System

Abstract

An object of a system according to an embodiment is to periodically grasp a health condition of an elderly parent and propose an appropriate countermeasure.SOLUTION: A system includes an interaction unit, an analysis unit, an evaluation unit, and a proposal unit. The interaction unit interacts with the senior parent. The analysis unit analyzes the dialogue content acquired by the dialogue unit. The evaluation unit evaluates a health condition on the basis of a result analyzed by the analysis unit. The proposal unit proposes an appropriate countermeasure on the basis of a result evaluated by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, it was difficult to regularly monitor the health status of elderly parents and suggest appropriate measures.

[0005] The system according to the embodiment aims to periodically monitor the health status of elderly parents and propose appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, an evaluation unit, and a proposal unit. The dialogue unit dialogues with the elderly parent. The analysis unit analyzes the dialogue content acquired by the dialogue unit. The evaluation unit evaluates the health condition based on the analysis result by the analysis unit. The proposal unit proposes appropriate measures based on the evaluation result by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can periodically monitor the health status of elderly parents and suggest appropriate measures. [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) The health management system according to an embodiment of the present invention is a system that installs an AI robot in the household of elderly parents, monitors their health status through regular communication, and supports the prevention and early detection of dementia in particular. This allows the health management system to monitor the health status of elderly parents and propose appropriate measures.

[0029] A health management system according to an embodiment includes a dialogue unit, an analysis unit, an evaluation unit, and a suggestion unit. The dialogue unit dialogues with an elderly parent. For example, the dialogue unit engages in daily conversations and health-related questions to understand the elderly parent's mood, physical condition, lifestyle habits, and the like. The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the analysis unit analyzes the dialogue content using natural language processing technology and extracts data for evaluating the elderly parent's health condition. The evaluation unit evaluates the health condition based on the results of the analysis by the analysis unit. For example, the evaluation unit detects mood fluctuations and changes in physical condition from the dialogue content and comprehensively evaluates the health condition. The suggestion unit suggests appropriate measures based on the results of the evaluation by the evaluation unit. For example, if the suggestion unit determines that the elderly parent is at high risk of dementia, the suggestion unit may recommend a brain training app or recommend that the parent see a specialist. This allows the health management system according to an embodiment to understand the elderly parent's health condition and suggest appropriate measures.

[0030] The dialogue unit can learn the lifestyle habits and preferences of the elderly parent and provide individually customized questions and topics. For example, the dialogue unit analyzes the content of the dialogue and learns the lifestyle habits of the elderly parent (for example, wake-up time and meal times). Based on this, it provides questions and topics at appropriate times. The dialogue unit also analyzes the content of the dialogue and learns the preferences of the elderly parent (for example, favorite foods and hobbies). Based on this, it provides topics that are likely to interest the elderly parent. The dialogue unit also analyzes the content of the dialogue and learns the elderly parent's past statements and behavior patterns. Based on this, it provides topics and questions that will help the elderly parent relax. In this way, it is possible to provide individually customized questions and topics for the elderly parent.

[0031] The dialogue unit can monitor the physical movements of the elderly parent and issue an alert if it detects abnormal movements. For example, the dialogue unit monitors the physical movements of the elderly parent with a camera during the dialogue and issues an alert if it detects abnormal movements (for example, a fall or unsteadiness). The dialogue unit can also monitor the physical movements of the elderly parent with a sensor during the dialogue and issue an alert if it detects abnormal movements (for example, a sudden movement or an unnatural posture). The dialogue unit can also monitor the physical movements of the elderly parent during the dialogue and build a system that automatically notifies family members or medical institutions if it detects abnormal movements. This allows for the detection of abnormal movements of the elderly parent and a prompt response.

[0032] The dialogue unit can provide information and news related to the elderly parent's hobbies and interests. For example, the dialogue unit analyzes the content of the dialogue and provides information and news related to the elderly parent's hobbies (e.g., gardening or reading). For example, it provides information on the latest gardening techniques or newly released books. The dialogue unit also analyzes the content of the dialogue and provides information and news related to the elderly parent's interests (e.g., history or science). For example, it provides information on historical events or the latest scientific research. The dialogue unit also analyzes the content of the dialogue and provides information and news related to the elderly parent's interests (e.g., local events or social issues). For example, it provides information on local events or news on social issues. This makes it possible to provide information related to the elderly parent's hobbies and interests.

