changes. The response promotion unit encourages appropriate responses based on the signs of mental illness detected by the symptom detection unit. For example, the response promotion unit may ask the user, "You seem to be feeling down lately. Are you okay?" and recommend that the user consult a specialist doctor or counselor if necessary. The response promotion unit may also introduce the user to an appropriate medical institution or counseling service if the user so desires. In this way, the mental illness detection system according to the embodiment can detect signs of mental illness early through the user's daily chats and encourage appropriate responses.

[0030] The generation AI can refer to the user's past conversation history and generate questions optimized for each individual user. For example, the generation AI analyzes the user's past conversation history and generates questions based on the user's interests. For example, it poses questions related to hobbies or interests that the user has previously discussed. The generation AI also refers to the user's past conversation history and generates questions based on the user's emotional state and stress level. For example, if the user is feeling stressed, it provides topics that will help them relax. The generation AI also generates follow-up questions for unresolved issues or questions that the user has previously discussed based on the user's past conversation history. For example, it asks about the progress of a health problem that the user has previously discussed. In this way, the generation AI generates questions optimized based on the user's past conversation history, enabling more natural conversations.

[0031] The generation AI can learn the user's lifestyle and behavioral patterns and start a conversation at the appropriate time. For example, the generation AI can analyze the user's lifestyle and start a conversation when the user is relaxed. For example, it can ask questions after the user has returned home from work. The generation AI can also learn the user's behavioral patterns and start a conversation when the user is most likely to respond. For example, it can start a conversation while the user is commuting or during a break. The generation AI can also provide topics that will help the user relax during times when they are likely to feel stressed, based on the user's lifestyle. For example, it can ask questions that will help the user refresh themselves between work sessions. This allows for more effective communication by starting a conversation at the appropriate time based on the user's lifestyle and behavioral patterns.

[0032] Generative AI can suggest topics based on the user's hobbies and interests, diversifying conversations. For example, generative AI can analyze the user's hobbies and interests and suggest topics based on them. For example, if the user likes movies, it can suggest topics about movies they have recently seen. Generative AI can also suggest new hobbies and activities based on the user's interests. For example, if the user is interested in the outdoors, it can introduce new hiking trails. Generative AI can also provide the latest news and trends related to the user's hobbies and interests. For example, if the user is interested in music, it can provide information on the latest music releases and concerts. In this way, by suggesting topics based on the user's hobbies and interests, it can diversify conversations and pique the user's interest.

[0033] Generative AI can introduce a multilingual conversation model so that it can accommodate users with different languages ​​and cultural backgrounds. For example, generative AI can use a multilingual conversation model to accommodate users who speak different languages. For example, it can hold conversations in multiple languages, such as English, French, and Chinese. Generative AI can also generate culturally appropriate responses for users with different cultural backgrounds. For example, it can provide responses that take into account the etiquette and customs of a particular culture. Generative AI can also use a multilingual conversation model to support communication between users who speak different languages. For example, it can provide a real-time translation function. This allows it to accommodate a wider variety of users by being able to accommodate users with different languages ​​and cultural backgrounds.

[0034] The conversation analysis unit deeply understands the context of a user's statements and can detect signs of mental illness from subtle nuances and word choice. For example, the generation AI analyzes the context of a user's statements and detects signs of mental illness from subtle nuances and word choice. For example, if a user frequently uses negative language, it detects this as a sign of mental illness. The conversation analysis unit also deeply understands the context of a user's statements and analyzes the user's emotional and psychological state. For example, if a user increasingly makes statements that lower their self-esteem, it detects this as a sign of mental illness. The conversation analysis unit also analyzes the user's psychological state in detail based on the context of the user's statements. For example, if a user overreacts to past events, it detects this as a sign of mental illness. This allows for a deep understanding of the context of a user's statements and detects signs of mental illness from subtle nuances and word choice, enabling more accurate detection of signs of mental illness.

[0035] The conversation analysis unit can analyze the frequency and patterns of a user's speech and detect abnormal changes. For example, the generation AI analyzes the frequency of a user's speech and detects abnormal changes. For example, if the user's speech frequency suddenly decreases, this is detected as a sign of mental illness. The generation AI also analyzes the patterns of a user's speech and detects abnormal changes. For example, if the user suddenly starts making more negative comments, this is detected as a sign of mental illness. The generation AI also analyzes the user's psychological state based on the frequency and patterns of the user's speech. For example, if the user starts speaking only during certain hours of the day, this is detected as a sign of mental illness. In this way, by analyzing the frequency and patterns of a user's speech and detecting abnormal changes, signs of mental illness can be discovered early.

[0036] The conversation analysis unit analyzes not only the user's speech but also behavioral data such as typing speed and input intervals to detect signs of mental illness. For example, the generation AI in the conversation analysis unit analyzes the user's typing speed to detect abnormal changes. For example, if the user's typing speed suddenly slows, it is detected as a sign of mental illness. The generation AI in the conversation analysis unit also analyzes the intervals between user inputs to detect abnormal changes. For example, if the user's input intervals become irregular, it is detected as a sign of mental illness. The generation AI in the conversation analysis unit also analyzes the user's psychological state based on the user's typing speed and input intervals. For example, if the user's typing speed slows only during certain hours, it is detected as a sign of mental illness. This allows for a more comprehensive detection of signs of mental illness by analyzing not only the user's speech but also behavioral data such as typing speed and input intervals.

