System

Abstract

To provide a system for grasping a change in emotions and physical conditions of a family member who lives separately in real time and notifying it to a person concerned.SOLUTION: A system comprises a voice recognition device, a generation part, and a notification part. The voice recognition device is installed in a place where a family member spends time. The generation part analyzes conversations or life sounds collected by the voice recognition device and determines emotions and physical conditions of the family member. The notification part transmits a notification to a person concerned on the basis of a result of determination by the generation part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to grasp changes in the emotions or physical condition of family members who lived far away in real time.

[0005] The system according to the embodiment aims to grasp changes in the emotions and physical condition of family members living far away in real time and notify relevant parties. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition device, a generation unit, and a notification unit. The voice recognition device is installed in a place where the family spends time. The generation unit analyzes the conversation or living sounds of the family collected by the voice recognition device and determines the emotions or physical conditions of the family. The notification unit transmits notifications to relevant parties based on the results determined by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can grasp changes in the emotions and physical condition of family members living far away in real time and notify relevant parties. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The living situation grasping system according to the embodiment of the present invention is a system that grasps the living situation of a family member using a voice recognition device installed in the house. In this system, the voice recognition device installed in the house collects conversations and living sounds of the family member. This voice data is input to the generation AI. The generation AI analyzes the collected voice data and judges the living situation of the family member. For example, it judges the situation such as whether the family member is living well, whether they look lonely, whether there is a risk of illness, depression, or mental abnormality. Next, based on the result of the judgment by the generation AI, a notification is sent to the relevant person (basic relatives). For example, if a family member is living well, it is notified that "there is no problem", and if they look lonely, it is notified that "they look lonely". If there is a risk of illness, depression, or mental abnormality, it is notified that an immediate response is required. This system can grasp the living situation of family members living apart, and can create an opportunity to help or communicate in advance. For example, if they look lonely, it is possible to ease the family member's feelings by talking to them by phone or video call. Also, if there is a risk of illness, depression, or mental abnormality, it is possible to contact a medical institution immediately and take appropriate measures. This system can grasp the living conditions of family members in real time, which can increase the sense of security of family members living far away. In addition, by taking appropriate measures according to the living conditions of family members, the health and happiness of the family can be maintained. As a result, the living conditions grasping system can grasp the living conditions of family members in real time and take appropriate measures.

[0029] The living situation grasping system according to the embodiment includes a voice recognition device, a generation unit, and a notification unit. The voice recognition device is installed in a place where the family often spends time. The places where the family often spends time include, for example, a living room, a dining room, a kitchen, and the like, but are not limited to such examples. The voice recognition device collects conversations and living sounds of the family. The conversations and living sounds of the family include, for example, talking, laughter, footsteps, sounds of home appliances, and the like, but are not limited to such examples. The generation unit uses a generation AI to analyze the conversations and living sounds of the family collected by the voice recognition device and determine the emotions and health conditions of the family. The generation AI analyzes, for example, tone of voice, speaking pattern, volume, and the like, and determines the emotions and health conditions of the family. The generation unit uses, for example, a machine learning algorithm or a voice analysis algorithm to determine the emotions and health conditions of the family. The notification unit transmits a notification to the relevant person based on the result determined by the generation unit. The notification unit transmits a notification, for example, via email or an app. The notification unit notifies, for example, content according to the living situation of the family. For example, if a family member is living well, a notification of "no problem" is made, and if the family member looks lonely, a notification of "looking lonely" is made. If there is a risk of illness, depression, or mental abnormality, a notification is made that immediate action is required. In this way, the life situation assessment system according to the embodiment can grasp the life situation of the family member in real time and take appropriate action. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can send a notification using an AI model that inputs the result determined by the generation unit and outputs the notification content.

[0030] The voice recognition device is installed in a place where the family often spends time. Examples of places where the family often spends time include, but are not limited to, a living room, a dining room, and a kitchen. The voice recognition device collects conversations and daily sounds of the family. Examples of conversations and daily sounds of the family include, but are not limited to, talking, laughter, footsteps, and sounds of home appliances. The voice recognition device is equipped with a highly sensitive microphone and can collect surrounding sounds clearly. Furthermore, by using noise canceling technology, background sounds and unnecessary noise can be removed and important voice data can be accurately obtained. The voice recognition device can process the collected voice data in real time and detect specific keywords and phrases. For example, if words such as "tired" and "painful" frequently appear in family conversations, these can be detected and used for subsequent analysis. In addition, the voice recognition device can also identify the direction of the sound source using multiple microphones and identify the voice of a specific person. This makes it possible to grasp the individual living conditions of each family member. Furthermore, the voice recognition device can temporarily store the collected data and analyze it later as necessary. This makes it possible to perform detailed analysis later even when real-time analysis is difficult.

[0031] The generation unit uses the generation AI to analyze the conversations and living sounds of the family collected by the voice recognition device and judge the emotions and health conditions of the family. The generation AI analyzes, for example, the tone of the voice, the speaking pattern, the volume, etc., and judges the emotions and health conditions of the family. The generation unit uses, for example, a machine learning algorithm or a voice analysis algorithm to judge the emotions and health conditions of the family. The generation AI has learned a large amount of voice data in advance and can identify voice patterns corresponding to various emotions and health conditions. For example, a low voice tone and slow speech may indicate fatigue or stress. Also, a loud voice tone and fast speech may indicate excitement or anger. The generation unit can analyze these patterns and judge the emotions and health conditions of the family with high accuracy. Furthermore, the generation unit can integrate and analyze not only voice data but also other sensor data. For example, by combining camera data that detects the movements and facial expressions of the family and data from a wearable device that measures heart rate and body temperature, more accurate judgments can be made. The generation unit can process these data in real time and continuously monitor the living conditions of the family. This enables the generation unit to quickly and accurately grasp the emotions and health status of family members and assist in appropriate responses.

