System

Abstract

To provide a system according to an embodiment that appropriately accepts a user' psychological worries and takes appropriate measures according to the seriousness of the worries.SOLUTION: A system according to an embodiment comprises an analysis unit, a seriousness determination unit, and a cooperation unit. The analysis unit receives input of information from a user by text or voice, and analyzes the received information. The seriousness determination unit determines the seriousness of the user's worries on the basis of the information analyzed by the analysis unit. The cooperation unit cooperates with a department of psychosomatic medicine on the basis of the degree of seriousness determined by the seriousness determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem of not adequately identifying the psychological concerns of users and providing appropriate responses according to their severity.

[0005] The system according to the embodiment aims to appropriately understand the psychological concerns of the user and take appropriate measures according to the severity of the concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a seriousness determination unit, and a linking unit. The analysis unit receives information input from a user by text or voice, and analyzes the received information. The seriousness determination unit determines the seriousness of the user's trouble based on the information analyzed by the analysis unit. The linking unit links to a psychosomatic department based on the degree of seriousness determined by the seriousness determination unit. Effect of the Invention

[0007] The system according to the embodiment can appropriately understand the psychological concerns of the user and take appropriate measures according to the seriousness of the concerns. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A psychological counseling system according to an embodiment of the present invention is a system that accepts psychological worries that a user finds difficult to discuss and provides simple counseling. In this psychological counseling system, a user inputs worries by text or voice, and an AI analyzes the information. Based on the analyzed information, the system judges the seriousness of the user's worries and cooperates with a psychosomatic medicine department as necessary. AI deals with mild worries, and AI and a psychosomatic medicine department cooperate to deal with severe worries. For example, a user inputs worries by text or voice. For example, the content is "Recently, work stress has increased, and I can't sleep at night." This information is input to an analysis unit. Next, the analysis unit analyzes the input information. The analysis unit estimates the user's emotions and adjusts the accuracy of the text analysis based on the estimated emotions. For example, if a user is very sad, the analysis is performed taking into account the emotions. In addition, the user's past consultation history, current psychological state, living situation, geographical location information, social media activity, etc. are also used in the analysis. Based on the analyzed information, a seriousness determination unit judges the seriousness of the user's worries. The seriousness determination unit estimates the user's emotions and adjusts the seriousness determination criteria based on the emotions. For example, if a user feels very anxious, the system judges the severity of the anxiety according to the degree of anxiety. Based on the judged severity, the collaboration unit collaborates with a psychosomatic medicine department. In the case of mild anxiety, the AI ​​responds and provides simple counseling to the user. For example, the AI ​​listens to the user and listens to deeper details to ease the user's feelings. In the case of severe anxiety, the AI ​​and the psychosomatic medicine department collaborate to respond and manage medication as necessary. This system allows users to consult with the AI ​​about psychological concerns that are difficult to discuss with others, and receive appropriate responses. For example, a user who is worried about work stress can consult with the AI ​​and receive advice on how to reduce stress. In addition, in the case of serious concerns, the system collaborates with a psychosomatic medicine department to provide appropriate treatment. This allows the psychological counseling system to understand the user's psychological concerns and provide appropriate responses.

[0029] The psychological counseling system according to the embodiment includes an analysis unit, a seriousness determination unit, and a linking unit. The analysis unit receives input of information from a user by text or voice, and analyzes the received information. Information from the user includes, for example, written documents, e-mails, recorded voices, and the like, but is not limited to such examples. The analysis unit, for example, analyzes text using natural language processing technology and analyzes voice using voice recognition technology. The analysis unit estimates the user's emotions and adjusts the accuracy of text analysis based on the estimated emotions. For example, if the user is very sad, the analysis is performed taking into account the emotions. In addition, the analysis unit analyzes the user's past consultation history and selects an appropriate analysis method. For example, the analysis method is selected based on the content of past consultations, consultation dates and times, information on the person who consulted, and the like. Furthermore, the analysis unit performs filtering based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, information related to stress is preferentially analyzed. The analysis unit preferentially analyzes information with high relevance based on the user's geographical location information. For example, if the user lives in a specific area, information related to that area is preferentially analyzed. The analysis unit analyzes the user's social media activity and analyzes related information. For example, the analysis unit analyzes related information based on the content posted by the user on social media. The seriousness determination unit determines the seriousness of the user's troubles based on the information analyzed by the analysis unit. The seriousness determination unit estimates the user's emotions and adjusts the seriousness determination criteria based on the estimated emotions. For example, if the user is feeling very anxious, the seriousness is determined according to the degree of anxiety. The seriousness determination unit analyzes the user's past consultation history and optimizes the seriousness determination algorithm. For example, the determination algorithm is optimized based on the past consultation content, consultation date and time, information on the person who consulted, etc. The seriousness determination unit determines the seriousness based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the seriousness related to the stress is determined. The linking unit links to a psychosomatic medicine department based on the seriousness degree determined by the seriousness determination unit. The linking unit estimates the user's emotions and adjusts the linking method with the psychosomatic medicine department based on the estimated emotions.For example, if the user is feeling very anxious, a collaboration method that provides a sense of security is selected based on that emotion. The collaboration unit analyzes the user's past consultation history and selects an optimal collaboration algorithm. For example, the collaboration algorithm is selected based on the content of the past consultation, the date and time of the consultation, and information about the person who consulted. The collaboration unit filters collaboration based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, collaboration results related to that stress are preferentially displayed. This allows the psychological counseling system according to the embodiment to understand the user's psychological concerns and provide an appropriate response.

[0030] The analysis unit accepts information input from the user by text or voice, and analyzes the accepted information. Information from the user includes, for example, written documents, e-mails, recorded voice, etc., but is not limited to such examples. The analysis unit, for example, analyzes text using natural language processing technology, and analyzes voice using voice recognition technology. Specifically, as natural language processing technology, technologies such as morphological analysis, grammatical analysis, and semantic analysis are used to analyze the user's input content in detail. For example, morphological analysis is used to divide text into words, grammatical analysis is used to analyze the structure of a sentence, and semantic analysis is used to understand the meaning of the sentence. As voice recognition technology, a technology that converts a voice signal into text is used, and the user's spoken content is accurately converted into text. The analysis unit estimates the user's emotion, and adjusts the accuracy of the text analysis based on the estimated emotion. For example, if the user is very sad, the analysis is performed taking into account that emotion. For emotion estimation, emotion recognition technology is used to estimate emotion from the user's text or voice. For example, the emotion expression in the text, tone, speed, strength, etc. of the voice are analyzed to estimate the user's emotion. The analysis unit also analyzes the user's past consultation history and selects an appropriate analysis method. For example, the analysis method is selected based on the content of the past consultation, the date and time of the consultation, and the information of the person consulting. Furthermore, the analysis unit performs filtering based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the analysis unit prioritizes analyzing information related to stress. The analysis unit prioritizes analyzing information that is highly relevant based on the user's geographical location information. For example, if the user lives in a specific area, the analysis unit prioritizes analyzing information related to that area. The analysis unit analyzes the user's social media activities and analyzes related information. For example, the analysis unit analyzes related information based on the content posted by the user on social media. This allows the analysis unit to comprehensively analyze the user's various information and accurately grasp the user's psychological state.

[0031] The seriousness determination unit determines the seriousness of the user's troubles based on the information analyzed by the analysis unit. The seriousness determination unit estimates the user's emotions and adjusts the criteria for determining the seriousness based on the estimated emotions. For example, if the user is feeling very anxious, the seriousness is determined according to the degree of anxiety. For emotion estimation, emotion recognition technology is used to estimate emotions from the user's text and voice. For example, the emotion of the user is estimated by analyzing the emotional expressions in the text and the tone, speed, strength, etc. of the voice. The seriousness determination unit analyzes the user's past consultation history and optimizes the seriousness determination algorithm. For example, the determination algorithm is optimized based on the past consultation contents, consultation dates and times, and information on the consultant. Specifically, the past consultation contents are clustered, and the seriousness of a new consultation content is determined based on the seriousness for similar consultation contents. In addition, the seriousness determination unit determines the seriousness based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the seriousness related to the stress is determined. In addition, the seriousness determination unit determines the seriousness taking into account the user's geographical location information and social media activity. For example, if a user lives in a particular area, the severity is determined taking into account the social situation and environmental factors of that area. This allows the severity determination unit to comprehensively consider a variety of information about the user and accurately determine the severity of the user's concern.

[0032] The linking unit links to the psychosomatic medicine department based on the degree of seriousness determined by the seriousness determination unit. The linking unit estimates the user's emotions and adjusts the linking method with the psychosomatic medicine department based on the estimated emotions. For example, if the user is feeling very anxious, the linking unit selects a linking method that gives a sense of security based on the emotions. Specifically, the linking unit sends the user gentle language and encouraging messages to give a sense of security. The linking unit also analyzes the user's past consultation history and selects an optimal linking algorithm. For example, the linking algorithm is selected based on the content of past consultations, the date and time of consultation, and information on the person who consulted. Specifically, a new linking method is selected based on a linking method that was effective in the past. Furthermore, the linking unit filters the linking based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the linking results related to that stress are preferentially displayed. The linking unit selects the linking method taking into account the user's geographical location information and social media activities. For example, if the user lives in a specific area, the linking with the psychosomatic medicine department in that area is prioritized. This allows the linking unit to comprehensively consider the user's diverse information and provide the user with linkage with a psychosomatic clinic that is optimal for the user. Furthermore, the linking unit can collect user feedback and continuously improve the accuracy and effectiveness of the linking method. For example, the linking unit evaluates the user's satisfaction with and effectiveness of the linking results and optimizes the linking algorithm. This allows the linking unit to provide the user with prompt and appropriate linkage and effectively resolve the user's psychological concerns.

