system

Abstract

The system according to the embodiment aims to help parents who are raising children to find appropriate ways to deal with sudden illness or injury to their children, thereby reducing their anxiety. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, a storage unit, an advice unit, a provision unit, and a collaboration unit. The reception unit accepts questions in a natural conversational format. The analysis unit analyzes symptoms based on the questions accepted by the reception unit. The proposal unit proposes a solution based on the symptoms analyzed by the analysis unit. The storage unit stores past symptom data. The advice unit gives advice based on the data stored by the storage unit. The provision unit provides information on nearby hospitals. The collaboration unit collaborates with medical professionals or pediatricians.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for parents to find appropriate ways to deal with sudden illness or injury in their children, and there has been a lack of means to alleviate their anxiety.

[0005] The system according to the embodiment aims to help parents who are raising children to find appropriate ways to deal with sudden illness or injury to their children, thereby reducing their anxiety. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a storage unit, an advice unit, a provision unit, and a collaboration unit. The reception unit accepts questions in a natural conversational format. The analysis unit analyzes symptoms based on the questions accepted by the reception unit. The proposal unit proposes a solution based on the symptoms analyzed by the analysis unit. The storage unit stores past symptom data. The advice unit gives advice based on the data stored by the storage unit. The provision unit provides information on nearby hospitals. The collaboration unit collaborates with medical professionals or pediatricians. [Effects of the Invention]

[0007] The system according to the embodiment allows parents raising children to find appropriate ways to deal with sudden illness or injury of their children, thereby reducing their anxiety. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An assistant system according to an embodiment of the present invention is a system for first-time parents experiencing childcare. It reduces anxiety over sudden illness or injury of a child (infant), suggests at-home measures to prevent incorrect treatment, and provides support for determining whether medical treatment is necessary. This assistant system responds to questions in a natural conversational format and to photos and videos of symptoms. For example, when a parent inputs their child's symptoms, the system suggests appropriate measures to address those symptoms. Furthermore, when a parent uploads photos or videos of their child's symptoms to the system, the system analyzes the images and suggests appropriate measures. Next, the system provides advice that takes into account past symptoms and diagnostic results, as well as long-term preventative measures. For example, it suggests future preventative measures based on past diagnostic results from a child's cold. It also stores past symptoms and diagnostic results in a database and uses them for future diagnoses and advice. Furthermore, it provides information on nearby hospitals with available care and connects with medical professionals and pediatricians. For example, the system searches for nearby hospitals based on the current location and provides information on available hospitals. Furthermore, it connects with medical professionals and pediatricians as needed to provide support for appropriate diagnosis and treatment. In this way, the present invention provides an assistance system that reduces anxiety for parents experiencing childcare for the first time about their child's sudden illness or injury and prevents incorrect responses.The assistance system thereby reduces anxiety for parents experiencing childcare for the first time about their child's sudden illness or injury and prevents incorrect responses.

[0029] The assistant system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, a storage unit, an advice unit, a provision unit, and a linking unit. The reception unit accepts questions in a natural conversational format. For example, when a parent asks a question about their child's symptoms, the reception unit can accept the question. The reception unit can also accept photos and videos of the child's symptoms taken by the parent. The analysis unit analyzes the symptoms based on the question accepted by the reception unit. For example, the analysis unit analyzes symptoms entered by the parent and suggests appropriate countermeasures for the symptoms. The analysis unit can also analyze photos and videos uploaded by the parent and suggest appropriate countermeasures based on the images. The suggestion unit suggests countermeasures based on the symptoms analyzed by the analysis unit. For example, the suggestion unit suggests specific countermeasures to the parent based on the analysis results by the analysis unit. The storage unit stores past symptom data. For example, the storage unit can store symptoms and diagnosis results previously entered by the parent in a database. The advice unit provides advice based on the data stored by the storage unit. For example, the advice unit refers to past symptom data and suggests future preventive measures to the guardian. The provision unit provides information on nearby hospitals. For example, the provision unit searches for nearby hospitals that are open to patients based on the current location and provides that information to the guardian. The collaboration unit collaborates with medical professionals and pediatricians. For example, the collaboration unit collaborates with medical professionals and pediatricians as needed to support the guardian in receiving appropriate diagnosis and treatment. In this way, the assistant system according to the embodiment can reduce the anxiety of guardians who are experiencing childcare for the first time about their child's sudden illness or injury and prevent incorrect handling.

[0030] When accepting a question, the reception unit can select a reception method by referring to the parent's past question history. For example, the reception unit can automatically display questions that the parent has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the parent has used in the past. Furthermore, the reception unit can predict and suggest question formats to be used during specific time periods based on the parent's past question history. In this way, by referring to the parent's past question history, it is possible to provide an optimal method for accepting questions. Some or all of the above-described 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 parent's past question history data into a generation AI and cause the generation AI to select an optimal reception method.

[0031] When receiving a question, the reception unit can filter the questions based on the guardian's current living situation or areas of interest. For example, if the guardian searches a lot for information about childcare, the reception unit can prioritize childcare-related questions. Furthermore, if the guardian is busy with work, the reception unit can provide a question format that can be answered in a short time. Furthermore, if the guardian is interested in health, the reception unit can prioritize health-related questions. This allows for more appropriate support to be provided by receiving questions according to the guardian's living situation and areas of interest. Some 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 guardian's living situation data into the generation AI and have the generation AI perform filtering.

[0032] When accepting questions, the reception unit can prioritize highly relevant questions based on the guardian's geographical location information. For example, if the guardian lives in a specific area, the reception unit can prioritize questions related to that area. Also, if the guardian is traveling, the reception unit can prioritize questions related to the travel destination. Furthermore, if the guardian is planning to move, the reception unit can prioritize questions related to the new area. In this way, by accepting questions based on the guardian's geographical location information, it is possible to provide support that is specialized for the area. Some 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 guardian's geographical location information data into a generation AI and cause the generation AI to select highly relevant questions.

[0033] When accepting a question, the reception unit can analyze the parent's social media posts and accept relevant questions. For example, if the parent posts frequently about childcare on social media, the reception unit can prioritize accepting questions related to childcare. Furthermore, if the parent posts frequently about health, the reception unit can prioritize accepting health-related questions. Furthermore, if the parent posts frequently about travel, the reception unit can prioritize accepting travel-related questions. By accepting questions based on the parent's social media activity, more personalized support can be provided. Some or all of the above-described 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 parent's social media data into a generation AI and have the generation AI select relevant questions.

