System

Abstract

An object of the system according to the embodiment is to monitor a state or a health condition of an elderly person or a disabled person in real time and predict and prevent an abnormal or dangerous condition.SOLUTION: A system according to an embodiment includes an interaction analysis unit, a proposal unit, a monitoring unit, an alert unit, a pattern analysis unit, and a prediction unit. The dialog analysis unit analyzes the user's voice and text and generates a response. The proposal unit proposes a game or a quiz on the basis of the response generated by the dialogue analysis unit. The monitoring unit monitors a state and a health condition of a user. The alert unit notifies the staff of the abnormality detected by the monitoring unit. The pattern analysis unit grasps a pattern of daily life and a health condition of the user. The prediction unit predicts and prevents an abnormal and dangerous situation based on the pattern grasped by the pattern analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately monitor the condition and health status of elderly people and people with disabilities in real time, and does not adequately predict and prevent abnormalities or dangerous situations, so there is room for improvement.

[0005] The system according to the embodiment aims to monitor the condition and health status of elderly people and people with disabilities in real time, and to predict and prevent abnormalities and dangerous situations. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue analysis unit, a suggestion unit, a monitoring unit, an alert unit, a pattern analysis unit, and a prediction unit. The dialogue analysis unit analyzes the user's voice and text and generates a response. The suggestion unit suggests a game or quiz based on the response generated by the dialogue analysis unit. The monitoring unit monitors the user's condition and health status. The alert unit notifies staff of any abnormalities detected by the monitoring unit. The pattern analysis unit identifies patterns in the user's daily life and health status. The prediction unit predicts and prevents abnormalities and dangerous situations based on the patterns identified by the pattern analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the condition and health status of elderly people and people with disabilities in real time, and can predict and prevent abnormalities and dangerous situations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to provide conversational companionship to elderly people and people with disabilities, offering empathy and encouragement. When a user speaks to the system, the AI ​​analyzes the voice and text and generates appropriate responses. The system then suggests fun games and quizzes to the user, supporting cognitive and memory training. Furthermore, the system collaborates with staff and caregivers at welfare facilities to monitor the user's condition and health status and send alerts to staff if any abnormalities are detected. The system also understands the user's daily life and health patterns and predicts and prevents abnormalities and dangerous situations. This helps ensure the user's safety and health. For example, when a user speaks to the system, the AI ​​analyzes the voice and text and generates an appropriate response. For example, if a user says, "The weather is nice today," the AI ​​responds, "Yes, it's very nice today. How about going for a walk?" This allows for natural dialogue with the user. The system then suggests fun games and quizzes to the user. For example, quizzes to train memory and puzzle games to improve cognitive function are available. This allows users to have fun while training their cognitive and memory functions. The system also works in conjunction with staff and caregivers at welfare facilities. It monitors the user's condition and health status and sends alerts to staff if any abnormalities are detected. For example, if a user's body temperature suddenly rises, the system will notify staff, "The user's temperature is rising. Please check." This allows for a prompt response. The system also understands patterns in the user's daily life and health status, predicting and preventing abnormalities and dangerous situations. For example, if a user has the habit of eating at the same time every day, the system can learn that pattern and notify staff if any abnormalities are detected. This ensures the user's safety.

[0029] A dialogue support system according to an embodiment includes a dialogue analysis unit, a suggestion unit, a monitoring unit, an alert unit, a pattern analysis unit, and a prediction unit. The dialogue analysis unit analyzes a user's voice and text and generates a response. For example, the dialogue analysis unit converts the user's voice into text using speech recognition technology and analyzes the text using natural language processing technology. The dialogue analysis unit can also generate an appropriate response using a generation AI. For example, when a user says, "The weather is nice today," the dialogue analysis unit generates a response such as, "Yes, it's very nice today. How about going for a walk?" The suggestion unit suggests a game or quiz based on the response generated by the dialogue analysis unit. For example, the suggestion unit suggests a quiz to train memory or a puzzle game to improve cognitive function. The suggestion unit can also suggest games or quizzes based on the user's interests using the generation AI. The monitoring unit monitors the user's condition and health status. For example, the monitoring unit measures vital signs such as body temperature and heart rate and collects data. The monitoring unit can also monitor the user's behavioral patterns and daily rhythms. The alert unit notifies staff of abnormalities detected by the monitoring unit. For example, the alert unit notifies staff if the user's body temperature suddenly rises. The alert unit can also notify staff if it detects an abnormal heart rate or a change in behavioral pattern. The pattern analysis unit understands patterns of the user's daily life and health condition. For example, the pattern analysis unit understands whether the user has a habit of eating meals at the same time every day. The pattern analysis unit can also analyze the user's sleep pattern and exercise pattern. The prediction unit predicts and prevents abnormalities and dangerous situations based on the patterns understood by the pattern analysis unit. For example, the prediction unit analyzes fluctuations in the user's daily rhythm in real time and predicts abnormalities early. The prediction unit can also predict changes in the user's health condition and suggest appropriate measures. As a result, the dialogue support system according to the embodiment can protect the safety and health of users.

[0030] The suggestion unit can suggest quizzes for training memory or puzzle games for improving cognitive function. The suggestion unit can, for example, suggest quizzes for training memory. For example, the suggestion unit can provide quizzes that encourage memorization of words or numbers. The suggestion unit can also suggest puzzle games for improving cognitive function. For example, the suggestion unit can provide games such as Sudoku or crossword puzzles. The suggestion unit can also use a generative AI to suggest games or quizzes based on the user's interests. This can support the training of the user's cognitive function and memory by suggesting quizzes for training memory or puzzle games for improving cognitive function.

[0031] The monitoring unit can monitor the user's vital signs, such as body temperature or heart rate. The monitoring unit, for example, monitors the user's body temperature. For example, the monitoring unit measures the user's body temperature using a thermometer and collects data. The monitoring unit can also monitor the heart rate. For example, the monitoring unit measures the user's heart rate using a heart rate monitor and collects data. The monitoring unit can also analyze vital sign data in real time to detect abnormalities early. In this way, by monitoring the user's vital signs, such as body temperature and heart rate, the user's health condition can be understood and any abnormalities can be dealt with promptly.

[0032] The alert unit can notify staff if the user's body temperature suddenly rises. For example, the alert unit notifies staff if the user's body temperature suddenly rises. For example, the alert unit detects an abnormality based on the rate and magnitude of body temperature rise and notifies staff. The alert unit can also notify if it detects an abnormal heart rate or a change in behavior pattern. For example, the alert unit detects a sudden change in heart rate or an abnormal behavior pattern and notifies staff. In this way, by notifying staff if the user's body temperature suddenly rises, a prompt response can be taken and the user's safety can be ensured.

[0033] The pattern analysis unit can determine whether the user has a habit of eating meals at the same time every day. The pattern analysis unit determines, for example, whether the user has a habit of eating meals at the same time every day. For example, the pattern analysis unit records the time and content of the user's meals and analyzes the data. The pattern analysis unit can also learn the user's eating patterns and notify staff if there is an abnormality. For example, the pattern analysis unit detects an abnormality if the user does not eat at their usual mealtimes and notifies staff. In this way, by determining whether the user has a habit of eating meals at the same time every day, it is possible to respond quickly if there is an abnormality.

[0034] The prediction unit can predict abnormalities and dangerous situations based on patterns of the user's daily life and health condition. The prediction unit predicts abnormalities and dangerous situations based on patterns of the user's daily life and health condition, for example. For example, the prediction unit analyzes fluctuations in the user's daily rhythm in real time to predict abnormalities early. The prediction unit can also predict changes in the user's health condition and propose appropriate countermeasures. For example, the prediction unit analyzes fluctuations in the user's sleeping patterns and eating patterns to predict abnormalities early. This makes it possible to protect the user's safety and health by predicting abnormalities and dangerous situations based on patterns of the user's daily life and health condition.

[0035] The dialogue analysis unit can analyze the user's past dialogue history and generate an optimal response. The dialogue analysis unit can, for example, analyze the user's past dialogue history and generate an optimal response. For example, the dialogue analysis unit can provide related topics based on content that the user has spoken in the past. The dialogue analysis unit can also provide topics that the user has preferred in the past with priority. The dialogue analysis unit can also generate a response that avoids topics that the user has avoided in the past. In this way, by analyzing the user's past dialogue history, a more appropriate response can be provided.