[0033] The analysis unit can track changes in the elderly parent's cognitive function over the long term and analyze trends based on the results of the questions and tests. For example, the analysis unit stores the results of regularly administered questions and tests in a database and tracks changes in the elderly parent's cognitive function over the long term. For example, it graphs monthly test results and analyzes trends in cognitive function. The analysis unit also analyzes the results of the questions and tests and builds a system for tracking changes in the elderly parent's cognitive function over the long term. For example, it compares the results with past data to detect abnormal changes. The analysis unit also tracks changes in the elderly parent's cognitive function over the long term and analyzes trends based on the results of the questions and tests. For example, it can suggest early measures if a decline in cognitive function is observed. This makes it possible to track changes in the elderly parent's cognitive function over the long term and analyze trends.

[0034] The analysis unit can customize the content of questions and tests to match the interests and concerns of the elderly parent, thereby improving the accuracy of the answers. For example, the analysis unit analyzes the interests and concerns of the elderly parent and customizes the content of questions and tests based on the analysis. For example, questions related to topics that interest the elderly parent are asked. The analysis unit also analyzes the content of past conversations between the elderly parent and customizes the content of questions and tests based on the analysis. For example, tests related to topics that interest the elderly parent are asked. The analysis unit also learns the preferences and lifestyle habits of the elderly parent and customizes the content of questions and tests based on the analysis. For example, questions related to activities that the elderly parent likes are asked. This allows the system to provide questions and tests that match the interests and concerns of the elderly parent, improving the accuracy of the answers.

[0035] The analysis unit can integrate the results of the questions and tests with other health data to perform a comprehensive health assessment. For example, the analysis unit can integrate the results of the questions and tests with data on the elderly parent's amount of exercise to perform a comprehensive health assessment. For example, the analysis unit can evaluate the impact of lack of exercise on cognitive function. The analysis unit can also integrate the results of the questions and tests with data on the elderly parent's dietary content to perform a comprehensive health assessment. For example, the analysis unit can evaluate the impact of nutritional balance on cognitive function. The analysis unit can also integrate the results of the questions and tests with other health data (for example, sleep patterns and weight fluctuations) to perform a comprehensive health assessment. For example, the analysis unit can evaluate the impact of lack of sleep on cognitive function. In this way, the analysis unit can integrate the results of the questions and tests with other health data to perform a comprehensive health assessment.

[0036] The analysis unit can share the results of the questions and tests with family members and caregivers, allowing them to jointly manage the health of their elderly parents. For example, the analysis unit can periodically create a report of the results of the questions and tests and send it to family members and caregivers by email. This allows them to jointly manage the health of their elderly parents. The analysis unit also stores the results of the questions and tests in the cloud, allowing family members and caregivers to access them at any time. This allows them to share the health status in real time. The analysis unit can also develop an app for sharing the results of the questions and tests with family members and caregivers, allowing them to jointly manage the health of their elderly parents. For example, the app can provide health status monitoring and advice. This allows them to share the results of the questions and tests with family members and caregivers, allowing them to jointly manage the health of their elderly parents.

[0037] The evaluation unit can evaluate the elderly parent's health condition from multiple angles based on the dialogue content and test results, and propose an individually customized health plan. The evaluation unit, for example, analyzes the dialogue content and test results to evaluate the elderly parent's health condition from multiple angles. For example, it integrates data on diet, exercise, sleep, etc. to perform a comprehensive health evaluation. The evaluation unit also evaluates the elderly parent's health condition from multiple angles based on the dialogue content and test results, and proposes an individually customized health plan. For example, it proposes a diet containing specific nutrients and an appropriate exercise plan. The evaluation unit also evaluates the elderly parent's health condition from multiple angles based on the dialogue content and test results, and proposes an individually customized health plan. For example, it proposes activities for stress management and relaxation. In this way, the health condition of the elderly parent can be evaluated from multiple angles, and an individually customized health plan can be proposed.

[0038] The evaluation unit compares the health status evaluation results with the elderly parent's past health data to detect abnormal changes early. The evaluation unit, for example, compares the health status evaluation results with the elderly parent's past health data to detect abnormal changes early. For example, it detects sudden weight gain or loss or abnormal blood pressure. The evaluation unit also compares the health status evaluation results with the elderly parent's past health data to build a system that detects abnormal changes early. For example, it graphs past data and current data to visually display abnormalities. The evaluation unit also compares the health status evaluation results with the elderly parent's past health data to detect abnormal changes early. For example, it detects declines in cognitive function and changes in motor skills. This makes it possible to compare the health status evaluation results with the past health data to detect abnormal changes early.

[0039] The evaluation unit can link the health condition evaluation results with other health management apps and devices to realize comprehensive health management. For example, the evaluation unit links the health condition evaluation results with other health management apps to realize comprehensive health management. For example, the evaluation unit shares data with a fitness app or a nutrition management app. The evaluation unit also links the health condition evaluation results with a health management device (for example, a smart watch or a blood pressure monitor) to realize comprehensive health management. For example, the evaluation unit integrates data from the device and performs an evaluation. The evaluation unit also links the health condition evaluation results with other health management apps and devices to build a system that realizes comprehensive health management. For example, the data can be stored in the cloud and made accessible from multiple devices. This allows the health condition evaluation results to be linked with other health management apps and devices to realize comprehensive health management.

