system

Abstract

The system according to the embodiment aims to understand the lifestyle patterns of elderly people and prompt them to perform tasks at appropriate times. [Solution] A system according to an embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data on the elderly person's daily life. The analysis unit analyzes the data collected by the data collection unit to understand the elderly person's lifestyle patterns. The notification unit vocally prompts the elderly person to perform a "task" based on the lifestyle patterns understood by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately understand the lifestyle patterns of elderly people and prompt them to perform tasks at the appropriate time, so there is room for improvement.

[0005] The system according to the embodiment aims to understand the lifestyle patterns of elderly people and prompt them to perform tasks at appropriate times. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data on the elderly person's daily life. The analysis unit analyzes the data collected by the data collection unit to understand the elderly person's lifestyle patterns. The notification unit vocally prompts the elderly person to perform a "task" based on the lifestyle patterns understood by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the lifestyle patterns of elderly people and prompt them to perform tasks at appropriate times. [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 generative AI to understand the lifestyle patterns of elderly people and vocally prompts them to do certain things, preventing forgetfulness and supporting independent living. Specifically, the system first collects data on the elderly person's daily life, such as meal times, medication times, and walk times. The system then analyzes the collected data to understand the elderly person's lifestyle patterns. For example, it identifies patterns such as eating at the same time each day and taking medication at a specific time. Based on the identified lifestyle patterns, the system then vocally prompts the elderly to do certain things. For example, it may prompt the elderly to do certain things when it's time to eat or take their medication. This helps to free the elderly from forgetfulness and enable them to live independently. This system can also reduce stress and prevent brain cell loss in the elderly. For example, by ensuring smooth daily life, stress is reduced and brain cell loss is prevented. Furthermore, this system can also help prevent dementia and depression in the elderly. For example, reducing forgetfulness helps maintain the mental health of the elderly and reduces the risk of developing dementia and depression. In this way, systems using generative AI can be an effective means of preventing forgetfulness in the elderly and supporting independent living. This allows the system to prevent forgetfulness in the elderly and support independent living.

[0029] The life support system according to the embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data on the elderly person's daily life. For example, it collects information such as meal times, medication times, and walk times. The data collection unit can collect information using, for example, sensors and smart devices. Examples of sensors include motion sensors and temperature sensors. Examples of smart devices include smartwatches and smartphones. The analysis unit analyzes the collected data to understand the elderly person's daily life patterns. For example, it identifies patterns such as eating meals at the same time every day or taking medication at a specific time. The analysis unit can analyze the data using machine learning algorithms. Examples of machine learning algorithms include decision trees and neural networks. The notification unit verbally prompts the elderly person to do certain tasks based on the understood daily life patterns. For example, it verbally notifies the elderly person when it is time to eat, saying "It's time to eat," and when it is time to take medication, saying "It's time to take your medication." The notification unit can verbally notify the elderly person using voice synthesis technology. As a result, the life support system according to the embodiment can prevent forgetfulness and support an independent life by collecting, analyzing, and notifying data on the daily life of elderly people.

[0030] The data collection unit can collect information on meal times, medication times, and walk times. The data collection unit, for example, collects meal times. Meal times include breakfast, lunch, and dinner times. The data collection unit, for example, collects medication times. Medication times include morning, midday, and evening times. The data collection unit, for example, collects walk times. Walk times include morning walks and evening walks. This allows for the collection of specific information on the daily life of elderly people, thereby enabling a more refined understanding of their lifestyle patterns.

[0031] The analysis unit analyzes the collected data and can identify patterns such as eating meals at fixed times every day or taking medicine at specific times. The analysis unit, for example, performs a frequency analysis of the data. For example, it identifies a pattern of eating meals at the same time every day. The analysis unit, for example, analyzes time zone coincidences. For example, it identifies a pattern of taking medicine at specific times. The analysis unit, for example, performs a statistical analysis of the data. For example, it analyzes statistics on meal times and medicine taking times to identify patterns. This makes it possible to understand the elderly person's lifestyle patterns in detail and provide appropriate notifications.

[0032] The notification unit can notify the elderly by saying "It's time to eat" when it is time to eat, and by saying "It's time to take your medicine" when it is time to take your medicine. The notification unit can notify the elderly by saying "It's time to eat" when it is time to eat, and by saying "It's time to take your medicine" when it is time to take your medicine. The notification unit can adjust the timing of the notification, for example. For example, the notification unit can notify the elderly by saying "It's time to eat" when it is time to take your medicine. This can prevent forgetfulness by notifying the elderly at the appropriate time.

[0033] The data collection unit can collect information using a sensor or a smart device. The data collection unit can collect information using, for example, a motion sensor. For example, the data collection unit detects the motion of the elderly person and collects data. The data collection unit can also collect information using a temperature sensor. For example, the data collection unit measures the temperature in a room and collects data. The data collection unit can collect information using, for example, a smart watch. For example, the data collection unit measures the elderly person's heart rate and activity level through the smart watch and collects data. The data collection unit can also collect information using a smartphone. For example, the data collection unit collects data on the elderly person's daily life through a smartphone app. This allows for more accurate data collection by using sensors and smart devices.

