System

Abstract

To provide a system for promoting early detection of disease at home.SOLUTION: A system includes an analysis part, a question part, and an identification part. The analysis part analyzes utterance contents of a user. The question part asks a question according to the information analyzed by the analysis part. The identification part identifies the disease according to the information heard by a question part.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 technology lacks effective interview tools to promote early detection of diseases at home, leaving room for improvement.

[0005] The system according to the embodiment aims to promote early detection of diseases in the home. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a questioning unit, and an identification unit. The analysis unit analyzes the content of a user's statement. The questioning unit asks a question based on the information analyzed by the analysis unit. The identification unit identifies a disease based on the information collected by the questioning unit. Effect of the Invention

[0007] The system according to the embodiment can facilitate early detection of diseases in the home. [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 home medical interview tool according to the embodiment of the present invention is a system that promotes early detection and early treatment at home in order to solve problems such as aging, shortage of medical personnel, and increasing social security costs. In this system, a user uses the medical interview tool at home, and a generation AI listens to the official interview in a conversational manner, thereby grasping the user's symptoms and condition, leading to early detection of diseases at home. For example, when a user uses the medical interview tool at home, the user only needs to talk about his or her symptoms and condition to the tool. For example, information such as "I've been having headaches lately" is input. This information is input to the generation AI. Next, the generation AI analyzes the input information and listens to the official interview in a conversational manner. The generation AI understands the content of the user's remarks and asks appropriate questions. For example, the generation AI asks questions such as "When did your headache start?" and "Which part hurts?" This allows the user's symptoms and condition to be grasped in detail. The generation AI analyzes the user's symptoms and condition based on the information it listens to. For example, the frequency of headaches, the intensity of pain, and the presence or absence of other symptoms are analyzed to identify the possibility of disease. This allows for early detection of diseases at home. This system allows for uniform interviews to be conducted even in areas with a shortage of elderly people or medical personnel. Users can easily undergo interviews at home, which allows for early detection of diseases. In addition, by understanding symptoms before going to a medical institution, examinations at medical institutions can proceed smoothly. For example, if an elderly person uses an interview tool at home and the generation AI listens to their headache symptoms, the generation AI can analyze the frequency of headaches, the intensity of pain, and the presence or absence of other symptoms to identify the possibility of stroke. This allows for early visits to medical institutions and appropriate treatment. In addition, even in areas with a shortage of medical personnel, uniform interviews can be conducted by using an interview tool at home. Since the generation AI listens to the official interview based on conversation, uniform interviews that are not personalized are possible. This improves the quality of medical services throughout the region and suppresses the increase in social security costs. As a result, the home medical interview tool can promote early detection and treatment within the home, and help resolve issues such as an aging population, a shortage of medical personnel, and rising social security costs.

[0029] The home medical interview tool according to the embodiment includes an analysis unit, a questioning unit, and a specification unit. The analysis unit analyzes the content of the user's statement. The content of the user's statement includes, for example, daily conversation, medical consultation, and emotional expression, but is not limited to such examples. The analysis unit analyzes the content of the user's statement using, for example, natural language processing technology. The analysis unit can also convert the content of the user's statement into text data using voice recognition technology. Furthermore, the analysis unit can estimate the user's emotion using emotion analysis technology. For example, the analysis unit analyzes the content of the user's statement using natural language processing technology to understand the intention and content of the statement. The voice recognition technology converts the user's statement into text data with high accuracy and uses it as basic data for analysis. The emotion analysis technology estimates emotions from the content of the user's statement and voice data, and reflects them in the analysis results. The questioning unit asks appropriate questions based on the information analyzed by the analysis unit. The questions include, for example, multiple choice questions, open-ended questions, follow-up questions, and the like, but are not limited to such examples. For example, the questioning unit asks the user questions such as "When did your headache start?" and "Which part hurts?" based on the information analyzed by the analysis unit. The questioning unit can also ask follow-up questions depending on the user's answer. For example, if the user answers "My headache continues," the questioning unit asks a follow-up question such as "How often do you get headaches?" The identification unit identifies the possibility of a disease based on the information heard by the questioning unit. Identification includes, but is not limited to, symptom matching, diagnostic algorithms, expert opinions, and the like. For example, the identification unit analyzes the frequency and intensity of the user's headaches, and the presence or absence of other symptoms, and identifies the possibility of a stroke. The identification unit can also use a diagnostic algorithm to identify the possibility of a disease based on the user's symptoms. Furthermore, the identification unit can also identify the possibility of a disease by referring to the opinions of experts. As a result, the home interview tool according to the embodiment analyzes the content of the user's remarks, asks appropriate questions, and identifies the possibility of a disease, thereby enabling early detection at home. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.For example, the identification unit can input information collected by the questioning unit into the generation AI, which can then identify the possibility of a disease.

[0030] The analysis unit analyzes the content of the user's speech. The content of the user's speech includes, for example, daily conversation, medical consultation, and emotional expression, but is not limited to such examples. The analysis unit analyzes the content of the user's speech using, for example, natural language processing technology. Specifically, the natural language processing technology grammatically analyzes the user's speech and extracts keywords and phrases. This makes it possible to understand the intention and content of the user's speech. The analysis unit can also convert the content of the user's speech into text data using voice recognition technology. The voice recognition technology converts the user's speech into text data with high accuracy and uses it as basic data for analysis. Furthermore, the analysis unit can also estimate the user's emotions using emotion analysis technology. The emotion analysis technology estimates emotions from the content of the user's speech and voice data and reflects them in the analysis results. For example, the analysis unit analyzes the content of the user's speech using natural language processing technology to understand the intention and content of the speech. The voice recognition technology converts the user's speech into text data with high accuracy and uses it as basic data for analysis. Emotion analysis technology estimates emotions from the user's remarks and voice data, and reflects them in the analysis results. This allows the analysis unit to analyze the user's remarks from multiple angles and accurately grasp the user's state and intentions. Furthermore, the analysis unit can perform more accurate analysis by referring to past remark data and data from other users. For example, by analyzing the user's remark patterns and tendencies based on past remark data and comparing them with the current remarks, abnormal remarks and changes can be detected. Furthermore, by referring to data from other users, common symptoms and patterns can be identified and reflected in the analysis results. This allows the analysis unit to analyze the user's remarks from multiple angles and accurately grasp the user's state and intentions.

