system

Abstract

The system according to the embodiment aims to suggest an appropriate way to deal with a disease without the user misdiagnosing the symptoms of the disease through self-diagnosis. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a proposal unit. The reception unit receives input of symptoms from a user. The analysis unit analyzes the symptoms received by the reception unit. The identification unit identifies a disease based on the results of the analysis by the analysis unit. The proposal unit proposes a method of dealing with the disease identified by the identification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that users may misdiagnose symptoms of illness through self-diagnosis, making it difficult to take appropriate measures.

[0005] The system according to the embodiment aims to suggest an appropriate way to deal with a disease without the user misdiagnosing the symptoms of the disease through self-diagnosis. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a proposal unit. The reception unit receives input of symptoms from a user. The analysis unit analyzes the symptoms received by the reception unit. The identification unit identifies a disease based on the analysis results obtained by the analysis unit. The proposal unit proposes a method of dealing with the disease identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate measures to be taken without causing the user to misdiagnose symptoms of an illness through self-diagnosis. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A generative AI system according to an embodiment of the present invention is a system designed to prevent misdiagnosis due to amateur judgment when a user contracts an illness. In this generative AI system, a user inputs their symptoms, and the generative AI analyzes the input symptoms and lists possible illnesses. The generative AI identifies illnesses that match the symptoms based on past medical data and case data. From the listed illnesses, the generative AI identifies the most likely illness and suggests appropriate treatment for that illness. Furthermore, if symptoms are severe or self-diagnosis is difficult, the generative AI recommends visiting an appropriate medical institution. This system allows users to learn appropriate treatments for their symptoms and prevents misdiagnosis due to amateur judgment. Furthermore, recommending visiting an appropriate medical institution enables early detection and early treatment, preventing the progression of illness. For example, when a user inputs symptoms such as fever or cough, the generative AI analyzes these symptoms and lists possible illnesses, such as influenza, colds, and pneumonia. Next, the generative AI identifies the most likely illness from these illnesses based on past medical data and case data and suggests appropriate treatment for that illness. For example, if there is a high possibility of influenza, the system will suggest rest, hydration, and the use of antiviral medication. Furthermore, if symptoms are severe or self-diagnosis is difficult, the system will recommend visiting an appropriate medical institution. This allows users to know the appropriate way to deal with their symptoms and prevents misdiagnosis due to amateur judgment. This allows the generative AI system to know the appropriate way to deal with the user's symptoms and prevents misdiagnosis due to amateur judgment. Furthermore, by recommending a visit to an appropriate medical institution, early detection and treatment are possible, preventing the progression of the disease.

[0029] A generative AI system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a proposal unit. The reception unit receives input of symptoms from a user. Examples of symptoms input by the user include, but are not limited to, physical symptoms such as fever, cough, headache, and stomachache, as well as mental symptoms such as anxiety, stress, and depression. The reception unit provides an interface through which the user inputs symptoms using, for example, a smartphone or a PC. The reception unit also supports voice input and image input, allowing the user to describe symptoms audibly or upload photos of the symptoms. The analysis unit analyzes the symptoms received by the reception unit. The analysis unit analyzes the input symptoms based on past medical data and case data and lists possible diseases. The analysis unit analyzes the association between symptoms and diseases, for example, using a machine learning algorithm. The analysis unit can also evaluate the frequency and severity of symptoms using statistical analysis. The identification unit identifies possible diseases based on the results of the analysis by the analysis unit. The identification unit identifies the most likely disease from the listed diseases. The identification unit evaluates the degree of correspondence between symptoms and a disease, for example, using a diagnostic algorithm. The identification unit can also evaluate the possibility of a disease by taking into account the severity and frequency of symptoms. The suggestion unit proposes appropriate measures for the disease identified by the identification unit. The suggestion unit proposes measures such as prescribing medication, improving lifestyle habits, resting, and hydration. Furthermore, if the symptoms are severe or if self-diagnosis is difficult, the suggestion unit recommends visiting an appropriate medical institution. This allows the generative AI system according to the embodiment to learn appropriate measures for the user's symptoms and prevent misdiagnosis due to amateur judgment. Furthermore, recommending a visit to an appropriate medical institution enables early detection and early treatment, thereby preventing the progression of the disease.

[0030] The reception unit can analyze the user's past symptom input history and select the optimal input format. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms that will be input during a specific time period based on the user's past input history. This improves input efficiency by providing the optimal input format based on the past input history. The analysis of the past symptom input history is performed, for example, using a database. The database stores symptoms and input methods that the user has previously input, and analysis is performed based on this. The optimal input format is selected based on the analysis results. This allows the reception unit to improve input efficiency by utilizing the user's past input history. Furthermore, the reception unit can accumulate the user's input history and analyze long-term input patterns. For example, the reception unit can analyze the user's input history in chronological order to identify changes in the input pattern. This allows the user's input history to be understood in detail and the optimal input format to be provided.

[0031] When inputting symptoms, the reception unit can filter the input content based on the user's current living situation or environment. For example, if the user is in a stressful environment, the reception unit can prioritize inputting stress-related symptoms. Furthermore, if the user is living a healthy lifestyle, the reception unit can also prioritize inputting general symptoms. Furthermore, if the user is in a specific environment (e.g., at work or home), the reception unit can also prioritize inputting symptoms related to that environment. This enables input of symptoms according to the user's living situation or environment. The filtering of the living situation or environment is performed, for example, based on information about the living situation or environment input by the user. The user answers questions about the living situation or environment, and filtering is performed based on the answers. This allows the reception unit to support input of symptoms according to the user's living situation or environment. Furthermore, the reception unit can accumulate data about the user's living situation or environment and analyze long-term trends. For example, the reception unit can analyze the user's living situation or environment data over time to identify changes in trends. This allows a detailed understanding of the user's living situation and environment and optimize the input content.