[0033] The analysis unit can analyze the user's past consultation history and select an analysis method. The analysis unit, for example, stores the user's past consultation content in a database and selects an analysis method based on the database. For example, the analysis unit analyzes the past consultation content using text mining technology and selects an appropriate analysis method. The analysis unit can also select an analysis method based on the user's past consultation date and time and the information of the consultant. For example, the analysis unit selects an analysis method according to a time period in consideration of the past consultation date and time. Furthermore, the analysis unit can extract a specific pattern from the user's past consultation history and select an analysis method based on the pattern. For example, the analysis unit extracts a common keyword in the past consultation content and selects an analysis method based on the keyword. This makes it possible to select an analysis method based on the past consultation history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal analysis method.

[0034] The analysis unit can perform filtering based on the user's current psychological state and living situation. For example, the analysis unit performs a psychological test to evaluate the user's current psychological state, and performs filtering based on the result. For example, the analysis unit evaluates the user's psychological state based on the result of the psychological test, and filters related information. The analysis unit can also evaluate the psychological state based on the user's self-report and perform filtering. For example, the analysis unit preferentially analyzes information related to stress based on the user's self-reported stress level. Furthermore, the analysis unit can evaluate the psychological state based on the user's behavioral observation and perform filtering. For example, the analysis unit observes the user's behavioral pattern and performs filtering based on the pattern. The analysis unit takes into account the user's lifestyle, home environment, occupational situation, etc. to evaluate the user's living situation. For example, the analysis unit filters related information based on the user's lifestyle. This enables filtering based on the current psychological state and living situation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's psychological test result to the generation AI and cause the generation AI to perform filtering.

[0035] The analysis unit can prioritize analysis of highly relevant information based on the geographical location information of the user. The analysis unit, for example, acquires the geographical location information of the user from GPS data, and prioritizes analysis of highly relevant information based on the information. For example, if the user lives in a specific area, the analysis unit prioritizes analysis of information related to the area. In addition, if the user is traveling, the analysis unit can also prioritize analysis of information related to the current location. For example, the analysis unit prioritizes analysis of information related to stress at the user's travel destination. In addition, if the user is in a specific location, the analysis unit can also prioritize analysis of information related to the location. For example, if the user is in a hospital, the analysis unit prioritizes analysis of information related to the hospital. This enables priority analysis of information based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's GPS data to the generation AI and cause the generation AI to execute analysis of highly relevant information.

[0036] The analysis unit can analyze the user's social media activity and analyze related information. The analysis unit, for example, collects the content posted by the user on social media and analyzes related information based on the content. For example, the analysis unit uses text mining technology to analyze the content posted by the user on social media and analyzes related information. The analysis unit can also analyze the user's social media friendships and analyze related information. For example, the analysis unit analyzes posts by users with similar concerns based on the user's friendships. Furthermore, the analysis unit can analyze related information based on the user's social media activity history. For example, the analysis unit analyzes related information based on the user's past postings, comments, the number of likes, etc. This makes it possible to analyze information based on social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute analysis of related information.

[0037] The seriousness determination unit can analyze the user's past consultation history and improve the seriousness determination algorithm. The seriousness determination unit, for example, stores the user's past consultation content in a database and improves the seriousness determination algorithm based on the database. For example, the seriousness determination unit analyzes the past consultation content using text mining technology and improves the seriousness determination algorithm. The seriousness determination unit can also improve the seriousness determination algorithm based on the user's past consultation date and time and the information of the person who consulted. For example, the seriousness determination unit improves the seriousness determination algorithm according to the time period, taking into account the past consultation date and time. Furthermore, the seriousness determination unit can extract a specific pattern from the user's past consultation history and improve the seriousness determination algorithm based on the pattern. For example, the seriousness determination unit extracts a common keyword in the past consultation content and improves the seriousness determination algorithm based on the keyword. This makes it possible to optimize the seriousness determination algorithm based on the past consultation history. A part or all of the above-mentioned processing in the seriousness determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the seriousness assessment unit can input past consultation history data to the generation AI and cause the generation AI to improve the seriousness assessment algorithm.

[0038] The seriousness determination unit can determine the seriousness based on the user's current psychological state and living situation. For example, the seriousness determination unit performs a psychological test to evaluate the user's current psychological state, and determines the seriousness based on the result. For example, the seriousness determination unit evaluates the user's psychological state based on the result of the psychological test, and determines the seriousness. The seriousness determination unit can also evaluate the psychological state based on the user's self-report, and determine the seriousness. For example, the seriousness determination unit determines the seriousness based on the stress level self-reported by the user. Furthermore, the seriousness determination unit can evaluate the psychological state based on the user's behavioral observation, and determine the seriousness. For example, the seriousness determination unit observes the user's behavioral pattern, and determines the seriousness based on the pattern. The seriousness determination unit considers the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's living situation. For example, the seriousness determination unit determines the seriousness based on the user's lifestyle. This makes it possible to determine the seriousness based on the current psychological state and living situation. A part or all of the above-mentioned processing in the seriousness determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the seriousness judgment unit can input the user's psychological test results to the generation AI and have the generation AI judge the seriousness.

[0039] The linking unit can analyze the user's past consultation history and select a linking algorithm. The linking unit, for example, stores the user's past consultation content in a database and selects a linking algorithm based on the database. For example, the linking unit analyzes the past consultation content using text mining technology and selects a linking algorithm. The linking unit can also select a linking algorithm based on the user's past consultation date and time and the information of the person who consulted. For example, the linking unit selects a linking algorithm according to a time period in consideration of the past consultation date and time. Furthermore, the linking unit can extract a specific pattern from the user's past consultation history and select a linking algorithm based on the pattern. For example, the linking unit extracts a common keyword in the past consultation content and selects a linking algorithm based on the keyword. This makes it possible to select an optimal linking algorithm based on the past consultation history. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal linking algorithm.

[0040] The linking unit can execute link filtering based on the user's current psychological state and life situation. For example, the linking unit performs a psychological test to evaluate the user's current psychological state, and filters the link based on the result. For example, the linking unit evaluates the user's psychological state based on the result of the psychological test, and filters the related link results. The linking unit can also evaluate the psychological state based on the user's self-report, and filter the link. For example, the linking unit preferentially displays link results related to stress based on the user's self-reported stress level. Furthermore, the linking unit can evaluate the psychological state based on the user's behavioral observation, and filter the link. For example, the linking unit observes the user's behavioral pattern, and filters the link results based on the pattern. The linking unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's life situation. For example, the linking unit filters the related link results based on the user's lifestyle. This enables link filtering based on the current psychological state and life situation. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's psychological test results to the generation AI and have the generation AI perform collaboration filtering.

[0041] The analysis unit can analyze the user's past consultation history and select an analysis algorithm. The analysis unit, for example, stores the user's past consultation content in a database and selects an analysis algorithm based on the database. For example, the analysis unit analyzes the past consultation content using text mining technology and selects an optimal analysis algorithm. The analysis unit can also select an analysis algorithm based on the user's past consultation date and time and the information of the consultant. For example, the analysis unit selects an analysis algorithm according to a time period in consideration of the past consultation date and time. Furthermore, the analysis unit can extract a specific pattern from the user's past consultation history and select an analysis algorithm based on the pattern. For example, the analysis unit extracts a common keyword in the past consultation content and selects an analysis algorithm based on the keyword. This makes it possible to select an optimal analysis algorithm based on the past consultation history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal analysis algorithm.

[0042] The analysis unit can perform filtering of the analysis based on the user's current psychological state and living situation. The analysis unit, for example, performs a psychological test to evaluate the user's current psychological state, and filters the analysis based on the result. For example, the analysis unit evaluates the user's psychological state based on the result of the psychological test, and filters related information. The analysis unit can also evaluate the psychological state based on the user's self-report, and filter the analysis. For example, the analysis unit preferentially analyzes information related to stress based on the user's self-reported stress level. Furthermore, the analysis unit can evaluate the psychological state based on the user's behavioral observation, and filter the analysis. For example, the analysis unit observes the user's behavioral pattern, and filters based on the pattern. The analysis unit takes into account the user's lifestyle, home environment, occupational situation, etc., in order to evaluate the user's living situation. For example, the analysis unit filters related information based on the user's lifestyle. This makes it possible to filter the analysis based on the current psychological state and living situation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's psychological test results into the generation AI and have the generation AI perform filtering.

[0043] The analysis unit can prioritize analysis of highly relevant information based on the geographical location information of the user. The analysis unit, for example, acquires the geographical location information of the user from GPS data, and prioritizes analysis of highly relevant information based on the information. For example, if the user lives in a specific area, the analysis unit prioritizes analysis of information related to the area. In addition, if the user is traveling, the analysis unit can also prioritize analysis of information related to the current location. For example, the analysis unit prioritizes analysis of information related to stress at the user's travel destination. In addition, if the user is in a specific location, the analysis unit can also prioritize analysis of information related to the location. For example, if the user is in a hospital, the analysis unit prioritizes analysis of information related to the hospital. This enables priority analysis of information based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's GPS data to the generation AI and cause the generation AI to execute analysis of highly relevant information.

[0044] The analysis unit can analyze the user's social media activity and analyze related information. The analysis unit, for example, collects the content posted by the user on social media and analyzes related information based on the content. For example, the analysis unit uses text mining technology to analyze the content posted by the user on social media and analyzes related information. The analysis unit can also analyze the user's social media friendships and analyze related information. For example, the analysis unit analyzes posts by users with similar concerns based on the user's friendships. Furthermore, the analysis unit can analyze related information based on the user's social media activity history. For example, the analysis unit analyzes related information based on the user's past postings, comments, the number of likes, etc. This makes it possible to analyze information based on social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute analysis of related information.