[0034] When analyzing symptoms, the analysis unit can improve the accuracy of the analysis by referring to past symptom data. For example, if a similar symptom has occurred in the past, the analysis unit performs the analysis based on that data. The analysis unit can also improve the reliability of the analysis results by referring to past symptom data. Furthermore, the analysis unit can predict the progression pattern of the symptom based on past symptom data. In this way, by referring to past symptom data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past symptom data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0035] When analyzing symptoms, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, the analysis unit applies a dedicated analysis algorithm to respiratory symptoms. The analysis unit can also apply a dedicated analysis algorithm to digestive symptoms. The analysis unit can also apply a dedicated analysis algorithm to skin symptoms. In this way, by applying an analysis algorithm according to the symptom category, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to select an appropriate analysis algorithm.

[0036] When analyzing symptoms, the analysis unit can perform the analysis based on the guardian's geographical location information. For example, the analysis unit performs the analysis taking into account illnesses and symptoms specific to the area where the guardian lives. Furthermore, if the guardian is traveling, the analysis unit can also perform the analysis taking into account illnesses and symptoms specific to the travel destination. Furthermore, if the guardian is planning to move, the analysis unit can also perform the analysis taking into account illnesses and symptoms specific to the new area. In this way, by performing the analysis based on the guardian's geographical location information, it is possible to provide analysis results that are specialized for the area. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the guardian's geographical location information data into the generation AI and cause the generation AI to perform an analysis specialized for the area.

[0037] When analyzing symptoms, the analysis unit can improve the accuracy of the analysis by referring to related medical literature. The analysis unit can, for example, refer to the latest medical literature to improve the reliability of the analysis results. The analysis unit can also predict the progression pattern of symptoms by referring to past medical literature. Furthermore, the analysis unit can also identify the cause of symptoms based on medical literature. This can improve the accuracy of the analysis by referring to related medical literature. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input medical literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0038] When proposing a countermeasure, the suggestion unit can adjust the level of detail of the suggestion based on the severity of the symptom. For example, the suggestion unit can suggest a detailed countermeasure for a symptom with a high severity. The suggestion unit can also suggest a concise countermeasure for a symptom with a low severity. Furthermore, the suggestion unit can determine the priority of the countermeasures according to the severity. This makes it possible to provide a more appropriate countermeasure by proposing a countermeasure according to the severity of the symptom. Some or all of the above-described processing in the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input symptom severity data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0039] When proposing a countermeasure, the suggestion unit can apply different suggestion algorithms depending on the symptom category. For example, the suggestion unit applies a dedicated suggestion algorithm to respiratory symptoms. The suggestion unit can also apply a dedicated suggestion algorithm to digestive symptoms. The suggestion unit can also apply a dedicated suggestion algorithm to skin symptoms. In this way, by applying a suggestion algorithm according to the symptom category, a more appropriate countermeasure can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input symptom category data to the generation AI and cause the generation AI to select an appropriate suggestion algorithm.

[0040] When proposing countermeasures, the suggestion unit can determine the priority of the suggestions based on the time of symptom onset. For example, if a symptom has just occurred, the suggestion unit can prioritize proposing a quick countermeasure. Also, if a symptom has continued for a long period of time, the suggestion unit can prioritize proposing a detailed countermeasure. Furthermore, the suggestion unit can also determine the priority of the countermeasures according to the time of symptom onset. In this way, by prioritizing the countermeasures according to the time of symptom onset, more appropriate countermeasures can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the time of symptom onset into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0041] When proposing countermeasures, the suggestion unit can adjust the order of the suggestions based on the relevance of the symptoms. For example, the suggestion unit can prioritize suggesting countermeasures for highly relevant symptoms. The suggestion unit can also postpone suggesting countermeasures for less relevant symptoms. Furthermore, the suggestion unit can determine the order of countermeasures based on the relevance of the symptoms. In this way, by adjusting the order of countermeasures based on the relevance of the symptoms, more appropriate countermeasures can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0042] When saving data, the storage unit can improve the storage algorithm by referring to previously saved data. The storage unit, for example, selects an optimal storage method based on previously saved data. The storage unit can also optimize the storage algorithm by referring to previously saved data. Furthermore, the storage unit can avoid data duplication based on previously saved data. In this way, by referring to previously saved data, the storage algorithm can be optimized and data duplication can be avoided. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input previously saved data into a generation AI and have the generation AI improve the storage algorithm.

[0043] The storage unit can weight the stored data based on the time when the symptom occurred when storing the data. For example, if the symptom has just occurred, the storage unit can weight the data higher. Also, if the symptom has continued for a long period of time, the storage unit can weight the data lower. Furthermore, the storage unit can adjust the weighting of the data according to the time when the symptom occurred. In this way, important data can be appropriately stored by weighting the data according to the time when the symptom occurred. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the time when the symptom occurred to the generation AI and cause the generation AI to weight the stored data.

[0044] When providing advice, the advice unit can select an advice method by referring to past advice data. The advice unit, for example, selects the optimal advice method based on advice provided in the past. The advice unit can also improve the accuracy of advice by referring to past advice data. Furthermore, the advice unit can also customize the content of advice based on past advice data. In this way, by referring to past advice data, it is possible to provide an optimal advice method. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input past advice data into a generation AI and have the generation AI select an advice method.

[0045] When providing advice, the advice unit can adjust the means of advice based on the guardian's current living situation. For example, if the guardian is busy, the advice unit can provide concise and quick advice. Also, if the guardian is relaxed, the advice unit can provide detailed advice. Furthermore, if the guardian is in a specific situation, the advice unit can provide advice tailored to that situation. This allows for more appropriate advice to be provided by providing means of advice tailored to the guardian's living situation. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the guardian's living situation data into the generation AI and have the generation AI adjust the means of advice.

[0046] When providing advice, the advice unit can select an advice method taking into account the guardian's geographical location information. For example, if the guardian lives in a specific area, the advice unit can provide advice related to that area. Furthermore, if the guardian is traveling, the advice unit can provide advice related to the travel destination. Furthermore, if the guardian is planning to move, the advice unit can provide advice related to the new area. In this way, by providing an advice method based on the guardian's geographical location information, advice specific to the area can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the guardian's geographical location information data into the generation AI and cause the generation AI to select the optimal advice method.