[0036] The dialogue analysis unit can customize responses according to the user's language or dialect. The dialogue analysis unit customizes responses according to the user's language or dialect, for example. For example, if the user speaks Kansai dialect, the dialogue analysis unit responds in Kansai dialect. Furthermore, if the user speaks English, the dialogue analysis unit can also respond in English. Furthermore, if the user speaks a specific dialect, the dialogue analysis unit can generate a response tailored to that dialect. In this way, by customizing responses according to the user's language or dialect, more natural dialogue can be achieved.

[0037] The dialogue analysis unit can generate a response based on the user's current activity and environment. The dialogue analysis unit generates a response based on the user's current activity and environment, for example. For example, if the user is out, the dialogue analysis unit can provide a topic related to going out. Furthermore, if the user is eating, the dialogue analysis unit can also provide a topic related to eating. Furthermore, if the user is relaxing, the dialogue analysis unit can also provide a topic related to relaxation. In this way, by generating a response based on the user's current activity and environment, it is possible to provide a more appropriate response.

[0038] The dialogue analysis unit can generate a response based on the geographical background of the user. The dialogue analysis unit generates a response based on, for example, the geographical background of the user. For example, the dialogue analysis unit provides weather information for the area where the user lives. The dialogue analysis unit can also provide event information for the area where the user lives. The dialogue analysis unit can also provide news for the area where the user lives. In this way, by generating a response based on the geographical background of the user, a more appropriate response can be provided.

[0039] The dialogue analysis unit can analyze the user's social media activity and provide related topics. The dialogue analysis unit can, for example, analyze the user's social media activity and provide related topics. For example, the dialogue analysis unit can generate a response based on the content that the user is talking about on social media. The dialogue analysis unit can also generate a response based on the content that the user's friends are talking about on social media. The dialogue analysis unit can also provide topics that the user is interested in on social media. In this way, by analyzing the user's social media activity, more appropriate topics can be provided.

[0040] The dialogue analysis unit can improve the quality of responses by reflecting the user's past feedback. The dialogue analysis unit can improve the quality of responses by reflecting the user's past feedback, for example. For example, the dialogue analysis unit can preferentially use a response style that the user has preferred in the past. The dialogue analysis unit can also avoid a response style that the user has avoided in the past. The dialogue analysis unit can also improve the content of the response based on the user's feedback. In this way, the quality of the response can be improved by reflecting the user's past feedback.

[0041] The suggestion unit can analyze the user's past game and quiz history and make optimal suggestions. The suggestion unit can, for example, analyze the user's past game and quiz history and make optimal suggestions. For example, the suggestion unit can re-suggest games and quizzes that the user enjoyed in the past. The suggestion unit can also re-suggest games and quizzes that the user attempted but was unable to complete in the past. The suggestion unit can also suggest new games and quizzes based on the user's past history. In this way, more appropriate suggestions can be made by analyzing the user's past game and quiz history.

[0042] The suggestion unit can customize games and quizzes according to the user's current cognitive function level. The suggestion unit customizes games and quizzes according to, for example, the user's current cognitive function level. For example, the suggestion unit adjusts the difficulty of games and quizzes according to the user's cognitive function level. The suggestion unit can also customize the content of games and quizzes according to the user's cognitive function level. The suggestion unit can also adjust the progress speed of games and quizzes according to the user's cognitive function level. In this way, more appropriate suggestions can be made by customizing games and quizzes according to the user's current cognitive function level.

[0043] The suggestion unit can select the content of games and quizzes based on the user's interests and concerns. The suggestion unit selects the content of games and quizzes based on, for example, the user's interests and concerns. For example, the suggestion unit selects games and quizzes based on themes in which the user is interested. The suggestion unit can also select games and quizzes based on themes that the user has enjoyed in the past. The suggestion unit can also suggest games and quizzes with new themes based on the user's interests and concerns. This allows for more appropriate suggestions to be made by selecting the content of games and quizzes based on the user's interests and concerns.

[0044] The suggestion unit can suggest region-specific games and quizzes based on the user's geographical background. The suggestion unit, for example, suggests region-specific games and quizzes based on the user's geographical background. For example, the suggestion unit can suggest quizzes based on the history of the region where the user lives. The suggestion unit can also suggest games based on the culture of the region where the user lives. The suggestion unit can also suggest quizzes based on events in the region where the user lives. This allows more appropriate suggestions to be made by suggesting region-specific games and quizzes based on the user's geographical background.

[0045] The suggestion unit can analyze the user's social media activity and suggest related games and quizzes. The suggestion unit can, for example, analyze the user's social media activity and suggest related games and quizzes. For example, the suggestion unit can suggest games and quizzes based on topics that the user is talking about on social media. The suggestion unit can also suggest games and quizzes based on topics that the user's friends are talking about on social media. The suggestion unit can also suggest games and quizzes based on topics that the user is interested in on social media. In this way, more appropriate games and quizzes can be suggested by analyzing the user's social media activity.

[0046] The suggestion unit can improve the quality of suggestions by reflecting the user's past feedback. The suggestion unit can improve the quality of suggestions by reflecting the user's past feedback, for example. For example, the suggestion unit can preferentially suggest games and quizzes that the user liked in the past. The suggestion unit can also avoid games and quizzes that the user avoided in the past. The suggestion unit can also improve the content of suggestions based on the user's feedback. In this way, the quality of suggestions can be improved by reflecting the user's past feedback.

[0047] The monitoring unit can analyze the user's past health data and select the optimal monitoring method. The monitoring unit, for example, analyzes the user's past health data and selects the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the user's past health data. The monitoring unit can also adjust the monitoring frequency based on the user's past health data. The monitoring unit can also customize the monitoring items based on the user's past health data. In this way, a more appropriate monitoring method can be selected by analyzing the user's past health data.

[0048] The monitoring unit can customize the monitoring items based on the user's current living situation. The monitoring unit customizes the monitoring items based on the user's current living situation, for example. For example, the monitoring unit customizes the monitoring items based on the user's current living situation. The monitoring unit can also adjust the monitoring frequency based on the user's current living situation. The monitoring unit can also customize the monitoring method based on the user's current living situation. In this way, by customizing the monitoring items based on the user's current living situation, more appropriate monitoring can be performed.

[0049] The monitoring unit analyzes fluctuations in the user's vital signs in real time and can detect abnormalities early. The monitoring unit, for example, analyzes fluctuations in the user's vital signs in real time and can detect abnormalities early. For example, the monitoring unit analyzes fluctuations in the user's body temperature in real time and can detect abnormalities early. The monitoring unit can also analyze fluctuations in the user's heart rate in real time and can detect abnormalities early. The monitoring unit can also analyze fluctuations in the user's blood pressure in real time and can detect abnormalities early. In this way, by analyzing fluctuations in the user's vital signs in real time, abnormalities can be detected early.

[0050] The monitoring unit can select monitoring items based on the geographical background of the user. The monitoring unit selects monitoring items based on, for example, the geographical background of the user. For example, if the user lives in a hot and humid region, the monitoring unit monitors the risk of heatstroke. Also, if the user lives in a cold region, the monitoring unit can monitor the risk of hypothermia. Also, if the user lives at high altitude, the monitoring unit can monitor the risk of altitude sickness. In this way, by selecting monitoring items based on the geographical background of the user, more appropriate monitoring can be performed.

[0051] The monitoring unit can analyze the user's social media activities and monitor related health information. The monitoring unit, for example, analyzes the user's social media activities and monitors related health information. For example, if the user posts health-related information on social media, the monitoring unit selects monitoring items based on the content of the posts. The monitoring unit can also select monitoring items based on health information shared by the user's friends on social media. The monitoring unit can also select monitoring items based on health information in which the user is interested on social media. In this way, more appropriate health information can be monitored by analyzing the user's social media activities.

[0052] The monitoring unit can improve the monitoring method by reflecting the user's past feedback. The monitoring unit improves the monitoring method by reflecting the user's past feedback, for example. For example, the monitoring unit preferentially uses monitoring methods that the user has preferred in the past. The monitoring unit can also avoid monitoring methods that the user has avoided in the past. The monitoring unit can also improve the content of monitoring based on the user's feedback. In this way, the monitoring method can be improved by reflecting the user's past feedback.

[0053] The alert unit can analyze the user's past health data and set optimal alert criteria. The alert unit, for example, analyzes the user's past health data and sets optimal alert criteria. For example, the alert unit sets criteria for detecting abnormal body temperature based on the user's past health data. The alert unit can also set criteria for detecting abnormal heart rate based on the user's past health data. The alert unit can also set criteria for detecting abnormal blood pressure based on the user's past health data. In this way, more appropriate alert criteria can be set by analyzing the user's past health data.

