system

Abstract

The system according to the embodiment aims to analyze conversation data, detect signs of mental illness early, and provide appropriate advice. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects conversation data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects signs of mental illness based on the data analyzed by the analysis unit. The provision unit provides advice based on the signs detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to detect signs of mental illness early and provide appropriate advice.

[0005] The system according to the embodiment aims to analyze conversation data, detect signs of mental illness early, and provide appropriate advice. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects conversation data. The analysis unit analyzes the data collected by the collection unit. The detection unit detects signs of psychosis based on the data analyzed by the analysis unit. The provision unit provides advice based on the signs detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze conversation data, detect early signs of mental illness, and provide appropriate advice. [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 detects signs of mental illness in advance through a regular chat app, preventing escalation and achieving early resolution. This mental illness detection system naturally detects signs of mental illness through ongoing, casual communication with a virtual character created by a generative AI. For example, a user uses a regular chat app to converse with a virtual character created by a generative AI. The generative AI analyzes the user's conversation data to detect signs of mental illness. For example, if the user frequently makes negative comments, the generative AI detects these signs and suggests appropriate responses. Next, the generative AI analyzes the user's daily conversation patterns, including the topics the user is interested in and the tone of their speech. This allows the type and degree of stress the user is experiencing to be identified. For example, if a user frequently feels stressed about work, the generative AI can identify the type and degree of stress and provide appropriate advice. This system allows users to easily understand their own mental state and take appropriate measures early on. For example, the generative AI can prevent mental illness from worsening by suggesting relaxation techniques or consulting a specialist. Furthermore, by analyzing the user's daily conversation patterns, the cause of stress can be identified and appropriate measures can be taken. This allows the mental illness detection system to grasp the user's mental state early and take appropriate measures.

[0029] A psychosis detection system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects conversation data of a user. The conversation data includes, but is not limited to, voice data, text data, and video data. The collection unit collects, for example, conversations the user has through a chat app in real time. The collection unit can also collect past conversation histories. For example, the collection unit stores data on past conversations the user has had and uses the data for analysis as needed. The analysis unit analyzes the conversation data collected by the collection unit. Examples of analysis include, but are not limited to, voice analysis, text analysis, and emotion analysis. For example, the analysis unit analyzes the content, tone, and frequency of the user's statements to evaluate the user's mental state. The analysis unit can use a generation AI to perform a detailed analysis of the user's conversation data. For example, the generation AI estimates the user's emotions based on the content of the user's statements and reflects the results in the analysis. The detection unit detects signs of psychosis based on the data analyzed by the analysis unit. Signs of mental illness include, but are not limited to, depression, anxiety disorders, and schizophrenia. The detection unit can detect signs of mental illness with high accuracy using the generation AI. For example, the generation AI detects signs of mental illness based on the content, tone, frequency, etc. of a user's speech. The provision unit provides appropriate advice based on the signs detected by the detection unit. The advice can include, but is not limited to, methods for relaxing or consulting a specialist. The provision unit can provide appropriate advice to the user using the generation AI. For example, the generation AI suggests methods for relaxing or consulting a specialist depending on the user's mental state. This allows the mental illness detection system according to the embodiment to quickly grasp the user's mental state and take appropriate measures.

[0030] The analysis unit can analyze the user's daily conversation patterns and identify the stress the user is experiencing. The analysis unit, for example, analyzes the user's daily conversation patterns. For example, it analyzes what topics the user is interested in and the tone of their speech. The analysis unit can use the generation AI to analyze the user's daily conversation patterns in detail. For example, the generation AI can identify the type and level of stress based on the content, tone, and frequency of the user's speech. This allows the type and level of stress the user is experiencing to be identified and appropriate measures to be taken. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's daily conversation patterns into the generation AI and have the generation AI identify the type and level of stress.

[0031] The providing unit can suggest relaxation methods or consultation with a specialist to the user based on the identified type and level of stress. The providing unit can suggest relaxation methods or consultation with a specialist to the user based on, for example, the identified type and level of stress. For example, if the user frequently feels stressed about work-related topics, the providing unit can suggest deep breathing or meditation as relaxation methods. The providing unit can also suggest consultation with a specialist if the user feels stressed about home-related topics. For example, the providing unit can suggest counseling or a consultation with a psychiatrist. By suggesting appropriate relaxation methods or consultation with a specialist to the user, stress can be reduced and mental illness can be prevented. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the identified type and level of stress into the generation AI and cause the generation AI to suggest relaxation methods or consultation with a specialist.

[0032] The collection unit can collect conversation data such as the content, tone, and frequency of user speech. The collection unit, for example, collects the content of user speech. The content of speech includes, for example, text, audio, video, etc., but is not limited to these examples. The collection unit, for example, collects the content of user speech in real time. The collection unit can also collect the user's tone. The tone includes, for example, but is not limited to, the pitch, strength, and emotional expression of the voice. The collection unit, for example, analyzes the user's tone and collects the emotional expression. The collection unit can also collect the frequency of user speech. The frequency includes, for example, but is not limited to, the number of times a user speaks per day or per week. The collection unit, for example, records the frequency of user speech and uses it for analysis. By collecting conversation data such as the content, tone, and frequency of user speech, more detailed analysis is possible. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the content, tone, and frequency of a user's speech into the generation AI and cause the generation AI to collect conversation data.

[0033] The detection unit can detect symptoms of psychosis based on the collected conversation data. The detection unit detects symptoms of psychosis based on, for example, the collected conversation data. Examples of symptoms of psychosis include, but are not limited to, depression, anxiety, and hallucinations. The detection unit can detect symptoms of psychosis with high accuracy using a generation AI. For example, the generation AI detects symptoms of psychosis based on the content, tone, frequency, etc. of a user's speech. This enables early detection of symptoms of psychosis by detecting them based on the collected conversation data. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input collected conversation data to the generation AI and cause the generation AI to detect symptoms of psychosis.

