System

Abstract

To evaluate the seriousness or emergency of symptoms of an aged individual and provide an appropriate response.SOLUTION: A system according to an embodiment of the present invention comprises a reception unit, an analysis unit, a classification unit. The reception unit receives input of symptoms. The analysis unit analyzes information received by the reception unit and evaluates the seriousness or emergency of the symptom. The classification unit classifies aged individuals into those who require visiting a medical care facility and those for whom welfare responses need to be taken based on the evaluation results obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to properly assess the severity and urgency of an elderly person's symptoms and determine whether they needed to visit a medical facility.

[0005] The system according to the embodiment aims to evaluate the severity and urgency of symptoms of elderly people and provide appropriate responses. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a classification unit. The reception unit receives input of symptoms. The analysis unit analyzes the information received by the reception unit and evaluates the severity or urgency of the symptoms. The classification unit classifies elderly people into those who need to visit a medical facility and those who need welfare support based on the evaluation result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can assess the severity and urgency of symptoms of elderly people and provide appropriate responses. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A counseling system according to an embodiment of the present invention is a system in which an elderly person inputs his / her symptoms and sensations, and an AI analyzes the information to provide an appropriate response. In this counseling system, an elderly person accesses the system and inputs his / her symptoms and sensations. Next, an AI analyzes the input information and evaluates the severity and urgency of the symptoms. After that, based on the evaluation result, the AI ​​classifies the elderly into those who should visit a medical facility and those who need welfare support. If it is determined that the elderly should visit a medical facility, the AI ​​guides them to an appropriate medical institution and takes steps to make an appointment. On the other hand, if it is determined that welfare support is required, the AI ​​provides counseling and introduces an institution that provides welfare services as necessary. This system reduces the burden on medical facilities and enables appropriate responses to the elderly. For example, for symptoms that are not medically urgent, welfare support can be provided to provide a sense of security to the elderly and make efficient use of the resources of medical facilities. First, an elderly person accesses the system and inputs his / her symptoms and sensations. For example, specific symptoms such as "I've been feeling tight in my chest recently" and "I can't sleep at night" are input. This information is input into the AI. Next, the AI ​​analyzes the input information and evaluates the severity and urgency of the symptoms. Based on past data and medical knowledge, the AI ​​judges whether a symptom is a medical emergency. For example, if chest pain is a possible precursor to a heart attack, the AI ​​judges it to be highly urgent. Then, based on the evaluation results, the AI ​​classifies the elderly into those who should visit a medical facility and those who need welfare support. If it is judged that a medical facility should be visited, the AI ​​will guide the elderly to an appropriate medical institution and take steps to make an appointment. On the other hand, if it is judged that welfare support is required, the AI ​​will provide counseling and introduce institutions that provide welfare services as necessary. This system reduces the burden on medical facilities and enables appropriate responses to the elderly. For example, for symptoms that are not medically urgent, providing welfare support provides a sense of security to the elderly and makes efficient use of the resources of medical facilities. This allows the counseling system to efficiently evaluate the symptoms of the elderly and provide appropriate responses.

[0029] The counseling system according to the embodiment includes a reception unit, an analysis unit, and a classification unit. The reception unit receives symptoms and sensations input by the elderly. The symptoms input by the elderly include, but are not limited to, chest pain, shortness of breath, and sleep disorders. The reception unit can receive symptoms by, for example, text input, voice input, selection from options, and the like. For example, the reception unit can input symptoms using a keyboard or a touch screen as text input. The reception unit can also input symptoms using a microphone as voice input. Furthermore, the reception unit can select a corresponding symptom from a list of symptoms prepared in advance as selection from options. The analysis unit uses AI to analyze the information received by the reception unit and evaluate the severity and urgency of the symptom. The analysis unit determines whether the symptom is medically urgent based on, for example, past data and medical knowledge. For example, the analysis unit determines that the urgency is high if chest pain is likely to be a precursor to a heart attack. The analysis unit can also determine that the severity is high if shortness of breath is a sign of a respiratory disease. Furthermore, the analysis unit can determine that the urgency is low if the sleep disorder is the result of mental stress or anxiety. The classification unit classifies the elderly into those who should visit a medical facility and those who need welfare support based on the evaluation result obtained by the analysis unit. For example, when the urgency is determined to be high, the classification unit classifies the elderly into those who should visit a medical facility. Also, when the urgency is determined to be low, the classification unit can classify the elderly into those who need welfare support. Furthermore, when the classification unit determines that the elderly should visit a medical facility, it can guide the elderly to an appropriate medical institution and take a procedure to make an appointment. For example, when the urgency is determined to be high, the classification unit guides the elderly to the nearest emergency hospital and takes a procedure to make an appointment. Also, when the classification unit determines that welfare support is required, it can provide counseling and introduce an institution that provides welfare services as necessary. For example, when the urgency is determined to be low, the classification unit introduces the elderly to a local welfare center and provides counseling. In this way, the counseling system according to the embodiment can efficiently evaluate the symptoms of the elderly and provide an appropriate response.

[0030] The reception unit receives symptoms and sensations input by the elderly person. Examples of symptoms input by the elderly person include, but are not limited to, chest pain, shortness of breath, and sleep disorders. The reception unit can receive symptoms by, for example, text input, voice input, selection from options, and other methods. Specifically, symptoms can be input using a keyboard or touch screen as text input. This allows the elderly person to describe their symptoms in detail, and the system can accurately receive the information. Symptoms can also be input using a microphone as voice input. Voice input is particularly convenient for elderly people who have limited vision and hand movements, and allows them to convey symptoms in a natural conversational format. Furthermore, as a selection from options, the relevant symptom can be selected from a list of symptoms prepared in advance. This simplifies input and allows information to be provided quickly, especially for common symptoms. By combining these input methods, the reception unit can respond to the diverse needs of the elderly person and receive symptoms accurately and quickly. Furthermore, the reception unit can centrally manage the input information and quickly provide it to the analysis unit and classification unit. This improves the efficiency of the entire system and enables quick response to the elderly person.

[0031] The analysis unit uses AI to analyze the information received by the reception unit and evaluate the severity and urgency of the symptoms. Specifically, the AI ​​uses natural language processing technology to analyze text input and voice input and understand the content of the symptoms. For example, if chest pain is input, the AI ​​analyzes detailed information such as the location, intensity, and duration of the pain to evaluate the possibility of a heart attack. In addition, based on past data and medical knowledge, it determines whether the symptoms are medically urgent. For example, if shortness of breath is input, the AI ​​analyzes its frequency and situation to evaluate the possibility of a respiratory disease. Furthermore, if a sleep disorder is input, the AI ​​analyzes its cause and effect and determines whether it is the result of mental stress or anxiety. The analysis unit can comprehensively evaluate this information and quickly determine the severity and urgency of the symptoms. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past symptom data, it predicts what kind of disease a specific symptom is related to and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] Based on the evaluation results obtained by the analysis unit, the classification unit classifies the elderly into those who should visit a medical facility and those who need welfare support. Specifically, if the urgency is determined to be high, the classification unit classifies the elderly as those who should visit a medical facility. For example, if it is determined that chest pain is likely to be a precursor to a heart attack, the classification unit guides the elderly to the nearest emergency hospital and takes steps to make an appointment. If shortness of breath is a sign of a respiratory disease, the classification unit determines that the elderly should be examined by a specialist and guides the elderly to an appropriate medical institution. On the other hand, if the urgency is determined to be low, the classification unit classifies the elderly as those who need welfare support. For example, if the sleep disorder is the result of mental stress or anxiety, the classification unit introduces the elderly to a local welfare center and provides counseling. Furthermore, if the classification unit determines that the elderly should visit a medical facility, the classification unit can also guide the elderly to an appropriate medical institution and take steps to make an appointment. For example, if the urgency is determined to be high, the classification unit guides the elderly to the nearest emergency hospital and takes steps to make an appointment. If the classification unit determines that welfare support is required, the classification unit can provide counseling and, if necessary, introduce an institution that provides welfare services. For example, if the emergency is judged to be low, the system will refer the patient to a local welfare center and provide counseling. This allows the classification unit to quickly provide appropriate treatment according to the elderly person's symptoms and support efficient use of medical resources. Furthermore, the classification unit can provide feedback based on the evaluation results to the analysis unit, continuously improving the accuracy and reliability of the entire system.