[0047] When providing advice, the advice unit can analyze the parent's social media activity and suggest advice methods. For example, if the parent posts many times about childcare on social media, the advice unit can provide parenting-related advice. Furthermore, if the parent posts many times about health, the advice unit can also provide health-related advice. Furthermore, if the parent posts many times about travel, the advice unit can also provide travel-related advice. This allows for more personalized advice by providing advice methods based on the parent's social media activity. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the parent's social media data into the generation AI and have the generation AI execute the suggestion of advice methods.

[0048] When providing hospital information, the providing unit can improve the providing algorithm by referring to past provided data. The providing unit, for example, selects an optimal providing method based on hospital information provided in the past. The providing unit can also optimize the providing algorithm by referring to past provided data. Furthermore, the providing unit can avoid duplication of hospital information based on past provided data. In this way, by referring to past provided data, the providing algorithm can be optimized and duplication of information can be avoided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past provided data into the generating AI and cause the generating AI to improve the providing algorithm.

[0049] When providing hospital information, the providing unit can provide optimal hospital information by taking into account the guardian's geographical location information. For example, if the guardian lives in a specific area, the providing unit can provide hospital information related to that area. Furthermore, if the guardian is traveling, the providing unit can also provide hospital information related to the travel destination. Furthermore, if the guardian is planning to move, the providing unit can also provide hospital information related to the new area. In this way, by providing hospital information based on the guardian's geographical location information, it is possible to provide information specific to the area. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the guardian's geographical location information data into the generating AI and cause the generating AI to provide optimal hospital information.

[0050] When collaborating with a medical professional, the collaboration unit can optimize the collaboration algorithm by referring to past collaboration data. The collaboration unit, for example, selects an optimal collaboration method based on collaboration performed in the past. The collaboration unit can also optimize the collaboration algorithm by referring to the past collaboration data. Furthermore, the collaboration unit can customize the content of collaboration based on the past collaboration data. In this way, by referring to the past collaboration data, the collaboration algorithm can be optimized and the accuracy of collaboration can be improved. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input past collaboration data into the generation AI and cause the generation AI to optimize the collaboration algorithm.

[0051] When collaborating with a medical professional, the collaboration unit can select a collaboration method based on the guardian's geographic location information. For example, if the guardian lives in a specific area, the collaboration unit can provide a collaboration method related to that area. Furthermore, if the guardian is traveling, the collaboration unit can provide a collaboration method related to the travel destination. Furthermore, if the guardian is planning to move, the collaboration unit can provide a collaboration method related to the new area. In this way, by providing a collaboration method based on the guardian's geographic location information, collaboration specific to the area can be performed. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the guardian's geographic location information data into the generation AI and cause the generation AI to select the optimal collaboration method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can refer to data on similar past cases for the symptoms entered by the guardian and predict the progression of the symptoms. For example, based on data on children who have had the same symptoms in the past, the analysis unit can predict the future progression of the symptoms and suggest points to watch out for to the guardian. The analysis unit can also suggest necessary measures in stages based on the predicted progression of the symptoms. Furthermore, the analysis unit can also suggest the timing of a medical examination based on the predicted progression of the symptoms. This makes it possible to provide more accurate analysis results by utilizing past case data.

[0054] The suggestion unit can adjust the timing of suggesting solutions based on the parent's lifestyle. For example, if the parent asks many questions at night, the suggestion unit can suggest solutions that are suitable for the night. If the parent is busy during the day, the suggestion unit can also suggest solutions that can be implemented in a short time. Furthermore, if the parent has more time on the weekends, the suggestion unit can suggest detailed solutions that can be implemented on the weekends. In this way, by suggesting solutions that suit the parent's lifestyle, the suggestion unit can provide suggestions that are easier to implement.

[0055] The providing unit can provide optimal hospital information by referring to the guardian's past hospital visit history. For example, it can suggest hospitals that can treat similar symptoms based on the evaluations and treatment details of hospitals visited in the past. It can also preferentially suggest hospitals with treatment styles preferred by the guardian based on the past visit history. Furthermore, it can also exclude hospitals that the guardian wants to avoid based on the past visit history. In this way, it is possible to provide more appropriate hospital information by utilizing the guardian's past visit history.

[0056] The collaboration unit can analyze parents' social media activities and customize how they collaborate with medical professionals. For example, if a parent shares a lot of information about childcare on social media, it can prioritize collaboration with medical professionals specializing in childcare. Alternatively, if a parent shares a lot of information about health, it can prioritize collaboration with medical professionals specializing in health. Furthermore, if a parent shares a lot of information about a specific illness, it can prioritize collaboration with medical professionals who are knowledgeable about that illness. This makes it possible to provide more personalized support by collaborating with medical professionals based on parents' social media activities.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit accepts questions in a natural conversational format. For example, if a parent asks about their child's symptoms, the reception unit can accept the question. It can also accept photos or videos of the child's symptoms taken by the parent. Step 2: The analysis unit analyzes the symptoms based on the questions received by the reception unit. For example, it analyzes the symptoms entered by the guardian and suggests appropriate measures to deal with those symptoms. It can also analyze photos and videos uploaded by the guardian and suggest appropriate measures to deal with them based on the images. Step 3: The suggestion unit suggests a countermeasure based on the symptoms analyzed by the analysis unit. For example, the suggestion unit suggests a specific countermeasure to the guardian based on the results of the analysis by the analysis unit. Step 4: The storage unit stores past symptom data. For example, symptoms and diagnosis results previously entered by the guardian can be stored in a database. Step 5: The advice unit provides advice based on the data stored by the storage unit. For example, the advice unit refers to past symptom data and suggests future preventive measures to the guardian. Step 6: The provider provides information about nearby hospitals. For example, it searches for nearby hospitals that are available for treatment based on the current location and provides the information to the guardian. Step 7: The Liaison Department will work with medical professionals and pediatricians, for example, to provide support to parents to ensure they receive appropriate diagnosis and treatment, as needed.

