system

Abstract

The system according to the embodiment aims to provide information on services available to caregivers and provide psychological care. [Solution] A system according to an embodiment includes a question generation unit, an analysis unit, an advice provision unit, and a care provision unit. The question generation unit generates questions to understand the current situation of a user. The analysis unit analyzes the user's answers to the questions generated by the question generation unit. The advice provision unit provides advice based on the results of the analysis by the analysis unit. The care provision unit provides psychological care for the caregiver based on the advice provided by the advice provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for caregivers to properly grasp information about available services and provide psychological care.

[0005] The system according to the embodiment aims to provide information on services available to caregivers and provide psychological care. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, an analysis unit, an advice provision unit, and a care provision unit. The question generation unit generates questions to understand the current situation of the user. The analysis unit analyzes the user's answers to the questions generated by the question generation unit. The advice provision unit provides advice based on the results of the analysis by the analysis unit. The care provision unit provides psychological care for the caregiver based on the advice provided by the advice provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide caregivers with information about services available to them and provide psychological care. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A care support system according to an embodiment of the present invention is a system that grasps a user's current situation and provides appropriate advice on care services and mental care. The user launches an application and answers questions interactively, which allows the system to grasp the user's current situation. The system then analyzes the user's answers to grasp the user's current situation. Based on the analysis results, the system advises the user on available care services. Furthermore, the system also provides advice on mental care for caregivers. For example, if a user inputs, "I have a family member who needs care, but I don't know what services are available," the system interactively collects detailed information and suggests appropriate care services. Furthermore, if the caregiver is feeling stressed, the system also provides advice on mental care. This system enables people in need of care to receive appropriate services and reduces the burden on caregivers. In this way, the care support system can grasp the user's current situation and provide appropriate advice on care services and mental care.

[0029] A care support system according to an embodiment includes a question generation unit, an analysis unit, an advice providing unit, and a care providing unit. The question generation unit generates questions to understand a user's current situation. For example, the question generation unit can generate specific questions regarding the user's health condition, living situation, psychological state, etc. The question generation unit can generate questions in a format such as multiple choice, free description, or scale. The analysis unit analyzes the user's answers to the questions generated by the question generation unit. For example, the analysis unit can analyze the user's answers using methods such as text analysis, statistical analysis, and machine learning algorithms. The analysis unit understands the current situation based on the user's answers and extracts information for providing advice on appropriate care services. The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide advice in a format such as text, audio, or video. The advice providing unit provides specific advice regarding care services available to the user. The care providing unit provides mental care for the caregiver based on the advice provided by the advice providing unit. For example, the care providing unit can provide mental care to the caregiver using methods such as counseling, relaxation techniques, and stress management. The care providing unit measures the stress level of the caregiver and provides appropriate care advice. This allows the care support system according to the embodiment to grasp the current situation of the user and provide appropriate advice on care services and mental care.

[0030] The question generation unit can analyze the user's past answer history and generate appropriate questions. For example, the question generation unit can generate questions that do not overlap based on the content of the user's past answers. The question generation unit can also generate questions that focus on specific issues from the user's past answer history. Furthermore, the question generation unit can analyze the user's past answer history and generate questions that fill in missing answers. This allows optimal questions to be generated based on the past answer history, avoiding overlaps and efficiently collecting information. The past answer history includes, for example, text data, option data, score data, etc. Some or all of the above-described processing in the question generation unit may be performed using, or without, AI. For example, the question generation unit can input the user's past answer data into a generation AI and cause the generation AI to generate optimal questions.

[0031] The question generation unit can adjust the level of detail of the question when generating a question depending on the user's living situation and the progress of care. For example, when the user's living situation is stable, the question generation unit can ask detailed questions to collect in-depth information. Furthermore, when the user's care progress is in the early stages, the question generation unit can ask basic questions to collect necessary information. Furthermore, when the user's living situation is changing, the question generation unit can ask flexible questions to collect the latest information. This allows necessary information to be accurately collected by asking questions according to the user's situation. Examples of living situations include daily living activities, health status, and home environment. Examples of the progress of care include care level and evaluation of activities of daily living (ADL). Some or all of the above-mentioned processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can input the user's living situation data into a generation AI and cause the generation AI to adjust the level of detail of the question.

[0032] When generating a question, the question generation unit can generate a question regarding nursing care services specific to the region based on the user's geographical location information. The question generation unit can generate a question regarding nursing care services specific to the region based on, for example, the user's location. The question generation unit can also generate a question regarding nursing care facilities specific to the region by taking the user's geographical location information into consideration. The question generation unit can also generate a question regarding nursing care support organizations specific to the region based on the user's location. This allows information appropriate to the region to be collected by generating a question regarding nursing care services specific to the region. The geographical location information includes, for example, GPS data and address information. The nursing care services specific to the region include, for example, nursing care facilities specific to the region, nursing care support services specific to the region, and the like. Some or all of the above-described processing by the question generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the question generation unit can input the user's geographical location data to the generation AI and cause the generation AI to generate a question regarding nursing care services specific to the region.