[0031] The questioning unit asks an appropriate question based on the information analyzed by the analysis unit. The questions include, but are not limited to, multiple choice questions, open-ended questions, and follow-up questions. For example, the questioning unit asks the user questions such as "When did your headache start?" and "Which part hurts?" based on the information analyzed by the analysis unit. The questioning unit can also ask follow-up questions according to the user's answer. For example, if the user answers "My headache continues," the questioning unit asks a follow-up question such as "How often do you get headaches?" The questioning unit can analyze the user's answer in real time and generate the next question quickly. This allows the user to smoothly proceed with the medical interview. Furthermore, the questioning unit can record the content of the user's answer and use it for later analysis and diagnosis. For example, the content of the user's answer can be saved in a database and compared with past answers to grasp changes and progress of symptoms. The questioning unit can also feed back the content of the user's answer to the analysis unit and update the analysis result. This allows the questioning unit to ask appropriate questions according to the user's condition and symptoms, and improve the accuracy of the medical interview. Furthermore, the questioning unit can automatically generate the next question based on the user's answer. For example, if the user answers "I have a persistent headache," the questioning unit automatically generates a follow-up question such as "How often do you get headaches?" This allows the questioning unit to quickly ask appropriate questions according to the user's condition and symptoms.

[0032] The identification unit identifies the possibility of a disease based on the information heard by the question unit. Identification includes, for example, symptom matching, diagnostic algorithms, expert opinions, and the like, but is not limited to such examples. For example, the identification unit analyzes the frequency of the user's headaches, the intensity of the pain, and the presence or absence of other symptoms, and identifies the possibility of a stroke. The identification unit can also use a diagnostic algorithm to identify the possibility of a disease based on the user's symptoms. The diagnostic algorithm is built based on past diagnostic data and medical knowledge, and can identify the possibility of a disease with high accuracy. Furthermore, the identification unit can also identify the possibility of a disease by referring to the opinions of experts. For example, the identification unit incorporates the opinions of doctors and experts and identifies the possibility of a disease based on the user's symptoms. As a result, the identification unit can identify the possibility of a disease based on the user's symptoms with high accuracy. Furthermore, the identification unit can also identify the possibility of a disease using a generation AI. For example, the identification unit inputs the information heard by the question unit into the generation AI, and the generation AI identifies the possibility of a disease. The generation AI learns based on a large amount of data, and can identify the possibility of a disease with high accuracy. As a result, the identification unit can identify the possibility of a disease with high accuracy using the generation AI. Furthermore, the identification unit can also suggest appropriate measures and treatments according to the user's symptoms and condition. For example, the identification unit can recommend a visit to an appropriate medical institution based on the user's symptoms. This allows the identification unit to identify the possibility of a disease based on the user's symptoms with high accuracy and suggest appropriate measures and treatments.

[0033] The analysis unit can analyze the user's past speech history and select an analysis algorithm. For example, the analysis unit selects an analysis algorithm related to a specific disease by the generation AI based on symptoms frequently mentioned by the user in the past. The analysis unit can also have the generation AI find a specific pattern from the user's past speech history and perform analysis based on the pattern. Furthermore, the analysis unit can analyze the user's past speech history and select the most effective analysis algorithm for the generation AI. This improves the accuracy of the analysis by selecting the optimal analysis algorithm based on the past speech history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past speech history into the generation AI, and the generation AI can select the optimal analysis algorithm.

[0034] When analyzing the content of the statement, the analysis unit can improve the accuracy of the analysis based on the user's lifestyle or medical history. For example, the analysis unit considers the user's lifestyle (eating, exercise, sleep, etc.) and the generation AI analyzes the content of the statement. The analysis unit can also have the generation AI analyze the content of the statement based on the user's medical history (past medical history) and identify the possibility of a disease. Furthermore, the analysis unit can consider the user's lifestyle and medical history and the generation AI can improve the analysis accuracy of the content of the statement. As a result, the accuracy of the analysis is improved by considering the lifestyle and medical history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs data on the user's lifestyle and medical history into the generation AI, and the generation AI can improve the analysis accuracy of the content of the statement.

[0035] When analyzing the content of the statement, the analysis unit can prioritize analysis of highly relevant information based on the geographical location information of the user. For example, the analysis unit allows the generation AI to prioritize analysis of information related to diseases specific to the region based on the geographical location information of the user. The analysis unit can also take into account the geographical location information of the user and allow the generation AI to prioritize analysis of information related to diseases prevalent in the region. Furthermore, the analysis unit can also allow the generation AI to prioritize analysis of information related to medical resources in the region based on the geographical location information of the user. As a result, by taking into account the geographical location information, information related to diseases specific to the region can be prioritized. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the geographical location information of the user to the generation AI, and the generation AI can prioritize analysis of highly relevant information.

[0036] The analysis unit can analyze the user's social media activity and analyze related information when analyzing the content of the comment. For example, the analysis unit can analyze posts related to health by the generation AI based on the user's social media activity. The analysis unit can also analyze the user's social media activity and analyze health information related to the activity by the generation AI. Furthermore, the analysis unit can analyze the possibility of a disease related to the activity by the generation AI based on the user's social media activity. In this way, the related health information can be analyzed by analyzing the social media activity. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's social media activity data to the generation AI, and the generation AI can analyze the related information.

[0037] The questioning unit can adjust the level of detail of the question in consideration of the importance of the user's statement when asking a question. For example, when the user's statement is important, the questioning unit has the generation AI ask a detailed question to draw out deeper information. Also, when the user's statement is general, the questioning unit can have the generation AI ask a concise question to quickly collect information. Furthermore, the questioning unit can adjust the level of detail of the question based on the importance of the user's statement. This makes it possible to draw out deeper information by adjusting the level of detail of the question based on the importance of the statement. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input importance data of the user's statement to the generation AI, and the generation AI can adjust the level of detail of the question.