[0034] The psychosis detection system further includes a collection unit that analyzes the user's past conversation history and selects the optimal collection method. The collection unit, for example, analyzes the user's past conversation history and selects the optimal collection method. For example, the generation AI selects the optimal collection method based on conversation patterns frequently used by the user in the past. It is also possible to concentrate collection during specific time periods based on the user's past conversation history. Furthermore, it is also possible to analyze the user's past conversation history and prioritize the collection of important conversation data. This allows the analysis of the user's past conversation history to select the optimal collection method and enable efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the user's past conversation history into the generation AI and have the generation AI select the optimal collection method.

[0035] The mental illness detection system further includes a collection unit that filters conversation data based on the user's current living situation and areas of interest when collecting the conversation data. For example, the collection unit filters the conversation data based on the user's current living situation and areas of interest when collecting the conversation data. For example, the generation AI prioritizes collecting relevant conversation data based on the user's current living situation. The generation AI can also collect conversation data related to specific topics based on the user's areas of interest. Furthermore, the generation AI can filter unnecessary data taking into account the user's living situation and areas of interest. This allows for more relevant data to be collected by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the user's living situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0036] The psychosis detection system further includes a collection unit that, when collecting conversation data, prioritizes the collection of highly relevant data based on the user's geographical location information. For example, when collecting conversation data, the collection unit prioritizes the collection of highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, the generation AI prioritizes the collection of conversation data related to that location. The generation AI can also collect region-specific conversation data based on the user's geographical location information. Furthermore, when the user is traveling, the generation AI can prioritize the collection of conversation data related to the user's destination. This allows the collection of highly relevant data to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0037] The psychosis detection system further includes a collection unit that analyzes the user's social media activity and collects related data when collecting conversation data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting conversation data. For example, the generation AI collects related conversation data based on keywords frequently used by the user on social media. The generation AI can also collect conversation data related to topics of interest from the user's social media activity. Furthermore, the generation AI can analyze the content of the user's social media posts and collect related conversation data. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0038] Furthermore, the mental illness detection system includes an analysis unit that adjusts the accuracy of the analysis based on the importance of the conversation data during analysis. The analysis unit, for example, adjusts the accuracy of the analysis based on the importance of the conversation data during analysis. For example, the generation AI performs a detailed analysis of important conversation data. The generation AI can also perform a simplified analysis of general conversation data. Furthermore, the generation AI can adjust the accuracy of the analysis according to the importance of the conversation data. This enables efficient analysis by adjusting the accuracy of the analysis based on the importance of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the importance of the conversation data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0039] Furthermore, the mental illness detection system includes an analysis unit that applies different algorithms depending on the category of conversation data during analysis. The analysis unit, for example, applies different algorithms depending on the category of conversation data during analysis. For example, the generation AI applies a specific analysis algorithm to negative conversation data. The generation AI can also apply a different analysis algorithm to positive conversation data. Furthermore, the generation AI can select the optimal analysis algorithm depending on the category of conversation data. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the category of conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the category of conversation data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0040] Furthermore, the psychosis detection system includes an analysis unit that determines the order of analysis based on the submission time of the conversation data during analysis. The analysis unit, for example, determines the order of analysis based on the submission time of the conversation data during analysis. For example, the generation AI prioritizes analysis of the most recent conversation data. The generation AI can also postpone analysis of older conversation data. Furthermore, the generation AI can determine the analysis priority based on the submission time of the conversation data. This enables efficient analysis by determining the analysis priority based on the submission time of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the submission time of the conversation data to the generation AI and have the generation AI determine the order of analysis.

[0041] Furthermore, the psychosis detection system includes an analysis unit that adjusts the analysis order based on the relevance of the conversation data during analysis. The analysis unit, for example, adjusts the analysis order based on the relevance of the conversation data during analysis. For example, the generation AI prioritizes analysis of highly relevant conversation data. The generation AI can also postpone analysis of less relevant conversation data. Furthermore, the generation AI can adjust the analysis order based on the relevance of the conversation data. This enables efficient analysis by adjusting the analysis order based on the relevance of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the conversation data to the generation AI and have the generation AI adjust the analysis order.

[0042] Furthermore, the mental illness detection system includes a detection unit that improves detection accuracy based on the interrelationships of conversation data during detection. The detection unit, for example, improves detection accuracy based on the interrelationships of conversation data during detection. For example, the detection unit analyzes the interrelationships of conversation data and the generation AI detects signs of mental illness with high accuracy. The generation AI can also reduce false detections by taking into account the interrelationships of conversation data. Furthermore, the generation AI can improve detection accuracy based on the interrelationships of conversation data. In this way, the accuracy of detecting signs of mental illness is improved by taking into account the interrelationships of conversation data. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit may input the interrelationships of conversation data into the generation AI and cause the generation AI to improve detection accuracy.

[0043] Furthermore, the mental illness detection system includes a detection unit that performs detection based on attribute information of the person who submitted the conversation data at the time of detection. The detection unit, for example, performs detection based on attribute information of the person who submitted the conversation data at the time of detection. For example, the generation AI detects signs of mental illness taking into account the age and gender of the person who submitted the conversation data. The generation AI can also perform detection taking into account the occupation and living environment of the person who submitted the conversation data. Furthermore, the generation AI can improve the accuracy of detection based on the attribute information of the person who submitted the conversation data. In this way, by taking into account the attribute information of the person who submitted the conversation data, the accuracy of detecting signs of mental illness is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input attribute information of the person who submitted the conversation data into the generation AI and cause the generation AI to perform detection.

[0044] Furthermore, the mental illness detection system includes a detection unit that performs detection based on the geographic distribution of the conversation data at the time of detection. The detection unit, for example, performs detection based on the geographic distribution of the conversation data at the time of detection. For example, the geographic distribution of the conversation data is analyzed and the generation AI detects signs of mental illness specific to a region. The generation AI can also improve the accuracy of detection by taking the geographic distribution of the conversation data into consideration. Furthermore, the generation AI can detect signs of mental illness with high accuracy based on the geographic distribution of the conversation data. In this way, by taking the geographic distribution of the conversation data into consideration, signs of mental illness specific to a region can be detected with high accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the geographic distribution of the conversation data to the generation AI and have the generation AI perform detection.