[0034] The analysis unit can analyze the data using a machine learning algorithm. The analysis unit analyzes the data using, for example, a decision tree algorithm. For example, the analysis unit classifies and analyzes the elderly's lifestyle patterns using a decision tree. The analysis unit can also analyze the data using a neural network algorithm. For example, the analysis unit predicts and analyzes the elderly's lifestyle patterns using a neural network. The analysis unit can also analyze the data using a support vector machine (SVM) algorithm. For example, the analysis unit classifies and analyzes the elderly's lifestyle patterns using an SVM. This improves the accuracy of the data analysis by using the machine learning algorithm.

[0035] The data collection unit can analyze the elderly person's past lifestyle data and select the optimal data collection method. The data collection unit, for example, selects the optimal data collection device based on data from devices and sensors used by the elderly person in the past. For example, the data collection unit analyzes data from smartwatches and smartphones used in the past and selects the optimal device. The data collection unit can also analyze the elderly person's past lifestyle rhythm and determine the optimal timing for data collection. For example, the data collection unit determines that data collection in the morning is optimal based on the elderly person's past lifestyle rhythm. Furthermore, the data collection unit can adjust the frequency and method of data collection based on the elderly person's past data collection history. For example, the data collection unit analyzes the elderly person's past data collection history and determines that data collection once a week is optimal. This allows the elderly person to select the optimal data collection method by analyzing past lifestyle data, enabling efficient data collection.

[0036] The data collection unit can filter data based on the elderly person's current health condition and activity level when collecting data. For example, the data collection unit monitors the elderly person's heart rate and blood pressure in real time and pauses data collection if an abnormality is detected. For example, the data collection unit pauses data collection if the elderly person's heart rate is abnormally high and resumes it when the heart rate returns to normal. The data collection unit can also reduce the frequency of data collection when the elderly person's activity level is low and resume it when the elderly person becomes more active. For example, the data collection unit reduces the frequency of data collection if the elderly person spends a lot of time sitting and resumes it when the elderly person is walking or exercising. Furthermore, the data collection unit can collect detailed data when the elderly person's health condition is good to refine an understanding of their lifestyle patterns. For example, the data collection unit collects detailed data on diet and exercise when the elderly person is in good health. This allows for more accurate data collection by adjusting data collection according to the elderly person's health condition and activity level.

[0037] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the elderly person. The data collection unit collects the geographical location information of the elderly person using, for example, GPS data. For example, when the elderly person is at home, the data collection unit prioritizes collecting indoor activity data. Furthermore, when the elderly person is out, the data collection unit can also prioritize collecting activity data while out. Furthermore, when the elderly person is in a specific location, the data collection unit can prioritize collecting data related to that location. For example, when the elderly person is in a park, the data collection unit collects activity data in the park. In this way, by taking into account the geographical location information of the elderly person, highly relevant data can be prioritized and collected.

[0038] The data collection unit can analyze the social media activities of the elderly person during data collection and collect related data. The data collection unit, for example, collects data on daily life based on information shared by the elderly person on social media. For example, the data collection unit analyzes photos and comments about meals posted by the elderly person on social media to determine the time and content of meals. The data collection unit can also analyze the content posted on social media by the elderly person to collect data related to their lifestyle patterns. For example, the data collection unit analyzes photos and comments about exercise and walks posted by the elderly person to determine the time and frequency of exercise. Furthermore, the data collection unit can collect related data based on information about accounts followed by the elderly person on social media. For example, the data collection unit analyzes information about health-related accounts followed by the elderly person to collect health-related data. In this way, related data can be efficiently collected by analyzing the social media activities of the elderly person.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle pattern. The analysis unit, for example, analyzes data related to important lifestyle patterns in detail. For example, the analysis unit analyzes data related to important lifestyle patterns, such as meal times and contents, and medication times, in detail. The analysis unit can also simply analyze data related to less important lifestyle patterns. For example, the analysis unit simply analyzes data related to less important lifestyle patterns, such as the duration and frequency of walks. Furthermore, the analysis unit can analyze lifestyle patterns that are directly related to the health of elderly people in particular in detail. For example, the analysis unit analyzes data related to health conditions and exercise amounts in detail to refine the understanding of lifestyle patterns. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis according to the importance of the lifestyle pattern.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the lifestyle pattern. For example, the analysis unit analyzes data related to meals using an algorithm that takes nutritional balance into consideration. For example, the analysis unit analyzes data using an algorithm that evaluates the content of meals and nutritional balance. The analysis unit can also analyze data related to medication use using an algorithm that emphasizes the accuracy of medication time. For example, the analysis unit analyzes data using an algorithm that evaluates the time and frequency of medication use. Furthermore, the analysis unit can analyze data related to walks using an algorithm that evaluates the amount of exercise. For example, the analysis unit analyzes data using an algorithm that evaluates the duration, distance, and amount of exercise of a walk. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the lifestyle pattern.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent dietary data or exercise data. The analysis unit can also prioritize analysis of data from the time when an important event occurred. For example, the analysis unit prioritizes analysis of health checkup results or hospital visit data. Furthermore, the analysis unit can also prioritize analysis of data from the time when an elderly person's health condition changed. For example, the analysis unit analyzes data from the time when the health condition worsened in detail to understand changes in lifestyle patterns. In this way, by determining the priority of analysis based on the time when the data was collected, important data can be prioritized for analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of data directly related to the health of the elderly person. For example, the analysis unit prioritizes analysis of heart rate and blood pressure data. The analysis unit can also prioritize analysis of data that has a significant impact on the elderly person's daily life. For example, the analysis unit prioritizes analysis of meal times and contents, and exercise time and frequency. Furthermore, the analysis unit can also prioritize analysis of data related to the elderly person's lifestyle patterns. For example, the analysis unit prioritizes analysis of data such as meal times and contents, and medication taking times. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0043] The notification unit can adjust the level of detail of the notification based on the importance of the lifestyle pattern when making a notification. For example, the notification unit provides detailed notifications regarding important lifestyle patterns. For example, the notification unit provides detailed notifications regarding important lifestyle patterns such as meal times and contents, and medication times. The notification unit can also provide simplified notifications regarding less important lifestyle patterns. For example, the notification unit provides simplified notifications regarding less important lifestyle patterns such as the duration and frequency of walks. Furthermore, the notification unit can also provide particularly detailed notifications regarding lifestyle patterns that are directly related to the health of elderly people. For example, the notification unit provides detailed notifications regarding health conditions and exercise amounts, thereby refining the understanding of lifestyle patterns. In this way, important information can be notified in detail by adjusting the level of detail of the notification according to the importance of the lifestyle pattern.