[0033] The reception unit can analyze the elderly person's past symptom input history and provide an appropriate input interface. The reception unit, for example, automatically displays symptoms that the elderly person frequently inputs in the past as candidates. For example, the reception unit lists symptoms that are frequently input from the past input history and displays them as candidates. The reception unit can also preferentially suggest an input method (such as voice or text) that the elderly person has used in the past. For example, if voice input has been used in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest symptoms to be used in a specific time period from the elderly person's past input history. For example, the reception unit predicts symptoms to be input in a specific time period from past data and provides an input interface suitable for that time period. This improves input efficiency by providing an optimal interface based on the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the elderly person's past input data to the generation AI and cause the generation AI to execute a suggestion of an optimal input interface.

[0034] The reception unit can filter the input contents based on the elderly person's current living situation and areas of interest when inputting symptoms. The reception unit preferentially displays related symptoms based on the elderly person's current living situation (e.g., living alone or living with others). For example, the reception unit preferentially displays symptoms related to loneliness and anxiety for elderly people living alone. The reception unit can also suggest related symptoms as input candidates based on the elderly person's areas of interest (e.g., exercise and diet). For example, the reception unit suggests symptoms related to fatigue and muscle pain caused by exercise for elderly people who are interested in exercise. The reception unit can also filter appropriate input contents based on the elderly person's life rhythm (e.g., whether they are a night owl or a morning owl). For example, the reception unit preferentially displays symptoms related to nighttime insomnia and sleep disorders for elderly people who are night owls. This improves the accuracy of input by providing input contents according to the elderly person's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the living conditions of an elderly person into the generation AI and have the generation AI make suggestions on related symptoms.

[0035] The reception unit can preferentially input highly relevant symptoms based on the geographical location information of the elderly person when inputting symptoms. For example, if the elderly person lives in a specific area, the reception unit urges the elderly person to preferentially input diseases and symptoms that are prevalent in that area. For example, the reception unit suggests that the elderly person preferentially input related symptoms based on infectious disease information in the area. In addition, the reception unit can also urge the elderly person to input symptoms based on health risks at the travel destination when the elderly person is traveling. For example, the reception unit suggests that the elderly person preferentially input related symptoms based on the infectious disease risk at the travel destination. In addition, the reception unit can also urge the elderly person to input symptoms based on the medical treatment content at the medical institution when the elderly person visits a specific medical institution. For example, the reception unit suggests that the elderly person preferentially input related symptoms based on the medical treatment content at the medical institution. As a result, the accuracy of input is improved by preferentially inputting highly relevant symptoms based on geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographic location data of an elderly person to the generation AI and have the generation AI execute suggestions for related symptoms.

[0036] The reception unit can analyze the social media activity of the elderly person and input related symptoms when inputting symptoms. The reception unit, for example, urges the elderly person to input related symptoms based on health information shared on social media. For example, the reception unit analyzes the content of social media posts and suggests related symptoms. The reception unit can also urge the elderly person to input symptoms based on information obtained from health-related accounts that the elderly person follows on social media. For example, the reception unit analyzes the content of posts of the accounts followed and suggests related symptoms. The reception unit can also urge the elderly person to input symptoms based on information on health-related groups that the elderly person participates in on social media. For example, the reception unit analyzes the content of posts of the participating groups and suggests related symptoms. As a result, the accuracy of input is improved by inputting related symptoms based on social media activities. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the elderly person's social media data to the generation AI and cause the generation AI to execute the suggestion of related symptoms.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. For example, the analysis unit performs a detailed analysis on symptoms with high importance. For example, when the risk of a heart attack is high, the analysis unit performs an analysis using detailed data. The analysis unit can also perform a concise analysis on symptoms with low importance. For example, the analysis unit provides a simple analysis result for a mild headache. The analysis unit can also perform an analysis with a moderate level of detail on symptoms with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on moderate lower back pain. This improves the accuracy of the analysis result by providing a level of detail according to the importance of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to adjust the level of detail.

[0038] The analysis unit can apply different analysis algorithms depending on the category of the symptom during the analysis. For example, the analysis unit applies a specialized analysis algorithm to symptoms of the respiratory system. For example, the analysis unit performs the analysis using an algorithm specialized for diseases of the respiratory system. The analysis unit can also apply another analysis algorithm to symptoms of the digestive system. For example, the analysis unit performs the analysis using an algorithm specialized for diseases of the digestive system. The analysis unit can also apply yet another analysis algorithm to symptoms of the nervous system. For example, the analysis unit performs the analysis using an algorithm specialized for diseases of the nervous system. As a result, the accuracy of the analysis result is improved by applying an analysis algorithm according to the category of the symptom. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to select an appropriate analysis algorithm.

[0039] The analysis unit can determine the priority of the analysis based on the time of submission of the symptoms during the analysis. For example, the analysis unit gives priority to the analysis of recently submitted symptoms. For example, the analysis unit gives priority to the analysis of symptoms with a recent submission date and time. The analysis unit can also postpone the analysis of symptoms submitted in the past. For example, the analysis unit postpones symptoms with an old submission date and time. The analysis unit can also perform analysis with moderate priority for symptoms submitted at a medium time. For example, the analysis unit analyzes symptoms submitted at a medium time with moderate priority. This improves the efficiency of the analysis by providing a priority based on the submission date and time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to determine the priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit performs analysis with priority on symptoms with high relevance. For example, the analysis unit performs analysis taking into consideration the relevance between symptoms of the respiratory system and symptoms of the digestive system. The analysis unit can also perform analysis on symptoms with low relevance at a later date. For example, the analysis unit performs analysis taking into consideration the relevance between symptoms of the skin system and symptoms of the immune system. The analysis unit can also perform analysis in a moderate order for symptoms with moderate relevance. For example, the analysis unit performs analysis taking into consideration the relevance between symptoms of the nervous system and symptoms of the circulatory system. This improves the efficiency of analysis by providing an order based on relevance. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] The classification unit can improve the accuracy of classification based on the interrelationships between symptoms during classification. The classification unit performs classification, for example, taking into consideration the interrelationships between symptoms of the respiratory system and symptoms of the digestive system. For example, the classification unit performs classification based on the correlation between symptoms of the respiratory system and symptoms of the digestive system. The classification unit can also perform classification taking into consideration the interrelationships between symptoms of the nervous system and symptoms of the circulatory system. For example, the classification unit performs classification based on the correlation between symptoms of the nervous system and symptoms of the circulatory system. The classification unit can also perform classification taking into consideration the interrelationships between symptoms of the skin system and symptoms of the immune system. For example, the classification unit performs classification based on the correlation between symptoms of the skin system and symptoms of the immune system. As a result, the accuracy of classification is improved by taking into consideration the interrelationships between symptoms. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input symptom data to the generation AI and cause the generation AI to execute an analysis of the interrelationships.

