System

Abstract

To provide a system according to an embodiment, the system intending to analyze a care condition on the basis of action histories of a care person and a care-receiver, and notify the family.SOLUTION: A system according to an embodiment includes: a collection part; an analysis part; and a notification part. The collection part collects respective action histories of a care person and a care-receiver. The analysis part analyzes a condition of care given to the care-receiver by the care person on the basis of the information collected by the collection part. The notification part notifies the care-receiver's family of the analysis result by the analysis 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] Conventional technology does not adequately grasp the care situation based on the behavioral history of the caregiver and the care recipient, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the care situation based on the behavioral history of the caregiver and the care recipient, and notify the family. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects behavioral histories of the caregiver and the care recipient. The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The notification unit notifies the family of the care recipient of the analysis result by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the care situation based on the behavioral history of the caregiver and the care recipient, and notify the family. [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 collaboration support system according to the embodiment of the present invention is a system that supports collaboration between a helper (nursery school teacher, teacher), a care recipient (child), and a family. This collaboration support system collects the behavioral history of the caregiver and the care recipient, analyzes the situation regarding the care of the care recipient by the caregiver based on the collected information, and notifies the family of the care recipient of the analysis result. For example, the behavior, remarks, and emotional values ​​of the caregiver and the care recipient are analyzed and notified to the family. In particular, a mechanism is included in which notification is made only when the emotional value exceeds a threshold value or when the emotional value is negative. For example, the history of the movements, facial expressions, and words and behavior of the caregiver and the care recipient is collected. For example, detailed data such as what movements the caregiver made, what facial expressions the care recipient had, and what words and behaviors were used is collected. This data is collected by a collection unit. Next, the situation regarding the care of the care recipient by the caregiver is analyzed based on the collected data. For example, what kind of care the caregiver provided, and what kind of reaction the care recipient showed are analyzed. This analysis is performed by an analysis unit. Furthermore, the emotional values ​​of the family are estimated based on the analysis result. For example, it estimates what emotions the family members have about the care situation. This estimation is performed by the estimation unit. Next, it is determined whether the estimated emotion value exceeds a threshold value. For example, a notification is made only when the emotion value exceeds a certain threshold value. This determination is performed by the determination unit. Finally, a notification is made to the family based on the determination result. For example, a notification is made to the family when the emotion value exceeds a threshold value. This notification is performed by the notification unit. This mechanism can be used by helper dispatch companies to evaluate helpers. In addition, the family can grasp the behavior, remarks, and emotion values ​​of the helper and the care recipient, making it possible to detect whether they are doing well or any trouble. In particular, since notifications are only made when the emotion value exceeds the threshold value or when the emotion is negative, important information can be grasped without missing it. As a result, the collaboration support system can effectively support collaboration between the helper (nursery school teacher, teacher), the care recipient (child), and the family.

[0029] The collaboration support system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects the behavioral history of each of the caregiver and the care recipient. The behavioral history includes, for example, movements, facial expressions, and speech, but is not limited to such examples. The collection unit collects detailed data such as, for example, what movements the caregiver performed, what facial expressions the care recipient had, and what words and actions were made. For example, the collection unit uses a camera and a microphone to record the movements and speech of the caregiver and the care recipient. The collection unit can also collect biological data of the caregiver and the care recipient using a sensor. For example, the collection unit collects biological data such as heart rate and skin electrodermal activity. Furthermore, the collection unit can also collect location information of the caregiver and the care recipient. For example, the collection unit uses a GPS to record the location information of the caregiver and the care recipient. The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The analysis includes, for example, what kind of care the caregiver performed and what kind of reaction the care recipient showed, but is not limited to such examples. For example, the analysis unit analyzes the collected motion data and identifies the motion pattern of the caregiver. The analysis unit can also analyze the collected facial expression data and estimate the emotional state of the care recipient. For example, the analysis unit uses facial expression recognition technology to estimate emotions from the facial expressions of the care recipient. Furthermore, the analysis unit can analyze the collected speech data and identify the communication patterns of the caregiver and the care recipient. For example, the analysis unit uses natural language processing technology to analyze the content of remarks and evaluate the quality of communication. The notification unit notifies the family of the care recipient of the analysis result by the analysis unit. The notification is performed, for example, only when the emotional value exceeds a threshold value or when the emotion is negative, but is not limited to such examples. For example, the notification unit notifies the family when the emotional value exceeds a certain threshold value. The notification unit can also notify only when a negative emotion is detected. For example, the notification unit notifies the family when the care recipient shows negative emotions such as anxiety or anger. As a result, the collaboration support system according to the embodiment can collect behavioral histories of the caregiver and the care recipient, and notify the family of the analysis results, thereby making it possible to grasp the status of care.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 perform notification using an AI model that receives the analysis result generated by the analysis unit as input and outputs the notification content.

[0030] The collecting unit collects the behavioral history of each of the caregiver and the care recipient. The behavioral history includes, for example, movements, facial expressions, and speech, but is not limited to such examples. The collecting unit collects detailed data, such as what movements the caregiver performed, what facial expressions the care recipient had, and what speech was performed. Specifically, the collecting unit uses a camera and a microphone to record the movements and speech of the caregiver and the care recipient. The camera is installed, for example, on the ceiling or wall of the room, and has a wide field of view, so that the movements of the caregiver and the care recipient can be captured in detail. The microphones are placed in multiple locations in the room and clearly capture the speech content. Furthermore, the collecting unit can also collect biological data of the caregiver and the care recipient using a sensor. For example, the collecting unit collects biological data such as heart rate and electrodermal activity. These sensors are attached to the bodies of the caregiver and the care recipient and collect data in real time. The heart rate sensor detects fluctuations in heart rate and is useful for understanding stress and excitement. The electrodermal activity sensor can measure changes in the electrical resistance of the skin and capture changes in emotions. Furthermore, the collection unit can also collect location information of the caregiver and the care recipient. For example, the collection unit records location information of the caregiver and the care recipient using a GPS. The GPS device is attached to the belongings and clothes of the caregiver and the care recipient, and records the movement routes and places of stay in detail. This allows the collection unit to collect the behavioral history of the caregiver and the care recipient from various angles and provide detailed data.

[0031] The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The analysis includes, for example, what kind of care the caregiver provided and what kind of reaction the care recipient showed, but is not limited to such examples. Specifically, the analysis unit analyzes the collected motion data and identifies the motion pattern of the caregiver. For example, the analysis unit analyzes the motion of the caregiver when supporting the care recipient and the procedure of daily care to identify an efficient care method. The analysis unit can also analyze the collected facial expression data and estimate the emotional state of the care recipient. For example, the analysis unit uses facial expression recognition technology to estimate emotions from the facial expression of the care recipient. The facial expression recognition technology analyzes the characteristic points of the face and captures subtle changes in facial expressions to estimate emotions such as joy, sadness, and anger with high accuracy. Furthermore, the analysis unit can analyze the collected speech and behavior data and identify the communication patterns of the caregiver and the care recipient. For example, the analysis unit uses natural language processing technology to analyze the content of remarks and evaluate the quality of communication. Natural language processing technology analyzes the content, tone, and frequency of statements to identify the smoothness and problems of communication between the caregiver and the care recipient. This allows the analysis unit to find specific improvements to improve the quality of care. In addition, the analysis unit can analyze the collected biometric data to evaluate the health status of the caregiver and the care recipient. For example, data on heart rate and electrodermal activity can be analyzed to detect early signs of stress and fatigue. This allows the analysis unit to support the health management of the caregiver and the care recipient and improve the quality of care.

[0032] The notification unit notifies the family of the care recipient of the analysis result by the analysis unit. The notification is performed, for example, only when the emotion value exceeds a threshold value or when the emotion is negative, but is not limited to such examples. Specifically, the notification unit notifies the family when the emotion value exceeds a certain threshold value. For example, when the care recipient is in a very high stress state, an alert is sent to the family to prompt a prompt response. The notification unit can also notify only when a negative emotion is detected. For example, when the care recipient shows negative emotions such as anxiety or anger, the family is notified. The notification is performed through a smartphone app, email, SMS, etc., so that the family can quickly grasp the situation. Furthermore, the notification unit can customize the notification content. For example, the frequency and content of notifications can be adjusted according to the wishes of the family. This allows the family to receive the necessary information at an appropriate time. 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 perform notification using an AI model that inputs the analysis result generated by the analysis unit and outputs the notification content. Specifically, the AI ​​model selects information that is important to the family based on the analysis results and generates appropriate notification content. This allows the notification unit to quickly provide appropriate information to the family and help them understand the status of care. Furthermore, the notification unit can collect feedback from the family and continuously improve the accuracy and effectiveness of the notification content. For example, by providing feedback on the notification received by the family, the notification unit can learn and improve more effective notification methods. This allows the notification unit to quickly provide appropriate information to the family and improve the quality of care.

[0033] The analysis unit includes an estimation unit that estimates the emotional value of the family member with respect to the care provided by the caregiver to the care recipient. The emotional value is, for example, a value indicating how the family member feels about the care situation, but is not limited to such an example. For example, the estimation unit analyzes the facial expressions and remarks of the family member to estimate the emotional value. The estimation unit can also analyze biological data of the family member to estimate the emotional value. For example, the estimation unit analyzes biological data such as heart rate and electrodermal activity to estimate the emotional value. Furthermore, the estimation unit can analyze social media activities of the family member to estimate the emotional value. For example, the estimation unit analyzes social media posts of the family member to estimate the emotional value. As a result, the analysis unit estimates the emotional value of the family member with respect to the care, thereby making it possible to grasp the care situation taking into account the emotions of the family member. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input facial expression data of the family member to the generation AI and cause the generation AI to execute the estimation of the emotional value.