[0032] The notification unit transmits a notification to the relevant person based on the result determined by the generation unit. The notification unit transmits the notification, for example, via email or an app. The notification unit notifies, for example, the content according to the living situation of the family. For example, if the family member is living well, the notification is "no problem", and if the family member looks lonely, the notification is "looking lonely". If there is a risk of illness, depression, or mental abnormality, the notification is made that immediate action is required. The notification unit can transmit the notification using an AI model that inputs the result determined by the generation unit and outputs the notification content. The AI ​​model can optimize the notification content and prioritize the information that is most important to the relevant person. For example, if the health condition of a family member suddenly deteriorates, an emergency notification can be sent to encourage a prompt response. In addition, the notification unit can flexibly select the method of sending the notification. For example, it is possible to send notifications not only via email or app notifications, but also via voice calls, SMS, and even smart home devices. This ensures that the relevant person receives the notification. Furthermore, the notification unit can save the notification history and refer to it later. This makes it possible to check the past notification content and evaluate the effectiveness of the response. The notification department can also collect feedback from the relevant parties and continuously improve the content and method of notifications. This enables the notification department to quickly and reliably provide appropriate notifications according to the family's living situation and support the relevant parties' response.

[0033] The generation unit can use an algorithm that analyzes the voice data and determines the emotions and health status of the family members. The generation unit can analyze the voice data using, for example, a machine learning algorithm. For example, the generation unit can analyze the tone of the voice and the speaking pattern to determine the emotions of the family members. The generation unit can also analyze the volume and rhythm of the voice to determine the health status of the family members. For example, the generation unit can determine that the family members look lonely if the tone of the voice is low and the speaking manner is slow. The generation unit can also determine that the family members may be sick or depressed if the volume of the voice is low and the rhythm is irregular. This allows the generation unit to accurately determine the emotions and health status of the family members by analyzing the voice data. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the voice data to the generation AI and cause the generation AI to execute the judgment of the emotions and health status.

[0034] The notification unit can transmit the notification via email or an app. The notification unit transmits the notification via email, for example. For example, the notification unit transmits content according to the family's living situation via email. The notification unit can also transmit the notification via an app. For example, the notification unit uses a dedicated app to notify content according to the family's living situation. This allows the notification unit to quickly transmit the notification to relevant parties. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can transmit the notification using an AI model that inputs the result determined by the generation unit and outputs the notification content.

[0035] The notification unit can notify the content according to the living situation of the family. For example, if the family is living well, the notification unit notifies "no problem". In addition, if the family looks lonely, the notification unit can also notify "looking lonely". In addition, if the family is at risk of illness, depression, or mental abnormality, the notification unit can also notify that immediate action is required. In this way, the notification unit can transmit an appropriate notification according to the living situation of the family. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can transmit a notification using an AI model that inputs the result determined by the generation unit and outputs the notification content.

[0036] The voice recognition device can be installed in the living room or bedroom in the house. The voice recognition device is installed in, for example, the living room. The living room is a place where the family often spends time, and many conversations and living sounds are collected. The voice recognition device can also be installed in the bedroom. The bedroom is a place where the family often spends time relaxing, and it is easy to grasp changes in emotions and health conditions. Furthermore, the voice recognition device can also be installed in other places where the family often spends time, such as the dining room or kitchen. In this way, the voice recognition device can collect voice data in places where the family often spends time. Some or all of the above-mentioned processing in the voice recognition device may be performed, for example, using AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to the generation AI and have the generation AI perform the analysis.

[0037] The voice recognition device can expand the type of voice data to be collected to include not only conversation sounds but also environmental sounds and operation sounds. For example, the voice recognition device collects sounds of family members watching TV or listening to music to grasp the rhythm of their lives. The voice recognition device can also collect sounds of family members cooking or cleaning to grasp their daily activities. Furthermore, the voice recognition device can collect sounds of family members opening and closing doors and walking to grasp the movement patterns in the house. In this way, the voice recognition device can grasp the living conditions of the family in more detail by collecting various voice data. Part or all of the above-mentioned processing in the voice recognition device may be performed, for example, using AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to a generation AI and have the generation AI perform analysis.

[0038] The voice recognition device can add a noise canceling function to improve the accuracy of collected voice data. For example, the voice recognition device can remove noise from outside the house and collect the conversation sounds of family members clearly. The voice recognition device can also remove the operating sounds of home appliances and collect daily life sounds accurately. Furthermore, even when multiple sound sources overlap, the voice recognition device can also collect by emphasizing a specific voice. As a result, the voice recognition device improves the accuracy of the voice data by the noise canceling function. A part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to a generation AI and cause the generation AI to execute a noise canceling process.

[0039] The voice recognition device can filter the collected voice data based on the life patterns of the family. For example, if the family wakes up at the same time every morning, the voice recognition device focuses on collecting voice data for that time period. In addition, if the family performs a specific activity on a specific day of the week, the voice recognition device can also focus on collecting voice data for that day. Furthermore, if the family spends the night quietly, the voice recognition device can also filter and collect nighttime voice data. This allows the voice recognition device to filter the voice data based on the life patterns of the family. A part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to a generation AI and have the generation AI perform filtering.

[0040] The voice recognition device can detect anomalies by comparing collected voice data with past voice data of family members. For example, the voice recognition device can detect abnormal silences or abnormal statements by comparing with past conversation sounds of family members. The voice recognition device can also detect abnormal noises or abnormal silences by comparing with past environmental sounds of family members. Furthermore, the voice recognition device can detect abnormal movements or abnormal stops by comparing with past operation sounds of family members. In this way, the voice recognition device can detect abnormalities by comparing with past voice data. Part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition device can input collected voice data to a generation AI and cause the generation AI to detect abnormalities.

[0041] The generation unit can improve the accuracy of the analysis by combining the voice data to be analyzed with the family's past health data and life patterns. The generation unit analyzes the current health state based on, for example, the family's past health check results. The generation unit can also detect abnormal behavior based on the family's past life patterns. Furthermore, the generation unit can analyze the current emotional state based on the family's past emotional data. As a result, the generation unit improves the analysis accuracy by combining the past health data and life patterns. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected voice data to the generation AI and have the generation AI execute an analysis combining the voice data with the past data.

[0042] The generation unit can apply an algorithm that takes into account the individual characteristics of family members to the voice data to be analyzed. For example, the generation unit performs analysis while taking into account health risks according to the age of the family members. The generation unit can also perform analysis while taking into account the way emotions are expressed according to the gender of the family members. Furthermore, the generation unit can also perform analysis while taking into account signs of abnormalities according to the health condition of the family members. As a result, the generation unit improves the accuracy of analysis by taking into account the individual characteristics of the family members. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input collected voice data to the generation AI and cause the generation AI to perform analysis while taking into account the individual characteristics.