[0044] The notification unit can apply different notification algorithms depending on the lifestyle pattern category when making a notification. For example, the notification unit makes a notification regarding meals using an algorithm that takes nutritional balance into consideration. For example, the notification unit makes a notification using an algorithm that evaluates the content and nutritional balance of the meal. The notification unit can also make a notification regarding taking medicine using an algorithm that emphasizes the accuracy of the medicine taking time. For example, the notification unit makes a notification using an algorithm that evaluates the time and frequency of taking medicine. Furthermore, the notification unit can also make a notification regarding walks using an algorithm that evaluates the amount of exercise. For example, the notification unit makes a notification using an algorithm that evaluates the time, distance, and amount of exercise of the walk. This improves the accuracy of notifications by applying an appropriate notification algorithm depending on the lifestyle pattern category.

[0045] When making a notification, the notification unit can select the optimal notification method by taking into consideration the elderly person's current activity status. For example, when the elderly person is resting, the notification unit can provide a notification in a quiet voice. For example, when the elderly person is resting, the notification unit can provide a notification in a gentle voice. Furthermore, when the elderly person is active, the notification unit can provide a visual notification that is displayed on a screen. Furthermore, when the elderly person is out, the notification unit can provide a notification through a mobile device. For example, when the elderly person is out, the notification unit can provide a notification through a smartphone or a smartwatch. In this way, the optimal notification method can be selected by taking into consideration the elderly person's current activity status.

[0046] The notification unit can optimize the effectiveness of the notification by referring to the elderly person's past response history when providing a notification. For example, the notification unit prioritizes using a notification method to which the elderly person has responded in the past. For example, the notification unit prioritizes using audio or visual notifications to which the elderly person has responded in the past. The notification unit can also determine the optimal notification timing based on the elderly person's past response history. For example, the notification unit determines the optimal notification timing based on the time period or situation in which the elderly person responded in the past. Furthermore, the notification unit can analyze the elderly person's past response history and adjust the notification content. For example, the notification unit adjusts the notification content based on the notification content to which the elderly person has responded in the past. In this way, the effectiveness of the notification can be optimized by referring to the elderly person's past response history.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The data collection unit can also collect information on the dietary content and nutritional balance of elderly people. For example, it can take photos of meals and use image analysis technology to identify the contents of the meal. It can also evaluate the nutritional balance of meals and suggest appropriate meals if necessary nutrients are lacking. This allows for a more detailed understanding of the elderly person's health status and supports appropriate nutritional management.

[0049] The data collection unit can also collect data on the elderly's sleep patterns. For example, a smartwatch can be used to monitor the elderly's sleep duration and quality. It can also detect heart rate and movement during sleep, and analyze the depth of sleep and frequency of interruptions. This allows for a detailed understanding of the elderly's sleep status and provides advice on improving sleep if necessary.

[0050] The analysis unit can also analyze the elderly person's social activity patterns. For example, it can analyze the frequency of communication with friends and family and the number of times they go out to evaluate the strength of their social connections. It can also suggest appropriate social activities if the elderly person feels isolated. This can support the elderly person's social health and contribute to reducing feelings of isolation.

[0051] The notification unit can also customize the content of notifications based on the elderly person's hobbies and interests. For example, it can notify them of their favorite music or movies. It can also provide information about events and activities related to their hobbies. This can bring enjoyment to the elderly's lives and support their mental health.

[0052] The data collection unit can also collect the elderly's exercise patterns. For example, a smartwatch can be used to monitor the elderly's step count and exercise time. It can also analyze the type and intensity of exercise and suggest appropriate exercise plans. This can support the elderly's exercise habits and contribute to maintaining their health.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data collection unit collects data on the elderly person's daily life. For example, it collects information such as meal times, medication times, and walk times. The data collection unit can collect information using sensors and smart devices. Sensors include motion sensors and temperature sensors, and smart devices include smart watches and smartphones. Step 2: The analysis unit analyzes the collected data to understand the elderly person's lifestyle patterns. For example, it can identify patterns such as eating meals at the same time every day or taking medicine at a specific time. The analysis unit can analyze the data using machine learning algorithms. Machine learning algorithms include decision trees and neural networks. Step 3: The notification unit will prompt the elderly person to do certain things by voice based on the lifestyle patterns it has identified. For example, when it is time to eat, it will say "It's time to eat," and when it is time to take medicine, it will say "It's time to take your medicine." The notification unit can use voice synthesis technology to give voice prompts.