[0040] The evaluation unit can analyze the health status evaluation results in association with the elderly parent's living environment and make suggestions for improving the environment. For example, the evaluation unit can analyze the health status evaluation results in association with living environment data of the elderly parent (e.g., temperature and humidity of the home) and make suggestions for improving the environment. For example, it can suggest appropriate temperature and humidity settings. The evaluation unit can also analyze the health status evaluation results in association with living environment data and build a system that makes suggestions for improving the environment. For example, it can suggest adjustments to air quality and lighting. The evaluation unit can also analyze the health status evaluation results in association with living environment data of the elderly parent and make suggestions for improving the environment. For example, it can suggest adjusting noise levels and lighting brightness. In this way, the health status evaluation results can be analyzed in association with the living environment and make suggestions for improving the environment.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The health management system can further include an exercise recommendation unit. The exercise recommendation unit can monitor the exercise habits of elderly parents and suggest appropriate exercise plans. For example, the exercise recommendation unit can record the elderly parents' number of steps and exercise time, and evaluate the degree of goal achievement. The exercise recommendation unit can also suggest exercise plans based on the elderly parents' physical strength and health condition. For example, it can provide plans for light stretching and walking. The exercise recommendation unit can also analyze the elderly parents' exercise history and evaluate the effectiveness of exercise. For example, it can evaluate weight loss and muscle strength improvement resulting from exercise. This can support the elderly parents' exercise habits and promote health maintenance.

[0043] The health management system may further include a nutrition management unit. The nutrition management unit can monitor the dietary content of the elderly parent and evaluate the nutritional balance. For example, the nutrition management unit records the content of the meals eaten by the elderly parent and calculates the amount of nutrients consumed. The nutrition management unit also proposes a meal plan based on the elderly parent's health condition. For example, it may recommend ingredients that are rich in specific nutrients. The nutrition management unit also analyzes the elderly parent's dietary history and suggests areas for improving the nutritional balance. For example, it may suggest ingredients to make up for vitamin and mineral deficiencies. This supports the nutritional balance of the elderly parent and promotes health maintenance.

[0044] The health management system may further include a sleep management unit. The sleep management unit can monitor the sleep patterns of the elderly parent and evaluate the quality of their sleep. For example, the sleep management unit records the bedtime and wake-up time of the elderly parent and calculates the sleep duration. The sleep management unit also monitors the movements and breathing patterns during sleep to evaluate the quality of the elderly parent's sleep. For example, it analyzes the frequency of movements during sleep and the rhythm of breathing. The sleep management unit also provides advice to improve the quality of the elderly parent's sleep. For example, it makes suggestions for creating a comfortable sleeping environment. This can improve the quality of the elderly parent's sleep and promote health maintenance.

[0045] The health management system may further include a social participation recommendation unit. The social participation recommendation unit may monitor the social activities of the elderly parent and make suggestions to promote social participation. For example, the social participation recommendation unit may introduce local events and club activities based on the elderly parent's hobbies and interests. The social participation recommendation unit may also provide advice to help the elderly parent maintain their social connections. For example, the social participation recommendation unit may make suggestions to promote communication with friends and family. The social participation recommendation unit may also analyze the elderly parent's social activity history and evaluate the effectiveness of social participation. For example, the social participation recommendation unit may evaluate the impact of social activities on cognitive function and mental health. This may support the elderly parent's social participation and promote health maintenance.

[0046] The health management system can further include a hobby recommendation unit. The hobby recommendation unit can monitor the hobbies and interests of elderly parents and suggest new hobbies and activities. For example, the hobby recommendation unit can analyze the elderly parents' past conversations and behavioral history to suggest new hobbies that they may be interested in. The hobby recommendation unit can also provide information and news related to the elderly parents' hobbies. For example, it can provide information on the latest gardening techniques and newly released books. The hobby recommendation unit can also analyze the elderly parents' hobby activity history and evaluate the effectiveness of the hobbies. For example, it can evaluate the impact of hobby activities on mental health and cognitive function. This can support the elderly parents' hobby activities and promote health maintenance.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The dialogue unit engages in a dialogue with the elderly parent. For example, the dialogue unit engages in daily conversations and health-related questions to understand the elderly parent's mood, physical condition, lifestyle habits, etc. Step 2: The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the analysis unit analyzes the dialogue content using natural language processing technology and extracts data for evaluating the health status of the elderly parent. Step 3: The evaluation unit evaluates the health state based on the results of the analysis by the analysis unit. For example, the evaluation unit detects mood fluctuations and changes in physical condition from the content of the conversation and performs a comprehensive evaluation of the health state. Step 4: The proposal department proposes appropriate measures based on the results of the evaluation by the evaluation department. For example, if the proposal department determines that the risk of dementia is high, it may recommend a brain training app or a consultation with a specialist.