[0045] The analysis unit can analyze the user's past consultation history and select a feedback method. The analysis unit, for example, stores the user's past consultation content in a database and selects a feedback method based on the database. For example, the analysis unit analyzes the past consultation content using text mining technology and selects the optimal feedback method. The analysis unit can also select a feedback method based on the user's past consultation date and time and the information of the consultant. For example, the analysis unit selects a feedback method according to a time period in consideration of the past consultation date and time. Furthermore, the analysis unit can extract a specific pattern from the user's past consultation history and select a feedback method based on the pattern. For example, the analysis unit extracts a common keyword in the past consultation content and selects a feedback method based on the keyword. This makes it possible to select an optimal feedback method based on the past consultation history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal feedback method.

[0046] The analysis unit can filter the analysis results based on the user's current psychological state and living situation. For example, the analysis unit performs a psychological test to evaluate the user's current psychological state, and filters the analysis results based on the result. For example, the analysis unit evaluates the user's psychological state based on the result of the psychological test, and filters the related analysis results. The analysis unit can also evaluate the psychological state based on the user's self-report, and filter the analysis results. For example, the analysis unit preferentially displays analysis results related to stress based on the user's self-reported stress level. Furthermore, the analysis unit can evaluate the psychological state based on the user's behavioral observation, and filter the analysis results. For example, the analysis unit observes the user's behavioral pattern, and filters the analysis results based on the pattern. The analysis unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's living situation. For example, the analysis unit filters the related analysis results based on the user's lifestyle. This makes it possible to filter the analysis results based on the current psychological state and living situation. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's psychological test results to the generation AI and have the generation AI filter the analysis results.

[0047] The analysis unit can preferentially display analysis results with high relevance based on the geographical location information of the user. The analysis unit, for example, acquires the geographical location information of the user from GPS data, and preferentially displays analysis results with high relevance based on the information. For example, if the user lives in a specific area, the analysis unit preferentially displays analysis results related to the area. In addition, if the user is traveling, the analysis unit can also preferentially display analysis results related to the current location. For example, the analysis unit preferentially displays analysis results related to stress at the user's travel destination. Furthermore, if the user is in a specific location, the analysis unit can also preferentially display analysis results related to the location. For example, if the user is in a hospital, the analysis unit preferentially displays analysis results related to the hospital. This makes it possible to preferentially display analysis results based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's GPS data to the generation AI and cause the generation AI to execute display of analysis results with high relevance.

[0048] The analysis unit can analyze the user's social media activity and display related analysis results. The analysis unit, for example, collects the user's social media posting content and displays related analysis results based on the content. For example, the analysis unit uses text mining technology to analyze the content posted by the user on social media and displays related analysis results. The analysis unit can also analyze the user's social media friendships and display related analysis results. For example, the analysis unit analyzes posts by users with similar concerns based on the user's friendships. Furthermore, the analysis unit can display related analysis results based on the user's social media activity history. For example, the analysis unit displays related analysis results based on the user's past posting content, comments, number of likes, etc. This makes it possible to display analysis results based on social media activities. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute display of related analysis results.

[0049] The seriousness determination unit can analyze the user's past consultation history and select a feedback method. The seriousness determination unit, for example, stores the user's past consultation content in a database and selects a feedback method based on the database. For example, the seriousness determination unit analyzes the past consultation content using text mining technology and selects an optimal feedback method. The seriousness determination unit can also select a feedback method based on the user's past consultation date and time and the information of the person who consulted. For example, the seriousness determination unit selects a feedback method according to a time period in consideration of the past consultation date and time. Furthermore, the seriousness determination unit can extract a specific pattern from the user's past consultation history and select a feedback method based on the pattern. For example, the seriousness determination unit extracts a common keyword in the past consultation content and selects a feedback method based on the keyword. This makes it possible to select an optimal feedback method based on the past consultation history. A part or all of the above-mentioned processing in the seriousness determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the seriousness determination unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal feedback method.

[0050] The seriousness determination unit can filter the seriousness determination results based on the user's current psychological state and living situation. The seriousness determination unit, for example, performs a psychological test to evaluate the user's current psychological state, and filters the seriousness determination results based on the result. For example, the seriousness determination unit evaluates the user's psychological state based on the result of the psychological test, and filters the related seriousness determination results. The seriousness determination unit can also evaluate the psychological state based on the user's self-report, and filter the seriousness determination results. For example, the seriousness determination unit preferentially displays the stress-related seriousness determination results based on the user's self-reported stress level. Furthermore, the seriousness determination unit can evaluate the psychological state based on the user's behavioral observation, and filter the seriousness determination results. For example, the seriousness determination unit observes the user's behavioral pattern, and filters the seriousness determination results based on the pattern. The seriousness determination unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's living situation. For example, the seriousness determination unit filters the related seriousness determination results based on the user's lifestyle. This enables filtering of the seriousness judgment results based on the current psychological state and living situation. Some or all of the above-mentioned processing in the seriousness judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the seriousness judgment unit may input the user's psychological test results to the generation AI and cause the generation AI to filter the seriousness judgment results.

[0051] The linking unit can analyze the user's past consultation history and select a feedback method. The linking unit, for example, stores the user's past consultation content in a database and selects a feedback method based on the database. For example, the linking unit analyzes the past consultation content using text mining technology and selects the optimal feedback method. The linking unit can also select a feedback method based on the user's past consultation date and time and the information of the person who consulted. For example, the linking unit selects a feedback method according to a time period in consideration of the past consultation date and time. Furthermore, the linking unit can extract a specific pattern from the user's past consultation history and select a feedback method based on the pattern. For example, the linking unit extracts a common keyword in the past consultation content and selects a feedback method based on the keyword. This makes it possible to select an optimal feedback method based on the past consultation history. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal feedback method.

[0052] The linking unit can filter the linking results based on the user's current psychological state and life situation. For example, the linking unit performs a psychological test to evaluate the user's current psychological state, and filters the linking results based on the result. For example, the linking unit evaluates the user's psychological state based on the result of the psychological test, and filters the related linking results. The linking unit can also evaluate the psychological state based on the user's self-report, and filter the linking results. For example, the linking unit preferentially displays linking results related to stress based on the user's self-reported stress level. Furthermore, the linking unit can evaluate the psychological state based on the user's behavioral observation, and filter the linking results. For example, the linking unit observes the user's behavioral pattern, and filters the linking results based on the pattern. The linking unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's life situation. For example, the linking unit filters the related linking results based on the user's lifestyle. This makes it possible to filter the linking results based on the current psychological state and life situation. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's psychological test results to the generation AI and have the generation AI filter the collaboration results.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The psychological counseling system may further include a health condition monitoring unit that monitors the user's physical health condition. The health condition monitoring unit collects biometric data such as the user's heart rate, blood pressure, and sleep pattern, and provides these data to the analysis unit. For example, when the user is stressed, the heart rate and blood pressure may increase, so by analyzing these data, the user's stress level can be more accurately evaluated. In addition, by analyzing the sleep pattern data, it is possible to detect signs of the user's lack of sleep or insomnia and provide appropriate counseling. Furthermore, the health condition monitoring unit collects records of the user's amount of exercise and meals, and by analyzing these data, it is possible to evaluate the user's overall health condition. This makes it possible to provide comprehensive counseling that takes into account not only psychological concerns but also physical health conditions.

[0055] The psychological counseling system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit collects information about activities that the user has enjoyed in the past and areas of interest, and provides this information to the analysis unit. For example, if the user likes listening to music, music therapy may be suggested. Also, if the user is interested in sports, a method of reducing stress through exercise may be suggested. Furthermore, the hobby analysis unit may analyze the history of events and activities that the user has participated in in the past, and suggest activities that the user can enjoy again. This enables individual counseling based on the user's hobbies and interests.

[0056] The psychological counseling system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes the user's relationships with friends and family, and evaluates the impact of these relationships on the user's psychological state. For example, if the user feels lonely, the social network analysis unit may suggest that the user contact friends and family that the user can trust. Also, if the user feels stressed in a particular relationship, the social network analysis unit may provide advice for improving the relationship. Furthermore, the social network analysis unit may suggest a way for the user to build new social connections. This enables counseling that takes the user's social network into consideration.

[0057] The psychological counseling system may further include a work environment evaluation unit that evaluates the user's work environment. The work environment evaluation unit analyzes the stress factors and human relationships at the user's workplace, and provides this information to the analysis unit. For example, if the user feels stressed about his or her relationship with his or her superior at work, advice for improving the relationship may be provided. Also, if the user feels oppressed by the amount of work at work, advice on improving work efficiency and prioritizing tasks may be provided. Furthermore, the work environment evaluation unit may suggest relaxation methods and stress management techniques for the user to reduce stress at work. This makes it possible to provide counseling that takes the work environment into consideration.