[0032] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting symptoms related to diseases prevalent in that area. Furthermore, when the user is traveling, the reception unit can prioritize inputting symptoms related to health risks at the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting symptoms related to health risks at the user's home. This enables symptom input based on geographical location information. The geographical location information can be used, for example, based on GPS data. The GPS data provides the user's current location information, and highly relevant symptoms are identified based on this information. This allows the reception unit to prioritize inputting highly relevant symptoms by utilizing the user's geographical location information. Furthermore, the reception unit can accumulate geographical location information and analyze long-term trends in the location information. For example, the reception unit can analyze the user's location information data over time to identify patterns of change in the location information. This allows the user's geographical location information to be grasped in detail and symptom input to be optimized.

[0033] The reception unit can analyze the user's social media activity when entering symptoms and input related symptoms. For example, if the user posts about health on social media, the reception unit can input symptoms based on the content of the post. Furthermore, if the user shares information about a specific illness on social media, the reception unit can input symptoms related to the illness. Furthermore, if the user posts about stress or anxiety on social media, the reception unit can input symptoms related to the emotion. This enables input of symptoms based on social media activity. Analysis of social media activity is performed, for example, based on the content of the post and the use of hashtags. Keywords are extracted from the content of the post, and related symptoms are identified based on these keywords. This allows the reception unit to input related symptoms using the user's social media activity. Furthermore, the reception unit can accumulate social media activity and analyze long-term activity patterns. For example, the reception unit can analyze the user's social media activity data over time to identify changes in activity patterns. This allows for a detailed understanding of the user's social media activity and optimize symptom input.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the symptom. For example, the analysis unit performs a detailed analysis in the case of a serious symptom. The analysis unit can also perform a basic analysis in the case of a general symptom. Furthermore, the analysis unit can perform a simple analysis in the case of a minor symptom. This makes it possible to adjust the level of detail of the analysis according to the importance of the symptom. The importance of the symptom is evaluated based on, for example, the severity and urgency of the symptom. A detailed analysis is performed for symptoms with high severity or urgency, and a simple analysis is performed for symptoms with low severity or urgency. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the symptom. Furthermore, the analysis unit can accumulate symptom importance data and analyze long-term trends in importance. For example, the analysis unit can analyze the symptom importance data in chronological order to identify patterns of change in importance. This makes it possible to grasp the importance of the symptom in detail and optimize the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, in the case of a respiratory symptom, the analysis unit can apply an analysis algorithm specialized for the respiratory system. Furthermore, in the case of a digestive symptom, the analysis unit can also apply an analysis algorithm specialized for the digestive system. Furthermore, in the case of a nervous system symptom, the analysis unit can also apply an analysis algorithm specialized for the nervous system. This makes it possible to apply an analysis algorithm depending on the symptom category. Symptom categories are classified based on criteria such as physical symptoms and mental symptoms. This allows the analysis unit to apply an optimal analysis algorithm depending on the symptom category. Furthermore, the analysis unit can accumulate symptom category data and analyze long-term category trends. For example, the analysis unit can analyze symptom category data in chronological order to identify pattern of category change. This allows for a detailed understanding of symptom categories and optimization of the analysis algorithm.

[0036] During analysis, the analysis unit can determine the analysis priority based on the time of symptom submission. For example, the analysis unit prioritizes analysis of recently submitted symptoms. The analysis unit can also perform analysis by referring to symptoms submitted in the past. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. This makes it possible to determine the analysis priority based on the time of symptom submission. The submission time is evaluated based on, for example, the submission date and time or the submission frequency. Symptoms submitted more recently are analyzed preferentially, and symptoms submitted earlier are analyzed later. This allows the analysis unit to determine the analysis priority based on the time of symptom submission. Furthermore, the analysis unit can accumulate submission time data and analyze long-term trends in the submission time. For example, the analysis unit can analyze the submission time data in chronological order to identify patterns of change in the submission time. This makes it possible to grasp the time of symptom submission in detail and optimize the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of symptoms. This makes it possible to adjust the order of analysis based on the relevance of symptoms. The relevance of symptoms is evaluated based on, for example, co-occurrence network analysis or symptom correlation analysis. Highly relevant symptoms are analyzed preferentially, and less relevant symptoms are postponed. This allows the analysis unit to adjust the order of analysis based on the relevance of symptoms. Furthermore, the analysis unit can accumulate symptom relevance data and analyze long-term trends in the relevance. For example, the analysis unit analyzes symptom relevance data in chronological order to identify patterns of change in the relevance. This makes it possible to grasp the relevance of symptoms in detail and optimize the order of analysis.

[0038] The identification unit can improve the accuracy of identification by taking into account the interrelationships between symptoms during identification. For example, when multiple symptoms are related, the identification unit can comprehensively analyze the symptoms to improve the accuracy of identification. The identification unit can also perform identification by taking into account not only a single symptom but also related symptoms. Furthermore, the identification unit can analyze the interrelationships between symptoms to improve the accuracy of identification. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between symptoms. The evaluation of the interrelationships between symptoms is performed based on, for example, co-occurrence network analysis or symptom correlation analysis. Highly related symptoms are analyzed comprehensively, and less related symptoms are analyzed individually. This allows the identification unit to improve the accuracy of identification by taking into account the interrelationships between symptoms. Furthermore, the identification unit can accumulate data on the interrelationships between symptoms and analyze long-term trends in the interrelationships. For example, the identification unit can analyze the data on the interrelationships between symptoms over time to identify patterns of change in the interrelationships. This makes it possible to understand the interrelationships between symptoms in detail and optimize the accuracy of identification.

[0039] The identification unit can perform identification by taking into consideration attribute information of the person who submitted the symptom. The identification unit performs identification by taking into consideration, for example, the age and gender of the person who submitted the symptom. The identification unit can also perform identification by taking into consideration the medical history of the person who submitted the symptom. Furthermore, the identification unit can perform identification by taking into consideration the lifestyle habits of the person who submitted the symptom. This enables identification by taking into consideration the attribute information of the person who submitted the symptom. The attribute information is used based on data such as age, gender, and medical history. This allows the identification unit to improve the accuracy of identification by utilizing the attribute information of the person who submitted the symptom. Furthermore, the identification unit can accumulate attribute information data and analyze long-term trends in the attribute information. For example, the identification unit can analyze the attribute information data in chronological order to identify patterns of change in the attribute information. This allows the attribute information of the person who submitted the symptom to be understood in detail and the accuracy of identification to be optimized.

