System

Abstract

To provide a system that efficiently evaluates and manages the health condition of an elderly person.SOLUTION: A system according to the embodiment comprises a collection unit, an evaluation unit, and a management unit. The collection unit collects voices and images of an elderly person. The evaluation unit evaluates a user's health condition from the elderly person's tone of voice or appearance based on the information collected by the collection unit. The management unit collects and manages the evaluation result from the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 techniques are inadequate for efficiently assessing and managing the health status of elderly people, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently evaluate and manage the health status of elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an evaluation unit, and a management unit. The collection unit collects voices and images of the elderly. The evaluation unit evaluates the health condition of the user from the tone of voice or appearance of the elderly based on the information collected by the collection unit. The management unit collects and manages the evaluation results by the evaluation unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently assess and manage the health status of elderly people. [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 health management system according to the embodiment of the present invention is a health management system that can be used by all elderly people. This health management system is a system that collects voices and images of elderly people, and evaluates and manages their health conditions from the tone of their voices and appearances. For example, the health management system includes a collection unit that collects voices and images of elderly people, and the collection unit collects information for evaluating the health conditions of elderly people from the tone of their voices and appearances. Next, the evaluation unit evaluates the health conditions based on the collected information, and the management unit collects and manages the results. The management unit includes a matching unit that matches elderly people with the same hobbies, and also includes a dialogue support unit that supports dialogue between elderly people. In addition, there is a calling unit that calls out to users, and the collection unit collects voices and images that show the user's reaction to the call. As a result, the AI ​​records the health conditions of elderly people one by one, and not only physical records but also trends among elderly people can be shared between apps. This aims to eliminate feelings of loneliness and keep people mentally healthy. For example, if the AI ​​analyzes the tone of the elderly person's voice and determines that they are not in good spirits, the calling unit calls out to them, saying, "How are you doing today?" When the elderly responds, their voice and image are collected and the evaluation unit evaluates their health condition again. The evaluation results are sent to the management unit, and the dialogue support unit supports dialogue between the elderly as necessary. This allows the elderly to maintain their health without feeling lonely. This allows the health management system to evaluate and manage the health condition of the elderly from their voice and image.

[0029] The health management system according to the embodiment includes a collection unit, an evaluation unit, and a management unit. The collection unit collects voices and images of the elderly. The voices and images of the elderly include, for example, daily conversations, specific actions, and facial expressions, but are not limited to such examples. The collection unit collects, for example, the tone of the elderly's voice using voice recognition technology, and collects the elderly's appearance using image recognition technology. The collection unit can also collect voices and images using AI. For example, the collection unit analyzes the elderly's tone of voice using voice recognition technology and evaluates the pitch, strength, and expression of emotions. Furthermore, the collection unit analyzes the elderly's appearance using image recognition technology and evaluates the complexion, facial expression, posture, and the like. The evaluation unit evaluates the health condition from the elderly's tone of voice or appearance based on the information collected by the collection unit. The evaluation unit analyzes the voice and image using, for example, AI, and evaluates the health condition. For example, the evaluation unit analyzes the elderly's tone of voice using voice recognition technology and evaluates the health condition. Furthermore, the evaluation unit analyzes the elderly's appearance using image recognition technology and evaluates the health condition. The management unit collects and manages the evaluation results by the evaluation unit. The management unit can also manage the evaluation results using, for example, AI. For example, the management unit stores the evaluation results in a database and refers to the evaluation results as necessary. In this way, the health management system according to the embodiment can evaluate and manage the health state of elderly people from voice and images.

[0030] The collection unit collects the voice and image of the elderly person. The voice and image of the elderly person include, for example, daily conversation, specific actions and facial expressions, but are not limited to such examples. For example, the collection unit collects the tone of the elderly person's voice using voice recognition technology, and collects the appearance of the elderly person using image recognition technology. Specifically, the collection unit uses a high-sensitivity microphone and a high-resolution camera to collect voices and images that occur naturally in daily life. The tone of the elderly person's voice, pitch, strength, emotional expression, etc. when speaking are recorded in detail, and these data are analyzed in real time. In addition, the camera captures the elderly person's facial color, facial expression, posture, movements, etc., and collects this information as image data. The collection unit can also collect voice and images using AI. For example, the collection unit uses voice recognition technology to analyze the tone of the elderly person's voice and evaluate the pitch, strength, emotional expression, etc. Furthermore, the collection unit uses image recognition technology to analyze the appearance of the elderly person and evaluate the facial color, facial expression, posture, etc. This allows the collection unit to efficiently collect various data on the elderly person's health condition and analyze it in real time. The collected data is sent to a cloud server and stored in a central database. This enables the collection unit to monitor the health condition of elderly people even from remote locations. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, data collection can be strengthened during specific time periods or when specific actions are performed. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The evaluation unit evaluates the health condition of the elderly person from the tone of voice or appearance based on the information collected by the collection unit. The evaluation unit, for example, uses AI to analyze voice and images to evaluate the health condition. Specifically, the evaluation unit uses voice recognition technology to analyze the tone of the elderly person's voice and evaluate the health condition. For example, if the tone of voice is lower than normal or the strength of the voice is weakened, these are evaluated as signs of fatigue or stress. In addition, if there is a trembling voice or an unnatural pause, these may indicate nervous system problems or emotional anxiety. Furthermore, the evaluation unit uses image recognition technology to analyze the appearance of the elderly person and evaluate the health condition. For example, if the face is paler than normal or the facial expression is stiff, these are evaluated as signs of poor health or mental stress. Changes in posture and movement are also important evaluation points, and for example, if walking is unstable or movements are slow, these may indicate muscle weakness or joint problems. The evaluation unit can comprehensively evaluate these analysis results and grasp the health condition of the elderly person in real time. In addition, the evaluation unit can use past data and statistical information to analyze long-term changes and trends in health status. For example, based on past voice data and image data, the evaluation unit can evaluate fluctuations in health status over a specific period of time and predict future risks. The evaluation unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and respond quickly. This allows the evaluation unit to not only grasp real-time health status, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The management department collects and manages the evaluation results by the evaluation department. The management department can also manage the evaluation results using AI, for example. Specifically, the management department stores the evaluation results in a database and refers to the evaluation results as necessary. The evaluation results are organized for each individual elderly person, and by comparing them with past data, changes and trends in health conditions can be grasped. The management department can create an appropriate health management plan for each elderly person based on the evaluation results. For example, if a specific elderly person shows signs of fatigue or stress, the management department recommends rest and relaxation for that elderly person. In addition, the management department can share the evaluation results with medical professionals and care staff and take necessary measures promptly. For example, if an abnormal evaluation result is detected, the management department immediately notifies a medical professional and makes arrangements for the elderly person to receive appropriate diagnosis and treatment. In addition, the management department can create statistical information and reports based on the evaluation results and evaluate the performance and effectiveness of the entire system. This allows the management department to always understand the operation status of the system and take improvement measures as necessary. The management department also takes into consideration data security and privacy protection, and takes appropriate security measures in storing and sharing the evaluation results. For example, unauthorized access and data leakage are prevented by strictly controlling access rights to the database and encrypting the evaluation results. This enables the management department to efficiently and safely manage the evaluation results and continuously monitor the health status of the elderly.