[0043] The generation unit can apply an algorithm that takes into account the living environment of the family to the voice data to be analyzed. For example, when the weather is bad, the generation unit performs the analysis while taking into account the impact on the family's emotions and health. The generation unit can also perform the analysis while taking into account changes in the family's health at the change of seasons. Furthermore, the generation unit can also perform the analysis while taking into account the activity patterns of the family depending on the time of day. This allows the generation unit to improve the analysis accuracy by taking into account the living environment. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected voice data to the generation AI and cause the generation AI to perform an analysis that takes into account the living environment.

[0044] The generation unit can improve the analysis accuracy by combining the voice data to be analyzed with the family's social media activities and online behavior. For example, the generation unit analyzes the contents of the family's social media posts to complement the emotional state. The generation unit can also analyze the family's online behavior (search history and browsing history) to grasp their interests and concerns. Furthermore, the generation unit can analyze the family's online communication (chats and emails) to detect changes in emotions. This allows the generation unit to improve the analysis accuracy by combining social media activities and online behavior. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected voice data to the generation AI and cause the generation AI to perform an analysis combining the social media activities and online behavior.

[0045] The notification unit can expand the format of the notification to be sent to include not only text messages but also voice messages and video messages. For example, the notification unit sends a text message according to the emotional state of the family. The notification unit can also send a voice message according to the emotional state of the family. Furthermore, the notification unit can also send a video message according to the emotional state of the family. This allows the notification unit to send notifications in a variety of formats. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the collected voice data to a generation AI and cause the generation AI to select a notification format.

[0046] The notification unit can customize the content of the notification to be sent based on the past notification history and reactions of the family members. For example, the notification unit customizes the notification based on notification content that the family members have favorably received in the past. The notification unit can also improve the notification based on notification content that the family members have ignored in the past. Furthermore, the notification unit can analyze the past reactions of the family members and propose optimal notification content. This allows the notification unit to customize the notification content based on the past notification history and reactions, thereby enabling more effective notification. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the collected voice data to a generation AI and have the generation AI execute customization of the notification content.

[0047] The notification unit can use different templates for the content of the notification to be sent depending on the living situation and health condition of the family. For example, when the family is healthy, the notification unit uses a simple notification template. In addition, when the family is sick, the notification unit can also use a detailed notification template. Furthermore, when the family is stressed, the notification unit can also use a notification template including an encouraging message. This allows the notification unit to use an appropriate notification template according to the living situation and health condition, thereby enabling more effective notification. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the collected voice data to the generation AI and cause the generation AI to select a notification template.

[0048] The notification unit can customize the content of the notification to be sent based on the attributes of the family member concerned. For example, when the family member concerned is elderly, the notification unit provides simple and easy-to-understand notification content. In addition, when the family member concerned is young, the notification unit can also provide detailed and specific notification content. Furthermore, when the family member concerned lives far away, the notification unit can also provide notification content with high urgency. This allows the notification unit to customize the notification content based on the attributes of the family member concerned, thereby enabling more effective notification. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the collected voice data to a generation AI and cause the generation AI to customize the notification content.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The living situation grasping system may further include a location information acquiring unit. The location information acquiring unit may acquire location information from the family member's smartphone or wearable device and grasp the family member's movement pattern. For example, if the family member stays in a particular place for a long time, the location information acquiring unit may focus on collecting environmental sounds and conversation sounds at that place. In addition, if the family member frequently moves along a particular route, the location information acquiring unit may collect voice data on that route and analyze the living pattern. Furthermore, if the family member shows an abnormal movement pattern, the location information acquiring unit may send a notification to relevant parties. This allows the living situation grasping system to grasp the living situation in more detail based on the family member's movement pattern.

[0051] The life situation grasping system may further include a vital data acquiring unit. The vital data acquiring unit may acquire vital data such as heart rate, blood pressure, and body temperature from the family member's smart watch or fitness tracker, and grasp the health condition. For example, if the heart rate is abnormally high, it may be analyzed taking into consideration the effects of stress and exercise. Also, if the body temperature is high, it may be analyzed taking into consideration the possibility of fever. Furthermore, the vital data acquiring unit may acquire sleep data and evaluate the quality of sleep of the family member. This allows the life situation grasping system to grasp the health condition of the family member more accurately based on the vital data.

[0052] The life situation awareness system may further include a home appliance control unit. The home appliance control unit may control the operation of home appliances according to the life situation of the family members. For example, when the family members are relaxed, the brightness of the lights may be adjusted to provide a comfortable environment. Also, when the family members are stressed, music may be played to enhance the relaxation effect. Furthermore, when the family members are in the bedroom, the temperature of the air conditioner may be adjusted to provide a comfortable sleeping environment. In this way, the life situation awareness system may improve the quality of life of the family members through the home appliances.

[0053] The lifestyle situation awareness system may further include a diet management unit. The diet management unit may record the dietary content and calorie intake of family members to support health management. For example, it may determine whether the family members are eating a balanced diet and make suggestions to supplement missing nutrients. If the family members are consuming excessive calories, it may also make suggestions to review the dietary content. Furthermore, the diet management unit may analyze the dietary patterns of the family members and evaluate the association with the health condition. In this way, the lifestyle situation awareness system may maintain the health of the family members through diet management.

[0054] The lifestyle status awareness system may further include an exercise management unit. The exercise management unit may record the amount of exercise and patterns of the family members to support health management. For example, it may determine whether the family members are exercising sufficiently, and if they are not exercising enough, may suggest that they should exercise. If the family members are exercising too much, it may also suggest that they take a rest. Furthermore, the exercise management unit may analyze the exercise patterns of the family members and evaluate the correlation with their health conditions. In this way, the lifestyle status awareness system may maintain the health of the family members through exercise management.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The voice recognition device is installed in a place where the family often spends time. Places where the family often spends time include, but are not limited to, the living room, dining room, kitchen, etc. The voice recognition device collects the family's conversations and daily life sounds. The collected sounds include talking, laughter, footsteps, and the sounds of home appliances. Step 2: The generator analyzes the family's conversations and everyday sounds collected by the voice recognition device to determine the family's emotions and health status. The generator uses generation AI to analyze the tone of voice, speaking patterns, volume, etc., and makes a judgment using machine learning algorithms and voice analysis algorithms. Step 3: The notification unit sends notifications to relevant parties based on the results determined by the generation unit. The notification unit sends notifications via email or app, and notifies the content according to the family member's living situation. For example, if a family member is living happily, the notification will say "no problem," and if they seem lonely, the notification will say "looking lonely." If there is a risk of illness, depression, or mental abnormality, the notification will be made that immediate action is required.