[0054] The alert unit can customize the content of the alert based on the user's current living situation. The alert unit customizes the content of the alert based on the user's current living situation, for example. For example, when the user is out, the alert unit provides an alert related to going out. Furthermore, when the user is eating, the alert unit can also provide an alert related to eating. Furthermore, when the user is relaxing, the alert unit can also provide an alert related to relaxation. In this way, by customizing the content of the alert based on the user's current living situation, more appropriate alerts can be provided.

[0055] The alert unit can analyze fluctuations in the user's vital signs in real time and provide early notification of abnormalities. The alert unit, for example, analyzes fluctuations in the user's vital signs in real time and provides early notification of abnormalities. For example, the alert unit can analyze fluctuations in the user's body temperature in real time and provide early notification of abnormalities. The alert unit can also analyze fluctuations in the user's heart rate in real time and provide early notification of abnormalities. The alert unit can also analyze fluctuations in the user's blood pressure in real time and provide early notification of abnormalities. In this way, by analyzing fluctuations in the user's vital signs in real time, it is possible to provide early notification of abnormalities.

[0056] The alert unit can select the content of the alert based on the geographical background of the user. The alert unit selects the content of the alert based on, for example, the geographical background of the user. For example, if the user lives in a hot and humid region, the alert unit can provide an alert regarding the risk of heatstroke. Also, if the user lives in a cold region, the alert unit can provide an alert regarding the risk of hypothermia. Also, if the user lives at high altitude, the alert unit can provide an alert regarding the risk of altitude sickness. In this way, by selecting the content of the alert based on the geographical background of the user, more appropriate alerts can be provided.

[0057] The alert unit can analyze the user's social media activity and generate a related alert. The alert unit can, for example, analyze the user's social media activity and generate a related alert. For example, if the user posts health-related information on social media, the alert unit can generate an alert based on the content of the post. The alert unit can also generate an alert based on health information shared by the user's friends on social media. The alert unit can also generate an alert based on health information in which the user is interested on social media. In this way, more appropriate alerts can be generated by analyzing the user's social media activity.

[0058] The alert unit can improve the quality of alerts by reflecting the user's past feedback. The alert unit can improve the quality of alerts by reflecting the user's past feedback, for example. For example, the alert unit can preferentially use alert styles that the user has previously preferred. The alert unit can also avoid alert styles that the user has previously avoided. The alert unit can also improve the content of alerts based on the user's feedback. In this way, the quality of alerts can be improved by reflecting the user's past feedback.

[0059] The pattern analysis unit can analyze the user's past lifestyle data and select the optimal pattern analysis method. The pattern analysis unit, for example, analyzes the user's past lifestyle data and selects the optimal pattern analysis method. For example, the pattern analysis unit selects the optimal pattern analysis method based on the user's past lifestyle data. The pattern analysis unit can also adjust the accuracy of the pattern analysis based on the user's past lifestyle data. The pattern analysis unit can also customize the pattern analysis items based on the user's past lifestyle data. In this way, a more appropriate pattern analysis method can be selected by analyzing the user's past lifestyle data.

[0060] The pattern analysis unit can customize the pattern analysis items based on the user's current living situation. The pattern analysis unit customizes the pattern analysis items based on the user's current living situation, for example. For example, the pattern analysis unit customizes the pattern analysis items based on the user's current living situation. The pattern analysis unit can also adjust the accuracy of the pattern analysis based on the user's current living situation. The pattern analysis unit can also customize the pattern analysis method based on the user's current living situation. In this way, by customizing the pattern analysis items based on the user's current living situation, more appropriate pattern analysis can be performed.

[0061] The pattern analysis unit analyzes fluctuations in the user's lifestyle rhythm in real time, and can detect abnormalities early. The pattern analysis unit, for example, analyzes fluctuations in the user's lifestyle rhythm in real time, and can detect abnormalities early. For example, the pattern analysis unit analyzes fluctuations in the user's sleep pattern in real time, and can detect abnormalities early. The pattern analysis unit can also analyze fluctuations in the user's eating pattern in real time, and can detect abnormalities early. The pattern analysis unit can also analyze fluctuations in the user's exercise pattern in real time, and can detect abnormalities early. In this way, by analyzing fluctuations in the user's lifestyle rhythm in real time, abnormalities can be detected early.

[0062] The pattern analysis unit can select pattern analysis items based on the user's geographical background. The pattern analysis unit selects pattern analysis items based on, for example, the user's geographical background. For example, if the user lives in a hot and humid region, the pattern analysis unit analyzes patterns related to the risk of heatstroke. Furthermore, if the user lives in a cold region, the pattern analysis unit can also analyze patterns related to the risk of hypothermia. Furthermore, if the user lives at high altitude, the pattern analysis unit can analyze patterns related to the risk of altitude sickness. In this way, by selecting pattern analysis items based on the user's geographical background, more appropriate pattern analysis can be performed.

[0063] The pattern analysis unit can analyze the user's social media activities and analyze related patterns. The pattern analysis unit, for example, analyzes the user's social media activities and analyzes related patterns. For example, if the user posts health-related information on social media, the pattern analysis unit analyzes patterns based on the content of the posts. The pattern analysis unit can also analyze patterns based on health information shared by the user's friends on social media. The pattern analysis unit can also analyze patterns based on health information in which the user is interested on social media. This makes it possible to analyze more appropriate patterns by analyzing the user's social media activities.

[0064] The pattern analysis unit can improve the pattern analysis method by reflecting the user's past feedback. The pattern analysis unit improves the pattern analysis method by reflecting the user's past feedback, for example. For example, the pattern analysis unit preferentially uses pattern analysis methods that the user has previously preferred. The pattern analysis unit can also avoid pattern analysis methods that the user has previously avoided. The pattern analysis unit can also improve the content of the pattern analysis based on the user's feedback. In this way, the pattern analysis method can be improved by reflecting the user's past feedback.

[0065] The prediction unit can analyze the user's past lifestyle data and select the optimal prediction method. The prediction unit, for example, analyzes the user's past lifestyle data and selects the optimal prediction method. For example, the prediction unit selects the optimal prediction method based on the user's past lifestyle data. The prediction unit can also adjust the accuracy of prediction based on the user's past lifestyle data. The prediction unit can also customize prediction items based on the user's past lifestyle data. In this way, a more appropriate prediction method can be selected by analyzing the user's past lifestyle data.

[0066] The prediction unit can customize the prediction items based on the user's current living situation. The prediction unit customizes the prediction items based on the user's current living situation, for example. For example, the prediction unit customizes the prediction items based on the user's current living situation. The prediction unit can also adjust the accuracy of the prediction based on the user's current living situation. The prediction unit can also customize the prediction method based on the user's current living situation. In this way, by customizing the prediction items based on the user's current living situation, more appropriate predictions can be made.

[0067] The prediction unit analyzes fluctuations in the user's lifestyle rhythm in real time and can predict abnormalities early. The prediction unit, for example, analyzes fluctuations in the user's lifestyle rhythm in real time and can predict abnormalities early. For example, the prediction unit analyzes fluctuations in the user's sleep pattern in real time and can predict abnormalities early. The prediction unit can also analyze fluctuations in the user's eating pattern in real time and can predict abnormalities early. The prediction unit can also analyze fluctuations in the user's exercise pattern in real time and can predict abnormalities early. In this way, by analyzing fluctuations in the user's lifestyle rhythm in real time, abnormalities can be predicted early.

[0068] The prediction unit can select prediction items based on the geographical background of the user. The prediction unit selects prediction items based on, for example, the geographical background of the user. For example, if the user lives in a hot and humid region, the prediction unit can predict the risk of heatstroke. Furthermore, if the user lives in a cold region, the prediction unit can also predict the risk of hypothermia. Furthermore, if the user lives at high altitude, the prediction unit can also predict the risk of altitude sickness. Thus, by selecting prediction items based on the geographical background of the user, more appropriate predictions can be made.

[0069] The prediction unit can analyze the user's social media activity and make related predictions. The prediction unit can, for example, analyze the user's social media activity and make related predictions. For example, if the user posts health-related information on social media, the prediction unit can make a prediction based on the content of the posts. The prediction unit can also make a prediction based on health information shared by the user's friends on social media. The prediction unit can also make a prediction based on health information in which the user is interested on social media. This allows for more appropriate predictions to be made by analyzing the user's social media activity.