[0033] The question generation unit can analyze the user's social media activities and generate related questions when generating questions. For example, the question generation unit can analyze the user's social media posts and generate questions based on concerns about caregiving. The question generation unit can also extract topics related to caregiving from the user's social media activities and generate related questions. Furthermore, the question generation unit can analyze the user's social media interactions and generate specific questions related to caregiving. This allows information based on the user's interests to be collected by generating related questions based on the social media activities. Social media activities include, for example, the content of posts, the number of likes, and the content of comments. Some or all of the above-described processing in the question generation unit may be performed using, or without, AI. For example, the question generation unit can input the user's social media data into a generation AI and cause the generation AI to generate related questions.

[0034] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past response data. The analysis unit can, for example, optimize the analysis algorithm based on the user's past response data. The analysis unit can also extract specific patterns from the user's past response data and adjust the analysis algorithm. Furthermore, the analysis unit can analyze the user's past response data to improve the accuracy of the analysis algorithm. By optimizing the analysis algorithm based on the past response data, analysis accuracy is improved. The past response data includes, for example, text data, choice data, score data, etc. Adjustment of the analysis algorithm includes, for example, algorithm parameter adjustment, feature engineering, etc. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's past response data into a generation AI and cause the generation AI to adjust the analysis algorithm.

[0035] During analysis, the analysis unit can adjust the level of detail of the analysis based on the user's living situation and the progress of care. For example, when the user's living situation is stable, the analysis unit can perform a detailed analysis and provide in-depth information. Furthermore, when the user's care progress is in the early stages, the analysis unit can perform a basic analysis and provide necessary information. Furthermore, when the user's living situation is fluctuating, the analysis unit can perform a flexible analysis and provide the latest information. This allows for accurate provision of necessary information by performing an analysis tailored to the user's situation. Examples of living situations include daily living activities, health status, and home environment. Examples of the progress of care include care level and assessment of activities of daily living (ADL). Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's living situation data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0036] During the analysis, the analysis unit can perform an analysis regarding area-specific nursing care services based on the user's geographical location information. The analysis unit can perform an analysis regarding area-specific nursing care services based on, for example, the user's location. The analysis unit can also perform an analysis regarding local nursing care facilities taking into account the user's geographical location information. Furthermore, the analysis unit can perform an analysis regarding local nursing care support organizations based on the user's location. This allows for analysis regarding area-specific nursing care services to provide information appropriate for the area. Geographical location information includes, for example, GPS data, address information, etc. Area-specific nursing care services include, for example, local nursing care facilities, local nursing care support services, etc. Some or all of the above-described processing by 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 user's geographical location data into the generation AI and cause the generation AI to perform an analysis regarding area-specific nursing care services.

[0037] During the analysis, the analysis unit can analyze the user's social media activities and perform related analysis. For example, the analysis unit can analyze the user's social media posts and perform analysis based on caregiving interests. The analysis unit can also extract topics related to caregiving from the user's social media activities and perform related analysis. Furthermore, the analysis unit can analyze the content of the user's social media interactions and perform specific analysis related to caregiving. This makes it possible to provide information tailored to the user's interests by performing related analysis based on social media activities. Social media activities include, for example, the content of posts, the number of likes, the content of comments, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI perform related analysis.

[0038] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the user's living situation and the progress of care. For example, when the user's living situation is stable, the advice providing unit can provide detailed advice. Furthermore, when the user's care progress is in the early stages, the advice providing unit can provide basic advice. Furthermore, when the user's living situation is fluctuating, the advice providing unit can provide flexible advice. This allows necessary information to be accurately provided by providing advice according to the user's situation. Examples of living situations include daily living activities, health status, and home environment. Examples of the progress of care include care level and evaluation of activities of daily living (ADL). Examples of adjustment of the level of detail of the advice include the specificity of the advice and the depth of the advice. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's living situation data to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0039] When providing advice, the advice providing unit can provide appropriate advice by referring to the user's past answer data. The advice providing unit can, for example, provide optimal advice based on the user's past answer data. The advice providing unit can also provide advice focused on a specific problem based on the user's past answer data. The advice providing unit can also analyze the user's past answer data and provide advice that complements missing answers. This allows optimal advice to be provided based on the past answer data, avoiding duplication and providing information efficiently. Past answer data includes, for example, text data, option data, score data, etc. Providing appropriate advice includes, for example, the relevance of the advice, the importance of the advice, etc. Some or all of the above-described processing by the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input the user's past answer data into a generation AI and cause the generation AI to provide appropriate advice.

[0040] When providing advice, the advice providing unit can provide advice on nursing care services specific to the region based on the user's geographical location information. The advice providing unit can provide advice on nursing care services specific to the region based on, for example, the user's location. The advice providing unit can also provide advice on nursing care facilities specific to the region, taking into account the user's geographical location information. Furthermore, the advice providing unit can provide advice on nursing care support organizations specific to the region based on the user's location. This allows the provision of advice on nursing care services specific to the region to provide information appropriate for the region. Geographical location information includes, for example, GPS data and address information. Regional nursing care services include, for example, regional nursing care facilities and regional nursing care support services. Some or all of the above-described processing by the advice providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice providing unit can input the user's geographical location data to the generation AI and cause the generation AI to provide advice on nursing care services specific to the region.