[0059] (Example 2) An assistant system according to an embodiment of the present invention is a system for first-time parents experiencing childcare. It reduces anxiety over sudden illness or injury of a child (infant), suggests at-home measures to prevent incorrect treatment, and provides support for determining whether medical treatment is necessary. This assistant system responds to questions in a natural conversational format and to photos and videos of symptoms. For example, when a parent inputs their child's symptoms, the system suggests appropriate measures to address those symptoms. Furthermore, when a parent uploads photos or videos of their child's symptoms to the system, the system analyzes the images and suggests appropriate measures. Next, the system provides advice that takes into account past symptoms and diagnostic results, as well as long-term preventative measures. For example, it suggests future preventative measures based on past diagnostic results from a child's cold. It also stores past symptoms and diagnostic results in a database and uses them for future diagnoses and advice. Furthermore, it provides information on nearby hospitals with available care and connects with medical professionals and pediatricians. For example, the system searches for nearby hospitals based on the current location and provides information on available hospitals. Furthermore, it connects with medical professionals and pediatricians as needed to provide support for appropriate diagnosis and treatment. In this way, the present invention provides an assistance system that reduces anxiety for parents experiencing childcare for the first time about their child's sudden illness or injury and prevents incorrect responses.The assistance system thereby reduces anxiety for parents experiencing childcare for the first time about their child's sudden illness or injury and prevents incorrect responses.

[0060] The assistant system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, a storage unit, an advice unit, a provision unit, and a linking unit. The reception unit accepts questions in a natural conversational format. For example, when a parent asks a question about their child's symptoms, the reception unit can accept the question. The reception unit can also accept photos and videos of the child's symptoms taken by the parent. The analysis unit analyzes the symptoms based on the question accepted by the reception unit. For example, the analysis unit analyzes symptoms entered by the parent and suggests appropriate countermeasures for the symptoms. The analysis unit can also analyze photos and videos uploaded by the parent and suggest appropriate countermeasures based on the images. The suggestion unit suggests countermeasures based on the symptoms analyzed by the analysis unit. For example, the suggestion unit suggests specific countermeasures to the parent based on the analysis results by the analysis unit. The storage unit stores past symptom data. For example, the storage unit can store symptoms and diagnosis results previously entered by the parent in a database. The advice unit provides advice based on the data stored by the storage unit. For example, the advice unit refers to past symptom data and suggests future preventive measures to the guardian. The provision unit provides information on nearby hospitals. For example, the provision unit searches for nearby hospitals that are open to patients based on the current location and provides that information to the guardian. The collaboration unit collaborates with medical professionals and pediatricians. For example, the collaboration unit collaborates with medical professionals and pediatricians as needed to support the guardian in receiving appropriate diagnosis and treatment. In this way, the assistant system according to the embodiment can reduce the anxiety of guardians who are experiencing childcare for the first time about their child's sudden illness or injury and prevent incorrect handling.

[0061] The reception unit can estimate the parent's emotions and adjust the method of accepting questions based on the estimated parent's emotions. For example, if the parent is feeling anxious, the reception unit can accept questions in a gentle tone and provide an interface that gives the parent a sense of security. Furthermore, if the parent is feeling anxious, the reception unit can provide a simple input form to quickly accept questions. Furthermore, if the parent is feeling relaxed, the reception unit can provide many options to accept detailed questions. This can alleviate the parent's anxiety by providing an appropriate method of accepting questions according to the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the parent's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0062] When accepting a question, the reception unit can select a reception method by referring to the parent's past question history. For example, the reception unit can automatically display questions that the parent has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the parent has used in the past. Furthermore, the reception unit can predict and suggest question formats to be used during specific time periods based on the parent's past question history. In this way, by referring to the parent's past question history, it is possible to provide an optimal method for accepting questions. Some or all of the above-described 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 parent's past question history data into a generation AI and cause the generation AI to select an optimal reception method.

[0063] When receiving a question, the reception unit can filter the questions based on the guardian's current living situation or areas of interest. For example, if the guardian searches a lot for information about childcare, the reception unit can prioritize childcare-related questions. Furthermore, if the guardian is busy with work, the reception unit can provide a question format that can be answered in a short time. Furthermore, if the guardian is interested in health, the reception unit can prioritize health-related questions. This allows for more appropriate support to be provided by receiving questions according to the guardian's living situation and areas of interest. Some 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 guardian's living situation data into the generation AI and have the generation AI perform filtering.

[0064] The reception unit can estimate the parent's emotions and determine the priority of questions to be received based on the estimated parent's emotions. For example, if the parent is feeling anxious, the reception unit can prioritize urgent questions. Furthermore, if the parent is relaxed, the reception unit can prioritize detailed questions. Furthermore, if the parent is feeling impatient, the reception unit can prioritize concise questions. This allows for prompt responses to urgent questions by determining the priority of questions according to the parent's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input parent's emotion data into the generation AI and have the generation AI determine the priority of questions.

[0065] When accepting questions, the reception unit can prioritize highly relevant questions based on the guardian's geographical location information. For example, if the guardian lives in a specific area, the reception unit can prioritize questions related to that area. Also, if the guardian is traveling, the reception unit can prioritize questions related to the travel destination. Furthermore, if the guardian is planning to move, the reception unit can prioritize questions related to the new area. In this way, by accepting questions based on the guardian's geographical location information, it is possible to provide support that is specialized for the area. Some 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 guardian's geographical location information data into a generation AI and cause the generation AI to select highly relevant questions.

[0066] When accepting a question, the reception unit can analyze the parent's social media posts and accept relevant questions. For example, if the parent posts frequently about childcare on social media, the reception unit can prioritize accepting questions related to childcare. Furthermore, if the parent posts frequently about health, the reception unit can prioritize accepting health-related questions. Furthermore, if the parent posts frequently about travel, the reception unit can prioritize accepting travel-related questions. By accepting questions based on the parent's social media activity, more personalized support can be provided. Some or all of the above-described 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 parent's social media data into a generation AI and have the generation AI select relevant questions.

[0067] The analysis unit can estimate the guardian's emotions and adjust the symptom analysis method based on the estimated guardian's emotions. For example, if the guardian is feeling anxious, the analysis unit can quickly provide an analysis result. Furthermore, if the guardian is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the guardian is feeling anxious, the analysis unit can also provide a concise analysis result. This allows for a symptom analysis method tailored to the guardian's emotions to be provided, thereby providing a more appropriate analysis result. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the guardian's emotion data into the generation AI and cause the generation AI to adjust the symptom analysis method.