[0070] The prediction unit can improve the prediction method by reflecting the user's past feedback. The prediction unit improves the prediction method by reflecting the user's past feedback, for example. For example, the prediction unit preferentially uses a prediction method that the user has previously preferred. The prediction unit can also avoid a prediction method that the user has previously avoided. The prediction unit can also improve the content of the prediction based on the user's feedback. In this way, the prediction method can be improved by reflecting the user's past feedback.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The suggestion unit can analyze the user's past game and quiz history and re-suggest games and quizzes that the user particularly enjoyed. For example, it can re-suggest puzzle games that the user enjoyed in the past. It can also re-suggest quizzes that the user attempted but was unable to complete in the past. It can also preferentially suggest games and quizzes that the user previously gave high ratings to. In this way, by analyzing the user's past game and quiz history, more appropriate suggestions can be made.

[0073] The monitoring unit can analyze fluctuations in the user's vital signs in real time and detect abnormalities early. For example, it can analyze fluctuations in the user's body temperature in real time and detect abnormalities early. It can also analyze fluctuations in the user's heart rate in real time and detect abnormalities early. It can also analyze fluctuations in the user's blood pressure in real time and detect abnormalities early. As a result, by analyzing fluctuations in the user's vital signs in real time, it is possible to detect abnormalities early.

[0074] The pattern analysis unit can analyze the user's past lifestyle data and select the optimal pattern analysis method. For example, the optimal pattern analysis method is selected based on the user's past lifestyle data. The accuracy of the pattern analysis can also be adjusted based on the user's past lifestyle data. Furthermore, the pattern analysis items can be customized based on the user's past lifestyle data. This makes it possible to select a more appropriate pattern analysis method by analyzing the user's past lifestyle data.

[0075] The dialogue analysis unit can analyze the user's past dialogue history and generate optimal responses. For example, it can provide related topics based on what the user has said in the past. It can also provide topics that the user has liked in the past with priority. It can also generate responses that avoid topics that the user has avoided in the past. In this way, by analyzing the user's past dialogue history, it is possible to provide more appropriate responses.

[0076] The alert unit can analyze the user's past health data and set optimal alert criteria. For example, it can set criteria for detecting abnormal body temperature based on the user's past health data. It can also set criteria for detecting abnormal heart rate based on the user's past health data. It can also set criteria for detecting abnormal blood pressure based on the user's past health data. In this way, by analyzing the user's past health data, it is possible to set more appropriate alert criteria.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The dialogue analysis unit analyzes the user's voice and text and generates a response. For example, it uses speech recognition technology to convert the voice into text and natural language processing technology to analyze the text. It can also use generative AI to generate an appropriate response. For example, if a user says, "The weather is nice today," the response generated is, "Yes, it's very nice today. How about going for a walk?" Step 2: The suggestion unit suggests games or quizzes based on the responses generated by the dialogue analysis unit. For example, it suggests quizzes to train memory or puzzle games to improve cognitive function. It is also possible to use generation AI to suggest games or quizzes based on the user's interests. Step 3: The monitoring unit monitors the user's condition and health status. For example, it measures vital signs such as body temperature and heart rate and collects data. It can also monitor the user's behavioral patterns and daily rhythms. Step 4: The alert unit notifies staff of any abnormalities detected by the monitoring unit. For example, if the user's body temperature suddenly rises, or if an abnormality in their heart rate or change in their behavior pattern is detected, the alert unit notifies staff. Step 5: The pattern analysis unit identifies the user's daily life and health patterns, such as eating habits at the same time every day, sleep patterns, and exercise patterns. Step 6: The prediction unit predicts and prevents abnormalities and dangerous situations based on the patterns identified by the pattern analysis unit. For example, it can analyze fluctuations in daily rhythms in real time to predict abnormalities early. It can also predict changes in health status and suggest appropriate countermeasures.

[0079] (Example 2) A system according to an embodiment of the present invention uses AI to provide conversational companionship to elderly people and people with disabilities, offering empathy and encouragement. When a user speaks to the system, the AI ​​analyzes the voice and text and generates appropriate responses. The system then suggests fun games and quizzes to the user, supporting cognitive and memory training. Furthermore, the system collaborates with staff and caregivers at welfare facilities to monitor the user's condition and health status and send alerts to staff if any abnormalities are detected. The system also understands the user's daily life and health patterns and predicts and prevents abnormalities and dangerous situations. This helps ensure the user's safety and health. For example, when a user speaks to the system, the AI ​​analyzes the voice and text and generates an appropriate response. For example, if a user says, "The weather is nice today," the AI ​​responds, "Yes, it's very nice today. How about going for a walk?" This allows for natural dialogue with the user. The system then suggests fun games and quizzes to the user. For example, quizzes to train memory and puzzle games to improve cognitive function are available. This allows users to have fun while training their cognitive and memory functions. The system also works in conjunction with staff and caregivers at welfare facilities. It monitors the user's condition and health status and sends alerts to staff if any abnormalities are detected. For example, if a user's body temperature suddenly rises, the system will notify staff, "The user's temperature is rising. Please check." This allows for a prompt response. The system also understands patterns in the user's daily life and health status, predicting and preventing abnormalities and dangerous situations. For example, if a user has the habit of eating at the same time every day, the system can learn that pattern and notify staff if any abnormalities are detected. This ensures the user's safety.

[0080] A dialogue support system according to an embodiment includes a dialogue analysis unit, a suggestion unit, a monitoring unit, an alert unit, a pattern analysis unit, and a prediction unit. The dialogue analysis unit analyzes a user's voice and text and generates a response. For example, the dialogue analysis unit converts the user's voice into text using speech recognition technology and analyzes the text using natural language processing technology. The dialogue analysis unit can also generate an appropriate response using a generation AI. For example, when a user says, "The weather is nice today," the dialogue analysis unit generates a response such as, "Yes, it's very nice today. How about going for a walk?" The suggestion unit suggests a game or quiz based on the response generated by the dialogue analysis unit. For example, the suggestion unit suggests a quiz to train memory or a puzzle game to improve cognitive function. The suggestion unit can also suggest games or quizzes based on the user's interests using the generation AI. The monitoring unit monitors the user's condition and health status. For example, the monitoring unit measures vital signs such as body temperature and heart rate and collects data. The monitoring unit can also monitor the user's behavioral patterns and daily rhythms. The alert unit notifies staff of abnormalities detected by the monitoring unit. For example, the alert unit notifies staff if the user's body temperature suddenly rises. The alert unit can also notify staff if it detects an abnormal heart rate or a change in behavioral pattern. The pattern analysis unit understands patterns of the user's daily life and health condition. For example, the pattern analysis unit understands whether the user has a habit of eating meals at the same time every day. The pattern analysis unit can also analyze the user's sleep pattern and exercise pattern. The prediction unit predicts and prevents abnormalities and dangerous situations based on the patterns understood by the pattern analysis unit. For example, the prediction unit analyzes fluctuations in the user's daily rhythm in real time and predicts abnormalities early. The prediction unit can also predict changes in the user's health condition and suggest appropriate measures. As a result, the dialogue support system according to the embodiment can protect the safety and health of users.

[0081] The suggestion unit can suggest quizzes for training memory or puzzle games for improving cognitive function. The suggestion unit can, for example, suggest quizzes for training memory. For example, the suggestion unit can provide quizzes that encourage memorization of words or numbers. The suggestion unit can also suggest puzzle games for improving cognitive function. For example, the suggestion unit can provide games such as Sudoku or crossword puzzles. The suggestion unit can also use a generative AI to suggest games or quizzes based on the user's interests. This can support the training of the user's cognitive function and memory by suggesting quizzes for training memory or puzzle games for improving cognitive function.

[0082] The monitoring unit can monitor the user's vital signs, such as body temperature or heart rate. The monitoring unit, for example, monitors the user's body temperature. For example, the monitoring unit measures the user's body temperature using a thermometer and collects data. The monitoring unit can also monitor the heart rate. For example, the monitoring unit measures the user's heart rate using a heart rate monitor and collects data. The monitoring unit can also analyze vital sign data in real time to detect abnormalities early. In this way, by monitoring the user's vital signs, such as body temperature and heart rate, the user's health condition can be understood and any abnormalities can be dealt with promptly.

[0083] The alert unit can notify staff if the user's body temperature suddenly rises. For example, the alert unit notifies staff if the user's body temperature suddenly rises. For example, the alert unit detects an abnormality based on the rate and magnitude of body temperature rise and notifies staff. The alert unit can also notify if it detects an abnormal heart rate or a change in behavior pattern. For example, the alert unit detects a sudden change in heart rate or an abnormal behavior pattern and notifies staff. In this way, by notifying staff if the user's body temperature suddenly rises, a prompt response can be taken and the user's safety can be ensured.