[0045] Furthermore, the mental illness detection system includes a detection unit that improves detection accuracy based on literature related to the conversation data during detection. The detection unit, for example, improves detection accuracy based on literature related to the conversation data during detection. For example, the generation AI refers to literature related to the conversation data and detects signs of mental illness with high accuracy. The generation AI can also reduce false detections based on literature related to the conversation data. Furthermore, the generation AI can improve detection accuracy by taking into account literature related to the conversation data. As a result, the accuracy of detecting signs of mental illness is improved by referring to literature related to the conversation data. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit may input literature related to the conversation data into the generation AI and cause the generation AI to improve detection accuracy.

[0046] The mental illness detection system further includes a providing unit that adjusts the accuracy of advice based on the severity of the mental illness signs when providing advice. The providing unit, for example, adjusts the accuracy of advice based on the severity of the mental illness signs when providing advice. For example, the generation AI provides detailed advice for severe mental illness signs. The generation AI can also provide simplified advice for mild mental illness signs. Furthermore, the generation AI can adjust the accuracy of the advice according to the severity of the mental illness signs. This allows appropriate advice to be provided by adjusting the level of detail of the advice based on the severity of the mental illness signs. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the severity of the mental illness signs into the generation AI and cause the generation AI to adjust the accuracy of the advice.

[0047] Furthermore, the mental illness detection system includes a providing unit that applies different algorithms depending on the category of the mental illness sign when providing advice. The providing unit, for example, applies different algorithms depending on the category of the mental illness sign when providing advice. For example, the generation AI applies a specific advice algorithm to negative mental illness signs. The generation AI can also apply a different advice algorithm to positive mental illness signs. Furthermore, the generation AI can select an optimal advice algorithm depending on the category of the mental illness sign. This makes it possible to provide appropriate advice by applying the optimal advice algorithm depending on the category of the mental illness sign. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the category of the mental illness sign to the generation AI and cause the generation AI to apply the optimal advice algorithm.

[0048] Furthermore, the mental illness detection system includes a providing unit that, when providing advice, determines the order of advice based on the timing of the presentation of mental illness symptoms. The providing unit, for example, determines the order of advice based on the timing of the presentation of mental illness symptoms when providing advice. For example, the generation AI prioritizes providing advice for the most recent mental illness symptoms. The generation AI can also provide advice for older mental illness symptoms later. Furthermore, the generation AI can determine the priority of advice based on the timing of the presentation of mental illness symptoms. In this way, by determining the priority of advice based on the timing of the presentation of mental illness symptoms, advice can be provided at an appropriate time. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the timing of the presentation of mental illness symptoms to the generation AI and cause the generation AI to determine the order of advice.

[0049] Furthermore, the mental illness detection system includes a providing unit that adjusts the order of advice based on the relevance of mental illness signs when providing advice. The providing unit, for example, adjusts the order of advice based on the relevance of mental illness signs when providing advice. For example, the generation AI prioritizes providing advice for highly relevant mental illness signs. The generation AI can also provide advice later for less relevant mental illness signs. Furthermore, the generation AI can adjust the order of advice based on the relevance of mental illness signs. In this way, by adjusting the order of advice based on the relevance of mental illness signs, important advice can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the relevance of mental illness signs to the generation AI and cause the generation AI to adjust the order of advice.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The mental illness detection system includes a collection unit that collects a user's conversation data, but it can also include a biometric data collection unit that collects the user's biometric data (e.g., heart rate, electrodermal activity, and body temperature). For example, biometric data can be collected from devices such as smartwatches and fitness trackers while the user is using a chat app. This allows for a more comprehensive assessment of the user's mental state. The collected biometric data is then integrated with the conversation data in the analysis unit, enabling a more accurate assessment of the user's mental state. For example, if the user is feeling stressed, their heart rate may increase and their electrodermal activity may change. Analyzing this biometric data can more accurately identify the user's stress level and provide appropriate advice. Furthermore, monitoring the user's biometric data over a long period of time can enable early detection of changes in the user's mental state and enable preventive measures to be taken.

[0052] The analysis unit analyzes the user's daily conversation patterns, but can also analyze the user's social media activity. For example, it can analyze the keywords and hashtags frequently used by the user to identify the user's interests. This allows for a more detailed understanding of the topics the user is interested in. For example, if the user frequently uses keywords such as "stress" or "anxiety" on social media, the analysis unit can detect this and determine that the user is likely feeling stressed. Furthermore, by analyzing the time periods and frequency of the user's social media activity, it can also identify the user's daily rhythm and peak stress times. This makes it possible to provide advice to the user at more appropriate times.

[0053] The providing unit suggests relaxation methods or consulting a specialist based on the identified type and level of stress, but can also suggest relaxation methods based on the user's hobbies and interests. For example, if the user likes listening to music, the providing unit can suggest a specific music playlist as a relaxation method. Also, if the user likes exercise, the providing unit can suggest yoga or light exercise. This makes it possible to provide a more effective relaxation method for the user. Furthermore, it is possible to analyze the user's past relaxation method history and prioritize suggesting methods that were effective. For example, if meditation was effective in the past, the providing unit can suggest meditation again. This makes it possible to provide the optimal relaxation method for the user and reduce stress.

[0054] The collection unit collects conversational data on the content, tone, and frequency of a user's speech, but can also collect the user's non-verbal behavior (gestures, facial expressions, etc.). For example, if a user is using video chat, the collection unit collects the user's facial expressions and gestures through the camera. This allows for a more detailed understanding of the user's emotions and mental state. For example, if the user frequently frowns, the collection unit can detect this and determine that the user is likely feeling stressed. Furthermore, by monitoring the user's non-verbal behavior over a long period of time, it is possible to detect changes in the user's mental state early and take preventive measures. This allows for a more comprehensive evaluation of the user's mental state and provide appropriate advice.