[0041] The advice providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the advice providing unit can analyze the user's social media posts and provide advice based on the user's concerns about caregiving. The advice providing unit can also extract topics related to caregiving from the user's social media activity and provide relevant advice. Furthermore, the advice providing unit can analyze the user's social media interactions and provide specific advice related to caregiving. This allows the system to provide information tailored to the user's interests by providing relevant advice based on the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the content of comments, etc. Some or all of the above-described processing in the advice providing unit may be performed using AI, or may be performed without AI. For example, the advice providing unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant advice.

[0042] When providing care, the care providing unit can provide an appropriate care method by referring to the user's past stress level data. For example, the care providing unit can provide an optimal care method based on the user's past stress level data. The care providing unit can also provide a care method focused on a specific problem based on the user's past stress level data. Furthermore, the care providing unit can analyze the user's past stress level data and provide a care method appropriate for periods of high stress. This allows the user's stress to be effectively reduced by providing an optimal care method based on the past stress level data. Examples of past stress level data include stress assessment scores and stress-related questionnaire results. Providing an appropriate care method includes, for example, the relevance of care and the importance of care. Some or all of the above-described processing in the care providing unit may be performed using, or without, AI. For example, the care providing unit can input the user's past stress level data into a generation AI and cause the generation AI to provide an appropriate care method.

[0043] The care providing unit can adjust the level of detail of care based on the user's living situation and the progress of care when providing care. For example, when the user's living situation is stable, the care providing unit can provide a detailed care method. Furthermore, when the user's care progress is in the early stages, the care providing unit can provide a basic care method. Furthermore, when the user's living situation is fluctuating, the care providing unit can provide a flexible care method. This allows necessary information to be accurately provided by providing a care method according to the user's situation. Examples of living situations include activities of daily living, health status, and home environment. Examples of the progress of care include care level and assessment of activities of daily living (ADL). Examples of adjustment of the level of detail of care include specificity of care and depth of care. Some or all of the above-mentioned processing in the care providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the care providing unit can input the user's living situation data to a generation AI and cause the generation AI to adjust the level of detail of care.

[0044] When providing care, the care providing unit can provide region-specific mental health advice based on the user's geographical location information. The care providing unit can provide region-specific mental health advice based on, for example, the user's location. The care providing unit can also provide advice about local mental health facilities, taking into account the user's geographical location information. Furthermore, the care providing unit can provide advice about local mental health support organizations based on the user's location. This allows the provision of region-specific mental health advice to provide information appropriate for the region. Geographical location information includes, for example, GPS data, address information, etc. Region-specific mental health advice includes, for example, local counseling services, local support groups, etc. Some or all of the above-described processing in the care providing unit can be performed using, for example, AI, or without AI. For example, the care providing unit can input the user's geographical location data into a generation AI and cause the generation AI to provide region-specific mental health advice.

[0045] The care providing unit can analyze the user's social media activities and provide relevant care methods when providing care. For example, the care providing unit can analyze the user's social media posts and provide care methods based on the user's concerns regarding mental health care. The care providing unit can also extract topics related to mental health care from the user's social media activities and provide relevant care methods. Furthermore, the care providing unit can analyze the content of the user's social media interactions and provide specific care methods related to mental health care. This allows the provision of relevant care methods based on the user's social media activities to provide information tailored to the user's interests. Social media activities include, for example, the content of posts, the number of likes, the content of comments, etc. Some or all of the above-described processing in the care providing unit may be performed using, or without, AI. For example, the care providing unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant care methods.

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

[0047] The question generation unit can customize questions based on the user's hobbies and interests. For example, if the user is interested in music, music-related questions can be generated to increase the user's level of relaxation. If the user is interested in sports, sports-related questions can be generated to understand the user's health condition. If the user is interested in reading, reading-related questions can be generated to deepen the user's knowledge and interests. This allows more appropriate information to be collected by asking questions that match the user's hobbies and interests.

[0048] The analysis unit can acquire the user's health data in real time and reflect it in the analysis results. For example, by acquiring the user's heart rate and blood pressure in real time and reflecting them in the analysis results, a more accurate understanding of the user's health condition can be achieved. In addition, by acquiring the user's number of steps and exercise volume in real time and reflecting them in the analysis results, the user's activity level can be evaluated. Furthermore, by acquiring the user's sleep data in real time and reflecting them in the analysis results, the user's sleep quality can be evaluated. In this way, analysis can be performed based on real-time health data, allowing for more accurate information to be provided.

[0049] The advice providing unit can customize advice based on the user's family structure. For example, if the user lives with elderly parents, advice on nursing care services for the elderly can be provided. If the user has small children, advice on balancing child care and nursing care can be provided. Furthermore, if the user lives alone, advice to support independent living can be provided. This allows for more appropriate information to be provided by providing advice according to the user's family structure.

[0050] The care provider can customize mental care methods based on the user's hobbies and interests. For example, if the user is interested in music, music therapy can be suggested. If the user is interested in art, art therapy can be suggested. Furthermore, if the user is interested in nature, nature walks or gardening can be suggested. This allows for more effective care by providing mental care methods that match the user's hobbies and interests.

[0051] The care providing unit can customize the mental care method based on the user's living environment. For example, if the user lives in an urban area, it can suggest a relaxation method suited to the urban environment. If the user lives in the countryside, it can suggest a relaxation method that utilizes the natural environment. Furthermore, if the user leads a busy life, it can suggest a relaxation method that can be performed in a short amount of time. This allows for more effective care by providing a mental care method that suits the user's living environment.