[0037] The conversation analysis unit can also accept the user's speech as voice input and detect signs of mental illness from the tone and rhythm of the voice. For example, the conversation analysis unit uses a generation AI to analyze the user's speech input and detect signs of mental illness from the tone and rhythm of the voice. For example, if the user's voice suddenly becomes lower, this is detected as a sign of mental illness. The conversation analysis unit also uses a generation AI to analyze the tone and rhythm of the voice based on the user's speech input and identify signs of mental illness. For example, if the user's voice begins to tremble, this is detected as a sign of mental illness. The conversation analysis unit also uses a generation AI to analyze the user's speech input and perform a detailed analysis of the user's psychological state based on fluctuations in the tone and rhythm of the voice. For example, if the user's voice suddenly becomes faster, this is detected as a sign of mental illness. This allows signs of mental illness to be detected from a wider variety of data by accepting the user's speech as voice input and detecting signs of mental illness from the tone and rhythm of the voice.

[0038] If signs of mental illness are detected, the response promotion unit can suggest specific relaxation methods or stress relief methods to the user. For example, if the generation AI detects signs of mental illness in the user, the response promotion unit will suggest specific relaxation methods. For example, it will explain how to do deep breathing or meditation and encourage the user to practice it. The response promotion unit also analyzes the user's stress level and suggests appropriate stress relief methods. For example, it may recommend light exercise or immersing oneself in a hobby. The response promotion unit also takes the user's mental state into consideration and suggests music or videos that are useful for relaxation. For example, it may provide a relaxing music playlist or videos of natural scenery. In this way, if signs of mental illness are detected, the response promotion unit can improve the user's mental state by suggesting specific relaxation methods or stress relief methods.

[0039] The response promotion unit can refer to the user's past conversation history and re-suggest previously effective response measures. In the response promotion unit, for example, the generation AI analyzes the user's past conversation history and re-suggests previously effective relaxation methods. For example, if meditation was previously effective for the user, the generation AI recommends meditation again. In addition, the response promotion unit re-suggests previously effective stress relief methods based on the user's past conversation history. For example, if jogging was previously effective for the user, the generation AI recommends jogging again. In addition, the response promotion unit refers to the user's past conversation history and re-suggests previously effective response measures. For example, if listening to relaxing music was previously effective for the user, the generation AI recommends that music again. In this way, by referring to the user's past conversation history and re-suggesting previously effective response measures, it is possible to provide the user with the optimal response measures.

[0040] If signs of mental illness are detected, the response promotion unit can introduce the user to available online mental health resources and communities. For example, if the generating AI detects signs of mental illness in the user, the response promotion unit introduces available online mental health resources. For example, it provides online counseling services and mental health websites. The response promotion unit also considers the user's mental state and introduces appropriate online communities. For example, it suggests support groups where people with the same concerns gather. If the generating AI detects signs of mental illness in the user, the response promotion unit introduces available online mental health resources and communities. For example, it provides mental health forums and chat rooms. In this way, if signs of mental illness are detected, the user can receive appropriate support by being introduced to available online mental health resources and communities.

[0041] The response promotion unit can provide a function to notify trusted friends and family of the situation with the user's consent. For example, if the generation AI detects signs of mental illness in the user, the response promotion unit provides a function to notify trusted friends and family of the situation with the user's consent. For example, an email or message is sent to friends and family only if the user consents. The response promotion unit also provides a function to notify trusted friends and family of the situation by the generation AI taking the user's mental state into consideration and with the user's consent. For example, a phone call is made to friends and family only if the user consents. The response promotion unit also provides a function to notify trusted friends and family of the situation with the user's consent if the generation AI detects signs of mental illness in the user, with the user's consent. For example, a message explaining the situation is sent to friends and family only if the user consents. This allows the user to receive appropriate support by notifying trusted friends and family of the situation with the user's consent.

[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 generation AI can learn the user's lifestyle and behavioral patterns and start a conversation at the appropriate time. For example, the generation AI can analyze the user's lifestyle and start a conversation when the user is relaxed. For example, it can ask questions after the user has returned home from work. The generation AI can also learn the user's behavioral patterns and start a conversation when the user is most likely to respond. For example, it can start a conversation while the user is commuting or during a break. The generation AI can also provide topics that will help the user relax during times when they are likely to feel stressed, based on the user's lifestyle. For example, it can ask questions that will help the user refresh themselves between work sessions. This allows for more effective communication by starting a conversation at the appropriate time based on the user's lifestyle and behavioral patterns.