[0042] The classification unit can classify based on the attribute information of the elderly person at the time of classification. The classification unit performs classification taking into consideration, for example, the age of the elderly person. For example, the classification unit performs appropriate classification based on the age of the elderly person. The classification unit can also perform classification taking into consideration the gender of the elderly person. For example, the classification unit performs appropriate classification based on the gender of the elderly person. The classification unit can also perform classification taking into consideration the living environment of the elderly person (whether they live alone or live together). For example, the classification unit performs appropriate classification based on the living environment of the elderly person. Thereby, by providing classification based on the attribute information of the elderly person, the accuracy of classification is improved. Part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input attribute data of the elderly person to the generation AI and cause the generation AI to execute the classification criteria.

[0043] The classification unit can classify the elderly taking into consideration the geographical distribution of symptoms when classifying the elderly. For example, the classification unit performs classification taking into consideration the distribution of symptoms in the area where the elderly person lives. For example, the classification unit performs classification based on infectious disease information of the area. In addition, when the elderly person is traveling, the classification unit can also perform classification taking into consideration the distribution of symptoms at the travel destination. For example, the classification unit performs classification based on the infectious disease risk at the travel destination. In addition, when the elderly person visits a specific medical institution, the classification unit can also perform classification taking into consideration the distribution of symptoms at the medical institution. For example, the classification unit performs classification based on the medical treatment content of the medical institution visited. As a result, by providing classification based on geographical distribution, the accuracy of classification is improved. Part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input geographical data of symptoms to the generation AI and cause the generation AI to execute the classification criteria.

[0044] The classification unit can improve the accuracy of classification by referring to literature related to the symptoms during classification. The classification unit performs classification by referring to, for example, the latest medical literature on respiratory symptoms. For example, the classification unit performs classification based on the latest medical papers on respiratory diseases. The classification unit can also perform classification by referring to the latest medical literature on digestive symptoms. For example, the classification unit performs classification based on the latest medical papers on digestive diseases. The classification unit can also perform classification by referring to the latest medical literature on nervous symptoms. For example, the classification unit performs classification based on the latest medical papers on nervous diseases. This improves the accuracy of classification by providing classification based on related literature. Part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input literature data on symptoms to the generation AI and cause the generation AI to execute classification criteria.

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

[0046] The reception unit can provide individual health advice based on the elderly person's input. For example, the reception unit suggests points to be careful about and measures to improve in daily life based on the symptoms input by the elderly person. For example, for an elderly person who complains of chest pain, stress management and moderate exercise are recommended. The reception unit can also provide dietary advice related to the input symptoms. For example, for an elderly person who complains of sleep disorders, it is suggested to refrain from consuming caffeine. The reception unit can also suggest appropriate self-care methods based on the symptoms input by the elderly person. For example, a hot compress and stretching are recommended for an elderly person who complains of mild muscle pain. This allows the elderly person to know specific measures for their symptoms, improving health management in daily life.

[0047] The reception unit can analyze the elderly person's past symptom input history and provide preventive health advice. For example, the reception unit suggests preventive measures based on symptoms frequently input in the past. For example, for an elderly person who has had colds many times in the past, it recommends thorough hand washing and gargling. The reception unit can also predict symptoms that are common in a particular season from the past input history and suggest measures in advance. For example, during hay fever season, it recommends wearing a mask and ventilating indoors. The reception unit can also suggest regular health checks based on the elderly person's past input history. For example, for an elderly person who has complained of high blood pressure in the past, it recommends regular blood pressure measurements. This improves health management for the elderly by providing preventive advice based on the past input history.

[0048] When the symptoms are input, the reception unit can provide health information based on the elderly person's current living situation and areas of interest. For example, the reception unit can suggest community activities to reduce loneliness to elderly people who live alone. For example, it can recommend participation in local clubs or volunteer activities. The reception unit can also suggest appropriate exercise programs to elderly people who are interested in exercise. For example, it can introduce walking or yoga classes. The reception unit can also suggest nutritionally balanced meal menus to elderly people who are interested in diet. For example, it can introduce recipes using seasonal vegetables. This allows health management to be more effective by providing information according to the elderly person's living situation and areas of interest.

[0049] When the elderly person inputs the symptoms, the reception unit can suggest local health events and services based on the geographical location information of the elderly person. For example, the reception unit can suggest health seminars and medical examination events held in the area where the elderly person lives. For example, the reception unit can introduce a free health check at a local health center. In addition, if the elderly person is traveling, the reception unit can also suggest medical services available at the travel destination. For example, the reception unit can provide information on hospitals and clinics at the travel destination. In addition, if the elderly person visits a specific medical institution, the reception unit can also suggest health programs offered at the medical institution. For example, the reception unit can introduce a rehabilitation program offered at the hospital where the elderly person visits. In this way, health management of the elderly person is improved by providing health events and services based on geographical location information.

[0050] When the symptoms are entered, the reception unit can analyze the social media activity of the elderly person and provide trend information on health. For example, the reception unit can suggest the latest health trends based on information obtained from health-related accounts that the elderly person follows on social media. For example, the reception unit can introduce health methods and diet methods that are currently popular. The reception unit can also provide related health information based on information on health-related groups that the elderly person participates in on social media. For example, the reception unit can introduce health-related articles and videos shared within the group. The reception unit can also provide individual health advice based on the health information shared by the elderly person on social media. For example, the reception unit can provide appropriate exercise and diet advice based on the content of the post. In this way, the health awareness of the elderly person can be improved by providing health information based on social media activities.

[0051] During the analysis, the analysis unit can adjust the display method of the analysis results based on the importance of the symptoms. For example, for symptoms with high importance, the analysis results are highlighted. For example, if there is a high risk of a heart attack, the analysis results are highlighted in red. The analysis unit can also provide a concise display for symptoms with low importance. For example, a mild headache is displayed in simple text. The analysis unit can also provide a display with a moderate level of detail for symptoms with medium importance. For example, a moderate level of detail is displayed for lower back pain. This improves understanding of the analysis results by providing a display method according to the importance of the symptoms.

[0052] During analysis, the analysis unit can apply different display formats of the analysis results depending on the category of the symptom. For example, a specialized display format is applied to respiratory symptoms. For example, graphs and charts specialized for respiratory diseases are used for display. The analysis unit can also apply a different display format to digestive symptoms. For example, text and data specialized for digestive diseases are used for display. The analysis unit can also apply yet another display format to nervous system symptoms. For example, icons and diagrams specialized for nervous system diseases are used for display. This improves understanding of the analysis results by providing a display format according to the symptom category.

