System

Abstract

To provide a system according to an embodiment, the system intending to analyze characteristics of voice and determine condition of a throat.SOLUTION: A system according to an embodiment includes: a reception part; an analysis part; a determination part; and a provision part. The reception part receives a voice input. The analysis part analyzes characteristics of the voice received by the reception part. The determination part determines condition of a throat on the basis of the characteristics of the voice analyzed by the analysis part. The provision part provides a diagnosis result on the basis of the determined results.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 do not adequately assess the condition of the throat from voice characteristics, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the characteristics of a voice and determine the condition of the throat. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a determination unit, and a provision unit. The reception unit receives a voice input. The analysis unit analyzes characteristics of the voice received by the reception unit. The determination unit determines the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The provision unit provides a diagnosis result based on the result of the determination. Effect of the Invention

[0007] The system according to the embodiment can analyze the characteristics of the voice and determine the condition of the throat. [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 the 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) The voice assistant system according to the embodiment of the present invention is a system that detects the condition of the user's throat or mental discomfort. This system operates when the user inputs voice to a voice assistant app. The voice input is analyzed by a generation AI. The generation AI analyzes the characteristics of the voice and detects whether there is any discomfort in the throat or mental discomfort. For example, the generation AI judges the condition of the throat by analyzing the tone and rhythm of the voice, the clarity of the pronunciation, etc. In addition, the generation AI also detects mental discomfort by taking into account the content of the dialogue and the response speed. For example, if the user speaks at a slower response speed than usual or if the content of the dialogue is negative, it is judged that there is mental discomfort. Next, if the generation AI judges that a diagnosis is necessary, it prompts the user to do so. For example, a message such as "You seem to be having trouble with your throat recently. We recommend that you get a doctor's diagnosis" is displayed. In this way, the user can detect an abnormality early and take appropriate measures. For example, if a user asks the voice assistant app, "What's the weather today?", the system analyzes the voice and evaluates the tone and rhythm of the voice and the clarity of the pronunciation. If the user's voice is hoarse or the rhythm is irregular, the system will determine that the user's throat is not in good condition. If the user says, "I've been feeling down lately," the system will analyze the content and detect any mental discomfort. This allows the user to easily monitor their health condition. In particular, early detection of throat condition or mental discomfort can prevent serious symptoms from developing. In addition, by using generative AI, the system can grasp the user's condition more accurately through interactive dialogue. This allows the voice assistant system to detect the user's health condition early and encourage appropriate measures.

[0029] The voice assistant system according to the embodiment includes a reception unit, an analysis unit, a judgment unit, and a provision unit. The reception unit accepts voice input made by a user to a voice assistant app. The voice input includes, for example, input using a microphone and uploading of a voice file, but is not limited to such examples. The reception unit, for example, accepts a voice spoken by a user into a microphone in real time. The reception unit can also upload a voice file recorded in advance by the user. The analysis unit analyzes the characteristics of the voice accepted by the reception unit. The analysis unit analyzes, for example, the frequency components, volume, tone, etc. of the voice. For example, the analysis unit analyzes the frequency components of the voice and evaluates the vibration pattern of the vocal cords. The analysis unit can also analyze the volume of the voice and evaluate the strength of the voice. Furthermore, the analysis unit can analyze the tone of the voice and evaluate the pitch and timbre of the voice. The judgment unit judges the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The judgment unit judges the condition of the throat based on, for example, the vibration pattern of the vocal cords and the clarity of the voice. For example, the determination unit determines that the throat is not in good condition when the vibration pattern of the vocal cords is irregular. The determination unit can also determine that the throat is not in good condition when the clarity of the voice is low. The provision unit provides a diagnosis result based on the result determined by the determination unit. The provision unit provides the diagnosis result in the form of, for example, a text message or voice feedback. For example, the provision unit displays a message such as "Your throat seems to be in bad condition recently. We recommend that you receive a doctor's diagnosis." The provision unit can also provide the diagnosis result through voice feedback. For example, the provision unit conveys the diagnosis result to the user by voice through a voice assistant app. As a result, the voice assistant system according to the embodiment can analyze the user's voice input, detect the condition of the throat or mental discomfort, and provide a diagnosis result.

[0030] The reception unit receives voice input from the user to the voice assistant app. Voice input includes, but is not limited to, input using a microphone and uploading of a voice file. For example, the reception unit receives the voice of the user speaking into the microphone in real time. The reception unit can also upload a voice file recorded in advance by the user. Specifically, the reception unit uses a high-sensitivity microphone to clearly capture the user's voice. The microphone has a noise canceling function and can accurately capture only the user's voice by removing ambient noise. Furthermore, the voice file upload function allows the user to provide previously recorded voice data to the system. This allows the user to receive a diagnosis using not only real-time voice input but also previously recorded voice. The reception unit receives the voice data and simultaneously performs pre-processing of the data. For example, noise removal and normalization of the voice data are performed so that the analysis unit can efficiently analyze it. This allows the reception unit to provide the user's voice input as high-quality data and improve the accuracy of the entire system.

[0031] The analysis unit analyzes the characteristics of the voice received by the reception unit. The analysis unit analyzes, for example, the frequency components, volume, and tone of the voice. Specifically, the analysis unit uses an advanced voice analysis algorithm to extract detailed characteristics of the voice data. For example, by analyzing the frequency components of the voice, the vibration pattern of the vocal cords is evaluated and the characteristics of the user's voice are grasped. In the volume analysis, the strength of the voice is evaluated and the user's voice production method and the health condition of the vocal cords are estimated. In the tone analysis, the pitch and timbre of the voice are evaluated and the user's emotional state and stress level can be estimated. The analysis unit integrates these analysis results and grasps the overall characteristics of the user's voice. Furthermore, the analysis unit uses a machine learning model to learn patterns from the voice data and perform more accurate analysis. For example, it is possible to detect abnormal patterns by comparing with past voice data and to detect throat condition and mental discomfort at an early stage. As a result, the analysis unit can analyze the user's voice data in detail and provide accurate information to the judgment unit.

[0032] The judgment unit judges the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The judgment unit judges the condition of the throat based on, for example, the vibration pattern of the vocal cords and the clarity of the voice. Specifically, the judgment unit executes an algorithm for evaluating the health condition of the throat of the user based on the data provided by the analysis unit. For example, if the vibration pattern of the vocal cords is irregular, it is judged that the throat is in a bad condition. It can also be judged that the throat is in a bad condition if the clarity of the voice is low. The judgment unit combines these evaluation criteria to make a comprehensive judgment and evaluate the health condition of the throat of the user. Furthermore, the judgment unit can detect a change in the condition of the throat by evaluating the current condition in comparison with past data. For example, if the current voice is clearly different compared with past voice data, it is judged that the condition of the throat is deteriorating. In this way, the judgment unit can accurately evaluate the health condition of the throat of the user and provide an appropriate diagnosis result to the provision unit.

[0033] The providing unit provides a diagnosis result based on the result determined by the determining unit. The providing unit provides the diagnosis result in the form of, for example, a text message or voice feedback. Specifically, the providing unit has an interface for conveying the diagnosis result to the user in an easy-to-understand format. For example, the providing unit displays a message such as, "Your throat seems to be in bad condition recently. We recommend that you get a doctor's diagnosis." The providing unit can also provide the diagnosis result through voice feedback. For example, the providing unit conveys the diagnosis result to the user by voice through a voice assistant app. Furthermore, the providing unit can also provide advice and recommendations to the user based on the diagnosis result. For example, the providing unit provides advice such as, "We recommend that you drink a lot of water to improve the condition of your throat." This allows the providing unit to quickly provide the user with appropriate diagnosis results and advice, and support the user's health management. Furthermore, the providing unit also has a function of recording the diagnosis result and managing the history of the user's health condition. This allows the user to refer to past diagnosis results and grasp changes in their health condition.

[0034] The analysis unit includes a tone analysis unit that analyzes the tone or rhythm of the voice. The tone analysis unit analyzes the tone or rhythm of the voice. The tone analysis unit analyzes, for example, the pitch or timbre of the sound. For example, the tone analysis unit analyzes the frequency components of the voice to evaluate the pitch. The tone analysis unit can also analyze the timbre of the voice to evaluate the texture of the voice. Furthermore, the tone analysis unit can analyze the rhythm of the voice to evaluate the intervals and tempo of the sounds. For example, the tone analysis unit can determine that the throat is not in good condition when the rhythm of the voice is irregular. As a result, the tone analysis unit can more accurately determine the condition of the throat by analyzing the tone and rhythm of the voice.

[0035] The analysis unit includes a pronunciation analysis unit that analyzes the clarity of pronunciation. The pronunciation analysis unit analyzes the clarity of pronunciation. The pronunciation analysis unit, for example, identifies phonemes and analyzes the consistency of pronunciation. For example, the pronunciation analysis unit identifies each phoneme in the voice and evaluates whether the pronunciation is clear. The pronunciation analysis unit can also analyze the consistency of the pronunciation of the voice and evaluate whether the pronunciation is stable. Furthermore, the pronunciation analysis unit can analyze the clarity of the voice and evaluate whether the pronunciation is clear. For example, the pronunciation analysis unit can determine that the throat is in poor condition when the clarity of the voice is low. As a result, the pronunciation analysis unit can more accurately determine the condition of the throat by analyzing the clarity of the pronunciation.

[0036] The analysis unit includes a speed analysis unit that analyzes the response speed. The speed analysis unit analyzes the response speed. The speed analysis unit analyzes, for example, the voice reaction time and the response delay. For example, the speed analysis unit evaluates how quickly the user responds to a question. The speed analysis unit can also analyze the voice reaction time and evaluate whether the response is delayed. Furthermore, the speed analysis unit can analyze the voice response delay and evaluate whether the response is delayed. For example, the speed analysis unit can determine that there is mental discomfort when the user's response speed is slow. Thereby, the speed analysis unit can more accurately determine mental discomfort by analyzing the response speed.

[0037] The judgment unit includes a dialogue analysis unit that analyzes the dialogue content. The dialogue analysis unit analyzes the dialogue content. The dialogue analysis unit, for example, analyzes the extraction of keywords and the understanding of context. For example, the dialogue analysis unit extracts important keywords from the voice and evaluates the content. The dialogue analysis unit can also analyze the context of the dialogue and understand the content of the dialogue. Furthermore, the dialogue analysis unit can evaluate whether the content of the dialogue is negative. For example, the dialogue analysis unit can determine that there is a mental discomfort when the user is talking about negative content. In this way, the dialogue analysis unit can more accurately determine the mental discomfort by analyzing the dialogue content.

[0038] The dialogue analysis unit includes a content determination unit that determines negative content. The content determination unit determines negative content. The content determination unit, for example, performs emotion analysis and detects negative keywords. For example, the content determination unit detects keywords that express negative emotions from the voice and evaluates the content. The content determination unit can also perform emotion analysis of the voice and evaluate whether the user has negative emotions. Furthermore, the content determination unit can analyze the context of the dialogue and evaluate whether the content of the dialogue is negative. For example, the content determination unit can determine that the user has a mental discomfort when the user is talking about negative content. As a result, the content determination unit can more accurately determine mental discomfort by determining negative content.

[0039] The providing unit includes a notification unit that notifies the user when it is determined that a diagnosis is necessary. The notification unit notifies the user when it is determined that a diagnosis is necessary. The notification unit provides notification in the form of, for example, a text message or voice feedback. For example, the notification unit displays a message such as "Your throat seems to be in a bad condition recently. We recommend that you get a diagnosis from a doctor." The notification unit can also provide notification through voice feedback. For example, the notification unit conveys the diagnosis result to the user by voice through a voice assistant app. In this way, the notification unit can detect abnormalities early by notifying the user when it is determined that a diagnosis is necessary.