[0038] The questioning unit can apply a question algorithm based on the user's category when asking a question. For example, if the user is an elderly person, the generation AI can apply a question algorithm for elderly people. Also, if the user is a child, the questioning unit can apply a question algorithm for children. Furthermore, the questioning unit can apply an optimal question algorithm for the generation AI according to the user's category. This makes it possible to ask more appropriate questions by applying a question algorithm according to the user's category. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input the user's category data to the generation AI, and the generation AI can apply the optimal question algorithm.

[0039] The questioning unit can determine the priority of questions based on the time when the user speaks at the time of asking a question. In the questioning unit, for example, the generation AI prioritizes questions based on the content of a recent statement made by the user. In addition, the questioning unit can also cause the generation AI to prioritize questions about important information based on the time when the user speaks. Furthermore, the questioning unit can also determine the optimal priority of questions by considering the time when the user speaks. As a result, important information can be collected preferentially by determining the priority of questions based on the time when the user speaks. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input the user's statement time data to the generation AI, and the generation AI can determine the priority of questions.

[0040] The questioning unit can adjust the order of questions in consideration of the relevance of the user when asking a question. In the questioning unit, for example, the generation AI determines the optimal order of questions based on the relevance of the user's statement content. In addition, the questioning unit can also cause the generation AI to prioritize important questions based on the relevance of the user's statement content. Furthermore, the questioning unit can also cause the generation AI to adjust the order of questions in consideration of the relevance of the user's statement content. As a result, important questions can be prioritized by adjusting the order of questions based on the relevance. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input relevance data of the user's statement content to the generation AI, and the generation AI can adjust the order of questions.

[0041] The identification unit can select the identification method by analyzing the user's past medical history when identifying a disease. For example, the identification unit selects the optimal disease identification method by the generation AI based on the user's past medical history. The identification unit can also analyze the user's past medical history, and the generation AI can preferentially analyze information related to the specific disease. Furthermore, the identification unit can also select the most effective disease identification method by the generation AI, taking into account the user's past medical history. As a result, the accuracy of identification is improved by selecting the optimal identification method based on the past medical history. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's past medical history data to the generation AI, and the generation AI can select the optimal identification method.

[0042] The identification unit can adjust the specific means in consideration of the user's current living situation when identifying a disease. For example, the identification unit can allow the generation AI to customize the specific means in consideration of the user's current living situation (work, home environment, etc.). The identification unit can also allow the generation AI to select an optimal disease identification method based on the user's current living situation. Furthermore, the identification unit can allow the generation AI to customize the specific means in consideration of the user's current living situation. This enables more appropriate identification by customizing the specific means based on the current living situation. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's current living situation data into the generation AI, and the generation AI can customize the specific means.

[0043] The identification unit can select an identification method based on the geographical location information of the user when identifying a disease. For example, the identification unit allows the generation AI to preferentially identify information related to diseases specific to the region based on the geographical location information of the user. The identification unit can also allow the generation AI to preferentially identify information related to diseases that are prevalent in the region, taking into account the geographical location information of the user. Furthermore, the identification unit can also allow the generation AI to preferentially identify information related to medical resources in the region, based on the geographical location information of the user. As a result, by taking into account the geographical location information, information related to diseases specific to the region can be preferentially identified. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the geographical location information data of the user to the generation AI, and the generation AI can select the optimal identification method.

[0044] The identification unit can analyze the user's social media activity and suggest a measure when identifying a disease. For example, the identification unit can have the generation AI analyze posts related to health based on the user's social media activity and suggest a specific measure. The identification unit can also analyze the user's social media activity, have the generation AI identify health information related to the activity, and suggest a measure. Furthermore, the identification unit can also have the generation AI identify the possibility of a disease related to the activity based on the user's social media activity and suggest a measure. In this way, by analyzing social media activity, a specific measure can be suggested based on related health information. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's social media activity data to the generation AI, and the generation AI can suggest a specific measure.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When analyzing the content of a user's speech, the analysis unit can improve the accuracy of the analysis by taking into account the user's tone of voice and speaking speed. For example, if the user speaks quickly, the generation AI will prioritize analyzing information that is more urgent. Also, if the user speaks slowly, the generation AI can perform analysis to extract more detailed information. Furthermore, if the tone of the user's voice changes, the generation AI can detect the change and adjust the analysis method. This improves the accuracy of the analysis by taking into account the characteristics of the user's voice.

[0047] When analyzing the user's past statement history, the analysis unit can detect changes in the user's health condition and dynamically adjust the analysis algorithm. For example, if the symptoms the user previously described are worsening, the generation AI can perform a more detailed analysis. Also, if the user's health condition is improving, the generation AI can perform a simpler analysis. Furthermore, the generation AI can select the optimal analysis algorithm based on changes in the user's health condition. This improves the accuracy of the analysis by taking changes in health condition into account.

[0048] When analyzing the content of a statement, the analysis unit can improve the accuracy of the analysis by taking into account the user's family history in addition to the user's lifestyle habits and medical history. For example, if a particular disease is prevalent in the user's family, the generation AI will prioritize analyzing information related to that disease. The generation AI can also identify specific risk factors based on the user's family history. Furthermore, the generation AI can improve the accuracy of the analysis of the content of a statement by taking into account the user's family history. In this way, the accuracy of the analysis is improved by taking into account the family history.

[0049] When analyzing the content of a comment, the analysis unit can improve the accuracy of the analysis by taking into account seasonal and weather information in addition to the user's geographic location information. For example, priority is given to analyzing information related to diseases that are common in a particular season. Also, if the weather has an effect on health, the generation AI can take that effect into account when performing the analysis. Furthermore, the generation AI can improve the accuracy of the analysis of the content of a comment based on seasonal and weather information. This improves the accuracy of the analysis by taking into account seasonal and weather information.

[0050] When analyzing the content of comments, the analysis unit can analyze the user's online search history in addition to the user's social media activity and analyze related information. For example, based on health information searched for by the user in the past, the generation AI can reflect that information in the analysis. It is also possible to prioritize analysis of information related to a specific disease from the user's search history. Furthermore, the generation AI can select the optimal analysis algorithm based on the user's online search history. This improves the accuracy of the analysis by taking into account the online search history.