[0055] The detection unit detects symptoms of mental illness based on the collected conversation data, but can also adjust the detection criteria based on the user's living environment (workplace, home, etc.). For example, if a user frequently reports stress at work, the detection unit applies detection criteria specialized for the work environment. Also, if a user reports problems at home, the detection unit can apply detection criteria specialized for the home environment. This enables appropriate detection according to the user's living environment. Furthermore, it is possible to monitor changes in the user's living environment (moving, changing jobs, etc.) and dynamically adjust the detection criteria. This enables flexible responses according to the user's living environment, enabling early detection and prevention of mental illness.

[0056] The collection unit analyzes the user's past conversation history to select the optimal collection method, but it can also analyze the user's past emotional history to adjust the collection method. For example, it can analyze conversation data from periods when the user felt stressed in the past and identify the collection method that was effective at that time. This makes it possible to apply the optimal collection method if a similar situation recurs. Furthermore, by monitoring the user's emotional history over a long period of time and identifying emotional patterns, preventive data collection becomes possible. For example, if a user tends to feel stressed at a certain time of year, collection can be strengthened at that time. This allows for flexible data collection based on the user's emotional history.

[0057] When collecting conversation data, the collection unit filters the data based on the user's current lifestyle and areas of interest, but can also filter the data based on the user's health condition (sleep patterns, eating habits, etc.). For example, if the user is sleep-deprived, the generation AI will take that into account when filtering the conversation data. Also, if the user is eating a healthy diet, the generation AI can take that into account when filtering the conversation data. This makes it possible to collect appropriate data according to the user's health condition. Furthermore, it is possible to monitor changes in the user's health condition and dynamically adjust the filtering criteria. For example, if the user suddenly becomes ill, the generation AI will immediately change the filtering criteria. This enables flexible data collection according to the user's health condition.

[0058] When collecting conversation data, the collection unit prioritizes collecting highly relevant data based on the user's geographic location information, but it can also analyze the user's movement patterns and adjust the collection timing. For example, if a user has a specific movement pattern, such as during commuting or traveling, the generation AI adjusts the collection timing based on that movement pattern. This allows data collection to be strengthened during times when the user is likely to feel stressed while traveling. Furthermore, it is possible to monitor changes in the user's movement pattern and dynamically adjust the collection timing. For example, if the user chooses a new commuting route, the generation AI will immediately change the collection timing. This enables flexible data collection according to the user's movement patterns.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects user conversation data. The conversation data includes voice data, text data, video data, etc. The collection unit collects conversations that users have through chat apps in real time. It can also collect past conversation histories. Step 2: The analysis unit analyzes the conversation data collected by the collection unit. The analysis includes voice analysis, text analysis, and sentiment analysis. The analysis unit analyzes the content, tone, and frequency of the user's speech to evaluate the user's mental state. A detailed analysis can be performed using generative AI. Step 3: The detection unit detects signs of mental illness based on the data analyzed by the analysis unit. Signs of mental illness include depression, anxiety disorders, and schizophrenia. Generative AI can be used to detect these signs with high accuracy. Step 4: The provision unit provides appropriate advice based on the symptoms detected by the detection unit. The advice may include ways to relax or consulting a specialist. Appropriate advice can be provided to the user using the generation AI.

[0061] (Example 2) A mental illness detection system according to an embodiment of the present invention detects signs of mental illness in advance through a regular chat app, preventing escalation and achieving early resolution. This mental illness detection system naturally detects signs of mental illness through ongoing, casual communication with a virtual character created by a generative AI. For example, a user uses a regular chat app to converse with a virtual character created by a generative AI. The generative AI analyzes the user's conversation data to detect signs of mental illness. For example, if the user frequently makes negative comments, the generative AI detects these signs and suggests appropriate responses. Next, the generative AI analyzes the user's daily conversation patterns, including the topics the user is interested in and the tone of their speech. This allows the type and degree of stress the user is experiencing to be identified. For example, if a user frequently feels stressed about work, the generative AI can identify the type and degree of stress and provide appropriate advice. This system allows users to easily understand their own mental state and take appropriate measures early on. For example, the generative AI can prevent mental illness from worsening by suggesting relaxation techniques or consulting a specialist. Furthermore, by analyzing the user's daily conversation patterns, the cause of stress can be identified and appropriate measures can be taken. This allows the mental illness detection system to grasp the user's mental state early and take appropriate measures.

[0062] A psychosis detection system according to an embodiment includes a collection unit, an analysis unit, a detection unit, and a provision unit. The collection unit collects conversation data of a user. The conversation data includes, but is not limited to, voice data, text data, and video data. The collection unit collects, for example, conversations the user has through a chat app in real time. The collection unit can also collect past conversation histories. For example, the collection unit stores data on past conversations the user has had and uses the data for analysis as needed. The analysis unit analyzes the conversation data collected by the collection unit. Examples of analysis include, but are not limited to, voice analysis, text analysis, and emotion analysis. For example, the analysis unit analyzes the content, tone, and frequency of the user's statements to evaluate the user's mental state. The analysis unit can use a generation AI to perform a detailed analysis of the user's conversation data. For example, the generation AI estimates the user's emotions based on the content of the user's statements and reflects the results in the analysis. The detection unit detects signs of psychosis based on the data analyzed by the analysis unit. Signs of mental illness include, but are not limited to, depression, anxiety disorders, and schizophrenia. The detection unit can detect signs of mental illness with high accuracy using the generation AI. For example, the generation AI detects signs of mental illness based on the content, tone, frequency, etc. of a user's speech. The provision unit provides appropriate advice based on the signs detected by the detection unit. The advice can include, but is not limited to, methods for relaxing or consulting a specialist. The provision unit can provide appropriate advice to the user using the generation AI. For example, the generation AI suggests methods for relaxing or consulting a specialist depending on the user's mental state. This allows the mental illness detection system according to the embodiment to quickly grasp the user's mental state and take appropriate measures.

[0063] The analysis unit can analyze the user's daily conversation patterns and identify the stress the user is experiencing. The analysis unit, for example, analyzes the user's daily conversation patterns. For example, it analyzes what topics the user is interested in and the tone of their speech. The analysis unit can use the generation AI to analyze the user's daily conversation patterns in detail. For example, the generation AI can identify the type and level of stress based on the content, tone, and frequency of the user's speech. This allows the type and level of stress the user is experiencing to be identified and appropriate measures to be taken. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's daily conversation patterns into the generation AI and have the generation AI identify the type and level of stress.