[0068] When analyzing symptoms, the analysis unit can improve the accuracy of the analysis by referring to past symptom data. For example, if a similar symptom has occurred in the past, the analysis unit performs the analysis based on that data. The analysis unit can also improve the reliability of the analysis results by referring to past symptom data. Furthermore, the analysis unit can predict the progression pattern of the symptom based on past symptom data. In this way, by referring to past symptom data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past symptom data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0069] When analyzing symptoms, the analysis unit can apply different analysis algorithms depending on the symptom category. For example, the analysis unit applies a dedicated analysis algorithm to respiratory symptoms. The analysis unit can also apply a dedicated analysis algorithm to digestive symptoms. The analysis unit can also apply a dedicated analysis algorithm to skin symptoms. In this way, by applying an analysis algorithm according to the symptom category, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input symptom category data to the generation AI and cause the generation AI to select an appropriate analysis algorithm.

[0070] The analysis unit can estimate the parent's emotions and adjust the display method of the analysis results based on the estimated parent's emotions. For example, if the parent is feeling anxious, the analysis unit can provide a simple, highly visible display method. Furthermore, if the parent is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the parent is feeling anxious, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate information to be provided by providing a display method of the analysis results that corresponds to the parent's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input parent's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0071] When analyzing symptoms, the analysis unit can perform the analysis based on the guardian's geographical location information. For example, the analysis unit performs the analysis taking into account illnesses and symptoms specific to the area where the guardian lives. Furthermore, if the guardian is traveling, the analysis unit can also perform the analysis taking into account illnesses and symptoms specific to the travel destination. Furthermore, if the guardian is planning to move, the analysis unit can also perform the analysis taking into account illnesses and symptoms specific to the new area. In this way, by performing the analysis based on the guardian's geographical location information, it is possible to provide analysis results that are specialized for the area. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the guardian's geographical location information data into the generation AI and cause the generation AI to perform an analysis specialized for the area.

[0072] When analyzing symptoms, the analysis unit can improve the accuracy of the analysis by referring to related medical literature. The analysis unit can, for example, refer to the latest medical literature to improve the reliability of the analysis results. The analysis unit can also predict the progression pattern of symptoms by referring to past medical literature. Furthermore, the analysis unit can also identify the cause of symptoms based on medical literature. This can improve the accuracy of the analysis by referring to related medical literature. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input medical literature data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0073] The suggestion unit can estimate the parent's emotions and adjust the proposed method of coping strategies based on the estimated parent's emotions. For example, if the parent feels anxious, the suggestion unit can suggest a coping strategy that provides a sense of security. Furthermore, if the parent feels relaxed, the suggestion unit can also suggest a detailed coping strategy. Furthermore, if the parent feels anxious, the suggestion unit can also suggest a concise and quick coping strategy. This allows for a more appropriate coping strategy to be proposed by providing a coping strategy proposal method that matches the parent's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input parent's emotion data into the generation AI and cause the generation AI to adjust the proposed method of coping strategies.

[0074] When proposing a countermeasure, the suggestion unit can adjust the level of detail of the suggestion based on the severity of the symptom. For example, the suggestion unit can suggest a detailed countermeasure for a symptom with a high severity. The suggestion unit can also suggest a concise countermeasure for a symptom with a low severity. Furthermore, the suggestion unit can determine the priority of the countermeasures according to the severity. This makes it possible to provide a more appropriate countermeasure by proposing a countermeasure according to the severity of the symptom. Some or all of the above-described processing in the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input symptom severity data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0075] When proposing a countermeasure, the suggestion unit can apply different suggestion algorithms depending on the symptom category. For example, the suggestion unit applies a dedicated suggestion algorithm to respiratory symptoms. The suggestion unit can also apply a dedicated suggestion algorithm to digestive symptoms. The suggestion unit can also apply a dedicated suggestion algorithm to skin symptoms. In this way, by applying a suggestion algorithm according to the symptom category, a more appropriate countermeasure can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input symptom category data to the generation AI and cause the generation AI to select an appropriate suggestion algorithm.

[0076] The suggestion unit can estimate the parent's emotions and adjust the length of the suggestion based on the estimated parent's emotions. For example, if the parent feels anxious, the suggestion unit can provide a short, concise suggestion. If the parent feels relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the parent feels impatient, the suggestion unit can provide a quick, concise suggestion. This allows the length of the suggestion to be tailored to the parent's emotions, thereby suggesting a more appropriate course of action. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input parent's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0077] When proposing countermeasures, the suggestion unit can determine the priority of the suggestions based on the time of symptom onset. For example, if a symptom has just occurred, the suggestion unit can prioritize proposing a quick countermeasure. Also, if a symptom has continued for a long period of time, the suggestion unit can prioritize proposing a detailed countermeasure. Furthermore, the suggestion unit can also determine the priority of the countermeasures according to the time of symptom onset. In this way, by prioritizing the countermeasures according to the time of symptom onset, more appropriate countermeasures can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the time of symptom onset into the generation AI and cause the generation AI to determine the priority of the suggestions.

[0078] When proposing countermeasures, the suggestion unit can adjust the order of the suggestions based on the relevance of the symptoms. For example, the suggestion unit can prioritize suggesting countermeasures for highly relevant symptoms. The suggestion unit can also postpone suggesting countermeasures for less relevant symptoms. Furthermore, the suggestion unit can determine the order of countermeasures based on the relevance of the symptoms. In this way, by adjusting the order of countermeasures based on the relevance of the symptoms, more appropriate countermeasures can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input symptom relevance data to the generation AI and cause the generation AI to adjust the order of suggestions.

[0079] The storage unit can estimate the guardian's emotions and select data to be saved based on the estimated emotions. For example, if the guardian is feeling anxious, the storage unit prioritizes saving important data. The storage unit can also save detailed data if the guardian is relaxed. Furthermore, the storage unit can save concise data if the guardian is feeling anxious. This allows for more appropriate data to be saved by selecting data to be saved according to the guardian's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the storage unit can be performed using, for example, AI, or without AI. For example, the storage unit can input the guardian's emotion data into the generation AI and have the generation AI select data to be saved.