[0049] (Example 2) The health management system according to an embodiment of the present invention is a system that installs an AI robot in the household of elderly parents, monitors their health status through regular communication, and supports the prevention and early detection of dementia in particular. This allows the health management system to monitor the health status of elderly parents and propose appropriate measures.

[0050] A health management system according to an embodiment includes a dialogue unit, an analysis unit, an evaluation unit, and a suggestion unit. The dialogue unit dialogues with an elderly parent. For example, the dialogue unit engages in daily conversations and health-related questions to understand the elderly parent's mood, physical condition, lifestyle habits, and the like. The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the analysis unit analyzes the dialogue content using natural language processing technology and extracts data for evaluating the elderly parent's health condition. The evaluation unit evaluates the health condition based on the results of the analysis by the analysis unit. For example, the evaluation unit detects mood fluctuations and changes in physical condition from the dialogue content and comprehensively evaluates the health condition. The suggestion unit suggests appropriate measures based on the results of the evaluation by the evaluation unit. For example, if the suggestion unit determines that the elderly parent is at high risk of dementia, the suggestion unit may recommend a brain training app or recommend that the parent see a specialist. This allows the health management system according to an embodiment to understand the elderly parent's health condition and suggest appropriate measures.

[0051] The dialogue unit can analyze the facial expressions and tone of voice of the elderly parent to detect changes in emotion in real time. For example, the dialogue unit captures the facial expressions of the elderly parent with a camera and detects changes in emotion in real time using facial expression analysis technology. For example, it analyzes facial expressions such as smiling and furrowing of the brow and quantifies changes in emotion. The dialogue unit also collects the tone of the elderly parent's voice with a microphone and detects changes in emotion in real time using voice analysis technology. For example, it analyzes changes in voice pitch, intensity, and rhythm to identify changes in emotion. The dialogue unit also simultaneously analyzes the facial expressions and tone of voice of the elderly parent to detect changes in emotion in a comprehensive manner in real time. For example, it can more accurately identify changes in emotion based on the degree of agreement between facial expressions and tone of voice. This makes it possible to grasp changes in the elderly parent's emotions in real time.

[0052] The dialogue unit can learn the lifestyle habits and preferences of the elderly parent and provide individually customized questions and topics. For example, the dialogue unit analyzes the content of the dialogue and learns the lifestyle habits of the elderly parent (for example, wake-up time and meal times). Based on this, it provides questions and topics at appropriate times. The dialogue unit also analyzes the content of the dialogue and learns the preferences of the elderly parent (for example, favorite foods and hobbies). Based on this, it provides topics that are likely to interest the elderly parent. The dialogue unit also analyzes the content of the dialogue and learns the elderly parent's past statements and behavior patterns. Based on this, it provides topics and questions that will help the elderly parent relax. In this way, it is possible to provide individually customized questions and topics for the elderly parent.

[0053] The dialogue unit can use the emotion estimation function to grasp the emotional state of the elderly parent and conduct a dialogue to draw out positive emotions. For example, the dialogue unit uses the emotion estimation function to analyze the emotional state of the elderly parent in real time and conduct a dialogue to draw out positive emotions. For example, it provides topics that will make the elderly parent smile. The dialogue unit also uses the emotion estimation function to grasp the emotional state of the elderly parent and conduct a dialogue to reduce negative emotions. For example, it provides topics that will help the elderly parent relax. The dialogue unit also uses the emotion estimation function to grasp the emotional state of the elderly parent and adjust the content of the dialogue according to changes in emotion. For example, it switches to a topic that is likely to interest the elderly parent. This makes it possible to conduct a dialogue that draws out positive emotions from the elderly parent.

[0054] The dialogue unit can monitor the physical movements of the elderly parent and issue an alert if it detects abnormal movements. For example, the dialogue unit monitors the physical movements of the elderly parent with a camera during the dialogue and issues an alert if it detects abnormal movements (for example, a fall or unsteadiness). The dialogue unit can also monitor the physical movements of the elderly parent with a sensor during the dialogue and issue an alert if it detects abnormal movements (for example, a sudden movement or an unnatural posture). The dialogue unit can also monitor the physical movements of the elderly parent during the dialogue and build a system that automatically notifies family members or medical institutions if it detects abnormal movements. This allows for the detection of abnormal movements of the elderly parent and a prompt response.