[0084] The pattern analysis unit can determine whether the user has a habit of eating meals at the same time every day. The pattern analysis unit determines, for example, whether the user has a habit of eating meals at the same time every day. For example, the pattern analysis unit records the time and content of the user's meals and analyzes the data. The pattern analysis unit can also learn the user's eating patterns and notify staff if there is an abnormality. For example, the pattern analysis unit detects an abnormality if the user does not eat at their usual mealtimes and notifies staff. In this way, by determining whether the user has a habit of eating meals at the same time every day, it is possible to respond quickly if there is an abnormality.

[0085] The prediction unit can predict abnormalities and dangerous situations based on patterns of the user's daily life and health condition. The prediction unit predicts abnormalities and dangerous situations based on patterns of the user's daily life and health condition, for example. For example, the prediction unit analyzes fluctuations in the user's daily rhythm in real time to predict abnormalities early. The prediction unit can also predict changes in the user's health condition and propose appropriate countermeasures. For example, the prediction unit analyzes fluctuations in the user's sleeping patterns and eating patterns to predict abnormalities early. This makes it possible to protect the user's safety and health by predicting abnormalities and dangerous situations based on patterns of the user's daily life and health condition.

[0086] The dialogue analysis unit can estimate the user's emotions and adjust the tone and content of a response based on the estimated user emotions. For example, the dialogue analysis unit can estimate the user's emotions and adjust the tone and content of a response based on the estimated user emotions. For example, if the user is sad, the dialogue analysis unit can provide encouraging words in a gentle tone. If the user is excited, the dialogue analysis unit can speak to the user in a calm tone to help them relax. If the user is tired, the dialogue analysis unit can provide a short and concise response to reduce their burden. This allows for a more appropriate response by adjusting the tone and content of a response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The dialogue analysis unit can analyze the user's past dialogue history and generate an optimal response. The dialogue analysis unit can, for example, analyze the user's past dialogue history and generate an optimal response. For example, the dialogue analysis unit can provide related topics based on content that the user has spoken in the past. The dialogue analysis unit can also provide topics that the user has preferred in the past with priority. The dialogue analysis unit can also generate a response that avoids topics that the user has avoided in the past. In this way, by analyzing the user's past dialogue history, a more appropriate response can be provided.

[0088] The dialogue analysis unit can customize responses according to the user's language or dialect. The dialogue analysis unit customizes responses according to the user's language or dialect, for example. For example, if the user speaks Kansai dialect, the dialogue analysis unit responds in Kansai dialect. Furthermore, if the user speaks English, the dialogue analysis unit can also respond in English. Furthermore, if the user speaks a specific dialect, the dialogue analysis unit can generate a response tailored to that dialect. In this way, by customizing responses according to the user's language or dialect, more natural dialogue can be achieved.

[0089] The dialogue analysis unit can generate a response based on the user's current activity and environment. The dialogue analysis unit generates a response based on the user's current activity and environment, for example. For example, if the user is out, the dialogue analysis unit can provide a topic related to going out. Furthermore, if the user is eating, the dialogue analysis unit can also provide a topic related to eating. Furthermore, if the user is relaxing, the dialogue analysis unit can also provide a topic related to relaxation. In this way, by generating a response based on the user's current activity and environment, it is possible to provide a more appropriate response.

[0090] The dialogue analysis unit can estimate the user's emotions and adjust the dialogue progression speed based on the estimated user emotions. The dialogue analysis unit, for example, estimates the user's emotions and adjusts the dialogue progression speed based on the estimated user emotions. For example, the dialogue analysis unit can speed up the dialogue progression speed when the user is in a hurry. The dialogue analysis unit can also slow down the dialogue progression speed when the user is relaxed. The dialogue analysis unit can also adjust the dialogue progression speed to reduce the burden on the user when the user is tired. In this way, by adjusting the dialogue progression speed according to the user's emotions, a more appropriate dialogue can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The dialogue analysis unit can generate a response based on the geographical background of the user. The dialogue analysis unit generates a response based on, for example, the geographical background of the user. For example, the dialogue analysis unit provides weather information for the area where the user lives. The dialogue analysis unit can also provide event information for the area where the user lives. The dialogue analysis unit can also provide news for the area where the user lives. In this way, by generating a response based on the geographical background of the user, a more appropriate response can be provided.

[0092] The dialogue analysis unit can analyze the user's social media activity and provide related topics. The dialogue analysis unit can, for example, analyze the user's social media activity and provide related topics. For example, the dialogue analysis unit can generate a response based on the content that the user is talking about on social media. The dialogue analysis unit can also generate a response based on the content that the user's friends are talking about on social media. The dialogue analysis unit can also provide topics that the user is interested in on social media. In this way, by analyzing the user's social media activity, more appropriate topics can be provided.

[0093] The dialogue analysis unit can improve the quality of responses by reflecting the user's past feedback. The dialogue analysis unit can improve the quality of responses by reflecting the user's past feedback, for example. For example, the dialogue analysis unit can preferentially use a response style that the user has preferred in the past. The dialogue analysis unit can also avoid a response style that the user has avoided in the past. The dialogue analysis unit can also improve the content of the response based on the user's feedback. In this way, the quality of the response can be improved by reflecting the user's past feedback.

[0094] The suggestion unit can estimate the user's emotions and adjust the difficulty of games and quizzes based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the difficulty of games and quizzes based on the estimated user emotions. For example, the suggestion unit can suggest games and quizzes with low difficulty when the user is relaxed. The suggestion unit can also suggest games and quizzes with high difficulty when the user is concentrating. The suggestion unit can also suggest easy games and quizzes when the user is tired. This allows for more appropriate suggestions to be made by adjusting the difficulty of games and quizzes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The suggestion unit can analyze the user's past game and quiz history and make optimal suggestions. The suggestion unit can, for example, analyze the user's past game and quiz history and make optimal suggestions. For example, the suggestion unit can re-suggest games and quizzes that the user enjoyed in the past. The suggestion unit can also re-suggest games and quizzes that the user attempted but was unable to complete in the past. The suggestion unit can also suggest new games and quizzes based on the user's past history. In this way, more appropriate suggestions can be made by analyzing the user's past game and quiz history.

[0096] The suggestion unit can customize games and quizzes according to the user's current cognitive function level. The suggestion unit customizes games and quizzes according to, for example, the user's current cognitive function level. For example, the suggestion unit adjusts the difficulty of games and quizzes according to the user's cognitive function level. The suggestion unit can also customize the content of games and quizzes according to the user's cognitive function level. The suggestion unit can also adjust the progress speed of games and quizzes according to the user's cognitive function level. In this way, more appropriate suggestions can be made by customizing games and quizzes according to the user's current cognitive function level.

[0097] The suggestion unit can select the content of games and quizzes based on the user's interests and concerns. The suggestion unit selects the content of games and quizzes based on, for example, the user's interests and concerns. For example, the suggestion unit selects games and quizzes based on themes in which the user is interested. The suggestion unit can also select games and quizzes based on themes that the user has enjoyed in the past. The suggestion unit can also suggest games and quizzes with new themes based on the user's interests and concerns. This allows for more appropriate suggestions to be made by selecting the content of games and quizzes based on the user's interests and concerns.

[0098] The suggestion unit can estimate the user's emotion and adjust the timing of suggestions based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the timing of suggestions based on the estimated user's emotion. For example, the suggestion unit can delay the timing of suggestions when the user is relaxed. Furthermore, the suggestion unit can also advance the timing of suggestions when the user is concentrating. Furthermore, the suggestion unit can adjust the timing of suggestions when the user is tired to reduce the burden on the user. In this way, by adjusting the timing of suggestions according to the user's emotion, more appropriate suggestions can be made. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The suggestion unit can suggest region-specific games and quizzes based on the user's geographical background. The suggestion unit, for example, suggests region-specific games and quizzes based on the user's geographical background. For example, the suggestion unit can suggest quizzes based on the history of the region where the user lives. The suggestion unit can also suggest games based on the culture of the region where the user lives. The suggestion unit can also suggest quizzes based on events in the region where the user lives. This allows more appropriate suggestions to be made by suggesting region-specific games and quizzes based on the user's geographical background.