[0058] The psychological counseling system may further include a goal analysis unit that analyzes the user's future goals and hopes. The goal analysis unit collects the user's goals and hopes for the future and provides this information to the analysis unit. For example, if the user is aiming to advance in his / her career, specific steps for achieving that goal may be suggested. Also, if the user wants to learn a new skill, resources and methods for acquiring that skill may be provided. Furthermore, the goal analysis unit may provide advice to help the user maintain motivation to progress toward his / her future goals. This makes it possible to provide counseling based on the user's future goals and hopes.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The analysis unit accepts information input from the user via text or voice, and analyzes the accepted information. Information from the user includes written documents, emails, recorded voice, etc. The analysis unit analyzes the text using natural language processing technology and the voice using voice recognition technology. In addition, the analysis unit estimates the user's emotions and adjusts the accuracy of the text analysis based on the estimated emotions. The analysis unit analyzes the user's past consultation history and selects an appropriate analysis method. Filtering is performed based on the user's current psychological state and living situation, and the analysis prioritizes highly relevant information by analyzing the user's geographical location information and social media activity. Step 2: The seriousness determination unit determines the seriousness of the user's trouble based on the information analyzed by the analysis unit. The seriousness determination unit estimates the user's emotions and adjusts the seriousness determination criteria based on the estimated emotions. It analyzes the user's past consultation history and optimizes the seriousness determination algorithm. It determines the seriousness based on the user's current psychological state and living situation. Step 3: The linking unit links to the psychosomatic medicine department based on the degree of seriousness determined by the seriousness determination unit. The linking unit estimates the user's emotions and adjusts the method of linking with the psychosomatic medicine department based on the estimated emotions. It analyzes the user's past consultation history and selects the optimal linking algorithm. It filters the link based on the user's current psychological state and living situation.

[0061] (Example 2) A psychological counseling system according to an embodiment of the present invention is a system that accepts psychological worries that a user finds difficult to discuss and provides simple counseling. In this psychological counseling system, a user inputs worries by text or voice, and an AI analyzes the information. Based on the analyzed information, the system judges the seriousness of the user's worries and cooperates with a psychosomatic medicine department as necessary. AI deals with mild worries, and AI and a psychosomatic medicine department cooperate to deal with severe worries. For example, a user inputs worries by text or voice. For example, the content is "Recently, work stress has increased, and I can't sleep at night." This information is input to an analysis unit. Next, the analysis unit analyzes the input information. The analysis unit estimates the user's emotions and adjusts the accuracy of the text analysis based on the estimated emotions. For example, if a user is very sad, the analysis is performed taking into account the emotions. In addition, the user's past consultation history, current psychological state, living situation, geographical location information, social media activity, etc. are also used in the analysis. Based on the analyzed information, a seriousness determination unit judges the seriousness of the user's worries. The seriousness determination unit estimates the user's emotions and adjusts the seriousness determination criteria based on the emotions. For example, if a user feels very anxious, the system judges the severity of the anxiety according to the degree of anxiety. Based on the judged severity, the collaboration unit collaborates with a psychosomatic medicine department. In the case of mild anxiety, the AI ​​responds and provides simple counseling to the user. For example, the AI ​​listens to the user and listens to deeper details to ease the user's feelings. In the case of severe anxiety, the AI ​​and the psychosomatic medicine department collaborate to respond and manage medication as necessary. This system allows users to consult with the AI ​​about psychological concerns that are difficult to discuss with others, and receive appropriate responses. For example, a user who is worried about work stress can consult with the AI ​​and receive advice on how to reduce stress. In addition, in the case of serious concerns, the system collaborates with a psychosomatic medicine department to provide appropriate treatment. This allows the psychological counseling system to understand the user's psychological concerns and provide appropriate responses.

[0062] The psychological counseling system according to the embodiment includes an analysis unit, a seriousness determination unit, and a linking unit. The analysis unit receives input of information from a user by text or voice, and analyzes the received information. Information from the user includes, for example, written documents, e-mails, recorded voices, and the like, but is not limited to such examples. The analysis unit, for example, analyzes text using natural language processing technology and analyzes voice using voice recognition technology. The analysis unit estimates the user's emotions and adjusts the accuracy of text analysis based on the estimated emotions. For example, if the user is very sad, the analysis is performed taking into account the emotions. In addition, the analysis unit analyzes the user's past consultation history and selects an appropriate analysis method. For example, the analysis method is selected based on the content of past consultations, consultation dates and times, information on the person who consulted, and the like. Furthermore, the analysis unit performs filtering based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, information related to stress is preferentially analyzed. The analysis unit preferentially analyzes information with high relevance based on the user's geographical location information. For example, if the user lives in a specific area, information related to that area is preferentially analyzed. The analysis unit analyzes the user's social media activity and analyzes related information. For example, the analysis unit analyzes related information based on the content posted by the user on social media. The seriousness determination unit determines the seriousness of the user's troubles based on the information analyzed by the analysis unit. The seriousness determination unit estimates the user's emotions and adjusts the seriousness determination criteria based on the estimated emotions. For example, if the user is feeling very anxious, the seriousness is determined according to the degree of anxiety. The seriousness determination unit analyzes the user's past consultation history and optimizes the seriousness determination algorithm. For example, the determination algorithm is optimized based on the past consultation content, consultation date and time, information on the person who consulted, etc. The seriousness determination unit determines the seriousness based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the seriousness related to the stress is determined. The linking unit links to a psychosomatic medicine department based on the seriousness degree determined by the seriousness determination unit. The linking unit estimates the user's emotions and adjusts the linking method with the psychosomatic medicine department based on the estimated emotions.For example, if the user is feeling very anxious, a collaboration method that provides a sense of security is selected based on that emotion. The collaboration unit analyzes the user's past consultation history and selects an optimal collaboration algorithm. For example, the collaboration algorithm is selected based on the content of the past consultation, the date and time of the consultation, and information about the person who consulted. The collaboration unit filters collaboration based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, collaboration results related to that stress are preferentially displayed. This allows the psychological counseling system according to the embodiment to understand the user's psychological concerns and provide an appropriate response.

[0063] The analysis unit accepts information input from the user by text or voice, and analyzes the accepted information. Information from the user includes, for example, written documents, e-mails, recorded voice, etc., but is not limited to such examples. The analysis unit, for example, analyzes text using natural language processing technology, and analyzes voice using voice recognition technology. Specifically, as natural language processing technology, technologies such as morphological analysis, grammatical analysis, and semantic analysis are used to analyze the user's input content in detail. For example, morphological analysis is used to divide text into words, grammatical analysis is used to analyze the structure of a sentence, and semantic analysis is used to understand the meaning of the sentence. As voice recognition technology, a technology that converts a voice signal into text is used, and the user's spoken content is accurately converted into text. The analysis unit estimates the user's emotion, and adjusts the accuracy of the text analysis based on the estimated emotion. For example, if the user is very sad, the analysis is performed taking into account that emotion. For emotion estimation, emotion recognition technology is used to estimate emotion from the user's text or voice. For example, the emotion expression in the text, tone, speed, strength, etc. of the voice are analyzed to estimate the user's emotion. The analysis unit also analyzes the user's past consultation history and selects an appropriate analysis method. For example, the analysis method is selected based on the content of the past consultation, the date and time of the consultation, and the information of the person consulting. Furthermore, the analysis unit performs filtering based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the analysis unit prioritizes analyzing information related to stress. The analysis unit prioritizes analyzing information that is highly relevant based on the user's geographical location information. For example, if the user lives in a specific area, the analysis unit prioritizes analyzing information related to that area. The analysis unit analyzes the user's social media activities and analyzes related information. For example, the analysis unit analyzes related information based on the content posted by the user on social media. This allows the analysis unit to comprehensively analyze the user's various information and accurately grasp the user's psychological state.

[0064] The seriousness determination unit determines the seriousness of the user's troubles based on the information analyzed by the analysis unit. The seriousness determination unit estimates the user's emotions and adjusts the criteria for determining the seriousness based on the estimated emotions. For example, if the user is feeling very anxious, the seriousness is determined according to the degree of anxiety. For emotion estimation, emotion recognition technology is used to estimate emotions from the user's text and voice. For example, the emotion of the user is estimated by analyzing the emotional expressions in the text and the tone, speed, strength, etc. of the voice. The seriousness determination unit analyzes the user's past consultation history and optimizes the seriousness determination algorithm. For example, the determination algorithm is optimized based on the past consultation contents, consultation dates and times, and information on the consultant. Specifically, the past consultation contents are clustered, and the seriousness of a new consultation content is determined based on the seriousness for similar consultation contents. In addition, the seriousness determination unit determines the seriousness based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the seriousness related to the stress is determined. In addition, the seriousness determination unit determines the seriousness taking into account the user's geographical location information and social media activity. For example, if a user lives in a particular area, the severity is determined taking into account the social situation and environmental factors of that area. This allows the severity determination unit to comprehensively consider a variety of information about the user and accurately determine the severity of the user's concern.

[0065] The linking unit links to the psychosomatic medicine department based on the degree of seriousness determined by the seriousness determination unit. The linking unit estimates the user's emotions and adjusts the linking method with the psychosomatic medicine department based on the estimated emotions. For example, if the user is feeling very anxious, the linking unit selects a linking method that gives a sense of security based on the emotions. Specifically, the linking unit sends the user gentle language and encouraging messages to give a sense of security. The linking unit also analyzes the user's past consultation history and selects an optimal linking algorithm. For example, the linking algorithm is selected based on the content of past consultations, the date and time of consultation, and information on the person who consulted. Specifically, a new linking method is selected based on a linking method that was effective in the past. Furthermore, the linking unit filters the linking based on the user's current psychological state and living situation. For example, if the user is currently feeling stressed, the linking results related to that stress are preferentially displayed. The linking unit selects the linking method taking into account the user's geographical location information and social media activities. For example, if the user lives in a specific area, the linking with the psychosomatic medicine department in that area is prioritized. This allows the linking unit to comprehensively consider the user's diverse information and provide the user with linkage with a psychosomatic clinic that is optimal for the user. Furthermore, the linking unit can collect user feedback and continuously improve the accuracy and effectiveness of the linking method. For example, the linking unit evaluates the user's satisfaction with and effectiveness of the linking results and optimizes the linking algorithm. This allows the linking unit to provide the user with prompt and appropriate linkage and effectively resolve the user's psychological concerns.