[0044] Generative AI can suggest topics based on the user's hobbies and interests, diversifying conversations. For example, generative AI can analyze the user's hobbies and interests and suggest topics based on them. For example, if the user likes movies, it can suggest topics about movies they have recently seen. Generative AI can also suggest new hobbies and activities based on the user's interests. For example, if the user is interested in the outdoors, it can introduce new hiking trails. Generative AI can also provide the latest news and trends related to the user's hobbies and interests. For example, if the user is interested in music, it can provide information on the latest music releases and concerts. In this way, by suggesting topics based on the user's hobbies and interests, it can diversify conversations and pique the user's interest.

[0045] Generative AI can introduce a multilingual conversation model so that it can accommodate users with different languages ​​and cultural backgrounds. For example, generative AI can use a multilingual conversation model to accommodate users who speak different languages. For example, it can hold conversations in multiple languages, such as English, French, and Chinese. Generative AI can also generate culturally appropriate responses for users with different cultural backgrounds. For example, it can provide responses that take into account the etiquette and customs of a particular culture. Generative AI can also use a multilingual conversation model to support communication between users who speak different languages. For example, it can provide a real-time translation function. This allows it to accommodate a wider variety of users by being able to accommodate users with different languages ​​and cultural backgrounds.

[0046] The generation AI can refer to the user's past conversation history and generate questions optimized for each individual user. For example, the generation AI analyzes the user's past conversation history and generates questions based on the user's interests. For example, it poses questions related to hobbies or interests that the user has previously discussed. The generation AI also refers to the user's past conversation history and generates questions based on the user's emotional state and stress level. For example, if the user is feeling stressed, it provides topics that will help them relax. The generation AI also generates follow-up questions for unresolved issues or questions that the user has previously discussed based on the user's past conversation history. For example, it asks about the progress of a health problem that the user has previously discussed. In this way, the generation AI generates questions optimized based on the user's past conversation history, enabling more natural conversations.

[0047] The conversation analysis unit deeply understands the context of a user's statements and can detect signs of mental illness from subtle nuances and word choice. For example, the generation AI analyzes the context of a user's statements and detects signs of mental illness from subtle nuances and word choice. For example, if a user frequently uses negative language, it detects this as a sign of mental illness. The conversation analysis unit also deeply understands the context of a user's statements and analyzes the user's emotional and psychological state. For example, if a user increasingly makes statements that lower their self-esteem, it detects this as a sign of mental illness. The conversation analysis unit also analyzes the user's psychological state in detail based on the context of the user's statements. For example, if a user overreacts to past events, it detects this as a sign of mental illness. This allows for a deep understanding of the context of a user's statements and detects signs of mental illness from subtle nuances and word choice, enabling more accurate detection of signs of mental illness.

[0048] The conversation analysis unit can analyze the frequency and patterns of a user's speech and detect abnormal changes. For example, the generation AI analyzes the frequency of a user's speech and detects abnormal changes. For example, if the user's speech frequency suddenly decreases, this is detected as a sign of mental illness. The generation AI also analyzes the patterns of a user's speech and detects abnormal changes. For example, if the user suddenly starts making more negative comments, this is detected as a sign of mental illness. The generation AI also analyzes the user's psychological state based on the frequency and patterns of the user's speech. For example, if the user starts speaking only during certain hours of the day, this is detected as a sign of mental illness. In this way, by analyzing the frequency and patterns of a user's speech and detecting abnormal changes, signs of mental illness can be discovered early.

[0049] If signs of mental illness are detected, the response promotion unit can suggest specific relaxation methods or stress relief methods to the user. For example, if the generation AI detects signs of mental illness in the user, the response promotion unit will suggest specific relaxation methods. For example, it will explain how to do deep breathing or meditation and encourage the user to practice it. The response promotion unit also analyzes the user's stress level and suggests appropriate stress relief methods. For example, it may recommend light exercise or immersing oneself in a hobby. The response promotion unit also takes the user's mental state into consideration and suggests music or videos that are useful for relaxation. For example, it may provide a relaxing music playlist or videos of natural scenery. In this way, if signs of mental illness are detected, the response promotion unit can improve the user's mental state by suggesting specific relaxation methods or stress relief methods.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The generative AI carries out the same interactions that users have on a daily basis. For example, the generative AI asks the user questions such as "How was your day today?" and advances the conversation based on the user's answers. The generative AI also generates appropriate responses to the user's input, allowing for a natural conversation to continue. Step 2: The conversation analysis unit analyzes the conversation content generated by the generation AI. For example, the conversation analysis unit uses natural language processing technology to analyze the conversation content and extract the user's emotional state and speech patterns. The conversation analysis unit can also perform sentiment analysis to identify the emotions contained in the user's speech. Step 3: The symptom detection unit detects symptoms of mental illness from the conversation content analyzed by the conversation analysis unit. For example, the symptom detection unit detects symptoms of mental illness based on specific keywords or patterns of emotional fluctuations. The symptom detection unit can also analyze the frequency and patterns of the user's speech to detect abnormal changes. Step 4: The response promotion unit encourages the user to take an appropriate action based on the signs of mental illness detected by the sign detection unit. For example, the response promotion unit may ask the user, "You seem to be feeling depressed lately. Are you okay?" and recommend that the user consult a specialist doctor or counselor if necessary. The response promotion unit may also introduce the user to an appropriate medical institution or counseling service if the user so desires.