[0055] The dialogue unit can provide information and news related to the elderly parent's hobbies and interests. For example, the dialogue unit analyzes the content of the dialogue and provides information and news related to the elderly parent's hobbies (e.g., gardening or reading). For example, it provides information on the latest gardening techniques or newly released books. The dialogue unit also analyzes the content of the dialogue and provides information and news related to the elderly parent's interests (e.g., history or science). For example, it provides information on historical events or the latest scientific research. The dialogue unit also analyzes the content of the dialogue and provides information and news related to the elderly parent's interests (e.g., local events or social issues). For example, it provides information on local events or news on social issues. This makes it possible to provide information related to the elderly parent's hobbies and interests.

[0056] The dialogue unit can use the emotion estimation function to automatically play music or video that corresponds to the emotional state of the elderly parent, thereby promoting a relaxing effect. For example, the dialogue unit can use the emotion estimation function to analyze the emotional state of the elderly parent and automatically play music to promote a relaxing effect. For example, classical music that helps the elderly parent relax can be played. The dialogue unit can also use the emotion estimation function to analyze the emotional state of the elderly parent and automatically play video that promotes a relaxing effect. For example, videos of natural landscapes or animals can be played. The dialogue unit can also use the emotion estimation function to analyze the emotional state of the elderly parent and switch music or video according to changes in emotion. For example, the music or video can be adjusted to help the elderly parent relax. In this way, music or video that corresponds to the emotional state of the elderly parent can be played, promoting a relaxing effect.

[0057] The analysis unit can track changes in the elderly parent's cognitive function over the long term and analyze trends based on the results of the questions and tests. For example, the analysis unit stores the results of regularly administered questions and tests in a database and tracks changes in the elderly parent's cognitive function over the long term. For example, it graphs monthly test results and analyzes trends in cognitive function. The analysis unit also analyzes the results of the questions and tests and builds a system for tracking changes in the elderly parent's cognitive function over the long term. For example, it compares the results with past data to detect abnormal changes. The analysis unit also tracks changes in the elderly parent's cognitive function over the long term and analyzes trends based on the results of the questions and tests. For example, it can suggest early measures if a decline in cognitive function is observed. This makes it possible to track changes in the elderly parent's cognitive function over the long term and analyze trends.

[0058] The analysis unit can customize the content of questions and tests to match the interests and concerns of the elderly parent, thereby improving the accuracy of the answers. For example, the analysis unit analyzes the interests and concerns of the elderly parent and customizes the content of questions and tests based on the analysis. For example, questions related to topics that interest the elderly parent are asked. The analysis unit also analyzes the content of past conversations between the elderly parent and customizes the content of questions and tests based on the analysis. For example, tests related to topics that interest the elderly parent are asked. The analysis unit also learns the preferences and lifestyle habits of the elderly parent and customizes the content of questions and tests based on the analysis. For example, questions related to activities that the elderly parent likes are asked. This allows the system to provide questions and tests that match the interests and concerns of the elderly parent, improving the accuracy of the answers.

[0059] The analysis unit can use the emotion estimation function to evaluate the stress level of the elderly parent during the test and suggest measures to reduce stress. For example, the analysis unit can use the emotion estimation function to analyze the stress level of the elderly parent during the test in real time and suggest measures to reduce stress. For example, by playing relaxing music. The analysis unit can also use the emotion estimation function to evaluate the stress level of the elderly parent during the test and provide advice to reduce stress. For example, by recommending deep breathing or light exercise. The analysis unit can also use the emotion estimation function to evaluate the stress level of the elderly parent during the test and create an environment to reduce stress. For example, by adjusting lighting or volume. In this way, the analysis unit can evaluate the stress level of the elderly parent during the test and suggest measures to reduce stress.

[0060] The analysis unit can integrate the results of the questions and tests with other health data to perform a comprehensive health assessment. For example, the analysis unit can integrate the results of the questions and tests with data on the elderly parent's amount of exercise to perform a comprehensive health assessment. For example, the analysis unit can evaluate the impact of lack of exercise on cognitive function. The analysis unit can also integrate the results of the questions and tests with data on the elderly parent's dietary content to perform a comprehensive health assessment. For example, the analysis unit can evaluate the impact of nutritional balance on cognitive function. The analysis unit can also integrate the results of the questions and tests with other health data (for example, sleep patterns and weight fluctuations) to perform a comprehensive health assessment. For example, the analysis unit can evaluate the impact of lack of sleep on cognitive function. In this way, the analysis unit can integrate the results of the questions and tests with other health data to perform a comprehensive health assessment.