[0055] (Example 2) A system according to an embodiment of the present invention uses generative AI to understand the lifestyle patterns of elderly people and vocally prompts them to do certain things, preventing forgetfulness and supporting independent living. Specifically, the system first collects data on the elderly person's daily life, such as meal times, medication times, and walk times. The system then analyzes the collected data to understand the elderly person's lifestyle patterns. For example, it identifies patterns such as eating at the same time each day and taking medication at a specific time. Based on the identified lifestyle patterns, the system then vocally prompts the elderly to do certain things. For example, it may prompt the elderly to do certain things when it's time to eat or take their medication. This helps to free the elderly from forgetfulness and enable them to live independently. This system can also reduce stress and prevent brain cell loss in the elderly. For example, by ensuring smooth daily life, stress is reduced and brain cell loss is prevented. Furthermore, this system can also help prevent dementia and depression in the elderly. For example, reducing forgetfulness helps maintain the mental health of the elderly and reduces the risk of developing dementia and depression. In this way, systems using generative AI can be an effective means of preventing forgetfulness in the elderly and supporting independent living. This allows the system to prevent forgetfulness in the elderly and support independent living.

[0056] The life support system according to the embodiment includes a data collection unit, an analysis unit, and a notification unit. The data collection unit collects data on the elderly person's daily life. For example, it collects information such as meal times, medication times, and walk times. The data collection unit can collect information using, for example, sensors and smart devices. Examples of sensors include motion sensors and temperature sensors. Examples of smart devices include smartwatches and smartphones. The analysis unit analyzes the collected data to understand the elderly person's daily life patterns. For example, it identifies patterns such as eating meals at the same time every day or taking medication at a specific time. The analysis unit can analyze the data using machine learning algorithms. Examples of machine learning algorithms include decision trees and neural networks. The notification unit verbally prompts the elderly person to do certain tasks based on the understood daily life patterns. For example, it verbally notifies the elderly person when it is time to eat, saying "It's time to eat," and when it is time to take medication, saying "It's time to take your medication." The notification unit can verbally notify the elderly person using voice synthesis technology. As a result, the life support system according to the embodiment can prevent forgetfulness and support an independent life by collecting, analyzing, and notifying data on the daily life of elderly people.

[0057] The data collection unit can collect information on meal times, medication times, and walk times. The data collection unit, for example, collects meal times. Meal times include breakfast, lunch, and dinner times. The data collection unit, for example, collects medication times. Medication times include morning, midday, and evening times. The data collection unit, for example, collects walk times. Walk times include morning walks and evening walks. This allows for the collection of specific information on the daily life of elderly people, thereby enabling a more refined understanding of their lifestyle patterns.

[0058] The analysis unit analyzes the collected data and can identify patterns such as eating meals at fixed times every day or taking medicine at specific times. The analysis unit, for example, performs a frequency analysis of the data. For example, it identifies a pattern of eating meals at the same time every day. The analysis unit, for example, analyzes time zone coincidences. For example, it identifies a pattern of taking medicine at specific times. The analysis unit, for example, performs a statistical analysis of the data. For example, it analyzes statistics on meal times and medicine taking times to identify patterns. This makes it possible to understand the elderly person's lifestyle patterns in detail and provide appropriate notifications.

[0059] The notification unit can notify the elderly by saying "It's time to eat" when it is time to eat, and by saying "It's time to take your medicine" when it is time to take your medicine. The notification unit can notify the elderly by saying "It's time to eat" when it is time to eat, and by saying "It's time to take your medicine" when it is time to take your medicine. The notification unit can adjust the timing of the notification, for example. For example, the notification unit can notify the elderly by saying "It's time to eat" when it is time to take your medicine. This can prevent forgetfulness by notifying the elderly at the appropriate time.

[0060] The data collection unit can collect information using a sensor or a smart device. The data collection unit can collect information using, for example, a motion sensor. For example, the data collection unit detects the motion of the elderly person and collects data. The data collection unit can also collect information using a temperature sensor. For example, the data collection unit measures the temperature in a room and collects data. The data collection unit can collect information using, for example, a smart watch. For example, the data collection unit measures the elderly person's heart rate and activity level through the smart watch and collects data. The data collection unit can also collect information using a smartphone. For example, the data collection unit collects data on the elderly person's daily life through a smartphone app. This allows for more accurate data collection by using sensors and smart devices.

[0061] The analysis unit can analyze the data using a machine learning algorithm. The analysis unit analyzes the data using, for example, a decision tree algorithm. For example, the analysis unit classifies and analyzes the elderly's lifestyle patterns using a decision tree. The analysis unit can also analyze the data using a neural network algorithm. For example, the analysis unit predicts and analyzes the elderly's lifestyle patterns using a neural network. The analysis unit can also analyze the data using a support vector machine (SVM) algorithm. For example, the analysis unit classifies and analyzes the elderly's lifestyle patterns using an SVM. This improves the accuracy of the data analysis by using the machine learning algorithm.