[0040] The identification unit can perform identification by taking into account the geographical distribution of symptoms. For example, the identification unit performs identification by taking into account diseases that are prevalent in a specific region. The identification unit can also identify related diseases based on the geographical distribution. Furthermore, the identification unit can perform identification by taking into account health risks for each region. This enables identification based on geographical distribution. The geographical distribution is used based on, for example, disease information specific to a region or the prevalence of infectious diseases. This allows the identification unit to improve the accuracy of identification by utilizing the geographical distribution. Furthermore, the identification unit can accumulate geographical distribution data and analyze long-term trends in the geographical distribution. For example, the identification unit analyzes the geographical distribution data over time to identify changing patterns in the geographical distribution. This makes it possible to grasp the geographical distribution of symptoms in detail and optimize the accuracy of identification.

[0041] The identification unit can improve the accuracy of identification by referring to literature related to the symptoms during identification. The identification unit, for example, performs identification by referring to the latest medical literature related to the symptoms. The identification unit can also perform identification based on past case data. Furthermore, the identification unit can improve the accuracy of identification by utilizing a medical literature database. This makes it possible to improve the accuracy of identification by referring to related literature. The use of related literature is based on, for example, medical papers and clinical guidelines. This allows the identification unit to improve the accuracy of identification by utilizing related literature. Furthermore, the identification unit can accumulate related literature data and analyze long-term trends in literature. For example, the identification unit analyzes related literature data over time to identify patterns of change in literature. This makes it possible to obtain a detailed understanding of literature related to the symptoms and optimize the accuracy of identification.

[0042] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the illness when making a suggestion. For example, the suggestion unit can suggest detailed measures for serious illnesses. The suggestion unit can also suggest basic measures for common illnesses. Furthermore, the suggestion unit can also suggest simple measures for minor illnesses. This enables the level of detail of the suggestion to be adjusted according to the importance of the illness. The importance of the illness is evaluated based on, for example, the severity and urgency of the illness. Detailed measures are suggested for illnesses with high severity or urgency, and simple measures are suggested for illnesses with low severity or urgency. This allows the suggestion unit to adjust the level of detail of the suggestion based on the importance of the illness. Furthermore, the suggestion unit can accumulate illness importance data and analyze long-term trends in importance. For example, the suggestion unit can analyze illness importance data over time to identify patterns of change in importance. This makes it possible to grasp the importance of the illness in detail and optimize the level of detail of the suggestion.

[0043] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the disease category. For example, in the case of a respiratory disease, the suggestion unit can apply a suggestion algorithm specialized for the respiratory system. Furthermore, in the case of a digestive disease, the suggestion unit can apply a suggestion algorithm specialized for the digestive system. Furthermore, in the case of a nervous system disease, the suggestion unit can apply a suggestion algorithm specialized for the nervous system. This makes it possible to apply a suggestion algorithm depending on the disease category. Disease categories are classified based on criteria such as infectious diseases and chronic diseases. This allows the suggestion unit to apply an optimal suggestion algorithm depending on the disease category. Furthermore, the suggestion unit can accumulate disease category data and analyze long-term category trends. For example, the suggestion unit can analyze disease category data over time to identify pattern changes in categories. This makes it possible to grasp disease categories in detail and optimize the suggestion algorithm.

[0044] The suggestion unit can determine the priority of the suggestions based on the time of submission of the illness when making a suggestion. For example, the suggestion unit prioritizes the suggestions of recently submitted illnesses. The suggestion unit can also make suggestions by referring to illnesses submitted in the past. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions based on the time of submission. This makes it possible to determine the priority of the suggestions based on the time of submission of the illness. The submission time is evaluated based on, for example, the submission date and time or the submission frequency. Illnesses with more recent submission dates and times are given priority in proposals, and illnesses with older submission dates and times are postponed. This allows the suggestion unit to determine the priority of the suggestions based on the time of submission of the illness. Furthermore, the suggestion unit can accumulate submission time data and analyze long-term trends in the submission time. For example, the suggestion unit can analyze the submission time data in chronological order to identify patterns of change in the submission time. This makes it possible to grasp the time of submission of illnesses in detail and optimize the priority of the suggestions.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of diseases when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant diseases. The suggestion unit can also postpone suggesting less relevant diseases. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of diseases. This makes it possible to adjust the order of suggestions based on the relevance of diseases. The evaluation of disease relevance is performed based on, for example, co-occurrence network analysis or disease correlation analysis. Highly relevant diseases are prioritized and less relevant diseases are postponed. This allows the suggestion unit to adjust the order of suggestions based on the relevance of diseases. Furthermore, the suggestion unit can accumulate disease relevance data and analyze long-term trends in relevance. For example, the suggestion unit can analyze disease relevance data over time to identify patterns of change in relevance. This makes it possible to grasp the relevance of diseases in detail and optimize the order of suggestions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can refer to the user's past medical history and evaluate the correlation between the input symptoms and past medical history. For example, if the user has had a specific illness in the past, it will prioritize analyzing symptoms related to that illness. The reception unit can also evaluate whether a specific treatment was effective based on the user's past medical history and suggest appropriate measures. Furthermore, the reception unit can analyze the user's past medical data in chronological order and identify patterns of symptom change. This makes it possible to utilize the user's past medical history to perform more accurate analysis and suggestions.

[0048] The reception unit collects lifestyle habit data of the user and can customize the input content by taking that data into consideration when inputting symptoms. For example, if the user is not getting enough exercise, exercise-related symptoms can be input with priority. Also, if the user is a smoker, smoking-related symptoms can be input with priority. Furthermore, if the user has specific eating habits, symptoms related to those eating habits can be input with priority. This makes it possible to input symptoms according to the user's lifestyle habits.