[0080] When saving data, the storage unit can improve the storage algorithm by referring to previously saved data. The storage unit, for example, selects an optimal storage method based on previously saved data. The storage unit can also optimize the storage algorithm by referring to previously saved data. Furthermore, the storage unit can avoid data duplication based on previously saved data. In this way, by referring to previously saved data, the storage algorithm can be optimized and data duplication can be avoided. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input previously saved data into a generation AI and have the generation AI improve the storage algorithm.

[0081] The storage unit can estimate the guardian's emotions and determine the priority of stored data based on the estimated guardian's emotions. For example, if the guardian is feeling anxious, the storage unit can prioritize saving important data. Furthermore, if the guardian is relaxed, the storage unit can prioritize saving detailed data. Furthermore, if the guardian is feeling anxious, the storage unit can prioritize saving concise data. Thus, by determining the priority of stored data according to the guardian's emotions, important data can be prioritized for storage. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit can be performed using, for example, an AI, or without an AI. For example, the storage unit can input the guardian's emotion data into the generation AI and have the generation AI determine the priority of stored data.

[0082] The storage unit can weight the stored data based on the time when the symptom occurred when storing the data. For example, if the symptom has just occurred, the storage unit can weight the data higher. Also, if the symptom has continued for a long period of time, the storage unit can weight the data lower. Furthermore, the storage unit can adjust the weighting of the data according to the time when the symptom occurred. In this way, important data can be appropriately stored by weighting the data according to the time when the symptom occurred. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the time when the symptom occurred to the generation AI and cause the generation AI to weight the stored data.

[0083] The advice unit can estimate the parent's emotions and adjust the advice method based on the estimated parent's emotions. For example, if the parent feels anxious, the advice unit can provide reassuring advice. Furthermore, if the parent feels relaxed, the advice unit can provide detailed advice. Furthermore, if the parent feels anxious, the advice unit can provide concise and quick advice. This allows for more appropriate advice to be provided by providing an advice method that suits the parent's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the advice unit may be performed using an AI, for example, or without an AI. For example, the advice unit can input parent's emotion data into the generation AI and cause the generation AI to adjust the advice method.

[0084] When providing advice, the advice unit can select an advice method by referring to past advice data. The advice unit, for example, selects the optimal advice method based on advice provided in the past. The advice unit can also improve the accuracy of advice by referring to past advice data. Furthermore, the advice unit can also customize the content of advice based on past advice data. In this way, by referring to past advice data, it is possible to provide an optimal advice method. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input past advice data into a generation AI and have the generation AI select an advice method.

[0085] When providing advice, the advice unit can adjust the means of advice based on the guardian's current living situation. For example, if the guardian is busy, the advice unit can provide concise and quick advice. Also, if the guardian is relaxed, the advice unit can provide detailed advice. Furthermore, if the guardian is in a specific situation, the advice unit can provide advice tailored to that situation. This allows for more appropriate advice to be provided by providing means of advice tailored to the guardian's living situation. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input the guardian's living situation data into the generation AI and have the generation AI adjust the means of advice.

[0086] The advice unit can estimate the parent's emotions and determine the priority of advice based on the estimated parent's emotions. For example, if the parent is feeling anxious, the advice unit can prioritize important advice. Furthermore, if the parent is relaxed, the advice unit can prioritize detailed advice. Furthermore, if the parent is feeling impatient, the advice unit can prioritize concise advice. In this way, by determining the priority of advice according to the parent's emotions, important advice can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input parent's emotion data into the generation AI and cause the generation AI to determine the priority of advice.

[0087] When providing advice, the advice unit can select an advice method taking into account the guardian's geographical location information. For example, if the guardian lives in a specific area, the advice unit can provide advice related to that area. Furthermore, if the guardian is traveling, the advice unit can provide advice related to the travel destination. Furthermore, if the guardian is planning to move, the advice unit can provide advice related to the new area. In this way, by providing an advice method based on the guardian's geographical location information, advice specific to the area can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the guardian's geographical location information data into the generation AI and cause the generation AI to select the optimal advice method.

[0088] When providing advice, the advice unit can analyze the parent's social media activity and suggest advice methods. For example, if the parent posts many times about childcare on social media, the advice unit can provide parenting-related advice. Furthermore, if the parent posts many times about health, the advice unit can also provide health-related advice. Furthermore, if the parent posts many times about travel, the advice unit can also provide travel-related advice. This allows for more personalized advice by providing advice methods based on the parent's social media activity. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the parent's social media data into the generation AI and have the generation AI execute the suggestion of advice methods.

[0089] The providing unit can estimate the guardian's emotions and adjust the method of providing hospital information based on the estimated guardian's emotions. For example, if the guardian is feeling anxious, the providing unit can provide hospital information that gives a sense of security. Furthermore, if the guardian is relaxed, the providing unit can provide detailed hospital information. Furthermore, if the guardian is feeling anxious, the providing unit can provide concise and quick hospital information. This allows for providing more appropriate information by providing a method of providing hospital information that suits the guardian's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the guardian's emotion data into the generation AI and cause the generation AI to adjust the method of providing hospital information.

[0090] When providing hospital information, the providing unit can improve the providing algorithm by referring to past provided data. The providing unit, for example, selects an optimal providing method based on hospital information provided in the past. The providing unit can also optimize the providing algorithm by referring to past provided data. Furthermore, the providing unit can avoid duplication of hospital information based on past provided data. In this way, by referring to past provided data, the providing algorithm can be optimized and duplication of information can be avoided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past provided data into the generating AI and cause the generating AI to improve the providing algorithm.

[0091] The providing unit can estimate the guardian's emotions and determine the priority of hospital information based on the estimated guardian's emotions. For example, if the guardian is feeling anxious, the providing unit can prioritize providing important hospital information. Furthermore, if the guardian is relaxed, the providing unit can prioritize providing detailed hospital information. Furthermore, if the guardian is feeling anxious, the providing unit can prioritize providing concise hospital information. In this way, by determining the priority of hospital information according to the guardian's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the guardian's emotion data into the generation AI and cause the generation AI to determine the priority of hospital information.