[0062] The data collection unit can estimate the elderly person's emotions and adjust the timing of data collection based on the estimated elderly person's emotions. The data collection unit can estimate the elderly person's emotions using, for example, facial expression recognition technology. For example, the data collection unit can capture the elderly person's facial expressions with a camera and estimate their emotions. The data collection unit can also estimate the elderly person's emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the elderly person's voice to estimate their emotions. Furthermore, the data collection unit can estimate the elderly person's emotions using a biosensor. For example, the data collection unit can measure the elderly person's heart rate and electrodermal activity to estimate their emotions. The data collection unit adjusts the timing of data collection based on the estimated elderly person's emotions. For example, if the elderly person is feeling stressed, the frequency of data collection can be reduced and data collection can be performed when the elderly person is relaxed. Furthermore, when the elderly person is relaxed, detailed data can be collected to refine an understanding of their lifestyle patterns. Furthermore, if the elderly person is tired, data collection can be temporarily stopped and resumed after the elderly person has rested. In this way, adjusting the timing of data collection according to the elderly person's emotions reduces stress and enables more accurate data collection.

[0063] The data collection unit can analyze the elderly person's past lifestyle data and select the optimal data collection method. The data collection unit, for example, selects the optimal data collection device based on data from devices and sensors used by the elderly person in the past. For example, the data collection unit analyzes data from smartwatches and smartphones used in the past and selects the optimal device. The data collection unit can also analyze the elderly person's past lifestyle rhythm and determine the optimal timing for data collection. For example, the data collection unit determines that data collection in the morning is optimal based on the elderly person's past lifestyle rhythm. Furthermore, the data collection unit can adjust the frequency and method of data collection based on the elderly person's past data collection history. For example, the data collection unit analyzes the elderly person's past data collection history and determines that data collection once a week is optimal. This allows the elderly person to select the optimal data collection method by analyzing past lifestyle data, enabling efficient data collection.

[0064] The data collection unit can filter data based on the elderly person's current health condition and activity level when collecting data. For example, the data collection unit monitors the elderly person's heart rate and blood pressure in real time and pauses data collection if an abnormality is detected. For example, the data collection unit pauses data collection if the elderly person's heart rate is abnormally high and resumes it when the heart rate returns to normal. The data collection unit can also reduce the frequency of data collection when the elderly person's activity level is low and resume it when the elderly person becomes more active. For example, the data collection unit reduces the frequency of data collection if the elderly person spends a lot of time sitting and resumes it when the elderly person is walking or exercising. Furthermore, the data collection unit can collect detailed data when the elderly person's health condition is good to refine an understanding of their lifestyle patterns. For example, the data collection unit collects detailed data on diet and exercise when the elderly person is in good health. This allows for more accurate data collection by adjusting data collection according to the elderly person's health condition and activity level.

[0065] The data collection unit can estimate the elderly person's emotions and determine the priority of data to be collected based on the estimated elderly person's emotions. The data collection unit can estimate the elderly person's emotions using, for example, facial expression recognition technology. For example, the data collection unit can capture the elderly person's facial expressions with a camera and estimate the emotions. The data collection unit can also estimate the elderly person's emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the elderly person's voice to estimate the emotions. The data collection unit can also estimate the elderly person's emotions using a biosensor. For example, the data collection unit can measure the elderly person's heart rate and electrodermal activity to estimate the emotions. The data collection unit determines the priority of data to be collected based on the estimated elderly person's emotions. For example, if the elderly person is feeling stressed, data related to stress reduction can be preferentially collected. Also, if the elderly person is relaxed, detailed data on daily life can be preferentially collected. Furthermore, if the elderly person is tired, data related to rest can be preferentially collected. In this way, by determining the priority of data to be collected according to the elderly person's emotions, important data can be preferentially collected.

[0066] When collecting data, the data collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the elderly person. The data collection unit collects the geographical location information of the elderly person using, for example, GPS data. For example, when the elderly person is at home, the data collection unit prioritizes collecting indoor activity data. Furthermore, when the elderly person is out, the data collection unit can also prioritize collecting activity data while out. Furthermore, when the elderly person is in a specific location, the data collection unit can prioritize collecting data related to that location. For example, when the elderly person is in a park, the data collection unit collects activity data in the park. In this way, by taking into account the geographical location information of the elderly person, highly relevant data can be prioritized and collected.

[0067] The data collection unit can analyze the social media activities of the elderly person during data collection and collect related data. The data collection unit, for example, collects data on daily life based on information shared by the elderly person on social media. For example, the data collection unit analyzes photos and comments about meals posted by the elderly person on social media to determine the time and content of meals. The data collection unit can also analyze the content posted on social media by the elderly person to collect data related to their lifestyle patterns. For example, the data collection unit analyzes photos and comments about exercise and walks posted by the elderly person to determine the time and frequency of exercise. Furthermore, the data collection unit can collect related data based on information about accounts followed by the elderly person on social media. For example, the data collection unit analyzes information about health-related accounts followed by the elderly person to collect health-related data. In this way, related data can be efficiently collected by analyzing the social media activities of the elderly person.