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

[0054] Step 1: The reception unit receives symptoms and sensations input by the elderly person. Examples of symptoms input by the elderly person include, but are not limited to, chest pain, shortness of breath, and sleep disorders. The reception unit can receive symptoms by, for example, text input, voice input, selection from options, and the like. For example, the reception unit can input symptoms using a keyboard or a touch screen as text input. The reception unit can also input symptoms using a microphone as voice input. Furthermore, the reception unit can select a relevant symptom from a list of symptoms prepared in advance as selection from options. Step 2: The analysis unit uses AI to analyze the information received by the reception unit and evaluate the severity and urgency of the symptoms. The analysis unit determines whether the symptoms are medically urgent based on, for example, past data and medical knowledge. For example, the analysis unit may determine that the urgency is high if chest pain is a possible precursor to a heart attack. The analysis unit may also determine that the severity is high if shortness of breath is a sign of a respiratory disease. Furthermore, the analysis unit may determine that the urgency is low if sleep disorders are a result of mental stress or anxiety. Step 3: The classification unit classifies the elderly into those who should visit a medical facility and those who need welfare support based on the evaluation results obtained by the analysis unit. For example, if the classification unit determines that the urgency is high, the classification unit classifies the elderly into those who should visit a medical facility. Also, if the classification unit determines that the urgency is low, the classification unit can classify the elderly into those who need welfare support. Furthermore, if the classification unit determines that the elderly should visit a medical facility, the classification unit can guide the elderly to an appropriate medical institution and take a procedure to make an appointment. For example, if the classification unit determines that the urgency is high, the classification unit can guide the elderly to the nearest emergency hospital and take a procedure to make an appointment. Also, if the classification unit determines that welfare support is necessary, the classification unit can provide counseling and introduce an institution that provides welfare services as necessary. For example, if the classification unit determines that the urgency is low, the classification unit can introduce the elderly to a local welfare center and provide counseling.

[0055] (Example 2) A counseling system according to an embodiment of the present invention is a system in which an elderly person inputs his / her symptoms and sensations, and an AI analyzes the information to provide an appropriate response. In this counseling system, an elderly person accesses the system and inputs his / her symptoms and sensations. Next, an AI analyzes the input information and evaluates the severity and urgency of the symptoms. After that, based on the evaluation result, the AI ​​classifies the elderly into those who should visit a medical facility and those who need welfare support. If it is determined that the elderly should visit a medical facility, the AI ​​guides them to an appropriate medical institution and takes steps to make an appointment. On the other hand, if it is determined that welfare support is required, the AI ​​provides counseling and introduces an institution that provides welfare services as necessary. This system reduces the burden on medical facilities and enables appropriate responses to the elderly. For example, for symptoms that are not medically urgent, welfare support can be provided to provide a sense of security to the elderly and make efficient use of the resources of medical facilities. First, an elderly person accesses the system and inputs his / her symptoms and sensations. For example, specific symptoms such as "I've been feeling tight in my chest recently" and "I can't sleep at night" are input. This information is input into the AI. Next, the AI ​​analyzes the input information and evaluates the severity and urgency of the symptoms. Based on past data and medical knowledge, the AI ​​judges whether a symptom is a medical emergency. For example, if chest pain is a possible precursor to a heart attack, the AI ​​judges it to be highly urgent. Then, based on the evaluation results, the AI ​​classifies the elderly into those who should visit a medical facility and those who need welfare support. If it is judged that a medical facility should be visited, the AI ​​will guide the elderly to an appropriate medical institution and take steps to make an appointment. On the other hand, if it is judged that welfare support is required, the AI ​​will provide counseling and introduce institutions that provide welfare services as necessary. This system reduces the burden on medical facilities and enables appropriate responses to the elderly. For example, for symptoms that are not medically urgent, providing welfare support provides a sense of security to the elderly and makes efficient use of the resources of medical facilities. This allows the counseling system to efficiently evaluate the symptoms of the elderly and provide appropriate responses.

[0056] The counseling system according to the embodiment includes a reception unit, an analysis unit, and a classification unit. The reception unit receives symptoms and sensations input by the elderly. The symptoms input by the elderly include, but are not limited to, chest pain, shortness of breath, and sleep disorders. The reception unit can receive symptoms by, for example, text input, voice input, selection from options, and the like. For example, the reception unit can input symptoms using a keyboard or a touch screen as text input. The reception unit can also input symptoms using a microphone as voice input. Furthermore, the reception unit can select a corresponding symptom from a list of symptoms prepared in advance as selection from options. The analysis unit uses AI to analyze the information received by the reception unit and evaluate the severity and urgency of the symptom. The analysis unit determines whether the symptom is medically urgent based on, for example, past data and medical knowledge. For example, the analysis unit determines that the urgency is high if chest pain is likely to be a precursor to a heart attack. The analysis unit can also determine that the severity is high if shortness of breath is a sign of a respiratory disease. Furthermore, the analysis unit can determine that the urgency is low if the sleep disorder is the result of mental stress or anxiety. The classification unit classifies the elderly into those who should visit a medical facility and those who need welfare support based on the evaluation result obtained by the analysis unit. For example, when the urgency is determined to be high, the classification unit classifies the elderly into those who should visit a medical facility. Also, when the urgency is determined to be low, the classification unit can classify the elderly into those who need welfare support. Furthermore, when the classification unit determines that the elderly should visit a medical facility, it can guide the elderly to an appropriate medical institution and take a procedure to make an appointment. For example, when the urgency is determined to be high, the classification unit guides the elderly to the nearest emergency hospital and takes a procedure to make an appointment. Also, when the classification unit determines that welfare support is required, it can provide counseling and introduce an institution that provides welfare services as necessary. For example, when the urgency is determined to be low, the classification unit introduces the elderly to a local welfare center and provides counseling. In this way, the counseling system according to the embodiment can efficiently evaluate the symptoms of the elderly and provide an appropriate response.

[0057] The reception unit receives symptoms and sensations input by the elderly person. Examples of symptoms input by the elderly person include, but are not limited to, chest pain, shortness of breath, and sleep disorders. The reception unit can receive symptoms by, for example, text input, voice input, selection from options, and other methods. Specifically, symptoms can be input using a keyboard or touch screen as text input. This allows the elderly person to describe their symptoms in detail, and the system can accurately receive the information. Symptoms can also be input using a microphone as voice input. Voice input is particularly convenient for elderly people who have limited vision and hand movements, and allows them to convey symptoms in a natural conversational format. Furthermore, as a selection from options, the relevant symptom can be selected from a list of symptoms prepared in advance. This simplifies input and allows information to be provided quickly, especially for common symptoms. By combining these input methods, the reception unit can respond to the diverse needs of the elderly person and receive symptoms accurately and quickly. Furthermore, the reception unit can centrally manage the input information and quickly provide it to the analysis unit and classification unit. This improves the efficiency of the entire system and enables quick response to the elderly person.