[0057] (Example 2) The living situation grasping system according to the embodiment of the present invention is a system that grasps the living situation of a family member using a voice recognition device installed in the house. In this system, the voice recognition device installed in the house collects conversations and living sounds of the family member. This voice data is input to the generation AI. The generation AI analyzes the collected voice data and judges the living situation of the family member. For example, it judges the situation such as whether the family member is living well, whether they look lonely, whether there is a risk of illness, depression, or mental abnormality. Next, based on the result of the judgment by the generation AI, a notification is sent to the relevant person (basic relatives). For example, if a family member is living well, it is notified that "there is no problem", and if they look lonely, it is notified that "they look lonely". If there is a risk of illness, depression, or mental abnormality, it is notified that an immediate response is required. This system can grasp the living situation of family members living apart, and can create an opportunity to help or communicate in advance. For example, if they look lonely, it is possible to ease the family member's feelings by talking to them by phone or video call. Also, if there is a risk of illness, depression, or mental abnormality, it is possible to contact a medical institution immediately and take appropriate measures. This system can grasp the living conditions of family members in real time, which can increase the sense of security of family members living far away. In addition, by taking appropriate measures according to the living conditions of family members, the health and happiness of the family can be maintained. As a result, the living conditions grasping system can grasp the living conditions of family members in real time and take appropriate measures.

[0058] The living situation grasping system according to the embodiment includes a voice recognition device, a generation unit, and a notification unit. The voice recognition device is installed in a place where the family often spends time. The places where the family often spends time include, for example, a living room, a dining room, a kitchen, and the like, but are not limited to such examples. The voice recognition device collects conversations and living sounds of the family. The conversations and living sounds of the family include, for example, talking, laughter, footsteps, sounds of home appliances, and the like, but are not limited to such examples. The generation unit uses a generation AI to analyze the conversations and living sounds of the family collected by the voice recognition device and determine the emotions and health conditions of the family. The generation AI analyzes, for example, tone of voice, speaking pattern, volume, and the like, and determines the emotions and health conditions of the family. The generation unit uses, for example, a machine learning algorithm or a voice analysis algorithm to determine the emotions and health conditions of the family. The notification unit transmits a notification to the relevant person based on the result determined by the generation unit. The notification unit transmits a notification, for example, via email or an app. The notification unit notifies, for example, content according to the living situation of the family. For example, if a family member is living well, a notification of "no problem" is made, and if the family member looks lonely, a notification of "looking lonely" is made. If there is a risk of illness, depression, or mental abnormality, a notification is made that immediate action is required. In this way, the life situation assessment system according to the embodiment can grasp the life situation of the family member in real time and take appropriate action. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can send a notification using an AI model that inputs the result determined by the generation unit and outputs the notification content.

[0059] The voice recognition device is installed in a place where the family often spends time. Examples of places where the family often spends time include, but are not limited to, a living room, a dining room, and a kitchen. The voice recognition device collects conversations and daily sounds of the family. Examples of conversations and daily sounds of the family include, but are not limited to, talking, laughter, footsteps, and sounds of home appliances. The voice recognition device is equipped with a highly sensitive microphone and can collect surrounding sounds clearly. Furthermore, by using noise canceling technology, background sounds and unnecessary noise can be removed and important voice data can be accurately obtained. The voice recognition device can process the collected voice data in real time and detect specific keywords and phrases. For example, if words such as "tired" and "painful" frequently appear in family conversations, these can be detected and used for subsequent analysis. In addition, the voice recognition device can also identify the direction of the sound source using multiple microphones and identify the voice of a specific person. This makes it possible to grasp the individual living conditions of each family member. Furthermore, the voice recognition device can temporarily store the collected data and analyze it later as necessary. This makes it possible to perform detailed analysis later even when real-time analysis is difficult.

[0060] The generation unit uses the generation AI to analyze the conversations and living sounds of the family collected by the voice recognition device and judge the emotions and health conditions of the family. The generation AI analyzes, for example, the tone of the voice, the speaking pattern, the volume, etc., and judges the emotions and health conditions of the family. The generation unit uses, for example, a machine learning algorithm or a voice analysis algorithm to judge the emotions and health conditions of the family. The generation AI has learned a large amount of voice data in advance and can identify voice patterns corresponding to various emotions and health conditions. For example, a low voice tone and slow speech may indicate fatigue or stress. Also, a loud voice tone and fast speech may indicate excitement or anger. The generation unit can analyze these patterns and judge the emotions and health conditions of the family with high accuracy. Furthermore, the generation unit can integrate and analyze not only voice data but also other sensor data. For example, by combining camera data that detects the movements and facial expressions of the family and data from a wearable device that measures heart rate and body temperature, more accurate judgments can be made. The generation unit can process these data in real time and continuously monitor the living conditions of the family. This enables the generation unit to quickly and accurately grasp the emotions and health status of family members and assist in appropriate responses.

[0061] The notification unit transmits a notification to the relevant person based on the result determined by the generation unit. The notification unit transmits the notification, for example, via email or an app. The notification unit notifies, for example, the content according to the living situation of the family. For example, if the family member is living well, the notification is "no problem", and if the family member looks lonely, the notification is "looking lonely". If there is a risk of illness, depression, or mental abnormality, the notification is made that immediate action is required. The notification unit can transmit the notification using an AI model that inputs the result determined by the generation unit and outputs the notification content. The AI ​​model can optimize the notification content and prioritize the information that is most important to the relevant person. For example, if the health condition of a family member suddenly deteriorates, an emergency notification can be sent to encourage a prompt response. In addition, the notification unit can flexibly select the method of sending the notification. For example, it is possible to send notifications not only via email or app notifications, but also via voice calls, SMS, and even smart home devices. This ensures that the relevant person receives the notification. Furthermore, the notification unit can save the notification history and refer to it later. This makes it possible to check the past notification content and evaluate the effectiveness of the response. The notification department can also collect feedback from the relevant parties and continuously improve the content and method of notifications. This enables the notification department to quickly and reliably provide appropriate notifications according to the family's living situation and support the relevant parties' response.