[0100] The suggestion unit can analyze the user's social media activity and suggest related games and quizzes. The suggestion unit can, for example, analyze the user's social media activity and suggest related games and quizzes. For example, the suggestion unit can suggest games and quizzes based on topics that the user is talking about on social media. The suggestion unit can also suggest games and quizzes based on topics that the user's friends are talking about on social media. The suggestion unit can also suggest games and quizzes based on topics that the user is interested in on social media. In this way, more appropriate games and quizzes can be suggested by analyzing the user's social media activity.

[0101] The suggestion unit can improve the quality of suggestions by reflecting the user's past feedback. The suggestion unit can improve the quality of suggestions by reflecting the user's past feedback, for example. For example, the suggestion unit can preferentially suggest games and quizzes that the user liked in the past. The suggestion unit can also avoid games and quizzes that the user avoided in the past. The suggestion unit can also improve the content of suggestions based on the user's feedback. In this way, the quality of suggestions can be improved by reflecting the user's past feedback.

[0102] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the monitoring frequency based on the estimated user emotions. For example, the monitoring unit can increase the monitoring frequency when the user is stressed. The monitoring unit can also decrease the monitoring frequency when the user is relaxed. The monitoring unit can also adjust the monitoring frequency when the user is tired to reduce the burden. In this way, by adjusting the monitoring frequency according to the user's emotions, more appropriate monitoring can be performed. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The monitoring unit can analyze the user's past health data and select the optimal monitoring method. The monitoring unit, for example, analyzes the user's past health data and selects the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the user's past health data. The monitoring unit can also adjust the monitoring frequency based on the user's past health data. The monitoring unit can also customize the monitoring items based on the user's past health data. In this way, a more appropriate monitoring method can be selected by analyzing the user's past health data.

[0104] The monitoring unit can customize the monitoring items based on the user's current living situation. The monitoring unit customizes the monitoring items based on the user's current living situation, for example. For example, the monitoring unit customizes the monitoring items based on the user's current living situation. The monitoring unit can also adjust the monitoring frequency based on the user's current living situation. The monitoring unit can also customize the monitoring method based on the user's current living situation. In this way, by customizing the monitoring items based on the user's current living situation, more appropriate monitoring can be performed.

[0105] The monitoring unit analyzes fluctuations in the user's vital signs in real time and can detect abnormalities early. The monitoring unit, for example, analyzes fluctuations in the user's vital signs in real time and can detect abnormalities early. For example, the monitoring unit analyzes fluctuations in the user's body temperature in real time and can detect abnormalities early. The monitoring unit can also analyze fluctuations in the user's heart rate in real time and can detect abnormalities early. The monitoring unit can also analyze fluctuations in the user's blood pressure in real time and can detect abnormalities early. In this way, by analyzing fluctuations in the user's vital signs in real time, abnormalities can be detected early.

[0106] The monitoring unit can estimate the user's emotions and determine the monitoring priority based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and determines the monitoring priority based on the estimated user emotions. For example, the monitoring unit can increase the monitoring priority when the user is stressed. The monitoring unit can also decrease the monitoring priority when the user is relaxed. The monitoring unit can also adjust the monitoring priority to reduce the burden when the user is tired. This allows for more appropriate monitoring by determining the monitoring priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The monitoring unit can select monitoring items based on the geographical background of the user. The monitoring unit selects monitoring items based on, for example, the geographical background of the user. For example, if the user lives in a hot and humid region, the monitoring unit monitors the risk of heatstroke. Also, if the user lives in a cold region, the monitoring unit can monitor the risk of hypothermia. Also, if the user lives at high altitude, the monitoring unit can monitor the risk of altitude sickness. In this way, by selecting monitoring items based on the geographical background of the user, more appropriate monitoring can be performed.

[0108] The monitoring unit can analyze the user's social media activities and monitor related health information. The monitoring unit, for example, analyzes the user's social media activities and monitors related health information. For example, if the user posts health-related information on social media, the monitoring unit selects monitoring items based on the content of the posts. The monitoring unit can also select monitoring items based on health information shared by the user's friends on social media. The monitoring unit can also select monitoring items based on health information in which the user is interested on social media. In this way, more appropriate health information can be monitored by analyzing the user's social media activities.

[0109] The monitoring unit can improve the monitoring method by reflecting the user's past feedback. The monitoring unit improves the monitoring method by reflecting the user's past feedback, for example. For example, the monitoring unit preferentially uses monitoring methods that the user has preferred in the past. The monitoring unit can also avoid monitoring methods that the user has avoided in the past. The monitoring unit can also improve the content of monitoring based on the user's feedback. In this way, the monitoring method can be improved by reflecting the user's past feedback.

[0110] The alert unit can estimate the user's emotion and adjust the urgency of the alert based on the estimated user's emotion. The alert unit, for example, estimates the user's emotion and adjusts the urgency of the alert based on the estimated user's emotion. For example, if the user is feeling stressed, the alert unit can prioritize notifying a high-urgency alert. Furthermore, if the user is relaxed, the alert unit can postpone a low-urgency alert. Furthermore, if the user is tired, the alert unit can adjust the urgency to reduce the user's burden. In this way, by adjusting the urgency of the alert according to the user's emotion, more appropriate alerts can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] The alert unit can analyze the user's past health data and set optimal alert criteria. The alert unit, for example, analyzes the user's past health data and sets optimal alert criteria. For example, the alert unit sets criteria for detecting abnormal body temperature based on the user's past health data. The alert unit can also set criteria for detecting abnormal heart rate based on the user's past health data. The alert unit can also set criteria for detecting abnormal blood pressure based on the user's past health data. In this way, more appropriate alert criteria can be set by analyzing the user's past health data.

[0112] The alert unit can customize the content of the alert based on the user's current living situation. The alert unit customizes the content of the alert based on the user's current living situation, for example. For example, when the user is out, the alert unit provides an alert related to going out. Furthermore, when the user is eating, the alert unit can also provide an alert related to eating. Furthermore, when the user is relaxing, the alert unit can also provide an alert related to relaxation. In this way, by customizing the content of the alert based on the user's current living situation, more appropriate alerts can be provided.

[0113] The alert unit can analyze fluctuations in the user's vital signs in real time and provide early notification of abnormalities. The alert unit, for example, analyzes fluctuations in the user's vital signs in real time and provides early notification of abnormalities. For example, the alert unit can analyze fluctuations in the user's body temperature in real time and provide early notification of abnormalities. The alert unit can also analyze fluctuations in the user's heart rate in real time and provide early notification of abnormalities. The alert unit can also analyze fluctuations in the user's blood pressure in real time and provide early notification of abnormalities. In this way, by analyzing fluctuations in the user's vital signs in real time, it is possible to provide early notification of abnormalities.

[0114] The alert unit can estimate the user's emotion and adjust the alert display method based on the estimated user emotion. For example, the alert unit can estimate the user's emotion and adjust the alert display method based on the estimated user emotion. For example, if the user is nervous, the alert unit can provide a simple, highly visible display method. If the user is relaxed, the alert unit can also provide a display method that includes detailed information. If the user is in a hurry, the alert unit can also provide a display method that focuses on the main points. This allows for adjusting the alert display method according to the user's emotion, thereby providing a more appropriate alert. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The alert unit can select the content of the alert based on the geographical background of the user. The alert unit selects the content of the alert based on, for example, the geographical background of the user. For example, if the user lives in a hot and humid region, the alert unit can provide an alert regarding the risk of heatstroke. Also, if the user lives in a cold region, the alert unit can provide an alert regarding the risk of hypothermia. Also, if the user lives at high altitude, the alert unit can provide an alert regarding the risk of altitude sickness. In this way, by selecting the content of the alert based on the geographical background of the user, more appropriate alerts can be provided.

[0116] The alert unit can analyze the user's social media activity and generate a related alert. The alert unit can, for example, analyze the user's social media activity and generate a related alert. For example, if the user posts health-related information on social media, the alert unit can generate an alert based on the content of the post. The alert unit can also generate an alert based on health information shared by the user's friends on social media. The alert unit can also generate an alert based on health information in which the user is interested on social media. In this way, more appropriate alerts can be generated by analyzing the user's social media activity.

[0117] The alert unit can improve the quality of alerts by reflecting the user's past feedback. The alert unit can improve the quality of alerts by reflecting the user's past feedback, for example. For example, the alert unit can preferentially use alert styles that the user has previously preferred. The alert unit can also avoid alert styles that the user has previously avoided. The alert unit can also improve the content of alerts based on the user's feedback. In this way, the quality of alerts can be improved by reflecting the user's past feedback.