[0040] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit preferentially suggests a voice input method that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and suggest the most efficient reception method. As a result, the reception unit can select the optimal reception method by analyzing the user's past voice input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice data to the generation AI and cause the generation AI to select the optimal reception method.

[0041] The reception unit can filter the user's current environmental sound to remove noise when receiving the voice input. For example, when the user is in a noisy environment, the reception unit filters the environmental sound to remove noise. In addition, when the user is in a quiet environment, the reception unit can also receive the voice input with the environmental sound minimized. Furthermore, when the user is moving, the reception unit can also filter the environmental sound in real time to remove noise. As a result, the reception unit improves the accuracy of the voice input by filtering the environmental sound to remove noise. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's environmental sound data to the generation AI and cause the generation AI to remove noise.

[0042] The reception unit can preferentially receive highly relevant voices in consideration of the geographical location information of the user when receiving the voice input. For example, when the user is in a specific location, the reception unit preferentially receives voice inputs related to the location. In addition, when the user is moving, the reception unit can also preferentially receive voice inputs related to the current location. Furthermore, when the user is in a specific area, the reception unit can also preferentially receive voice inputs related to the area. Thereby, the reception unit can preferentially receive highly relevant voices by considering the geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant voices.

[0043] The reception unit can analyze the user's social media activity and receive related voices when receiving the voice input. For example, the reception unit preferentially receives voice inputs related to topics that the user is talking about on social media. The reception unit can also preferentially receive voice inputs related to topics of interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and preferentially receive related voice inputs. In this way, the reception unit can preferentially receive related voices by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the priority order of related voices.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice when analyzing the features of the voice. For example, in the case of important voice, the analysis unit performs detailed feature analysis. In addition, in the case of normal voice, the analysis unit can also perform standard feature analysis. Furthermore, in the case of urgent voice, the analysis unit can perform feature analysis quickly. As a result, the analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. 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 voice data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the voice when analyzing the features of the voice. For example, when the voice is a conversation, the analysis unit applies a conversation analysis algorithm. Furthermore, when the voice is music, the analysis unit can also apply a music analysis algorithm. Furthermore, when the voice is environmental sound, the analysis unit can also apply an environmental sound analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the voice, thereby enabling more appropriate analysis. 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 voice data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0046] The analysis unit can determine the priority of analysis based on the time of submission of the voice when analyzing the features of the voice. For example, the analysis unit prioritizes analysis of the most recently submitted voice. The analysis unit can also analyze the voice submitted in the past later. Furthermore, the analysis unit can also analyze urgent voice with the highest priority. In this way, the analysis unit can prioritize analysis of the latest voice by determining the priority of analysis based on the time of submission of the voice. 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 voice data to the generation AI and cause the generation AI to execute the determination of the priority of analysis based on the time of submission.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the voice when analyzing the features of the voice. For example, the analysis unit prioritizes analysis of important voice. The analysis unit can also analyze normal voice later. Furthermore, the analysis unit can prioritize analysis of highly relevant voice. As a result, the analysis unit can adjust the order of analysis based on the relevance of the voice, thereby analyzing highly relevant voice with priority. 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 voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0048] The judgment unit can improve the accuracy of judgment when judging the condition of the throat by considering the interrelationship of voices. The judgment unit judges the condition of the throat by considering, for example, the interrelationship between the tone and rhythm of the voice. The judgment unit can also judge the condition of the throat by considering the interrelationship between the clarity of the pronunciation of the voice and the rhythm. Furthermore, the judgment unit can also judge the condition of the throat by considering the interrelationship between the tone of the voice and the clarity of the pronunciation. In this way, the judgment unit can improve the accuracy of judgment of the condition of the throat by considering the interrelationship of the voices. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input voice data to the generation AI and cause the generation AI to execute improvement of the accuracy of judgment based on the interrelationship.

[0049] When determining the condition of the throat, the determination unit can make a determination by taking into consideration attribute information of the person who submitted the voice. The determination unit can determine the condition of the throat by taking into consideration, for example, the age of the person who submitted the voice. The determination unit can also determine the condition of the throat by taking into consideration the gender of the person who submitted the voice. Furthermore, the determination unit can also determine the condition of the throat by taking into consideration the health condition of the person who submitted the voice. In this way, the determination unit can improve the accuracy of determining the condition of the throat by taking into consideration the attribute information of the person who submitted the voice. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the attribute information of the person who submitted the voice to the generation AI and cause the generation AI to execute a judgment based on the attribute information.

[0050] The judgment unit can make a judgment taking into consideration the geographical distribution of the voice when judging the condition of the throat. For example, the judgment unit gives priority to judging voices from a specific region. The judgment unit can also give priority to judging voices with high geographical relevance. Furthermore, the judgment unit can judge geographically distributed voices equally. In this way, the judgment unit can improve the accuracy of judging the condition of the throat by taking into consideration the geographical distribution of the voice. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the voice data to a generation AI and cause the generation AI to execute a judgment based on the geographical distribution.

[0051] The judgment unit can improve the accuracy of the judgment when judging the condition of the throat by referring to the related literature of the voice. The judgment unit judges the condition of the throat by referring to the related literature of the voice, for example. The judgment unit can also improve the accuracy of the judgment based on the related literature of the voice. Furthermore, the judgment unit can also judge the condition of the throat by analyzing the related literature of the voice. In this way, the judgment unit can improve the accuracy of the judgment of the condition of the throat by referring to the related literature of the voice. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature data to the generation AI and cause the generation AI to execute a judgment based on the literature.

[0052] When providing a diagnostic result, the providing unit can select an optimal providing method by referring to the user's past diagnostic history. The providing unit selects an optimal providing method based on, for example, the user's past diagnostic history. The providing unit can also suggest an appropriate diagnostic result providing method from the user's past diagnostic history. Furthermore, the providing unit can analyze the user's past diagnostic history and select the most effective providing method. As a result, the providing unit can select an optimal providing method by referring to the user's past diagnostic history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past diagnostic history data to the generating AI and cause the generating AI to select an optimal providing method.

[0053] The providing unit can customize the means of providing based on the user's current living situation when providing the diagnosis result. For example, when the user is busy, the providing unit provides a concise diagnosis result. In addition, the providing unit can also provide a detailed diagnosis result when the user is relaxed. Furthermore, when the user is on the move, the providing unit can provide the diagnosis result by voice. Thereby, the providing unit can provide a more appropriate diagnosis result by customizing the means of providing based on the user's current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's living situation data to the generating AI and cause the generating AI to select the customized means of providing.

[0054] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing the diagnostic result. For example, when the user is in a specific location, the providing unit provides a diagnostic result related to the location. In addition, when the user is moving, the providing unit can also provide a diagnostic result related to the current location. Furthermore, when the user is in a specific area, the providing unit can also provide a diagnostic result related to the area. Thereby, the providing unit can select the optimal providing method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0055] The providing unit can analyze the user's social media activity and suggest a means of providing the diagnostic result when providing the diagnostic result. The providing unit provides, for example, a diagnostic result related to the content that the user is talking about on social media. The providing unit can also provide a diagnostic result related to a topic of interest from the user's social media activity. Furthermore, the providing unit can analyze the user's social media activity and provide a related diagnostic result. In this way, the providing unit can provide a related diagnostic result by analyzing the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide the related diagnostic result.

[0056] The tone analysis unit can adjust the level of detail of the analysis based on the importance of the voice during tone analysis. For example, in the case of important voice, the tone analysis unit performs detailed tone analysis. In addition, in the case of normal voice, the tone analysis unit can also perform standard tone analysis. Furthermore, in the case of urgent voice, the tone analysis unit can perform tone analysis quickly. As a result, the tone analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. A part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input voice data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0057] The tone analysis unit can apply different analysis algorithms depending on the category of the sound during tone analysis. For example, when the sound is a conversation, the tone analysis unit applies a conversation analysis algorithm. In addition, when the sound is music, the tone analysis unit can also apply a music analysis algorithm. Furthermore, when the sound is environmental sound, the tone analysis unit can also apply an environmental sound analysis algorithm. This allows the tone analysis unit to apply different analysis algorithms depending on the category of the sound, thereby enabling more appropriate analysis. Part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0058] The tone analysis unit can determine the analysis priority based on the time of submission of the voice during tone analysis. For example, the tone analysis unit prioritizes analysis of the most recently submitted voice. The tone analysis unit can also analyze the voice submitted in the past later. Furthermore, the tone analysis unit can analyze the urgent voice with the highest priority. As a result, the tone analysis unit can prioritize analysis of the latest voice by determining the analysis priority based on the time of submission of the voice. A part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tone analysis unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the analysis priority based on the time of submission.

[0059] The tone analysis unit can adjust the order of analysis based on the relevance of the voice during tone analysis. For example, the tone analysis unit prioritizes analysis of important voice. The tone analysis unit can also prioritize analysis of normal voice. Furthermore, the tone analysis unit can prioritize analysis of highly relevant voice. As a result, the tone analysis unit can prioritize analysis of highly relevant voice by adjusting the order of analysis based on the relevance of the voice. A part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0060] The pronunciation analysis unit can adjust the level of detail of the analysis based on the importance of the voice during the pronunciation analysis. For example, in the case of important voice, the pronunciation analysis unit performs detailed pronunciation analysis. In addition, in the case of normal voice, the pronunciation analysis unit can also perform standard pronunciation analysis. Furthermore, in the case of urgent voice, the pronunciation analysis unit can also perform quick pronunciation analysis. As a result, the pronunciation analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. A part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input voice data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0061] The pronunciation analysis unit can apply different analysis algorithms depending on the category of the sound during pronunciation analysis. For example, when the sound is a conversation, the pronunciation analysis unit applies a conversation analysis algorithm. Furthermore, when the sound is music, the pronunciation analysis unit can also apply a music analysis algorithm. Furthermore, when the sound is environmental sound, the pronunciation analysis unit can also apply an environmental sound analysis algorithm. This allows the pronunciation analysis unit to apply different analysis algorithms depending on the category of the sound, thereby enabling more appropriate analysis. Part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the pronunciation analysis unit can input voice data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0062] The pronunciation analysis unit can determine the priority of analysis based on the time of submission of the voice during the pronunciation analysis. For example, the pronunciation analysis unit prioritizes analysis of the most recently submitted voice. The pronunciation analysis unit can also analyze the voice submitted in the past later. Furthermore, the pronunciation analysis unit can also analyze urgent voice with the highest priority. As a result, the pronunciation analysis unit can prioritize analysis of the latest voice by determining the priority of analysis based on the time of submission of the voice. A part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the priority of analysis based on the time of submission.

[0063] The pronunciation analysis unit can adjust the order of analysis based on the relevance of the sounds during the pronunciation analysis. For example, the pronunciation analysis unit prioritizes analysis of important sounds. The pronunciation analysis unit can also prioritize analysis of normal sounds. Furthermore, the pronunciation analysis unit can also prioritize analysis of highly relevant sounds. As a result, the pronunciation analysis unit can adjust the order of analysis based on the relevance of the sounds, thereby analyzing highly relevant sounds with priority. A part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input the voice data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0064] The speed analysis unit can adjust the level of detail of the analysis based on the importance of the voice during the speed analysis. For example, in the case of important voice, the speed analysis unit performs detailed speed analysis. In addition, in the case of normal voice, the speed analysis unit can also perform standard speed analysis. Furthermore, in the case of urgent voice, the speed analysis unit can also perform speed analysis quickly. As a result, the speed analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. A part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input voice data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0065] The speed analysis unit can apply different analysis algorithms depending on the category of the audio during speed analysis. For example, when the audio is a conversation, the speed analysis unit applies a conversation analysis algorithm. In addition, when the audio is music, the speed analysis unit can also apply a music analysis algorithm. Furthermore, when the audio is environmental sound, the speed analysis unit can also apply an environmental sound analysis algorithm. This allows the speed analysis unit to apply different analysis algorithms depending on the category of the audio, thereby enabling more appropriate analysis. Part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0066] The speed analysis unit can determine the priority of analysis based on the time of submission of the voice during the speed analysis. For example, the speed analysis unit prioritizes analysis of the most recently submitted voice. The speed analysis unit can also analyze the voice submitted in the past later. Furthermore, the speed analysis unit can also analyze the urgent voice with the highest priority. In this way, the speed analysis unit can prioritize analysis of the latest voice by determining the priority of analysis based on the time of submission of the voice. A part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the priority of analysis based on the time of submission.