[0066] The analysis unit can estimate the user's emotion and change the accuracy of text analysis based on the estimated emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotion using a voice tone analysis technology. For example, the analysis unit records the user's voice and estimates the emotion using a voice tone analysis algorithm. Furthermore, the analysis unit can also estimate the user's emotion using a text emotion analysis technology. For example, the analysis unit analyzes the text entered by the user and estimates the emotion using a text emotion analysis algorithm. This makes it possible to adjust the analysis accuracy according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0067] The analysis unit can analyze the user's past consultation history and select an analysis method. The analysis unit, for example, stores the user's past consultation content in a database and selects an analysis method based on the database. For example, the analysis unit analyzes the past consultation content using text mining technology and selects an appropriate analysis method. The analysis unit can also select an analysis method based on the user's past consultation date and time and the information of the consultant. For example, the analysis unit selects an analysis method according to a time period in consideration of the past consultation date and time. Furthermore, the analysis unit can extract a specific pattern from the user's past consultation history and select an analysis method based on the pattern. For example, the analysis unit extracts a common keyword in the past consultation content and selects an analysis method based on the keyword. This makes it possible to select an analysis method based on the past consultation history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal analysis method.

[0068] The analysis unit can perform filtering based on the user's current psychological state and living situation. For example, the analysis unit performs a psychological test to evaluate the user's current psychological state, and performs filtering based on the result. For example, the analysis unit evaluates the user's psychological state based on the result of the psychological test, and filters related information. The analysis unit can also evaluate the psychological state based on the user's self-report and perform filtering. For example, the analysis unit preferentially analyzes information related to stress based on the user's self-reported stress level. Furthermore, the analysis unit can evaluate the psychological state based on the user's behavioral observation and perform filtering. For example, the analysis unit observes the user's behavioral pattern and performs filtering based on the pattern. The analysis unit takes into account the user's lifestyle, home environment, occupational situation, etc. to evaluate the user's living situation. For example, the analysis unit filters related information based on the user's lifestyle. This enables filtering based on the current psychological state and living situation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's psychological test result to the generation AI and cause the generation AI to perform filtering.

[0069] The analysis unit can estimate the user's emotion and set a priority order for the text to be analyzed based on the estimated emotion. The analysis unit can estimate the user's emotion using, for example, facial expression recognition technology and set a priority order for the text based on the emotion. For example, the analysis unit captures the user's facial expression with a camera, estimates the emotion using a facial expression recognition algorithm, and preferentially analyzes important text. The analysis unit can also estimate the user's emotion using voice tone analysis technology and set a priority order for the text based on the emotion. For example, the analysis unit records the user's voice, estimates the emotion using a voice tone analysis algorithm, and preferentially analyzes text that gives a sense of security. Furthermore, the analysis unit can estimate the user's emotion using text emotion analysis technology and set a priority order for the text based on the emotion. For example, the analysis unit analyzes text entered by the user, estimates the emotion using a text emotion analysis algorithm, and preferentially analyzes text to regain composure. This makes it possible to determine the priority order for the text based on the emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI, or the like, using an emotion estimation function. The generation AI may be 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-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0070] The analysis unit can prioritize analysis of highly relevant information based on the geographical location information of the user. The analysis unit, for example, acquires the geographical location information of the user from GPS data, and prioritizes analysis of highly relevant information based on the information. For example, if the user lives in a specific area, the analysis unit prioritizes analysis of information related to the area. In addition, if the user is traveling, the analysis unit can also prioritize analysis of information related to the current location. For example, the analysis unit prioritizes analysis of information related to stress at the user's travel destination. In addition, if the user is in a specific location, the analysis unit can also prioritize analysis of information related to the location. For example, if the user is in a hospital, the analysis unit prioritizes analysis of information related to the hospital. This enables priority analysis of information based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's GPS data to the generation AI and cause the generation AI to execute analysis of highly relevant information.

[0071] The analysis unit can analyze the user's social media activity and analyze related information. The analysis unit, for example, collects the content posted by the user on social media and analyzes related information based on the content. For example, the analysis unit uses text mining technology to analyze the content posted by the user on social media and analyzes related information. The analysis unit can also analyze the user's social media friendships and analyze related information. For example, the analysis unit analyzes posts by users with similar concerns based on the user's friendships. Furthermore, the analysis unit can analyze related information based on the user's social media activity history. For example, the analysis unit analyzes related information based on the user's past postings, comments, the number of likes, etc. This makes it possible to analyze information based on social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute analysis of related information.

[0072] The seriousness determination unit can estimate the user's emotion and change the seriousness determination criteria based on the estimated emotion. The seriousness determination unit can estimate the user's emotion using, for example, facial expression recognition technology and change the seriousness determination criteria based on the emotion. For example, the seriousness determination unit can capture the user's facial expression with a camera, estimate the emotion using a facial expression recognition algorithm, and adjust the seriousness determination criteria. The seriousness determination unit can also estimate the user's emotion using a voice tone analysis technology and change the seriousness determination criteria based on the emotion. For example, the seriousness determination unit can record the user's voice, estimate the emotion using a voice tone analysis algorithm, and adjust the seriousness determination criteria. Furthermore, the seriousness determination unit can estimate the user's emotion using a text emotion analysis technology and change the seriousness determination criteria based on the emotion. For example, the seriousness determination unit can analyze the text entered by the user, estimate the emotion using a text emotion analysis algorithm, and adjust the seriousness determination criteria. This makes it possible to adjust the seriousness determination criteria based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the seriousness determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the seriousness determination unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0073] The seriousness determination unit can analyze the user's past consultation history and improve the seriousness determination algorithm. The seriousness determination unit, for example, stores the user's past consultation content in a database and improves the seriousness determination algorithm based on the database. For example, the seriousness determination unit analyzes the past consultation content using text mining technology and improves the seriousness determination algorithm. The seriousness determination unit can also improve the seriousness determination algorithm based on the user's past consultation date and time and the information of the person who consulted. For example, the seriousness determination unit improves the seriousness determination algorithm according to the time period, taking into account the past consultation date and time. Furthermore, the seriousness determination unit can extract a specific pattern from the user's past consultation history and improve the seriousness determination algorithm based on the pattern. For example, the seriousness determination unit extracts a common keyword in the past consultation content and improves the seriousness determination algorithm based on the keyword. This makes it possible to optimize the seriousness determination algorithm based on the past consultation history. A part or all of the above-mentioned processing in the seriousness determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the seriousness assessment unit can input past consultation history data to the generation AI and cause the generation AI to improve the seriousness assessment algorithm.

[0074] The seriousness determination unit can determine the seriousness based on the user's current psychological state and living situation. For example, the seriousness determination unit performs a psychological test to evaluate the user's current psychological state, and determines the seriousness based on the result. For example, the seriousness determination unit evaluates the user's psychological state based on the result of the psychological test, and determines the seriousness. The seriousness determination unit can also evaluate the psychological state based on the user's self-report, and determine the seriousness. For example, the seriousness determination unit determines the seriousness based on the stress level self-reported by the user. Furthermore, the seriousness determination unit can evaluate the psychological state based on the user's behavioral observation, and determine the seriousness. For example, the seriousness determination unit observes the user's behavioral pattern, and determines the seriousness based on the pattern. The seriousness determination unit considers the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's living situation. For example, the seriousness determination unit determines the seriousness based on the user's lifestyle. This makes it possible to determine the seriousness based on the current psychological state and living situation. A part or all of the above-mentioned processing in the seriousness determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the seriousness judgment unit can input the user's psychological test results to the generation AI and have the generation AI judge the seriousness.

[0075] The linking unit can estimate the user's emotion and change the linking method with the psychosomatic department based on the estimated emotion. The linking unit can estimate the user's emotion using, for example, facial expression recognition technology and change the linking method with the psychosomatic department based on the emotion. For example, the linking unit captures the user's facial expression with a camera, estimates the emotion using a facial expression recognition algorithm, and selects a linking method that gives a sense of security. The linking unit can also estimate the user's emotion using a voice tone analysis technology and change the linking method with the psychosomatic department based on the emotion. For example, the linking unit records the user's voice, estimates the emotion using a voice tone analysis algorithm, and selects a linking method that gives a sense of security. Furthermore, the linking unit can estimate the user's emotion using a text emotion analysis technology and change the linking method with the psychosomatic department based on the emotion. For example, the linking unit analyzes the text entered by the user, estimates the emotion using a text emotion analysis algorithm, and selects a linking method that gives a sense of security. This makes it possible to adjust the linking method with the psychosomatic department based on the emotion. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collaboration unit may be performed, for example, using an AI or may be performed without using an AI. For example, the collaboration unit may input the user's facial expression data to the generation AI and cause the generation AI to perform emotion estimation.

[0076] The linking unit can analyze the user's past consultation history and select a linking algorithm. The linking unit, for example, stores the user's past consultation content in a database and selects a linking algorithm based on the database. For example, the linking unit analyzes the past consultation content using text mining technology and selects a linking algorithm. The linking unit can also select a linking algorithm based on the user's past consultation date and time and the information of the person who consulted. For example, the linking unit selects a linking algorithm according to a time period in consideration of the past consultation date and time. Furthermore, the linking unit can extract a specific pattern from the user's past consultation history and select a linking algorithm based on the pattern. For example, the linking unit extracts a common keyword in the past consultation content and selects a linking algorithm based on the keyword. This makes it possible to select an optimal linking algorithm based on the past consultation history. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal linking algorithm.