[0051] When asking a question, the questioning unit can adjust the level of detail of the question taking into account the consistency of the user's answer in addition to the importance of the content of the user's statement. For example, if the user's answers are consistent, the generation AI can ask detailed questions to elicit deeper information. Also, if the user's answers are inconsistent, the generation AI can ask concise questions to quickly collect information. Furthermore, the generation AI can adjust the level of detail of the question based on the consistency of the user's answer. This makes it possible to elicit deeper information by adjusting the level of detail of the question based on the consistency of the answer.

[0052] When asking a question, the questioning unit can apply a question algorithm taking into account the user's educational level in addition to the user's category. For example, if the user has a high level of education, the generation AI can apply a specialized question algorithm. Also, if the user has a low level of education, the generation AI can apply a simple and easy-to-understand question algorithm. Furthermore, the generation AI can apply an optimal question algorithm depending on the user's educational level. This allows for more appropriate questions to be asked by applying a question algorithm according to the user's educational level.

[0053] The process flow of the first embodiment will be briefly described below.

[0054] Step 1: The analysis unit analyzes the user's utterances. The user's utterances include daily conversations, medical consultations, and emotional expressions. The analysis unit can analyze the user's utterances using natural language processing technology and convert the user's utterances into text data using voice recognition technology. It can also estimate the user's emotions using emotion analysis technology. Step 2: The questioning unit asks appropriate questions based on the information analyzed by the analysis unit. The questions include multiple choice questions, open-ended questions, follow-up questions, etc. For example, based on the information analyzed by the analysis unit, the questioning unit asks the user questions such as "When did your headache start?" and "Which part hurts?". It can also ask follow-up questions depending on the user's answers. Step 3: The identification unit identifies the possibility of a disease based on the information heard by the questioning unit. Identification includes symptom matching, diagnostic algorithms, expert opinions, and the like. For example, the identification unit analyzes the frequency and intensity of the user's headaches, and the presence or absence of other symptoms, to identify the possibility of a stroke. The identification unit can also use diagnostic algorithms to identify the possibility of a disease based on the user's symptoms. Furthermore, the identification unit can also identify the possibility of a disease by referring to the opinions of experts.

[0055] (Example 2) The home medical interview tool according to the embodiment of the present invention is a system that promotes early detection and early treatment at home in order to solve problems such as aging, shortage of medical personnel, and increasing social security costs. In this system, a user uses the medical interview tool at home, and a generation AI listens to the official interview in a conversational manner, thereby grasping the user's symptoms and condition, leading to early detection of diseases at home. For example, when a user uses the medical interview tool at home, the user only needs to talk about his or her symptoms and condition to the tool. For example, information such as "I've been having headaches lately" is input. This information is input to the generation AI. Next, the generation AI analyzes the input information and listens to the official interview in a conversational manner. The generation AI understands the content of the user's remarks and asks appropriate questions. For example, the generation AI asks questions such as "When did your headache start?" and "Which part hurts?" This allows the user's symptoms and condition to be grasped in detail. The generation AI analyzes the user's symptoms and condition based on the information it listens to. For example, the frequency of headaches, the intensity of pain, and the presence or absence of other symptoms are analyzed to identify the possibility of disease. This allows for early detection of diseases at home. This system allows for uniform interviews to be conducted even in areas with a shortage of elderly people or medical personnel. Users can easily undergo interviews at home, which allows for early detection of diseases. In addition, by understanding symptoms before going to a medical institution, examinations at medical institutions can proceed smoothly. For example, if an elderly person uses an interview tool at home and the generation AI listens to their headache symptoms, the generation AI can analyze the frequency of headaches, the intensity of pain, and the presence or absence of other symptoms to identify the possibility of stroke. This allows for early visits to medical institutions and appropriate treatment. In addition, even in areas with a shortage of medical personnel, uniform interviews can be conducted by using an interview tool at home. Since the generation AI listens to the official interview based on conversation, uniform interviews that are not personalized are possible. This improves the quality of medical services throughout the region and suppresses the increase in social security costs. As a result, the home medical interview tool can promote early detection and treatment within the home, and help resolve issues such as an aging population, a shortage of medical personnel, and rising social security costs.

[0056] The home medical interview tool according to the embodiment includes an analysis unit, a questioning unit, and a specification unit. The analysis unit analyzes the content of the user's statement. The content of the user's statement includes, for example, daily conversation, medical consultation, and emotional expression, but is not limited to such examples. The analysis unit analyzes the content of the user's statement using, for example, natural language processing technology. The analysis unit can also convert the content of the user's statement into text data using voice recognition technology. Furthermore, the analysis unit can estimate the user's emotion using emotion analysis technology. For example, the analysis unit analyzes the content of the user's statement using natural language processing technology to understand the intention and content of the statement. The voice recognition technology converts the user's statement into text data with high accuracy and uses it as basic data for analysis. The emotion analysis technology estimates emotions from the content of the user's statement and voice data, and reflects them in the analysis results. The questioning unit asks appropriate questions based on the information analyzed by the analysis unit. The questions include, for example, multiple choice questions, open-ended questions, follow-up questions, and the like, but are not limited to such examples. For example, the questioning unit asks the user questions such as "When did your headache start?" and "Which part hurts?" based on the information analyzed by the analysis unit. The questioning unit can also ask follow-up questions depending on the user's answer. For example, if the user answers "My headache continues," the questioning unit asks a follow-up question such as "How often do you get headaches?" The identification unit identifies the possibility of a disease based on the information heard by the questioning unit. Identification includes, but is not limited to, symptom matching, diagnostic algorithms, expert opinions, and the like. For example, the identification unit analyzes the frequency and intensity of the user's headaches, and the presence or absence of other symptoms, and identifies the possibility of a stroke. The identification unit can also use a diagnostic algorithm to identify the possibility of a disease based on the user's symptoms. Furthermore, the identification unit can also identify the possibility of a disease by referring to the opinions of experts. As a result, the home interview tool according to the embodiment analyzes the content of the user's remarks, asks appropriate questions, and identifies the possibility of a disease, thereby enabling early detection at home. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI.For example, the identification unit can input information collected by the questioning unit into the generation AI, which can then identify the possibility of a disease.