[0067] The speed analysis unit can adjust the order of analysis based on the relevance of the voices during the speed analysis. For example, the speed analysis unit prioritizes analysis of important voices. The speed analysis unit can also prioritize analysis of normal voices. Furthermore, the speed analysis unit can prioritize analysis of highly relevant voices. As a result, the speed analysis unit can prioritize analysis of highly relevant voices by adjusting the order of analysis based on the relevance of the voices. A part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0068] The dialogue analysis unit can adjust the level of detail of the analysis based on the importance of the dialogue during dialogue analysis. For example, in the case of an important dialogue, the dialogue analysis unit performs a detailed dialogue analysis. In addition, in the case of a normal dialogue, the dialogue analysis unit can also perform a standard dialogue analysis. Furthermore, in the case of an urgent dialogue, the dialogue analysis unit can quickly perform dialogue analysis. As a result, the dialogue analysis unit can analyze important dialogues in more detail by adjusting the level of detail of the analysis based on the importance of the dialogue. A part or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input voice data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0069] The dialogue analysis unit can apply different analysis algorithms depending on the category of the dialogue when analyzing the dialogue. For example, if the dialogue is business, the dialogue analysis unit applies a business analysis algorithm. Also, if the dialogue is casual, the dialogue analysis unit can apply a casual analysis algorithm. Furthermore, if the dialogue is technical, the dialogue analysis unit can apply a technical analysis algorithm. This allows the dialogue analysis unit to apply different analysis algorithms depending on the category of the dialogue, thereby enabling more appropriate analysis. Some or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue analysis unit can input voice data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0070] The dialogue analysis unit can determine the priority of analysis based on the time of submission of the dialogue when analyzing the dialogue. For example, the dialogue analysis unit prioritizes the analysis of the most recently submitted dialogue. The dialogue analysis unit can also postpone the analysis of dialogues submitted in the past. Furthermore, the dialogue analysis unit can also give top priority to analyzing urgent dialogues. In this way, the dialogue analysis unit can prioritize the analysis of the latest dialogue by determining the priority of analysis based on the time of submission of the dialogue. A part or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input voice data to the generation AI and cause the generation AI to determine the priority of analysis based on the time of submission.

[0071] The dialogue analysis unit can adjust the order of analysis based on the relevance of the dialogue during dialogue analysis. For example, the dialogue analysis unit prioritizes analysis of important dialogues. The dialogue analysis unit can also postpone analysis of normal dialogues. Furthermore, the dialogue analysis unit can prioritize analysis of highly relevant dialogues. As a result, the dialogue analysis unit can adjust the order of analysis based on the relevance of the dialogues, thereby analyzing highly relevant dialogues with priority. A part or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0072] The content determination unit can adjust the level of detail of the determination based on the importance of the dialogue when determining the content. For example, in the case of an important dialogue, the content determination unit performs a detailed content determination. In addition, in the case of a normal dialogue, the content determination unit can also perform a standard content determination. Furthermore, in the case of an urgent dialogue, the content determination unit can perform a quick content determination. As a result, the content determination unit can determine an important dialogue in more detail by adjusting the level of detail of the determination based on the importance of the dialogue. A part or all of the above-mentioned processing in the content determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the content determination unit can input voice data to a generation AI and cause the generation AI to adjust the level of detail of the determination based on the importance.

[0073] The content determination unit can apply different determination algorithms depending on the category of the dialogue when determining the content. For example, if the dialogue is business, the content determination unit applies a business determination algorithm. In addition, if the dialogue is casual, the content determination unit can also apply a casual determination algorithm. In addition, if the dialogue is technical, the content determination unit can also apply a technical determination algorithm. This allows the content determination unit to make a more appropriate determination by applying different determination algorithms depending on the category of the dialogue. Part or all of the above-mentioned processing in the content determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the content determination unit can input voice data to a generation AI and cause the generation AI to apply a determination algorithm according to the category.

[0074] The content determination unit can determine the priority of the determination based on the time of submission of the dialogue when determining the content. For example, the content determination unit gives priority to the most recently submitted dialogue. The content determination unit can also give a later determination to dialogues submitted in the past. Furthermore, the content determination unit can also give top priority to urgent dialogues. In this way, the content determination unit can determine the priority of the determination based on the time of submission of the dialogue, thereby giving priority to the latest dialogue. A part or all of the above-mentioned processing in the content determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the content determination unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the priority of the determination based on the time of submission.

[0075] The content determination unit can adjust the order of determination based on the relevance of the dialogue when determining the content. For example, the content determination unit prioritizes the determination of an important dialogue. The content determination unit can also prioritize the determination of a normal dialogue. Furthermore, the content determination unit can prioritize the determination of a highly relevant dialogue. As a result, the content determination unit can prioritize the determination of a highly relevant dialogue by adjusting the order of determination based on the relevance of the dialogue. A part or all of the above-mentioned processing in the content determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the content determination unit can input voice data to a generation AI and cause the generation AI to adjust the order of determination based on the relevance.

[0076] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit selects the optimal notification method based on the user's past notification history, for example. The notification unit can also suggest an appropriate notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. As a result, the notification unit can select the optimal notification method by referring to the user's past notification history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past notification history data to the generation AI and cause the generation AI to select the optimal notification method.

[0077] The notification unit can customize the notification means based on the user's current living situation at the time of notification. For example, when the user is busy, the notification unit provides a brief notification. In addition, when the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, when the user is on the move, the notification unit can also provide a voice notification. This allows the notification unit to customize the notification means based on the user's current living situation, thereby enabling more appropriate notification. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's living situation data to the generation AI and cause the generation AI to select the customized notification means.

[0078] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. For example, when the user is in a specific location, the notification unit performs a notification related to the location. In addition, when the user is moving, the notification unit can also perform a notification related to the current location. Furthermore, when the user is in a specific area, the notification unit can also perform a notification related to the area. Thereby, the notification unit can select the optimal notification method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal notification method.

[0079] At the time of notification, the notification unit can analyze the user's social media activity and suggest a means of notification. The notification unit, for example, provides a notification related to the content that the user is talking about on social media. The notification unit can also provide a notification related to a topic of interest from the user's social media activity. Furthermore, the notification unit can analyze the user's social media activity and provide a related notification. In this way, the notification unit can provide a related notification by analyzing the social media activity. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's social media data to the generation AI and cause the generation AI to execute a related means of notification.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can analyze background sounds of the voice input and estimate the user's environment. For example, the analysis unit can analyze car sounds and people's voices heard during voice input and estimate that the user is out. The analysis unit can also analyze keyboard typing sounds and telephone ringtones heard during voice input and estimate that the user is in an office. Furthermore, the analysis unit can analyze television sounds and operating sounds of home appliances heard during voice input and estimate that the user is at home. In this way, the analysis unit can improve the accuracy of analysis of the voice input by estimating the user's environment.

[0082] The providing unit can provide advice regarding the user's health condition. For example, when the providing unit determines that the user's throat is not feeling well, the providing unit can suggest a specific method for resting the throat. Also, when the providing unit determines that the user is feeling unwell mentally, the providing unit can suggest a method for relaxing or an activity for relieving stress. Furthermore, the providing unit can introduce the user to an appropriate medical institution or specialist depending on the user's health condition. In this way, the providing unit can provide specific advice regarding the user's health condition, allowing the user to take appropriate measures.

[0083] The analysis unit can estimate the user's interests and concerns based on the content of the voice input. For example, if the user frequently talks about a particular topic, the analysis unit can estimate the interest in that topic. Also, if the user frequently uses a particular keyword, the analysis unit can estimate the interest related to that keyword. Furthermore, the analysis unit can analyze the user's dialogue patterns and topic transitions to estimate changes in the user's interests and concerns. In this way, the analysis unit can provide a more personalized service by estimating the user's interests and concerns.

[0084] The providing unit can provide personalized content based on the user's voice input. For example, when the user talks about a specific music genre, the providing unit can recommend music related to the genre. Also, when the user talks about a specific movie or drama, the providing unit can recommend content related to the work. Furthermore, when the user talks about a specific hobby or interest, the providing unit can provide information or events related to the hobby or interest. In this way, the providing unit can improve the user's satisfaction by providing personalized content based on the user's voice input.

[0085] The analysis unit can analyze the voice input pattern and estimate the user's habits and behavioral patterns. For example, if the user asks a specific question at the same time every morning, the analysis unit estimates that the user is engaged in a specific behavior during that time period. In addition, if the user talks about a specific topic on a specific day of the week, the analysis unit can estimate that the user is engaged in a specific activity on that day of the week. Furthermore, if the user talks about a specific content in a specific season, the analysis unit can estimate that the user has a specific interest or concern in that season. In this way, the analysis unit can more accurately grasp the user's habits and behavioral patterns by analyzing the voice input pattern.

[0086] The process flow of the first embodiment will be briefly described below.

[0087] Step 1: The reception unit receives voice input from the user to the voice assistant app. Voice input includes, for example, input using a microphone and uploading of a voice file. The reception unit can receive the voice of the user speaking into the microphone in real time, and can also upload a pre-recorded voice file. Step 2: The analysis unit analyzes the characteristics of the voice received by the reception unit. The analysis unit analyzes the frequency components, volume, tone, etc. of the voice, and evaluates, for example, the vibration pattern of the vocal cords, the strength of the voice, the pitch, and the timbre of the voice. Step 3: The judgment unit judges the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The judgment unit judges the condition of the throat based on the vibration pattern of the vocal cords and the clarity of the voice, and judges that the throat is not in good condition if, for example, the vibration pattern of the vocal cords is irregular or the clarity of the voice is low. Step 4: The providing unit provides a diagnosis result based on the result determined by the determining unit. The providing unit provides the diagnosis result in the form of a text message or voice feedback, for example, displaying a message such as "Your throat seems to be in a bad condition recently. We recommend that you see a doctor." The providing unit can also convey the diagnosis result to the user by voice through a voice assistant app.

[0088] (Example 2) The voice assistant system according to the embodiment of the present invention is a system that detects the condition of the user's throat or mental discomfort. This system operates when the user inputs voice to a voice assistant app. The voice input is analyzed by a generation AI. The generation AI analyzes the characteristics of the voice and detects whether there is any discomfort in the throat or mental discomfort. For example, the generation AI judges the condition of the throat by analyzing the tone and rhythm of the voice, the clarity of the pronunciation, etc. In addition, the generation AI also detects mental discomfort by taking into account the content of the dialogue and the response speed. For example, if the user speaks at a slower response speed than usual or if the content of the dialogue is negative, it is judged that there is mental discomfort. Next, if the generation AI judges that a diagnosis is necessary, it prompts the user to do so. For example, a message such as "You seem to be having trouble with your throat recently. We recommend that you get a doctor's diagnosis" is displayed. In this way, the user can detect an abnormality early and take appropriate measures. For example, if a user asks the voice assistant app, "What's the weather today?", the system analyzes the voice and evaluates the tone and rhythm of the voice and the clarity of the pronunciation. If the user's voice is hoarse or the rhythm is irregular, the system will determine that the user's throat is not in good condition. If the user says, "I've been feeling down lately," the system will analyze the content and detect any mental discomfort. This allows the user to easily monitor their health condition. In particular, early detection of throat condition or mental discomfort can prevent serious symptoms from developing. In addition, by using generative AI, the system can grasp the user's condition more accurately through interactive dialogue. This allows the voice assistant system to detect the user's health condition early and encourage appropriate measures.