[0049] The reception unit can provide information related to region-specific health risks based on the user's geographical location information. For example, if the user is in a specific region, information about diseases prevalent in that region can be provided. If the user is traveling, information about health risks at the travel destination can also be provided. Furthermore, if the user is at home, information about health risks within the home can also be provided. This makes it possible to provide health risk information based on geographical location information.

[0050] The analysis unit can evaluate individual health risks when analyzing symptoms based on the user's past medical data. For example, if the user has had a specific disease in the past, the analysis unit can evaluate the risk associated with that disease. It can also evaluate genetic risks by taking the user's family history into account. Furthermore, it can evaluate risks related to lifestyle habits based on the user's lifestyle data. This enables analysis that takes into account the user's individual health risks.

[0051] When analyzing symptoms, the analysis unit can take into account data on the user's living environment. For example, if the user is in a high-humidity environment, it can prioritize analysis of humidity-related symptoms. Also, if the user is in a high-temperature environment, it can prioritize analysis of heatstroke-related symptoms. Furthermore, if the user lives in an urban area, it can prioritize analysis of symptoms related to health risks specific to urban areas. This makes it possible to analyze symptoms according to the user's living environment.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception unit accepts symptom input from the user. Symptoms input by the user include physical symptoms such as fever, cough, headache, and stomachache, as well as mental symptoms such as anxiety, stress, and depression. The reception unit provides an interface that allows users to input symptoms using a smartphone or computer, and also supports voice and image input. Step 2: The analysis unit analyzes the symptoms received by the reception unit. Based on past medical data and case data, the analysis unit analyzes the input symptoms and lists possible diseases. Using machine learning algorithms and statistical analysis, the analysis unit evaluates the correlation between symptoms and diseases, their frequency of occurrence, and their severity. Step 3: The identification unit identifies possible diseases based on the results of the analysis by the analysis unit. The identification unit identifies the most likely disease from the listed diseases and evaluates the degree of match between the symptoms and the disease using a diagnostic algorithm. The unit also evaluates the possibility of the disease taking into account the severity and frequency of symptoms. Step 4: The suggestion unit proposes appropriate measures for the illness identified by the identification unit. The suggestion unit suggests measures such as prescribing medication, improving lifestyle habits, resting, and hydration, and if symptoms are severe or self-diagnosis is difficult, it recommends visiting an appropriate medical institution.

[0054] (Example 2) A generative AI system according to an embodiment of the present invention is a system designed to prevent misdiagnosis due to amateur judgment when a user contracts an illness. In this generative AI system, a user inputs their symptoms, and the generative AI analyzes the input symptoms and lists possible illnesses. The generative AI identifies illnesses that match the symptoms based on past medical data and case data. From the listed illnesses, the generative AI identifies the most likely illness and suggests appropriate treatment for that illness. Furthermore, if symptoms are severe or self-diagnosis is difficult, the generative AI recommends visiting an appropriate medical institution. This system allows users to learn appropriate treatments for their symptoms and prevents misdiagnosis due to amateur judgment. Furthermore, recommending visiting an appropriate medical institution enables early detection and early treatment, preventing the progression of illness. For example, when a user inputs symptoms such as fever or cough, the generative AI analyzes these symptoms and lists possible illnesses, such as influenza, colds, and pneumonia. Next, the generative AI identifies the most likely illness from these illnesses based on past medical data and case data and suggests appropriate treatment for that illness. For example, if there is a high possibility of influenza, the system will suggest rest, hydration, and the use of antiviral medication. Furthermore, if symptoms are severe or self-diagnosis is difficult, the system will recommend visiting an appropriate medical institution. This allows users to know the appropriate way to deal with their symptoms and prevents misdiagnosis due to amateur judgment. This allows the generative AI system to know the appropriate way to deal with the user's symptoms and prevents misdiagnosis due to amateur judgment. Furthermore, by recommending a visit to an appropriate medical institution, early detection and treatment are possible, preventing the progression of the disease.

[0055] A generative AI system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a proposal unit. The reception unit receives input of symptoms from a user. Examples of symptoms input by the user include, but are not limited to, physical symptoms such as fever, cough, headache, and stomachache, as well as mental symptoms such as anxiety, stress, and depression. The reception unit provides an interface through which the user inputs symptoms using, for example, a smartphone or a PC. The reception unit also supports voice input and image input, allowing the user to describe symptoms audibly or upload photos of the symptoms. The analysis unit analyzes the symptoms received by the reception unit. The analysis unit analyzes the input symptoms based on past medical data and case data and lists possible diseases. The analysis unit analyzes the association between symptoms and diseases, for example, using a machine learning algorithm. The analysis unit can also evaluate the frequency and severity of symptoms using statistical analysis. The identification unit identifies possible diseases based on the results of the analysis by the analysis unit. The identification unit identifies the most likely disease from the listed diseases. The identification unit evaluates the degree of correspondence between symptoms and a disease, for example, using a diagnostic algorithm. The identification unit can also evaluate the possibility of a disease by taking into account the severity and frequency of symptoms. The suggestion unit proposes appropriate measures for the disease identified by the identification unit. The suggestion unit proposes measures such as prescribing medication, improving lifestyle habits, resting, and hydration. Furthermore, if the symptoms are severe or if self-diagnosis is difficult, the suggestion unit recommends visiting an appropriate medical institution. This allows the generative AI system according to the embodiment to learn appropriate measures for the user's symptoms and prevent misdiagnosis due to amateur judgment. Furthermore, recommending a visit to an appropriate medical institution enables early detection and early treatment, thereby preventing the progression of the disease.

[0056] The reception unit can estimate the user's emotions and adjust the symptom input method based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick symptom input. This allows for more appropriate symptom input by providing an input method tailored to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the reception unit to grasp the user's emotions in real time and provide an appropriate input method. Furthermore, the reception unit can accumulate user emotion data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows for a detailed understanding of the user's emotions and allows for the optimization of input methods.