[0068] The analysis unit can estimate the elderly person's emotions and adjust the way the analysis is presented based on the estimated elderly person's emotions. The analysis unit can estimate the elderly person's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the elderly person's facial expressions with a camera and estimate the emotions. The analysis unit can also estimate the elderly person's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the elderly person's voice to estimate the emotions. The analysis unit can also estimate the elderly person's emotions using a biosensor. For example, the analysis unit can measure the elderly person's heart rate and electrodermal activity to estimate the emotions. The analysis unit adjusts the way the analysis is presented based on the estimated elderly person's emotions. For example, if the elderly person is relaxed, a detailed analysis result can be provided. If the elderly person is stressed, a concise and to-the-point analysis result can be provided. If the elderly person is tired, a visually easy-to-understand analysis result can be provided. In this way, by adjusting the way the analysis is presented based on the elderly person's emotions, it is possible to provide more understandable analysis results.

[0069] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the lifestyle pattern. The analysis unit, for example, analyzes data related to important lifestyle patterns in detail. For example, the analysis unit analyzes data related to important lifestyle patterns, such as meal times and contents, and medication times, in detail. The analysis unit can also simply analyze data related to less important lifestyle patterns. For example, the analysis unit simply analyzes data related to less important lifestyle patterns, such as the duration and frequency of walks. Furthermore, the analysis unit can analyze lifestyle patterns that are directly related to the health of elderly people in particular in detail. For example, the analysis unit analyzes data related to health conditions and exercise amounts in detail to refine the understanding of lifestyle patterns. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis according to the importance of the lifestyle pattern.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the lifestyle pattern. For example, the analysis unit analyzes data related to meals using an algorithm that takes nutritional balance into consideration. For example, the analysis unit analyzes data using an algorithm that evaluates the content of meals and nutritional balance. The analysis unit can also analyze data related to medication use using an algorithm that emphasizes the accuracy of medication time. For example, the analysis unit analyzes data using an algorithm that evaluates the time and frequency of medication use. Furthermore, the analysis unit can analyze data related to walks using an algorithm that evaluates the amount of exercise. For example, the analysis unit analyzes data using an algorithm that evaluates the duration, distance, and amount of exercise of a walk. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the lifestyle pattern.

[0071] The analysis unit can estimate the elderly person's emotions and adjust the length of the analysis based on the estimated elderly person's emotions. The analysis unit can estimate the elderly person's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the elderly person's facial expressions with a camera and estimate the emotions. The analysis unit can also estimate the elderly person's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the elderly person's voice to estimate the emotions. The analysis unit can also estimate the elderly person's emotions using a biosensor. For example, the analysis unit can measure the elderly person's heart rate and electrodermal activity to estimate the emotions. The analysis unit adjusts the length of the analysis based on the estimated elderly person's emotions. For example, if the elderly person is relaxed, a detailed analysis result can be provided. If the elderly person is stressed, a concise and to-the-point analysis result can be provided. If the elderly person is tired, a visually easy-to-understand analysis result can be provided. As a result, by adjusting the length of the analysis according to the elderly person's emotions, more appropriate analysis results can be provided.

[0072] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent dietary data or exercise data. The analysis unit can also prioritize analysis of data from the time when an important event occurred. For example, the analysis unit prioritizes analysis of health checkup results or hospital visit data. Furthermore, the analysis unit can also prioritize analysis of data from the time when an elderly person's health condition changed. For example, the analysis unit analyzes data from the time when the health condition worsened in detail to understand changes in lifestyle patterns. In this way, by determining the priority of analysis based on the time when the data was collected, important data can be prioritized for analysis.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of data directly related to the health of the elderly person. For example, the analysis unit prioritizes analysis of heart rate and blood pressure data. The analysis unit can also prioritize analysis of data that has a significant impact on the elderly person's daily life. For example, the analysis unit prioritizes analysis of meal times and contents, and exercise time and frequency. Furthermore, the analysis unit can also prioritize analysis of data related to the elderly person's lifestyle patterns. For example, the analysis unit prioritizes analysis of data such as meal times and contents, and medication taking times. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0074] The notification unit can estimate the elderly person's emotion and adjust the notification expression method based on the estimated elderly person's emotion. The notification unit can estimate the elderly person's emotion using, for example, facial expression recognition technology. For example, the notification unit can capture the elderly person's facial expression with a camera and estimate the emotion. The notification unit can also estimate the elderly person's emotion using voice analysis technology. For example, the notification unit can analyze the tone and speed of the elderly person's voice to estimate the emotion. The notification unit can also estimate the elderly person's emotion using a biosensor. For example, the notification unit can measure the elderly person's heart rate and electrodermal activity to estimate the emotion. The notification unit adjusts the notification expression method based on the estimated elderly person's emotion. For example, if the elderly person is relaxed, the notification can be made in a calm voice. If the elderly person is stressed, the notification can be made concise and to the point. If the elderly person is tired, the notification can be made visually easy to understand. As a result, more appropriate notifications can be provided by adjusting the notification expression method according to the elderly person's emotion.