[0089] The voice assistant system according to the embodiment includes a reception unit, an analysis unit, a judgment unit, and a provision unit. The reception unit accepts voice input made by a user to a voice assistant app. The voice input includes, for example, input using a microphone and uploading of a voice file, but is not limited to such examples. The reception unit, for example, accepts a voice spoken by a user into a microphone in real time. The reception unit can also upload a voice file recorded in advance by the user. The analysis unit analyzes the characteristics of the voice accepted by the reception unit. The analysis unit analyzes, for example, the frequency components, volume, tone, etc. of the voice. For example, the analysis unit analyzes the frequency components of the voice and evaluates the vibration pattern of the vocal cords. The analysis unit can also analyze the volume of the voice and evaluate the strength of the voice. Furthermore, the analysis unit can analyze the tone of the voice and evaluate the pitch and timbre of the voice. The judgment unit judges the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The judgment unit judges the condition of the throat based on, for example, the vibration pattern of the vocal cords and the clarity of the voice. For example, the determination unit determines that the throat is not in good condition when the vibration pattern of the vocal cords is irregular. The determination unit can also determine that the throat is not in good condition when the clarity of the voice is low. The provision unit provides a diagnosis result based on the result determined by the determination unit. The provision unit provides the diagnosis result in the form of, for example, a text message or voice feedback. For example, the provision unit displays a message such as "Your throat seems to be in bad condition recently. We recommend that you receive a doctor's diagnosis." The provision unit can also provide the diagnosis result through voice feedback. For example, the provision unit conveys the diagnosis result to the user by voice through a voice assistant app. As a result, the voice assistant system according to the embodiment can analyze the user's voice input, detect the condition of the throat or mental discomfort, and provide a diagnosis result.

[0090] The reception unit receives voice input from the user to the voice assistant app. Voice input includes, but is not limited to, input using a microphone and uploading of a voice file. For example, the reception unit receives the voice of the user speaking into the microphone in real time. The reception unit can also upload a voice file recorded in advance by the user. Specifically, the reception unit uses a high-sensitivity microphone to clearly capture the user's voice. The microphone has a noise canceling function and can accurately capture only the user's voice by removing ambient noise. Furthermore, the voice file upload function allows the user to provide previously recorded voice data to the system. This allows the user to receive a diagnosis using not only real-time voice input but also previously recorded voice. The reception unit receives the voice data and simultaneously performs pre-processing of the data. For example, noise removal and normalization of the voice data are performed so that the analysis unit can efficiently analyze it. This allows the reception unit to provide the user's voice input as high-quality data and improve the accuracy of the entire system.

[0091] The analysis unit analyzes the characteristics of the voice received by the reception unit. The analysis unit analyzes, for example, the frequency components, volume, and tone of the voice. Specifically, the analysis unit uses an advanced voice analysis algorithm to extract detailed characteristics of the voice data. For example, by analyzing the frequency components of the voice, the vibration pattern of the vocal cords is evaluated and the characteristics of the user's voice are grasped. In the volume analysis, the strength of the voice is evaluated and the user's voice production method and the health condition of the vocal cords are estimated. In the tone analysis, the pitch and timbre of the voice are evaluated and the user's emotional state and stress level can be estimated. The analysis unit integrates these analysis results and grasps the overall characteristics of the user's voice. Furthermore, the analysis unit uses a machine learning model to learn patterns from the voice data and perform more accurate analysis. For example, it is possible to detect abnormal patterns by comparing with past voice data and to detect throat condition and mental discomfort at an early stage. As a result, the analysis unit can analyze the user's voice data in detail and provide accurate information to the judgment unit.

[0092] The judgment unit judges the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The judgment unit judges the condition of the throat based on, for example, the vibration pattern of the vocal cords and the clarity of the voice. Specifically, the judgment unit executes an algorithm for evaluating the health condition of the throat of the user based on the data provided by the analysis unit. For example, if the vibration pattern of the vocal cords is irregular, it is judged that the throat is in a bad condition. It can also be judged that the throat is in a bad condition if the clarity of the voice is low. The judgment unit combines these evaluation criteria to make a comprehensive judgment and evaluate the health condition of the throat of the user. Furthermore, the judgment unit can detect a change in the condition of the throat by evaluating the current condition in comparison with past data. For example, if the current voice is clearly different compared with past voice data, it is judged that the condition of the throat is deteriorating. In this way, the judgment unit can accurately evaluate the health condition of the throat of the user and provide an appropriate diagnosis result to the provision unit.

[0093] The providing unit provides a diagnosis result based on the result determined by the determining unit. The providing unit provides the diagnosis result in the form of, for example, a text message or voice feedback. Specifically, the providing unit has an interface for conveying the diagnosis result to the user in an easy-to-understand format. For example, the providing unit displays a message such as, "Your throat seems to be in bad condition recently. We recommend that you get a doctor's diagnosis." The providing unit can also provide the diagnosis result through voice feedback. For example, the providing unit conveys the diagnosis result to the user by voice through a voice assistant app. Furthermore, the providing unit can also provide advice and recommendations to the user based on the diagnosis result. For example, the providing unit provides advice such as, "We recommend that you drink a lot of water to improve the condition of your throat." This allows the providing unit to quickly provide the user with appropriate diagnosis results and advice, and support the user's health management. Furthermore, the providing unit also has a function of recording the diagnosis result and managing the history of the user's health condition. This allows the user to refer to past diagnosis results and grasp changes in their health condition.

[0094] The analysis unit includes a tone analysis unit that analyzes the tone or rhythm of the voice. The tone analysis unit analyzes the tone or rhythm of the voice. The tone analysis unit analyzes, for example, the pitch or timbre of the sound. For example, the tone analysis unit analyzes the frequency components of the voice to evaluate the pitch. The tone analysis unit can also analyze the timbre of the voice to evaluate the texture of the voice. Furthermore, the tone analysis unit can analyze the rhythm of the voice to evaluate the intervals and tempo of the sounds. For example, the tone analysis unit can determine that the throat is not in good condition when the rhythm of the voice is irregular. As a result, the tone analysis unit can more accurately determine the condition of the throat by analyzing the tone and rhythm of the voice.

[0095] The analysis unit includes a pronunciation analysis unit that analyzes the clarity of pronunciation. The pronunciation analysis unit analyzes the clarity of pronunciation. The pronunciation analysis unit, for example, identifies phonemes and analyzes the consistency of pronunciation. For example, the pronunciation analysis unit identifies each phoneme in the voice and evaluates whether the pronunciation is clear. The pronunciation analysis unit can also analyze the consistency of the pronunciation of the voice and evaluate whether the pronunciation is stable. Furthermore, the pronunciation analysis unit can analyze the clarity of the voice and evaluate whether the pronunciation is clear. For example, the pronunciation analysis unit can determine that the throat is in poor condition when the clarity of the voice is low. As a result, the pronunciation analysis unit can more accurately determine the condition of the throat by analyzing the clarity of the pronunciation.

[0096] The analysis unit includes a speed analysis unit that analyzes the response speed. The speed analysis unit analyzes the response speed. The speed analysis unit analyzes, for example, the voice reaction time and the response delay. For example, the speed analysis unit evaluates how quickly the user responds to a question. The speed analysis unit can also analyze the voice reaction time and evaluate whether the response is delayed. Furthermore, the speed analysis unit can analyze the voice response delay and evaluate whether the response is delayed. For example, the speed analysis unit can determine that there is mental discomfort when the user's response speed is slow. Thereby, the speed analysis unit can more accurately determine mental discomfort by analyzing the response speed.

[0097] The judgment unit includes a dialogue analysis unit that analyzes the dialogue content. The dialogue analysis unit analyzes the dialogue content. The dialogue analysis unit, for example, analyzes the extraction of keywords and the understanding of context. For example, the dialogue analysis unit extracts important keywords from the voice and evaluates the content. The dialogue analysis unit can also analyze the context of the dialogue and understand the content of the dialogue. Furthermore, the dialogue analysis unit can evaluate whether the content of the dialogue is negative. For example, the dialogue analysis unit can determine that there is a mental discomfort when the user is talking about negative content. In this way, the dialogue analysis unit can more accurately determine the mental discomfort by analyzing the dialogue content.

[0098] The dialogue analysis unit includes a content determination unit that determines negative content. The content determination unit determines negative content. The content determination unit, for example, performs emotion analysis and detects negative keywords. For example, the content determination unit detects keywords that express negative emotions from the voice and evaluates the content. The content determination unit can also perform emotion analysis of the voice and evaluate whether the user has negative emotions. Furthermore, the content determination unit can analyze the context of the dialogue and evaluate whether the content of the dialogue is negative. For example, the content determination unit can determine that the user has a mental discomfort when the user is talking about negative content. As a result, the content determination unit can more accurately determine mental discomfort by determining negative content.

[0099] The providing unit includes a notification unit that notifies the user when it is determined that a diagnosis is necessary. The notification unit notifies the user when it is determined that a diagnosis is necessary. The notification unit provides notification in the form of, for example, a text message or voice feedback. For example, the notification unit displays a message such as "Your throat seems to be in a bad condition recently. We recommend that you get a diagnosis from a doctor." The notification unit can also provide notification through voice feedback. For example, the notification unit conveys the diagnosis result to the user by voice through a voice assistant app. In this way, the notification unit can detect abnormalities early by notifying the user when it is determined that a diagnosis is necessary.

[0100] The reception unit can estimate the user's emotion and adjust the timing of receiving the voice input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit delays the timing of receiving the voice input to give the user time to relax. Also, when the user is relaxed, the reception unit can advance the timing of receiving the voice input to promote smooth dialogue. Furthermore, when the user is in a hurry, the reception unit can immediately receive the voice input to enable a quick response. Thereby, the reception unit can receive the voice input at a more appropriate timing by adjusting the timing of receiving the voice input according to the user's emotion. The estimation of the emotion 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0101] The reception unit can analyze the user's past voice input history and select the optimal reception method. For example, the reception unit preferentially suggests a voice input method that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past voice input history. Furthermore, the reception unit can analyze the user's past voice input history and suggest the most efficient reception method. As a result, the reception unit can select the optimal reception method by analyzing the user's past voice input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past voice data to the generation AI and cause the generation AI to select the optimal reception method.

[0102] The reception unit can filter the user's current environmental sound to remove noise when receiving the voice input. For example, when the user is in a noisy environment, the reception unit filters the environmental sound to remove noise. In addition, when the user is in a quiet environment, the reception unit can also receive the voice input with the environmental sound minimized. Furthermore, when the user is moving, the reception unit can also filter the environmental sound in real time to remove noise. As a result, the reception unit improves the accuracy of the voice input by filtering the environmental sound to remove noise. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's environmental sound data to the generation AI and cause the generation AI to remove noise.