[0052] (Example 2) A mental illness detection system according to an embodiment of the present invention is a system that detects signs of mental illness in a user's daily interactions through a chat app that utilizes generative AI and encourages early resolution. As a result, the mental illness detection system can detect signs of mental illness early through the user's daily chats and encourage appropriate responses.

[0053] A mental illness detection system according to an embodiment includes a generation AI, a conversation analysis unit, a symptom detection unit, and a response promotion unit. The generation AI performs daily user interactions. For example, the generation AI asks the user a question such as, "How was your day today?" and advances the conversation based on the user's response. The generation AI also generates appropriate responses to the user's input, allowing for a natural conversation. The conversation analysis unit analyzes the conversation content generated by the generation AI. For example, the conversation analysis unit analyzes the conversation content using natural language processing technology to extract the user's emotional state and speech patterns. The conversation analysis unit can also perform emotion analysis to identify emotions contained in the user's speech. The symptom detection unit detects signs of mental illness from the conversation content analyzed by the conversation analysis unit. For example, the symptom detection unit detects signs of mental illness based on specific keywords or patterns of emotional fluctuations. The symptom detection unit can also analyze the frequency and patterns of the user's speech to detect abnormal changes. The response promotion unit encourages appropriate responses based on the signs of mental illness detected by the symptom detection unit. For example, the response promotion unit may ask the user, "You seem to be feeling down lately. Are you okay?" and recommend that the user consult a specialist doctor or counselor if necessary. The response promotion unit may also introduce the user to an appropriate medical institution or counseling service if the user so desires. In this way, the mental illness detection system according to the embodiment can detect signs of mental illness early through the user's daily chats and encourage appropriate responses.

[0054] The generation AI can refer to the user's past conversation history and generate questions optimized for each individual user. For example, the generation AI analyzes the user's past conversation history and generates questions based on the user's interests. For example, it poses questions related to hobbies or interests that the user has previously discussed. The generation AI also refers to the user's past conversation history and generates questions based on the user's emotional state and stress level. For example, if the user is feeling stressed, it provides topics that will help them relax. The generation AI also generates follow-up questions for unresolved issues or questions that the user has previously discussed based on the user's past conversation history. For example, it asks about the progress of a health problem that the user has previously discussed. In this way, the generation AI generates questions optimized based on the user's past conversation history, enabling more natural conversations.

[0055] The generation AI can learn the user's lifestyle and behavioral patterns and start a conversation at the appropriate time. For example, the generation AI can analyze the user's lifestyle and start a conversation when the user is relaxed. For example, it can ask questions after the user has returned home from work. The generation AI can also learn the user's behavioral patterns and start a conversation when the user is most likely to respond. For example, it can start a conversation while the user is commuting or during a break. The generation AI can also provide topics that will help the user relax during times when they are likely to feel stressed, based on the user's lifestyle. For example, it can ask questions that will help the user refresh themselves between work sessions. This allows for more effective communication by starting a conversation at the appropriate time based on the user's lifestyle and behavioral patterns.

[0056] Using its emotion estimation function, the generation AI can generate questions and responses that correspond to the user's emotional state and draw out the user's emotions. For example, the generation AI can analyze the user's emotional state in real time, and if the user is feeling positive, generate questions that further draw out those emotions. For example, if the user is happy, it can ask the reason for their joy. The generation AI can also analyze the user's emotional state, and if the user is feeling negative, it can generate responses that alleviate those emotions. For example, if the user is sad, it can provide words of encouragement. The generation AI can also provide topics that will help the user relax based on the user's emotional state. For example, if the user is feeling stressed, it can ask questions about hobbies and interests that will help them relax. In this way, by generating questions and responses that correspond to the user's emotional state, the generation AI can draw out the user's emotions and achieve deeper communication.

[0057] Generative AI can suggest topics based on the user's hobbies and interests, diversifying conversations. For example, generative AI can analyze the user's hobbies and interests and suggest topics based on them. For example, if the user likes movies, it can suggest topics about movies they have recently seen. Generative AI can also suggest new hobbies and activities based on the user's interests. For example, if the user is interested in the outdoors, it can introduce new hiking trails. Generative AI can also provide the latest news and trends related to the user's hobbies and interests. For example, if the user is interested in music, it can provide information on the latest music releases and concerts. In this way, by suggesting topics based on the user's hobbies and interests, it can diversify conversations and pique the user's interest.

[0058] Generative AI can introduce a multilingual conversation model so that it can accommodate users with different languages ​​and cultural backgrounds. For example, generative AI can use a multilingual conversation model to accommodate users who speak different languages. For example, it can hold conversations in multiple languages, such as English, French, and Chinese. Generative AI can also generate culturally appropriate responses for users with different cultural backgrounds. For example, it can provide responses that take into account the etiquette and customs of a particular culture. Generative AI can also use a multilingual conversation model to support communication between users who speak different languages. For example, it can provide a real-time translation function. This allows it to accommodate a wider variety of users by being able to accommodate users with different languages ​​and cultural backgrounds.