[0075] The notification unit can adjust the level of detail of the notification based on the importance of the lifestyle pattern when making a notification. For example, the notification unit provides detailed notifications regarding important lifestyle patterns. For example, the notification unit provides detailed notifications regarding important lifestyle patterns such as meal times and contents, and medication times. The notification unit can also provide simplified notifications regarding less important lifestyle patterns. For example, the notification unit provides simplified notifications regarding less important lifestyle patterns such as the duration and frequency of walks. Furthermore, the notification unit can also provide particularly detailed notifications regarding lifestyle patterns that are directly related to the health of elderly people. For example, the notification unit provides detailed notifications regarding health conditions and exercise amounts, thereby refining the understanding of lifestyle patterns. In this way, important information can be notified in detail by adjusting the level of detail of the notification according to the importance of the lifestyle pattern.

[0076] The notification unit can apply different notification algorithms depending on the lifestyle pattern category when making a notification. For example, the notification unit makes a notification regarding meals using an algorithm that takes nutritional balance into consideration. For example, the notification unit makes a notification using an algorithm that evaluates the content and nutritional balance of the meal. The notification unit can also make a notification regarding taking medicine using an algorithm that emphasizes the accuracy of the medicine taking time. For example, the notification unit makes a notification using an algorithm that evaluates the time and frequency of taking medicine. Furthermore, the notification unit can also make a notification regarding walks using an algorithm that evaluates the amount of exercise. For example, the notification unit makes a notification using an algorithm that evaluates the time, distance, and amount of exercise of the walk. This improves the accuracy of notifications by applying an appropriate notification algorithm depending on the lifestyle pattern category.

[0077] The notification unit can estimate the elderly person's emotion and adjust the timing of the notification based on the estimated elderly person's emotion. The notification unit can estimate the elderly person's emotion using, for example, facial expression recognition technology. For example, the notification unit can capture the elderly person's facial expression with a camera and estimate the emotion. The notification unit can also estimate the elderly person's emotion using voice analysis technology. For example, the notification unit can analyze the tone and speed of the elderly person's voice to estimate the emotion. The notification unit can also estimate the elderly person's emotion using a biosensor. For example, the notification unit can measure the elderly person's heart rate and electrodermal activity to estimate the emotion. The notification unit adjusts the timing of the notification based on the estimated elderly person's emotion. For example, if the elderly person is relaxed, the notification can be sent at a gentle timing. If the elderly person is feeling stressed, the notification can be sent at a brief and to-the-point timing. Furthermore, if the elderly person is tired, the notification can be sent after the elderly person has rested. In this way, by adjusting the timing of the notification according to the elderly person's emotion, the notification can be sent at a more appropriate timing.

[0078] When making a notification, the notification unit can select the optimal notification method by taking into consideration the elderly person's current activity status. For example, when the elderly person is resting, the notification unit can provide a notification in a quiet voice. For example, when the elderly person is resting, the notification unit can provide a notification in a gentle voice. Furthermore, when the elderly person is active, the notification unit can provide a visual notification that is displayed on a screen. Furthermore, when the elderly person is out, the notification unit can provide a notification through a mobile device. For example, when the elderly person is out, the notification unit can provide a notification through a smartphone or a smartwatch. In this way, the optimal notification method can be selected by taking into consideration the elderly person's current activity status.

[0079] The notification unit can optimize the effectiveness of the notification by referring to the elderly person's past response history when providing a notification. For example, the notification unit prioritizes using a notification method to which the elderly person has responded in the past. For example, the notification unit prioritizes using audio or visual notifications to which the elderly person has responded in the past. The notification unit can also determine the optimal notification timing based on the elderly person's past response history. For example, the notification unit determines the optimal notification timing based on the time period or situation in which the elderly person responded in the past. Furthermore, the notification unit can analyze the elderly person's past response history and adjust the notification content. For example, the notification unit adjusts the notification content based on the notification content to which the elderly person has responded in the past. In this way, the effectiveness of the notification can be optimized by referring to the elderly person's past response history. === Hard Collateral 1-1 === Each of the multiple elements including the data collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects data on the elderly person's daily life using a sensor or a smartwatch of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand the elderly person's lifestyle patterns. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and uses voice synthesis technology to verbally prompt the elderly person to "do their thing." === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects data on the elderly person's daily life using sensors in the smart glasses 214 or a smart watch. The analysis unit, realized, for example, by the specific processing unit 290 in the data processing device 12, analyzes the collected data and understands the elderly person's lifestyle patterns. The notification unit, realized, for example, by the control unit 46A of the smart glasses 214, uses voice synthesis technology to verbally prompt the elderly person to "do their thing." === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data collection unit collects data on the elderly person's daily life using a sensor or a smart watch of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand the elderly person's lifestyle patterns. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and uses voice synthesis technology to verbally prompt the elderly person to "do something." === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects data on the elderly person's daily life using sensors or a smartwatch of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand the elderly person's lifestyle patterns. The notification unit is realized, for example, by the control unit 46A of the robot 414, and uses voice synthesis technology to verbally prompt the elderly person to "do their thing."

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

[0081] The data collection unit can also collect information on the dietary content and nutritional balance of elderly people. For example, it can take photos of meals and use image analysis technology to identify the contents of the meal. It can also evaluate the nutritional balance of meals and suggest appropriate meals if necessary nutrients are lacking. This allows for a more detailed understanding of the elderly person's health status and supports appropriate nutritional management.