[0034] The estimation unit includes a determination unit that determines whether or not the emotion value estimated by the estimation unit exceeds a threshold value. The threshold value is, for example, a reference value for notifying when the emotion value exceeds a certain range, but is not limited to such an example. For example, the determination unit notifies when the emotion value exceeds the threshold value. The determination unit can also notify only when the emotion value indicates a negative emotion. For example, the determination unit notifies when the emotion value indicates a negative emotion such as anxiety or anger. In this way, the determination unit can notify important information without missing it by determining whether or not the emotion value exceeds the threshold value. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the emotion value estimated by the estimation unit to the generation AI and cause the generation AI to execute the threshold value judgment.

[0035] The notification unit notifies the family when the determination unit determines that the emotion value exceeds the threshold. The notification is performed, for example, when the emotion value exceeds a certain threshold, but is not limited to such an example. For example, the notification unit notifies the family when the emotion value exceeds the threshold. The notification unit can also notify only when a negative emotion is detected. For example, the notification unit notifies the family when the care recipient shows a negative emotion such as anxiety or anger. In this way, the notification unit can efficiently convey important information by notifying only when the emotion value exceeds the threshold. 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 emotion value determined by the determination unit to the generation AI and cause the generation AI to generate the notification content.

[0036] The collection unit collects the history of the actions, facial expressions, and words of the caregiver and the care recipient as a behavior history. The behavior history includes, for example, actions, facial expressions, and words, but is not limited to such examples. For example, the collection unit uses a camera and a microphone to record the actions and remarks of the caregiver and the care recipient. The collection unit can also collect biological data of the caregiver and the care recipient using a sensor. For example, the collection unit collects biological data such as heart rate and skin electrodermal activity. Furthermore, the collection unit can also collect location information of the caregiver and the care recipient. For example, the collection unit uses a GPS to record location information of the caregiver and the care recipient. In this way, the collection unit can grasp a detailed behavior history by collecting the history of actions, facial expressions, and words. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera to the generation AI and cause the generation AI to perform an analysis of the actions and facial expressions.

[0037] The determination unit uses a threshold according to the attributes of the family members to determine whether or not the emotion value exceeds the threshold. The threshold is set based on attributes such as the age, sex, and occupation of the family members, but is not limited to such examples. For example, the determination unit sets a threshold according to the age of the family members and determines whether or not the emotion value exceeds the threshold. The determination unit can also set a threshold according to the sex of the family members and determine whether or not the emotion value exceeds the threshold. For example, the determination unit sets a threshold according to the occupation of the family members and determines whether or not the emotion value exceeds the threshold. Thereby, the determination unit can make a more appropriate determination by using a threshold according to the attributes of the family members. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input attribute data of the family members to the generation AI and cause the generation AI to set the threshold.

[0038] The emotion value is a value indicating the degree of a negative emotion. Negative emotions include, but are not limited to, anger, anxiety, sadness, and the like. For example, the emotion value is high when the care recipient feels anger. The emotion value can also be high when the care recipient feels anxiety. For example, the emotion value is high when the care recipient feels sadness. As a result, the emotion value is a value indicating the degree of a negative emotion, so that important information can be notified without being overlooked. A part or all of the above-mentioned processing in the emotion value may be performed, for example, using AI, or may be performed without using AI. For example, the emotion value can be generated by inputting the emotion data of the care recipient into the generation AI and causing the generation AI to execute the degree of the negative emotion.

[0039] The collection unit can analyze the past behavioral history of the caregiver and the care recipient and select the optimal collection method. The optimal collection method is selected based on, for example, the collection frequency, the collection means, etc., but is not limited to such examples. For example, the collection unit analyzes the tendency of important events to occur in a specific time period from the past behavioral history, and collects data in a focused manner during that time period. In addition, when a specific behavioral pattern is observed based on the past behavioral history, the collection unit can select a collection method that matches the pattern. For example, the collection unit analyzes the past behavioral history and adds a specific sensor or device to improve the accuracy of the collected data. This allows the collection unit to select the optimal collection method by analyzing the past behavioral history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past behavioral history data to the generation AI and cause the generation AI to select the optimal collection method.

[0040] When collecting the behavioral history, the collection unit can perform filtering based on the current living conditions and areas of interest of the caregiver and the care recipient. The living conditions and areas of interest include, but are not limited to, daily activities, health conditions, hobbies, and topics of interest. For example, when the caregiver is trying a new care method, the collection unit preferentially collects behavioral history related to that method. In addition, when the care recipient is interested in a specific activity, the collection unit can also collect behavioral history related to that activity. For example, when the living conditions of the caregiver and the care recipient change, the collection unit collects behavioral history corresponding to the change. In this way, the collection unit can collect more relevant behavioral history by filtering based on the living conditions and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the living conditions data of the caregiver and the care recipient to the generation AI and cause the generation AI to perform filtering.

[0041] When collecting the behavioral history, the collection unit can preferentially collect highly relevant histories in consideration of the geographical location information of the caregiver and the care recipient. The geographical location information includes, but is not limited to, GPS data, address information, and the like. For example, when the caregiver is providing care at a specific location, the collection unit preferentially collects behavioral histories related to that location. In addition, when the care recipient is active at a specific location, the collection unit can also preferentially collect behavioral histories related to that location. For example, the collection unit preferentially collects behavioral histories at a location where the caregiver and the care recipient are together. In this way, the collection unit can preferentially collect highly relevant behavioral histories by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant histories.

[0042] The collection unit can analyze the social media activities of the caregiver and the care recipient when collecting the behavioral history, and collect related histories. Social media activities include, for example, the content of posts, the number of likes, and the like, but are not limited to such examples. For example, the collection unit collects related behavioral histories based on information shared by the caregiver on social media. The collection unit can also collect related behavioral histories based on the field of interest shown by the care recipient on social media. For example, the collection unit analyzes the social media activities of the caregiver and the care recipient, and collects behavioral histories related to the common field of interest. In this way, the collection unit can collect related behavioral histories by analyzing the social media activities. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data to the generation AI and cause the generation AI to select related histories.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of care during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of care during the analysis. The importance of care includes, for example, the frequency of care and the quality of care, but is not limited to such examples. For example, when an important care event occurs, the analysis unit performs a detailed analysis of the event. In addition, in the case of daily care activities, the analysis unit can also perform a simplified analysis. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of care. Thereby, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of care. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of care data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of care during analysis. The categories of care include, for example, physical care, life assistance, etc., but are not limited to such examples. For example, in the case of physical care, the analysis unit applies a physical movement analysis algorithm. In addition, in the case of mental care, the analysis unit can also apply an emotion analysis algorithm. For example, in the case of social care, the analysis unit applies a communication analysis algorithm. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of care. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input care category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the time of performing care during the analysis. The time of performing care includes, for example, the start time of care and the frequency of care, but is not limited to such examples. For example, the analysis unit performs the analysis immediately after care is performed and creates a report with priority. In addition, in the case of regular care activities, the analysis unit can perform the analysis with normal priority. For example, the analysis unit adjusts the priority of the analysis step by step according to the time of performing care. Thereby, the analysis unit can prioritize analysis of important information by determining the priority of the analysis based on the time of performing care. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of performing care to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of care during analysis. The relevance of care includes, for example, the type of care, the recipient of care, and the like, but is not limited to such examples. For example, when the relationship between the caregiver and the care recipient is strong, the analysis unit prioritizes the order of analysis based on the relationship. In addition, when the relevance of care is low, the analysis unit can also perform analysis in a normal order. For example, the analysis unit gradually adjusts the order of analysis according to the relevance of care. As a result, the analysis unit can prioritize analysis of important information by adjusting the order of analysis based on the relevance of care. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of care to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of care at the time of notification. The importance of care includes, for example, the frequency of care and the quality of care, but is not limited to such examples. For example, when an important care event occurs, the notification unit provides a detailed notification regarding the event. In addition, in the case of daily care activities, the notification unit can also provide a simplified notification. For example, the notification unit gradually adjusts the level of detail of the notification according to the importance of care. As a result, the notification unit can notify important information in detail by adjusting the level of detail of the notification based on the importance of care. 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 importance of care data to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0048] The notification unit can apply different notification algorithms depending on the attributes of the family members at the time of notification. The attributes of the family members include, but are not limited to, for example, age, sex, and occupation. For example, when the family members are elderly, the notification unit applies a notification method with high visibility. In addition, when the family members are young, the notification unit can also apply a notification method optimized for smartphones. For example, the notification unit gradually adjusts the notification algorithm depending on the attributes of the family members. As a result, the notification unit can apply a notification algorithm according to the attributes of the family members to enable more appropriate 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 attribute data of the family members to the generation AI and cause the generation AI to apply the notification algorithm.