[0062] The generation unit can use an algorithm that analyzes the voice data and determines the emotions and health status of the family members. The generation unit can analyze the voice data using, for example, a machine learning algorithm. For example, the generation unit can analyze the tone of the voice and the speaking pattern to determine the emotions of the family members. The generation unit can also analyze the volume and rhythm of the voice to determine the health status of the family members. For example, the generation unit can determine that the family members look lonely if the tone of the voice is low and the speaking manner is slow. The generation unit can also determine that the family members may be sick or depressed if the volume of the voice is low and the rhythm is irregular. This allows the generation unit to accurately determine the emotions and health status of the family members by analyzing the voice data. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the voice data to the generation AI and cause the generation AI to execute the judgment of the emotions and health status.

[0063] The notification unit can transmit the notification via email or an app. The notification unit transmits the notification via email, for example. For example, the notification unit transmits content according to the family's living situation via email. The notification unit can also transmit the notification via an app. For example, the notification unit uses a dedicated app to notify content according to the family's living situation. This allows the notification unit to quickly transmit the notification to relevant parties. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can transmit the notification using an AI model that inputs the result determined by the generation unit and outputs the notification content.

[0064] The notification unit can notify the content according to the living situation of the family. For example, if the family is living well, the notification unit notifies "no problem". In addition, if the family looks lonely, the notification unit can also notify "looking lonely". In addition, if the family is at risk of illness, depression, or mental abnormality, the notification unit can also notify that immediate action is required. In this way, the notification unit can transmit an appropriate notification according to the living situation of the family. A part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can transmit a notification using an AI model that inputs the result determined by the generation unit and outputs the notification content.

[0065] The voice recognition device can be installed in the living room or bedroom in the house. The voice recognition device is installed in, for example, the living room. The living room is a place where the family often spends time, and many conversations and living sounds are collected. The voice recognition device can also be installed in the bedroom. The bedroom is a place where the family often spends time relaxing, and it is easy to grasp changes in emotions and health conditions. Furthermore, the voice recognition device can also be installed in other places where the family often spends time, such as the dining room or kitchen. In this way, the voice recognition device can collect voice data in places where the family often spends time. Some or all of the above-mentioned processing in the voice recognition device may be performed, for example, using AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to the generation AI and have the generation AI perform the analysis.

[0066] The voice recognition device can estimate the emotions of the family members and adjust the timing of collecting voice data based on the estimated emotions of the family members. For example, when it is estimated that the family members are relaxed, the voice recognition device reduces the frequency of collecting voice data and respects privacy. Also, when it is estimated that the family members are stressed, the voice recognition device can increase the frequency of collecting voice data and grasp the detailed situation. Furthermore, when it is estimated that the family members are excited, the voice recognition device can also concentrate on collecting voice data during a specific time period. This allows the voice recognition device to adjust the timing of collecting voice data according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice recognition device can input the collected voice data to the generation AI and cause the generation AI to execute emotion estimation.

[0067] The voice recognition device can expand the type of voice data to be collected to include not only conversation sounds but also environmental sounds and operation sounds. For example, the voice recognition device collects sounds of family members watching TV or listening to music to grasp the rhythm of their lives. The voice recognition device can also collect sounds of family members cooking or cleaning to grasp their daily activities. Furthermore, the voice recognition device can collect sounds of family members opening and closing doors and walking to grasp the movement patterns in the house. In this way, the voice recognition device can grasp the living conditions of the family in more detail by collecting various voice data. Part or all of the above-mentioned processing in the voice recognition device may be performed, for example, using AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to a generation AI and have the generation AI perform analysis.

[0068] The voice recognition device can add a noise canceling function to improve the accuracy of collected voice data. For example, the voice recognition device can remove noise from outside the house and collect the conversation sounds of family members clearly. The voice recognition device can also remove the operating sounds of home appliances and collect daily life sounds accurately. Furthermore, even when multiple sound sources overlap, the voice recognition device can also collect by emphasizing a specific voice. As a result, the voice recognition device improves the accuracy of the voice data by the noise canceling function. A part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to a generation AI and cause the generation AI to execute a noise canceling process.

[0069] The voice recognition device can estimate the emotions of the family members and determine the priority of the voice data to be collected based on the estimated emotions of the family members. For example, when it is estimated that the family members are sad, the voice recognition device prioritizes collecting conversation sounds to grasp the change in emotions in detail. In addition, when it is estimated that the family members are having fun, the voice recognition device can also prioritize collecting environmental sounds to evaluate the quality of life. Furthermore, when it is estimated that the family members are tired, the voice recognition device can also prioritize collecting operation sounds to determine the need for rest. In this way, the voice recognition device can determine the priority of the voice data to be collected according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice recognition device can input the collected voice data to the generation AI and cause the generation AI to execute the determination of the priority.

[0070] The voice recognition device can filter the collected voice data based on the life patterns of the family. For example, if the family wakes up at the same time every morning, the voice recognition device focuses on collecting voice data for that time period. In addition, if the family performs a specific activity on a specific day of the week, the voice recognition device can also focus on collecting voice data for that day. Furthermore, if the family spends the night quietly, the voice recognition device can also filter and collect nighttime voice data. This allows the voice recognition device to filter the voice data based on the life patterns of the family. A part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition device can input the collected voice data to a generation AI and have the generation AI perform filtering.

[0071] The voice recognition device can detect anomalies by comparing collected voice data with past voice data of family members. For example, the voice recognition device can detect abnormal silences or abnormal statements by comparing with past conversation sounds of family members. The voice recognition device can also detect abnormal noises or abnormal silences by comparing with past environmental sounds of family members. Furthermore, the voice recognition device can detect abnormal movements or abnormal stops by comparing with past operation sounds of family members. In this way, the voice recognition device can detect abnormalities by comparing with past voice data. Part or all of the above-mentioned processing in the voice recognition device may be performed using, for example, AI, or may be performed without using AI. For example, the voice recognition device can input collected voice data to a generation AI and cause the generation AI to detect abnormalities.