[0057] The analysis unit analyzes the content of the user's speech. The content of the user's speech includes, for example, daily conversation, medical consultation, and emotional expression, but is not limited to such examples. The analysis unit analyzes the content of the user's speech using, for example, natural language processing technology. Specifically, the natural language processing technology grammatically analyzes the user's speech and extracts keywords and phrases. This makes it possible to understand the intention and content of the user's speech. The analysis unit can also convert the content of the user's speech into text data using voice recognition technology. The voice recognition technology converts the user's speech into text data with high accuracy and uses it as basic data for analysis. Furthermore, the analysis unit can also estimate the user's emotions using emotion analysis technology. The emotion analysis technology estimates emotions from the content of the user's speech and voice data and reflects them in the analysis results. For example, the analysis unit analyzes the content of the user's speech using natural language processing technology to understand the intention and content of the speech. The voice recognition technology converts the user's speech into text data with high accuracy and uses it as basic data for analysis. Emotion analysis technology estimates emotions from the user's remarks and voice data, and reflects them in the analysis results. This allows the analysis unit to analyze the user's remarks from multiple angles and accurately grasp the user's state and intentions. Furthermore, the analysis unit can perform more accurate analysis by referring to past remark data and data from other users. For example, by analyzing the user's remark patterns and tendencies based on past remark data and comparing them with the current remarks, abnormal remarks and changes can be detected. Furthermore, by referring to data from other users, common symptoms and patterns can be identified and reflected in the analysis results. This allows the analysis unit to analyze the user's remarks from multiple angles and accurately grasp the user's state and intentions.

[0058] The questioning unit asks an appropriate question based on the information analyzed by the analysis unit. The questions include, but are not limited to, multiple choice questions, open-ended questions, and follow-up questions. For example, the questioning unit asks the user questions such as "When did your headache start?" and "Which part hurts?" based on the information analyzed by the analysis unit. The questioning unit can also ask follow-up questions according to the user's answer. For example, if the user answers "My headache continues," the questioning unit asks a follow-up question such as "How often do you get headaches?" The questioning unit can analyze the user's answer in real time and generate the next question quickly. This allows the user to smoothly proceed with the medical interview. Furthermore, the questioning unit can record the content of the user's answer and use it for later analysis and diagnosis. For example, the content of the user's answer can be saved in a database and compared with past answers to grasp changes and progress of symptoms. The questioning unit can also feed back the content of the user's answer to the analysis unit and update the analysis result. This allows the questioning unit to ask appropriate questions according to the user's condition and symptoms, and improve the accuracy of the medical interview. Furthermore, the questioning unit can automatically generate the next question based on the user's answer. For example, if the user answers "I have a persistent headache," the questioning unit automatically generates a follow-up question such as "How often do you get headaches?" This allows the questioning unit to quickly ask appropriate questions according to the user's condition and symptoms.

[0059] The identification unit identifies the possibility of a disease based on the information heard by the question unit. Identification includes, for example, symptom matching, diagnostic algorithms, expert opinions, and the like, but is not limited to such examples. For example, the identification unit analyzes the frequency of the user's headaches, the intensity of the pain, and the presence or absence of other symptoms, and identifies the possibility of a stroke. The identification unit can also use a diagnostic algorithm to identify the possibility of a disease based on the user's symptoms. The diagnostic algorithm is built based on past diagnostic data and medical knowledge, and can identify the possibility of a disease with high accuracy. Furthermore, the identification unit can also identify the possibility of a disease by referring to the opinions of experts. For example, the identification unit incorporates the opinions of doctors and experts and identifies the possibility of a disease based on the user's symptoms. As a result, the identification unit can identify the possibility of a disease based on the user's symptoms with high accuracy. Furthermore, the identification unit can also identify the possibility of a disease using a generation AI. For example, the identification unit inputs the information heard by the question unit into the generation AI, and the generation AI identifies the possibility of a disease. The generation AI learns based on a large amount of data, and can identify the possibility of a disease with high accuracy. As a result, the identification unit can identify the possibility of a disease with high accuracy using the generation AI. Furthermore, the identification unit can also suggest appropriate measures and treatments according to the user's symptoms and condition. For example, the identification unit can recommend a visit to an appropriate medical institution based on the user's symptoms. This allows the identification unit to identify the possibility of a disease based on the user's symptoms with high accuracy and suggest appropriate measures and treatments.

[0060] The analysis unit can estimate the user's emotions and adjust the analysis method of the remark contents based on the estimated user's emotions. For example, when the user feels anxious, the analysis unit causes the generation AI to ask questions in a gentle tone to give a sense of security. In addition, when the user is relaxed, the analysis unit can also cause the generation AI to ask detailed questions to extract deeper information. Furthermore, when the user is in a hurry, the analysis unit can also cause the generation AI to ask concise questions to quickly collect information. This allows for more appropriate analysis by adjusting the analysis method of the remark contents 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the analysis method of the remark contents.

[0061] The analysis unit can analyze the user's past speech history and select an analysis algorithm. For example, the analysis unit selects an analysis algorithm related to a specific disease by the generation AI based on symptoms frequently mentioned by the user in the past. The analysis unit can also have the generation AI find a specific pattern from the user's past speech history and perform analysis based on the pattern. Furthermore, the analysis unit can analyze the user's past speech history and select the most effective analysis algorithm for the generation AI. This improves the accuracy of the analysis by selecting the optimal analysis algorithm based on the past speech history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past speech history into the generation AI, and the generation AI can select the optimal analysis algorithm.

[0062] When analyzing the content of the statement, the analysis unit can improve the accuracy of the analysis based on the user's lifestyle or medical history. For example, the analysis unit considers the user's lifestyle (eating, exercise, sleep, etc.) and the generation AI analyzes the content of the statement. The analysis unit can also have the generation AI analyze the content of the statement based on the user's medical history (past medical history) and identify the possibility of a disease. Furthermore, the analysis unit can consider the user's lifestyle and medical history and the generation AI can improve the analysis accuracy of the content of the statement. As a result, the accuracy of the analysis is improved by considering the lifestyle and medical history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs data on the user's lifestyle and medical history into the generation AI, and the generation AI can improve the analysis accuracy of the content of the statement.

[0063] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user feels anxious, the analysis unit allows the generation AI to preferentially analyze important information to reduce the anxiety. In addition, when the user is relaxed, the analysis unit can also allow the generation AI to preferentially analyze detailed information. Furthermore, when the user is in a hurry, the analysis unit can also allow the generation AI to quickly and preferentially analyze important information. In this way, important information can be preferentially analyzed by determining the priority of the analysis results 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the analysis results.