[0061] The analysis unit can share the results of the questions and tests with family members and caregivers, allowing them to jointly manage the health of their elderly parents. For example, the analysis unit can periodically create a report of the results of the questions and tests and send it to family members and caregivers by email. This allows them to jointly manage the health of their elderly parents. The analysis unit also stores the results of the questions and tests in the cloud, allowing family members and caregivers to access them at any time. This allows them to share the health status in real time. The analysis unit can also develop an app for sharing the results of the questions and tests with family members and caregivers, allowing them to jointly manage the health of their elderly parents. For example, the app can provide health status monitoring and advice. This allows them to share the results of the questions and tests with family members and caregivers, allowing them to jointly manage the health of their elderly parents.

[0062] The analysis unit can use the emotion estimation function to monitor the emotional state of the elderly parent during the test and provide positive feedback. For example, the analysis unit can use the emotion estimation function to monitor the emotional state of the elderly parent during the test in real time and provide positive feedback. For example, the analysis unit can give encouraging words as the test progresses. The analysis unit can also use the emotion estimation function to monitor the emotional state of the elderly parent during the test and provide positive feedback. For example, the analysis unit can give compliments according to the test results. The analysis unit can also use the emotion estimation function to monitor the emotional state of the elderly parent during the test and provide positive feedback. For example, the analysis unit can play relaxing music after the test is completed. In this way, the analysis unit can monitor the emotional state of the elderly parent during the test and provide positive feedback.

[0063] The evaluation unit can evaluate the elderly parent's health condition from multiple angles based on the dialogue content and test results, and propose an individually customized health plan. The evaluation unit, for example, analyzes the dialogue content and test results to evaluate the elderly parent's health condition from multiple angles. For example, it integrates data on diet, exercise, sleep, etc. to perform a comprehensive health evaluation. The evaluation unit also evaluates the elderly parent's health condition from multiple angles based on the dialogue content and test results, and proposes an individually customized health plan. For example, it proposes a diet containing specific nutrients and an appropriate exercise plan. The evaluation unit also evaluates the elderly parent's health condition from multiple angles based on the dialogue content and test results, and proposes an individually customized health plan. For example, it proposes activities for stress management and relaxation. In this way, the health condition of the elderly parent can be evaluated from multiple angles, and an individually customized health plan can be proposed.

[0064] The evaluation unit compares the health status evaluation results with the elderly parent's past health data to detect abnormal changes early. The evaluation unit, for example, compares the health status evaluation results with the elderly parent's past health data to detect abnormal changes early. For example, it detects sudden weight gain or loss or abnormal blood pressure. The evaluation unit also compares the health status evaluation results with the elderly parent's past health data to build a system that detects abnormal changes early. For example, it graphs past data and current data to visually display abnormalities. The evaluation unit also compares the health status evaluation results with the elderly parent's past health data to detect abnormal changes early. For example, it detects declines in cognitive function and changes in motor skills. This makes it possible to compare the health status evaluation results with the past health data to detect abnormal changes early.

[0065] The evaluation unit uses the emotion estimation function to suggest health measures that take into account the emotional state of the elderly parent, thereby increasing motivation to implement the measures. For example, the evaluation unit uses the emotion estimation function to analyze the emotional state of the elderly parent and suggest health measures that take into account the emotions. For example, it suggests exercise or meal plans that bring out positive emotions. The evaluation unit also uses the emotion estimation function to suggest health measures that take into account the emotional state of the elderly parent and increase motivation to implement the measures. For example, it introduces words of encouragement or a reward system that corresponds to the emotions. The evaluation unit also uses the emotion estimation function to suggest health measures that take into account the emotional state of the elderly parent and correspond to changes in emotions. For example, it suggests relaxation methods to reduce stress. In this way, it is possible to suggest health measures that take into account the emotional state of the elderly parent and increase motivation to implement the measures.

[0066] The evaluation unit can link the health condition evaluation results with other health management apps and devices to realize comprehensive health management. For example, the evaluation unit links the health condition evaluation results with other health management apps to realize comprehensive health management. For example, the evaluation unit shares data with a fitness app or a nutrition management app. The evaluation unit also links the health condition evaluation results with a health management device (for example, a smart watch or a blood pressure monitor) to realize comprehensive health management. For example, the evaluation unit integrates data from the device and performs an evaluation. The evaluation unit also links the health condition evaluation results with other health management apps and devices to build a system that realizes comprehensive health management. For example, the data can be stored in the cloud and made accessible from multiple devices. This allows the health condition evaluation results to be linked with other health management apps and devices to realize comprehensive health management.