[0118] The pattern analysis unit can estimate the user's emotions and adjust the accuracy of the pattern analysis based on the estimated user's emotions. The pattern analysis unit, for example, estimates the user's emotions and adjusts the accuracy of the pattern analysis based on the estimated user's emotions. For example, the pattern analysis unit increases the accuracy of the pattern analysis when the user is stressed. The pattern analysis unit can also adjust the accuracy of the pattern analysis when the user is relaxed. The pattern analysis unit can also adjust the accuracy of the pattern analysis to reduce the user's burden when the user is tired. In this way, by adjusting the accuracy of the pattern analysis according to the user's emotions, more appropriate pattern analysis can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The pattern analysis unit can analyze the user's past lifestyle data and select the optimal pattern analysis method. The pattern analysis unit, for example, analyzes the user's past lifestyle data and selects the optimal pattern analysis method. For example, the pattern analysis unit selects the optimal pattern analysis method based on the user's past lifestyle data. The pattern analysis unit can also adjust the accuracy of the pattern analysis based on the user's past lifestyle data. The pattern analysis unit can also customize the pattern analysis items based on the user's past lifestyle data. In this way, a more appropriate pattern analysis method can be selected by analyzing the user's past lifestyle data.

[0120] The pattern analysis unit can customize the pattern analysis items based on the user's current living situation. The pattern analysis unit customizes the pattern analysis items based on the user's current living situation, for example. For example, the pattern analysis unit customizes the pattern analysis items based on the user's current living situation. The pattern analysis unit can also adjust the accuracy of the pattern analysis based on the user's current living situation. The pattern analysis unit can also customize the pattern analysis method based on the user's current living situation. In this way, by customizing the pattern analysis items based on the user's current living situation, more appropriate pattern analysis can be performed.

[0121] The pattern analysis unit analyzes fluctuations in the user's lifestyle rhythm in real time, and can detect abnormalities early. The pattern analysis unit, for example, analyzes fluctuations in the user's lifestyle rhythm in real time, and can detect abnormalities early. For example, the pattern analysis unit analyzes fluctuations in the user's sleep pattern in real time, and can detect abnormalities early. The pattern analysis unit can also analyze fluctuations in the user's eating pattern in real time, and can detect abnormalities early. The pattern analysis unit can also analyze fluctuations in the user's exercise pattern in real time, and can detect abnormalities early. In this way, by analyzing fluctuations in the user's lifestyle rhythm in real time, abnormalities can be detected early.

[0122] The pattern analysis unit can estimate the user's emotion and determine the priority of pattern analysis based on the estimated user's emotion. The pattern analysis unit, for example, estimates the user's emotion and determines the priority of pattern analysis based on the estimated user's emotion. For example, the pattern analysis unit can increase the priority of pattern analysis when the user is stressed. The pattern analysis unit can also decrease the priority of pattern analysis when the user is relaxed. The pattern analysis unit can also adjust the priority of pattern analysis to reduce the burden on the user when the user is tired. This allows for more appropriate pattern analysis by determining the priority of pattern analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The pattern analysis unit can select pattern analysis items based on the user's geographical background. The pattern analysis unit selects pattern analysis items based on, for example, the user's geographical background. For example, if the user lives in a hot and humid region, the pattern analysis unit analyzes patterns related to the risk of heatstroke. Furthermore, if the user lives in a cold region, the pattern analysis unit can also analyze patterns related to the risk of hypothermia. Furthermore, if the user lives at high altitude, the pattern analysis unit can analyze patterns related to the risk of altitude sickness. In this way, by selecting pattern analysis items based on the user's geographical background, more appropriate pattern analysis can be performed.

[0124] The pattern analysis unit can analyze the user's social media activities and analyze related patterns. The pattern analysis unit, for example, analyzes the user's social media activities and analyzes related patterns. For example, if the user posts health-related information on social media, the pattern analysis unit analyzes patterns based on the content of the posts. The pattern analysis unit can also analyze patterns based on health information shared by the user's friends on social media. The pattern analysis unit can also analyze patterns based on health information in which the user is interested on social media. This makes it possible to analyze more appropriate patterns by analyzing the user's social media activities.

[0125] The pattern analysis unit can improve the pattern analysis method by reflecting the user's past feedback. The pattern analysis unit improves the pattern analysis method by reflecting the user's past feedback, for example. For example, the pattern analysis unit preferentially uses pattern analysis methods that the user has previously preferred. The pattern analysis unit can also avoid pattern analysis methods that the user has previously avoided. The pattern analysis unit can also improve the content of the pattern analysis based on the user's feedback. In this way, the pattern analysis method can be improved by reflecting the user's past feedback.

[0126] The prediction unit can estimate the user's emotion and adjust the accuracy of the prediction based on the estimated user's emotion. For example, the prediction unit can estimate the user's emotion and adjust the accuracy of the prediction based on the estimated user's emotion. For example, the prediction unit can increase the accuracy of the prediction when the user is stressed. The prediction unit can also adjust the accuracy of the prediction when the user is relaxed. The prediction unit can also adjust the accuracy of the prediction when the user is tired to reduce the burden. In this way, by adjusting the accuracy of the prediction according to the user's emotion, more appropriate predictions can be made. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] The prediction unit can analyze the user's past lifestyle data and select the optimal prediction method. The prediction unit, for example, analyzes the user's past lifestyle data and selects the optimal prediction method. For example, the prediction unit selects the optimal prediction method based on the user's past lifestyle data. The prediction unit can also adjust the accuracy of prediction based on the user's past lifestyle data. The prediction unit can also customize prediction items based on the user's past lifestyle data. In this way, a more appropriate prediction method can be selected by analyzing the user's past lifestyle data.

[0128] The prediction unit can customize the prediction items based on the user's current living situation. The prediction unit customizes the prediction items based on the user's current living situation, for example. For example, the prediction unit customizes the prediction items based on the user's current living situation. The prediction unit can also adjust the accuracy of the prediction based on the user's current living situation. The prediction unit can also customize the prediction method based on the user's current living situation. In this way, by customizing the prediction items based on the user's current living situation, more appropriate predictions can be made.

[0129] The prediction unit analyzes fluctuations in the user's lifestyle rhythm in real time and can predict abnormalities early. The prediction unit, for example, analyzes fluctuations in the user's lifestyle rhythm in real time and can predict abnormalities early. For example, the prediction unit analyzes fluctuations in the user's sleep pattern in real time and can predict abnormalities early. The prediction unit can also analyze fluctuations in the user's eating pattern in real time and can predict abnormalities early. The prediction unit can also analyze fluctuations in the user's exercise pattern in real time and can predict abnormalities early. In this way, by analyzing fluctuations in the user's lifestyle rhythm in real time, abnormalities can be predicted early.

[0130] The prediction unit can estimate the user's emotion and determine the priority of prediction based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and determines the priority of prediction based on the estimated user's emotion. For example, the prediction unit can increase the priority of prediction when the user is stressed. The prediction unit can also decrease the priority of prediction when the user is relaxed. The prediction unit can also adjust the priority of prediction when the user is tired to reduce the burden. In this way, by determining the priority of prediction according to the user's emotion, more appropriate prediction can be made. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0131] The prediction unit can select prediction items based on the geographical background of the user. The prediction unit selects prediction items based on, for example, the geographical background of the user. For example, if the user lives in a hot and humid region, the prediction unit can predict the risk of heatstroke. Furthermore, if the user lives in a cold region, the prediction unit can also predict the risk of hypothermia. Furthermore, if the user lives at high altitude, the prediction unit can also predict the risk of altitude sickness. Thus, by selecting prediction items based on the geographical background of the user, more appropriate predictions can be made.

[0132] The prediction unit can analyze the user's social media activity and make related predictions. The prediction unit can, for example, analyze the user's social media activity and make related predictions. For example, if the user posts health-related information on social media, the prediction unit can make a prediction based on the content of the posts. The prediction unit can also make a prediction based on health information shared by the user's friends on social media. The prediction unit can also make a prediction based on health information in which the user is interested on social media. This allows for more appropriate predictions to be made by analyzing the user's social media activity.