[0033] The health management system includes a calling unit that calls the user. The calling unit calls the user. Specific contents and methods of the call include, but are not limited to, voice messages, text messages, and the like. The calling unit can also generate the contents of the call using, for example, AI. For example, the calling unit can analyze the tone of the user's voice using voice recognition technology to generate appropriate call contents. The calling unit can also generate a text message for the user using a text generation AI. This allows the health condition to be confirmed by calling the user. A part or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can make a call using an AI model that analyzes the tone of the user's voice and generates appropriate call contents.

[0034] The collection unit can collect voices and images showing the user's reaction in response to the call by the calling unit. The collection unit, for example, collects voices and images showing the user's reaction in response to the call by the calling unit. Specific types of reactions and evaluation methods include, for example, verbal responses, changes in facial expressions, and actions, but are not limited to such examples. The collection unit can also analyze the user's reaction by using AI, for example. For example, the collection unit can analyze the user's verbal responses using voice recognition technology and collect voice data. The collection unit can also analyze changes in the user's facial expressions using image recognition technology and collect image data. This makes it possible to evaluate the health condition in more detail by collecting the user's reaction in response to the call. 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 collect data using an AI model that collects voices and images showing the user's reaction in response to the call by the calling unit.

[0035] The management unit may include a matching unit that matches elderly people who have the same hobby. The management unit may include, for example, a matching unit that matches elderly people who have the same hobby. Specific criteria and methods for matching include, for example, hobbies, areas of interest, personality, etc., but are not limited to such examples. The management unit may also match elderly people using, for example, AI. For example, the management unit may perform matching using an AI model that matches elderly people based on hobbies and areas of interest. The management unit may also match elderly people based on personality diagnosis results. As a result, by matching elderly people who have the same hobby, it is possible to reduce a sense of loneliness. A part or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit may perform matching using an AI model that matches elderly people based on hobbies and areas of interest.

[0036] The management unit may include a dialogue support unit that supports dialogue between elderly people. The management unit may include, for example, a dialogue support unit that supports dialogue between elderly people. Specific methods and contents of dialogue support include, for example, conversation topics, question contents, and the like, but are not limited to such examples. The management unit may also support dialogue between elderly people using, for example, AI. For example, the management unit may support dialogue between elderly people using an AI model that suggests conversation topics. The management unit may also support dialogue between elderly people using an AI model that generates question contents. This makes it possible to maintain mental health by supporting dialogue between elderly people. Part or all of the above-mentioned processing in the dialogue support unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue support unit may support dialogue between elderly people using an AI model that suggests conversation topics.

[0037] The collection unit can analyze the elderly person's past health data and select an appropriate collection method. The collection unit, for example, analyzes the elderly person's past health data and selects an appropriate collection method. Specific types and collection methods of past health data include, for example, medical records, self-reported data, etc., but are not limited to such examples. The collection unit can also analyze the past health data using AI, for example, and select an optimal collection method. For example, the collection unit determines the frequency of voice and image collection based on the past health data. In addition, the collection unit can analyze the past health data and concentrate collection in a specific time period. Furthermore, the collection unit can select the type of data to be collected with reference to the past health data. As a result, efficient data collection is possible by selecting the optimal collection method based on the past health data. 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 collect data using an AI model that analyzes past health data and selects the optimal collection method.

[0038] The collection unit can perform filtering based on the elderly person's current living situation and areas of interest when collecting voice and images. For example, the collection unit performs filtering based on the elderly person's current living situation and areas of interest when collecting voice and images. Specific evaluation criteria and collection methods for the living situation and areas of interest include, but are not limited to, daily activity patterns, living environment, hobbies, and topics of interest. The collection unit can also analyze the elderly person's living situation and areas of interest using AI, for example, and perform filtering when collecting voice and images. For example, if the elderly person is immersed in a hobby, the collection unit excludes that situation from the collection target. In addition, if the elderly person is spending time with his / her family, the collection unit can refrain from collecting data during that time period. Furthermore, if the elderly person is performing a specific activity, the collection unit can preferentially collect data related to that activity. As a result, more relevant data can be collected by filtering based on the living situation and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can collect data using an AI model that analyzes and filters the living conditions and areas of interest of elderly people.

[0039] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the elderly person when collecting voice and images. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the elderly person when collecting voice and images. Specific collection and use methods of geographical location information include, but are not limited to, GPS data, address information, and the like. The collection unit can also, for example, use AI to analyze the geographical location information of the elderly person and preferentially collect highly relevant data when collecting voice and images. For example, when the elderly person is at home, the collection unit collects indoor voice and images. In addition, when the elderly person is out, the collection unit can also collect external voice and images. Furthermore, when the elderly person is in a specific location, the collection unit can also preferentially collect data related to that location. As a result, highly relevant data can be preferentially collected 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 collect data using an AI model that analyzes the geographical location information of the elderly person and preferentially collects highly relevant data.