[0059] Using its emotion estimation function, the generation AI can suggest music or videos related to a particular emotion when the user feels that emotion. For example, the generation AI can analyze the user's emotional state, and if the user feels positive, suggest music or videos that will further enhance that emotion. For example, when the user is happy, it can provide cheerful music. The generation AI can also analyze the user's emotional state, and if the user feels negative, it can suggest music or videos that will soothe that emotion. For example, when the user feels sad, it can provide relaxing videos. The generation AI can also suggest music or videos that will help the user relax based on the user's emotional state. For example, when the user is feeling stressed, it can provide relaxing music. In this way, when the user feels a particular emotion, it can suggest music or videos related to that emotion, thereby soothing the user's emotions.

[0060] The conversation analysis unit deeply understands the context of a user's statements and can detect signs of mental illness from subtle nuances and word choice. For example, the generation AI analyzes the context of a user's statements and detects signs of mental illness from subtle nuances and word choice. For example, if a user frequently uses negative language, it detects this as a sign of mental illness. The conversation analysis unit also deeply understands the context of a user's statements and analyzes the user's emotional and psychological state. For example, if a user increasingly makes statements that lower their self-esteem, it detects this as a sign of mental illness. The conversation analysis unit also analyzes the user's psychological state in detail based on the context of the user's statements. For example, if a user overreacts to past events, it detects this as a sign of mental illness. This allows for a deep understanding of the context of a user's statements and detects signs of mental illness from subtle nuances and word choice, enabling more accurate detection of signs of mental illness.

[0061] The conversation analysis unit can analyze the frequency and patterns of a user's speech and detect abnormal changes. For example, the generation AI analyzes the frequency of a user's speech and detects abnormal changes. For example, if the user's speech frequency suddenly decreases, this is detected as a sign of mental illness. The generation AI also analyzes the patterns of a user's speech and detects abnormal changes. For example, if the user suddenly starts making more negative comments, this is detected as a sign of mental illness. The generation AI also analyzes the user's psychological state based on the frequency and patterns of the user's speech. For example, if the user starts speaking only during certain hours of the day, this is detected as a sign of mental illness. In this way, by analyzing the frequency and patterns of a user's speech and detecting abnormal changes, signs of mental illness can be discovered early.

[0062] The conversation analysis unit uses the emotion estimation function to perform a detailed analysis of the emotional fluctuations contained in the user's speech and identify signs of mental illness. For example, the generation AI in the conversation analysis unit uses the emotion estimation function to perform a detailed analysis of the emotional fluctuations contained in the user's speech. For example, if the user frequently expresses negative emotions, it detects this as a sign of mental illness. The generation AI in the conversation analysis unit also uses the emotion estimation function to analyze the emotional fluctuations contained in the user's speech and identify signs of mental illness. For example, if the user's emotional fluctuations suddenly become more severe, it detects this as a sign of mental illness. The generation AI in the conversation analysis unit also uses the emotion estimation function to perform a detailed analysis of the user's psychological state based on the emotional fluctuations contained in the user's speech. For example, if the user makes overly emotional speech, it detects this as a sign of mental illness. In this way, by performing a detailed analysis of the emotional fluctuations contained in the user's speech, it is possible to more accurately identify signs of mental illness.

[0063] The conversation analysis unit analyzes not only the user's speech but also behavioral data such as typing speed and input intervals to detect signs of mental illness. For example, the generation AI in the conversation analysis unit analyzes the user's typing speed to detect abnormal changes. For example, if the user's typing speed suddenly slows, it is detected as a sign of mental illness. The generation AI in the conversation analysis unit also analyzes the intervals between user inputs to detect abnormal changes. For example, if the user's input intervals become irregular, it is detected as a sign of mental illness. The generation AI in the conversation analysis unit also analyzes the user's psychological state based on the user's typing speed and input intervals. For example, if the user's typing speed slows only during certain hours, it is detected as a sign of mental illness. This allows for a more comprehensive detection of signs of mental illness by analyzing not only the user's speech but also behavioral data such as typing speed and input intervals.

[0064] The conversation analysis unit can also accept the user's speech as voice input and detect signs of mental illness from the tone and rhythm of the voice. For example, the conversation analysis unit uses a generation AI to analyze the user's speech input and detect signs of mental illness from the tone and rhythm of the voice. For example, if the user's voice suddenly becomes lower, this is detected as a sign of mental illness. The conversation analysis unit also uses a generation AI to analyze the tone and rhythm of the voice based on the user's speech input and identify signs of mental illness. For example, if the user's voice begins to tremble, this is detected as a sign of mental illness. The conversation analysis unit also uses a generation AI to analyze the user's speech input and perform a detailed analysis of the user's psychological state based on fluctuations in the tone and rhythm of the voice. For example, if the user's voice suddenly becomes faster, this is detected as a sign of mental illness. This allows signs of mental illness to be detected from a wider variety of data by accepting the user's speech as voice input and detecting signs of mental illness from the tone and rhythm of the voice.