[0049] The notification unit can determine the priority of the notification based on the time of performing care at the time of notification. The time of performing care includes, for example, the start time of care and the frequency of care, but is not limited to such examples. For example, the notification unit performs notification immediately after care is performed, and provides information preferentially. In addition, in the case of regular care activities, the notification unit can also perform notification with normal priority. For example, the notification unit gradually adjusts the priority of the notification according to the time of performing care. Thereby, the notification unit can prioritize notifying important information by determining the priority of the notification based on the time of performing care. 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 data on the time of performing care to the generation AI and cause the generation AI to execute the determination of the priority of the notification.

[0050] The notification unit can adjust the order of notifications based on the relevance of care at the time of notification. The relevance of care includes, for example, the type of care, the recipient of care, and the like, but is not limited to such examples. For example, when the relationship between the caregiver and the care recipient is strong, the notification unit prioritizes the order of notifications based on the relationship. In addition, when the relevance of care is low, the notification unit can also perform notifications in a normal order. For example, the notification unit gradually adjusts the order of notifications according to the relevance of care. As a result, the notification unit can prioritize notifying important information by adjusting the order of notifications based on the relevance of care. 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 relevance data of care to the generation AI and cause the generation AI to adjust the order of notifications.

[0051] The estimation unit can improve the estimation accuracy of the emotion value by referring to past emotion data at the time of estimation. The past emotion data includes, for example, past emotion history, emotion change patterns, and the like, but is not limited to such examples. For example, the estimation unit estimates the current emotion value based on the past emotion data. The estimation unit can also analyze the past emotion data and improve the estimation accuracy of the emotion value. For example, the estimation unit refers to the past emotion data and identifies the change pattern of the emotion value. Thereby, the estimation unit can improve the estimation accuracy of the emotion value by referring to the past emotion data. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the past emotion data to the generation AI and cause the generation AI to improve the estimation accuracy of the emotion value.

[0052] The estimation unit can apply different estimation algorithms according to the attributes of the family members at the time of estimation. The attributes of the family members include, but are not limited to, for example, age, sex, and occupation. For example, when the family members are elderly, the estimation unit applies an emotion estimation algorithm for elderly people. Also, when the family members are young, the estimation unit can apply an emotion estimation algorithm for young people. For example, the estimation unit gradually adjusts the emotion estimation algorithm according to the attributes of the family members. As a result, the estimation unit can estimate a more appropriate emotion value by applying an estimation algorithm according to the attributes of the family members. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input attribute data of the family members to the generation AI and cause the generation AI to apply the estimation algorithm.

[0053] The estimation unit can determine the priority of the emotion values ​​based on the living conditions of the family members at the time of estimation. The living conditions include, for example, daily activities, health conditions, etc., but are not limited to such examples. For example, when the family members are busy, the estimation unit preferentially displays important emotion values. In addition, when the family members are relaxed, the estimation unit can also display detailed emotion values. For example, the estimation unit gradually adjusts the priority of the emotion values ​​according to the living conditions of the family members. In this way, the estimation unit can preferentially display important emotion values ​​by determining the priority of the emotion values ​​based on the living conditions of the family members. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the living conditions data of the family members to the generation AI and cause the generation AI to execute the determination of the priority of the emotion values.

[0054] The estimation unit can improve the estimation accuracy of the emotion value by analyzing the social media activities of the family at the time of estimation. The social media activities include, for example, the content of posts, the number of likes, and the like, but are not limited to such examples. For example, the estimation unit can analyze the social media posts of the family and improve the estimation accuracy of the emotion value. The estimation unit can also identify a pattern of emotion fluctuation from the social media activities of the family. For example, the estimation unit can refer to the social media activities of the family and improve the estimation accuracy of the emotion value. In this way, the estimation unit can improve the estimation accuracy of the emotion value by analyzing the social media activities. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input social media activity data to the generation AI and cause the generation AI to improve the estimation accuracy of the emotion value.

[0055] The judgment unit can improve the accuracy of threshold setting by referring to past judgment data at the time of judgment. The past judgment data includes, for example, past judgment history, change patterns of judgment, etc., but is not limited to such examples. For example, the judgment unit sets the current threshold based on the past judgment data. The judgment unit can also analyze the past judgment data and improve the accuracy of threshold setting. For example, the judgment unit refers to the past judgment data and identifies a change pattern of the threshold. Thereby, the judgment unit can improve the accuracy of threshold setting by referring to the past judgment data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the past judgment data to the generation AI and cause the generation AI to improve the accuracy of threshold setting.

[0056] The determination unit can apply different threshold setting algorithms according to the attributes of the family members at the time of determination. The attributes of the family members include, but are not limited to, for example, age, sex, and occupation. For example, when the family members are elderly, the determination unit applies a threshold setting algorithm for elderly people. Also, when the family members are young, the determination unit can apply a threshold setting algorithm for young people. For example, the determination unit gradually adjusts the threshold setting algorithm according to the attributes of the family members. This allows the determination unit to set a more appropriate threshold by applying a threshold setting algorithm according to the attributes of the family members. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input attribute data of the family members to the generation AI and cause the generation AI to apply the threshold setting algorithm.

[0057] The determination unit can determine the priority of the thresholds based on the living conditions of the family members at the time of determination. The living conditions include, for example, daily activities, health conditions, etc., but are not limited to such examples. For example, when the family members are busy, the determination unit preferentially displays important thresholds. In addition, when the family members are relaxed, the determination unit can also display detailed thresholds. For example, the determination unit gradually adjusts the priority of the thresholds according to the living conditions of the family members. As a result, the determination unit can preferentially display important thresholds by determining the priority of the thresholds based on the living conditions of the family members. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the living conditions data of the family members to the generation AI and cause the generation AI to execute the determination of the priority of the thresholds.

[0058] The determination unit can improve the accuracy of threshold setting by analyzing the social media activities of the family at the time of determination. The social media activities include, for example, the content of posts, the number of likes, and the like, but are not limited to such examples. For example, the determination unit analyzes the social media posts of the family and improves the accuracy of threshold setting. The determination unit can also identify a pattern of emotional fluctuations from the social media activities of the family. For example, the determination unit refers to the social media activities of the family and improves the accuracy of threshold setting. In this way, the determination unit can improve the accuracy of threshold setting by analyzing the social media activities. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input social media activity data to the generation AI and cause the generation AI to improve the accuracy of threshold setting.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting the behavioral history of the caregiver and the care recipient, the collection unit can convert the conversation content into text using voice recognition technology and transmit the text data to the analysis unit. For example, the collection unit converts the conversation between the caregiver and the care recipient into text in real time and transmits the text data to the analysis unit. In addition, when a specific keyword appears in the conversation, the collection unit can emphasize that part and transmit it to the analysis unit. For example, when a keyword with high urgency such as "ouch" or "help" is included, the collection unit emphasizes that part and transmits it. In addition, the collection unit can detect changes in tone and volume of the conversation and transmit it to the analysis unit as data for estimating changes in emotion. In this way, the collection unit can collect more detailed behavioral history by using voice recognition technology and provide it to the analysis unit.

[0061] The analysis unit can have a function of detecting abnormalities in behavioral patterns when analyzing the behavioral history of the caregiver and the care recipient. For example, the analysis unit detects actions or speech that are different from normal behavioral patterns and notifies the family of the abnormality. In addition, if an abnormality is found in the health condition of the care recipient, the analysis unit can detect the signs early and notify the family. For example, if the care recipient's movements suddenly slow down or their speech becomes unnatural, the analysis unit detects the abnormality and notifies the family. In addition, the analysis unit can analyze the stress level of the caregiver and propose appropriate measures when stress is increasing. In this way, the analysis unit can detect abnormalities in behavioral patterns early and encourage appropriate responses.

[0062] The notification unit can set the priority of notifications when notifying family members. For example, when an emotion value exceeds a threshold, the notification is given top priority. Furthermore, when multiple notifications occur simultaneously, the notification unit can adjust the order of notifications according to importance. For example, a notification regarding the health condition of the care recipient is given top priority, followed by a notification regarding the stress level of the caregiver. Furthermore, the notification unit can adjust the timing of notifications according to the family member's schedule. For example, when a family member is at work, the notification is delayed or only notified when there is a high level of urgency. In this way, the notification unit can efficiently convey important information by setting the priority of notifications.

[0063] The collection unit can collect environmental data when collecting the behavioral history of the caregiver and the care recipient. For example, the collection unit collects environmental data such as room temperature, humidity, and illuminance, and transmits the collected data to the analysis unit together with the behavioral history. The collection unit can also provide the data for analyzing the effect of changes in the environmental data on behavior. For example, the collection unit analyzes whether the care recipient tends to move slower when the room temperature is high. Furthermore, the collection unit can make suggestions for maintaining a comfortable environment based on the environmental data. For example, the collection unit makes a suggestion to adjust the air conditioner settings when the room temperature is too high. In this way, the collection unit can provide a more detailed behavioral history and provide support for maintaining a comfortable environment by collecting the environmental data.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The collection unit collects the behavioral history of the caregiver and the care recipient. The behavioral history includes movements, facial expressions, and speech. The collection unit records movements and speech using a camera and microphone, and collects biometric data such as heart rate and electrodermal activity using sensors. It can also record location information using GPS. Step 2: The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The analysis includes identifying the caregiver's movement patterns, estimating the emotional state of the care recipient, and identifying communication patterns. For example, the analysis is performed using facial expression recognition technology and natural language processing technology. Step 3: The notification unit notifies the family of the care recipient of the analysis results by the analysis unit. The notification is made when the emotion value exceeds a threshold value or when a negative emotion is detected. For example, a notification is made when the care recipient shows negative emotions such as anxiety or anger. The notification unit can also use an AI model that inputs the analysis results and outputs the notification content.