[0058] The analysis unit uses AI to analyze the information received by the reception unit and evaluate the severity and urgency of the symptoms. Specifically, the AI ​​uses natural language processing technology to analyze text input and voice input and understand the content of the symptoms. For example, if chest pain is input, the AI ​​analyzes detailed information such as the location, intensity, and duration of the pain to evaluate the possibility of a heart attack. In addition, based on past data and medical knowledge, it determines whether the symptoms are medically urgent. For example, if shortness of breath is input, the AI ​​analyzes its frequency and situation to evaluate the possibility of a respiratory disease. Furthermore, if a sleep disorder is input, the AI ​​analyzes its cause and effect and determines whether it is the result of mental stress or anxiety. The analysis unit can comprehensively evaluate this information and quickly determine the severity and urgency of the symptoms. In addition, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past symptom data, it predicts what kind of disease a specific symptom is related to and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0059] Based on the evaluation results obtained by the analysis unit, the classification unit classifies the elderly into those who should visit a medical facility and those who need welfare support. Specifically, if the urgency is determined to be high, the classification unit classifies the elderly as those who should visit a medical facility. For example, if it is determined that chest pain is likely to be a precursor to a heart attack, the classification unit guides the elderly to the nearest emergency hospital and takes steps to make an appointment. If shortness of breath is a sign of a respiratory disease, the classification unit determines that the elderly should be examined by a specialist and guides the elderly to an appropriate medical institution. On the other hand, if the urgency is determined to be low, the classification unit classifies the elderly as those who need welfare support. For example, if the sleep disorder is the result of mental stress or anxiety, the classification unit introduces the elderly to a local welfare center and provides counseling. Furthermore, if the classification unit determines that the elderly should visit a medical facility, the classification unit can also guide the elderly to an appropriate medical institution and take steps to make an appointment. For example, if the urgency is determined to be high, the classification unit guides the elderly to the nearest emergency hospital and takes steps to make an appointment. If the classification unit determines that welfare support is required, the classification unit can provide counseling and, if necessary, introduce an institution that provides welfare services. For example, if the emergency is judged to be low, the system will refer the patient to a local welfare center and provide counseling. This allows the classification unit to quickly provide appropriate treatment according to the elderly person's symptoms and support efficient use of medical resources. Furthermore, the classification unit can provide feedback based on the evaluation results to the analysis unit, continuously improving the accuracy and reliability of the entire system.

[0060] The reception unit can estimate the emotion of the elderly person and adjust the symptom input method based on the estimated emotion. For example, when the elderly person feels anxious, the reception unit provides a simple and intuitive interface and minimizes the input procedure. For example, the reception unit prompts the elderly person to input symptoms in a simple question format. Also, when the elderly person is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, the reception unit provides an option to select from a detailed symptom list. Also, when the elderly person is in a hurry, the reception unit can prioritize voice input and enable the elderly person to input symptoms quickly. For example, the reception unit uses voice recognition technology to convert voice input into text and input symptoms. This improves ease of input by providing an input method according to the elderly person's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input facial expression data of an elderly person to the generation AI and have the generation AI estimate the emotions.

[0061] The reception unit can analyze the elderly person's past symptom input history and provide an appropriate input interface. The reception unit, for example, automatically displays symptoms that the elderly person frequently inputs in the past as candidates. For example, the reception unit lists symptoms that are frequently input from the past input history and displays them as candidates. The reception unit can also preferentially suggest an input method (such as voice or text) that the elderly person has used in the past. For example, if voice input has been used in the past, the reception unit preferentially suggests voice input. The reception unit can also predict and suggest symptoms to be used in a specific time period from the elderly person's past input history. For example, the reception unit predicts symptoms to be input in a specific time period from past data and provides an input interface suitable for that time period. This improves input efficiency by providing an optimal interface based on the past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the elderly person's past input data to the generation AI and cause the generation AI to execute a suggestion of an optimal input interface.

[0062] The reception unit can filter the input contents based on the elderly person's current living situation and areas of interest when inputting symptoms. The reception unit preferentially displays related symptoms based on the elderly person's current living situation (e.g., living alone or living with others). For example, the reception unit preferentially displays symptoms related to loneliness and anxiety for elderly people living alone. The reception unit can also suggest related symptoms as input candidates based on the elderly person's areas of interest (e.g., exercise and diet). For example, the reception unit suggests symptoms related to fatigue and muscle pain caused by exercise for elderly people who are interested in exercise. The reception unit can also filter appropriate input contents based on the elderly person's life rhythm (e.g., whether they are a night owl or a morning owl). For example, the reception unit preferentially displays symptoms related to nighttime insomnia and sleep disorders for elderly people who are night owls. This improves the accuracy of input by providing input contents according to the elderly person's living situation and areas of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the living conditions of an elderly person into the generation AI and have the generation AI make suggestions on related symptoms.

[0063] The reception unit can estimate the emotion of the elderly person and determine the order of symptoms to be input based on the estimated emotion. For example, when the elderly person feels anxious, the reception unit urges the elderly person to input symptoms with high urgency first. For example, the reception unit suggests that symptoms with high urgency such as chest pain and shortness of breath be input with high urgency first. The reception unit can also urge the elderly person to input detailed symptoms when the elderly person is relaxed. For example, the reception unit suggests that detailed symptoms such as sleep disorders and loss of appetite be input. The reception unit can also urge the elderly person to input only important symptoms with high urgency first when the elderly person is in a hurry. For example, the reception unit suggests that symptoms with high urgency be input with high urgency first and other symptoms be put off until later. In this way, important symptoms can be input with high priority by providing a priority order according to the elderly person's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input facial expression data of an elderly person to a generation AI and cause the generation AI to estimate emotions.

[0064] The reception unit can preferentially input highly relevant symptoms based on the geographical location information of the elderly person when inputting symptoms. For example, if the elderly person lives in a specific area, the reception unit urges the elderly person to preferentially input diseases and symptoms that are prevalent in that area. For example, the reception unit suggests that the elderly person preferentially input related symptoms based on infectious disease information in the area. In addition, the reception unit can also urge the elderly person to input symptoms based on health risks at the travel destination when the elderly person is traveling. For example, the reception unit suggests that the elderly person preferentially input related symptoms based on the infectious disease risk at the travel destination. In addition, the reception unit can also urge the elderly person to input symptoms based on the medical treatment content at the medical institution when the elderly person visits a specific medical institution. For example, the reception unit suggests that the elderly person preferentially input related symptoms based on the medical treatment content at the medical institution. As a result, the accuracy of input is improved by preferentially inputting highly relevant symptoms based on geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographic location data of an elderly person to the generation AI and have the generation AI execute suggestions for related symptoms.

[0065] The reception unit can analyze the social media activity of the elderly person and input related symptoms when inputting symptoms. The reception unit, for example, urges the elderly person to input related symptoms based on health information shared on social media. For example, the reception unit analyzes the content of social media posts and suggests related symptoms. The reception unit can also urge the elderly person to input symptoms based on information obtained from health-related accounts that the elderly person follows on social media. For example, the reception unit analyzes the content of posts of the accounts followed and suggests related symptoms. The reception unit can also urge the elderly person to input symptoms based on information on health-related groups that the elderly person participates in on social media. For example, the reception unit analyzes the content of posts of the participating groups and suggests related symptoms. As a result, the accuracy of input is improved by inputting related symptoms based on social media activities. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the elderly person's social media data to the generation AI and cause the generation AI to execute the suggestion of related symptoms.