[0065] The conversation analysis unit uses the emotion estimation function to analyze other users' reactions to the user's comments and can detect signs of mental illness taking into account social impact. For example, the generation AI in the conversation analysis unit uses the emotion estimation function to analyze other users' reactions to the user's comments and detect signs of mental illness taking into account social impact. For example, if other users show negative reactions, it detects this as a sign of mental illness. The generation AI also uses the emotion estimation function to analyze other users' reactions to the user's comments and identify signs of mental illness. For example, if other users frequently show worried reactions, it detects this as a sign of mental illness. The conversation analysis unit also uses the emotion estimation function to analyze the user's psychological state in detail based on other users' reactions to the user's comments. For example, if other users overreact to the user's comments, it detects this as a sign of mental illness. This enables more comprehensive detection of signs of mental illness by analyzing other users' reactions to the user's comments and detecting signs of mental illness taking into account social impact.

[0066] If signs of mental illness are detected, the response promotion unit can suggest specific relaxation methods or stress relief methods to the user. For example, if the generation AI detects signs of mental illness in the user, the response promotion unit will suggest specific relaxation methods. For example, it will explain how to do deep breathing or meditation and encourage the user to practice it. The response promotion unit also analyzes the user's stress level and suggests appropriate stress relief methods. For example, it may recommend light exercise or immersing oneself in a hobby. The response promotion unit also takes the user's mental state into consideration and suggests music or videos that are useful for relaxation. For example, it may provide a relaxing music playlist or videos of natural scenery. In this way, if signs of mental illness are detected, the response promotion unit can improve the user's mental state by suggesting specific relaxation methods or stress relief methods.

[0067] The response promotion unit can refer to the user's past conversation history and re-suggest previously effective response measures. In the response promotion unit, for example, the generation AI analyzes the user's past conversation history and re-suggests previously effective relaxation methods. For example, if meditation was previously effective for the user, the generation AI recommends meditation again. In addition, the response promotion unit re-suggests previously effective stress relief methods based on the user's past conversation history. For example, if jogging was previously effective for the user, the generation AI recommends jogging again. In addition, the response promotion unit refers to the user's past conversation history and re-suggests previously effective response measures. For example, if listening to relaxing music was previously effective for the user, the generation AI recommends that music again. In this way, by referring to the user's past conversation history and re-suggesting previously effective response measures, it is possible to provide the user with the optimal response measures.

[0068] The response promotion unit uses the emotion estimation function to generate words of encouragement or comfort according to the user's emotional state, thereby soothing the user's feelings. In the response promotion unit, for example, the generation AI uses the emotion estimation function to analyze the user's emotional state and generate appropriate words of encouragement. For example, if the user is feeling down, the generation AI provides words of encouragement such as, "You're a wonderful person." In addition, the response promotion unit uses the emotion estimation function to analyze the user's emotional state and generate appropriate words of comfort. For example, if the user is sad, the generation AI provides words of comfort such as, "It's okay, you're not alone." In addition, the response promotion unit uses the emotion estimation function to generate words of comfort based on the user's emotional state. For example, if the user is feeling stressed, the generation AI provides words such as, "Relax and take a break." In this way, the emotion estimation function can be used to generate words of encouragement or comfort according to the user's emotional state, thereby soothing the user's feelings.

[0069] If signs of mental illness are detected, the response promotion unit can introduce the user to available online mental health resources and communities. For example, if the generating AI detects signs of mental illness in the user, the response promotion unit introduces available online mental health resources. For example, it provides online counseling services and mental health websites. The response promotion unit also considers the user's mental state and introduces appropriate online communities. For example, it suggests support groups where people with the same concerns gather. If the generating AI detects signs of mental illness in the user, the response promotion unit introduces available online mental health resources and communities. For example, it provides mental health forums and chat rooms. In this way, if signs of mental illness are detected, the user can receive appropriate support by being introduced to available online mental health resources and communities.

[0070] The response promotion unit can provide a function to notify trusted friends and family of the situation with the user's consent. For example, if the generation AI detects signs of mental illness in the user, the response promotion unit provides a function to notify trusted friends and family of the situation with the user's consent. For example, an email or message is sent to friends and family only if the user consents. The response promotion unit also provides a function to notify trusted friends and family of the situation by the generation AI taking the user's mental state into consideration and with the user's consent. For example, a phone call is made to friends and family only if the user consents. The response promotion unit also provides a function to notify trusted friends and family of the situation with the user's consent if the generation AI detects signs of mental illness in the user, with the user's consent. For example, a message explaining the situation is sent to friends and family only if the user consents. This allows the user to receive appropriate support by notifying trusted friends and family of the situation with the user's consent.