[0072] The generation unit can estimate the emotions of the family members and adjust the parameters of the analysis algorithm based on the estimated emotions of the family members. For example, when it is estimated that the family members are relaxed, the generation unit sets the sensitivity of the analysis algorithm low to respect privacy. In addition, when it is estimated that the family members are stressed, the generation unit can also set the sensitivity of the analysis algorithm high to grasp the detailed situation. Furthermore, when it is estimated that the family members are excited, the generation unit can adjust the parameters of the analysis algorithm for a specific emotion. This allows the generation unit to adjust the parameters of the analysis algorithm according to the emotions of the family members. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the collected voice data to the generation AI and cause the generation AI to execute parameter adjustment of the analysis algorithm.

[0073] The generation unit can improve the accuracy of the analysis by combining the voice data to be analyzed with the family's past health data and life patterns. The generation unit analyzes the current health state based on, for example, the family's past health check results. The generation unit can also detect abnormal behavior based on the family's past life patterns. Furthermore, the generation unit can analyze the current emotional state based on the family's past emotional data. As a result, the generation unit improves the analysis accuracy by combining the past health data and life patterns. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected voice data to the generation AI and have the generation AI execute an analysis combining the voice data with the past data.

[0074] The generation unit can apply an algorithm that takes into account the individual characteristics of family members to the voice data to be analyzed. For example, the generation unit performs analysis while taking into account health risks according to the age of the family members. The generation unit can also perform analysis while taking into account the way emotions are expressed according to the gender of the family members. Furthermore, the generation unit can also perform analysis while taking into account signs of abnormalities according to the health condition of the family members. As a result, the generation unit improves the accuracy of analysis by taking into account the individual characteristics of the family members. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input collected voice data to the generation AI and cause the generation AI to perform analysis while taking into account the individual characteristics.

[0075] The generation unit can estimate the emotions of the family members and adjust the display method of the analysis results based on the estimated emotions of the family members. For example, when it is estimated that the family members are relaxed, the generation unit displays the analysis results simply. Furthermore, when it is estimated that the family members are stressed, the generation unit can also display the analysis results in detail. Furthermore, when it is estimated that the family members are excited, the generation unit can also display the analysis results in a visually emphasized manner. This allows the generation unit to adjust the display method of the analysis results according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the collected voice data to the generation AI and cause the generation AI to adjust the display method.

[0076] The generation unit can apply an algorithm that takes into account the living environment of the family to the voice data to be analyzed. For example, when the weather is bad, the generation unit performs the analysis while taking into account the impact on the family's emotions and health. The generation unit can also perform the analysis while taking into account changes in the family's health at the change of seasons. Furthermore, the generation unit can also perform the analysis while taking into account the activity patterns of the family depending on the time of day. This allows the generation unit to improve the analysis accuracy by taking into account the living environment. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected voice data to the generation AI and cause the generation AI to perform an analysis that takes into account the living environment.

[0077] The generation unit can improve the analysis accuracy by combining the voice data to be analyzed with the family's social media activities and online behavior. For example, the generation unit analyzes the contents of the family's social media posts to complement the emotional state. The generation unit can also analyze the family's online behavior (search history and browsing history) to grasp their interests and concerns. Furthermore, the generation unit can analyze the family's online communication (chats and emails) to detect changes in emotions. This allows the generation unit to improve the analysis accuracy by combining social media activities and online behavior. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the collected voice data to the generation AI and cause the generation AI to perform an analysis combining the social media activities and online behavior.

[0078] The notification unit can estimate the emotions of the family members and adjust the content and timing of the notification based on the estimated emotions of the family members. For example, when it is estimated that the family members are relaxed, the notification unit can simplify the content of the notification and delay the timing. In addition, when it is estimated that the family members are stressed, the notification unit can also make the content of the notification more detailed and advance the timing. Furthermore, when it is estimated that the family members are excited, the notification unit can also visually emphasize the content of the notification and adjust the timing. This allows the notification unit to adjust the content and timing of the notification according to the emotions of the family members. The estimation of the emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the collected voice data to the generation AI and cause the generation AI to adjust the notification content and timing.

[0079] The notification unit can expand the format of the notification to be sent to include not only text messages but also voice messages and video messages. For example, the notification unit sends a text message according to the emotional state of the family. The notification unit can also send a voice message according to the emotional state of the family. Furthermore, the notification unit can also send a video message according to the emotional state of the family. This allows the notification unit to send notifications in a variety of formats. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the collected voice data to a generation AI and cause the generation AI to select a notification format.

[0080] The notification unit can customize the content of the notification to be sent based on the past notification history and reactions of the family members. For example, the notification unit customizes the notification based on notification content that the family members have favorably received in the past. The notification unit can also improve the notification based on notification content that the family members have ignored in the past. Furthermore, the notification unit can analyze the past reactions of the family members and propose optimal notification content. This allows the notification unit to customize the notification content based on the past notification history and reactions, thereby enabling more effective notification. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the collected voice data to a generation AI and have the generation AI execute customization of the notification content.

[0081] The notification unit can estimate the emotions of the family members and determine the priority of notifications based on the estimated emotions of the family members. For example, when it is estimated that the family members are sad, the notification unit sets the priority of notifications to high. In addition, when it is estimated that the family members are having fun, the notification unit can also set the priority of notifications to low. Furthermore, when it is estimated that the family members are tired, the notification unit can also set the priority of notifications to medium. In this way, the notification unit can prioritize important notifications by determining the priority of notifications according to the emotions of the family members. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the collected voice data to the generation AI and cause the generation AI to execute the determination of the priority of notifications.

[0082] The notification unit can use different templates for the content of the notification to be sent depending on the living situation and health condition of the family. For example, when the family is healthy, the notification unit uses a simple notification template. In addition, when the family is sick, the notification unit can also use a detailed notification template. Furthermore, when the family is stressed, the notification unit can also use a notification template including an encouraging message. This allows the notification unit to use an appropriate notification template according to the living situation and health condition, thereby enabling more effective notification. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the collected voice data to the generation AI and cause the generation AI to select a notification template.