[0064] The providing unit can suggest relaxation methods or consultation with a specialist to the user based on the identified type and level of stress. The providing unit can suggest relaxation methods or consultation with a specialist to the user based on, for example, the identified type and level of stress. For example, if the user frequently feels stressed about work-related topics, the providing unit can suggest deep breathing or meditation as relaxation methods. The providing unit can also suggest consultation with a specialist if the user feels stressed about home-related topics. For example, the providing unit can suggest counseling or a consultation with a psychiatrist. By suggesting appropriate relaxation methods or consultation with a specialist to the user, stress can be reduced and mental illness can be prevented. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the identified type and level of stress into the generation AI and cause the generation AI to suggest relaxation methods or consultation with a specialist.

[0065] The collection unit can collect conversation data such as the content, tone, and frequency of user speech. The collection unit, for example, collects the content of user speech. The content of speech includes, for example, text, audio, video, etc., but is not limited to these examples. The collection unit, for example, collects the content of user speech in real time. The collection unit can also collect the user's tone. The tone includes, for example, but is not limited to, the pitch, strength, and emotional expression of the voice. The collection unit, for example, analyzes the user's tone and collects the emotional expression. The collection unit can also collect the frequency of user speech. The frequency includes, for example, but is not limited to, the number of times a user speaks per day or per week. The collection unit, for example, records the frequency of user speech and uses it for analysis. By collecting conversation data such as the content, tone, and frequency of user speech, more detailed analysis is possible. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the content, tone, and frequency of a user's speech into the generation AI and cause the generation AI to collect conversation data.

[0066] The detection unit can detect symptoms of psychosis based on the collected conversation data. The detection unit detects symptoms of psychosis based on, for example, the collected conversation data. Examples of symptoms of psychosis include, but are not limited to, depression, anxiety, and hallucinations. The detection unit can detect symptoms of psychosis with high accuracy using a generation AI. For example, the generation AI detects symptoms of psychosis based on the content, tone, frequency, etc. of a user's speech. This enables early detection of symptoms of psychosis by detecting them based on the collected conversation data. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input collected conversation data to the generation AI and cause the generation AI to detect symptoms of psychosis.

[0067] The mental illness detection system further includes a collection unit that estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated emotions. For example, when the user is stressed, the generation AI collects conversation data frequently to collect detailed data. When the user is relaxed, the generation AI can collect conversation data at intervals to maintain a natural conversation. Furthermore, when the user is in a hurry, the generation AI can collect important conversation data in a short period of time. This enables more appropriate data collection by adjusting the timing of conversation data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0068] The psychosis detection system further includes a collection unit that analyzes the user's past conversation history and selects the optimal collection method. The collection unit, for example, analyzes the user's past conversation history and selects the optimal collection method. For example, the generation AI selects the optimal collection method based on conversation patterns frequently used by the user in the past. It is also possible to concentrate collection during specific time periods based on the user's past conversation history. Furthermore, it is also possible to analyze the user's past conversation history and prioritize the collection of important conversation data. This allows the analysis of the user's past conversation history to select the optimal collection method and enable efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the user's past conversation history into the generation AI and have the generation AI select the optimal collection method.

[0069] The mental illness detection system further includes a collection unit that filters conversation data based on the user's current living situation and areas of interest when collecting the conversation data. For example, the collection unit filters the conversation data based on the user's current living situation and areas of interest when collecting the conversation data. For example, the generation AI prioritizes collecting relevant conversation data based on the user's current living situation. The generation AI can also collect conversation data related to specific topics based on the user's areas of interest. Furthermore, the generation AI can filter unnecessary data taking into account the user's living situation and areas of interest. This allows for more relevant data to be collected by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the user's living situation and areas of interest into the generation AI and have the generation AI perform the filtering.

[0070] The mental illness detection system further includes a collection unit that estimates the user's emotions and prioritizes the conversation data to be collected based on the estimated emotions. The collection unit, for example, estimates the user's emotions and prioritizes the conversation data to be collected based on the estimated emotions. For example, if the user is stressed, the generation AI can prioritize collecting negative comments. Also, if the user is relaxed, the generation AI can prioritize collecting positive comments. Furthermore, if the user is in a hurry, the generation AI can prioritize collecting important conversation data in a short period of time. Thus, by prioritizing data based on the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the collected data.

[0071] The psychosis detection system further includes a collection unit that, when collecting conversation data, prioritizes the collection of highly relevant data based on the user's geographical location information. For example, when collecting conversation data, the collection unit prioritizes the collection of highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, the generation AI prioritizes the collection of conversation data related to that location. The generation AI can also collect region-specific conversation data based on the user's geographical location information. Furthermore, when the user is traveling, the generation AI can prioritize the collection of conversation data related to the user's destination. This allows the collection of highly relevant data to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0072] The psychosis detection system further includes a collection unit that analyzes the user's social media activity and collects related data when collecting conversation data. For example, the collection unit analyzes the user's social media activity and collects related data when collecting conversation data. For example, the generation AI collects related conversation data based on keywords frequently used by the user on social media. The generation AI can also collect conversation data related to topics of interest from the user's social media activity. Furthermore, the generation AI can analyze the content of the user's social media posts and collect related conversation data. This allows for efficient collection of related data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0073] The mental illness detection system further includes an analysis unit that estimates the user's emotions and adjusts the analysis presentation method based on the estimated emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis presentation method based on the estimated emotions. For example, if the user is stressed, the generation AI provides a concise and easy-to-understand analysis result. Furthermore, if the user is relaxed, the generation AI can provide a detailed analysis result. Furthermore, if the user is in a hurry, the generation AI can provide an analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's emotion data into the generation AI and have the generation AI adjust the analysis presentation method.