[0071] The response promotion unit uses the emotion estimation function to allow a user to share positive experiences or episodes related to a particular emotion when the user feels that emotion. For example, the generation AI uses the emotion estimation function to allow a user to share positive experiences or episodes related to that emotion when the user feels a positive emotion. For example, when the user is happy, the response promotion unit introduces other users' successful experiences. The generation AI also uses the emotion estimation function to share positive experiences or episodes that ease the user's negative emotions when the user feels that emotion. For example, when the user is sad, the response promotion unit provides an encouraging episode. The generation AI also uses the emotion estimation function to allow a user to share positive experiences or episodes related to a particular emotion when the user feels that emotion. For example, when the user is feeling stressed, the response promotion unit provides a relaxing experience. In this way, when a user feels a particular emotion, the user's feelings can be eased by sharing positive experiences or episodes related to that emotion.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The generation AI can learn the user's lifestyle and behavioral patterns and start a conversation at the appropriate time. For example, the generation AI can analyze the user's lifestyle and start a conversation when the user is relaxed. For example, it can ask questions after the user has returned home from work. The generation AI can also learn the user's behavioral patterns and start a conversation when the user is most likely to respond. For example, it can start a conversation while the user is commuting or during a break. The generation AI can also provide topics that will help the user relax during times when they are likely to feel stressed, based on the user's lifestyle. For example, it can ask questions that will help the user refresh themselves between work sessions. This allows for more effective communication by starting a conversation at the appropriate time based on the user's lifestyle and behavioral patterns.

[0074] Generative AI can suggest topics based on the user's hobbies and interests, diversifying conversations. For example, generative AI can analyze the user's hobbies and interests and suggest topics based on them. For example, if the user likes movies, it can suggest topics about movies they have recently seen. Generative AI can also suggest new hobbies and activities based on the user's interests. For example, if the user is interested in the outdoors, it can introduce new hiking trails. Generative AI can also provide the latest news and trends related to the user's hobbies and interests. For example, if the user is interested in music, it can provide information on the latest music releases and concerts. In this way, by suggesting topics based on the user's hobbies and interests, it can diversify conversations and pique the user's interest.

[0075] Generative AI can introduce a multilingual conversation model so that it can accommodate users with different languages ​​and cultural backgrounds. For example, generative AI can use a multilingual conversation model to accommodate users who speak different languages. For example, it can hold conversations in multiple languages, such as English, French, and Chinese. Generative AI can also generate culturally appropriate responses for users with different cultural backgrounds. For example, it can provide responses that take into account the etiquette and customs of a particular culture. Generative AI can also use a multilingual conversation model to support communication between users who speak different languages. For example, it can provide a real-time translation function. This allows it to accommodate a wider variety of users by being able to accommodate users with different languages ​​and cultural backgrounds.

[0076] Using its emotion estimation function, the generation AI can generate questions and responses that correspond to the user's emotional state and draw out the user's emotions. For example, the generation AI can analyze the user's emotional state in real time, and if the user is feeling positive, generate questions that further draw out those emotions. For example, if the user is happy, it can ask the reason for their joy. The generation AI can also analyze the user's emotional state, and if the user is feeling negative, it can generate responses that alleviate those emotions. For example, if the user is sad, it can provide words of encouragement. The generation AI can also provide topics that will help the user relax based on the user's emotional state. For example, if the user is feeling stressed, it can ask questions about hobbies and interests that will help them relax. In this way, by generating questions and responses that correspond to the user's emotional state, the generation AI can draw out the user's emotions and achieve deeper communication.

[0077] The generation AI can refer to the user's past conversation history and generate questions optimized for each individual user. For example, the generation AI analyzes the user's past conversation history and generates questions based on the user's interests. For example, it poses questions related to hobbies or interests that the user has previously discussed. The generation AI also refers to the user's past conversation history and generates questions based on the user's emotional state and stress level. For example, if the user is feeling stressed, it provides topics that will help them relax. The generation AI also generates follow-up questions for unresolved issues or questions that the user has previously discussed based on the user's past conversation history. For example, it asks about the progress of a health problem that the user has previously discussed. In this way, the generation AI generates questions optimized based on the user's past conversation history, enabling more natural conversations.

[0078] Using its emotion estimation function, the generation AI can suggest music or videos related to a particular emotion when the user feels that emotion. For example, the generation AI can analyze the user's emotional state, and if the user feels positive, suggest music or videos that will further enhance that emotion. For example, when the user is happy, it can provide cheerful music. The generation AI can also analyze the user's emotional state, and if the user feels negative, it can suggest music or videos that will soothe that emotion. For example, when the user feels sad, it can provide relaxing videos. The generation AI can also suggest music or videos that will help the user relax based on the user's emotional state. For example, when the user is feeling stressed, it can provide relaxing music. In this way, when the user feels a particular emotion, it can suggest music or videos related to that emotion, thereby soothing the user's emotions.