[0064] When analyzing the content of the statement, the analysis unit can prioritize analysis of highly relevant information based on the geographical location information of the user. For example, the analysis unit allows the generation AI to prioritize analysis of information related to diseases specific to the region based on the geographical location information of the user. The analysis unit can also take into account the geographical location information of the user and allow the generation AI to prioritize analysis of information related to diseases prevalent in the region. Furthermore, the analysis unit can also allow the generation AI to prioritize analysis of information related to medical resources in the region based on the geographical location information of the user. As a result, by taking into account the geographical location information, information related to diseases specific to the region can be prioritized. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the geographical location information of the user to the generation AI, and the generation AI can prioritize analysis of highly relevant information.

[0065] The analysis unit can analyze the user's social media activity and analyze related information when analyzing the content of the comment. For example, the analysis unit can analyze posts related to health by the generation AI based on the user's social media activity. The analysis unit can also analyze the user's social media activity and analyze health information related to the activity by the generation AI. Furthermore, the analysis unit can analyze the possibility of a disease related to the activity by the generation AI based on the user's social media activity. In this way, the related health information can be analyzed by analyzing the social media activity. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's social media activity data to the generation AI, and the generation AI can analyze the related information.

[0066] The questioning unit can estimate the user's emotions and adjust the way the question is expressed based on the estimated user's emotions. For example, when the user feels anxious, the questioning unit has the generation AI ask a question in a gentle tone to give a sense of security. In addition, when the user is relaxed, the questioning unit can also have the generation AI ask a detailed question to draw out deeper information. Furthermore, when the user is in a hurry, the questioning unit can also have the generation AI ask a concise question to quickly collect information. This allows for more appropriate questions to be asked by adjusting the way the question is expressed 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 questioning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input the user's emotion data to the generation AI, and the generation AI can adjust the way the question is expressed.

[0067] The questioning unit can adjust the level of detail of the question in consideration of the importance of the user's statement when asking a question. For example, when the user's statement is important, the questioning unit has the generation AI ask a detailed question to draw out deeper information. Also, when the user's statement is general, the questioning unit can have the generation AI ask a concise question to quickly collect information. Furthermore, the questioning unit can adjust the level of detail of the question based on the importance of the user's statement. This makes it possible to draw out deeper information by adjusting the level of detail of the question based on the importance of the statement. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input importance data of the user's statement to the generation AI, and the generation AI can adjust the level of detail of the question.

[0068] The questioning unit can apply a question algorithm based on the user's category when asking a question. For example, if the user is an elderly person, the generation AI can apply a question algorithm for elderly people. Also, if the user is a child, the questioning unit can apply a question algorithm for children. Furthermore, the questioning unit can apply an optimal question algorithm for the generation AI according to the user's category. This makes it possible to ask more appropriate questions by applying a question algorithm according to the user's category. Some or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input the user's category data to the generation AI, and the generation AI can apply the optimal question algorithm.

[0069] The questioning unit can estimate the user's emotion and adjust the length of the question based on the estimated user's emotion. For example, when the user feels anxious, the questioning unit causes the generation AI to ask a short and concise question to give a sense of security. In addition, when the user is relaxed, the questioning unit can also cause the generation AI to ask a detailed question to draw out deeper information. Furthermore, when the user is in a hurry, the questioning unit can also cause the generation AI to ask a short and to the point question to quickly collect information. This allows for more appropriate questions to be asked by adjusting the length of the question 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 questioning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input the user's emotion data to the generation AI, and the generation AI can adjust the length of the question.

[0070] The questioning unit can determine the priority of questions based on the time when the user speaks at the time of asking a question. In the questioning unit, for example, the generation AI prioritizes questions based on the content of a recent statement made by the user. In addition, the questioning unit can also cause the generation AI to prioritize questions about important information based on the time when the user speaks. Furthermore, the questioning unit can also determine the optimal priority of questions by considering the time when the user speaks. As a result, important information can be collected preferentially by determining the priority of questions based on the time when the user speaks. Part or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input the user's statement time data to the generation AI, and the generation AI can determine the priority of questions.

[0071] The questioning unit can adjust the order of questions in consideration of the relevance of the user when asking a question. In the questioning unit, for example, the generation AI determines the optimal order of questions based on the relevance of the user's statement content. In addition, the questioning unit can also cause the generation AI to prioritize important questions based on the relevance of the user's statement content. Furthermore, the questioning unit can also cause the generation AI to adjust the order of questions in consideration of the relevance of the user's statement content. As a result, important questions can be prioritized by adjusting the order of questions based on the relevance. A part or all of the above-mentioned processing in the questioning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the questioning unit can input relevance data of the user's statement content to the generation AI, and the generation AI can adjust the order of questions.

[0072] The identification unit can estimate the user's emotion and adjust the disease identification method based on the estimated user's emotion. For example, when the user feels anxious, the identification unit causes the generation AI to explain the disease identification method in a gentle tone. In addition, when the user is relaxed, the identification unit can also cause the generation AI to provide a detailed explanation and adjust the disease identification method. Furthermore, when the user is in a hurry, the identification unit can also cause the generation AI to provide a concise explanation and quickly identify the disease. This allows for more appropriate identification by adjusting the disease identification method 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 identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's emotion data to the generation AI, and the generation AI can adjust the disease identification method.

[0073] The identification unit can select the identification method by analyzing the user's past medical history when identifying a disease. For example, the identification unit selects the optimal disease identification method by the generation AI based on the user's past medical history. The identification unit can also analyze the user's past medical history, and the generation AI can preferentially analyze information related to the specific disease. Furthermore, the identification unit can also select the most effective disease identification method by the generation AI, taking into account the user's past medical history. As a result, the accuracy of identification is improved by selecting the optimal identification method based on the past medical history. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's past medical history data to the generation AI, and the generation AI can select the optimal identification method.