[0067] The evaluation unit can analyze the health status evaluation results in association with the elderly parent's living environment and make suggestions for improving the environment. For example, the evaluation unit can analyze the health status evaluation results in association with living environment data of the elderly parent (e.g., temperature and humidity of the home) and make suggestions for improving the environment. For example, it can suggest appropriate temperature and humidity settings. The evaluation unit can also analyze the health status evaluation results in association with living environment data and build a system that makes suggestions for improving the environment. For example, it can suggest adjustments to air quality and lighting. The evaluation unit can also analyze the health status evaluation results in association with living environment data of the elderly parent and make suggestions for improving the environment. For example, it can suggest adjusting noise levels and lighting brightness. In this way, the health status evaluation results can be analyzed in association with the living environment and make suggestions for improving the environment.

[0068] The evaluation unit can use the emotion estimation function to provide health advice according to the emotional state of the elderly parent, thereby increasing their motivation to take action. For example, the evaluation unit can use the emotion estimation function to analyze the emotional state of the elderly parent and provide health advice according to the emotion. For example, it can suggest exercise or meal plans to elicit positive emotions. The evaluation unit can also use the emotion estimation function to take the emotional state of the elderly parent into consideration and provide health advice to increase their motivation to take action. For example, it can introduce words of encouragement or a reward system according to the emotion. The evaluation unit can also use the emotion estimation function to take the emotional state of the elderly parent into consideration and provide health advice according to changes in emotion. For example, it can suggest relaxation methods to reduce stress. This allows the evaluation unit to provide health advice according to the emotional state of the elderly parent, thereby increasing their motivation to take action.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The health management system can further include an exercise recommendation unit. The exercise recommendation unit can monitor the exercise habits of elderly parents and suggest appropriate exercise plans. For example, the exercise recommendation unit can record the elderly parents' number of steps and exercise time, and evaluate the degree of goal achievement. The exercise recommendation unit can also suggest exercise plans based on the elderly parents' physical strength and health condition. For example, it can provide plans for light stretching and walking. The exercise recommendation unit can also analyze the elderly parents' exercise history and evaluate the effectiveness of exercise. For example, it can evaluate weight loss and muscle strength improvement resulting from exercise. This can support the elderly parents' exercise habits and promote health maintenance.

[0071] The health management system may further include a nutrition management unit. The nutrition management unit can monitor the dietary content of the elderly parent and evaluate the nutritional balance. For example, the nutrition management unit records the content of the meals eaten by the elderly parent and calculates the amount of nutrients consumed. The nutrition management unit also proposes a meal plan based on the elderly parent's health condition. For example, it may recommend ingredients that are rich in specific nutrients. The nutrition management unit also analyzes the elderly parent's dietary history and suggests areas for improving the nutritional balance. For example, it may suggest ingredients to make up for vitamin and mineral deficiencies. This supports the nutritional balance of the elderly parent and promotes health maintenance.

[0072] The health management system may further include a sleep management unit. The sleep management unit can monitor the sleep patterns of the elderly parent and evaluate the quality of their sleep. For example, the sleep management unit records the bedtime and wake-up time of the elderly parent and calculates the sleep duration. The sleep management unit also monitors the movements and breathing patterns during sleep to evaluate the quality of the elderly parent's sleep. For example, it analyzes the frequency of movements during sleep and the rhythm of breathing. The sleep management unit also provides advice to improve the quality of the elderly parent's sleep. For example, it makes suggestions for creating a comfortable sleeping environment. This can improve the quality of the elderly parent's sleep and promote health maintenance.

[0073] The health management system may further include a social participation recommendation unit. The social participation recommendation unit may monitor the social activities of the elderly parent and make suggestions to promote social participation. For example, the social participation recommendation unit may introduce local events and club activities based on the elderly parent's hobbies and interests. The social participation recommendation unit may also provide advice to help the elderly parent maintain their social connections. For example, the social participation recommendation unit may make suggestions to promote communication with friends and family. The social participation recommendation unit may also analyze the elderly parent's social activity history and evaluate the effectiveness of social participation. For example, the social participation recommendation unit may evaluate the impact of social activities on cognitive function and mental health. This may support the elderly parent's social participation and promote health maintenance.

[0074] The health management system can further include a hobby recommendation unit. The hobby recommendation unit can monitor the hobbies and interests of elderly parents and suggest new hobbies and activities. For example, the hobby recommendation unit can analyze the elderly parents' past conversations and behavioral history to suggest new hobbies that they may be interested in. The hobby recommendation unit can also provide information and news related to the elderly parents' hobbies. For example, it can provide information on the latest gardening techniques and newly released books. The hobby recommendation unit can also analyze the elderly parents' hobby activity history and evaluate the effectiveness of the hobbies. For example, it can evaluate the impact of hobby activities on mental health and cognitive function. This can support the elderly parents' hobby activities and promote health maintenance.