[0092] When providing hospital information, the providing unit can provide optimal hospital information by taking into account the guardian's geographical location information. For example, if the guardian lives in a specific area, the providing unit can provide hospital information related to that area. Furthermore, if the guardian is traveling, the providing unit can also provide hospital information related to the travel destination. Furthermore, if the guardian is planning to move, the providing unit can also provide hospital information related to the new area. In this way, by providing hospital information based on the guardian's geographical location information, it is possible to provide information specific to the area. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the guardian's geographical location information data into the generating AI and cause the generating AI to provide optimal hospital information.

[0093] The collaboration unit can estimate the parent's emotions and adjust the collaboration method with the medical professional based on the estimated parent's emotions. For example, if the parent feels anxious, the collaboration unit can provide a collaboration method that provides a sense of security. Furthermore, if the parent feels relaxed, the collaboration unit can provide a detailed collaboration method. Furthermore, if the parent feels anxious, the collaboration unit can provide a concise and quick collaboration method. This allows for more appropriate collaboration by providing a collaboration method with the medical professional that matches the parent's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collaboration unit can be performed using AI, for example, or without AI. For example, the collaboration unit can input parent's emotion data into the generation AI and cause the generation AI to adjust the collaboration method.

[0094] When collaborating with a medical professional, the collaboration unit can optimize the collaboration algorithm by referring to past collaboration data. The collaboration unit, for example, selects an optimal collaboration method based on collaboration performed in the past. The collaboration unit can also optimize the collaboration algorithm by referring to the past collaboration data. Furthermore, the collaboration unit can customize the content of collaboration based on the past collaboration data. In this way, by referring to the past collaboration data, the collaboration algorithm can be optimized and the accuracy of collaboration can be improved. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input past collaboration data into the generation AI and cause the generation AI to optimize the collaboration algorithm.

[0095] The collaboration unit can estimate the parent's emotions and determine the priority of collaboration based on the estimated parent's emotions. For example, if the parent is feeling anxious, the collaboration unit can prioritize important collaboration. Furthermore, if the parent is relaxed, the collaboration unit can prioritize detailed collaboration. Furthermore, if the parent is feeling anxious, the collaboration unit can prioritize concise collaboration. In this way, by determining the priority of collaboration according to the parent's emotions, important collaboration can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit may be performed using an AI, for example, or without an AI. For example, the collaboration unit can input parent's emotion data into the generation AI and cause the generation AI to determine the priority of collaboration.

[0096] When collaborating with a medical professional, the collaboration unit can select a collaboration method based on the guardian's geographic location information. For example, if the guardian lives in a specific area, the collaboration unit can provide a collaboration method related to that area. Furthermore, if the guardian is traveling, the collaboration unit can provide a collaboration method related to the travel destination. Furthermore, if the guardian is planning to move, the collaboration unit can provide a collaboration method related to the new area. In this way, by providing a collaboration method based on the guardian's geographic location information, collaboration specific to the area can be performed. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the guardian's geographic location information data into the generation AI and cause the generation AI to select the optimal collaboration method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, storage unit, advice unit, provision unit, and linking unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts questions from the parent and photos and videos of symptoms. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a solution based on the analysis results. The storage unit, for example, stores past symptom data in the database 24 of the data processing device 12. The advice unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and provides advice based on the stored data. The provision unit, for example, is implemented by the control unit 46A of the smart device 14 and provides information on nearby hospitals. The linking unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and facilitates collaboration with medical professionals and pediatricians. The reception unit can, for example, estimate the parent's emotion and adjust the method of receiving the question based on the estimated emotion. The estimation of emotion is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, storage unit, advice unit, provision unit, and collaboration 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 accepts questions from parents and photos and videos of symptoms. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes countermeasures based on the analysis results. The storage unit, for example, stores past symptom data in the database 24 of the data processing device 12. The advice unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on the stored data. The provision unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides information on nearby hospitals. The collaboration unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and cooperates with medical professionals and pediatricians. The reception unit can, for example, estimate the parent's emotion and adjust the method of receiving the question based on the estimated emotion. The estimation of emotion is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, storage unit, advice unit, provision unit, and linking unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314 and accepts questions from the parent and / or child and photos and videos of symptoms. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes a solution based on the analysis results. The storage unit, for example, stores past symptom data in the database 24 of the data processing device 12. The advice unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and provides advice based on the stored data. The provision unit, for example, is implemented by the control unit 46A of the headset-type terminal 314 and provides information on nearby hospitals. The linking unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and cooperates with medical professionals and pediatricians. The reception unit can, for example, estimate the parent's emotion and adjust the method of receiving the question based on the estimated emotion. The estimation of emotion is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, storage unit, advice unit, provision unit, and collaboration unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts questions from parents and photos and videos of symptoms. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the accepted information. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes countermeasures based on the analysis results. The storage unit, for example, stores past symptom data in the database 24 of the data processing device 12. The advice unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on the stored data. The provision unit, for example, is realized by the control unit 46A of the robot 414 and provides information on nearby hospitals. The collaboration unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and cooperates with medical professionals and pediatricians. The reception unit can, for example, estimate the parent's emotion and adjust the method of receiving the question based on the estimated emotion. The estimation of emotion is realized by, for example, the specific processing unit 290 of the data processing device 12.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the parent's voice tone and speaking speed to estimate the parent's stress level. For example, if the parent's voice is trembling or they speak quickly, it can determine that they are under high stress and provide quick and concise question formats. If the parent's voice is calm, it can accept detailed questions. Furthermore, if the parent's voice tone changes, it can detect that change and take appropriate action. This makes it possible to provide a more appropriate method of accepting questions based on the parent's voice tone and speaking speed.

[0099] The analysis unit can refer to data on similar past cases for the symptoms entered by the guardian and predict the progression of the symptoms. For example, based on data on children who have had the same symptoms in the past, the analysis unit can predict the future progression of the symptoms and suggest points to watch out for to the guardian. The analysis unit can also suggest necessary measures in stages based on the predicted progression of the symptoms. Furthermore, the analysis unit can also suggest the timing of a medical examination based on the predicted progression of the symptoms. This makes it possible to provide more accurate analysis results by utilizing past case data.

[0100] The suggestion unit can adjust the timing of suggesting solutions based on the parent's lifestyle. For example, if the parent asks many questions at night, the suggestion unit can suggest solutions that are suitable for the night. If the parent is busy during the day, the suggestion unit can also suggest solutions that can be implemented in a short time. Furthermore, if the parent has more time on the weekends, the suggestion unit can suggest detailed solutions that can be implemented on the weekends. In this way, by suggesting solutions that suit the parent's lifestyle, the suggestion unit can provide suggestions that are easier to implement.