[0103] The reception unit can estimate the user's emotion and determine the priority of the voice input to be received based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially receives important voice input. In addition, when the user is relaxed, the reception unit can also preferentially receive normal voice input. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive urgent voice input. In this way, the reception unit can preferentially receive important voice input by determining the priority of the voice input 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 reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0104] The reception unit can preferentially receive highly relevant voices in consideration of the geographical location information of the user when receiving the voice input. For example, when the user is in a specific location, the reception unit preferentially receives voice inputs related to the location. In addition, when the user is moving, the reception unit can also preferentially receive voice inputs related to the current location. Furthermore, when the user is in a specific area, the reception unit can also preferentially receive voice inputs related to the area. Thereby, the reception unit can preferentially receive highly relevant voices by considering the geographical location information. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the priority order of highly relevant voices.

[0105] The reception unit can analyze the user's social media activity and receive related voices when receiving the voice input. For example, the reception unit preferentially receives voice inputs related to topics that the user is talking about on social media. The reception unit can also preferentially receive voice inputs related to topics of interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and preferentially receive related voice inputs. In this way, the reception unit can preferentially receive related voices by analyzing the social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the priority order of related voices.

[0106] The analysis unit can estimate the user's emotion and adjust the method of voice feature analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit simplifies the method of voice feature analysis and performs analysis quickly. In addition, when the user is relaxed, the analysis unit can perform detailed voice feature analysis. Furthermore, when the user is in a hurry, the analysis unit can prioritize analysis of important voice features. This allows the analysis unit to adjust the method of voice feature analysis according to the user's emotion, thereby enabling more appropriate analysis. The estimation of emotion 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 the user's voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the voice when analyzing the features of the voice. For example, in the case of important voice, the analysis unit performs detailed feature analysis. In addition, in the case of normal voice, the analysis unit can also perform standard feature analysis. Furthermore, in the case of urgent voice, the analysis unit can perform feature analysis quickly. As a result, the analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. 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 voice data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0108] The analysis unit can apply different analysis algorithms depending on the category of the voice when analyzing the features of the voice. For example, when the voice is a conversation, the analysis unit applies a conversation analysis algorithm. Furthermore, when the voice is music, the analysis unit can also apply a music analysis algorithm. Furthermore, when the voice is environmental sound, the analysis unit can also apply an environmental sound analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the voice, thereby enabling more appropriate analysis. 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 voice data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0109] The analysis unit can estimate the user's emotion and determine the priority of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit prioritizes the analysis of important voices. In addition, when the user is relaxed, the analysis unit can also prioritize the analysis of normal voices. Furthermore, when the user is in a hurry, the analysis unit can also prioritize the analysis of urgent voices. In this way, the analysis unit can prioritize the analysis of important voices by determining the priority of the analysis according to the user's emotion. The estimation of the emotion 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 the user's voice data to the generation AI and cause the generation AI to execute the emotion estimation.

[0110] The analysis unit can determine the priority of analysis based on the time of submission of the voice when analyzing the features of the voice. For example, the analysis unit prioritizes analysis of the most recently submitted voice. The analysis unit can also analyze the voice submitted in the past later. Furthermore, the analysis unit can also analyze urgent voice with the highest priority. In this way, the analysis unit can prioritize analysis of the latest voice by determining the priority of analysis based on the time of submission of the voice. 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 voice data to the generation AI and cause the generation AI to execute the determination of the priority of analysis based on the time of submission.

[0111] The analysis unit can adjust the order of analysis based on the relevance of the voice when analyzing the features of the voice. For example, the analysis unit prioritizes analysis of important voice. The analysis unit can also analyze normal voice later. Furthermore, the analysis unit can prioritize analysis of highly relevant voice. As a result, the analysis unit can adjust the order of analysis based on the relevance of the voice, thereby analyzing highly relevant voice with priority. 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 voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0112] The judgment unit can estimate the user's emotion and adjust the judgment criteria for the throat condition based on the estimated emotion of the user. For example, when the user is stressed, the judgment unit relaxes the judgment criteria for the throat condition. In addition, when the user is relaxed, the judgment unit can also tighten the judgment criteria for the throat condition. Furthermore, when the user is in a hurry, the judgment unit can quickly apply the judgment criteria for the throat condition. This allows the judgment unit to adjust the judgment criteria for the throat condition according to the user's emotion, thereby making it possible to make a more appropriate judgment. The estimation of emotion 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0113] The judgment unit can improve the accuracy of judgment when judging the condition of the throat by considering the interrelationship of voices. The judgment unit judges the condition of the throat by considering, for example, the interrelationship between the tone and rhythm of the voice. The judgment unit can also judge the condition of the throat by considering the interrelationship between the clarity of the pronunciation of the voice and the rhythm. Furthermore, the judgment unit can also judge the condition of the throat by considering the interrelationship between the tone of the voice and the clarity of the pronunciation. In this way, the judgment unit can improve the accuracy of judgment of the condition of the throat by considering the interrelationship of the voices. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input voice data to the generation AI and cause the generation AI to execute improvement of the accuracy of judgment based on the interrelationship.

[0114] When determining the condition of the throat, the determination unit can make a determination by taking into consideration attribute information of the person who submitted the voice. The determination unit can determine the condition of the throat by taking into consideration, for example, the age of the person who submitted the voice. The determination unit can also determine the condition of the throat by taking into consideration the gender of the person who submitted the voice. Furthermore, the determination unit can also determine the condition of the throat by taking into consideration the health condition of the person who submitted the voice. In this way, the determination unit can improve the accuracy of determining the condition of the throat by taking into consideration the attribute information of the person who submitted the voice. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the attribute information of the person who submitted the voice to the generation AI and cause the generation AI to execute a judgment based on the attribute information.

[0115] The judgment unit can estimate the user's emotion and adjust the order of displaying the judgment results based on the estimated user's emotion. For example, when the user is feeling stressed, the judgment unit can preferentially display important results. In addition, when the user is relaxed, the judgment unit can also preferentially display normal results. Furthermore, when the user is in a hurry, the judgment unit can preferentially display urgent results. In this way, the judgment unit can adjust the order of displaying the judgment results according to the user's emotion, thereby preferentially displaying important results. 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the user's voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0116] The judgment unit can make a judgment taking into consideration the geographical distribution of the voice when judging the condition of the throat. For example, the judgment unit gives priority to judging voices from a specific region. The judgment unit can also give priority to judging voices with high geographical relevance. Furthermore, the judgment unit can judge geographically distributed voices equally. In this way, the judgment unit can improve the accuracy of judging the condition of the throat by taking into consideration the geographical distribution of the voice. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the voice data to a generation AI and cause the generation AI to execute a judgment based on the geographical distribution.

[0117] The judgment unit can improve the accuracy of the judgment when judging the condition of the throat by referring to the related literature of the voice. The judgment unit judges the condition of the throat by referring to the related literature of the voice, for example. The judgment unit can also improve the accuracy of the judgment based on the related literature of the voice. Furthermore, the judgment unit can also judge the condition of the throat by analyzing the related literature of the voice. In this way, the judgment unit can improve the accuracy of the judgment of the condition of the throat by referring to the related literature of the voice. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature data to the generation AI and cause the generation AI to execute a judgment based on the literature.

[0118] The providing unit can estimate the user's emotion and adjust the method of providing the diagnosis result based on the estimated user's emotion. For example, when the user is stressed, the providing unit provides a simple and easy-to-understand diagnosis result. In addition, the providing unit can also provide a detailed diagnosis result when the user is relaxed. Furthermore, the providing unit can quickly provide a diagnosis result when the user is in a hurry. In this way, the providing unit can provide a more appropriate diagnosis result by adjusting the method of providing the diagnosis result according to the user's emotion. The estimation of the emotion 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute adjustment of the provision method based on the emotion.

[0119] When providing a diagnostic result, the providing unit can select an optimal providing method by referring to the user's past diagnostic history. The providing unit selects an optimal providing method based on, for example, the user's past diagnostic history. The providing unit can also suggest an appropriate diagnostic result providing method from the user's past diagnostic history. Furthermore, the providing unit can analyze the user's past diagnostic history and select the most effective providing method. As a result, the providing unit can select an optimal providing method by referring to the user's past diagnostic history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past diagnostic history data to the generating AI and cause the generating AI to select an optimal providing method.

[0120] The providing unit can customize the means of providing based on the user's current living situation when providing the diagnosis result. For example, when the user is busy, the providing unit provides a concise diagnosis result. In addition, the providing unit can also provide a detailed diagnosis result when the user is relaxed. Furthermore, when the user is on the move, the providing unit can provide the diagnosis result by voice. Thereby, the providing unit can provide a more appropriate diagnosis result by customizing the means of providing based on the user's current living situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's living situation data to the generating AI and cause the generating AI to select the customized means of providing.

[0121] The providing unit can estimate the user's emotion and determine the priority of the diagnostic results based on the estimated user's emotion. For example, when the user is feeling stressed, the providing unit can provide important diagnostic results with priority. In addition, when the user is relaxed, the providing unit can also provide normal diagnostic results with priority. Furthermore, when the user is in a hurry, the providing unit can also provide urgent diagnostic results with priority. Thereby, the providing unit can provide important diagnostic results with priority by determining the priority of diagnostic results according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority based on the emotion.

[0122] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing the diagnostic result. For example, when the user is in a specific location, the providing unit provides a diagnostic result related to the location. In addition, when the user is moving, the providing unit can also provide a diagnostic result related to the current location. Furthermore, when the user is in a specific area, the providing unit can also provide a diagnostic result related to the area. Thereby, the providing unit can select the optimal providing method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0123] The providing unit can analyze the user's social media activity and suggest a means of providing the diagnostic result when providing the diagnostic result. The providing unit provides, for example, a diagnostic result related to the content that the user is talking about on social media. The providing unit can also provide a diagnostic result related to a topic of interest from the user's social media activity. Furthermore, the providing unit can analyze the user's social media activity and provide a related diagnostic result. In this way, the providing unit can provide a related diagnostic result by analyzing the social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to provide the related diagnostic result.

[0124] The tone analysis unit can estimate the user's emotion and adjust the tone analysis method based on the estimated user's emotion. For example, when the user is stressed, the tone analysis unit simplifies the tone analysis method and performs a quick analysis. In addition, when the user is relaxed, the tone analysis unit can perform a detailed tone analysis. Furthermore, when the user is in a hurry, the tone analysis unit can prioritize analysis of important tones. This allows the tone analysis unit to adjust the tone analysis method according to the user's emotion, thereby enabling more appropriate analysis. 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 tone analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tone analysis unit can input the user's voice data to the generation AI and cause the generation AI to adjust the tone analysis method based on the emotion.

[0125] The tone analysis unit can adjust the level of detail of the analysis based on the importance of the voice during tone analysis. For example, in the case of important voice, the tone analysis unit performs detailed tone analysis. In addition, in the case of normal voice, the tone analysis unit can also perform standard tone analysis. Furthermore, in the case of urgent voice, the tone analysis unit can perform tone analysis quickly. As a result, the tone analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. A part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input voice data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0126] The tone analysis unit can apply different analysis algorithms depending on the category of the sound during tone analysis. For example, when the sound is a conversation, the tone analysis unit applies a conversation analysis algorithm. In addition, when the sound is music, the tone analysis unit can also apply a music analysis algorithm. Furthermore, when the sound is environmental sound, the tone analysis unit can also apply an environmental sound analysis algorithm. This allows the tone analysis unit to apply different analysis algorithms depending on the category of the sound, thereby enabling more appropriate analysis. Part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0127] The tone analysis unit can estimate the user's emotion and determine the priority of tone analysis based on the estimated user's emotion. For example, when the user is stressed, the tone analysis unit prioritizes the analysis of important tones. In addition, when the user is relaxed, the tone analysis unit can also prioritize the analysis of normal tones. Furthermore, when the user is in a hurry, the tone analysis unit can also prioritize the analysis of urgent tones. Thereby, the tone analysis unit can prioritize the analysis of important tones by determining the priority of tone analysis according to the user's emotion. The estimation of emotion 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 tone analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tone analysis unit can input the user's voice data to the generation AI and cause the generation AI to execute the determination of the priority based on the emotion.