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

[0053] Step 1: The question generator generates questions to understand the user's current situation. For example, the question generator can generate specific questions about the user's health condition, lifestyle, psychological state, etc. The question generator can generate questions in multiple choice, free description, scale, or other formats. Step 2: The analysis unit analyzes the user's answers to the questions generated by the question generation unit. For example, the analysis unit can analyze the user's answers using methods such as text analysis, statistical analysis, and machine learning algorithms. The analysis unit understands the current situation based on the user's answers and extracts information to provide appropriate nursing care service advice. Step 3: The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide advice in a format such as text, audio, or video. The advice providing unit provides specific advice regarding care services available to the user. Step 4: The care delivery unit provides psychological care to the caregiver based on the advice provided by the advice delivery unit. For example, the care delivery unit can provide psychological care to the caregiver using methods such as counseling, relaxation techniques, and stress management. The care delivery unit measures the stress level of the caregiver and provides appropriate care advice.

[0054] (Example 2) A care support system according to an embodiment of the present invention is a system that grasps a user's current situation and provides appropriate advice on care services and mental care. The user launches an application and answers questions interactively, which allows the system to grasp the user's current situation. The system then analyzes the user's answers to grasp the user's current situation. Based on the analysis results, the system advises the user on available care services. Furthermore, the system also provides advice on mental care for caregivers. For example, if a user inputs, "I have a family member who needs care, but I don't know what services are available," the system interactively collects detailed information and suggests appropriate care services. Furthermore, if the caregiver is feeling stressed, the system also provides advice on mental care. This system enables people in need of care to receive appropriate services and reduces the burden on caregivers. In this way, the care support system can grasp the user's current situation and provide appropriate advice on care services and mental care.

[0055] A care support system according to an embodiment includes a question generation unit, an analysis unit, an advice providing unit, and a care providing unit. The question generation unit generates questions to understand a user's current situation. For example, the question generation unit can generate specific questions regarding the user's health condition, living situation, psychological state, etc. The question generation unit can generate questions in a format such as multiple choice, free description, or scale. The analysis unit analyzes the user's answers to the questions generated by the question generation unit. For example, the analysis unit can analyze the user's answers using methods such as text analysis, statistical analysis, and machine learning algorithms. The analysis unit understands the current situation based on the user's answers and extracts information for providing advice on appropriate care services. The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide advice in a format such as text, audio, or video. The advice providing unit provides specific advice regarding care services available to the user. The care providing unit provides mental care for the caregiver based on the advice provided by the advice providing unit. For example, the care providing unit can provide mental care to the caregiver using methods such as counseling, relaxation techniques, and stress management. The care providing unit measures the stress level of the caregiver and provides appropriate care advice. This allows the care support system according to the embodiment to grasp the current situation of the user and provide appropriate advice on care services and mental care.

[0056] The question generation unit can estimate the user's emotions and adjust the content and order of questions based on the estimated user's emotions. For example, if the user is feeling stressed, the question generation unit can start with simple, easy-to-answer questions and gradually move on to more detailed questions. Furthermore, if the user is relaxed, the question generation unit can ask detailed questions first, allowing the user to quickly understand the current situation. Furthermore, if the user is in a hurry, the question generation unit can prioritize important questions and collect necessary information in a short amount of time. This allows more appropriate information to be collected by asking questions according to the user's emotions. Emotions are estimated using methods such as facial expression analysis, voice analysis, and text analysis. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or without AI. For example, the question generation unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate emotions.

[0057] The question generation unit can analyze the user's past answer history and generate appropriate questions. For example, the question generation unit can generate questions that do not overlap based on the content of the user's past answers. The question generation unit can also generate questions that focus on specific issues from the user's past answer history. Furthermore, the question generation unit can analyze the user's past answer history and generate questions that fill in missing answers. This allows optimal questions to be generated based on the past answer history, avoiding overlaps and efficiently collecting information. The past answer history includes, for example, text data, option data, score data, etc. Some or all of the above-described processing in the question generation unit may be performed using, or without, AI. For example, the question generation unit can input the user's past answer data into a generation AI and cause the generation AI to generate optimal questions.

[0058] The question generation unit can adjust the level of detail of the question when generating a question depending on the user's living situation and the progress of care. For example, when the user's living situation is stable, the question generation unit can ask detailed questions to collect in-depth information. Furthermore, when the user's care progress is in the early stages, the question generation unit can ask basic questions to collect necessary information. Furthermore, when the user's living situation is changing, the question generation unit can ask flexible questions to collect the latest information. This allows necessary information to be accurately collected by asking questions according to the user's situation. Examples of living situations include daily living activities, health status, and home environment. Examples of the progress of care include care level and evaluation of activities of daily living (ADL). Some or all of the above-mentioned processing in the question generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the question generation unit can input the user's living situation data into a generation AI and cause the generation AI to adjust the level of detail of the question.