[0066] (Example 2) The collaboration support system according to the embodiment of the present invention is a system that supports collaboration between a helper (nursery school teacher, teacher), a care recipient (child), and a family. This collaboration support system collects the behavioral history of the caregiver and the care recipient, analyzes the situation regarding the care of the care recipient by the caregiver based on the collected information, and notifies the family of the care recipient of the analysis result. For example, the behavior, remarks, and emotional values ​​of the caregiver and the care recipient are analyzed and notified to the family. In particular, a mechanism is included in which notification is made only when the emotional value exceeds a threshold value or when the emotional value is negative. For example, the history of the movements, facial expressions, and words and behavior of the caregiver and the care recipient is collected. For example, detailed data such as what movements the caregiver made, what facial expressions the care recipient had, and what words and behaviors were used is collected. This data is collected by a collection unit. Next, the situation regarding the care of the care recipient by the caregiver is analyzed based on the collected data. For example, what kind of care the caregiver provided, and what kind of reaction the care recipient showed are analyzed. This analysis is performed by an analysis unit. Furthermore, the emotional values ​​of the family are estimated based on the analysis result. For example, it estimates what emotions the family members have about the care situation. This estimation is performed by the estimation unit. Next, it is determined whether the estimated emotion value exceeds a threshold value. For example, a notification is made only when the emotion value exceeds a certain threshold value. This determination is performed by the determination unit. Finally, a notification is made to the family based on the determination result. For example, a notification is made to the family when the emotion value exceeds a threshold value. This notification is performed by the notification unit. This mechanism can be used by helper dispatch companies to evaluate helpers. In addition, the family can grasp the behavior, remarks, and emotion values ​​of the helper and the care recipient, making it possible to detect whether they are doing well or any trouble. In particular, since notifications are only made when the emotion value exceeds the threshold value or when the emotion is negative, important information can be grasped without missing it. As a result, the collaboration support system can effectively support collaboration between the helper (nursery school teacher, teacher), the care recipient (child), and the family.

[0067] The collaboration support system according to the embodiment includes a collection unit, an analysis unit, and a notification unit. The collection unit collects the behavioral history of each of the caregiver and the care recipient. The behavioral history includes, for example, movements, facial expressions, and speech, but is not limited to such examples. The collection unit collects detailed data such as, for example, what movements the caregiver performed, what facial expressions the care recipient had, and what words and actions were made. For example, the collection unit uses a camera and a microphone to record the movements and speech of the caregiver and the care recipient. The collection unit can also collect biological data of the caregiver and the care recipient using a sensor. For example, the collection unit collects biological data such as heart rate and skin electrodermal activity. Furthermore, the collection unit can also collect location information of the caregiver and the care recipient. For example, the collection unit uses a GPS to record the location information of the caregiver and the care recipient. The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The analysis includes, for example, what kind of care the caregiver performed and what kind of reaction the care recipient showed, but is not limited to such examples. For example, the analysis unit analyzes the collected motion data and identifies the motion pattern of the caregiver. The analysis unit can also analyze the collected facial expression data and estimate the emotional state of the care recipient. For example, the analysis unit uses facial expression recognition technology to estimate emotions from the facial expressions of the care recipient. Furthermore, the analysis unit can analyze the collected speech data and identify the communication patterns of the caregiver and the care recipient. For example, the analysis unit uses natural language processing technology to analyze the content of remarks and evaluate the quality of communication. The notification unit notifies the family of the care recipient of the analysis result by the analysis unit. The notification is performed, for example, only when the emotional value exceeds a threshold value or when the emotion is negative, but is not limited to such examples. For example, the notification unit notifies the family when the emotional value exceeds a certain threshold value. The notification unit can also notify only when a negative emotion is detected. For example, the notification unit notifies the family when the care recipient shows negative emotions such as anxiety or anger. As a result, the collaboration support system according to the embodiment can collect behavioral histories of the caregiver and the care recipient, and notify the family of the analysis results, thereby making it possible to grasp the status of care.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 perform notification using an AI model that receives the analysis result generated by the analysis unit as input and outputs the notification content.

[0068] The collecting unit collects the behavioral history of each of the caregiver and the care recipient. The behavioral history includes, for example, movements, facial expressions, and speech, but is not limited to such examples. The collecting unit collects detailed data, such as what movements the caregiver performed, what facial expressions the care recipient had, and what speech was performed. Specifically, the collecting unit uses a camera and a microphone to record the movements and speech of the caregiver and the care recipient. The camera is installed, for example, on the ceiling or wall of the room, and has a wide field of view, so that the movements of the caregiver and the care recipient can be captured in detail. The microphones are placed in multiple locations in the room and clearly capture the speech content. Furthermore, the collecting unit can also collect biological data of the caregiver and the care recipient using a sensor. For example, the collecting unit collects biological data such as heart rate and electrodermal activity. These sensors are attached to the bodies of the caregiver and the care recipient and collect data in real time. The heart rate sensor detects fluctuations in heart rate and is useful for understanding stress and excitement. The electrodermal activity sensor can measure changes in the electrical resistance of the skin and capture changes in emotions. Furthermore, the collection unit can also collect location information of the caregiver and the care recipient. For example, the collection unit records location information of the caregiver and the care recipient using a GPS. The GPS device is attached to the belongings and clothes of the caregiver and the care recipient, and records the movement routes and places of stay in detail. This allows the collection unit to collect the behavioral history of the caregiver and the care recipient from various angles and provide detailed data.

[0069] The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The analysis includes, for example, what kind of care the caregiver provided and what kind of reaction the care recipient showed, but is not limited to such examples. Specifically, the analysis unit analyzes the collected motion data and identifies the motion pattern of the caregiver. For example, the analysis unit analyzes the motion of the caregiver when supporting the care recipient and the procedure of daily care to identify an efficient care method. The analysis unit can also analyze the collected facial expression data and estimate the emotional state of the care recipient. For example, the analysis unit uses facial expression recognition technology to estimate emotions from the facial expression of the care recipient. The facial expression recognition technology analyzes the characteristic points of the face and captures subtle changes in facial expressions to estimate emotions such as joy, sadness, and anger with high accuracy. Furthermore, the analysis unit can analyze the collected speech and behavior data and identify the communication patterns of the caregiver and the care recipient. For example, the analysis unit uses natural language processing technology to analyze the content of remarks and evaluate the quality of communication. Natural language processing technology analyzes the content, tone, and frequency of statements to identify the smoothness and problems of communication between the caregiver and the care recipient. This allows the analysis unit to find specific improvements to improve the quality of care. In addition, the analysis unit can analyze the collected biometric data to evaluate the health status of the caregiver and the care recipient. For example, data on heart rate and electrodermal activity can be analyzed to detect early signs of stress and fatigue. This allows the analysis unit to support the health management of the caregiver and the care recipient and improve the quality of care.

[0070] The notification unit notifies the family of the care recipient of the analysis result by the analysis unit. The notification is performed, for example, only when the emotion value exceeds a threshold value or when the emotion is negative, but is not limited to such examples. Specifically, the notification unit notifies the family when the emotion value exceeds a certain threshold value. For example, when the care recipient is in a very high stress state, an alert is sent to the family to prompt a prompt response. The notification unit can also notify only when a negative emotion is detected. For example, when the care recipient shows negative emotions such as anxiety or anger, the family is notified. The notification is performed through a smartphone app, email, SMS, etc., so that the family can quickly grasp the situation. Furthermore, the notification unit can customize the notification content. For example, the frequency and content of notifications can be adjusted according to the wishes of the family. This allows the family to receive the necessary information at an appropriate time. 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 perform notification using an AI model that inputs the analysis result generated by the analysis unit and outputs the notification content. Specifically, the AI ​​model selects information that is important to the family based on the analysis results and generates appropriate notification content. This allows the notification unit to quickly provide appropriate information to the family and help them understand the status of care. Furthermore, the notification unit can collect feedback from the family and continuously improve the accuracy and effectiveness of the notification content. For example, by providing feedback on the notification received by the family, the notification unit can learn and improve more effective notification methods. This allows the notification unit to quickly provide appropriate information to the family and improve the quality of care.

[0071] The analysis unit includes an estimation unit that estimates the emotional value of the family member with respect to the care provided by the caregiver to the care recipient. The emotional value is, for example, a value indicating how the family member feels about the care situation, but is not limited to such an example. For example, the estimation unit analyzes the facial expressions and remarks of the family member to estimate the emotional value. The estimation unit can also analyze biological data of the family member to estimate the emotional value. For example, the estimation unit analyzes biological data such as heart rate and electrodermal activity to estimate the emotional value. Furthermore, the estimation unit can analyze social media activities of the family member to estimate the emotional value. For example, the estimation unit analyzes social media posts of the family member to estimate the emotional value. As a result, the analysis unit estimates the emotional value of the family member with respect to the care, thereby making it possible to grasp the care situation taking into account the emotions of the family member. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input facial expression data of the family member to the generation AI and cause the generation AI to execute the estimation of the emotional value.