[0066] The analysis unit can estimate the emotion of the elderly person and adjust the expression method of the analysis based on the estimated emotion. For example, when the elderly person feels anxious, the analysis unit uses a simple and easy-to-understand expression method. For example, the analysis unit displays the analysis result using a simple graph or icon. In addition, the analysis unit can provide a detailed analysis result when the elderly person is relaxed. For example, the analysis unit displays the analysis result using detailed text or data. In addition, when the elderly person is in a hurry, the analysis unit can provide a concise analysis result that covers the main points. For example, the analysis unit displays only the main points in bullet points. This improves understanding of the analysis result by providing an expression method according to the emotion of the elderly person. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input facial expression data of an elderly person into the generation AI and have the generation AI estimate the emotions.

[0067] The analysis unit can adjust the level of detail of the analysis based on the importance of the symptom during the analysis. For example, the analysis unit performs a detailed analysis on symptoms with high importance. For example, when the risk of a heart attack is high, the analysis unit performs an analysis using detailed data. The analysis unit can also perform a concise analysis on symptoms with low importance. For example, the analysis unit provides a simple analysis result for a mild headache. The analysis unit can also perform an analysis with a moderate level of detail on symptoms with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on moderate lower back pain. This improves the accuracy of the analysis result by providing a level of detail according to the importance of the symptom. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to adjust the level of detail.

[0068] The analysis unit can apply different analysis algorithms depending on the category of the symptom during the analysis. For example, the analysis unit applies a specialized analysis algorithm to symptoms of the respiratory system. For example, the analysis unit performs the analysis using an algorithm specialized for diseases of the respiratory system. The analysis unit can also apply another analysis algorithm to symptoms of the digestive system. For example, the analysis unit performs the analysis using an algorithm specialized for diseases of the digestive system. The analysis unit can also apply yet another analysis algorithm to symptoms of the nervous system. For example, the analysis unit performs the analysis using an algorithm specialized for diseases of the nervous system. As a result, the accuracy of the analysis result is improved by applying an analysis algorithm according to the category of the symptom. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to select an appropriate analysis algorithm.

[0069] The analysis unit can estimate the elderly person's emotions and adjust the length of the analysis based on the estimated emotions. For example, when the elderly person feels anxious, the analysis unit provides a short and to the point analysis result. For example, the analysis unit displays the analysis result using concise text and graphs. The analysis unit can also provide a detailed analysis result when the elderly person is relaxed. For example, the analysis unit displays the analysis result using detailed data and explanations. The analysis unit can also provide a concise analysis result when the elderly person is in a hurry. For example, the analysis unit displays only the main points in bullet points. This improves the understanding of the analysis result by providing the length of the analysis according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input facial expression data of an elderly person into the generation AI and have the generation AI estimate the emotions.

[0070] The analysis unit can determine the priority of the analysis based on the time of submission of the symptoms during the analysis. For example, the analysis unit gives priority to the analysis of recently submitted symptoms. For example, the analysis unit gives priority to the analysis of symptoms with a recent submission date and time. The analysis unit can also postpone the analysis of symptoms submitted in the past. For example, the analysis unit postpones symptoms with an old submission date and time. The analysis unit can also perform analysis with moderate priority for symptoms submitted at a medium time. For example, the analysis unit analyzes symptoms submitted at a medium time with moderate priority. This improves the efficiency of the analysis by providing a priority based on the submission date and time. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to determine the priority.

[0071] The analysis unit can adjust the order of analysis based on the relevance of symptoms during analysis. For example, the analysis unit performs analysis with priority on symptoms with high relevance. For example, the analysis unit performs analysis taking into consideration the relevance between symptoms of the respiratory system and symptoms of the digestive system. The analysis unit can also perform analysis on symptoms with low relevance at a later date. For example, the analysis unit performs analysis taking into consideration the relevance between symptoms of the skin system and symptoms of the immune system. The analysis unit can also perform analysis in a moderate order for symptoms with moderate relevance. For example, the analysis unit performs analysis taking into consideration the relevance between symptoms of the nervous system and symptoms of the circulatory system. This improves the efficiency of analysis by providing an order based on relevance. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input symptom data to the generation AI and cause the generation AI to adjust the order of analysis.

[0072] The classification unit can estimate the emotion of the elderly person and adjust the classification criteria based on the estimated emotion. For example, when the elderly person feels anxious, the classification unit prioritizes classification of symptoms with high urgency. For example, the classification unit prioritizes classification of symptoms with high urgency, such as chest pain and shortness of breath. In addition, the classification unit can apply detailed classification criteria when the elderly person is relaxed. For example, the classification unit performs classification based on detailed data. In addition, the classification unit can apply concise classification criteria when the elderly person is in a hurry. For example, the classification unit performs classification based only on the main points. This improves the accuracy of classification by providing classification criteria according to the emotion of the elderly person. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit can input facial expression data of an elderly person into the generation AI and have the generation AI estimate the emotions.

[0073] The classification unit can improve the accuracy of classification based on the interrelationships between symptoms during classification. The classification unit performs classification, for example, taking into consideration the interrelationships between symptoms of the respiratory system and symptoms of the digestive system. For example, the classification unit performs classification based on the correlation between symptoms of the respiratory system and symptoms of the digestive system. The classification unit can also perform classification taking into consideration the interrelationships between symptoms of the nervous system and symptoms of the circulatory system. For example, the classification unit performs classification based on the correlation between symptoms of the nervous system and symptoms of the circulatory system. The classification unit can also perform classification taking into consideration the interrelationships between symptoms of the skin system and symptoms of the immune system. For example, the classification unit performs classification based on the correlation between symptoms of the skin system and symptoms of the immune system. As a result, the accuracy of classification is improved by taking into consideration the interrelationships between symptoms. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input symptom data to the generation AI and cause the generation AI to execute an analysis of the interrelationships.

[0074] The classification unit can classify based on the attribute information of the elderly person at the time of classification. The classification unit performs classification taking into consideration, for example, the age of the elderly person. For example, the classification unit performs appropriate classification based on the age of the elderly person. The classification unit can also perform classification taking into consideration the gender of the elderly person. For example, the classification unit performs appropriate classification based on the gender of the elderly person. The classification unit can also perform classification taking into consideration the living environment of the elderly person (whether they live alone or live together). For example, the classification unit performs appropriate classification based on the living environment of the elderly person. Thereby, by providing classification based on the attribute information of the elderly person, the accuracy of classification is improved. Part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input attribute data of the elderly person to the generation AI and cause the generation AI to execute the classification criteria.

[0075] The classification unit can estimate the emotion of the elderly person and adjust the order of displaying the classification results based on the estimated emotion. For example, when the elderly person feels anxious, the classification unit preferentially displays results with high urgency. For example, the classification unit preferentially displays results with high urgency such as chest pain or shortness of breath. The classification unit can also display detailed results when the elderly person is relaxed. For example, the classification unit displays results based on detailed data. The classification unit can also preferentially display concise results when the elderly person is in a hurry. For example, the classification unit displays results based only on the main points. This improves the understanding of the results by providing a display order according to the emotion of the elderly person. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit can input facial expression data of an elderly person into the generation AI and have the generation AI estimate the emotions.