[0059] The question generation unit can estimate the user's emotions and adjust the difficulty of questions based on the estimated user's emotions. For example, if the user is feeling stressed, the question generation unit can prioritize simple, easy-to-answer questions. Furthermore, if the user is relaxed, the question generation unit can ask detailed, difficult questions. Furthermore, if the user is in a hurry, the question generation unit can ask short, to-the-point questions. This allows the user to be provided with questions that are easy to answer by adjusting the difficulty of questions according to the user's emotions. Emotion estimation is performed using, for example, facial expression analysis, voice analysis, or text analysis. Some or all of the above-described processing in the question generation unit may be performed using, for example, AI, or may be performed without AI. For example, the question generation unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the difficulty of questions.

[0060] When generating a question, the question generation unit can generate a question regarding nursing care services specific to the region based on the user's geographical location information. The question generation unit can generate a question regarding nursing care services specific to the region based on, for example, the user's location. The question generation unit can also generate a question regarding nursing care facilities specific to the region by taking the user's geographical location information into consideration. The question generation unit can also generate a question regarding nursing care support organizations specific to the region based on the user's location. This allows information appropriate to the region to be collected by generating a question regarding nursing care services specific to the region. The geographical location information includes, for example, GPS data and address information. The nursing care services specific to the region include, for example, nursing care facilities specific to the region, nursing care support services specific to the region, and the like. Some or all of the above-described processing by the question generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the question generation unit can input the user's geographical location data to the generation AI and cause the generation AI to generate a question regarding nursing care services specific to the region.

[0061] The question generation unit can analyze the user's social media activities and generate related questions when generating questions. For example, the question generation unit can analyze the user's social media posts and generate questions based on concerns about caregiving. The question generation unit can also extract topics related to caregiving from the user's social media activities and generate related questions. Furthermore, the question generation unit can analyze the user's social media interactions and generate specific questions related to caregiving. This allows information based on the user's interests to be collected by generating related questions based on the social media activities. Social media activities include, for example, the content of posts, the number of likes, and the content of comments. Some or all of the above-described processing in the question generation unit may be performed using, or without, AI. For example, the question generation unit can input the user's social media data into a generation AI and cause the generation AI to generate related questions.

[0062] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can increase the accuracy of the analysis and provide more detailed information. Furthermore, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis and provide necessary information. Furthermore, if the user is in a hurry, the analysis unit can adjust the accuracy of the analysis and provide information quickly. This allows for adjusting the accuracy of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is performed using methods such as facial expression analysis, voice analysis, and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0063] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past response data. The analysis unit can, for example, optimize the analysis algorithm based on the user's past response data. The analysis unit can also extract specific patterns from the user's past response data and adjust the analysis algorithm. Furthermore, the analysis unit can analyze the user's past response data to improve the accuracy of the analysis algorithm. By optimizing the analysis algorithm based on the past response data, analysis accuracy is improved. The past response data includes, for example, text data, choice data, score data, etc. Adjustment of the analysis algorithm includes, for example, algorithm parameter adjustment, feature engineering, etc. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's past response data into a generation AI and cause the generation AI to adjust the analysis algorithm.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the user's living situation and the progress of care. For example, when the user's living situation is stable, the analysis unit can perform a detailed analysis and provide in-depth information. Furthermore, when the user's care progress is in the early stages, the analysis unit can perform a basic analysis and provide necessary information. Furthermore, when the user's living situation is fluctuating, the analysis unit can perform a flexible analysis and provide the latest information. This allows for accurate provision of necessary information by performing an analysis tailored to the user's situation. Examples of living situations include daily living activities, health status, and home environment. Examples of the progress of care include care level and assessment of activities of daily living (ADL). Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's living situation data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for a deeper understanding of the information by providing a display method that matches the user's emotions. Emotion estimation is performed using, for example, facial expression analysis, voice analysis, text analysis, etc. Adjustment of the display method of the analysis results includes, for example, the display format and the priority of the display content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the display method of the analysis results.

[0066] During the analysis, the analysis unit can perform an analysis regarding area-specific nursing care services based on the user's geographical location information. The analysis unit can perform an analysis regarding area-specific nursing care services based on, for example, the user's location. The analysis unit can also perform an analysis regarding local nursing care facilities taking into account the user's geographical location information. Furthermore, the analysis unit can perform an analysis regarding local nursing care support organizations based on the user's location. This allows for analysis regarding area-specific nursing care services to provide information appropriate for the area. Geographical location information includes, for example, GPS data, address information, etc. Area-specific nursing care services include, for example, local nursing care facilities, local nursing care support services, etc. Some or all of the above-described processing by 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 user's geographical location data into the generation AI and cause the generation AI to perform an analysis regarding area-specific nursing care services.

[0067] During the analysis, the analysis unit can analyze the user's social media activities and perform related analysis. For example, the analysis unit can analyze the user's social media posts and perform analysis based on caregiving interests. The analysis unit can also extract topics related to caregiving from the user's social media activities and perform related analysis. Furthermore, the analysis unit can analyze the content of the user's social media interactions and perform specific analysis related to caregiving. This makes it possible to provide information tailored to the user's interests by performing related analysis based on social media activities. Social media activities include, for example, the content of posts, the number of likes, the content of comments, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI perform related analysis.