[0128] The tone analysis unit can determine the analysis priority based on the time of submission of the voice during tone analysis. For example, the tone analysis unit prioritizes analysis of the most recently submitted voice. The tone analysis unit can also analyze the voice submitted in the past later. Furthermore, the tone analysis unit can analyze the urgent voice with the highest priority. As a result, the tone analysis unit can prioritize analysis of the latest voice by determining the analysis priority based on the time of submission of the voice. A part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the tone analysis unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the analysis priority based on the time of submission.

[0129] The tone analysis unit can adjust the order of analysis based on the relevance of the voice during tone analysis. For example, the tone analysis unit prioritizes analysis of important voice. The tone analysis unit can also prioritize analysis of normal voice. Furthermore, the tone analysis unit can prioritize analysis of highly relevant voice. As a result, the tone analysis unit can prioritize analysis of highly relevant voice by adjusting the order of analysis based on the relevance of the voice. A part or all of the above-mentioned processing in the tone analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the tone analysis unit can input voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0130] The pronunciation analysis unit can estimate the user's emotion and adjust the pronunciation analysis method based on the estimated user's emotion. For example, when the user is stressed, the pronunciation analysis unit simplifies the pronunciation analysis method and performs a quick analysis. In addition, when the user is relaxed, the pronunciation analysis unit can perform a detailed pronunciation analysis. Furthermore, when the user is in a hurry, the pronunciation analysis unit can prioritize the analysis of important pronunciations. This allows the pronunciation analysis unit to adjust the pronunciation analysis method according to the user's emotion, thereby enabling more appropriate analysis. 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 pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input the user's voice data to the generation AI and cause the generation AI to adjust the pronunciation analysis method based on the emotion.

[0131] The pronunciation analysis unit can adjust the level of detail of the analysis based on the importance of the voice during the pronunciation analysis. For example, in the case of important voice, the pronunciation analysis unit performs detailed pronunciation analysis. In addition, in the case of normal voice, the pronunciation analysis unit can also perform standard pronunciation analysis. Furthermore, in the case of urgent voice, the pronunciation analysis unit can also perform quick pronunciation analysis. As a result, the pronunciation analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. A part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input voice data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0132] The pronunciation analysis unit can apply different analysis algorithms depending on the category of the sound during pronunciation analysis. For example, when the sound is a conversation, the pronunciation analysis unit applies a conversation analysis algorithm. Furthermore, when the sound is music, the pronunciation analysis unit can also apply a music analysis algorithm. Furthermore, when the sound is environmental sound, the pronunciation analysis unit can also apply an environmental sound analysis algorithm. This allows the pronunciation analysis unit to apply different analysis algorithms depending on the category of the sound, thereby enabling more appropriate analysis. Part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the pronunciation analysis unit can input voice data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0133] The pronunciation analysis unit can estimate the user's emotion and determine the priority of the pronunciation analysis based on the estimated emotion of the user. For example, when the user is stressed, the pronunciation analysis unit prioritizes the analysis of important pronunciation. Also, when the user is relaxed, the pronunciation analysis unit can prioritize the analysis of normal pronunciation. Furthermore, when the user is in a hurry, the pronunciation analysis unit can prioritize the analysis of urgent pronunciation. In this way, the pronunciation analysis unit can prioritize the analysis of important pronunciation by determining the priority of the pronunciation analysis according to the emotion of the user. The estimation of emotion 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 pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input the user's voice data to the generation AI and cause the generation AI to execute the determination of the priority based on the emotion.

[0134] The pronunciation analysis unit can determine the priority of analysis based on the time of submission of the voice during the pronunciation analysis. For example, the pronunciation analysis unit prioritizes analysis of the most recently submitted voice. The pronunciation analysis unit can also analyze the voice submitted in the past later. Furthermore, the pronunciation analysis unit can also analyze urgent voice with the highest priority. As a result, the pronunciation analysis unit can prioritize analysis of the latest voice by determining the priority of analysis based on the time of submission of the voice. A part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the priority of analysis based on the time of submission.

[0135] The pronunciation analysis unit can adjust the order of analysis based on the relevance of the sounds during the pronunciation analysis. For example, the pronunciation analysis unit prioritizes analysis of important sounds. The pronunciation analysis unit can also prioritize analysis of normal sounds. Furthermore, the pronunciation analysis unit can also prioritize analysis of highly relevant sounds. As a result, the pronunciation analysis unit can adjust the order of analysis based on the relevance of the sounds, thereby analyzing highly relevant sounds with priority. A part or all of the above-mentioned processing in the pronunciation analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the pronunciation analysis unit can input the voice data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0136] The speed analysis unit can estimate the user's emotions and adjust the speed analysis method based on the estimated user's emotions. For example, when the user is stressed, the speed analysis unit simplifies the speed analysis method and performs a quick analysis. In addition, when the user is relaxed, the speed analysis unit can perform a detailed speed analysis. Furthermore, when the user is in a hurry, the speed analysis unit can prioritize analysis of important speeds. This allows the speed analysis unit to adjust the speed analysis method according to the user's emotions, thereby enabling more appropriate analysis. The estimation of emotions 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 speed analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speed analysis unit can input the user's voice data to the generation AI and cause the generation AI to adjust the speed analysis method based on emotions.

[0137] The speed analysis unit can adjust the level of detail of the analysis based on the importance of the voice during the speed analysis. For example, in the case of important voice, the speed analysis unit performs detailed speed analysis. In addition, in the case of normal voice, the speed analysis unit can also perform standard speed analysis. Furthermore, in the case of urgent voice, the speed analysis unit can also perform speed analysis quickly. As a result, the speed analysis unit can analyze important voice in more detail by adjusting the level of detail of the analysis based on the importance of the voice. A part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input voice data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0138] The speed analysis unit can apply different analysis algorithms depending on the category of the audio during speed analysis. For example, when the audio is a conversation, the speed analysis unit applies a conversation analysis algorithm. In addition, when the audio is music, the speed analysis unit can also apply a music analysis algorithm. Furthermore, when the audio is environmental sound, the speed analysis unit can also apply an environmental sound analysis algorithm. This allows the speed analysis unit to apply different analysis algorithms depending on the category of the audio, thereby enabling more appropriate analysis. Part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input audio data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0139] The speed analysis unit can estimate the user's emotion and determine the priority of the speed analysis based on the estimated user's emotion. For example, when the user is stressed, the speed analysis unit prioritizes the analysis of important speeds. In addition, when the user is relaxed, the speed analysis unit can also prioritize the analysis of normal speeds. Furthermore, when the user is in a hurry, the speed analysis unit can also prioritize the analysis of urgent speeds. In this way, the speed analysis unit can prioritize the analysis of important speeds by determining the priority of the speed analysis according to the user's emotion. The estimation of emotion 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 speed analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speed analysis unit can input the user's voice data to the generation AI and cause the generation AI to execute the determination of the priority based on the emotion.

[0140] The speed analysis unit can determine the priority of analysis based on the time of submission of the voice during the speed analysis. For example, the speed analysis unit prioritizes analysis of the most recently submitted voice. The speed analysis unit can also analyze the voice submitted in the past later. Furthermore, the speed analysis unit can also analyze the urgent voice with the highest priority. In this way, the speed analysis unit can prioritize analysis of the latest voice by determining the priority of analysis based on the time of submission of the voice. A part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the priority of analysis based on the time of submission.

[0141] The speed analysis unit can adjust the order of analysis based on the relevance of the voices during the speed analysis. For example, the speed analysis unit prioritizes analysis of important voices. The speed analysis unit can also prioritize analysis of normal voices. Furthermore, the speed analysis unit can prioritize analysis of highly relevant voices. As a result, the speed analysis unit can prioritize analysis of highly relevant voices by adjusting the order of analysis based on the relevance of the voices. A part or all of the above-mentioned processing in the speed analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the speed analysis unit can input voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0142] The dialogue analysis unit can estimate the user's emotions and adjust the dialogue analysis method based on the estimated user's emotions. For example, when the user is stressed, the dialogue analysis unit simplifies the dialogue analysis method and performs a quick analysis. In addition, when the user is relaxed, the dialogue analysis unit can perform a detailed dialogue analysis. Furthermore, when the user is in a hurry, the dialogue analysis unit can prioritize analysis of important dialogues. This allows the dialogue analysis unit to adjust the dialogue analysis method according to the user's emotions, thereby enabling more appropriate analysis. 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 dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input the user's voice data to the generation AI and cause the generation AI to adjust the dialogue analysis method based on emotions.

[0143] The dialogue analysis unit can adjust the level of detail of the analysis based on the importance of the dialogue during dialogue analysis. For example, in the case of an important dialogue, the dialogue analysis unit performs a detailed dialogue analysis. In addition, in the case of a normal dialogue, the dialogue analysis unit can also perform a standard dialogue analysis. Furthermore, in the case of an urgent dialogue, the dialogue analysis unit can quickly perform dialogue analysis. As a result, the dialogue analysis unit can analyze important dialogues in more detail by adjusting the level of detail of the analysis based on the importance of the dialogue. A part or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input voice data to a generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0144] The dialogue analysis unit can apply different analysis algorithms depending on the category of the dialogue when analyzing the dialogue. For example, if the dialogue is business, the dialogue analysis unit applies a business analysis algorithm. Also, if the dialogue is casual, the dialogue analysis unit can apply a casual analysis algorithm. Furthermore, if the dialogue is technical, the dialogue analysis unit can apply a technical analysis algorithm. This allows the dialogue analysis unit to apply different analysis algorithms depending on the category of the dialogue, thereby enabling more appropriate analysis. Some or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue analysis unit can input voice data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0145] The dialogue analysis unit can estimate the user's emotions and determine the priority of dialogue analysis based on the estimated user's emotions. For example, when the user is stressed, the dialogue analysis unit prioritizes the analysis of important dialogues. In addition, when the user is relaxed, the dialogue analysis unit can also prioritize the analysis of normal dialogues. Furthermore, when the user is in a hurry, the dialogue analysis unit can also prioritize the analysis of urgent dialogues. In this way, the dialogue analysis unit can prioritize the analysis of important dialogues by determining the priority of dialogue analysis according to the user's 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 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 dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input the user's voice data to the generation AI and cause the generation AI to execute the determination of the priority based on emotions.

[0146] The dialogue analysis unit can determine the priority of analysis based on the time of submission of the dialogue when analyzing the dialogue. For example, the dialogue analysis unit prioritizes the analysis of the most recently submitted dialogue. The dialogue analysis unit can also postpone the analysis of dialogues submitted in the past. Furthermore, the dialogue analysis unit can also give top priority to analyzing urgent dialogues. In this way, the dialogue analysis unit can prioritize the analysis of the latest dialogue by determining the priority of analysis based on the time of submission of the dialogue. A part or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input voice data to the generation AI and cause the generation AI to determine the priority of analysis based on the time of submission.

[0147] The dialogue analysis unit can adjust the order of analysis based on the relevance of the dialogue during dialogue analysis. For example, the dialogue analysis unit prioritizes analysis of important dialogues. The dialogue analysis unit can also postpone analysis of normal dialogues. Furthermore, the dialogue analysis unit can prioritize analysis of highly relevant dialogues. As a result, the dialogue analysis unit can adjust the order of analysis based on the relevance of the dialogues, thereby analyzing highly relevant dialogues with priority. A part or all of the above-mentioned processing in the dialogue analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue analysis unit can input voice data to a generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0148] The content determination unit can estimate the user's emotion and adjust the content determination method based on the estimated user's emotion. For example, when the user is stressed, the content determination unit simplifies the content determination method and makes a quick determination. In addition, when the user is relaxed, the content determination unit can also perform detailed content determination. Furthermore, when the user is in a hurry, the content determination unit can prioritize important content determination. This allows the content determination unit to adjust the content determination method according to the user's emotion and make a more appropriate determination. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 content determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the content determination unit can input the user's voice data to the generation AI and cause the generation AI to adjust the content determination method based on the emotion.