[0072] The estimation unit includes a determination unit that determines whether or not the emotion value estimated by the estimation unit exceeds a threshold value. The threshold value is, for example, a reference value for notifying when the emotion value exceeds a certain range, but is not limited to such an example. For example, the determination unit notifies when the emotion value exceeds the threshold value. The determination unit can also notify only when the emotion value indicates a negative emotion. For example, the determination unit notifies when the emotion value indicates a negative emotion such as anxiety or anger. In this way, the determination unit can notify important information without missing it by determining whether or not the emotion value exceeds the threshold value. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the emotion value estimated by the estimation unit to the generation AI and cause the generation AI to execute the threshold value judgment.

[0073] The notification unit notifies the family when the determination unit determines that the emotion value exceeds the threshold. The notification is performed, for example, when the emotion value exceeds a certain threshold, but is not limited to such an example. For example, the notification unit notifies the family when the emotion value exceeds the threshold. The notification unit can also notify only when a negative emotion is detected. For example, the notification unit notifies the family when the care recipient shows a negative emotion such as anxiety or anger. In this way, the notification unit can efficiently convey important information by notifying only when the emotion value exceeds the threshold. 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 emotion value determined by the determination unit to the generation AI and cause the generation AI to generate the notification content.

[0074] The collection unit collects the history of the actions, facial expressions, and words of the caregiver and the care recipient as a behavior history. The behavior history includes, for example, actions, facial expressions, and words, but is not limited to such examples. For example, the collection unit uses a camera and a microphone to record the actions and remarks of the caregiver and the care recipient. The collection unit can also collect biological data of the caregiver and the care recipient using a sensor. For example, the collection unit collects biological data such as heart rate and skin electrodermal activity. Furthermore, the collection unit can also collect location information of the caregiver and the care recipient. For example, the collection unit uses a GPS to record location information of the caregiver and the care recipient. In this way, the collection unit can grasp a detailed behavior history by collecting the history of actions, facial expressions, and words. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera to the generation AI and cause the generation AI to perform an analysis of the actions and facial expressions.

[0075] The determination unit uses a threshold according to the attributes of the family members to determine whether or not the emotion value exceeds the threshold. The threshold is set based on attributes such as the age, sex, and occupation of the family members, but is not limited to such examples. For example, the determination unit sets a threshold according to the age of the family members and determines whether or not the emotion value exceeds the threshold. The determination unit can also set a threshold according to the sex of the family members and determine whether or not the emotion value exceeds the threshold. For example, the determination unit sets a threshold according to the occupation of the family members and determines whether or not the emotion value exceeds the threshold. Thereby, the determination unit can make a more appropriate determination by using a threshold according to the attributes of the family members. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input attribute data of the family members to the generation AI and cause the generation AI to set the threshold.

[0076] The emotion value is a value indicating the degree of a negative emotion. Negative emotions include, but are not limited to, anger, anxiety, sadness, and the like. For example, the emotion value is high when the care recipient feels anger. The emotion value can also be high when the care recipient feels anxiety. For example, the emotion value is high when the care recipient feels sadness. As a result, the emotion value is a value indicating the degree of a negative emotion, so that important information can be notified without being overlooked. A part or all of the above-mentioned processing in the emotion value may be performed, for example, using AI, or may be performed without using AI. For example, the emotion value can be generated by inputting the emotion data of the care recipient into the generation AI and causing the generation AI to execute the degree of the negative emotion.

[0077] The collection unit can estimate the emotions of the caregiver and the care recipient, and adjust the collection timing of the behavior history based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the caregiver is stressed, the collection unit increases the collection frequency to collect detailed behavior history. In addition, when the care recipient is relaxed, the collection unit can also reduce the collection frequency to collect the minimum necessary behavior history. For example, when the emotions of the caregiver and the care recipient match, the collection unit collects behavior history at a normal collection frequency. As a result, the collection unit can collect more appropriate behavior history by adjusting the collection timing based on the emotions. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input emotion data of the caregiver and the care recipient to the generation AI and cause the generation AI to adjust the collection timing.

[0078] The collection unit can analyze the past behavioral history of the caregiver and the care recipient and select the optimal collection method. The optimal collection method is selected based on, for example, the collection frequency, the collection means, etc., but is not limited to such examples. For example, the collection unit analyzes the tendency of important events to occur in a specific time period from the past behavioral history, and collects data in a focused manner during that time period. In addition, when a specific behavioral pattern is observed based on the past behavioral history, the collection unit can select a collection method that matches the pattern. For example, the collection unit analyzes the past behavioral history and adds a specific sensor or device to improve the accuracy of the collected data. This allows the collection unit to select the optimal collection method by analyzing the past behavioral history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past behavioral history data to the generation AI and cause the generation AI to select the optimal collection method.

[0079] When collecting the behavioral history, the collection unit can perform filtering based on the current living conditions and areas of interest of the caregiver and the care recipient. The living conditions and areas of interest include, but are not limited to, daily activities, health conditions, hobbies, and topics of interest. For example, when the caregiver is trying a new care method, the collection unit preferentially collects behavioral history related to that method. In addition, when the care recipient is interested in a specific activity, the collection unit can also collect behavioral history related to that activity. For example, when the living conditions of the caregiver and the care recipient change, the collection unit collects behavioral history corresponding to the change. In this way, the collection unit can collect more relevant behavioral history by filtering based on the living conditions and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the living conditions data of the caregiver and the care recipient to the generation AI and cause the generation AI to perform filtering.

[0080] The collection unit can estimate the emotions of the caregiver and the care recipient, and determine the priority of the behavioral history to be collected based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the caregiver is stressed, the collection unit preferentially collects behavioral history that causes stress. In addition, when the care recipient is anxious, the collection unit can also preferentially collect behavioral history that causes anxiety. For example, when the emotions of the caregiver and the care recipient match, the collection unit preferentially collects common behavioral history. In this way, the collection unit can preferentially collect important behavioral history by determining the priority of behavioral history based on emotions. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input emotion data of the caregiver and the care recipient to the generation AI, and cause the generation AI to execute the determination of the priority of the behavioral history.

[0081] When collecting the behavioral history, the collection unit can preferentially collect highly relevant histories in consideration of the geographical location information of the caregiver and the care recipient. The geographical location information includes, but is not limited to, GPS data, address information, and the like. For example, when the caregiver is providing care at a specific location, the collection unit preferentially collects behavioral histories related to that location. In addition, when the care recipient is active at a specific location, the collection unit can also preferentially collect behavioral histories related to that location. For example, the collection unit preferentially collects behavioral histories at a location where the caregiver and the care recipient are together. In this way, the collection unit can preferentially collect highly relevant behavioral histories by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to select highly relevant histories.

[0082] The collection unit can analyze the social media activities of the caregiver and the care recipient when collecting the behavioral history, and collect related histories. Social media activities include, for example, the content of posts, the number of likes, and the like, but are not limited to such examples. For example, the collection unit collects related behavioral histories based on information shared by the caregiver on social media. The collection unit can also collect related behavioral histories based on the field of interest shown by the care recipient on social media. For example, the collection unit analyzes the social media activities of the caregiver and the care recipient, and collects behavioral histories related to the common field of interest. In this way, the collection unit can collect related behavioral histories by analyzing the social media activities. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data to the generation AI and cause the generation AI to select related histories.

[0083] The analysis unit can estimate the emotions of the caregiver and the care recipient, and adjust the expression method of the analysis based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the caregiver is stressed, the analysis unit performs a detailed analysis to identify the cause of stress. In addition, when the care recipient is relaxed, the analysis unit can also perform an analysis to identify the factor of relaxation. For example, when the emotions of the caregiver and the care recipient match, the analysis unit performs an analysis to identify a common factor. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the expression method of the analysis based on the emotions. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input emotion data of the caregiver and the care recipient to the generation AI, and cause the generation AI to adjust the expression method of the analysis.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of care during the analysis. The importance of care includes, for example, the frequency of care and the quality of care, but is not limited to such examples. For example, when an important care event occurs, the analysis unit performs a detailed analysis of the event. In addition, in the case of daily care activities, the analysis unit can also perform a simplified analysis. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of care. As a result, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of care. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of care data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of care during analysis. The categories of care include, for example, physical care, life assistance, etc., but are not limited to such examples. For example, in the case of physical care, the analysis unit applies a physical movement analysis algorithm. In addition, in the case of mental care, the analysis unit can also apply an emotion analysis algorithm. For example, in the case of social care, the analysis unit applies a communication analysis algorithm. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of care. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input care category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0086] The analysis unit can estimate the emotions of the caregiver and the care recipient, and adjust the length of the analysis based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the caregiver is stressed, the analysis unit performs a detailed analysis and creates a longer report. In addition, when the care recipient is relaxed, the analysis unit can also perform a concise analysis and create a shorter report. For example, when the emotions of the caregiver and the care recipient match, the analysis unit performs an analysis of normal length. As a result, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis based on the emotions. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input emotion data of the caregiver and the care recipient to the generation AI and cause the generation AI to adjust the length of the analysis.