[0068] The advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice providing unit can provide advice using gentle language. Furthermore, if the user is relaxed, the advice providing unit can provide advice including detailed information. Furthermore, if the user is in a hurry, the advice providing unit can provide concise and to-the-point advice. This improves the ease with which the advice is accepted by providing an expression method that matches the user's emotions. Emotion estimation is performed using methods such as facial expression analysis, voice analysis, and text analysis. Adjustment of the way the advice is expressed includes, for example, the expression format and the priority of the content of the expression. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the way the advice is expressed.

[0069] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the user's living situation and the progress of care. For example, when the user's living situation is stable, the advice providing unit can provide detailed advice. Furthermore, when the user's care progress is in the early stages, the advice providing unit can provide basic advice. Furthermore, when the user's living situation is fluctuating, the advice providing unit can provide flexible advice. This allows necessary information to be accurately provided by providing advice according to the user's situation. Examples of living situations include daily living activities, health status, and home environment. Examples of the progress of care include care level and evaluation of activities of daily living (ADL). Examples of adjustment of the level of detail of the advice include the specificity of the advice and the depth of the advice. Some or all of the above-mentioned processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can input the user's living situation data to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0070] When providing advice, the advice providing unit can provide appropriate advice by referring to the user's past answer data. The advice providing unit can, for example, provide optimal advice based on the user's past answer data. The advice providing unit can also provide advice focused on a specific problem based on the user's past answer data. The advice providing unit can also analyze the user's past answer data and provide advice that complements missing answers. This allows optimal advice to be provided based on the past answer data, avoiding duplication and providing information efficiently. Past answer data includes, for example, text data, option data, score data, etc. Providing appropriate advice includes, for example, the relevance of the advice, the importance of the advice, etc. Some or all of the above-described processing by the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input the user's past answer data into a generation AI and cause the generation AI to provide appropriate advice.

[0071] The advice providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice providing unit can prioritize advice related to mental care. Furthermore, if the user is relaxed, the advice providing unit can prioritize advice related to detailed nursing care services. Furthermore, if the user is in a hurry, the advice providing unit can prioritize advice that can be implemented quickly. This allows for providing more appropriate advice by providing priorities according to the user's emotions. Emotions can be estimated using methods such as facial expression analysis, voice analysis, and text analysis. The priority of advice can be determined based on, for example, the importance and urgency of the advice. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without AI. For example, the advice providing unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of advice.

[0072] When providing advice, the advice providing unit can provide advice on nursing care services specific to the region based on the user's geographical location information. The advice providing unit can provide advice on nursing care services specific to the region based on, for example, the user's location. The advice providing unit can also provide advice on nursing care facilities specific to the region, taking into account the user's geographical location information. Furthermore, the advice providing unit can provide advice on nursing care support organizations specific to the region based on the user's location. This allows the provision of advice on nursing care services specific to the region to provide information appropriate for the region. Geographical location information includes, for example, GPS data and address information. Regional nursing care services include, for example, regional nursing care facilities and regional nursing care support services. Some or all of the above-described processing by the advice providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice providing unit can input the user's geographical location data to the generation AI and cause the generation AI to provide advice on nursing care services specific to the region.

[0073] The advice providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the advice providing unit can analyze the user's social media posts and provide advice based on the user's concerns about caregiving. The advice providing unit can also extract topics related to caregiving from the user's social media activity and provide relevant advice. Furthermore, the advice providing unit can analyze the user's social media interactions and provide specific advice related to caregiving. This allows the system to provide information tailored to the user's interests by providing relevant advice based on the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the content of comments, etc. Some or all of the above-described processing in the advice providing unit may be performed using AI, or may be performed without AI. For example, the advice providing unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant advice.

[0074] The care providing unit can estimate the user's emotions and adjust the mental care method based on the estimated user's emotions. For example, if the user is feeling stressed, the care providing unit can suggest a relaxation method. Furthermore, if the user is relaxed, the care providing unit can suggest a detailed mental care method. Furthermore, if the user is in a hurry, the care providing unit can suggest a mental care method that can be performed quickly. This enables more appropriate care by providing a mental care method that corresponds to the user's emotions. Emotions are estimated using methods such as facial expression analysis, voice analysis, and text analysis. Adjustment of the mental care method includes, for example, the specificity of the care and the depth of the care. Some or all of the above-mentioned processing in the care providing unit may be performed using, for example, AI, or may be performed without AI. For example, the care providing unit can input the user's emotion data into a generation AI and have the generation AI adjust the mental care method.

[0075] When providing care, the care providing unit can provide an appropriate care method by referring to the user's past stress level data. For example, the care providing unit can provide an optimal care method based on the user's past stress level data. The care providing unit can also provide a care method focused on a specific problem based on the user's past stress level data. Furthermore, the care providing unit can analyze the user's past stress level data and provide a care method appropriate for periods of high stress. This allows the user's stress to be effectively reduced by providing an optimal care method based on the past stress level data. Examples of past stress level data include stress assessment scores and stress-related questionnaire results. Providing an appropriate care method includes, for example, the relevance of care and the importance of care. Some or all of the above-described processing in the care providing unit may be performed using, or without, AI. For example, the care providing unit can input the user's past stress level data into a generation AI and cause the generation AI to provide an appropriate care method.