[0076] The classification unit can classify the elderly taking into consideration the geographical distribution of symptoms when classifying the elderly. For example, the classification unit performs classification taking into consideration the distribution of symptoms in the area where the elderly person lives. For example, the classification unit performs classification based on infectious disease information of the area. In addition, when the elderly person is traveling, the classification unit can also perform classification taking into consideration the distribution of symptoms at the travel destination. For example, the classification unit performs classification based on the infectious disease risk at the travel destination. In addition, when the elderly person visits a specific medical institution, the classification unit can also perform classification taking into consideration the distribution of symptoms at the medical institution. For example, the classification unit performs classification based on the medical treatment content of the medical institution visited. As a result, by providing classification based on geographical distribution, the accuracy of classification is improved. Part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input geographical data of symptoms to the generation AI and cause the generation AI to execute the classification criteria.

[0077] The classification unit can improve the accuracy of classification by referring to literature related to the symptoms during classification. The classification unit performs classification by referring to, for example, the latest medical literature on respiratory symptoms. For example, the classification unit performs classification based on the latest medical papers on respiratory diseases. The classification unit can also perform classification by referring to the latest medical literature on digestive symptoms. For example, the classification unit performs classification based on the latest medical papers on digestive diseases. The classification unit can also perform classification by referring to the latest medical literature on nervous symptoms. For example, the classification unit performs classification based on the latest medical papers on nervous diseases. This improves the accuracy of classification by providing classification based on related literature. Part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input literature data on symptoms to the generation AI and cause the generation AI to execute classification criteria.

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

[0079] The reception unit can provide individual health advice based on the elderly person's input. For example, the reception unit suggests points to be careful about and measures to improve in daily life based on the symptoms input by the elderly person. For example, for an elderly person who complains of chest pain, stress management and moderate exercise are recommended. The reception unit can also provide dietary advice related to the input symptoms. For example, for an elderly person who complains of sleep disorders, it is suggested to refrain from consuming caffeine. The reception unit can also suggest appropriate self-care methods based on the symptoms input by the elderly person. For example, a hot compress and stretching are recommended for an elderly person who complains of mild muscle pain. This allows the elderly person to know specific measures for their symptoms, improving health management in daily life.

[0080] The reception unit can estimate the elderly person's emotions and check the input contents based on the estimated emotions. For example, if the elderly person is feeling anxious, the reception unit rechecks the input contents to check for any input errors. For example, an elderly person who has inputted chest pain is asked a confirmation question again. In addition, if the elderly person is relaxed, the reception unit can also check the input contents in detail. For example, an elderly person who has inputted sleep disorders is asked to confirm specific symptoms and frequency. In addition, if the elderly person is in a hurry, the reception unit can check only important input contents. For example, only symptoms with high urgency are rechecked. In this way, the accuracy of the input contents is improved by performing a confirmation task according to the elderly person's emotions.

[0081] The reception unit can analyze the elderly person's past symptom input history and provide preventive health advice. For example, the reception unit suggests preventive measures based on symptoms frequently input in the past. For example, for an elderly person who has had colds many times in the past, it recommends thorough hand washing and gargling. The reception unit can also predict symptoms that are common in a particular season from the past input history and suggest measures in advance. For example, during hay fever season, it recommends wearing a mask and ventilating indoors. The reception unit can also suggest regular health checks based on the elderly person's past input history. For example, for an elderly person who has complained of high blood pressure in the past, it recommends regular blood pressure measurements. This improves health management for the elderly by providing preventive advice based on the past input history.

[0082] When the symptoms are input, the reception unit can provide health information based on the elderly person's current living situation and areas of interest. For example, the reception unit can suggest community activities to reduce loneliness to elderly people who live alone. For example, it can recommend participation in local clubs or volunteer activities. The reception unit can also suggest appropriate exercise programs to elderly people who are interested in exercise. For example, it can introduce walking or yoga classes. The reception unit can also suggest nutritionally balanced meal menus to elderly people who are interested in diet. For example, it can introduce recipes using seasonal vegetables. This allows health management to be more effective by providing information according to the elderly person's living situation and areas of interest.

[0083] The reception unit can estimate the elderly person's emotions and provide feedback on the input contents based on the estimated emotions. For example, if the elderly person is feeling anxious, the reception unit provides feedback that gives a sense of security about the input contents. For example, a message such as "The input contents are accurate. Please rest assured" is displayed. The reception unit can also provide detailed feedback if the elderly person is relaxed. For example, a message such as "Based on the input contents, let's proceed to the next step." The reception unit can also provide concise feedback if the elderly person is in a hurry. For example, a message such as "The input contents have been confirmed. Please proceed to the next step." In this way, the stress of the input work is reduced by providing feedback according to the elderly person's emotions.

[0084] When the elderly person inputs the symptoms, the reception unit can suggest local health events and services based on the geographical location information of the elderly person. For example, the reception unit can suggest health seminars and medical examination events held in the area where the elderly person lives. For example, the reception unit can introduce a free health check at a local health center. In addition, if the elderly person is traveling, the reception unit can also suggest medical services available at the travel destination. For example, the reception unit can provide information on hospitals and clinics at the travel destination. In addition, if the elderly person visits a specific medical institution, the reception unit can also suggest health programs offered at the medical institution. For example, the reception unit can introduce a rehabilitation program offered at the hospital where the elderly person visits. In this way, health management of the elderly person is improved by providing health events and services based on geographical location information.

[0085] When the symptoms are entered, the reception unit can analyze the social media activity of the elderly person and provide trend information on health. For example, the reception unit can suggest the latest health trends based on information obtained from health-related accounts that the elderly person follows on social media. For example, the reception unit can introduce health methods and diet methods that are currently popular. The reception unit can also provide related health information based on information on health-related groups that the elderly person participates in on social media. For example, the reception unit can introduce health-related articles and videos shared within the group. The reception unit can also provide individual health advice based on the health information shared by the elderly person on social media. For example, the reception unit can provide appropriate exercise and diet advice based on the content of the post. In this way, the health awareness of the elderly person can be improved by providing health information based on social media activities.

[0086] The analysis unit can estimate the elderly person's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the elderly person is feeling anxious, the analysis unit can promptly notify the elderly person of the analysis results. For example, for symptoms with high urgency, the analysis unit can immediately notify the elderly person. The analysis unit can also provide detailed analysis results if the elderly person is relaxed. For example, the analysis results can be explained in detail by email or app notification. The analysis unit can also notify the elderly person of concise analysis results if the elderly person is in a hurry. For example, only the main points are notified by SMS. This improves the ease of receipt of the analysis results by providing a notification method according to the elderly person's emotions.

[0087] During the analysis, the analysis unit can adjust the display method of the analysis results based on the importance of the symptoms. For example, for symptoms with high importance, the analysis results are highlighted. For example, if there is a high risk of a heart attack, the analysis results are highlighted in red. The analysis unit can also provide a concise display for symptoms with low importance. For example, a mild headache is displayed in simple text. The analysis unit can also provide a display with a moderate level of detail for symptoms with medium importance. For example, a moderate level of detail is displayed for lower back pain. This improves understanding of the analysis results by providing a display method according to the importance of the symptoms.