[0082] The data collection unit can also collect data on the elderly's sleep patterns. For example, a smartwatch can be used to monitor the elderly's sleep duration and quality. It can also detect heart rate and movement during sleep, and analyze the depth of sleep and frequency of interruptions. This allows for a detailed understanding of the elderly's sleep status and provides advice on improving sleep if necessary.

[0083] The analysis unit can also analyze the elderly person's social activity patterns. For example, it can analyze the frequency of communication with friends and family and the number of times they go out to evaluate the strength of their social connections. It can also suggest appropriate social activities if the elderly person feels isolated. This can support the elderly person's social health and contribute to reducing feelings of isolation.

[0084] The notification unit can also customize the content of notifications based on the elderly person's hobbies and interests. For example, it can notify them of their favorite music or movies. It can also provide information about events and activities related to their hobbies. This can bring enjoyment to the elderly's lives and support their mental health.

[0085] The data collection unit can also collect the elderly's exercise patterns. For example, a smartwatch can be used to monitor the elderly's step count and exercise time. It can also analyze the type and intensity of exercise and suggest appropriate exercise plans. This can support the elderly's exercise habits and contribute to maintaining their health.

[0086] The analysis unit can estimate the emotions of the elderly person and determine the priority of analysis based on the estimated emotions of the elderly person. For example, if the elderly person is feeling stressed, data related to stress reduction can be analyzed with priority. Also, if the elderly person is relaxed, detailed data of daily life can be analyzed with priority. Furthermore, if the elderly person is tired, data related to rest can be analyzed with priority. In this way, by determining the priority of analysis according to the emotions of the elderly person, important data can be analyzed with priority.

[0087] The data collection unit can estimate the emotions of the elderly person and determine the priority of data to be collected based on the estimated emotions of the elderly person. For example, if the elderly person is feeling stressed, data related to stress reduction can be collected with priority. Also, if the elderly person is relaxed, detailed data on daily life can be collected with priority. Furthermore, if the elderly person is tired, data related to rest can be collected with priority. In this way, by determining the priority of data to be collected according to the emotions of the elderly person, important data can be collected with priority.

[0088] The notification unit can estimate the elderly person's emotions and adjust the way the notification is expressed based on the estimated elderly person's emotions. For example, if the elderly person is relaxed, the notification can be given in a calm voice. If the elderly person is feeling stressed, the notification can be given in a concise and to-the-point manner. Furthermore, if the elderly person is tired, the notification can be given in a visually easy-to-understand manner. This allows for more appropriate notifications to be provided by adjusting the way the notification is expressed based on the elderly person's emotions.

[0089] The analysis unit can estimate the elderly person's emotions and adjust the way the analysis is presented based on the estimated emotions of the elderly person. For example, if the elderly person is relaxed, detailed analysis results can be provided. If the elderly person is feeling stressed, concise analysis results that focus on the main points can be provided. Furthermore, if the elderly person is tired, analysis results that are easy to understand visually can be provided. In this way, by adjusting the way the analysis is presented according to the elderly person's emotions, it is possible to provide analysis results that are easier to understand.

[0090] The notification unit can estimate the elderly person's emotions and adjust the timing of the notification based on the estimated elderly person's emotions. For example, if the elderly person is relaxed, the notification can be made at a gentle timing. If the elderly person is feeling stressed, the notification can be made at a brief and to-the-point timing. Furthermore, if the elderly person is tired, the notification can be made after they have rested. In this way, by adjusting the timing of the notification according to the elderly person's emotions, it is possible to make the notification at a more appropriate timing.

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

[0092] Step 1: The data collection unit collects data on the elderly person's daily life. For example, it collects information such as meal times, medication times, and walk times. The data collection unit can collect information using sensors and smart devices. Sensors include motion sensors and temperature sensors, and smart devices include smart watches and smartphones. Step 2: The analysis unit analyzes the collected data to understand the elderly person's lifestyle patterns. For example, it can identify patterns such as eating meals at the same time every day or taking medicine at a specific time. The analysis unit can analyze the data using machine learning algorithms. Machine learning algorithms include decision trees and neural networks. Step 3: The notification unit will prompt the elderly person to do certain things by voice based on the lifestyle patterns it has identified. For example, when it is time to eat, it will say "It's time to eat," and when it is time to take medicine, it will say "It's time to take your medicine." The notification unit can use voice synthesis technology to give voice prompts.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0164] [Explanation of symbols]

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

Claims

1. a data collection unit that collects data on the daily lives of elderly people; an analysis unit that analyzes the data collected by the data collection unit and grasps the life patterns of the elderly person; a notification unit that prompts the elderly person to perform a task by voice based on the life pattern grasped by the analysis unit. A system characterized by:

2. The data collection unit Collect information on meal times, medication times, and walk times 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify patterns such as eating meals at specific times each day or taking medication at specific times.

2. The system of claim 1.

4. The data collection unit Use sensors or smart devices to collect information 2. The system of claim 1.

5. The analysis unit Analyze data using machine learning algorithms 2. The system of claim 1.

6. The data collection unit Estimate the emotions of the elderly and adjust the timing of data collection based on the estimated emotions of the elderly.

2. The system of claim 1.

7. The data collection unit Analyze past lifestyle data of elderly people and select the most appropriate data collection method 2. The system of claim 1.

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