[0077] The linking unit can execute link filtering based on the user's current psychological state and life situation. For example, the linking unit performs a psychological test to evaluate the user's current psychological state, and filters the link based on the result. For example, the linking unit evaluates the user's psychological state based on the result of the psychological test, and filters the related link results. The linking unit can also evaluate the psychological state based on the user's self-report, and filter the link. For example, the linking unit preferentially displays link results related to stress based on the user's self-reported stress level. Furthermore, the linking unit can evaluate the psychological state based on the user's behavioral observation, and filter the link. For example, the linking unit observes the user's behavioral pattern, and filters the link results based on the pattern. The linking unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's life situation. For example, the linking unit filters the related link results based on the user's lifestyle. This enables link filtering based on the current psychological state and life situation. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's psychological test results to the generation AI and have the generation AI perform collaboration filtering.

[0078] The analysis unit can estimate the user's emotion and change the expression method of the text to be analyzed based on the estimated emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion and changes the expression method of the text based on the emotion. For example, the analysis unit takes a picture of the user's facial expression with a camera, estimates the emotion using a facial expression recognition algorithm, and analyzes the text with gentle language. The analysis unit can also estimate the user's emotion using voice tone analysis technology and change the expression method of the text based on the emotion. For example, the analysis unit records the user's voice, estimates the emotion using a voice tone analysis algorithm, and analyzes the text with calm language. Furthermore, the analysis unit can estimate the user's emotion using text emotion analysis technology and change the expression method of the text based on the emotion. For example, the analysis unit analyzes the text entered by the user, estimates the emotion using a text emotion analysis algorithm, and analyzes the text with language that gives a sense of security. This makes it possible to adjust the expression method of the text based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0079] The analysis unit can analyze the user's past consultation history and select an analysis algorithm. The analysis unit, for example, stores the user's past consultation content in a database and selects an analysis algorithm based on the database. For example, the analysis unit analyzes the past consultation content using text mining technology and selects an optimal analysis algorithm. The analysis unit can also select an analysis algorithm based on the user's past consultation date and time and the information of the consultant. For example, the analysis unit selects an analysis algorithm according to a time period in consideration of the past consultation date and time. Furthermore, the analysis unit can extract a specific pattern from the user's past consultation history and select an analysis algorithm based on the pattern. For example, the analysis unit extracts a common keyword in the past consultation content and selects an analysis algorithm based on the keyword. This makes it possible to select an optimal analysis algorithm based on the past consultation history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal analysis algorithm.

[0080] The analysis unit can perform filtering of the analysis based on the user's current psychological state and living situation. The analysis unit, for example, performs a psychological test to evaluate the user's current psychological state, and filters the analysis based on the result. For example, the analysis unit evaluates the user's psychological state based on the result of the psychological test, and filters related information. The analysis unit can also evaluate the psychological state based on the user's self-report, and filter the analysis. For example, the analysis unit preferentially analyzes information related to stress based on the user's self-reported stress level. Furthermore, the analysis unit can evaluate the psychological state based on the user's behavioral observation, and filter the analysis. For example, the analysis unit observes the user's behavioral pattern, and filters based on the pattern. The analysis unit takes into account the user's lifestyle, home environment, occupational situation, etc., in order to evaluate the user's living situation. For example, the analysis unit filters related information based on the user's lifestyle. This makes it possible to filter the analysis based on the current psychological state and living situation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's psychological test results into the generation AI and have the generation AI perform filtering.

[0081] The analysis unit can estimate the user's emotion and set a priority order for the text to be analyzed based on the estimated emotion. The analysis unit can estimate the user's emotion using, for example, facial expression recognition technology and set a priority order for the text based on the emotion. For example, the analysis unit captures the user's facial expression with a camera, estimates the emotion using a facial expression recognition algorithm, and preferentially analyzes important text. The analysis unit can also estimate the user's emotion using voice tone analysis technology and set a priority order for the text based on the emotion. For example, the analysis unit records the user's voice, estimates the emotion using a voice tone analysis algorithm, and preferentially analyzes text that gives a sense of security. Furthermore, the analysis unit can estimate the user's emotion using text emotion analysis technology and set a priority order for the text based on the emotion. For example, the analysis unit analyzes text entered by the user, estimates the emotion using a text emotion analysis algorithm, and preferentially analyzes text to regain composure. This makes it possible to determine the priority order for the text based on the emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI, or the like, using an emotion estimation function. The generation AI may be 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-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0082] The analysis unit can prioritize analysis of highly relevant information based on the geographical location information of the user. The analysis unit, for example, acquires the geographical location information of the user from GPS data, and prioritizes analysis of highly relevant information based on the information. For example, if the user lives in a specific area, the analysis unit prioritizes analysis of information related to the area. In addition, if the user is traveling, the analysis unit can also prioritize analysis of information related to the current location. For example, the analysis unit prioritizes analysis of information related to stress at the user's travel destination. In addition, if the user is in a specific location, the analysis unit can also prioritize analysis of information related to the location. For example, if the user is in a hospital, the analysis unit prioritizes analysis of information related to the hospital. This enables priority analysis of information based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's GPS data to the generation AI and cause the generation AI to execute analysis of highly relevant information.

[0083] The analysis unit can analyze the user's social media activity and analyze related information. The analysis unit, for example, collects the content posted by the user on social media and analyzes related information based on the content. For example, the analysis unit uses text mining technology to analyze the content posted by the user on social media and analyzes related information. The analysis unit can also analyze the user's social media friendships and analyze related information. For example, the analysis unit analyzes posts by users with similar concerns based on the user's friendships. Furthermore, the analysis unit can analyze related information based on the user's social media activity history. For example, the analysis unit analyzes related information based on the user's past postings, comments, the number of likes, etc. This makes it possible to analyze information based on social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute analysis of related information.

[0084] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology, and change the display method of the analysis results based on the emotions. For example, the analysis unit can take a picture of the user's facial expression with a camera, estimate the emotions using a facial expression recognition algorithm, and display the analysis results in gentle language. The analysis unit can also estimate the user's emotions using voice tone analysis technology and change the display method of the analysis results based on the emotions. For example, the analysis unit can record the user's voice, estimate the emotions using a voice tone analysis algorithm, and display the analysis results in calm language. Furthermore, the analysis unit can estimate the user's emotions using text emotion analysis technology and change the display method of the analysis results based on the emotions. For example, the analysis unit can analyze the text entered by the user, estimate the emotions using a text emotion analysis algorithm, and display the analysis results in language that gives a sense of security. This makes it possible to adjust the display method of the analysis results based on emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0085] The analysis unit can analyze the user's past consultation history and select a feedback method. The analysis unit, for example, stores the user's past consultation content in a database and selects a feedback method based on the database. For example, the analysis unit analyzes the past consultation content using text mining technology and selects the optimal feedback method. The analysis unit can also select a feedback method based on the user's past consultation date and time and the information of the consultant. For example, the analysis unit selects a feedback method according to a time period in consideration of the past consultation date and time. Furthermore, the analysis unit can extract a specific pattern from the user's past consultation history and select a feedback method based on the pattern. For example, the analysis unit extracts a common keyword in the past consultation content and selects a feedback method based on the keyword. This makes it possible to select an optimal feedback method based on the past consultation history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal feedback method.

[0086] The analysis unit can filter the analysis results based on the user's current psychological state and living situation. For example, the analysis unit performs a psychological test to evaluate the user's current psychological state, and filters the analysis results based on the result. For example, the analysis unit evaluates the user's psychological state based on the result of the psychological test, and filters the related analysis results. The analysis unit can also evaluate the psychological state based on the user's self-report, and filter the analysis results. For example, the analysis unit preferentially displays analysis results related to stress based on the user's self-reported stress level. Furthermore, the analysis unit can evaluate the psychological state based on the user's behavioral observation, and filter the analysis results. For example, the analysis unit observes the user's behavioral pattern, and filters the analysis results based on the pattern. The analysis unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's living situation. For example, the analysis unit filters the related analysis results based on the user's lifestyle. This makes it possible to filter the analysis results based on the current psychological state and living situation. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's psychological test results to the generation AI and have the generation AI filter the analysis results.

[0087] The analysis unit can estimate the user's emotions and set the priority of the analysis results based on the estimated emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology and set the priority of the analysis results based on the emotions. For example, the analysis unit captures the user's facial expression with a camera, estimates the emotions using a facial expression recognition algorithm, and preferentially displays important analysis results. The analysis unit can also estimate the user's emotions using voice tone analysis technology and set the priority of the analysis results based on the emotions. For example, the analysis unit records the user's voice, estimates the emotions using a voice tone analysis algorithm, and preferentially displays analysis results that give a sense of security. Furthermore, the analysis unit can estimate the user's emotions using text emotion analysis technology and set the priority of the analysis results based on the emotions. For example, the analysis unit analyzes text entered by the user, estimates the emotions using a text emotion analysis algorithm, and preferentially displays analysis results that help the user regain their composure. This makes it possible to determine the priority of the analysis results based on emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0088] The analysis unit can preferentially display analysis results with high relevance based on the geographical location information of the user. The analysis unit, for example, acquires the geographical location information of the user from GPS data, and preferentially displays analysis results with high relevance based on the information. For example, if the user lives in a specific area, the analysis unit preferentially displays analysis results related to the area. In addition, if the user is traveling, the analysis unit can also preferentially display analysis results related to the current location. For example, the analysis unit preferentially displays analysis results related to stress at the user's travel destination. Furthermore, if the user is in a specific location, the analysis unit can also preferentially display analysis results related to the location. For example, if the user is in a hospital, the analysis unit preferentially displays analysis results related to the hospital. This makes it possible to preferentially display analysis results based on geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's GPS data to the generation AI and cause the generation AI to execute display of analysis results with high relevance.