[0149] The content determination unit can adjust the level of detail of the determination based on the importance of the dialogue when determining the content. For example, in the case of an important dialogue, the content determination unit performs a detailed content determination. In addition, in the case of a normal dialogue, the content determination unit can also perform a standard content determination. Furthermore, in the case of an urgent dialogue, the content determination unit can perform a quick content determination. As a result, the content determination unit can determine an important dialogue in more detail by adjusting the level of detail of the determination based on the importance of the dialogue. A part or all of the above-mentioned processing in the content determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the content determination unit can input voice data to a generation AI and cause the generation AI to adjust the level of detail of the determination based on the importance.

[0150] The content determination unit can apply different determination algorithms depending on the category of the dialogue when determining the content. For example, if the dialogue is business, the content determination unit applies a business determination algorithm. In addition, if the dialogue is casual, the content determination unit can also apply a casual determination algorithm. In addition, if the dialogue is technical, the content determination unit can also apply a technical determination algorithm. This allows the content determination unit to make a more appropriate determination by applying different determination algorithms depending on the category of the dialogue. Part or all of the above-mentioned processing in the content determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the content determination unit can input voice data to a generation AI and cause the generation AI to apply a determination algorithm according to the category.

[0151] The content determination unit can estimate the user's emotion and determine the priority of content determination based on the estimated user's emotion. For example, when the user is stressed, the content determination unit prioritizes the determination of important content. In addition, when the user is relaxed, the content determination unit can also prioritize the determination of normal content. Furthermore, when the user is in a hurry, the content determination unit can also prioritize the determination of urgent content. In this way, the content determination unit can determine the priority of content determination according to the user's emotion, thereby determining important content with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is 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 content determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the content determination unit can input the user's voice data to the generation AI and cause the generation AI to execute the determination of the priority based on the emotion.

[0152] The content determination unit can determine the priority of the determination based on the time of submission of the dialogue when determining the content. For example, the content determination unit gives priority to the most recently submitted dialogue. The content determination unit can also give a later determination to dialogues submitted in the past. Furthermore, the content determination unit can also give top priority to urgent dialogues. In this way, the content determination unit can determine the priority of the determination based on the time of submission of the dialogue, thereby giving priority to the latest dialogue. A part or all of the above-mentioned processing in the content determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the content determination unit can input the voice data to the generation AI and cause the generation AI to execute the determination of the priority of the determination based on the time of submission.

[0153] The content determination unit can adjust the order of determination based on the relevance of the dialogue when determining the content. For example, the content determination unit prioritizes the determination of an important dialogue. The content determination unit can also prioritize the determination of a normal dialogue. Furthermore, the content determination unit can prioritize the determination of a highly relevant dialogue. As a result, the content determination unit can prioritize the determination of a highly relevant dialogue by adjusting the order of determination based on the relevance of the dialogue. A part or all of the above-mentioned processing in the content determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the content determination unit can input voice data to a generation AI and cause the generation AI to adjust the order of determination based on the relevance.

[0154] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, when the user is stressed, the notification unit provides a simple and easy-to-understand notification. Furthermore, when the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, when the user is in a hurry, the notification unit can also provide a quick notification. This allows the notification unit to adjust the notification method according to the user's emotion, thereby enabling more appropriate notification. 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the notification method based on the emotion.

[0155] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit selects the optimal notification method based on the user's past notification history, for example. The notification unit can also suggest an appropriate notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and select the most effective notification method. As a result, the notification unit can select the optimal notification method by referring to the user's past notification history. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input past notification history data to the generation AI and cause the generation AI to select the optimal notification method.

[0156] The notification unit can customize the notification means based on the user's current living situation at the time of notification. For example, when the user is busy, the notification unit provides a brief notification. In addition, when the user is relaxed, the notification unit can also provide a detailed notification. Furthermore, when the user is on the move, the notification unit can also provide a voice notification. This allows the notification unit to customize the notification means based on the user's current living situation, thereby enabling more appropriate notification. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's living situation data to the generation AI and cause the generation AI to select the customized notification means.

[0157] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, when the user is feeling stressed, the notification unit can prioritize important notifications. Also, when the user is relaxed, the notification unit can also prioritize normal notifications. Furthermore, when the user is in a hurry, the notification unit can also prioritize emergency notifications. In this way, the notification unit can prioritize important notifications by determining the priority of notifications 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 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 notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority based on the emotion.

[0158] The notification unit can select the optimal notification method in consideration of the geographical location information of the user at the time of notification. For example, when the user is in a specific location, the notification unit performs a notification related to the location. In addition, when the user is moving, the notification unit can also perform a notification related to the current location. Furthermore, when the user is in a specific area, the notification unit can also perform a notification related to the area. Thereby, the notification unit can select the optimal notification method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the optimal notification method.

[0159] At the time of notification, the notification unit can analyze the user's social media activity and suggest a means of notification. The notification unit, for example, provides a notification related to the content that the user is talking about on social media. The notification unit can also provide a notification related to a topic of interest from the user's social media activity. Furthermore, the notification unit can analyze the user's social media activity and provide a related notification. In this way, the notification unit can provide a related notification by analyzing the social media activity. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the user's social media data to the generation AI and cause the generation AI to execute a related means of notification.

[0160] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0161] The analysis unit can analyze background sounds of the voice input and estimate the user's environment. For example, the analysis unit can analyze car sounds and people's voices heard during voice input and estimate that the user is out. The analysis unit can also analyze keyboard typing sounds and telephone ringtones heard during voice input and estimate that the user is in an office. Furthermore, the analysis unit can analyze television sounds and operating sounds of home appliances heard during voice input and estimate that the user is at home. In this way, the analysis unit can improve the accuracy of analysis of the voice input by estimating the user's environment.

[0162] The providing unit can provide advice regarding the user's health condition. For example, when the providing unit determines that the user's throat is not feeling well, the providing unit can suggest a specific method for resting the throat. Also, when the providing unit determines that the user is feeling unwell mentally, the providing unit can suggest a method for relaxing or an activity for relieving stress. Furthermore, the providing unit can introduce the user to an appropriate medical institution or specialist depending on the user's health condition. In this way, the providing unit can provide specific advice regarding the user's health condition, allowing the user to take appropriate measures.

[0163] The analysis unit can estimate the user's interests and concerns based on the content of the voice input. For example, if the user frequently talks about a particular topic, the analysis unit can estimate the interest in that topic. Also, if the user frequently uses a particular keyword, the analysis unit can estimate the interest related to that keyword. Furthermore, the analysis unit can analyze the user's dialogue patterns and topic transitions to estimate changes in the user's interests and concerns. In this way, the analysis unit can provide a more personalized service by estimating the user's interests and concerns.

[0164] The providing unit can provide personalized content based on the user's voice input. For example, when the user talks about a specific music genre, the providing unit can recommend music related to the genre. Also, when the user talks about a specific movie or drama, the providing unit can recommend content related to the work. Furthermore, when the user talks about a specific hobby or interest, the providing unit can provide information or events related to the hobby or interest. In this way, the providing unit can improve the user's satisfaction by providing personalized content based on the user's voice input.

[0165] The analysis unit can analyze the emotional tone of the voice input and estimate the emotional state of the user. For example, the analysis unit estimates that the user is excited if the tone of the voice is high and fast. The analysis unit can also estimate that the user is depressed if the tone of the voice is low and slow. Furthermore, the analysis unit can also estimate that the user is in an unstable emotional state if the tone of the voice is not constant. In this way, the analysis unit can more accurately grasp the emotional state of the user by analyzing the emotional tone of the voice input.

[0166] The providing unit can provide appropriate feedback based on the emotional state of the user. For example, when the providing unit estimates that the user is feeling stressed, the providing unit provides advice for relaxing. Also, when the providing unit estimates that the user is excited, the providing unit can suggest a method for calming down. Furthermore, when the providing unit estimates that the user is depressed, the providing unit can provide an encouraging message or positive information. In this way, the providing unit can support the mental health of the user by providing appropriate feedback based on the emotional state of the user.

[0167] The analysis unit can evaluate the overall state of the user by combining the contents of the voice input with the emotional state. For example, if the user speaks negative content and has a low emotional tone, the analysis unit evaluates that the user is feeling serious stress. In addition, if the user speaks positive content and has a high emotional tone, the analysis unit can also evaluate that the user is very satisfied. Furthermore, if the user speaks neutral content and has an inconsistent emotional tone, the analysis unit can evaluate that the user is in an unstable state. This allows the analysis unit to more accurately evaluate the overall state of the user by combining the contents of the voice input with the emotional state.

[0168] The providing unit can adjust the progress of the dialogue based on the emotional state of the user. For example, when the providing unit estimates that the user is feeling stressed, the providing unit can slowly progress the dialogue. Also, when the providing unit estimates that the user is relaxed, the providing unit can smoothly progress the dialogue. Furthermore, when the providing unit estimates that the user is in a hurry, the providing unit can quickly progress the dialogue. In this way, the providing unit can improve the satisfaction of the user by adjusting the progress of the dialogue based on the emotional state of the user.

[0169] The analysis unit can analyze the voice input pattern and estimate the user's habits and behavioral patterns. For example, if the user asks a specific question at the same time every morning, the analysis unit estimates that the user is engaged in a specific behavior during that time period. In addition, if the user talks about a specific topic on a specific day of the week, the analysis unit can estimate that the user is engaged in a specific activity on that day of the week. Furthermore, if the user talks about a specific content in a specific season, the analysis unit can estimate that the user has a specific interest or concern in that season. In this way, the analysis unit can more accurately grasp the user's habits and behavioral patterns by analyzing the voice input pattern.

[0170] The providing unit can provide an appropriate reminder based on the emotional state of the user. For example, when the providing unit estimates that the user is feeling stressed, the providing unit provides a reminder to take a break to relax. Also, when the providing unit estimates that the user is excited, the providing unit can provide a reminder to take a deep breath to calm down. Furthermore, when the providing unit estimates that the user is depressed, the providing unit can provide a reminder to encourage positive activity. In this way, the providing unit can support the mental health of the user by providing an appropriate reminder based on the emotional state of the user.

[0171] The process flow of the second embodiment will be briefly described below.