[0057] The reception unit can analyze the user's past symptom input history and select the optimal input format. For example, the reception unit automatically displays symptoms that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest symptoms that will be input during a specific time period based on the user's past input history. This improves input efficiency by providing the optimal input format based on the past input history. The analysis of the past symptom input history is performed, for example, using a database. The database stores symptoms and input methods that the user has previously input, and analysis is performed based on this. The optimal input format is selected based on the analysis results. This allows the reception unit to improve input efficiency by utilizing the user's past input history. Furthermore, the reception unit can accumulate the user's input history and analyze long-term input patterns. For example, the reception unit can analyze the user's input history in chronological order to identify changes in the input pattern. This allows the user's input history to be understood in detail and the optimal input format to be provided.

[0058] When inputting symptoms, the reception unit can filter the input content based on the user's current living situation or environment. For example, if the user is in a stressful environment, the reception unit can prioritize inputting stress-related symptoms. Furthermore, if the user is living a healthy lifestyle, the reception unit can also prioritize inputting general symptoms. Furthermore, if the user is in a specific environment (e.g., at work or home), the reception unit can also prioritize inputting symptoms related to that environment. This enables input of symptoms according to the user's living situation or environment. The filtering of the living situation or environment is performed, for example, based on information about the living situation or environment input by the user. The user answers questions about the living situation or environment, and filtering is performed based on the answers. This allows the reception unit to support input of symptoms according to the user's living situation or environment. Furthermore, the reception unit can accumulate data about the user's living situation or environment and analyze long-term trends. For example, the reception unit can analyze the user's living situation or environment data over time to identify changes in trends. This allows a detailed understanding of the user's living situation and environment and optimize the input content.

[0059] The reception unit can estimate the user's emotions and prioritize the input symptoms based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize analyzing serious symptoms. Furthermore, if the user is relaxed, the reception unit can prioritize analyzing general symptoms. Furthermore, if the user is in a hurry, the reception unit can prioritize analyzing symptoms that require immediate attention. This enables more appropriate analysis by prioritizing symptoms according to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the reception unit to grasp the user's emotions in real time and prioritize symptoms. Furthermore, the reception unit can accumulate the user's emotional data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotional data over time to identify patterns of emotional changes. This allows for a detailed understanding of the user's emotions and allows for optimal symptom prioritization.

[0060] When inputting symptoms, the reception unit can prioritize inputting highly relevant symptoms based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes inputting symptoms related to diseases prevalent in that area. Furthermore, when the user is traveling, the reception unit can prioritize inputting symptoms related to health risks at the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize inputting symptoms related to health risks at the user's home. This enables symptom input based on geographical location information. The geographical location information can be used, for example, based on GPS data. The GPS data provides the user's current location information, and highly relevant symptoms are identified based on this information. This allows the reception unit to prioritize inputting highly relevant symptoms by utilizing the user's geographical location information. Furthermore, the reception unit can accumulate geographical location information and analyze long-term trends in the location information. For example, the reception unit can analyze the user's location information data over time to identify patterns of change in the location information. This allows the user's geographical location information to be grasped in detail and symptom input to be optimized.

[0061] The reception unit can analyze the user's social media activity when entering symptoms and input related symptoms. For example, if the user posts about health on social media, the reception unit can input symptoms based on the content of the post. Furthermore, if the user shares information about a specific illness on social media, the reception unit can input symptoms related to the illness. Furthermore, if the user posts about stress or anxiety on social media, the reception unit can input symptoms related to the emotion. This enables input of symptoms based on social media activity. Analysis of social media activity is performed, for example, based on the content of the post and the use of hashtags. Keywords are extracted from the content of the post, and related symptoms are identified based on these keywords. This allows the reception unit to input related symptoms using the user's social media activity. Furthermore, the reception unit can accumulate social media activity and analyze long-term activity patterns. For example, the reception unit can analyze the user's social media activity data over time to identify changes in activity patterns. This allows for a detailed understanding of the user's social media activity and optimize symptom input.

[0062] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can increase the accuracy of the analysis and provide detailed results. The analysis unit can also provide general analysis results if the user is relaxed. Furthermore, the analysis unit can quickly provide analysis results if the user is in a hurry. This enables the accuracy of the analysis to be adjusted according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the analysis unit to grasp the user's emotions in real time and adjust the accuracy of the analysis. Furthermore, the analysis unit can accumulate user emotion data and analyze long-term emotional trends. For example, the analysis unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows the user's emotions to be grasped in detail and the accuracy of the analysis to be optimized.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the symptom. For example, the analysis unit performs a detailed analysis in the case of a serious symptom. The analysis unit can also perform a basic analysis in the case of a general symptom. Furthermore, the analysis unit can perform a simple analysis in the case of a minor symptom. This makes it possible to adjust the level of detail of the analysis according to the importance of the symptom. The importance of the symptom is evaluated based on, for example, the severity and urgency of the symptom. A detailed analysis is performed for symptoms with high severity or urgency, and a simple analysis is performed for symptoms with low severity or urgency. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the symptom. Furthermore, the analysis unit can accumulate symptom importance data and analyze long-term trends in importance. For example, the analysis unit can analyze the symptom importance data in chronological order to identify patterns of change in importance. This makes it possible to grasp the importance of the symptom in detail and optimize the level of detail of the analysis.

[0064] During analysis, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, in the case of a respiratory symptom, the analysis unit can apply an analysis algorithm specialized for the respiratory system. Furthermore, in the case of a digestive symptom, the analysis unit can also apply an analysis algorithm specialized for the digestive system. Furthermore, in the case of a nervous system symptom, the analysis unit can also apply an analysis algorithm specialized for the nervous system. This makes it possible to apply an analysis algorithm depending on the symptom category. Symptom categories are classified based on criteria such as physical symptoms and mental symptoms. This allows the analysis unit to apply an optimal analysis algorithm depending on the symptom category. Furthermore, the analysis unit can accumulate symptom category data and analyze long-term category trends. For example, the analysis unit can analyze symptom category data in chronological order to identify pattern of category change. This allows for a detailed understanding of symptom categories and optimization of the analysis algorithm.