[0076] The care providing unit can adjust the level of detail of care based on the user's living situation and the progress of care when providing care. For example, when the user's living situation is stable, the care providing unit can provide a detailed care method. Furthermore, when the user's care progress is in the early stages, the care providing unit can provide a basic care method. Furthermore, when the user's living situation is fluctuating, the care providing unit can provide a flexible care method. This allows necessary information to be accurately provided by providing a care method according to the user's situation. Examples of living situations include activities of daily living, health status, and home environment. Examples of the progress of care include care level and assessment of activities of daily living (ADL). Examples of adjustment of the level of detail of care include specificity of care and depth of care. Some or all of the above-mentioned processing in the care providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the care providing unit can input the user's living situation data to a generation AI and cause the generation AI to adjust the level of detail of care.

[0077] The care providing unit can estimate the user's emotions and determine care priorities based on the estimated user emotions. For example, if the user is feeling stressed, the care providing unit can prioritize mental care. Furthermore, if the user is relaxed, the care providing unit can prioritize detailed care methods. Furthermore, if the user is in a hurry, the care providing unit can prioritize care methods that can be performed quickly. This allows for providing more appropriate care by providing priorities according to the user's emotions. The estimation of emotions is performed using methods such as facial expression analysis, voice analysis, and text analysis. The determination of care priorities includes, for example, the importance and urgency of care. Some or all of the above-described processing in the care providing unit may be performed using, for example, AI, or without AI. For example, the care providing unit can input the user's emotion data into a generation AI and have the generation AI determine the care priorities.

[0078] When providing care, the care providing unit can provide region-specific mental health advice based on the user's geographical location information. The care providing unit can provide region-specific mental health advice based on, for example, the user's location. The care providing unit can also provide advice about local mental health facilities, taking into account the user's geographical location information. Furthermore, the care providing unit can provide advice about local mental health support organizations based on the user's location. This allows the provision of region-specific mental health advice to provide information appropriate for the region. Geographical location information includes, for example, GPS data, address information, etc. Region-specific mental health advice includes, for example, local counseling services, local support groups, etc. Some or all of the above-described processing in the care providing unit can be performed using, for example, AI, or without AI. For example, the care providing unit can input the user's geographical location data into a generation AI and cause the generation AI to provide region-specific mental health advice.

[0079] The care providing unit can analyze the user's social media activities and provide relevant care methods when providing care. For example, the care providing unit can analyze the user's social media posts and provide care methods based on the user's concerns regarding mental health care. The care providing unit can also extract topics related to mental health care from the user's social media activities and provide relevant care methods. Furthermore, the care providing unit can analyze the content of the user's social media interactions and provide specific care methods related to mental health care. This allows the provision of relevant care methods based on the user's social media activities to provide information tailored to the user's interests. Social media activities include, for example, the content of posts, the number of likes, the content of comments, etc. Some or all of the above-described processing in the care providing unit may be performed using, or without, AI. For example, the care providing unit can input the user's social media data into a generation AI and cause the generation AI to provide relevant care methods. === Hard Collateral 1-1 === Each of the multiple elements including the question generation unit, analysis unit, advice provision unit, and care provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the question generation unit is realized by the control unit 46A of the smart device 14 and generates questions regarding the user's health condition and living situation. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers. The advice provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides advice on nursing care services based on the analysis results. The care provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice on mental care for caregivers. === Hard Collateral 1-2 === Each of the multiple elements including the question generation unit, analysis unit, advice provision unit, and care provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the question generation unit is realized by the control unit 46A of the smart glasses 214 and generates questions regarding the user's health condition and living situation. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers. The advice provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice on care services based on the analysis results. The care provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice on mental care for caregivers. === Hard Collateral 1-3 === Each of the multiple elements including the question generation unit, analysis unit, advice provision unit, and care provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the question generation unit is realized by the control unit 46A of the headset type terminal 314 and generates questions regarding the user's health condition and living situation. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers. The advice provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides advice on nursing care services based on the analysis results. The care provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice on mental care for caregivers. === Hard Collateral 1-4 === Each of the multiple elements including the question generation unit, analysis unit, advice provision unit, and care provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the question generation unit is realized by the control unit 46A of the robot 414 and generates questions regarding the user's health condition and living situation. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's answers. The advice provision unit is realized, for example, by the control unit 46A of the robot 414 and provides advice on nursing care services based on the analysis results. The care provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice on mental care for the caregiver.

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

[0081] The question generation unit can customize questions based on the user's hobbies and interests. For example, if the user is interested in music, music-related questions can be generated to increase the user's level of relaxation. If the user is interested in sports, sports-related questions can be generated to understand the user's health condition. If the user is interested in reading, reading-related questions can be generated to deepen the user's knowledge and interests. This allows more appropriate information to be collected by asking questions that match the user's hobbies and interests.

[0082] The question generation unit can estimate the user's emotions and adjust the timing of questions based on the estimated user emotions. For example, if the user is feeling stressed, the intervals between questions can be increased to provide time for the user to relax. Also, if the user is relaxed, the intervals between questions can be decreased to efficiently collect information. Furthermore, if the user is in a hurry, important questions can be given priority, allowing necessary information to be collected in a short amount of time. In this way, more appropriate information can be collected by adjusting the timing of questions according to the user's emotions.