[0040] The collection unit can analyze the social media activity of the elderly person when collecting the voice and image, and collect related data. The collection unit, for example, analyzes the social media activity of the elderly person when collecting the voice and image, and collects related data. Specific analysis methods and target data of social media activity include, for example, the content of posts, comments, the number of likes, etc., but are not limited to such examples. The collection unit can also analyze the social media activity of the elderly person using AI, for example, and collect related data. For example, the collection unit selects data to be collected based on the content shared by the elderly person on social media. In addition, the collection unit can collect data related to topics of interest from the social media activity of the elderly person. Furthermore, the collection unit can collect the content of conversations between the elderly person and people with whom they interact on social media. In this way, by analyzing social media activity, related data can be efficiently collected. 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 collect data using an AI model that analyzes the social media activity of the elderly person and collects related data.

[0041] The evaluation unit can improve the accuracy of the evaluation by referring to past health data when evaluating the health condition. The evaluation unit can improve the accuracy of the evaluation by referring to past health data, for example, when evaluating the health condition. Specific types and collection methods of past health data include, for example, medical records, self-reported data, etc., but are not limited to such examples. The evaluation unit can also improve the accuracy of the evaluation by referring to past health data, for example, using AI. For example, the evaluation unit evaluates the current health condition based on the past health data. In addition, the evaluation unit can also correct the evaluation result by referring to the past health data. Furthermore, the evaluation unit can analyze the past health data and adjust the evaluation criteria. As a result, the accuracy of the evaluation is improved by referring to the past health data. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can evaluate the health condition by referring to the past health data and using an AI model that improves the accuracy of the evaluation.

[0042] The evaluation unit can perform the evaluation while taking into consideration the elderly's lifestyle and environmental factors when evaluating the health state. For example, the evaluation unit performs the evaluation while taking into consideration the elderly's lifestyle and environmental factors when evaluating the health state. Specific evaluation criteria and collection methods for lifestyle and environmental factors include, for example, diet, exercise, sleep patterns, living environment, climate, social environment, etc., but are not limited to such examples. The evaluation unit can also analyze the elderly's lifestyle and environmental factors using AI, for example, to evaluate the health state. For example, the evaluation unit evaluates the health state based on the elderly's lifestyle. In addition, the evaluation unit can also correct the evaluation result by taking into consideration the elderly's environmental factors. Furthermore, the evaluation unit can comprehensively evaluate the elderly's lifestyle and environmental factors and judge the health state. This makes it possible to evaluate the health state more accurately by taking into consideration the lifestyle and environmental factors. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that analyzes the elderly's lifestyle and environmental factors and evaluates the health state.

[0043] The evaluation unit can determine the priority of the evaluation by referring to the activity history of the elderly person when evaluating the health condition. The evaluation unit, for example, determines the priority of the evaluation by referring to the activity history of the elderly person when evaluating the health condition. Specific collection methods and usage methods of the activity history include, for example, daily activity records, exercise history, etc., but are not limited to such examples. The evaluation unit can also use AI to analyze the activity history of the elderly person and determine the priority of the evaluation. For example, the evaluation unit determines the priority of the evaluation based on the activity history of the elderly person. In addition, the evaluation unit can identify important evaluation items from the activity history of the elderly person. Furthermore, the evaluation unit can analyze the activity history of the elderly person and adjust the priority of the evaluation. As a result, the priority of the evaluation can be appropriately determined by referring to the activity history. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that analyzes the activity history of the elderly person and determines the priority of the evaluation.

[0044] The evaluation unit can improve the accuracy of the evaluation by referring to the elderly person's related medical data when evaluating the health condition. The evaluation unit can improve the accuracy of the evaluation by referring to the elderly person's related medical data when evaluating the health condition, for example. Specific types and collection methods of the related medical data include, for example, medical records, test results, etc., but are not limited to such examples. The evaluation unit can also improve the accuracy of the evaluation by analyzing the elderly person's related medical data using AI, for example. For example, the evaluation unit evaluates the health condition based on the elderly person's medical data. In addition, the evaluation unit can also correct the evaluation result by referring to the elderly person's medical data. Furthermore, the evaluation unit can analyze the elderly person's medical data and adjust the evaluation criteria. As a result, the accuracy of the evaluation is improved by referring to the related medical data. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can analyze the elderly person's related medical data and perform the evaluation using an AI model that improves the accuracy of the evaluation.

[0045] The management unit can select the optimal management method by referring to the past data management history during data management. For example, the management unit selects the optimal management method by referring to the past data management history during data management. Specific collection and use methods of the data management history include, for example, past data operation records, change history, etc., but are not limited to such examples. The management unit can also analyze the past data management history using AI to select the optimal management method. For example, the management unit selects the optimal management method based on the past data management history. In addition, the management unit can adjust the management method by referring to the past data management history. Furthermore, the management unit can analyze the past data management history and propose the optimal management method. As a result, the optimal management method can be selected by referring to the past data management history. Part or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the past data management history and selects the optimal management method.

[0046] The management unit can customize the data arrangement method based on the elderly person's living situation when managing data. The management unit, for example, customizes the data arrangement method based on the elderly person's living situation when managing data. Specific evaluation criteria and collection methods for the living situation include, for example, daily activity patterns and living environment, but are not limited to such examples. The management unit can also analyze the elderly person's living situation using AI and customize the data arrangement method. For example, the management unit customizes the data arrangement method based on the elderly person's living situation. In addition, the management unit can adjust the data arrangement method taking into account the elderly person's living situation. Furthermore, the management unit can analyze the elderly person's living situation and propose an optimal data arrangement method. As a result, more appropriate data management is possible by customizing the data arrangement method based on the living situation. Part or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the elderly person's living situation and customizes the data arrangement method.

[0047] The management unit can select the optimal management method in consideration of the geographical location information of the elderly person at the time of data management. For example, the management unit selects the optimal management method in consideration of the geographical location information of the elderly person at the time of data management. Specific collection methods and usage methods of geographical location information include, for example, GPS data, address information, etc., but are not limited to such examples. The management unit can also analyze the geographical location information of the elderly person using AI, for example, and select the optimal management method. For example, the management unit selects the optimal data management method based on the geographical location information of the elderly person. In addition, the management unit can adjust the management method by referring to the geographical location information of the elderly person. Furthermore, the management unit can analyze the geographical location information of the elderly person and propose the optimal management method. In this way, the optimal data management method can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the geographical location information of the elderly person and selects the optimal management method.