[0065] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This enables the display method of the analysis results to be tailored to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the analysis unit to grasp the user's emotions in real time and adjust the display method of the analysis results. Furthermore, the analysis unit can accumulate user emotion data and analyze long-term emotional trends. For example, the analysis unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows the user's emotions to be understood in detail and the display method of the analysis results to be optimized.

[0066] During analysis, the analysis unit can determine the analysis priority based on the time of symptom submission. For example, the analysis unit prioritizes analysis of recently submitted symptoms. The analysis unit can also perform analysis by referring to symptoms submitted in the past. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. This makes it possible to determine the analysis priority based on the time of symptom submission. The submission time is evaluated based on, for example, the submission date and time or the submission frequency. Symptoms submitted more recently are analyzed preferentially, and symptoms submitted earlier are analyzed later. This allows the analysis unit to determine the analysis priority based on the time of symptom submission. Furthermore, the analysis unit can accumulate submission time data and analyze long-term trends in the submission time. For example, the analysis unit can analyze the submission time data in chronological order to identify patterns of change in the submission time. This makes it possible to grasp the time of symptom submission in detail and optimize the analysis priority.

[0067] During analysis, the analysis unit can adjust the order of analysis based on the relevance of symptoms. For example, the analysis unit prioritizes analysis of highly relevant symptoms. The analysis unit can also postpone analysis of less relevant symptoms. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of symptoms. This makes it possible to adjust the order of analysis based on the relevance of symptoms. The relevance of symptoms is evaluated based on, for example, co-occurrence network analysis or symptom correlation analysis. Highly relevant symptoms are analyzed preferentially, and less relevant symptoms are postponed. This allows the analysis unit to adjust the order of analysis based on the relevance of symptoms. Furthermore, the analysis unit can accumulate symptom relevance data and analyze long-term trends in the relevance. For example, the analysis unit analyzes symptom relevance data in chronological order to identify patterns of change in the relevance. This makes it possible to grasp the relevance of symptoms in detail and optimize the order of analysis.

[0068] The identification unit can estimate the user's emotions and adjust the specific criteria based on the estimated user's emotions. For example, if the user is feeling anxious, the identification unit can tighten the specific criteria and provide detailed results. The identification unit can also apply general specific criteria when the user is relaxed. Furthermore, if the user is in a hurry, the identification unit can quickly provide specific results. This enables the specific criteria to be adjusted according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the identification unit to grasp the user's emotions in real time and adjust the specific criteria. Furthermore, the identification unit can accumulate user emotion data and analyze long-term emotional trends. For example, the identification unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows the user's emotions to be understood in detail and the specific criteria to be optimized.

[0069] The identification unit can improve the accuracy of identification by taking into account the interrelationships between symptoms during identification. For example, when multiple symptoms are related, the identification unit can comprehensively analyze the symptoms to improve the accuracy of identification. The identification unit can also perform identification by taking into account not only a single symptom but also related symptoms. Furthermore, the identification unit can analyze the interrelationships between symptoms to improve the accuracy of identification. This makes it possible to improve the accuracy of identification by taking into account the interrelationships between symptoms. The evaluation of the interrelationships between symptoms is performed based on, for example, co-occurrence network analysis or symptom correlation analysis. Highly related symptoms are analyzed comprehensively, and less related symptoms are analyzed individually. This allows the identification unit to improve the accuracy of identification by taking into account the interrelationships between symptoms. Furthermore, the identification unit can accumulate data on the interrelationships between symptoms and analyze long-term trends in the interrelationships. For example, the identification unit can analyze the data on the interrelationships between symptoms over time to identify patterns of change in the interrelationships. This makes it possible to understand the interrelationships between symptoms in detail and optimize the accuracy of identification.

[0070] The identification unit can perform identification by taking into consideration attribute information of the person who submitted the symptom. The identification unit performs identification by taking into consideration, for example, the age and gender of the person who submitted the symptom. The identification unit can also perform identification by taking into consideration the medical history of the person who submitted the symptom. Furthermore, the identification unit can perform identification by taking into consideration the lifestyle habits of the person who submitted the symptom. This enables identification by taking into consideration the attribute information of the person who submitted the symptom. The attribute information is used based on data such as age, gender, and medical history. This allows the identification unit to improve the accuracy of identification by utilizing the attribute information of the person who submitted the symptom. Furthermore, the identification unit can accumulate attribute information data and analyze long-term trends in the attribute information. For example, the identification unit can analyze the attribute information data in chronological order to identify patterns of change in the attribute information. This allows the attribute information of the person who submitted the symptom to be understood in detail and the accuracy of identification to be optimized.

[0071] The identification unit can estimate the user's emotions and adjust the display order of the identified results based on the estimated user's emotions. For example, if the user is feeling anxious, the identification unit can prioritize displaying serious illnesses. Furthermore, if the user is relaxed, the identification unit can prioritize displaying common illnesses. Furthermore, if the user is in a hurry, the identification unit can prioritize displaying illnesses that require prompt treatment. This enables the display order of the identified results to be adjusted according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the identification unit to grasp the user's emotions in real time and adjust the display order of the identified results. Furthermore, the identification unit can accumulate user emotion data and analyze long-term emotional trends. For example, the identification unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows the user's emotions to be understood in detail and the display order of the identified results to be optimized.

[0072] The identification unit can perform identification by taking into account the geographical distribution of symptoms. For example, the identification unit performs identification by taking into account diseases that are prevalent in a specific region. The identification unit can also identify related diseases based on the geographical distribution. Furthermore, the identification unit can perform identification by taking into account health risks for each region. This enables identification based on geographical distribution. The geographical distribution is used based on, for example, disease information specific to a region or the prevalence of infectious diseases. This allows the identification unit to improve the accuracy of identification by utilizing the geographical distribution. Furthermore, the identification unit can accumulate geographical distribution data and analyze long-term trends in the geographical distribution. For example, the identification unit analyzes the geographical distribution data over time to identify changing patterns in the geographical distribution. This makes it possible to grasp the geographical distribution of symptoms in detail and optimize the accuracy of identification.