[0079] The conversation analysis unit deeply understands the context of a user's statements and can detect signs of mental illness from subtle nuances and word choice. For example, the generation AI analyzes the context of a user's statements and detects signs of mental illness from subtle nuances and word choice. For example, if a user frequently uses negative language, it detects this as a sign of mental illness. The conversation analysis unit also deeply understands the context of a user's statements and analyzes the user's emotional and psychological state. For example, if a user increasingly makes statements that lower their self-esteem, it detects this as a sign of mental illness. The conversation analysis unit also analyzes the user's psychological state in detail based on the context of the user's statements. For example, if a user overreacts to past events, it detects this as a sign of mental illness. This allows for a deep understanding of the context of a user's statements and detects signs of mental illness from subtle nuances and word choice, enabling more accurate detection of signs of mental illness.

[0080] The conversation analysis unit can analyze the frequency and patterns of a user's speech and detect abnormal changes. For example, the generation AI analyzes the frequency of a user's speech and detects abnormal changes. For example, if the user's speech frequency suddenly decreases, this is detected as a sign of mental illness. The generation AI also analyzes the patterns of a user's speech and detects abnormal changes. For example, if the user suddenly starts making more negative comments, this is detected as a sign of mental illness. The generation AI also analyzes the user's psychological state based on the frequency and patterns of the user's speech. For example, if the user starts speaking only during certain hours of the day, this is detected as a sign of mental illness. In this way, by analyzing the frequency and patterns of a user's speech and detecting abnormal changes, signs of mental illness can be discovered early.

[0081] The conversation analysis unit uses the emotion estimation function to perform a detailed analysis of the emotional fluctuations contained in the user's speech and identify signs of mental illness. For example, the generation AI in the conversation analysis unit uses the emotion estimation function to perform a detailed analysis of the emotional fluctuations contained in the user's speech. For example, if the user frequently expresses negative emotions, it detects this as a sign of mental illness. The generation AI in the conversation analysis unit also uses the emotion estimation function to analyze the emotional fluctuations contained in the user's speech and identify signs of mental illness. For example, if the user's emotional fluctuations suddenly become more severe, it detects this as a sign of mental illness. The generation AI in the conversation analysis unit also uses the emotion estimation function to perform a detailed analysis of the user's psychological state based on the emotional fluctuations contained in the user's speech. For example, if the user makes overly emotional speech, it detects this as a sign of mental illness. In this way, by performing a detailed analysis of the emotional fluctuations contained in the user's speech, it is possible to more accurately identify signs of mental illness.

[0082] If signs of mental illness are detected, the response promotion unit can suggest specific relaxation methods or stress relief methods to the user. For example, if the generation AI detects signs of mental illness in the user, the response promotion unit will suggest specific relaxation methods. For example, it will explain how to do deep breathing or meditation and encourage the user to practice it. The response promotion unit also analyzes the user's stress level and suggests appropriate stress relief methods. For example, it may recommend light exercise or immersing oneself in a hobby. The response promotion unit also takes the user's mental state into consideration and suggests music or videos that are useful for relaxation. For example, it may provide a relaxing music playlist or videos of natural scenery. In this way, if signs of mental illness are detected, the response promotion unit can improve the user's mental state by suggesting specific relaxation methods or stress relief methods.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The generative AI carries out the same interactions that users have on a daily basis. For example, the generative AI asks the user questions such as "How was your day today?" and advances the conversation based on the user's answers. The generative AI also generates appropriate responses to the user's input, allowing for a natural conversation to continue. Step 2: The conversation analysis unit analyzes the conversation content generated by the generation AI. For example, the conversation analysis unit uses natural language processing technology to analyze the conversation content and extract the user's emotional state and speech patterns. The conversation analysis unit can also perform sentiment analysis to identify the emotions contained in the user's speech. Step 3: The symptom detection unit detects symptoms of mental illness from the conversation content analyzed by the conversation analysis unit. For example, the symptom detection unit detects symptoms of mental illness based on specific keywords or patterns of emotional fluctuations. The symptom detection unit can also analyze the frequency and patterns of the user's speech to detect abnormal changes. Step 4: The response promotion unit encourages the user to take an appropriate action based on the signs of mental illness detected by the sign detection unit. For example, the response promotion unit may ask the user, "You seem to be feeling depressed lately. Are you okay?" and recommend that the user consult a specialist doctor or counselor if necessary. The response promotion unit may also introduce the user to an appropriate medical institution or counseling service if the user so desires.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 generative AI that handles daily user interactions through a chat app using generative AI, and a conversation analysis unit that analyzes the conversation content generated by the generation AI; a symptom detection unit that detects a symptom of a mental illness from the conversation content analyzed by the conversation analysis unit; a response promotion unit that promotes appropriate responses based on the signs of mental illness detected by the sign detection unit. A system characterized by:

2. The generated AI is Refer to the user's past conversation history to generate questions optimized for each individual user 2. The system of claim 1.

3. The generated AI is Learns the user's daily rhythm and behavioral patterns and starts conversations at appropriate times 2. The system of claim 1.

4. The generated AI is Generate questions and responses according to the user's emotional state to elicit the user's emotions 2. The system of claim 1.

5. The generated AI is Diversify conversations by suggesting topics based on users' hobbies and interests 2. The system of claim 1.

Images