[0048] The management unit can analyze the social media activity of the elderly person at the time of data management and propose a means of data management. For example, the management unit can analyze the social media activity of the elderly person at the time of data management and propose a means of data management. Specific analysis methods and target data of social media activity include, for example, the content of posts, comments, the number of likes, etc., but are not limited to such examples. The management unit can also analyze the social media activity of the elderly person using AI and propose a means of data management. For example, the management unit proposes an optimal data management means based on the social media activity of the elderly person. In addition, the management unit can adjust the management means by referring to the social media activity of the elderly person. Furthermore, the management unit can analyze the social media activity of the elderly person and select an optimal data management means. In this way, the optimal data management means can be proposed by analyzing the social media activity. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the social media activity of the elderly person and proposes a means of data management.

[0049] The calling unit can select the optimal calling method by referring to the elderly person's past response history when calling. For example, the calling unit selects the optimal calling method by referring to the elderly person's past response history when calling. Specific collection and use methods of the response history include, for example, past response content, response time, etc., but are not limited to such examples. The calling unit can also analyze the elderly person's past response history using AI to select the optimal calling method. For example, the calling unit selects the optimal calling method based on the elderly person's past response history. In addition, the calling unit can adjust the calling content by referring to the elderly person's past response history. Furthermore, the calling unit can analyze the elderly person's past response history and determine the optimal calling timing. As a result, the optimal calling method can be selected by referring to the past response history. Part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can perform a call using an AI model that analyzes the elderly person's past response history and selects the optimal calling method.

[0050] The calling unit can adjust the timing of the call based on the current living situation of the elderly person when making a call. For example, the calling unit adjusts the timing of the call based on the current living situation of the elderly person when making a call. Specific evaluation criteria and collection methods for the living situation include, for example, daily activity patterns and living environment, but are not limited to such examples. The calling unit can also adjust the timing of the call by analyzing the living situation of the elderly person using AI, for example. For example, the calling unit can make a call during a time period when the elderly person is relaxing. In addition, the calling unit can also make a call by avoiding a time period when the elderly person is busy. Furthermore, the calling unit can make a call at an optimal timing in accordance with the elderly person's life rhythm. As a result, by adjusting the timing of the call based on the living situation, it is possible to make a call at a more appropriate timing. Part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can make a call using an AI model that analyzes the living situation of the elderly person and adjusts the timing of the call.

[0051] The calling unit can select the optimal calling method in consideration of the geographical location information of the elderly person when calling. For example, the calling unit selects the optimal calling method in consideration of the geographical location information of the elderly person when calling. Specific methods of collecting and using the geographical location information include, for example, GPS data, address information, and the like, but are not limited to such examples. The calling unit can also analyze the geographical location information of the elderly person using AI, for example, and select the optimal calling method. For example, the calling unit selects the optimal calling method based on the geographical location information of the elderly person. In addition, the calling unit can adjust the calling method by referring to the geographical location information of the elderly person. Furthermore, the calling unit can analyze the geographical location information of the elderly person and propose the optimal calling method. In this way, the optimal calling method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can perform a call using an AI model that analyzes the geographical location information of the elderly person and selects the optimal calling method.

[0052] The calling unit can analyze the social media activity of the elderly person at the time of the call and propose the content of the call. For example, the calling unit analyzes the social media activity of the elderly person at the time of the call and proposes the content of the call. Specific analysis methods and target data of social media activities include, for example, the content of posts, the number of comments, and likes, but are not limited to such examples. The calling unit can also analyze the social media activity of the elderly person using AI and propose the content of the call. For example, the calling unit proposes the optimal content of the call based on the social media activity of the elderly person. In addition, the calling unit can adjust the content of the call by referring to the social media activity of the elderly person. Furthermore, the calling unit can analyze the social media activity of the elderly person and select the optimal content of the call. In this way, the optimal content of the call can be proposed by analyzing the social media activity. Part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can make a call using an AI model that analyzes the social media activity of the elderly person and proposes the content of the call.

[0053] The matching unit can select the optimal matching method by referring to the elderly person's past interaction history at the time of matching. For example, the matching unit selects the optimal matching method by referring to the elderly person's past interaction history at the time of matching. Specific collection methods and usage methods of the interaction history include, for example, past dialogue content, interaction frequency, etc., but are not limited to such examples. The matching unit can also analyze the elderly person's past interaction history using AI, for example, and select the optimal matching method. For example, the matching unit selects the optimal matching method based on the elderly person's past interaction history. In addition, the matching unit can adjust the matching method by referring to the elderly person's past interaction history. Furthermore, the matching unit can analyze the elderly person's past interaction history and propose the optimal matching method. As a result, the optimal matching method can be selected by referring to the past interaction history. A part or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that analyzes the elderly person's past interaction history and selects the optimal matching method.

[0054] The matching unit can perform matching based on the hobbies and fields of interest of the elderly person at the time of matching. For example, the matching unit performs matching based on the hobbies and fields of interest of the elderly person at the time of matching. Specific definitions and collection methods of hobbies and fields of interest include, for example, sports, music, reading, etc., but are not limited to such examples. The matching unit can also perform matching by analyzing the hobbies and fields of interest of the elderly person using AI, for example. For example, the matching unit performs optimal matching based on the hobbies of the elderly person. In addition, the matching unit can also adjust the matching by referring to the fields of interest of the elderly person. Furthermore, the matching unit can analyze the hobbies and fields of interest of the elderly person and propose optimal matching. As a result, more appropriate matching is possible by performing matching based on the hobbies and fields of interest. Part or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can perform matching by analyzing the hobbies and fields of interest of the elderly person and using an AI model that performs matching.

[0055] The matching unit can select the optimal matching method in consideration of the geographical location information of the elderly person at the time of matching. For example, the matching unit selects the optimal matching method in consideration of the geographical location information of the elderly person at the time of matching. Specific collection methods and usage methods of the geographical location information include, for example, GPS data, address information, etc., but are not limited to such examples. For example, the matching unit can analyze the geographical location information of the elderly person using AI and select the optimal matching method. For example, the matching unit selects the optimal matching method based on the geographical location information of the elderly person. In addition, the matching unit can adjust the matching method by referring to the geographical location information of the elderly person. Furthermore, the matching unit can analyze the geographical location information of the elderly person and propose the optimal matching method. In this way, the optimal matching method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that analyzes the geographical location information of the elderly person and selects the optimal matching method.