[0075] The dialogue unit can use the emotion estimation function to grasp the emotional state of the elderly parent and suggest relaxation methods according to the emotion. For example, if the elderly parent is feeling stressed, the dialogue unit can suggest deep breathing or meditation. The dialogue unit can also play music or video according to the emotional state to help the elderly parent relax. For example, it can provide natural scenery or classical music. The dialogue unit can also analyze the emotional state of the elderly parent and adjust the relaxation method according to changes in emotion. For example, it can adjust the content and tone of the dialogue to help the elderly parent relax. This makes it possible to suggest relaxation methods according to the elderly parent's emotional state and reduce stress.

[0076] The dialogue unit can use the emotion estimation function to grasp the emotional state of the elderly parent and suggest exercises according to the emotion. For example, if the elderly parent is feeling positive, the dialogue unit can suggest light stretching or walking. If the elderly parent is feeling negative, the dialogue unit can suggest relaxing yoga or deep breathing exercises. The dialogue unit can also analyze the emotional state of the elderly parent and adjust the exercise content according to changes in emotion. For example, it can adjust the intensity and type of exercise to help the elderly parent relax. This makes it possible to suggest exercises according to the emotional state of the elderly parent and promote health maintenance.

[0077] The dialogue unit can use the emotion estimation function to grasp the emotional state of the elderly parent and propose a meal plan according to the emotion. For example, if the elderly parent is feeling positive, the dialogue unit will propose a nutritionally balanced meal. If the elderly parent is feeling negative, the dialogue unit will propose ingredients and dishes that will help them relax. The dialogue unit also analyzes the emotional state of the elderly parent and adjusts the meal plan according to changes in emotion. For example, it adjusts the content and timing of meals to help the elderly parent relax. This makes it possible to propose a meal plan according to the emotional state of the elderly parent and promote health maintenance.

[0078] The dialogue unit can use the emotion estimation function to grasp the emotional state of the elderly parent and suggest a communication method according to the emotion. For example, if the elderly parent is feeling positive, the dialogue unit can offer fun topics or jokes. If the elderly parent is feeling negative, the dialogue unit can offer relaxing topics or words of encouragement. The dialogue unit can also analyze the emotional state of the elderly parent and adjust the communication method according to changes in emotion. For example, it can adjust the tone and content of the dialogue to help the elderly parent relax. This makes it possible to suggest a communication method according to the elderly parent's emotional state and reduce stress.

[0079] The dialogue unit can use the emotion estimation function to grasp the emotional state of the elderly parent and introduce a reward system according to the emotion. For example, if the elderly parent has a positive emotion, the dialogue unit provides a reward that gives the parent a sense of accomplishment. On the other hand, if the elderly parent has a negative emotion, the dialogue unit provides a reward that helps the parent relax. The dialogue unit also analyzes the emotional state of the elderly parent and adjusts the reward system according to changes in emotion. For example, it adjusts the content and timing of rewards to help the elderly parent relax. This makes it possible to introduce a reward system according to the emotional state of the elderly parent and increase their motivation.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The dialogue unit engages in a dialogue with the elderly parent. For example, the dialogue unit engages in daily conversations and health-related questions to understand the elderly parent's mood, physical condition, lifestyle habits, etc. Step 2: The analysis unit analyzes the dialogue content acquired by the dialogue unit. For example, the analysis unit analyzes the dialogue content using natural language processing technology and extracts data for evaluating the health status of the elderly parent. Step 3: The evaluation unit evaluates the health state based on the results of the analysis by the analysis unit. For example, the evaluation unit detects mood fluctuations and changes in physical condition from the content of the conversation and performs a comprehensive evaluation of the health state. Step 4: The proposal department proposes appropriate measures based on the results of the evaluation by the evaluation department. For example, if the proposal department determines that the risk of dementia is high, it may recommend a brain training app or a consultation with a specialist.

[0082] 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.

[0083] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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, in order to avoid confusion and to 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.

[0148] 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. [Explanation of symbols]

[0149] 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 dialogue section where participants can talk with elderly parents, an analysis unit that analyzes the dialogue content acquired by the dialogue unit; an evaluation unit that evaluates a health state based on the results of the analysis by the analysis unit; a proposal unit that proposes appropriate measures based on the results of evaluation by the evaluation unit. A system characterized by:

2. The dialogue unit Analyzing facial expressions and tone of voice of elderly parents to detect emotional changes in real time 2. The system of claim 1.

3. The dialogue unit Learn about your elderly parent's lifestyle habits and preferences and provide personalized questions and topics 2. The system of claim 1.

4. The dialogue unit Understand the emotional state of elderly parents and engage in dialogue to bring out positive emotions 2. The system of claim 1.

5. The dialogue unit Monitor the physical movements of elderly parents and issue alerts if abnormal movements are detected 2. The system of claim 1.

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