[0087] The analysis unit can determine the priority of the analysis based on the time of performing care during the analysis. The time of performing care includes, for example, the start time of care and the frequency of care, but is not limited to such examples. For example, the analysis unit performs the analysis immediately after care is performed and creates a report with priority. In addition, in the case of regular care activities, the analysis unit can perform the analysis with normal priority. For example, the analysis unit adjusts the priority of the analysis step by step according to the time of performing care. Thereby, the analysis unit can prioritize analysis of important information by determining the priority of the analysis based on the time of performing care. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of performing care to the generation AI and cause the generation AI to execute the determination of the priority of the analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of care during analysis. The relevance of care includes, for example, the type of care, the recipient of care, and the like, but is not limited to such examples. For example, when the relationship between the caregiver and the care recipient is strong, the analysis unit prioritizes the order of analysis based on the relationship. In addition, when the relevance of care is low, the analysis unit can also perform analysis in a normal order. For example, the analysis unit gradually adjusts the order of analysis according to the relevance of care. As a result, the analysis unit can prioritize analysis of important information by adjusting the order of analysis based on the relevance of care. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of care to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The notification unit can estimate the emotions of the family members and adjust the notification expression method based on the estimated emotions. The emotion estimation is realized by 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. For example, when a family member feels anxious, the notification unit notifies the family member using an expression method that gives a sense of security. In addition, when a family member is relaxed, the notification unit can also notify the family member using an expression method that includes detailed information. For example, when a family member is in a hurry, the notification unit notifies the family member using a concise expression method that focuses on the main points. This allows the notification unit to adjust the notification expression method based on the family member's emotions, thereby enabling more appropriate notification. 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 emotion data of the family members to the generation AI and cause the generation AI to adjust the notification expression method.

[0090] The notification unit can adjust the level of detail of the notification based on the importance of care at the time of notification. The importance of care includes, for example, the frequency of care and the quality of care, but is not limited to such examples. For example, when an important care event occurs, the notification unit provides a detailed notification regarding the event. In addition, in the case of daily care activities, the notification unit can also provide a simplified notification. For example, the notification unit gradually adjusts the level of detail of the notification according to the importance of care. As a result, the notification unit can notify important information in detail by adjusting the level of detail of the notification based on the importance of care. 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 importance of care data to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0091] The notification unit can apply different notification algorithms depending on the attributes of the family members at the time of notification. The attributes of the family members include, but are not limited to, for example, age, sex, and occupation. For example, when the family members are elderly, the notification unit applies a notification method with high visibility. In addition, when the family members are young, the notification unit can also apply a notification method optimized for smartphones. For example, the notification unit gradually adjusts the notification algorithm depending on the attributes of the family members. As a result, the notification unit can apply a notification algorithm according to the attributes of the family members to enable more appropriate 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 attribute data of the family members to the generation AI and cause the generation AI to apply the notification algorithm.

[0092] The notification unit can estimate the emotions of the family members and adjust the length of the notification based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the family members feel anxious, the notification unit provides a detailed notification to give a sense of security. In addition, when the family members are relaxed, the notification unit can provide a concise notification and only necessary information. For example, when the family members are in a hurry, the notification unit provides a short notification that covers the main points. This allows the notification unit to adjust the length of the notification based on the emotions of the family members, thereby enabling more appropriate 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 emotion data of the family members to the generation AI and cause the generation AI to adjust the length of the notification.

[0093] The notification unit can determine the priority of the notification based on the time of performing care at the time of notification. The time of performing care includes, for example, the start time of care and the frequency of care, but is not limited to such examples. For example, the notification unit performs notification immediately after care is performed, and provides information preferentially. In addition, in the case of regular care activities, the notification unit can also perform notification with normal priority. For example, the notification unit gradually adjusts the priority of the notification according to the time of performing care. Thereby, the notification unit can prioritize notifying important information by determining the priority of the notification based on the time of performing care. 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 data on the time of performing care to the generation AI and cause the generation AI to execute the determination of the priority of the notification.

[0094] The notification unit can adjust the order of notifications based on the relevance of care at the time of notification. The relevance of care includes, for example, the type of care, the recipient of care, and the like, but is not limited to such examples. For example, when the relationship between the caregiver and the care recipient is strong, the notification unit prioritizes the order of notifications based on the relationship. In addition, when the relevance of care is low, the notification unit can also perform notifications in a normal order. For example, the notification unit gradually adjusts the order of notifications according to the relevance of care. As a result, the notification unit can prioritize notifying important information by adjusting the order of notifications based on the relevance of care. 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 relevance data of care to the generation AI and cause the generation AI to adjust the order of notifications.

[0095] The estimation unit can estimate the emotions of the family members and adjust the estimation method of the emotion value based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the family members feel anxious, the estimation unit performs a detailed emotion analysis to improve the accuracy of the emotion value. In addition, when the family members are relaxed, the estimation unit can also perform a simplified emotion analysis to estimate the emotion value. For example, when the family members are in a hurry, the estimation unit performs a quick emotion analysis to estimate the emotion value. This allows the estimation unit to adjust the estimation method of the emotion value based on the emotions of the family members, thereby enabling more appropriate estimation of the emotion value. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the emotion data of the family members to the generation AI and cause the generation AI to adjust the estimation method of the emotion value.

[0096] The estimation unit can improve the estimation accuracy of the emotion value by referring to past emotion data at the time of estimation. The past emotion data includes, for example, past emotion history, emotion change patterns, and the like, but is not limited to such examples. For example, the estimation unit estimates the current emotion value based on the past emotion data. The estimation unit can also analyze the past emotion data and improve the estimation accuracy of the emotion value. For example, the estimation unit refers to the past emotion data and identifies the change pattern of the emotion value. Thereby, the estimation unit can improve the estimation accuracy of the emotion value by referring to the past emotion data. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the past emotion data to the generation AI and cause the generation AI to improve the estimation accuracy of the emotion value.

[0097] The estimation unit can apply different estimation algorithms according to the attributes of the family members at the time of estimation. The attributes of the family members include, but are not limited to, for example, age, sex, and occupation. For example, when the family members are elderly, the estimation unit applies an emotion estimation algorithm for elderly people. Also, when the family members are young, the estimation unit can apply an emotion estimation algorithm for young people. For example, the estimation unit gradually adjusts the emotion estimation algorithm according to the attributes of the family members. As a result, the estimation unit can estimate a more appropriate emotion value by applying an estimation algorithm according to the attributes of the family members. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input attribute data of the family members to the generation AI and cause the generation AI to apply the estimation algorithm.

[0098] The estimation unit can estimate the emotions of the family members and adjust the display method of the emotion value based on the estimated emotion. The estimation of the emotion is realized by 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. For example, when the family members feel anxious, the estimation unit displays a detailed emotion value to give a sense of security. In addition, when the family members are relaxed, the estimation unit can display a concise emotion value and provide only the necessary information. For example, when the family members are in a hurry, the estimation unit displays an emotion value that covers the main points. This allows the estimation unit to adjust the display method of the emotion value based on the emotions of the family members, thereby enabling a more appropriate display of the emotion value. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the emotion data of the family members to the generation AI and cause the generation AI to adjust the display method of the emotion value.

[0099] The estimation unit can determine the priority of the emotion values ​​based on the living conditions of the family members at the time of estimation. The living conditions include, for example, daily activities, health conditions, etc., but are not limited to such examples. For example, when the family members are busy, the estimation unit preferentially displays important emotion values. In addition, when the family members are relaxed, the estimation unit can also display detailed emotion values. For example, the estimation unit gradually adjusts the priority of the emotion values ​​according to the living conditions of the family members. In this way, the estimation unit can preferentially display important emotion values ​​by determining the priority of the emotion values ​​based on the living conditions of the family members. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input the living conditions data of the family members to the generation AI and cause the generation AI to execute the determination of the priority of the emotion values.

[0100] The estimation unit can improve the estimation accuracy of the emotion value by analyzing the social media activities of the family at the time of estimation. The social media activities include, for example, the content of posts, the number of likes, and the like, but are not limited to such examples. For example, the estimation unit can analyze the social media posts of the family and improve the estimation accuracy of the emotion value. The estimation unit can also identify a pattern of emotion fluctuation from the social media activities of the family. For example, the estimation unit can refer to the social media activities of the family and improve the estimation accuracy of the emotion value. In this way, the estimation unit can improve the estimation accuracy of the emotion value by analyzing the social media activities. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI. For example, the estimation unit can input social media activity data to the generation AI and cause the generation AI to improve the estimation accuracy of the emotion value.

[0101] The determination unit can estimate the emotions of the family members and adjust the threshold setting method based on the estimated emotions. The estimation of emotions is realized by 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. For example, the determination unit sets a lower threshold and notifies early when the family members feel anxious. In addition, the determination unit can set a higher threshold and notify only when it is important when the family members are relaxed. For example, the determination unit gradually adjusts the threshold setting method according to the emotions of the family members. This allows the determination unit to set a more appropriate threshold by adjusting the threshold setting method based on the emotions of the family members. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input emotion data of the family members to the generation AI and cause the generation AI to adjust the threshold setting method.

[0102] The judgment unit can improve the accuracy of threshold setting by referring to past judgment data at the time of judgment. The past judgment data includes, for example, past judgment history, change patterns of judgment, etc., but is not limited to such examples. For example, the judgment unit sets the current threshold based on the past judgment data. The judgment unit can also analyze the past judgment data and improve the accuracy of threshold setting. For example, the judgment unit refers to the past judgment data and identifies a change pattern of the threshold. Thereby, the judgment unit can improve the accuracy of threshold setting by referring to the past judgment data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the past judgment data to the generation AI and cause the generation AI to improve the accuracy of threshold setting.