[0083] The notification unit can customize the content of the notification to be sent based on the attributes of the family member concerned. For example, when the family member concerned is elderly, the notification unit provides simple and easy-to-understand notification content. In addition, when the family member concerned is young, the notification unit can also provide detailed and specific notification content. Furthermore, when the family member concerned lives far away, the notification unit can also provide notification content with high urgency. This allows the notification unit to customize the notification content based on the attributes of the family member concerned, thereby enabling more effective notification. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or may be performed without using AI. For example, the notification unit can input the collected voice data to a generation AI and cause the generation AI to customize the notification content.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The living situation grasping system may further include a location information acquiring unit. The location information acquiring unit may acquire location information from the family member's smartphone or wearable device and grasp the family member's movement pattern. For example, if the family member stays in a particular place for a long time, the location information acquiring unit may focus on collecting environmental sounds and conversation sounds at that place. In addition, if the family member frequently moves along a particular route, the location information acquiring unit may collect voice data on that route and analyze the living pattern. Furthermore, if the family member shows an abnormal movement pattern, the location information acquiring unit may send a notification to relevant parties. This allows the living situation grasping system to grasp the living situation in more detail based on the family member's movement pattern.

[0086] The life situation grasping system may further include a vital data acquiring unit. The vital data acquiring unit may acquire vital data such as heart rate, blood pressure, and body temperature from the family member's smart watch or fitness tracker, and grasp the health condition. For example, if the heart rate is abnormally high, it may be analyzed taking into consideration the effects of stress and exercise. Also, if the body temperature is high, it may be analyzed taking into consideration the possibility of fever. Furthermore, the vital data acquiring unit may acquire sleep data and evaluate the quality of sleep of the family member. This allows the life situation grasping system to grasp the health condition of the family member more accurately based on the vital data.

[0087] The life situation awareness system may further include a home appliance control unit. The home appliance control unit may control the operation of home appliances according to the life situation of the family members. For example, when the family members are relaxed, the brightness of the lights may be adjusted to provide a comfortable environment. Also, when the family members are stressed, music may be played to enhance the relaxation effect. Furthermore, when the family members are in the bedroom, the temperature of the air conditioner may be adjusted to provide a comfortable sleeping environment. In this way, the life situation awareness system may improve the quality of life of the family members through the home appliances.

[0088] The lifestyle situation awareness system may further include a diet management unit. The diet management unit may record the dietary content and calorie intake of family members to support health management. For example, it may determine whether the family members are eating a balanced diet and make suggestions to supplement missing nutrients. If the family members are consuming excessive calories, it may also make suggestions to review the dietary content. Furthermore, the diet management unit may analyze the dietary patterns of the family members and evaluate the association with the health condition. In this way, the lifestyle situation awareness system may maintain the health of the family members through diet management.

[0089] The lifestyle status awareness system may further include an exercise management unit. The exercise management unit may record the amount of exercise and patterns of the family members to support health management. For example, it may determine whether the family members are exercising sufficiently, and if they are not exercising enough, may suggest that they should exercise. If the family members are exercising too much, it may also suggest that they take a rest. Furthermore, the exercise management unit may analyze the exercise patterns of the family members and evaluate the correlation with their health conditions. In this way, the lifestyle status awareness system may maintain the health of the family members through exercise management.

[0090] The life situation awareness system can estimate the emotions of family members and adjust the way of communication with the family members based on the estimated emotions. For example, if it is estimated that a family member looks lonely, the system can suggest that the family member be spoken to in a kind manner. If it is estimated that a family member is feeling stressed, the system can provide topics that will help them relax. Furthermore, if it is estimated that a family member is having fun, the system can provide topics for sharing those emotions. In this way, the life situation awareness system can support appropriate communication according to the emotions of the family members.

[0091] The life situation awareness system can estimate the emotions of family members and support stress management for the family members based on the estimated emotions. For example, if it is estimated that a family member is feeling stressed, the system can play relaxing music. Also, if it is estimated that a family member is feeling stressed, the system can provide a guide for deep breathing or meditation. Furthermore, if it is estimated that a family member is feeling stressed, the system can suggest exercises to relieve stress. In this way, the life situation awareness system can support stress management for the family members and maintain their health.

[0092] The life situation recognition system can estimate the emotions of family members and support sleep management of the family members based on the estimated emotions. For example, if it is estimated that a family member is tired, the system can suggest that the family members go to bed early. Also, if it is estimated that a family member is feeling anxious, the system can play relaxing music to encourage sound sleep. Furthermore, if it is estimated that a family member is excited, the system can provide a meditation guide to help them relax. In this way, the life situation recognition system can support appropriate sleep management according to the emotions of the family members.

[0093] The lifestyle situation recognition system can estimate the emotions of family members and support health management of the family members based on the estimated emotions. For example, if it is estimated that a family member is tired, the system can suggest that the family member take a rest. Also, if it is estimated that a family member is feeling stressed, the system can suggest activities that will help them relax. Furthermore, if it is estimated that a family member is in good health, the system can make suggestions to encourage exercise. In this way, the lifestyle situation recognition system can support appropriate health management according to the emotions of the family members.

[0094] The life situation recognition system can estimate the emotions of family members and support social activities of the family members based on the estimated emotions. For example, if it is estimated that a family member looks lonely, the system can suggest a video call with friends or relatives. Also, if it is estimated that a family member is having fun, the system can suggest an event or activity to share the emotion. Furthermore, if it is estimated that a family member is feeling stressed, the system can suggest a social activity to relax. In this way, the life situation recognition system can support appropriate social activities according to the emotions of the family members.

[0095] The process flow of the second embodiment will be briefly described below.

[0096] Step 1: The voice recognition device is installed in a place where the family often spends time. Places where the family often spends time include, but are not limited to, the living room, dining room, kitchen, etc. The voice recognition device collects the family's conversations and daily life sounds. The collected sounds include talking, laughter, footsteps, and the sounds of home appliances. Step 2: The generator analyzes the family's conversations and everyday sounds collected by the voice recognition device to determine the family's emotions and health status. The generator uses generation AI to analyze the tone of voice, speaking patterns, volume, etc., and makes a judgment using machine learning algorithms and voice analysis algorithms. Step 3: The notification unit sends notifications to relevant parties based on the results determined by the generation unit. The notification unit sends notifications via email or app, and notifies the content according to the family member's living situation. For example, if a family member is living happily, the notification will say "no problem," and if they seem lonely, the notification will say "looking lonely." If there is a risk of illness, depression, or mental abnormality, the notification will be made that immediate action is required.