[0133] The prediction unit can improve the prediction method by reflecting the user's past feedback. The prediction unit improves the prediction method by reflecting the user's past feedback, for example. For example, the prediction unit preferentially uses a prediction method that the user has previously preferred. The prediction unit can also avoid a prediction method that the user has previously avoided. The prediction unit can also improve the content of the prediction based on the user's feedback. In this way, the prediction method can be improved by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the dialogue analysis unit, suggestion unit, monitoring unit, alert unit, pattern analysis unit, and prediction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the dialogue analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's condition using, for example, the camera 42 or microphone 38B of the smart device 14 and analyzes the data using the specific processing unit 290 of the data processing device 12. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies staff when an abnormality is detected. The pattern analysis unit and prediction unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze patterns of the user's daily life and health condition to predict and prevent abnormalities and dangerous situations. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned dialogue analysis unit, suggestion unit, monitoring unit, alert unit, pattern analysis unit, and prediction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the dialogue analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's condition using, for example, the camera 42 or the microphone 238 of the smart glasses 214 and analyzes the data using the specific processing unit 290 of the data processing device 12. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies staff when an abnormality is detected. The pattern analysis unit and the prediction unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze patterns of the user's daily life and health condition to predict and prevent abnormalities and dangerous situations. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned dialogue analysis unit, suggestion unit, monitoring unit, alert unit, pattern analysis unit, and prediction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the dialogue analysis unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's condition using, for example, the camera 42 or the microphone 238 of the headset-type terminal 314 and analyzes the data using the specific processing unit 290 of the data processing device 12. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies staff when an abnormality is detected. The pattern analysis unit and the prediction unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze patterns of the user's daily life and health condition to predict and prevent abnormalities and dangerous situations. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned dialogue analysis unit, suggestion unit, monitoring unit, alert unit, pattern analysis unit, and prediction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the dialogue analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's condition using, for example, the camera 42 or microphone 238 of the robot 414 and analyzes the data using the specific processing unit 290 of the data processing device 12. The alert unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies staff when an abnormality is detected. The pattern analysis unit and the prediction unit are realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze patterns of the user's daily life and health condition to predict and prevent abnormalities and dangerous situations.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The dialogue analysis unit can analyze the user's tone of voice and speaking speed to infer the user's emotions. For example, if the user speaks slowly, the dialogue analysis unit can infer that the user is relaxed and generate a response in a relaxed tone. If the user speaks quickly, the dialogue analysis unit can infer that the user is nervous and generate a response in a calm tone. Furthermore, if the user speaks with a trembling voice, the dialogue analysis unit can infer that the user is feeling anxious and generate a reassuring response. This makes it possible to infer the user's emotions based on the user's tone of voice and speaking speed and provide a more appropriate response.

[0136] The suggestion unit can analyze the user's past game and quiz history and re-suggest games and quizzes that the user particularly enjoyed. For example, it can re-suggest puzzle games that the user enjoyed in the past. It can also re-suggest quizzes that the user attempted but was unable to complete in the past. It can also preferentially suggest games and quizzes that the user previously gave high ratings to. In this way, by analyzing the user's past game and quiz history, more appropriate suggestions can be made.

[0137] The monitoring unit can analyze fluctuations in the user's vital signs in real time and detect abnormalities early. For example, it can analyze fluctuations in the user's body temperature in real time and detect abnormalities early. It can also analyze fluctuations in the user's heart rate in real time and detect abnormalities early. It can also analyze fluctuations in the user's blood pressure in real time and detect abnormalities early. As a result, by analyzing fluctuations in the user's vital signs in real time, it is possible to detect abnormalities early.

[0138] The alert unit can estimate the user's emotions and adjust the urgency of the alert based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize alerts with a high level of urgency. Also, if the user is relaxed, it can postpone alerts with a low level of urgency. Furthermore, if the user is tired, it can adjust the urgency to reduce the burden on the user. In this way, by adjusting the urgency of the alert according to the user's emotions, it is possible to provide more appropriate alerts.

[0139] The pattern analysis unit can analyze the user's past lifestyle data and select the optimal pattern analysis method. For example, the optimal pattern analysis method is selected based on the user's past lifestyle data. The accuracy of the pattern analysis can also be adjusted based on the user's past lifestyle data. Furthermore, the pattern analysis items can be customized based on the user's past lifestyle data. This makes it possible to select a more appropriate pattern analysis method by analyzing the user's past lifestyle data.

[0140] The prediction unit can estimate the user's emotions and adjust the accuracy of the prediction based on the estimated user's emotions. For example, if the user is feeling stressed, the accuracy of the prediction can be increased. Also, if the user is relaxed, the accuracy of the prediction can be adjusted. Furthermore, if the user is tired, the accuracy of the prediction can be adjusted to reduce the burden on the user. In this way, by adjusting the accuracy of the prediction according to the user's emotions, more appropriate predictions can be made.

[0141] The dialogue analysis unit can analyze the user's past dialogue history and generate optimal responses. For example, it can provide related topics based on what the user has said in the past. It can also provide topics that the user has liked in the past with priority. It can also generate responses that avoid topics that the user has avoided in the past. In this way, by analyzing the user's past dialogue history, it is possible to provide more appropriate responses.

[0142] The suggestion unit can estimate the user's emotions and adjust the difficulty of games and quizzes based on the estimated user's emotions. For example, if the user is relaxed, it can suggest games and quizzes with low difficulty. Also, if the user is concentrating, it can suggest games and quizzes with high difficulty. Furthermore, if the user is tired, it can suggest easy games and quizzes. In this way, by adjusting the difficulty of games and quizzes according to the user's emotions, more appropriate suggestions can be made.

[0143] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring frequency can be increased. Also, if the user is relaxed, the monitoring frequency can be decreased. Furthermore, if the user is tired, the monitoring frequency can be adjusted to reduce the burden on the user. In this way, more appropriate monitoring can be performed by adjusting the monitoring frequency according to the user's emotions.

[0144] The alert unit can analyze the user's past health data and set optimal alert criteria. For example, it can set criteria for detecting abnormal body temperature based on the user's past health data. It can also set criteria for detecting abnormal heart rate based on the user's past health data. It can also set criteria for detecting abnormal blood pressure based on the user's past health data. In this way, by analyzing the user's past health data, it is possible to set more appropriate alert criteria.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The dialogue analysis unit analyzes the user's voice and text and generates a response. For example, it uses speech recognition technology to convert the voice into text and natural language processing technology to analyze the text. It can also use generative AI to generate an appropriate response. For example, if a user says, "The weather is nice today," the response generated is, "Yes, it's very nice today. How about going for a walk?" Step 2: The suggestion unit suggests games or quizzes based on the responses generated by the dialogue analysis unit. For example, it suggests quizzes to train memory or puzzle games to improve cognitive function. It is also possible to use generation AI to suggest games or quizzes based on the user's interests. Step 3: The monitoring unit monitors the user's condition and health status. For example, it measures vital signs such as body temperature and heart rate and collects data. It can also monitor the user's behavioral patterns and daily rhythms. Step 4: The alert unit notifies staff of any abnormalities detected by the monitoring unit. For example, if the user's body temperature suddenly rises, or if an abnormality in their heart rate or change in their behavior pattern is detected, the alert unit notifies staff. Step 5: The pattern analysis unit identifies the user's daily life and health patterns, such as eating habits at the same time every day, sleep patterns, and exercise patterns. Step 6: The prediction unit predicts and prevents abnormalities and dangerous situations based on the patterns identified by the pattern analysis unit. For example, it can analyze fluctuations in daily rhythms in real time to predict abnormalities early. It can also predict changes in health status and suggest appropriate countermeasures.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 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 system comprising: a dialogue analysis unit that analyzes a user's voice and text and generates a response; a suggestion unit that suggests games or quizzes based on the response generated by the dialogue analysis unit; a monitoring unit that monitors the user's condition and health status; an alert unit that notifies staff of any abnormalities detected by the monitoring unit; a pattern analysis unit that grasps patterns in the user's daily life and health status; and a prediction unit that predicts and prevents abnormalities and dangerous situations based on the patterns grasped by the pattern analysis unit.

2. The system according to claim 1 , wherein the suggestion unit suggests a quiz for training memory or a puzzle game for improving cognitive function.

3. The system of claim 1 , wherein the monitoring unit monitors the user's vital signs, such as body temperature or heart rate.

4. The alert unit Notify staff if a user's temperature suddenly rises 2. The system of claim 1.

5. The system according to claim 1 , wherein the pattern analysis unit identifies the user's habit of eating meals at the same time every day.

6. The prediction unit Predicting abnormalities and dangerous situations based on the user's daily life and health patterns 2. The system of claim 1.

7. The dialogue analysis unit Inferring user emotions and adjusting the tone and content of responses based on the inferred user emotions 2. The system of claim 1.

8. The dialogue analysis unit Analyze the user's past interaction history and generate the optimal response 2. The system of claim 1.

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