[0088] During analysis, the analysis unit can apply different display formats of the analysis results depending on the category of the symptom. For example, a specialized display format is applied to respiratory symptoms. For example, graphs and charts specialized for respiratory diseases are used for display. The analysis unit can also apply a different display format to digestive symptoms. For example, text and data specialized for digestive diseases are used for display. The analysis unit can also apply yet another display format to nervous system symptoms. For example, icons and diagrams specialized for nervous system diseases are used for display. This improves understanding of the analysis results by providing a display format according to the symptom category.

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

[0090] Step 1: The reception unit receives symptoms and sensations input by the elderly person. Examples of symptoms input by the elderly person include, but are not limited to, chest pain, shortness of breath, and sleep disorders. The reception unit can receive symptoms by, for example, text input, voice input, selection from options, and the like. For example, the reception unit can input symptoms using a keyboard or a touch screen as text input. The reception unit can also input symptoms using a microphone as voice input. Furthermore, the reception unit can select a relevant symptom from a list of symptoms prepared in advance as selection from options. Step 2: The analysis unit uses AI to analyze the information received by the reception unit and evaluate the severity and urgency of the symptoms. The analysis unit determines whether the symptoms are medically urgent based on, for example, past data and medical knowledge. For example, the analysis unit may determine that the urgency is high if chest pain is a possible precursor to a heart attack. The analysis unit may also determine that the severity is high if shortness of breath is a sign of a respiratory disease. Furthermore, the analysis unit may determine that the urgency is low if sleep disorders are a result of mental stress or anxiety. Step 3: The classification unit classifies the elderly into those who should visit a medical facility and those who need welfare support based on the evaluation results obtained by the analysis unit. For example, if the classification unit determines that the urgency is high, the classification unit classifies the elderly into those who should visit a medical facility. Also, if the classification unit determines that the urgency is low, the classification unit can classify the elderly into those who need welfare support. Furthermore, if the classification unit determines that the elderly should visit a medical facility, the classification unit can guide the elderly to an appropriate medical institution and take a procedure to make an appointment. For example, if the classification unit determines that the urgency is high, the classification unit can guide the elderly to the nearest emergency hospital and take a procedure to make an appointment. Also, if the classification unit determines that welfare support is necessary, the classification unit can provide counseling and introduce an institution that provides welfare services as necessary. For example, if the classification unit determines that the urgency is low, the classification unit can introduce the elderly to a local welfare center and provide counseling.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0093] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0094] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and classification 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 receives symptoms and sensations input by the elderly person. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI and evaluates the severity and urgency of the symptoms. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and classifies the elderly person into those who should visit a medical facility and those who need welfare support based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0102] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and classification 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 receives symptoms and sensations input by the elderly person. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI and evaluates the severity and urgency of the symptoms. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and classifies the elderly person into those who should visit a medical facility and those who need welfare support based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the elements including the reception unit, the analysis unit, and the classification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives symptoms and sensations input by the elderly person. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI and evaluates the severity and urgency of the symptoms. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and classifies the elderly person into those who should visit a medical facility and those who need welfare support based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

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

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0138] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] Each of the multiple elements including the reception unit, the analysis unit, and the classification unit described above 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 receives symptoms and sensations input by the elderly person. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the received information using AI and evaluates the severity and urgency of the symptoms. The classification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and classifies the elderly person into those who should visit a medical facility and those who need welfare support based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0144] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0145] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0146] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0147] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0148] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0149] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0150] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0152] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0153] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0154] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0155] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0156] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

[0158] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0159] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0160] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0161] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0162] (Appendix 1) A reception unit that receives input of symptoms; an analysis unit that analyzes the information received by the reception unit and evaluates the severity or urgency of a symptom; A classification unit that classifies elderly people into those who need to visit a medical facility and those who need welfare support based on the evaluation results obtained by the analysis unit. A system characterized by: (Appendix 2) The reception unit is Estimating the emotions of elderly people and adjusting the symptom input method based on the estimated emotions 2. The system of claim 1, (Appendix 3) The reception unit is Analyzing the symptom input history of elderly people and providing an appropriate input interface 2. The system of claim 1, (Appendix 4) The reception unit is When entering symptoms, filter input based on the senior’s current living situation or areas of interest 2. The system of claim 1, (Appendix 5) The reception unit is Estimate the emotions of the elderly person and determine the order of symptoms to be entered based on the estimated emotions 2. The system of claim 1, (Appendix 6) The reception unit is When entering symptoms, the system prioritizes relevant symptoms based on the senior's geographic location 2. The system of claim 1, (Appendix 7) The reception unit is When symptoms are entered, social media activity of the elderly is analyzed and related symptoms are entered. 2. The system of claim 1, (Appendix 8) The analysis unit is Estimate the emotions of the elderly and adjust the way the analysis is presented based on the estimated emotions. 2. The system of claim 1, (Appendix 9) The analysis unit is During analysis, adjust the level of detail based on the severity of symptoms 2. The system of claim 1, (Appendix 10) The analysis unit is During analysis, different analysis algorithms are applied depending on the symptom category. 2. The system of claim 1, (Appendix 11) The analysis unit is Estimate the emotions of the elderly and adjust the length of the analysis based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, analysis prioritization will be based on when symptoms were submitted. 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the order of analysis based on symptom relevance 2. The system of claim 1, (Appendix 14) The dividing unit is Estimate the emotions of the elderly and adjust classification criteria based on the estimated emotions. 2. The system of claim 1, (Appendix 15) The dividing unit is During segmentation, improve segmentation accuracy based on symptom correlations 2. The system of claim 1, (Appendix 16) The dividing unit is When classifying, the classification is based on the attribute information of the elderly. 2. The system of claim 1, (Appendix 17) The dividing unit is Estimate the emotions of the elderly and adjust the order in which the segmentation results are displayed based on the estimated emotions. 2. The system of claim 1, (Appendix 18) The dividing unit is When dividing the categories, the geographical distribution of symptoms should be taken into consideration. 2. The system of claim 1, (Appendix 19) The dividing unit is When classifying, refer to literature related to symptoms to improve classification accuracy. 2. The system of claim 1, [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input of symptoms; an analysis unit that analyzes the information received by the reception unit and evaluates the severity or urgency of a symptom; A classification unit that classifies elderly people into those who need to visit a medical facility and those who need welfare support based on the evaluation results obtained by the analysis unit. A system characterized by:

2. The reception unit is Estimating the emotions of elderly people and adjusting the symptom input method based on the estimated emotions 2. The system of claim 1 .

3. The reception unit is Analyzing the symptom input history of elderly people and providing an appropriate input interface 2. The system of claim 1 .

4. The reception unit is When entering symptoms, filter input based on the senior’s current living situation or areas of interest 2. The system of claim 1 .

5. The reception unit is Estimate the emotions of the elderly person and determine the order of symptoms to be entered based on the estimated emotions 2. The system of claim 1 .

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

7. The reception unit is When symptoms are entered, social media activity of the elderly is analyzed and related symptoms are entered.

2. The system of claim 1 .

8. The analysis unit is Estimate the emotions of the elderly and adjust the way the analysis is presented based on the estimated emotions.

2. The system of claim 1 .

9. The analysis unit is During analysis, adjust the level of detail based on the severity of symptoms 2. The system of claim 1 .

10. The analysis unit is During analysis, different analysis algorithms are applied depending on the symptom category.

2. The system of claim 1 .

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