[0056] The matching unit can analyze the social media activity of the elderly person at the time of matching and propose a matching means. The matching unit, for example, analyzes the social media activity of the elderly person at the time of matching and proposes a matching means. Specific analysis methods and target data of social media activity include, for example, the content of posts, comments, the number of likes, etc., but are not limited to such examples. The matching unit can also analyze the social media activity of the elderly person using AI and propose a matching means. For example, the matching unit proposes an optimal matching means based on the social media activity of the elderly person. In addition, the matching unit can adjust the matching means by referring to the social media activity of the elderly person. Furthermore, the matching unit can analyze the social media activity of the elderly person and select an optimal matching means. In this way, the optimal matching means can be proposed by analyzing the social media activity. A part or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that analyzes the social media activity of the elderly person and proposes a matching means.

[0057] The dialogue support unit can select the optimal support method by referring to the elderly person's past dialogue history during dialogue support. For example, the dialogue support unit selects the optimal support method by referring to the elderly person's past dialogue history during dialogue support. Specific collection and use methods of the dialogue history include, for example, past dialogue content, dialogue frequency, etc., but are not limited to such examples. The dialogue support unit can also analyze the elderly person's past dialogue history using AI to select the optimal support method. For example, the dialogue support unit selects the optimal support method based on the elderly person's past dialogue history. In addition, the dialogue support unit can adjust the support method by referring to the elderly person's past dialogue history. Furthermore, the dialogue support unit can analyze the elderly person's past dialogue history and propose the optimal support method. As a result, the optimal support method can be selected by referring to the past dialogue history. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the elderly person's past dialogue history and selects the optimal support method.

[0058] The dialogue support unit can adjust the timing of dialogue support based on the current living situation of the elderly person during dialogue support. The dialogue support unit, for example, adjusts the timing of dialogue support based on the current living situation of the elderly person during dialogue support. Specific evaluation criteria and collection methods for the living situation include, for example, daily activity patterns and living environment, but are not limited to such examples. The dialogue support unit can also analyze the living situation of the elderly person using AI and adjust the timing of dialogue support. For example, the dialogue support unit performs dialogue support during a time period when the elderly person is relaxed. In addition, the dialogue support unit can also perform dialogue support by avoiding a time period when the elderly person is busy. Furthermore, the dialogue support unit can perform dialogue support at an optimal timing in accordance with the elderly person's life rhythm. As a result, dialogue support can be performed at a more appropriate timing by adjusting the timing of dialogue support based on the living situation. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the living situation of the elderly person and adjusts the timing of dialogue support.

[0059] The dialogue support unit can select the optimal support method in consideration of the geographical location information of the elderly person at the time of dialogue support. For example, the dialogue support unit selects the optimal support method in consideration of the geographical location information of the elderly person at the time of dialogue support. Specific collection methods and usage methods of the geographical location information include, for example, GPS data, address information, etc., but are not limited to such examples. The dialogue support unit can also analyze the geographical location information of the elderly person using AI, for example, and select the optimal support method. For example, the dialogue support unit selects the optimal support method based on the geographical location information of the elderly person. In addition, the dialogue support unit can also adjust the support method by referring to the geographical location information of the elderly person. Furthermore, the dialogue support unit can analyze the geographical location information of the elderly person and propose the optimal support method. As a result, the optimal support method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the geographical location information of the elderly person and selects the optimal support method.

[0060] The dialogue support unit can analyze the social media activity of the elderly person at the time of dialogue support and propose the content of the dialogue support. For example, the dialogue support unit analyzes the social media activity of the elderly person at the time of dialogue support and proposes the content of the dialogue support. Specific analysis methods and target data of social media activity include, for example, the content of posts, the number of comments, and the like, but are not limited to such examples. The dialogue support unit can also analyze the social media activity of the elderly person using AI and propose the content of the dialogue support. For example, the dialogue support unit proposes optimal dialogue support content based on the social media activity of the elderly person. In addition, the dialogue support unit can adjust the dialogue support content by referring to the social media activity of the elderly person. Furthermore, the dialogue support unit can analyze the social media activity of the elderly person and select optimal dialogue support content. In this way, the optimal dialogue support content can be proposed by analyzing the social media activity. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the social media activity of the elderly person and proposes the content of dialogue support.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The health management system may further include a nutritional evaluation unit that records the dietary contents of the elderly person and evaluates the nutritional balance. For example, the collection unit collects images of the elderly person eating and analyzes the dietary contents using image recognition technology. The evaluation unit can evaluate the nutritional balance based on the analyzed dietary contents and recommend the intake of necessary nutrients. In addition, the management unit can store the nutritional evaluation results in a database and compare them with past dietary history to track changes in nutritional balance. This makes it possible to manage the dietary contents of the elderly person and help maintain their health.

[0063] The health management system may further include an exercise evaluation unit that records the amount of exercise of the elderly person and warns them of lack of exercise. For example, the collection unit collects the number of steps and exercise time of the elderly person, and the exercise evaluation unit analyzes the data. The evaluation unit can warn them of lack of exercise based on the analysis results and recommend appropriate exercise. In addition, the management unit can store the exercise evaluation results in a database and track changes in the amount of exercise by comparing them with past exercise history. This makes it possible to manage the exercise habits of the elderly person and help them maintain their health.

[0064] The health management system may further include a sleep evaluation unit that records the sleep patterns of the elderly and evaluates the quality of their sleep. For example, the collection unit collects the movements and sounds of the elderly while they are sleeping, and the sleep evaluation unit analyzes the data. The evaluation unit can evaluate the quality of sleep based on the analysis results and suggest necessary improvements. The management unit can also store the sleep evaluation results in a database and compare them with past sleep histories to track changes in sleep patterns. This can help manage the sleeping habits of the elderly and maintain their health.

[0065] The health management system may further include a stress assessment unit that assesses the elderly person's stress level and suggests relaxation methods. For example, the collection unit collects the elderly person's heart rate and breathing patterns, and the stress assessment unit analyzes the data. The assessment unit can assess the stress level based on the analysis results and suggest relaxation methods. The management unit can also store the stress assessment results in a database and compare them with past stress levels to track changes. This allows for stress management for the elderly person and helps maintain their health.