[0089] The analysis unit can analyze the user's social media activity and display related analysis results. The analysis unit, for example, collects the user's social media posting content and displays related analysis results based on the content. For example, the analysis unit uses text mining technology to analyze the content posted by the user on social media and displays related analysis results. The analysis unit can also analyze the user's social media friendships and display related analysis results. For example, the analysis unit analyzes posts by users with similar concerns based on the user's friendships. Furthermore, the analysis unit can display related analysis results based on the user's social media activity history. For example, the analysis unit displays related analysis results based on the user's past posting content, comments, number of likes, etc. This makes it possible to display analysis results based on social media activities. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media data to the generation AI and cause the generation AI to execute display of related analysis results.

[0090] The seriousness determination unit can estimate the user's emotion and change the display method of the seriousness determination result based on the estimated emotion. The seriousness determination unit can estimate the user's emotion using, for example, facial expression recognition technology and change the display method of the seriousness determination result based on the emotion. For example, the seriousness determination unit can take a picture of the user's facial expression with a camera, estimate the emotion using a facial expression recognition algorithm, and display the seriousness determination result in gentle language. The seriousness determination unit can also estimate the user's emotion using a voice tone analysis technology and change the display method of the seriousness determination result based on the emotion. For example, the seriousness determination unit can record the user's voice, estimate the emotion using a voice tone analysis algorithm, and display the seriousness determination result in language that gives a sense of security. Furthermore, the seriousness determination unit can estimate the user's emotion using a text emotion analysis technology and change the display method of the seriousness determination result based on the emotion. For example, the seriousness determination unit can analyze the text entered by the user, estimate the emotion using a text emotion analysis algorithm, and display the seriousness determination result in calm language. This makes it possible to adjust the display method of the seriousness determination result based on emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the seriousness determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the seriousness determination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0091] The seriousness determination unit can analyze the user's past consultation history and select a feedback method. The seriousness determination unit, for example, stores the user's past consultation content in a database and selects a feedback method based on the database. For example, the seriousness determination unit analyzes the past consultation content using text mining technology and selects an optimal feedback method. The seriousness determination unit can also select a feedback method based on the user's past consultation date and time and the information of the person who consulted. For example, the seriousness determination unit selects a feedback method according to a time period in consideration of the past consultation date and time. Furthermore, the seriousness determination unit can extract a specific pattern from the user's past consultation history and select a feedback method based on the pattern. For example, the seriousness determination unit extracts a common keyword in the past consultation content and selects a feedback method based on the keyword. This makes it possible to select an optimal feedback method based on the past consultation history. A part or all of the above-mentioned processing in the seriousness determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the seriousness determination unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal feedback method.

[0092] The seriousness determination unit can filter the seriousness determination results based on the user's current psychological state and living situation. The seriousness determination unit, for example, performs a psychological test to evaluate the user's current psychological state, and filters the seriousness determination results based on the result. For example, the seriousness determination unit evaluates the user's psychological state based on the result of the psychological test, and filters the related seriousness determination results. The seriousness determination unit can also evaluate the psychological state based on the user's self-report, and filter the seriousness determination results. For example, the seriousness determination unit preferentially displays the stress-related seriousness determination results based on the user's self-reported stress level. Furthermore, the seriousness determination unit can evaluate the psychological state based on the user's behavioral observation, and filter the seriousness determination results. For example, the seriousness determination unit observes the user's behavioral pattern, and filters the seriousness determination results based on the pattern. The seriousness determination unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's living situation. For example, the seriousness determination unit filters the related seriousness determination results based on the user's lifestyle. This enables filtering of the seriousness judgment results based on the current psychological state and living situation. Some or all of the above-mentioned processing in the seriousness judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the seriousness judgment unit may input the user's psychological test results to the generation AI and cause the generation AI to filter the seriousness judgment results.

[0093] The linking unit can estimate the user's emotion and change the display method of the linking result based on the estimated emotion. The linking unit, for example, uses facial expression recognition technology to estimate the user's emotion and changes the display method of the linking result based on the emotion. For example, the linking unit takes a picture of the user's facial expression with a camera, estimates the emotion using a facial expression recognition algorithm, and displays the linking result in gentle language. The linking unit can also estimate the user's emotion using voice tone analysis technology and change the display method of the linking result based on the emotion. For example, the linking unit records the user's voice, estimates the emotion using a voice tone analysis algorithm, and displays the linking result in language that gives a sense of security. Furthermore, the linking unit can estimate the user's emotion using text emotion analysis technology and change the display method of the linking result based on the emotion. For example, the linking unit analyzes the text entered by the user, estimates the emotion using a text emotion analysis algorithm, and displays the linking result in calm language. This makes it possible to adjust the display method of the linking result based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the collaboration unit may be performed, for example, using AI, or may be performed without using AI. For example, the collaboration unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0094] The linking unit can analyze the user's past consultation history and select a feedback method. The linking unit, for example, stores the user's past consultation content in a database and selects a feedback method based on the database. For example, the linking unit analyzes the past consultation content using text mining technology and selects the optimal feedback method. The linking unit can also select a feedback method based on the user's past consultation date and time and the information of the person who consulted. For example, the linking unit selects a feedback method according to a time period in consideration of the past consultation date and time. Furthermore, the linking unit can extract a specific pattern from the user's past consultation history and select a feedback method based on the pattern. For example, the linking unit extracts a common keyword in the past consultation content and selects a feedback method based on the keyword. This makes it possible to select an optimal feedback method based on the past consultation history. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input past consultation history data to the generation AI and cause the generation AI to select an optimal feedback method.

[0095] The linking unit can filter the linking results based on the user's current psychological state and life situation. For example, the linking unit performs a psychological test to evaluate the user's current psychological state, and filters the linking results based on the result. For example, the linking unit evaluates the user's psychological state based on the result of the psychological test, and filters the related linking results. The linking unit can also evaluate the psychological state based on the user's self-report, and filter the linking results. For example, the linking unit preferentially displays linking results related to stress based on the user's self-reported stress level. Furthermore, the linking unit can evaluate the psychological state based on the user's behavioral observation, and filter the linking results. For example, the linking unit observes the user's behavioral pattern, and filters the linking results based on the pattern. The linking unit takes into account the user's lifestyle, home environment, occupational situation, and the like in order to evaluate the user's life situation. For example, the linking unit filters the related linking results based on the user's lifestyle. This makes it possible to filter the linking results based on the current psychological state and life situation. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's psychological test results to the generation AI and have the generation AI filter the collaboration results.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The psychological counseling system may further include a health condition monitoring unit that monitors the user's physical health condition. The health condition monitoring unit collects biometric data such as the user's heart rate, blood pressure, and sleep pattern, and provides these data to the analysis unit. For example, when the user is stressed, the heart rate and blood pressure may increase, so by analyzing these data, the user's stress level can be more accurately evaluated. In addition, by analyzing the sleep pattern data, it is possible to detect signs of the user's lack of sleep or insomnia and provide appropriate counseling. Furthermore, the health condition monitoring unit collects records of the user's amount of exercise and meals, and by analyzing these data, it is possible to evaluate the user's overall health condition. This makes it possible to provide comprehensive counseling that takes into account not only psychological concerns but also physical health conditions.

[0098] The psychological counseling system may further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit collects information about activities that the user has enjoyed in the past and areas of interest, and provides this information to the analysis unit. For example, if the user likes listening to music, music therapy may be suggested. Also, if the user is interested in sports, a method of reducing stress through exercise may be suggested. Furthermore, the hobby analysis unit may analyze the history of events and activities that the user has participated in in the past, and suggest activities that the user can enjoy again. This enables individual counseling based on the user's hobbies and interests.

[0099] The psychological counseling system may further include a social network analysis unit that analyzes the user's social network. The social network analysis unit analyzes the user's relationships with friends and family, and evaluates the impact of these relationships on the user's psychological state. For example, if the user feels lonely, the social network analysis unit may suggest that the user contact friends and family that the user can trust. Also, if the user feels stressed in a particular relationship, the social network analysis unit may provide advice for improving the relationship. Furthermore, the social network analysis unit may suggest a way for the user to build new social connections. This enables counseling that takes the user's social network into consideration.

[0100] The psychological counseling system may further include a work environment evaluation unit that evaluates the user's work environment. The work environment evaluation unit analyzes the stress factors and human relationships at the user's workplace, and provides this information to the analysis unit. For example, if the user feels stressed about his or her relationship with his or her superior at work, advice for improving the relationship may be provided. Also, if the user feels oppressed by the amount of work at work, advice on improving work efficiency and prioritizing tasks may be provided. Furthermore, the work environment evaluation unit may suggest relaxation methods and stress management techniques for the user to reduce stress at work. This makes it possible to provide counseling that takes the work environment into consideration.

[0101] The psychological counseling system may further include a goal analysis unit that analyzes the user's future goals and hopes. The goal analysis unit collects the user's goals and hopes for the future and provides this information to the analysis unit. For example, if the user is aiming to advance in his / her career, specific steps for achieving that goal may be suggested. Also, if the user wants to learn a new skill, resources and methods for acquiring that skill may be provided. Furthermore, the goal analysis unit may provide advice to help the user maintain motivation to progress toward his / her future goals. This makes it possible to provide counseling based on the user's future goals and hopes.

[0102] The psychological counseling system can further estimate the user's emotions and adjust the timing of counseling based on the estimated emotions. For example, if the user is very sad, the timing of counseling can be adjusted to take into account the emotions and counseling can be provided when the user needs the most support. Also, if the user is very anxious, counseling can be provided immediately to reduce the anxiety. Furthermore, the frequency of counseling can be adjusted based on the user's emotions. For example, if the user is stressed for a long period of time, regular counseling can be provided to continuously manage the user's stress. This allows counseling to be provided at a time based on the user's emotions.