[0073] The identification unit can improve the accuracy of identification by referring to literature related to the symptoms during identification. The identification unit, for example, performs identification by referring to the latest medical literature related to the symptoms. The identification unit can also perform identification based on past case data. Furthermore, the identification unit can improve the accuracy of identification by utilizing a medical literature database. This makes it possible to improve the accuracy of identification by referring to related literature. The use of related literature is based on, for example, medical papers and clinical guidelines. This allows the identification unit to improve the accuracy of identification by utilizing related literature. Furthermore, the identification unit can accumulate related literature data and analyze long-term trends in literature. For example, the identification unit analyzes related literature data over time to identify patterns of change in literature. This makes it possible to obtain a detailed understanding of literature related to the symptoms and optimize the accuracy of identification.

[0074] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the suggestion unit can use an expression that gives a sense of security. If the user is relaxed, the suggestion unit can use an expression that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can use a concise and quick expression. This enables the suggestion to be expressed in a way that suits the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the suggestion unit to grasp the user's emotions in real time and adjust the way the suggestions are expressed. Furthermore, the suggestion unit can accumulate user emotion data and analyze long-term emotional trends. For example, the suggestion unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows the user's emotions to be understood in detail and the way the suggestions are expressed to be optimized.

[0075] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the illness when making a suggestion. For example, the suggestion unit can suggest detailed measures for serious illnesses. The suggestion unit can also suggest basic measures for common illnesses. Furthermore, the suggestion unit can also suggest simple measures for minor illnesses. This enables the level of detail of the suggestion to be adjusted according to the importance of the illness. The importance of the illness is evaluated based on, for example, the severity and urgency of the illness. Detailed measures are suggested for illnesses with high severity or urgency, and simple measures are suggested for illnesses with low severity or urgency. This allows the suggestion unit to adjust the level of detail of the suggestion based on the importance of the illness. Furthermore, the suggestion unit can accumulate illness importance data and analyze long-term trends in importance. For example, the suggestion unit can analyze illness importance data over time to identify patterns of change in importance. This makes it possible to grasp the importance of the illness in detail and optimize the level of detail of the suggestion.

[0076] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the disease category. For example, in the case of a respiratory disease, the suggestion unit can apply a suggestion algorithm specialized for the respiratory system. Furthermore, in the case of a digestive disease, the suggestion unit can apply a suggestion algorithm specialized for the digestive system. Furthermore, in the case of a nervous system disease, the suggestion unit can apply a suggestion algorithm specialized for the nervous system. This makes it possible to apply a suggestion algorithm depending on the disease category. Disease categories are classified based on criteria such as infectious diseases and chronic diseases. This allows the suggestion unit to apply an optimal suggestion algorithm depending on the disease category. Furthermore, the suggestion unit can accumulate disease category data and analyze long-term category trends. For example, the suggestion unit can analyze disease category data over time to identify pattern changes in categories. This makes it possible to grasp disease categories in detail and optimize the suggestion algorithm.

[0077] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can provide detailed suggestions. If the user is feeling relaxed, the suggestion unit can also provide general suggestions. Furthermore, if the user is in a hurry, the suggestion unit can also provide concise suggestions. This enables the length of suggestions to be adjusted according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. Facial expression recognition technology estimates emotions by capturing a user's facial expression with a camera and analyzing changes in facial expression. Voice analysis technology estimates emotions by analyzing the tone and speed of the user's voice. This allows the suggestion unit to grasp the user's emotions in real time and adjust the length of suggestions. Furthermore, the suggestion unit can accumulate user emotion data and analyze long-term emotional trends. For example, the suggestion unit can analyze the user's emotion data over time to identify patterns of emotional changes. This allows the user's emotions to be understood in detail and the length of suggestions to be optimized.

[0078] The suggestion unit can determine the priority of the suggestions based on the time of submission of the illness when making a suggestion. For example, the suggestion unit prioritizes the suggestions of recently submitted illnesses. The suggestion unit can also make suggestions by referring to illnesses submitted in the past. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions based on the time of submission. This makes it possible to determine the priority of the suggestions based on the time of submission of the illness. The submission time is evaluated based on, for example, the submission date and time or the submission frequency. Illnesses with more recent submission dates and times are given priority in proposals, and illnesses with older submission dates and times are postponed. This allows the suggestion unit to determine the priority of the suggestions based on the time of submission of the illness. Furthermore, the suggestion unit can accumulate submission time data and analyze long-term trends in the submission time. For example, the suggestion unit can analyze the submission time data in chronological order to identify patterns of change in the submission time. This makes it possible to grasp the time of submission of illnesses in detail and optimize the priority of the suggestions.

[0079] The suggestion unit can adjust the order of suggestions based on the relevance of diseases when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant diseases. The suggestion unit can also postpone suggesting less relevant diseases. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of diseases. This makes it possible to adjust the order of suggestions based on the relevance of diseases. The evaluation of disease relevance is performed based on, for example, co-occurrence network analysis or disease correlation analysis. Highly relevant diseases are prioritized and less relevant diseases are postponed. This allows the suggestion unit to adjust the order of suggestions based on the relevance of diseases. Furthermore, the suggestion unit can accumulate disease relevance data and analyze long-term trends in relevance. For example, the suggestion unit can analyze disease relevance data over time to identify patterns of change in relevance. This makes it possible to grasp the relevance of diseases in detail and optimize the order of suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface through which a user inputs symptoms using a smartphone or a personal computer. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes symptoms based on past medical data and case data. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the most likely disease from the listed diseases. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests an appropriate treatment for the identified disease. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, identification unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface through which the user inputs symptoms using the smart glasses. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes symptoms based on past medical data and case data. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the most likely disease from the listed diseases. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests an appropriate treatment for the identified disease. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface through which the user inputs symptoms using a headset. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes symptoms based on past medical data and case data. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the most likely disease from the listed diseases. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and suggests an appropriate treatment for the identified disease. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface through which the user inputs symptoms using the robot. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes symptoms based on past medical data and case data. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the most likely disease from the listed diseases. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests an appropriate treatment for the identified disease.