[0066] The health management system may further include a social activity evaluation unit for recording the social activities of the elderly and preventing isolation. For example, the collection unit collects the frequency of the elderly's going out and the duration of conversation, and the social activity evaluation unit analyzes the data. The evaluation unit can evaluate the level of social activity based on the analysis results and recommend necessary social activities. The management unit can also store the social activity evaluation results in a database and compare them with past social activity history to track changes. This makes it possible to manage the social activities of the elderly and prevent isolation.

[0067] The process flow of the first embodiment will be briefly described below.

[0068] Step 1: The collection unit collects voices and images of the elderly. The voices and images of the elderly include everyday conversations, specific actions, and facial expressions. The collection unit collects the elderly's tone of voice using voice recognition technology and collects the elderly's appearance using image recognition technology. AI can also be used to collect voices and images. Step 2: The evaluation unit evaluates the health condition of the elderly person from the tone of voice or appearance based on the information collected by the collection unit. The evaluation unit uses AI to analyze voice and images, analyzes the tone of voice of the elderly person using voice recognition technology, and analyzes the appearance of the elderly person using image recognition technology. Step 3: The management department collects and manages the evaluation results by the evaluation department. The management department uses AI to manage the evaluation results, stores the evaluation results in a database, and refers to the evaluation results as needed.

[0069] (Example 2) The health management system according to the embodiment of the present invention is a health management system that can be used by all elderly people. This health management system is a system that collects voices and images of elderly people, and evaluates and manages their health conditions from the tone of their voices and appearances. For example, the health management system includes a collection unit that collects voices and images of elderly people, and the collection unit collects information for evaluating the health conditions of elderly people from the tone of their voices and appearances. Next, the evaluation unit evaluates the health conditions based on the collected information, and the management unit collects and manages the results. The management unit includes a matching unit that matches elderly people with the same hobbies, and also includes a dialogue support unit that supports dialogue between elderly people. In addition, there is a calling unit that calls out to users, and the collection unit collects voices and images that show the user's reaction to the call. As a result, the AI ​​records the health conditions of elderly people one by one, and not only physical records but also trends among elderly people can be shared between apps. This aims to eliminate feelings of loneliness and keep people mentally healthy. For example, if the AI ​​analyzes the tone of the elderly person's voice and determines that they are not in good spirits, the calling unit calls out to them, saying, "How are you doing today?" When the elderly responds, their voice and image are collected and the evaluation unit evaluates their health condition again. The evaluation results are sent to the management unit, and the dialogue support unit supports dialogue between the elderly as necessary. This allows the elderly to maintain their health without feeling lonely. This allows the health management system to evaluate and manage the health condition of the elderly from their voice and image.

[0070] The health management system according to the embodiment includes a collection unit, an evaluation unit, and a management unit. The collection unit collects voices and images of the elderly. The voices and images of the elderly include, for example, daily conversations, specific actions, and facial expressions, but are not limited to such examples. The collection unit collects, for example, the tone of the elderly's voice using voice recognition technology, and collects the elderly's appearance using image recognition technology. The collection unit can also collect voices and images using AI. For example, the collection unit analyzes the elderly's tone of voice using voice recognition technology and evaluates the pitch, strength, and expression of emotions. Furthermore, the collection unit analyzes the elderly's appearance using image recognition technology and evaluates the complexion, facial expression, posture, and the like. The evaluation unit evaluates the health condition from the elderly's tone of voice or appearance based on the information collected by the collection unit. The evaluation unit analyzes the voice and image using, for example, AI, and evaluates the health condition. For example, the evaluation unit analyzes the elderly's tone of voice using voice recognition technology and evaluates the health condition. Furthermore, the evaluation unit analyzes the elderly's appearance using image recognition technology and evaluates the health condition. The management unit collects and manages the evaluation results by the evaluation unit. The management unit can also manage the evaluation results using, for example, AI. For example, the management unit stores the evaluation results in a database and refers to the evaluation results as necessary. In this way, the health management system according to the embodiment can evaluate and manage the health state of elderly people from voice and images.

[0071] The collection unit collects the voice and image of the elderly person. The voice and image of the elderly person include, for example, daily conversation, specific actions and facial expressions, but are not limited to such examples. For example, the collection unit collects the tone of the elderly person's voice using voice recognition technology, and collects the appearance of the elderly person using image recognition technology. Specifically, the collection unit uses a high-sensitivity microphone and a high-resolution camera to collect voices and images that occur naturally in daily life. The tone of the elderly person's voice, pitch, strength, emotional expression, etc. when speaking are recorded in detail, and these data are analyzed in real time. In addition, the camera captures the elderly person's facial color, facial expression, posture, movements, etc., and collects this information as image data. The collection unit can also collect voice and images using AI. For example, the collection unit uses voice recognition technology to analyze the tone of the elderly person's voice and evaluate the pitch, strength, emotional expression, etc. Furthermore, the collection unit uses image recognition technology to analyze the appearance of the elderly person and evaluate the facial color, facial expression, posture, etc. This allows the collection unit to efficiently collect various data on the elderly person's health condition and analyze it in real time. The collected data is sent to a cloud server and stored in a central database. This enables the collection unit to monitor the health condition of elderly people even from remote locations. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, data collection can be strengthened during specific time periods or when specific actions are performed. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0072] The evaluation unit evaluates the health condition of the elderly person from the tone of voice or appearance based on the information collected by the collection unit. The evaluation unit, for example, uses AI to analyze voice and images to evaluate the health condition. Specifically, the evaluation unit uses voice recognition technology to analyze the tone of the elderly person's voice and evaluate the health condition. For example, if the tone of voice is lower than normal or the strength of the voice is weakened, these are evaluated as signs of fatigue or stress. In addition, if there is a trembling voice or an unnatural pause, these may indicate nervous system problems or emotional anxiety. Furthermore, the evaluation unit uses image recognition technology to analyze the appearance of the elderly person and evaluate the health condition. For example, if the face is paler than normal or the facial expression is stiff, these are evaluated as signs of poor health or mental stress. Changes in posture and movement are also important evaluation points, and for example, if walking is unstable or movements are slow, these may indicate muscle weakness or joint problems. The evaluation unit can comprehensively evaluate these analysis results and grasp the health condition of the elderly person in real time. In addition, the evaluation unit can use past data and statistical information to analyze long-term changes and trends in health status. For example, based on past voice data and image data, the evaluation unit can evaluate fluctuations in health status over a specific period of time and predict future risks. The evaluation unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data early and respond quickly. This allows the evaluation unit to not only grasp real-time health status, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0073] The management department collects and manages the evaluation results by the evaluation department. The management department can also manage the evaluation results using AI, for example. Specifically, the management department stores the evaluation results in a database and refers to the evaluation results as necessary. The evaluation results are organized for each individual elderly person, and by comparing them with past data, changes and trends in health conditions can be grasped. The management department can create an appropriate health management plan for each elderly person based on the evaluation results. For example, if a specific elderly person shows signs of fatigue or stress, the management department recommends rest and relaxation for that elderly person. In addition, the management department can share the evaluation results with medical professionals and care staff and take necessary measures promptly. For example, if an abnormal evaluation result is detected, the management department immediately notifies a medical professional and makes arrangements for the elderly person to receive appropriate diagnosis and treatment. In addition, the management department can create statistical information and reports based on the evaluation results and evaluate the performance and effectiveness of the entire system. This allows the management department to always understand the operation status of the system and take improvement measures as necessary. The management department also takes into consideration data security and privacy protection, and takes appropriate security measures in storing and sharing the evaluation results. For example, unauthorized access and data leakage are prevented by strictly controlling access rights to the database and encrypting the evaluation results. This enables the management department to efficiently and safely manage the evaluation results and continuously monitor the health status of the elderly.