[0074] Furthermore, the mental illness detection system includes an analysis unit that adjusts the accuracy of the analysis based on the importance of the conversation data during analysis. The analysis unit, for example, adjusts the accuracy of the analysis based on the importance of the conversation data during analysis. For example, the generation AI performs a detailed analysis of important conversation data. The generation AI can also perform a simplified analysis of general conversation data. Furthermore, the generation AI can adjust the accuracy of the analysis according to the importance of the conversation data. This enables efficient analysis by adjusting the accuracy of the analysis based on the importance of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the importance of the conversation data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0075] Furthermore, the mental illness detection system includes an analysis unit that applies different algorithms depending on the category of conversation data during analysis. The analysis unit, for example, applies different algorithms depending on the category of conversation data during analysis. For example, the generation AI applies a specific analysis algorithm to negative conversation data. The generation AI can also apply a different analysis algorithm to positive conversation data. Furthermore, the generation AI can select the optimal analysis algorithm depending on the category of conversation data. This improves the accuracy of analysis by applying the optimal analysis algorithm depending on the category of conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the category of conversation data into the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0076] The mental illness detection system further includes an analysis unit that estimates the user's emotions and adjusts the analysis time based on the estimated emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis time based on the estimated emotions. For example, if the user is stressed, the generation AI performs a short, concise analysis. Alternatively, if the user is relaxed, the generation AI can perform a detailed analysis. Furthermore, if the user is in a hurry, the generation AI can perform a concise analysis. This allows for adjusting the length of the analysis according to the user's emotions, thereby providing more appropriate analysis results. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's emotion data into the generation AI and have the generation AI adjust the analysis time.

[0077] Furthermore, the psychosis detection system includes an analysis unit that determines the order of analysis based on the submission time of the conversation data during analysis. The analysis unit, for example, determines the order of analysis based on the submission time of the conversation data during analysis. For example, the generation AI prioritizes analysis of the most recent conversation data. The generation AI can also postpone analysis of older conversation data. Furthermore, the generation AI can determine the analysis priority based on the submission time of the conversation data. This enables efficient analysis by determining the analysis priority based on the submission time of the conversation data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the submission time of the conversation data to the generation AI and have the generation AI determine the order of analysis.

[0078] Furthermore, the psychosis detection system includes an analysis unit that adjusts the analysis order based on the relevance of the conversation data during analysis. The analysis unit, for example, adjusts the analysis order based on the relevance of the conversation data during analysis. For example, the generation AI prioritizes analysis of highly relevant conversation data. The generation AI can also postpone analysis of less relevant conversation data. Furthermore, the generation AI can adjust the analysis order based on the relevance of the conversation data. This enables efficient analysis by adjusting the analysis order based on the relevance of the conversation data. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the conversation data to the generation AI and have the generation AI adjust the analysis order.

[0079] The mental illness detection system further includes a detection unit that estimates the user's emotions and adjusts the detection criteria for mental illness symptoms based on the estimated emotions. The detection unit, for example, estimates the user's emotions and adjusts the detection criteria for mental illness symptoms based on the estimated emotions. For example, if the user is feeling stressed, the generation AI may apply strict detection criteria. Alternatively, if the user is relaxed, the generation AI may apply lenient detection criteria. Furthermore, the generation AI may adjust the detection criteria for mental illness symptoms according to the user's emotions. This allows for more appropriate detection of signs of mental illness by adjusting the detection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the detection criteria.

[0080] Furthermore, the mental illness detection system includes a detection unit that improves detection accuracy based on the interrelationships of conversation data during detection. The detection unit, for example, improves detection accuracy based on the interrelationships of conversation data during detection. For example, the detection unit analyzes the interrelationships of conversation data and the generation AI detects signs of mental illness with high accuracy. The generation AI can also reduce false detections by taking into account the interrelationships of conversation data. Furthermore, the generation AI can improve detection accuracy based on the interrelationships of conversation data. In this way, the accuracy of detecting signs of mental illness is improved by taking into account the interrelationships of conversation data. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit may input the interrelationships of conversation data into the generation AI and cause the generation AI to improve detection accuracy.

[0081] Furthermore, the mental illness detection system includes a detection unit that performs detection based on attribute information of the person who submitted the conversation data at the time of detection. The detection unit, for example, performs detection based on attribute information of the person who submitted the conversation data at the time of detection. For example, the generation AI detects signs of mental illness taking into account the age and gender of the person who submitted the conversation data. The generation AI can also perform detection taking into account the occupation and living environment of the person who submitted the conversation data. Furthermore, the generation AI can improve the accuracy of detection based on the attribute information of the person who submitted the conversation data. In this way, by taking into account the attribute information of the person who submitted the conversation data, the accuracy of detecting signs of mental illness is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input attribute information of the person who submitted the conversation data into the generation AI and cause the generation AI to perform detection.

[0082] The mental illness detection system further includes a detection unit that estimates the user's emotion and adjusts the order of the detection results based on the estimated emotion. The detection unit, for example, estimates the user's emotion and adjusts the order of the detection results based on the estimated emotion. For example, if the user is feeling stressed, the generation AI can prioritize displaying important detection results. Furthermore, if the user is relaxed, the generation AI can also display detailed detection results. Furthermore, if the user is in a hurry, the generation AI can prioritize displaying detection results that highlight the key points. This allows important information to be prioritized by adjusting the display order of the detection results based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI. For example, the detection unit may input the user's emotion data into the generation AI and have the generation AI adjust the order of the detection results.

[0083] Furthermore, the mental illness detection system includes a detection unit that performs detection based on the geographic distribution of the conversation data at the time of detection. The detection unit, for example, performs detection based on the geographic distribution of the conversation data at the time of detection. For example, the geographic distribution of the conversation data is analyzed and the generation AI detects signs of mental illness specific to a region. The generation AI can also improve the accuracy of detection by taking the geographic distribution of the conversation data into consideration. Furthermore, the generation AI can detect signs of mental illness with high accuracy based on the geographic distribution of the conversation data. In this way, by taking the geographic distribution of the conversation data into consideration, signs of mental illness specific to a region can be detected with high accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the geographic distribution of the conversation data to the generation AI and have the generation AI perform detection.