[0101] The providing unit can provide optimal hospital information by referring to the guardian's past hospital visit history. For example, it can suggest hospitals that can treat similar symptoms based on the evaluations and treatment details of hospitals visited in the past. It can also preferentially suggest hospitals with treatment styles preferred by the guardian based on the past visit history. Furthermore, it can also exclude hospitals that the guardian wants to avoid based on the past visit history. In this way, it is possible to provide more appropriate hospital information by utilizing the guardian's past visit history.

[0102] The collaboration unit can analyze parents' social media activities and customize how they collaborate with medical professionals. For example, if a parent shares a lot of information about childcare on social media, it can prioritize collaboration with medical professionals specializing in childcare. Alternatively, if a parent shares a lot of information about health, it can prioritize collaboration with medical professionals specializing in health. Furthermore, if a parent shares a lot of information about a specific illness, it can prioritize collaboration with medical professionals who are knowledgeable about that illness. This makes it possible to provide more personalized support by collaborating with medical professionals based on parents' social media activities.

[0103] The reception unit can estimate the parent's emotions and, based on the estimated parent's emotions, provide relaxing music and images when accepting questions. For example, if the parent is feeling anxious, relaxing music can be played to give the parent a sense of security. If the parent is feeling anxious, calming images can be displayed to encourage the parent to calm down. Furthermore, if the parent is relaxed, interactive images can be provided to allow for more detailed questions to be accepted. This allows for a smooth acceptance of questions by providing content with a relaxing effect that matches the parent's emotions.

[0104] The analysis unit can estimate the parent's emotions and adjust the notification method of the analysis results based on the estimated parent's emotions. For example, if the parent is feeling anxious, the analysis results can be quickly notified to provide a sense of security. If the parent is relaxed, detailed analysis results can be notified. Furthermore, if the parent is anxious, a concise analysis result can be notified to encourage a prompt response. This makes it possible to provide more appropriate information by providing a notification method of the analysis results according to the parent's emotions.

[0105] The suggestion unit can estimate the parent's emotions and add an encouraging message when proposing a solution based on the estimated parent's emotions. For example, if the parent is feeling anxious, a message such as "It's okay, you're a great parent" can be displayed to reassure the parent. If the parent is feeling impatient, a message such as "Calm down and take it easy" can be displayed to encourage the parent to stay calm. Furthermore, if the parent is relaxed, a message such as "Keep up the good work" can be displayed to maintain motivation. In this way, by providing encouraging messages that correspond to the parent's emotions, solution suggestions can be made more effectively.

[0106] The storage unit can estimate the parent's emotions and adjust the display method of the stored data based on the estimated parent's emotions. For example, if the parent is feeling anxious, important data can be highlighted to provide a sense of security. If the parent is relaxed, detailed data can be displayed. Furthermore, if the parent is anxious, simple data can be displayed to encourage a quick response. In this way, by providing a display method of the stored data according to the parent's emotions, more appropriate information can be provided.

[0107] The collaboration unit can estimate the parent's emotions and provide emotional support when collaborating with a medical professional based on the estimated parent's emotions. For example, if the parent is feeling anxious, the collaboration unit can inform the medical professional of the parent's emotional state and encourage them to take measures to reassure the parent. If the parent is feeling anxious, the collaboration unit can request the medical professional to take prompt action. Furthermore, if the parent is feeling relaxed, the collaboration unit can request the medical professional to provide a detailed explanation. This allows for more effective collaboration with a medical professional by providing emotional support according to the parent's emotions.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit accepts questions in a natural conversational format. For example, if a parent asks about their child's symptoms, the reception unit can accept the question. It can also accept photos or videos of the child's symptoms taken by the parent. Step 2: The analysis unit analyzes the symptoms based on the questions received by the reception unit. For example, it analyzes the symptoms entered by the guardian and suggests appropriate measures to deal with those symptoms. It can also analyze photos and videos uploaded by the guardian and suggest appropriate measures to deal with them based on the images. Step 3: The suggestion unit suggests a countermeasure based on the symptoms analyzed by the analysis unit. For example, the suggestion unit suggests a specific countermeasure to the guardian based on the results of the analysis by the analysis unit. Step 4: The storage unit stores past symptom data. For example, symptoms and diagnosis results previously entered by the guardian can be stored in a database. Step 5: The advice unit provides advice based on the data stored by the storage unit. For example, the advice unit refers to past symptom data and suggests future preventive measures to the guardian. Step 6: The provider provides information about nearby hospitals. For example, it searches for nearby hospitals that are available for treatment based on the current location and provides the information to the guardian. Step 7: The Liaison Department will work with medical professionals and pediatricians, for example, to provide support to parents to ensure they receive appropriate diagnosis and treatment, as needed.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] 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.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] 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.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 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 section that accepts questions in a natural conversational format; an analysis unit that analyzes symptoms based on the questions received by the reception unit; a suggestion unit that suggests a countermeasure based on the symptoms analyzed by the analysis unit; a storage unit for storing past symptom data; an advice unit that provides advice based on the data stored by the storage unit; A provision department that provides information on nearby hospitals, A liaison department that coordinates with medical professionals or pediatricians; Equipped with A system characterized by:

2. The reception unit Estimate the parent's feelings and adjust the way questions are accepted based on the estimated parent's feelings 2. The system of claim 1.

3. The reception unit When accepting questions, the method of acceptance is determined by referring to the parent's past question history.

2. The system of claim 1.

4. The reception unit When questions are received, they are filtered based on the parent's current life situation or areas of interest.

2. The system of claim 1.

5. The reception unit Estimate the parent's feelings and prioritize questions to be accepted based on the estimated parent's feelings.

2. The system of claim 1.

6. The reception unit Prioritize questions based on the parent's geographic location to prioritize relevant questions 2. The system of claim 1.

7. The reception unit When accepting questions, analyze parents' social media posts and accept related questions.

2. The system of claim 1.

8. The analysis unit Estimate the parent's emotions and adjust the symptom analysis method based on the estimated parent's emotions.

2. The system of claim 1.

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