[0074] The identification unit can adjust the specific means in consideration of the user's current living situation when identifying a disease. For example, the identification unit can allow the generation AI to customize the specific means in consideration of the user's current living situation (work, home environment, etc.). The identification unit can also allow the generation AI to select an optimal disease identification method based on the user's current living situation. Furthermore, the identification unit can allow the generation AI to customize the specific means in consideration of the user's current living situation. This enables more appropriate identification by customizing the specific means based on the current living situation. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's current living situation data into the generation AI, and the generation AI can customize the specific means.

[0075] The identification unit can estimate the user's emotion and determine the priority of disease identification based on the estimated user's emotion. For example, when the user feels anxious, the identification unit allows the generation AI to preferentially identify diseases that are important for reducing anxiety. In addition, when the user is relaxed, the identification unit can also allow the generation AI to preferentially identify detailed information. Furthermore, when the user is in a hurry, the identification unit can also allow the generation AI to quickly preferentially identify important diseases. In this way, by determining the priority of the specific disease according to the user's emotion, important diseases can be preferentially identified. 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 identification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of disease identification.

[0076] The identification unit can select an identification method based on the geographical location information of the user when identifying a disease. For example, the identification unit allows the generation AI to preferentially identify information related to diseases specific to the region based on the geographical location information of the user. The identification unit can also allow the generation AI to preferentially identify information related to diseases that are prevalent in the region, taking into account the geographical location information of the user. Furthermore, the identification unit can also allow the generation AI to preferentially identify information related to medical resources in the region, based on the geographical location information of the user. As a result, by taking into account the geographical location information, information related to diseases specific to the region can be preferentially identified. A part or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the geographical location information data of the user to the generation AI, and the generation AI can select the optimal identification method.

[0077] The identification unit can analyze the user's social media activity and suggest a measure when identifying a disease. For example, the identification unit can have the generation AI analyze posts related to health based on the user's social media activity and suggest a specific measure. The identification unit can also analyze the user's social media activity, have the generation AI identify health information related to the activity, and suggest a measure. Furthermore, the identification unit can also have the generation AI identify the possibility of a disease related to the activity based on the user's social media activity and suggest a measure. In this way, by analyzing social media activity, a specific measure can be suggested based on related health information. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the identification unit can input the user's social media activity data to the generation AI, and the generation AI can suggest a specific measure.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] When analyzing the content of a user's speech, the analysis unit can improve the accuracy of the analysis by taking into account the user's tone of voice and speaking speed. For example, if the user speaks quickly, the generation AI will prioritize analyzing information that is more urgent. Also, if the user speaks slowly, the generation AI can perform analysis to extract more detailed information. Furthermore, if the tone of the user's voice changes, the generation AI can detect the change and adjust the analysis method. This improves the accuracy of the analysis by taking into account the characteristics of the user's voice.

[0080] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user's emotions. For example, if the user feels anxious, the generation AI can provide feedback that gives a sense of security. Also, if the user is relaxed, the generation AI can provide detailed feedback to encourage deeper understanding. Furthermore, if the user is in a hurry, the generation AI can provide concise and quick feedback. This makes it possible to provide more appropriate feedback by adjusting the feedback method according to the user's emotions.

[0081] When analyzing the user's past statement history, the analysis unit can detect changes in the user's health condition and dynamically adjust the analysis algorithm. For example, if the symptoms the user previously described are worsening, the generation AI can perform a more detailed analysis. Also, if the user's health condition is improving, the generation AI can perform a simpler analysis. Furthermore, the generation AI can select the optimal analysis algorithm based on changes in the user's health condition. This improves the accuracy of the analysis by taking changes in health condition into account.

[0082] When analyzing the content of a statement, the analysis unit can improve the accuracy of the analysis by taking into account the user's family history in addition to the user's lifestyle habits and medical history. For example, if a particular disease is prevalent in the user's family, the generation AI will prioritize analyzing information related to that disease. The generation AI can also identify specific risk factors based on the user's family history. Furthermore, the generation AI can improve the accuracy of the analysis of the content of a statement by taking into account the user's family history. In this way, the accuracy of the analysis is improved by taking into account the family history.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user feels anxious, the generation AI can adopt a display method that gives a sense of security. In addition, if the user is relaxed, the generation AI can display detailed information to encourage deeper understanding. Furthermore, if the user is in a hurry, the generation AI can adopt a simple and quick display method. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotions.

[0084] When analyzing the content of a comment, the analysis unit can improve the accuracy of the analysis by taking into account seasonal and weather information in addition to the user's geographic location information. For example, priority is given to analyzing information related to diseases that are common in a particular season. Also, if the weather has an effect on health, the generation AI can take that effect into account when performing the analysis. Furthermore, the generation AI can improve the accuracy of the analysis of the content of a comment based on seasonal and weather information. This improves the accuracy of the analysis by taking into account seasonal and weather information.

[0085] When analyzing the content of comments, the analysis unit can analyze the user's online search history in addition to the user's social media activity and analyze related information. For example, based on health information searched for by the user in the past, the generation AI can reflect that information in the analysis. It is also possible to prioritize analysis of information related to a specific disease from the user's search history. Furthermore, the generation AI can select the optimal analysis algorithm based on the user's online search history. This improves the accuracy of the analysis by taking into account the online search history.

[0086] The questioning unit can estimate the user's emotions and adjust the timing of questions based on the estimated user's emotions. For example, if the user feels anxious, the generation AI can delay the timing of questions to give the user a sense of security. Also, if the user is relaxed, the generation AI can advance the timing of questions to draw out more detailed information. Furthermore, if the user is in a hurry, the generation AI can ask questions quickly and collect information quickly. This allows for more appropriate questions to be asked by adjusting the timing of questions according to the user's emotions.

[0087] When asking a question, the questioning unit can adjust the level of detail of the question taking into account the consistency of the user's answer in addition to the importance of the content of the user's statement. For example, if the user's answers are consistent, the generation AI can ask detailed questions to elicit deeper information. Also, if the user's answers are inconsistent, the generation AI can ask concise questions to quickly collect information. Furthermore, the generation AI can adjust the level of detail of the question based on the consistency of the user's answer. This makes it possible to elicit deeper information by adjusting the level of detail of the question based on the consistency of the answer.