[0084] Furthermore, the mental illness detection system includes a detection unit that improves detection accuracy based on literature related to the conversation data during detection. The detection unit, for example, improves detection accuracy based on literature related to the conversation data during detection. For example, the generation AI refers to literature related to the conversation data and detects signs of mental illness with high accuracy. The generation AI can also reduce false detections based on literature related to the conversation data. Furthermore, the generation AI can improve detection accuracy by taking into account literature related to the conversation data. As a result, the accuracy of detecting signs of mental illness is improved by referring to literature related to the conversation data. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit may input literature related to the conversation data into the generation AI and cause the generation AI to improve detection accuracy.

[0085] The mental illness detection system further includes a providing unit that adjusts the accuracy of advice based on the severity of the mental illness signs when providing advice. The providing unit, for example, adjusts the accuracy of advice based on the severity of the mental illness signs when providing advice. For example, the generation AI provides detailed advice for severe mental illness signs. The generation AI can also provide simplified advice for mild mental illness signs. Furthermore, the generation AI can adjust the accuracy of the advice according to the severity of the mental illness signs. This allows appropriate advice to be provided by adjusting the level of detail of the advice based on the severity of the mental illness signs. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the severity of the mental illness signs into the generation AI and cause the generation AI to adjust the accuracy of the advice.

[0086] Furthermore, the mental illness detection system includes a providing unit that applies different algorithms depending on the category of the mental illness sign when providing advice. The providing unit, for example, applies different algorithms depending on the category of the mental illness sign when providing advice. For example, the generation AI applies a specific advice algorithm to negative mental illness signs. The generation AI can also apply a different advice algorithm to positive mental illness signs. Furthermore, the generation AI can select an optimal advice algorithm depending on the category of the mental illness sign. This makes it possible to provide appropriate advice by applying the optimal advice algorithm depending on the category of the mental illness sign. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the category of the mental illness sign to the generation AI and cause the generation AI to apply the optimal advice algorithm.

[0087] The mental illness detection system further includes a providing unit that estimates the user's emotions and adjusts the duration of advice based on the estimated emotions. The providing unit, for example, estimates the user's emotions and adjusts the duration of advice based on the estimated emotions. For example, if the user is feeling stressed, the generation AI provides short, concise advice. Also, if the user is relaxed, the generation AI can provide detailed advice. Furthermore, if the user is in a hurry, the generation AI can provide concise advice. This allows appropriate advice to be provided by adjusting the length of advice based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the duration of advice.

[0088] Furthermore, the mental illness detection system includes a providing unit that, when providing advice, determines the order of advice based on the timing of the presentation of mental illness symptoms. The providing unit, for example, determines the order of advice based on the timing of the presentation of mental illness symptoms when providing advice. For example, the generation AI prioritizes providing advice for the most recent mental illness symptoms. The generation AI can also provide advice for older mental illness symptoms later. Furthermore, the generation AI can determine the priority of advice based on the timing of the presentation of mental illness symptoms. In this way, by determining the priority of advice based on the timing of the presentation of mental illness symptoms, advice can be provided at an appropriate time. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the timing of the presentation of mental illness symptoms to the generation AI and cause the generation AI to determine the order of advice.

[0089] Furthermore, the mental illness detection system includes a providing unit that adjusts the order of advice based on the relevance of mental illness signs when providing advice. The providing unit, for example, adjusts the order of advice based on the relevance of mental illness signs when providing advice. For example, the generation AI prioritizes providing advice for highly relevant mental illness signs. The generation AI can also provide advice later for less relevant mental illness signs. Furthermore, the generation AI can adjust the order of advice based on the relevance of mental illness signs. In this way, by adjusting the order of advice based on the relevance of mental illness signs, important advice can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit may input the relevance of mental illness signs to the generation AI and cause the generation AI to adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, detection unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user conversation data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of psychosis based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides appropriate advice to the user based on the detected signs. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, detection unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user conversation data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected conversation data. The detection unit, realized, for example, by the specific processing unit 290 of the data processing device 12, detects signs of psychosis based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the user with appropriate advice based on the detected signs. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, detection unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects conversation data of the user using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of psychosis based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the user with appropriate advice based on the detected signs. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, detection unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects conversation data of the user using the camera 42 or microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected conversation data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of psychosis based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides appropriate advice to the user based on the detected signs.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The mental illness detection system includes a collection unit that collects a user's conversation data, but it can also include a biometric data collection unit that collects the user's biometric data (e.g., heart rate, electrodermal activity, and body temperature). For example, biometric data can be collected from devices such as smartwatches and fitness trackers while the user is using a chat app. This allows for a more comprehensive assessment of the user's mental state. The collected biometric data is then integrated with the conversation data in the analysis unit, enabling a more accurate assessment of the user's mental state. For example, if the user is feeling stressed, their heart rate may increase and their electrodermal activity may change. Analyzing this biometric data can more accurately identify the user's stress level and provide appropriate advice. Furthermore, monitoring the user's biometric data over a long period of time can enable early detection of changes in the user's mental state and enable preventive measures to be taken.

[0092] The analysis unit analyzes the user's daily conversation patterns, but can also analyze the user's social media activity. For example, it can analyze the keywords and hashtags frequently used by the user to identify the user's interests. This allows for a more detailed understanding of the topics the user is interested in. For example, if the user frequently uses keywords such as "stress" or "anxiety" on social media, the analysis unit can detect this and determine that the user is likely feeling stressed. Furthermore, by analyzing the time periods and frequency of the user's social media activity, it can also identify the user's daily rhythm and peak stress times. This makes it possible to provide advice to the user at more appropriate times.

[0093] The providing unit suggests relaxation methods or consulting a specialist based on the identified type and level of stress, but can also suggest relaxation methods based on the user's hobbies and interests. For example, if the user likes listening to music, the providing unit can suggest a specific music playlist as a relaxation method. Also, if the user likes exercise, the providing unit can suggest yoga or light exercise. This makes it possible to provide a more effective relaxation method for the user. Furthermore, it is possible to analyze the user's past relaxation method history and prioritize suggesting methods that were effective. For example, if meditation was effective in the past, the providing unit can suggest meditation again. This makes it possible to provide the optimal relaxation method for the user and reduce stress.