[0103] The determination unit can apply different threshold setting algorithms according to the attributes of the family members at the time of determination. The attributes of the family members include, but are not limited to, for example, age, sex, and occupation. For example, when the family members are elderly, the determination unit applies a threshold setting algorithm for elderly people. Also, when the family members are young, the determination unit can apply a threshold setting algorithm for young people. For example, the determination unit gradually adjusts the threshold setting algorithm according to the attributes of the family members. This allows the determination unit to set a more appropriate threshold by applying a threshold setting algorithm according to the attributes of the family members. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input attribute data of the family members to the generation AI and cause the generation AI to apply the threshold setting algorithm.

[0104] The determination unit can estimate the emotions of the family members and adjust the display method of the threshold value based on the estimated emotions. The estimation of emotions is realized by 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. For example, when the family members feel anxious, the determination unit displays a detailed threshold value to provide a sense of security. In addition, when the family members are relaxed, the determination unit can display a concise threshold value to provide only necessary information. For example, when the family members are in a hurry, the determination unit displays a threshold value that covers the main points. This allows the determination unit to adjust the display method of the threshold value based on the emotions of the family members, thereby enabling the display of a more appropriate threshold value. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input emotion data of the family members to the generation AI and cause the generation AI to adjust the display method of the threshold value.

[0105] The determination unit can determine the priority of the thresholds based on the living conditions of the family members at the time of determination. The living conditions include, for example, daily activities, health conditions, etc., but are not limited to such examples. For example, when the family members are busy, the determination unit preferentially displays important thresholds. In addition, when the family members are relaxed, the determination unit can also display detailed thresholds. For example, the determination unit gradually adjusts the priority of the thresholds according to the living conditions of the family members. As a result, the determination unit can preferentially display important thresholds by determining the priority of the thresholds based on the living conditions of the family members. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the living conditions data of the family members to the generation AI and cause the generation AI to execute the determination of the priority of the thresholds.

[0106] The determination unit can improve the accuracy of threshold setting by analyzing the social media activities of the family at the time of determination. The social media activities include, for example, the content of posts, the number of likes, and the like, but are not limited to such examples. For example, the determination unit analyzes the social media posts of the family and improves the accuracy of threshold setting. The determination unit can also identify a pattern of emotional fluctuations from the social media activities of the family. For example, the determination unit refers to the social media activities of the family and improves the accuracy of threshold setting. In this way, the determination unit can improve the accuracy of threshold setting by analyzing the social media activities. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input social media activity data to the generation AI and cause the generation AI to improve the accuracy of threshold setting.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When collecting the behavioral history of the caregiver and the care recipient, the collection unit can convert the conversation content into text using voice recognition technology and transmit the text data to the analysis unit. For example, the collection unit converts the conversation between the caregiver and the care recipient into text in real time and transmits the text data to the analysis unit. In addition, when a specific keyword appears in the conversation, the collection unit can emphasize that part and transmit it to the analysis unit. For example, when a keyword with high urgency such as "ouch" or "help" is included, the collection unit emphasizes that part and transmits it. In addition, the collection unit can detect changes in tone and volume of the conversation and transmit it to the analysis unit as data for estimating changes in emotion. In this way, the collection unit can collect more detailed behavioral history by using voice recognition technology and provide it to the analysis unit.

[0109] The analysis unit can have a function of detecting abnormalities in behavioral patterns when analyzing the behavioral history of the caregiver and the care recipient. For example, the analysis unit detects actions or speech that are different from normal behavioral patterns and notifies the family of the abnormality. In addition, if an abnormality is found in the health condition of the care recipient, the analysis unit can detect the signs early and notify the family. For example, if the care recipient's movements suddenly slow down or their speech becomes unnatural, the analysis unit detects the abnormality and notifies the family. In addition, the analysis unit can analyze the stress level of the caregiver and propose appropriate measures when stress is increasing. In this way, the analysis unit can detect abnormalities in behavioral patterns early and encourage appropriate responses.

[0110] The determination unit can take into account the change pattern of the emotion value when determining whether the estimated emotion value exceeds the threshold value. For example, if the emotion value changes suddenly, the change pattern is analyzed and notified to the family. In addition, if the emotion value continues to be high for a certain period of time, the determination unit can determine that the state is abnormal and notify the family. For example, if the emotional value of the care recipient continues to be high for several days, the determination unit can determine that the state is abnormal and notify the family. Furthermore, the determination unit can predict future emotion changes based on the change pattern of the emotion value and propose preventive measures. As a result, the determination unit can make a more appropriate determination by taking into account the change pattern of the emotion value.

[0111] The notification unit can set the priority of notifications when notifying family members. For example, when an emotion value exceeds a threshold, the notification is given top priority. Furthermore, when multiple notifications occur simultaneously, the notification unit can adjust the order of notifications according to importance. For example, a notification regarding the health condition of the care recipient is given top priority, followed by a notification regarding the stress level of the caregiver. Furthermore, the notification unit can adjust the timing of notifications according to the family member's schedule. For example, when a family member is at work, the notification is delayed or only notified when there is a high level of urgency. In this way, the notification unit can efficiently convey important information by setting the priority of notifications.

[0112] The collection unit can collect environmental data when collecting the behavioral history of the caregiver and the care recipient. For example, the collection unit collects environmental data such as room temperature, humidity, and illuminance, and transmits the collected data to the analysis unit together with the behavioral history. The collection unit can also provide the data for analyzing the effect of changes in the environmental data on behavior. For example, the collection unit analyzes whether the care recipient tends to move slower when the room temperature is high. Furthermore, the collection unit can make suggestions for maintaining a comfortable environment based on the environmental data. For example, the collection unit makes a suggestion to adjust the air conditioner settings when the room temperature is too high. In this way, the collection unit can provide a more detailed behavioral history and provide support for maintaining a comfortable environment by collecting the environmental data.

[0113] The determination unit can refer to past emotion data of the family when determining whether the emotion value exceeds the threshold value using a threshold value according to the attributes of the family. For example, the determination unit refers to the emotion that the family member felt in a similar situation in the past and compares it with the current emotion value. The determination unit can also analyze the emotion fluctuation pattern of the family member and adjust the threshold value based on the past data. For example, if the emotion value fluctuated rapidly in the past, the threshold value is set taking into consideration the fluctuation pattern. Furthermore, the determination unit can set a more appropriate threshold value by accumulating the emotion data of the family member over the long term and grasping the trend of emotion fluctuation. As a result, the determination unit can make a more accurate determination by referring to the past emotion data of the family member.

[0114] The collection unit can estimate the emotions of the caregiver and the care recipient, and adjust the timing of collecting the behavioral history based on the estimated emotions. For example, when the caregiver is stressed, the collection frequency is increased to collect a detailed behavioral history. In addition, when the care recipient is relaxed, the collection unit can also reduce the collection frequency to collect the minimum necessary behavioral history. For example, when the emotions of the caregiver and the care recipient match, the collection unit collects the behavioral history at the normal collection frequency. This allows the collection unit to collect a more appropriate behavioral history by adjusting the collection timing based on emotions.

[0115] The analysis unit can estimate the emotions of the caregiver and the care recipient, and adjust the way the analysis is expressed based on the estimated emotions. For example, if the caregiver is feeling stressed, the analysis unit performs a detailed analysis to identify the cause of the stress. In addition, if the care recipient is relaxed, the analysis unit can also perform an analysis to identify the factors that cause the care recipient to relax. For example, if the emotions of the caregiver and the care recipient match, the analysis unit performs an analysis to identify common factors. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is expressed based on emotions.

[0116] The notification unit can estimate the emotions of the family members and adjust the manner of expression of the notification based on the estimated emotions. For example, if the family members feel anxious, the notification unit can provide a notification using an expression that gives a sense of security. Furthermore, if the family members are relaxed, the notification unit can also provide a notification using an expression that includes detailed information. For example, if the family members are in a hurry, the notification unit can provide a notification using a concise expression that gets to the point. This allows the notification unit to adjust the manner of expression of the notification based on the emotions of the family members, thereby enabling more appropriate notification.

[0117] The estimation unit can estimate the emotions of family members and adjust the display method of the emotion value based on the estimated emotion. For example, if a family member feels anxious, a detailed emotion value is displayed to provide a sense of security. The estimation unit can also display a concise emotion value and provide only necessary information if a family member is relaxed. For example, if a family member is in a hurry, the estimation unit displays an emotion value that focuses on the main points. This allows the estimation unit to adjust the display method of the emotion value based on the emotions of the family members, thereby enabling a more appropriate display of the emotion value.

[0118] The process flow of the second embodiment will be briefly described below.