[0074] The health management system includes a calling unit that calls the user. The calling unit calls the user. Specific contents and methods of the call include, but are not limited to, voice messages, text messages, and the like. The calling unit can also generate the contents of the call using, for example, AI. For example, the calling unit can analyze the tone of the user's voice using voice recognition technology to generate appropriate call contents. The calling unit can also generate a text message for the user using a text generation AI. This allows the health condition to be confirmed by calling the user. A part or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the calling unit can make a call using an AI model that analyzes the tone of the user's voice and generates appropriate call contents.

[0075] The collection unit can collect voices and images showing the user's reaction in response to the call by the calling unit. The collection unit, for example, collects voices and images showing the user's reaction in response to the call by the calling unit. Specific types of reactions and evaluation methods include, for example, verbal responses, changes in facial expressions, and actions, but are not limited to such examples. The collection unit can also analyze the user's reaction by using AI, for example. For example, the collection unit can analyze the user's verbal responses using voice recognition technology and collect voice data. The collection unit can also analyze changes in the user's facial expressions using image recognition technology and collect image data. This makes it possible to evaluate the health condition in more detail by collecting the user's reaction in response to the call. 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 collect data using an AI model that collects voices and images showing the user's reaction in response to the call by the calling unit.

[0076] The management unit may include a matching unit that matches elderly people who have the same hobby. The management unit may include, for example, a matching unit that matches elderly people who have the same hobby. Specific criteria and methods for matching include, for example, hobbies, areas of interest, personality, etc., but are not limited to such examples. The management unit may also match elderly people using, for example, AI. For example, the management unit may perform matching using an AI model that matches elderly people based on hobbies and areas of interest. The management unit may also match elderly people based on personality diagnosis results. As a result, by matching elderly people who have the same hobby, it is possible to reduce a sense of loneliness. A part or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit may perform matching using an AI model that matches elderly people based on hobbies and areas of interest.

[0077] The management unit may include a dialogue support unit that supports dialogue between elderly people. The management unit may include, for example, a dialogue support unit that supports dialogue between elderly people. Specific methods and contents of dialogue support include, for example, conversation topics, question contents, and the like, but are not limited to such examples. The management unit may also support dialogue between elderly people using, for example, AI. For example, the management unit may support dialogue between elderly people using an AI model that suggests conversation topics. The management unit may also support dialogue between elderly people using an AI model that generates question contents. This makes it possible to maintain mental health by supporting dialogue between elderly people. Part or all of the above-mentioned processing in the dialogue support unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue support unit may support dialogue between elderly people using an AI model that suggests conversation topics.

[0078] The collection unit can estimate the user's emotion and adjust the timing of collecting voice and images based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting voice and images based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis, facial expression analysis, and the like, but are not limited to such examples. The collection unit can also estimate the user's emotion using, for example, AI and adjust the timing of collecting voice and images. For example, the collection unit uses voice recognition technology to analyze the tone of the user's voice and estimate the emotion. The collection unit can also use image recognition technology to analyze the user's facial expression and estimate the emotion. This enables more appropriate data collection by adjusting the collection timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that infers a user's emotions and adjusts the timing of audio and image collection based on the inferred emotions.

[0079] The collection unit can analyze the elderly person's past health data and select an appropriate collection method. The collection unit, for example, analyzes the elderly person's past health data and selects an appropriate collection method. Specific types and collection methods of past health data include, for example, medical records, self-reported data, etc., but are not limited to such examples. The collection unit can also analyze the past health data using AI, for example, and select an optimal collection method. For example, the collection unit determines the frequency of voice and image collection based on the past health data. In addition, the collection unit can analyze the past health data and concentrate collection in a specific time period. Furthermore, the collection unit can select the type of data to be collected with reference to the past health data. As a result, efficient data collection is possible by selecting the optimal collection method based on the past health data. 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 collect data using an AI model that analyzes past health data and selects the optimal collection method.

[0080] The collection unit can perform filtering based on the elderly person's current living situation and areas of interest when collecting voice and images. For example, the collection unit performs filtering based on the elderly person's current living situation and areas of interest when collecting voice and images. Specific evaluation criteria and collection methods for the living situation and areas of interest include, but are not limited to, daily activity patterns, living environment, hobbies, and topics of interest. The collection unit can also analyze the elderly person's living situation and areas of interest using AI, for example, and perform filtering when collecting voice and images. For example, if the elderly person is immersed in a hobby, the collection unit excludes that situation from the collection target. In addition, if the elderly person is spending time with his / her family, the collection unit can refrain from collecting data during that time period. Furthermore, if the elderly person is performing a specific activity, the collection unit can preferentially collect data related to that activity. As a result, more relevant data can be collected by filtering based on the living situation and areas of interest. A part or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can collect data using an AI model that analyzes and filters the living conditions and areas of interest of elderly people.