[0094] The collection unit collects conversational data on the content, tone, and frequency of a user's speech, but can also collect the user's non-verbal behavior (gestures, facial expressions, etc.). For example, if a user is using video chat, the collection unit collects the user's facial expressions and gestures through the camera. This allows for a more detailed understanding of the user's emotions and mental state. For example, if the user frequently frowns, the collection unit can detect this and determine that the user is likely feeling stressed. Furthermore, by monitoring the user's non-verbal behavior over a long period of time, it is possible to detect changes in the user's mental state early and take preventive measures. This allows for a more comprehensive evaluation of the user's mental state and provide appropriate advice.

[0095] The detection unit detects symptoms of mental illness based on the collected conversation data, but can also adjust the detection criteria based on the user's living environment (workplace, home, etc.). For example, if a user frequently reports stress at work, the detection unit applies detection criteria specialized for the work environment. Also, if a user reports problems at home, the detection unit can apply detection criteria specialized for the home environment. This enables appropriate detection according to the user's living environment. Furthermore, it is possible to monitor changes in the user's living environment (moving, changing jobs, etc.) and dynamically adjust the detection criteria. This enables flexible responses according to the user's living environment, enabling early detection and prevention of mental illness.

[0096] The collection unit estimates the user's emotions and adjusts the timing of conversation data collection based on the estimated emotions. It can also select the type of data to collect based on the user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize collecting text data. Also, if the user is relaxed, the generation AI can prioritize collecting audio data and video data. This enables optimal data collection according to the user's emotions. Furthermore, it is possible to dynamically adjust the type of data to be collected according to changes in the user's emotions. For example, if the user suddenly begins to feel stressed, the generation AI can immediately switch to collecting text data. This enables flexible data collection according to the user's emotions.

[0097] The collection unit analyzes the user's past conversation history to select the optimal collection method, but it can also analyze the user's past emotional history to adjust the collection method. For example, it can analyze conversation data from periods when the user felt stressed in the past and identify the collection method that was effective at that time. This makes it possible to apply the optimal collection method if a similar situation recurs. Furthermore, by monitoring the user's emotional history over a long period of time and identifying emotional patterns, preventive data collection becomes possible. For example, if a user tends to feel stressed at a certain time of year, collection can be strengthened at that time. This allows for flexible data collection based on the user's emotional history.

[0098] When collecting conversation data, the collection unit filters the data based on the user's current lifestyle and areas of interest, but can also filter the data based on the user's health condition (sleep patterns, eating habits, etc.). For example, if the user is sleep-deprived, the generation AI will take that into account when filtering the conversation data. Also, if the user is eating a healthy diet, the generation AI can take that into account when filtering the conversation data. This makes it possible to collect appropriate data according to the user's health condition. Furthermore, it is possible to monitor changes in the user's health condition and dynamically adjust the filtering criteria. For example, if the user suddenly becomes ill, the generation AI will immediately change the filtering criteria. This enables flexible data collection according to the user's health condition.

[0099] The collection unit estimates the user's emotions and prioritizes the conversation data to be collected based on the estimated emotions, but it can also adjust the accuracy of the data to be collected based on the user's emotions. For example, if the user is feeling stressed, the generation AI will collect data with high accuracy. On the other hand, if the user is relaxed, the generation AI can collect data with low accuracy. This makes it possible to collect optimal data according to the user's emotions. Furthermore, it is possible to dynamically adjust the accuracy of the data to be collected according to changes in the user's emotions. For example, if the user suddenly begins to feel stressed, the generation AI will immediately switch to collecting high-accuracy data. This enables flexible data collection according to the user's emotions.

[0100] When collecting conversation data, the collection unit prioritizes collecting highly relevant data based on the user's geographic location information, but it can also analyze the user's movement patterns and adjust the collection timing. For example, if a user has a specific movement pattern, such as during commuting or traveling, the generation AI adjusts the collection timing based on that movement pattern. This allows data collection to be strengthened during times when the user is likely to feel stressed while traveling. Furthermore, it is possible to monitor changes in the user's movement pattern and dynamically adjust the collection timing. For example, if the user chooses a new commuting route, the generation AI will immediately change the collection timing. This enables flexible data collection according to the user's movement patterns.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects user conversation data. The conversation data includes voice data, text data, video data, etc. The collection unit collects conversations that users have through chat apps in real time. It can also collect past conversation histories. Step 2: The analysis unit analyzes the conversation data collected by the collection unit. The analysis includes voice analysis, text analysis, and sentiment analysis. The analysis unit analyzes the content, tone, and frequency of the user's speech to evaluate the user's mental state. A detailed analysis can be performed using generative AI. Step 3: The detection unit detects signs of mental illness based on the data analyzed by the analysis unit. Signs of mental illness include depression, anxiety disorders, and schizophrenia. Generative AI can be used to detect these signs with high accuracy. Step 4: The provision unit provides appropriate advice based on the symptoms detected by the detection unit. The advice may include ways to relax or consulting a specialist. Appropriate advice can be provided to the user using the generation AI.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 collection unit that collects conversation data; an analysis unit that analyzes the data collected by the collection unit; a detection unit that detects signs of psychosis based on the data analyzed by the analysis unit; a providing unit that provides advice based on the symptom detected by the detecting unit; Equipped with A system characterized by:

2. The analysis unit Analyzing the user's daily conversation patterns to identify the stress the user is experiencing 2. The system of claim 1.

3. The providing unit Based on the type and level of stress identified, the system suggests relaxation techniques or professional consultation to the user.

2. The system of claim 1.

4. The collecting unit Collect conversational data on what users say, their tone, and frequency 2. The system of claim 1.

5. The detection unit Detecting psychosis symptoms based on collected conversation data 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of conversation data collection based on the estimated emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past conversation history and select the collection method 2. The system of claim 1.

8. The collecting unit As conversation data is collected, it is filtered based on the user's current life situation or areas of interest.

2. The system of claim 1.

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