[0088] When asking a question, the questioning unit can apply a question algorithm taking into account the user's educational level in addition to the user's category. For example, if the user has a high level of education, the generation AI can apply a specialized question algorithm. Also, if the user has a low level of education, the generation AI can apply a simple and easy-to-understand question algorithm. Furthermore, the generation AI can apply an optimal question algorithm depending on the user's educational level. This allows for more appropriate questions to be asked by applying a question algorithm according to the user's educational level.

[0089] The process flow of the second embodiment will be briefly described below.

[0090] Step 1: The analysis unit analyzes the user's utterances. The user's utterances include daily conversations, medical consultations, and emotional expressions. The analysis unit can analyze the user's utterances using natural language processing technology and convert the user's utterances into text data using voice recognition technology. It can also estimate the user's emotions using emotion analysis technology. Step 2: The questioning unit asks appropriate questions based on the information analyzed by the analysis unit. The questions include multiple choice questions, open-ended questions, follow-up questions, etc. For example, based on the information analyzed by the analysis unit, the questioning unit asks the user questions such as "When did your headache start?" and "Which part hurts?". It can also ask follow-up questions depending on the user's answers. Step 3: The identification unit identifies the possibility of a disease based on the information heard by the questioning unit. Identification includes symptom matching, diagnostic algorithms, expert opinions, and the like. For example, the identification unit analyzes the frequency and intensity of the user's headaches, and the presence or absence of other symptoms, to identify the possibility of a stroke. The identification unit can also use diagnostic algorithms to identify the possibility of a disease based on the user's symptoms. Furthermore, the identification unit can also identify the possibility of a disease by referring to the opinions of experts.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] Each of the elements including the above-mentioned analysis unit, question unit, and identification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14, and analyzes the content of the user's statement using natural language processing technology or voice recognition technology. The question unit is realized, for example, by the control unit 46A of the smart device 14, and asks an appropriate question based on the analyzed information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of a disease based on the information heard by the question unit. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements including the above-mentioned analysis unit, question unit, and identification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214, and analyzes the content of the user's utterance using natural language processing technology or voice recognition technology. The question unit is realized, for example, by the control unit 46A of the smart glasses 214, and asks an appropriate question based on the analyzed information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of a disease based on the information heard by the question unit. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the elements including the above-mentioned analysis unit, question unit, and identification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314, and analyzes the content of the user's utterance using natural language processing technology or voice recognition technology. The question unit is realized, for example, by the control unit 46A of the headset type terminal 314, and asks an appropriate question based on the analyzed information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of a disease based on the information heard by the question unit. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements including the above-mentioned analysis unit, question unit, and identification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414, and analyzes the content of the user's statement using natural language processing technology or voice recognition technology. The question unit is realized, for example, by the control unit 46A of the robot 414, and asks an appropriate question based on the analyzed information. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the possibility of a disease based on the information heard by the question unit. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0144] 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.

[0145] 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.

[0146] These emotions are distributed in the three 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.

[0147] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] (Appendix 1) an analysis unit that analyzes the content of a user's statement; a questioning unit that asks a question based on the information analyzed by the analysis unit; An identification unit that identifies a disease based on the information collected by the questioning unit. A system characterized by: (Appendix 2) The analysis unit is Estimate the user's emotions and adjust the analysis method of the speech content based on the estimated user emotions. 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the user's past comment history and select an analysis algorithm 2. The system of claim 1, (Appendix 4) The analysis unit is When analyzing speech content, the accuracy of the analysis can be improved based on the user's lifestyle or medical history. 2. The system of claim 1, (Appendix 5) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 6) The analysis unit is When analyzing the content of comments, the system prioritizes information that is more relevant based on the user's geographic location. 2. The system of claim 1, (Appendix 7) The analysis unit is When analyzing the content of comments, the user's social media activity is analyzed and related information is analyzed. 2. The system of claim 1, (Appendix 8) The interrogation unit is Infer user sentiment and adjust question wording based on the inferred sentiment 2. The system of claim 1, (Appendix 9) The interrogation unit is When asking a question, adjust the level of detail of the question by considering the importance of the user's comment 2. The system of claim 1, (Appendix 10) The interrogation unit is Applying question algorithms based on user categories when asking a question 2. The system of claim 1, (Appendix 11) The interrogation unit is Infer user sentiment and adjust question length based on the estimated user sentiment 2. The system of claim 1, (Appendix 12) The interrogation unit is When asking a question, prioritize the question by considering when the user spoke. 2. The system of claim 1, (Appendix 13) The interrogation unit is When asking questions, adjust the order of questions based on user relevance 2. The system of claim 1, (Appendix 14) The identification unit is Inferring a user's emotions and adjusting a disease identification method based on the estimated user's emotions 2. The system of claim 1, (Appendix 15) The identification unit is When identifying a disease, the method of identification is selected by analyzing the user's past medical history. 2. The system of claim 1, (Appendix 16) The identification unit is Upon identification of a disease, specific measures are adjusted taking into account the user's current life situation. 2. The system of claim 1, (Appendix 17) The identification unit is Estimating a user's emotions and prioritizing disease identification based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The identification unit is When identifying a disease, a method of identification is selected based on the user's geographic location information. 2. The system of claim 1, (Appendix 19) The identification unit is When a disease is identified, measures are suggested by analyzing the user's social media activity 2. The system of claim 1, [Explanation of symbols]

[0163] 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. An analysis unit that analyzes the content of a user's comment; a questioning unit that asks a question based on the information analyzed by the analysis unit; An identification unit that identifies a disease based on the information collected by the questioning unit. A system characterized by:

2. The analysis unit is Estimate the user's emotions and adjust the analysis method of the speech content based on the estimated user emotions.

2. The system of claim 1 .

3. The analysis unit is Analyze the user's past comment history and select an analysis algorithm 2. The system of claim 1 .

4. The analysis unit is When analyzing speech content, the accuracy of the analysis can be improved based on the user's lifestyle or medical history.

2. The system of claim 1 .

5. The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions.

2. The system of claim 1 .

6. The analysis unit is When analyzing the content of comments, the system prioritizes information that is more relevant based on the user's geographic location.

2. The system of claim 1 .

7. The analysis unit is When analyzing the content of comments, the user's social media activity is analyzed and related information is analyzed.

2. The system of claim 1 .

8. The interrogation unit is Infer user sentiment and adjust question wording based on the inferred sentiment 2. The system of claim 1 .

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