[0172] Step 1: The reception unit receives voice input from the user to the voice assistant app. Voice input includes, for example, input using a microphone and uploading of a voice file. The reception unit can receive the voice of the user speaking into the microphone in real time, and can also upload a pre-recorded voice file. Step 2: The analysis unit analyzes the characteristics of the voice received by the reception unit. The analysis unit analyzes the frequency components, volume, tone, etc. of the voice, and evaluates, for example, the vibration pattern of the vocal cords, the strength of the voice, the pitch, and the timbre of the voice. Step 3: The judgment unit judges the condition of the throat based on the characteristics of the voice analyzed by the analysis unit. The judgment unit judges the condition of the throat based on the vibration pattern of the vocal cords and the clarity of the voice, and judges that the throat is not in good condition if, for example, the vibration pattern of the vocal cords is irregular or the clarity of the voice is low. Step 4: The providing unit provides a diagnosis result based on the result determined by the determining unit. The providing unit provides the diagnosis result in the form of a text message or voice feedback, for example, displaying a message such as "Your throat seems to be in a bad condition recently. We recommend that you see a doctor." The providing unit can also convey the diagnosis result to the user by voice through a voice assistant app.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements including the above-mentioned reception unit, analysis unit, judgment unit, and provision unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts voice input using the microphone 38B of the smart device 14. The analysis unit analyzes the characteristics of the voice by the specific processing unit 290 of the data processing device 12. The judgment unit judges the condition of the throat or mental discomfort by the specific processing unit 290 of the data processing device 12. The provision unit provides the diagnosis result using the display 40A or speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0177] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Each of the multiple elements including the above-mentioned reception unit, analysis unit, judgment unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit accepts voice input using the microphone 238 of the smart glasses 214. The analysis unit analyzes the characteristics of the voice by the specific processing unit 290 of the data processing device 12. The judgment unit judges the condition of the throat and mental discomfort by the specific processing unit 290 of the data processing device 12. The provision unit provides the diagnosis result using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0193] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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 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.

[0206] 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.

[0207] 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.

[0208] Each of the multiple elements including the above-mentioned reception unit, analysis unit, judgment unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the characteristics of the voice by the specific processing unit 290 of the data processing device 12. The judgment unit judges the condition of the throat and mental discomfort by the specific processing unit 290 of the data processing device 12. The provision unit provides the diagnosis result using the speaker 240 of the headset type terminal 314. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0209] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0210] 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.

[0211] 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.

[0212] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] Each of the multiple elements including the above-mentioned reception unit, analysis unit, judgment unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414. The analysis unit analyzes the characteristics of the voice by the specific processing unit 290 of the data processing device 12. The judgment unit judges the condition of the throat and mental discomfort by the specific processing unit 290 of the data processing device 12. The provision unit provides the diagnosis result by using the speaker 240 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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."

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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 in which a processor is used 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] (Appendix 1) A reception unit that receives voice input; an analysis unit that analyzes features of the voice received by the reception unit; a judgment unit for judging a throat condition based on the voice characteristics analyzed by the analysis unit; A providing unit that provides a diagnosis result based on the determined result. A system characterized by: (Appendix 2) The analysis unit is Equipped with a tone analysis unit that analyzes the tone or rhythm of the voice 2. The system of claim 1, (Appendix 3) The analysis unit is Equipped with a pronunciation analyzer that analyzes the clarity of pronunciation 2. The system of claim 1, (Appendix 4) The analysis unit is Equipped with a speed analysis section that analyzes response speed 2. The system of claim 1, (Appendix 5) The determination unit is Equipped with a dialogue analysis unit that analyzes dialogue content 2. The system of claim 1, (Appendix 6) The dialogue analysis unit Equipped with a content judgment unit that judges negative content 6. The system of claim 5, (Appendix 7) The providing unit is Equipped with a notification unit that notifies the user when it is determined that a diagnosis is necessary 2. The system of claim 1, (Appendix 8) The reception unit is To estimate a user's emotion and adjust the timing of accepting voice input based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past voice input history and select the optimal reception method 2. The system of claim 1, (Appendix 10) The reception unit is When accepting voice input, it filters the user's current ambient sound to remove noise. 2. The system of claim 1, (Appendix 11) The reception unit is Estimate the user's emotions and prioritize the voice inputs to be accepted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 12) The reception unit is When accepting voice input, the system takes into account the user's geographic location information and prioritizes relevant voice input. 2. The system of claim 1, (Appendix 13) The reception unit is When receiving voice input, analyze the user's social media activity and receive related voice. 2. The system of claim 1, (Appendix 14) The analysis unit is To estimate a user's emotion and adjust a method of analyzing voice features based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing voice features, adjust the level of analysis detail based on the importance of the voice 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing voice features, different analysis algorithms are applied depending on the voice category. 2. The system of claim 1, (Appendix 17) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing audio features, priority is given to analysis based on when the audio was submitted 2. The system of claim 1, (Appendix 19) The analysis unit is When analyzing audio features, adjust the order of analysis based on audio relevance 2. The system of claim 1, (Appendix 20) The determination unit is To estimate a user's emotion and adjust a criterion for judging a throat condition based on the estimated user's emotion. 2. The system of claim 1, (Appendix 21) The determination unit is Improve accuracy of judgment when judging throat condition by taking into account the correlation of sounds 2. The system of claim 1, (Appendix 22) The determination unit is When judging the condition of the voice, the attribute information of the person submitting the voice is taken into consideration. 2. The system of claim 1, (Appendix 23) The determination unit is Inferring user emotions and adjusting the order in which judgment results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 24) The determination unit is When judging the condition of the throat, the geographical distribution of the voice is taken into account. 2. The system of claim 1, (Appendix 25) The determination unit is When judging the condition of the throat, improve the accuracy of the judgment by referring to related literature on speech. 2. The system of claim 1, (Appendix 26) The providing unit is To estimate a user's emotion and adjust a method of providing a diagnosis result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 27) The providing unit is When providing diagnostic results, the optimal method of providing them is selected by referring to the user's past diagnostic history. 2. The system of claim 1, (Appendix 28) The providing unit is When providing diagnostic results, the means of providing them is customized based on the user's current living situation. 2. The system of claim 1, (Appendix 29) The providing unit is Estimating a user's emotions and prioritizing diagnostic results based on the estimated user's emotions 2. The system of claim 1, (Appendix 30) The providing unit is When providing diagnostic results, the optimal method of providing them is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The providing unit is When providing diagnostic results, we analyze the user's social media activity to suggest ways to provide them. 2. The system of claim 1, (Appendix 32) The tone analysis unit To estimate a user's emotion and adjust a tone analysis method based on the estimated user's emotion. 3. The system of claim 2, (Appendix 33) The tone analysis unit When analyzing tones, adjust the analysis detail based on the importance of the sound. 3. The system of claim 2, (Appendix 34) The tone analysis unit When analyzing tones, apply different analysis algorithms depending on the category of the sound 3. The system of claim 2, (Appendix 35) The tone analysis unit To estimate a user's emotion and determine a priority of tone analysis based on the estimated user's emotion. 3. The system of claim 2, (Appendix 36) The tone analysis unit During tone analysis, priority is given to analysis based on when the audio was submitted 3. The system of claim 2, (Appendix 37) The tone analysis unit When analyzing tones, adjust the order of analysis based on audio relevance 3. The system of claim 2, (Appendix 38) The pronunciation analysis unit is To estimate a user's emotion and adjust a pronunciation analysis method based on the estimated user's emotion. 4. The system of claim 3. (Appendix 39) The pronunciation analysis unit is Adjust the level of detail during pronunciation analysis based on the importance of the sound 4. The system of claim 3. (Appendix 40) The pronunciation analysis unit is When analyzing pronunciation, different analysis algorithms are applied depending on the phonetic category. 4. The system of claim 3. (Appendix 41) The pronunciation analysis unit is A user's emotion is estimated, and a priority order for pronunciation analysis is determined based on the estimated user's emotion. 4. The system of claim 3. (Appendix 42) The pronunciation analysis unit is During pronunciation analysis, priority is determined based on when the audio was submitted. 4. The system of claim 3. (Appendix 43) The pronunciation analysis unit is When analyzing pronunciation, adjust the analysis order based on phonetic relevance 4. The system of claim 3. (Appendix 44) The velocity analysis unit Estimate user emotion and adjust speed analysis method based on the estimated user emotion. 5. The system of claim 4. (Appendix 45) The velocity analysis unit When analyzing speed, adjust the analysis detail level based on the importance of the audio 5. The system of claim 4. (Appendix 46) The velocity analysis unit When analyzing speed, different analysis algorithms are applied depending on the audio category. 5. The system of claim 4. (Appendix 47) The velocity analysis unit Estimate user's emotions and prioritize speed analysis based on the estimated user's emotions. 5. The system of claim 4. (Appendix 48) The velocity analysis unit During speed analysis, priority is given to analysis based on when the audio was submitted. 5. The system of claim 4. (Appendix 49) The velocity analysis unit When analyzing speed, adjust the analysis order based on audio relevance 5. The system of claim 4. (Appendix 50) The dialogue analysis unit Estimate user emotions and adjust the dialogue analysis method based on the estimated user emotions. 6. The system of claim 5, (Appendix 51) The dialogue analysis unit During dialogue analysis, adjust the level of analysis detail based on the importance of the dialogue 6. The system of claim 5, (Appendix 52) The dialogue analysis unit When analyzing dialogue, apply different analysis algorithms depending on the category of the dialogue. 6. The system of claim 5, (Appendix 53) The dialogue analysis unit Estimate user emotions and determine the priority of dialogue analysis based on the estimated user emotions. 6. The system of claim 5, (Appendix 54) The dialogue analysis unit When analyzing dialogue, prioritize analysis based on when the dialogue was submitted 6. The system of claim 5, (Appendix 55) The dialogue analysis unit During dialogue analysis, adjust the analysis order based on dialogue relevance 6. The system of claim 5, (Appendix 56) The content determination unit is To estimate a user's emotion and adjust a content judgment method based on the estimated user's emotion. 7. The system of claim 6, (Appendix 57) The content determination unit is When judging content, adjust the level of detail of the judgment based on the importance of the dialogue 7. The system of claim 6, (Appendix 58) The content determination unit is When determining content, different determination algorithms are applied depending on the category of the dialogue. 7. The system of claim 6, (Appendix 59) The content determination unit is Estimate the user's emotions and determine the priority of content judgment based on the estimated user's emotions. 7. The system of claim 6, (Appendix 60) The content determination unit is When judging content, the priority of the judgment is determined based on the time of submission of the dialogue. 7. The system of claim 6, (Appendix 61) The content determination unit is When judging content, adjust the order of judgments based on the relevance of the dialogue 7. The system of claim 6, (Appendix 62) The notification unit is Infer user emotions and adjust notification methods based on the estimated user emotions 8. The system of claim 7, (Appendix 63) The notification unit is When notifying users, the system selects the optimal notification method by referring to the user's past notification history. 8. The system of claim 7, (Appendix 64) The notification unit is When notifying users, customize the notification experience based on their current life situation 8. The system of claim 7, (Appendix 65) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 8. The system of claim 7, (Appendix 66) The notification unit is When notifying users, the optimal notification method is selected based on the user's geographic location. 8. The system of claim 7, (Appendix 67) The notification unit is When notifying users, the app analyzes their social media activity to suggest a method of notification. 8. The system of claim 7, [Explanation of symbols]

[0245] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives voice input; an analysis unit that analyzes features of the voice received by the reception unit; a judgment unit for judging a throat condition based on the voice characteristics analyzed by the analysis unit; A providing unit that provides a diagnosis result based on the determined result. A system characterized by:

2. The analysis unit is Equipped with a tone analysis unit that analyzes the tone or rhythm of the voice 2. The system of claim 1 .

3. The analysis unit is Equipped with a pronunciation analyzer that analyzes the clarity of pronunciation 2. The system of claim 1 .

4. The analysis unit is Equipped with a speed analysis section that analyzes response speed 2. The system of claim 1 .

5. The determination unit is Equipped with a dialogue analysis unit that analyzes dialogue content 2. The system of claim 1 .

6. The dialogue analysis unit Equipped with a content judgment unit that judges negative content 6. The system of claim 5.

7. The providing unit is Equipped with a notification unit that notifies the user when it is determined that a diagnosis is necessary 2. The system of claim 1 .

8. The reception unit is To estimate a user's emotion and adjust the timing of accepting voice input based on the estimated user's emotion.

2. The system of claim 1 .

9. The reception unit is Analyze the user's past voice input history and select the optimal reception method 2. The system of claim 1 .

Images