[0083] The analysis unit can acquire the user's health data in real time and reflect it in the analysis results. For example, by acquiring the user's heart rate and blood pressure in real time and reflecting them in the analysis results, a more accurate understanding of the user's health condition can be achieved. In addition, by acquiring the user's number of steps and exercise volume in real time and reflecting them in the analysis results, the user's activity level can be evaluated. Furthermore, by acquiring the user's sleep data in real time and reflecting them in the analysis results, the user's sleep quality can be evaluated. In this way, analysis can be performed based on real-time health data, allowing for more accurate information to be provided.

[0084] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, positive feedback can be given priority. If the user is relaxed, detailed feedback can be provided. If the user is in a hurry, brief feedback that focuses on the main points can be provided. In this way, providing a feedback method that corresponds to the user's emotions improves the user's acceptance of information.

[0085] The advice providing unit can customize advice based on the user's family structure. For example, if the user lives with elderly parents, advice on nursing care services for the elderly can be provided. If the user has small children, advice on balancing child care and nursing care can be provided. Furthermore, if the user lives alone, advice to support independent living can be provided. This allows for more appropriate information to be provided by providing advice according to the user's family structure.

[0086] The advice providing unit can estimate the user's emotions and adjust the frequency of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of advice can be reduced to provide time for relaxation. Also, if the user is relaxed, the frequency of advice can be increased to proactively provide information. Furthermore, if the user is in a hurry, important advice can be provided preferentially. In this way, by adjusting the frequency of advice according to the user's emotions, more appropriate information can be provided.

[0087] The care provider can customize mental care methods based on the user's hobbies and interests. For example, if the user is interested in music, music therapy can be suggested. If the user is interested in art, art therapy can be suggested. Furthermore, if the user is interested in nature, nature walks or gardening can be suggested. This allows for more effective care by providing mental care methods that match the user's hobbies and interests.

[0088] The care providing unit can estimate the user's emotions and adjust the timing of mental care based on the estimated user's emotions. For example, if the user is feeling stressed, mental care can be provided to provide time for relaxation. Also, if the user is relaxed, mental care can be provided to promote deep relaxation. Furthermore, if the user is in a hurry, a mental care method that can be performed in a short time can be provided. This allows for more effective care by adjusting the timing of mental care according to the user's emotions.

[0089] The care providing unit can customize the mental care method based on the user's living environment. For example, if the user lives in an urban area, it can suggest a relaxation method suited to the urban environment. If the user lives in the countryside, it can suggest a relaxation method that utilizes the natural environment. Furthermore, if the user leads a busy life, it can suggest a relaxation method that can be performed in a short amount of time. This allows for more effective care by providing a mental care method that suits the user's living environment.

[0090] The care providing unit can estimate the user's emotions and adjust the content of mental care based on the estimated user's emotions. For example, if the user is feeling stressed, mental care centered on relaxation techniques can be provided. Also, if the user is relaxed, mental care to promote self-growth can be provided. Furthermore, if the user is in a hurry, a mental care method that can be performed in a short time can be provided. This allows for more effective care by adjusting the content of mental care according to the user's emotions.

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

[0092] Step 1: The question generator generates questions to understand the user's current situation. For example, the question generator can generate specific questions about the user's health condition, lifestyle, psychological state, etc. The question generator can generate questions in multiple choice, free description, scale, or other formats. Step 2: The analysis unit analyzes the user's answers to the questions generated by the question generation unit. For example, the analysis unit can analyze the user's answers using methods such as text analysis, statistical analysis, and machine learning algorithms. The analysis unit understands the current situation based on the user's answers and extracts information to provide appropriate nursing care service advice. Step 3: The advice providing unit provides advice based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide advice in a format such as text, audio, or video. The advice providing unit provides specific advice regarding care services available to the user. Step 4: The care delivery unit provides psychological care to the caregiver based on the advice provided by the advice delivery unit. For example, the care delivery unit can provide psychological care to the caregiver using methods such as counseling, relaxation techniques, and stress management. The care delivery unit measures the stress level of the caregiver and provides appropriate care advice.

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

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

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

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

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

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

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

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

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

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

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

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

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

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

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

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

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

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

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

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

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

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

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

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

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

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

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

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

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

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

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is 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.

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

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

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

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

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

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

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

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

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

[0164] [Explanation of symbols]

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

Claims

1. a question generation unit that generates questions for grasping the current situation of the user; an analysis unit that analyzes a user's answer to the question generated by the question generation unit; an advice providing unit that provides advice based on the results of the analysis by the analysis unit; a care providing unit that provides psychological care to the caregiver based on the advice provided by the advice providing unit; A system characterized by:

2. The question generation unit Inferring user emotions and adjusting the content and order of questions based on the estimated user emotions 2. The system of claim 1.

3. The question generation unit Analyze the user's past answer history and generate appropriate questions 2. The system of claim 1.

4. The question generation unit When generating questions, the level of detail of the questions is adjusted according to the user's living situation and the progress of care.

2. The system of claim 1.

5. The question generation unit Inferring user sentiment and adjusting question detail based on the estimated sentiment 2. The system of claim 1.

6. The question generation unit When generating questions, generate questions about area-specific nursing care services based on the user's geographic location information.

2. The system of claim 1.

7. The question generation unit When generating questions, analyze users' social media activity and generate relevant questions.

2. The system of claim 1.

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

2. The system of claim 1.

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