[0103] The psychological counseling system can further estimate the user's emotions and adjust the content of the counseling based on the estimated emotions. For example, if the user is very angry, the content of the counseling can be adjusted taking into account the emotions and providing advice to help the user regain their composure. Also, if the user is very sad, the emotions can be taken into account and words of comfort and encouragement can be provided. Furthermore, the counseling approach can also be adjusted based on the user's emotions. For example, if the user is feeling very anxious, relaxation techniques and stress management methods can be suggested to reduce the anxiety. This enables individual counseling based on the user's emotions.

[0104] The psychological counseling system can further estimate the user's emotions and select a counseling method based on the estimated emotions. For example, if the user feels very lonely, group counseling can be suggested taking into account the emotions. Also, if the user feels very stressed, individual counseling can be suggested taking into account the emotions. Furthermore, the form of counseling can be selected based on the user's emotions. For example, if the user desires face-to-face counseling, face-to-face counseling can be provided in accordance with the desire. This makes it possible to select the optimal counseling method based on the user's emotions.

[0105] The psychological counseling system can further estimate the user's emotions and adjust the speed of counseling based on the estimated emotions. For example, if the user is feeling very frustrated, the speed of counseling can be increased in consideration of the emotions. Also, if the user is feeling very anxious, the speed of counseling can be decreased in consideration of the emotions, allowing the user to feel at ease. Furthermore, the steps of counseling can be adjusted based on the user's emotions. For example, if the user is feeling very sad, the counseling steps can be divided into small steps in consideration of the emotions, allowing the user to proceed step by step. This makes it possible to progress in counseling based on the user's emotions.

[0106] The psychological counseling system can further estimate the user's emotions and adjust the counseling feedback based on the estimated emotions. For example, if the user is very depressed, positive feedback can be provided taking into account the emotions. Also, if the user is very angry, calm feedback can be provided taking into account the emotions. Furthermore, the frequency of feedback can be adjusted based on the user's emotions. For example, if the user feels anxious for a long period of time, the user's anxiety can be continuously managed by providing periodic feedback. This enables feedback based on the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The analysis unit accepts information input from the user via text or voice, and analyzes the accepted information. Information from the user includes written documents, emails, recorded voice, etc. The analysis unit analyzes the text using natural language processing technology and the voice using voice recognition technology. In addition, the analysis unit estimates the user's emotions and adjusts the accuracy of the text analysis based on the estimated emotions. The analysis unit analyzes the user's past consultation history and selects an appropriate analysis method. Filtering is performed based on the user's current psychological state and living situation, and the analysis prioritizes highly relevant information by analyzing the user's geographical location information and social media activity. Step 2: The seriousness determination unit determines the seriousness of the user's trouble based on the information analyzed by the analysis unit. The seriousness determination unit estimates the user's emotions and adjusts the seriousness determination criteria based on the estimated emotions. It analyzes the user's past consultation history and optimizes the seriousness determination algorithm. It determines the seriousness based on the user's current psychological state and living situation. Step 3: The linking unit links to the psychosomatic medicine department based on the degree of seriousness determined by the seriousness determination unit. The linking unit estimates the user's emotions and adjusts the method of linking with the psychosomatic medicine department based on the estimated emotions. It analyzes the user's past consultation history and selects the optimal linking algorithm. It filters the link based on the user's current psychological state and living situation.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] For example, the analysis unit is realized by either the smart device 14 or the data processing device 12. For example, the processor 46 of the smart device 14 accepts text or voice input and performs analysis. The seriousness determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and determines the seriousness of the user's worries based on the analyzed information. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and links with a psychosomatic department. The analysis unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the seriousness determination unit and the linking unit may be realized by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] For example, the analysis unit is realized by either the smart glasses 214 or the data processing device 12. For example, the processor 46 of the smart glasses 214 accepts text or voice input and performs analysis. The seriousness determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and determines the seriousness of the user's troubles based on the analyzed information. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and links with a psychosomatic department. The analysis unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the seriousness determination unit and the linking unit may be realized by, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 a voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] For example, the analysis unit is realized by either the headset type terminal 314 or the data processing device 12. For example, the processor 46 of the headset type terminal 314 accepts input of text or voice and performs analysis. The seriousness determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and determines the seriousness of the user's troubles based on the analyzed information. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and links with a psychosomatic department. The analysis unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the seriousness determination unit and the linking unit may be realized by, for example, the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0161] For example, the analysis unit is realized by either the robot 414 or the data processing device 12. For example, the processor 46 of the robot 414 accepts text or voice input and performs analysis. The seriousness determination unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and determines the seriousness of the user's worries based on the analyzed information. The linking unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and links with a psychosomatic department. The analysis unit may be realized by, for example, the specific processing unit 290 of the data processing device 12, and the seriousness determination unit and the linking unit may be realized by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) Accepting input of information from a user via text or voice; an analysis unit that analyzes the received information; a severity determination unit that determines the severity of the user's concern based on the information analyzed by the analysis unit; A unit for linking to a psychosomatic department based on the degree of seriousness determined by the seriousness determination unit. A system characterized by: (Appendix 2) The analysis unit is Infer user sentiment and change text analysis accuracy based on the estimated sentiment 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the user's past consultation history and select an analysis method 2. The system of claim 1, (Appendix 4) The analysis unit is Perform filtering based on the user's current state of mind and life situation 2. The system of claim 1, (Appendix 5) The analysis unit is Infer user sentiment and prioritize texts to analyze based on the inferred sentiment 2. The system of claim 1, (Appendix 6) The analysis unit is Prioritize and analyze relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 7) The analysis unit is Analyze your social media activity and parse related information 2. The system of claim 1, (Appendix 8) The severity determination unit is Inferring the user's emotions and changing the criteria for determining the severity based on the estimated emotions 2. The system of claim 1, (Appendix 9) The severity determination unit is Analyze users' past consultation history and improve the severity assessment algorithm 2. The system of claim 1, (Appendix 10) The severity determination unit is Determine the severity based on the user's current mental state and life situation 2. The system of claim 1, (Appendix 11) The linking portion is Estimate the user's emotions and change the method of collaboration with the psychosomatic clinic based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The linking portion is Analyze the user's past consultation history and select a linking algorithm 2. The system of claim 1, (Appendix 13) The linking portion is Perform collaboration filtering based on the user's current state of mind and life situation 2. The system of claim 1, (Appendix 14) The analysis unit is Infer the user's sentiment and change the way the parsed text is presented based on the inferred sentiment 2. The system of claim 1, (Appendix 15) The analysis unit is Analyze the user's past consultation history and select an analysis algorithm 2. The system of claim 1, (Appendix 16) The analysis unit is Filtering analytics based on the user's current state of mind and life circumstances 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and prioritize texts to analyze based on the inferred sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is Prioritize and analyze relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 19) The analysis unit is Analyze your social media activity and parse related information 2. The system of claim 1, (Appendix 20) The analysis unit is Infer user emotions and change the way analysis results are displayed based on the inferred emotions 2. The system of claim 1, (Appendix 21) The analysis unit is Analyze the user's past consultation history and select the feedback method 2. The system of claim 1, (Appendix 22) The analysis unit is Filtering the analysis results based on the user's current state of mind and life situation 2. The system of claim 1, (Appendix 23) The analysis unit is Infer user emotions and prioritize analysis results based on the inferred emotions 2. The system of claim 1, (Appendix 24) The analysis unit is Prioritize the display of relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 25) The analysis unit is Analyze your social media activity and display relevant information 2. The system of claim 1, (Appendix 26) The severity determination unit is Infer the user's emotions and change the way the severity judgment result is displayed based on the estimated emotions 2. The system of claim 1, (Appendix 27) The severity determination unit is Analyze the user's past consultation history and select the feedback method 2. The system of claim 1, (Appendix 28) The severity determination unit is Filtering the severity judgment results based on the user's current psychological state and life situation 2. The system of claim 1, (Appendix 29) The linking portion is Infer user emotions and change the way collaboration results are displayed based on the inferred emotions 2. The system of claim 1, (Appendix 30) The linking portion is Analyze the user's past consultation history and select the feedback method 2. The system of claim 1, (Appendix 31) The linking portion is Filtering collaboration results based on the user's current state of mind and life situation 2. The system of claim 1, [Explanation of symbols]

[0181] 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. Accepting input of information from a user via text or voice; an analysis unit that analyzes the received information; a severity determination unit that determines the severity of the user's concern based on the information analyzed by the analysis unit; A unit for linking to a psychosomatic department based on the degree of seriousness determined by the seriousness determination unit. A system characterized by:

2. The analysis unit is Estimating the user's emotion and changing the accuracy of text analysis based on the estimated emotion.

2. The system of claim 1 .

3. The analysis unit is Analyze the user's past consultation history and select an analysis method.

2. The system of claim 1 .

4. The analysis unit is Perform filtering based on the user's current state of mind and life situation 2. The system of claim 1 .

5. The analysis unit is Estimating the user's sentiment and prioritizing the texts to be analyzed based on the estimated sentiment.

2. The system of claim 1 .

6. The analysis unit is Prioritizing and analyzing information that is highly relevant based on the geographic location information of the user 2. The system of claim 1 .

7. The analysis unit is Analyzing the user's social media activity and parsing related information 2. The system of claim 1 .

8. The severity determination unit is Estimating the user's emotion and changing the criteria for determining the severity based on the estimated emotion.

2. The system of claim 1 .

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