[0097] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0098] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0099] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0100] Each of the multiple elements including the voice recognition device, the generation unit, and the notification unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the voice recognition device collects conversations and daily sounds of family members using the microphone 38B of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data to determine the emotions and health conditions of family members. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits a notification to relevant parties based on the determination result. The notification unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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, the RAM 30, and the 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.

[0104] 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.

[0105] 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 the voice according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0107] 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 performed in a secure state.

[0108] Fig. 4 shows an example of 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.

[0109] 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.

[0110] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0114] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0115] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned voice recognition device, generation unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition device collects conversations and daily sounds of family members using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data to determine the emotions and health conditions of the family members. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits a notification to related parties based on the determination result. The notification unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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, the RAM 30, and the 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] 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.

[0125] 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.

[0126] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 voice indicating a user input for 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned voice recognition device, generation unit, and notification unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the voice recognition device collects conversations and daily sounds of family members using the microphone 238 of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data to determine the emotions and health conditions of family members. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits a notification to related parties based on the determination result. The notification unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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, the RAM 30, and the 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0139] 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 performed in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0141] 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.

[0142] 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.

[0143] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0145] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] 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 a voice indicating a user input for 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.

[0147] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0149] Each of the multiple elements including the voice recognition device, the generation unit, and the 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 voice recognition device collects conversations and daily sounds of the family using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data to determine the emotions and health conditions of the family. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits a notification to relevant parties based on the determination result. The notification unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0150] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0151] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0153] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0154] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0155] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0157] 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.

[0158] 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 Universal Serial Bus (USB) 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.

[0159] In addition, 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 upon request from the data processing device 12.

[0160] 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.

[0161] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0162] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0163] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0164] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0166] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0168] (Appendix 1) A voice recognition device installed in a place where the family spends time; A generation unit that analyzes conversations or daily sounds of family members collected by the voice recognition device and determines emotions or physical conditions of family members; A notification unit that transmits a notification to a related person based on the result determined by the generation unit. A system characterized by: (Appendix 2) The generation unit is Uses algorithms to analyze voice data and determine family members' emotions and health conditions 2. The system of claim 1, (Appendix 3) The notification unit is Send notifications via email or app 2. The system of claim 1, (Appendix 4) The notification unit is Provide information appropriate to the family's living situation 2. The system of claim 1, (Appendix 5) The speech recognition device includes: Placed in the living room or bedroom of the house 2. The system of claim 1, (Appendix 6) The speech recognition device includes: The emotions of family members are estimated, and the timing of voice data collection is adjusted based on the estimated emotions of family members. 2. The system of claim 1, (Appendix 7) The speech recognition device includes: Expand the types of audio data collected to include not only conversational sounds but also environmental and motion sounds. 2. The system of claim 1, (Appendix 8) The speech recognition device includes: Adding noise-canceling functionality to improve the accuracy of collected voice data 2. The system of claim 1, (Appendix 9) The speech recognition device includes: Estimate the emotions of family members and prioritize the voice data to be collected based on the estimated emotions of family members 2. The system of claim 1, (Appendix 10) The speech recognition device includes: Filtering collected voice data based on family life patterns 2. The system of claim 1, (Appendix 11) The speech recognition device includes: The collected voice data is compared with past voice data of family members to detect anomalies. 2. The system of claim 1, (Appendix 12) The generation unit is Estimate the family's emotions and adjust the parameters of the analysis algorithm based on the estimated family's emotions. 2. The system of claim 1, (Appendix 13) The generation unit is Improve the accuracy of analysis by combining the voice data to be analyzed with the family's past health data and lifestyle patterns 2. The system of claim 1, (Appendix 14) The generation unit is Applying algorithms that take into account the individual characteristics of family members to the analyzed voice data 2. The system of claim 1, (Appendix 15) The generation unit is Infer family members' emotions and adjust the way the analysis results are displayed based on the inferred family members' emotions 2. The system of claim 1, (Appendix 16) The generation unit is Applying algorithms that take into account the family's living environment to the voice data being analyzed 2. The system of claim 1, (Appendix 17) The generation unit is Improve analysis accuracy by combining voice data with family members' social media activities and online behavior 2. The system of claim 1, (Appendix 18) The notification unit is Infer family members' emotions and adjust the content and timing of notifications based on the inferred emotions of family members 2. The system of claim 1, (Appendix 19) The notification unit is Expand the types of notifications you send to include voice and video messages in addition to text messages 2. The system of claim 1, (Appendix 20) The notification unit is Customize notifications based on your family members' past notification history and responses 2. The system of claim 1, (Appendix 21) The notification unit is Estimate family members' emotions and prioritize notifications based on the estimated family members' emotions 2. The system of claim 1, (Appendix 22) The notification unit is Use different templates for sending notifications depending on the family's living situation and health condition 2. The system of claim 1, (Appendix 23) The notification unit is Customize notifications sent based on family member attributes 2. The system of claim 1, [Explanation of symbols]

[0169] 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 voice recognition device installed in a place where the family spends time; A generation unit that analyzes conversations or daily sounds of family members collected by the voice recognition device and determines emotions or physical conditions of family members; A notification unit that transmits a notification to a related person based on the result determined by the generation unit. A system characterized by:

2. The generation unit is Uses algorithms to analyze voice data and determine family members' emotions and health conditions 2. The system of claim 1 .

3. The notification unit is Send notifications via email or app 2. The system of claim 1 .

4. The notification unit is Provide information appropriate to the family's living situation 2. The system of claim 1 .

5. The speech recognition device includes: Placed in the living room or bedroom of the house 2. The system of claim 1 .

6. The speech recognition device includes: The emotions of family members are estimated, and the timing of voice data collection is adjusted based on the estimated emotions of family members.

2. The system of claim 1 .

7. The speech recognition device includes: Expand the types of audio data collected to include not only conversational sounds but also environmental and motion sounds.

2. The system of claim 1 .

8. The speech recognition device includes: Adding noise-canceling functionality to improve the accuracy of collected voice data 2. The system of claim 1 .

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