[0119] Step 1: The collection unit collects the behavioral history of the caregiver and the care recipient. The behavioral history includes movements, facial expressions, and speech. The collection unit records movements and speech using a camera and microphone, and collects biometric data such as heart rate and electrodermal activity using sensors. It can also record location information using GPS. Step 2: The analysis unit analyzes the situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit. The analysis includes identifying the caregiver's movement patterns, estimating the emotional state of the care recipient, and identifying communication patterns. For example, the analysis is performed using facial expression recognition technology and natural language processing technology. Step 3: The notification unit notifies the family of the care recipient of the analysis results by the analysis unit. The notification is made when the emotion value exceeds a threshold value or when a negative emotion is detected. For example, a notification is made when the care recipient shows negative emotions such as anxiety or anger. The notification unit can also use an AI model that inputs the analysis results and outputs the notification content.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] For example, the collection unit is realized by either the data processing device 12 or the smart device 14. For example, the collection unit records the actions and remarks of the caregiver and the care recipient using the camera 42 and the microphone 38B of the smart device 14. For example, the collection unit collects biological data of the caregiver and the care recipient by the specific processing unit 290 of the data processing device 12. For example, the collection unit records the location information of the caregiver and the care recipient using the GPS of the smart device 14. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected action data and identifies the action pattern of the caregiver. For example, the analysis unit analyzes the collected facial expression data and estimates the emotional state of the care recipient. The notification unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the notification unit notifies the family when the emotional value exceeds a threshold value. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] For example, the collection unit is realized by either the data processing device 12 or the smart glasses 214. For example, the collection unit records the actions and remarks of the caregiver and the care recipient using the camera 42 and the microphone 238 of the smart glasses 214. For example, the collection unit collects biological data of the caregiver and the care recipient by the specific processing unit 290 of the data processing device 12. For example, the collection unit records the location information of the caregiver and the care recipient using the GPS of the smart glasses 214. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected action data and identifies the action pattern of the caregiver. For example, the analysis unit analyzes the collected facial expression data and estimates the emotional state of the care recipient. The notification unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the notification unit notifies the family when the emotional value exceeds a threshold value. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] For example, the collecting unit is realized by either the data processing device 12 or the headset type terminal 314. For example, the collecting unit records the actions and remarks of the caregiver and the care recipient using the camera 42 and the microphone 238 of the headset type terminal 314. For example, the collecting unit collects the biological data of the caregiver and the care recipient by the specific processing unit 290 of the data processing device 12. For example, the collecting unit records the position information of the caregiver and the care recipient using the GPS of the headset type terminal 314. The analyzing unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the analyzing unit analyzes the collected action data and identifies the action pattern of the caregiver. For example, the analyzing unit analyzes the collected facial expression data and estimates the emotional state of the care recipient. The notifying unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the notifying unit notifies the family when the emotional value exceeds a threshold value. The correspondence relationship between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] For example, the collection unit is realized by either the data processing device 12 or the robot 414. For example, the collection unit records the actions and remarks of the caregiver and the care recipient using the camera 42 and the microphone 238 of the robot 414. For example, the collection unit collects biological data of the caregiver and the care recipient by the specific processing unit 290 of the data processing device 12. For example, the collection unit records the location information of the caregiver and the care recipient using the GPS of the robot 414. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected action data and identifies the action pattern of the caregiver. For example, the analysis unit analyzes the collected facial expression data and estimates the emotional state of the care recipient. The notification unit is realized by, for example, the specific processing unit 290 of the data processing device 12. For example, the notification unit notifies the family when the emotional value exceeds a threshold value. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Appendix 1) A collection unit that collects behavioral histories of the caregiver and the care recipient; an analysis unit that analyzes a situation regarding care given by a caregiver to a care recipient based on the information collected by the collection unit; A notification unit that notifies a family member of the care recipient of the analysis result by the analysis unit. A system characterized by: (Appendix 2) The analysis part is The device includes an estimation unit that estimates the emotional value of the family member with respect to the care provided by the caregiver to the care recipient. 2. The system of claim 1, (Appendix 3) A determination unit is provided to determine whether or not the emotion value estimated by the estimation unit exceeds a threshold value. 3. The system of claim 2, (Appendix 4) The notification section: When the determination unit determines that the emotion value exceeds a threshold, a notification is given to the family. 4. The system of claim 3. (Appendix 5) The collection department: Collect the movements, expressions, and behavior of the caregiver and the care recipient as behavioral history. 2. The system of claim 1, (Appendix 6) The determination unit is Using a threshold according to the family's attributes, it is determined whether the emotion value exceeds the threshold. 4. The system of claim 3. (Appendix 7) The emotional value is It is a value that indicates the degree of negative emotion. 3. The system of claim 2, (Appendix 8) The collection department: To estimate the emotions of a caregiver and a care recipient, and adjust the timing of collecting behavioral history based on the estimated emotions. 2. The system of claim 1, (Appendix 9) The collection department: Analyze the past behavioral history of caregivers and care recipients and select the most appropriate collection method 2. The system of claim 1, (Appendix 10) The collection department: When collecting behavioral history, filtering based on the current living situation and areas of interest of the caregiver and care recipient 2. The system of claim 1, (Appendix 11) The collection department: To estimate the emotions of a caregiver and a care recipient, and determine the priority of behavioral histories to be collected based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The collection department: When collecting behavioral histories, the geographic location information of the caregiver and the care recipient is taken into consideration to prioritize collection of relevant histories. 2. The system of claim 1, (Appendix 13) The collection department: When collecting behavioral histories, analyze the social media activities of caregivers and care recipients and collect related histories. 2. The system of claim 1, (Appendix 14) The analysis part is To estimate the emotions of a caregiver and a care recipient, and to adjust the expression method of the analysis based on the estimated emotions. 2. The system of claim 1, (Appendix 15) The analysis part is During analysis, adjust the level of detail of the analysis based on the importance of care. 2. The system of claim 1, (Appendix 16) The analysis part is During analysis, different analysis algorithms should be applied depending on the category of care. 2. The system of claim 1, (Appendix 17) The analysis part is To estimate the emotions of a caregiver and a care recipient, and to adjust the length of analysis based on the estimated emotions. 2. The system of claim 1, (Appendix 18) The analysis part is During analysis, prioritize analysis based on when care was provided. 2. The system of claim 1, (Appendix 19) The analysis part is During analysis, adjust the order of analysis based on relevance to care. 2. The system of claim 1, (Appendix 20) The notification section: Inferring the emotions of family members and adjusting the way notifications are expressed based on the inferred emotions 2. The system of claim 1, (Appendix 21) The notification section: When notifying, adjust the level of detail of the notification based on the importance of the care. 2. The system of claim 1, (Appendix 22) The notification section: Apply different notification algorithms depending on family attributes at the time of notification 2. The system of claim 1, (Appendix 23) The notification section: Inferring the emotions of family members and adjusting the length of notifications based on the inferred emotions 2. The system of claim 1, (Appendix 24) The notification section: At the time of notification, prioritize notifications based on when care was provided. 2. The system of claim 1, (Appendix 25) The notification section: When notifying, adjust the order of notifications based on the relevance of care. 2. The system of claim 1, (Appendix 26) The estimation unit is To estimate emotions of family members and adjust an emotion value estimation method based on the estimated emotions. 3. The system of claim 2, (Appendix 27) The estimation unit is When making an estimation, the accuracy of the emotion value estimation is improved by referring to past emotion data. 3. The system of claim 2, (Appendix 28) The estimation unit is Apply different estimation algorithms depending on family attributes during estimation. 3. The system of claim 2, (Appendix 29) The estimation unit is To estimate the emotions of family members and adjust the display method of the emotion value based on the estimated emotions. 3. The system of claim 2, (Appendix 30) The estimation unit is During estimation, priority is given to emotion values ​​based on the living conditions of the family. 3. The system of claim 2, (Appendix 31) The estimation unit is During estimation, the accuracy of emotion value estimation is improved by analyzing the social media activities of family members. 3. The system of claim 2, (Appendix 32) The determination unit is To estimate the emotions of family members and adjust the threshold setting method based on the estimated emotions. 4. The system of claim 3, (Appendix 33) The determination unit is When making a judgment, the accuracy of threshold setting is improved by referring to past judgment data. 4. The system of claim 3, (Appendix 34) The determination unit is Apply different threshold setting algorithms depending on family attributes when making judgments 4. The system of claim 3, (Appendix 35) The determination unit is Estimating the emotions of family members and adjusting the threshold display method based on the estimated emotions 4. The system of claim 3, (Appendix 36) The determination unit is When making the decision, prioritize thresholds based on the family's living situation. 4. The system of claim 3, (Appendix 37) The determination unit is When making a judgment, analyze the social media activity of family members to improve the accuracy of threshold setting. 4. The system of claim 3, [Explanation of symbols]

[0192] 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 collection unit that collects behavioral histories of the caregiver and the care recipient; an analysis unit that analyzes a situation regarding the care provided by the caregiver to the care recipient based on the information collected by the collection unit; A notification unit that notifies a family member of the care recipient of an analysis result by the analysis unit. A system characterized by:

2. The analysis unit is an estimation unit that estimates an emotional value of a family member regarding the care provided by the caregiver to the care recipient; 2. The system of claim 1 .

3. a determination unit that determines whether the emotion value estimated by the estimation unit exceeds a threshold value; 3. The system of claim 2.

4. The notification unit is When the determination unit determines that the emotion value exceeds a threshold, a notification is given to a family member.

4. The system of claim 3.

5. The collecting unit includes: Collecting the movements, expressions, and behaviors of the caregiver and the care recipient as behavioral history.

2. The system of claim 1 .

6. The determination unit is Using a threshold according to the family's attributes, it is determined whether the emotion value exceeds the threshold.

4. The system of claim 3.

7. The emotional value is It is a value that indicates the degree of negative emotion. The system of claim 2 .

8. The collecting unit includes: Estimating emotions of the caregiver and the care recipient, and adjusting the timing of collecting behavioral histories based on the estimated emotions. The system of claim 1 .

9. The collecting unit includes: Analyzing the past behavioral history of the caregiver and the care recipient and selecting the optimal collection method The system of claim 1 .

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