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

[0081] The reception unit can refer to the user's past medical history and evaluate the correlation between the input symptoms and past medical history. For example, if the user has had a specific illness in the past, it will prioritize analyzing symptoms related to that illness. The reception unit can also evaluate whether a specific treatment was effective based on the user's past medical history and suggest appropriate measures. Furthermore, the reception unit can analyze the user's past medical data in chronological order and identify patterns of symptom change. This makes it possible to utilize the user's past medical history to perform more accurate analysis and suggestions.

[0082] The reception unit can estimate the user's emotions and display reminders or encouraging messages based on the estimated user emotions when the user is entering symptoms. For example, if the user is feeling anxious, an encouraging message can be displayed to provide a sense of security. If the user is feeling relaxed, a reminder can be displayed to encourage input. Furthermore, if the user is in a hurry, a concise message can be displayed to support quick input. This allows for smoother symptom input by displaying messages that correspond to the user's emotions.

[0083] The reception unit collects lifestyle habit data of the user and can customize the input content by taking that data into consideration when inputting symptoms. For example, if the user is not getting enough exercise, exercise-related symptoms can be input with priority. Also, if the user is a smoker, smoking-related symptoms can be input with priority. Furthermore, if the user has specific eating habits, symptoms related to those eating habits can be input with priority. This makes it possible to input symptoms according to the user's lifestyle habits.

[0084] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. For example, if the user is feeling anxious, an interface with subdued colors can be provided. If the user is relaxed, an interface with bright colors can be provided. Furthermore, if the user is in a hurry, a simple and intuitive interface can be provided. In this way, a more comfortable input environment can be realized by providing an interface design that corresponds to the user's emotions.

[0085] The reception unit can provide information related to region-specific health risks based on the user's geographical location information. For example, if the user is in a specific region, information about diseases prevalent in that region can be provided. If the user is traveling, information about health risks at the travel destination can also be provided. Furthermore, if the user is at home, information about health risks within the home can also be provided. This makes it possible to provide health risk information based on geographical location information.

[0086] The analysis unit can estimate the user's emotions and adjust the reliability of the analysis results based on the estimated user emotions. For example, if the user feels anxious, the reliability of the analysis results can be increased and more detailed information can be provided. If the user feels relaxed, analysis results of general reliability can be provided. Furthermore, if the user is in a hurry, analysis results of high reliability can be provided quickly. This makes it possible to adjust the reliability of the analysis results according to the user's emotions.

[0087] The analysis unit can evaluate individual health risks when analyzing symptoms based on the user's past medical data. For example, if the user has had a specific disease in the past, the analysis unit can evaluate the risk associated with that disease. It can also evaluate genetic risks by taking the user's family history into account. Furthermore, it can evaluate risks related to lifestyle habits based on the user's lifestyle data. This enables analysis that takes into account the user's individual health risks.

[0088] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis results can be notified in gentle words. If the user is relaxed, detailed analysis results can be notified. Furthermore, if the user is in a hurry, concise analysis results can be notified quickly. This makes it possible to notify the analysis results in a way that suits the user's emotions.

[0089] When analyzing symptoms, the analysis unit can take into account data on the user's living environment. For example, if the user is in a high-humidity environment, it can prioritize analysis of humidity-related symptoms. Also, if the user is in a high-temperature environment, it can prioritize analysis of heatstroke-related symptoms. Furthermore, if the user lives in an urban area, it can prioritize analysis of symptoms related to health risks specific to urban areas. This makes it possible to analyze symptoms according to the user's living environment.

[0090] The analysis unit can estimate the user's emotions and provide feedback of the analysis results based on the estimated user emotions. For example, if the user feels anxious, it can provide feedback that gives a sense of security. If the user feels relaxed, it can also provide detailed feedback. Furthermore, if the user is in a hurry, it can also provide quick, concise feedback. This makes it possible to provide feedback of the analysis results according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts symptom input from the user. Symptoms input by the user include physical symptoms such as fever, cough, headache, and stomachache, as well as mental symptoms such as anxiety, stress, and depression. The reception unit provides an interface that allows users to input symptoms using a smartphone or computer, and also supports voice and image input. Step 2: The analysis unit analyzes the symptoms received by the reception unit. Based on past medical data and case data, the analysis unit analyzes the input symptoms and lists possible diseases. Using machine learning algorithms and statistical analysis, the analysis unit evaluates the correlation between symptoms and diseases, their frequency of occurrence, and their severity. Step 3: The identification unit identifies possible diseases based on the results of the analysis by the analysis unit. The identification unit identifies the most likely disease from the listed diseases and evaluates the degree of match between the symptoms and the disease using a diagnostic algorithm. The unit also evaluates the possibility of the disease taking into account the severity and frequency of symptoms. Step 4: The suggestion unit proposes appropriate measures for the illness identified by the identification unit. The suggestion unit suggests measures such as prescribing medication, improving lifestyle habits, resting, and hydration, and if symptoms are severe or self-diagnosis is difficult, it recommends visiting an appropriate medical institution.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] 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.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an input of symptoms from a user; an analysis unit that analyzes the symptoms received by the reception unit; an identification unit that identifies a disease based on the results of the analysis by the analysis unit; a suggestion unit that suggests a way to deal with the disease identified by the identification unit. A system characterized by:

2. The reception unit Inferring the user's emotions and adjusting the symptom input method based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's symptom input history and select the input format 2. The system of claim 1.

4. The reception unit When entering symptoms, filter input based on the user's current life situation or environment 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the input symptoms based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When entering symptoms, prioritize relevant symptoms based on the user's geographic location 2. The system of claim 1.

7. The reception unit When a symptom is entered, the system analyzes the user's social media activity and enters related symptoms.

2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

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