[0081] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis, facial expression analysis, etc., but are not limited to such examples. The collection unit can also estimate the user's emotion using, for example, AI and determine the priority of data to be collected. For example, the collection unit uses voice recognition technology to analyze the tone of the user's voice and estimate the emotion. The collection unit can also analyze the user's facial expression and estimate the emotion using image recognition technology. As a result, important data can be preferentially collected by determining the priority of data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect data using an AI model that infers a user's emotions and prioritizes the data to collect based on the inferred emotions.

[0082] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the elderly person when collecting voice and images. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the elderly person when collecting voice and images. Specific collection and use methods of geographical location information include, but are not limited to, GPS data, address information, and the like. The collection unit can also, for example, use AI to analyze the geographical location information of the elderly person and preferentially collect highly relevant data when collecting voice and images. For example, when the elderly person is at home, the collection unit collects indoor voice and images. In addition, when the elderly person is out, the collection unit can also collect external voice and images. Furthermore, when the elderly person is in a specific location, the collection unit can also preferentially collect data related to that location. As a result, highly relevant data can be preferentially collected 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 collect data using an AI model that analyzes the geographical location information of the elderly person and preferentially collects highly relevant data.

[0083] The collection unit can analyze the social media activity of the elderly person when collecting the voice and image, and collect related data. The collection unit, for example, analyzes the social media activity of the elderly person when collecting the voice and image, and collects related data. Specific analysis methods and target data of social media activity include, for example, the content of posts, comments, the number of likes, etc., but are not limited to such examples. The collection unit can also analyze the social media activity of the elderly person using AI, for example, and collect related data. For example, the collection unit selects data to be collected based on the content shared by the elderly person on social media. In addition, the collection unit can collect data related to topics of interest from the social media activity of the elderly person. Furthermore, the collection unit can collect the content of conversations between the elderly person and people with whom they interact on social media. In this way, by analyzing social media activity, related data can be efficiently collected. 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 collect data using an AI model that analyzes the social media activity of the elderly person and collects related data.

[0084] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria of the health state based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the evaluation criteria of the health state based on the estimated user's emotion. Specific estimation methods and criteria for emotion include, for example, voice analysis, facial expression analysis, etc., but are not limited to such examples. The evaluation unit can also estimate the user's emotion using, for example, AI and adjust the evaluation criteria of the health state. For example, the evaluation unit can analyze the tone of the user's voice using voice recognition technology and estimate the emotion. The evaluation unit can also analyze the user's facial expression using image recognition technology and estimate the emotion. This enables a more accurate evaluation of the health state by adjusting the evaluation criteria based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can evaluate the health condition using an AI model that estimates the user's emotions and adjusts the evaluation criteria for the health condition based on the estimated emotions.

[0085] The evaluation unit can improve the accuracy of the evaluation by referring to past health data when evaluating the health condition. The evaluation unit can improve the accuracy of the evaluation by referring to past health data, for example, when evaluating the health condition. Specific types and collection methods of past health data include, for example, medical records, self-reported data, etc., but are not limited to such examples. The evaluation unit can also improve the accuracy of the evaluation by referring to past health data, for example, using AI. For example, the evaluation unit evaluates the current health condition based on the past health data. In addition, the evaluation unit can also correct the evaluation result by referring to the past health data. Furthermore, the evaluation unit can analyze the past health data and adjust the evaluation criteria. As a result, the accuracy of the evaluation is improved by referring to the past health data. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can evaluate the health condition by referring to the past health data and using an AI model that improves the accuracy of the evaluation.

[0086] The evaluation unit can perform the evaluation while taking into consideration the elderly's lifestyle and environmental factors when evaluating the health state. For example, the evaluation unit performs the evaluation while taking into consideration the elderly's lifestyle and environmental factors when evaluating the health state. Specific evaluation criteria and collection methods for lifestyle and environmental factors include, for example, diet, exercise, sleep patterns, living environment, climate, social environment, etc., but are not limited to such examples. The evaluation unit can also analyze the elderly's lifestyle and environmental factors using AI, for example, to evaluate the health state. For example, the evaluation unit evaluates the health state based on the elderly's lifestyle. In addition, the evaluation unit can also correct the evaluation result by taking into consideration the elderly's environmental factors. Furthermore, the evaluation unit can comprehensively evaluate the elderly's lifestyle and environmental factors and judge the health state. This makes it possible to evaluate the health state more accurately by taking into consideration the lifestyle and environmental factors. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that analyzes the elderly's lifestyle and environmental factors and evaluates the health state.

[0087] The evaluation unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the display method of the evaluation result based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis, facial expression analysis, etc., but are not limited to such examples. The evaluation unit can also estimate the user's emotion using, for example, AI and adjust the display method of the evaluation result. For example, the evaluation unit can analyze the tone of the user's voice using voice recognition technology and estimate the emotion. The evaluation unit can also analyze the user's facial expression using image recognition technology and estimate the emotion. This makes it possible to display a more appropriate evaluation result by adjusting the display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can display the evaluation results using an AI model that estimates the user's emotions and adjusts the way the evaluation results are displayed based on the estimated emotions.

[0088] The evaluation unit can determine the priority of the evaluation by referring to the activity history of the elderly person when evaluating the health condition. The evaluation unit, for example, determines the priority of the evaluation by referring to the activity history of the elderly person when evaluating the health condition. Specific collection methods and usage methods of the activity history include, for example, daily activity records, exercise history, etc., but are not limited to such examples. The evaluation unit can also use AI to analyze the activity history of the elderly person and determine the priority of the evaluation. For example, the evaluation unit determines the priority of the evaluation based on the activity history of the elderly person. In addition, the evaluation unit can identify important evaluation items from the activity history of the elderly person. Furthermore, the evaluation unit can analyze the activity history of the elderly person and adjust the priority of the evaluation. As a result, the priority of the evaluation can be appropriately determined by referring to the activity history. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can perform the evaluation using an AI model that analyzes the activity history of the elderly person and determines the priority of the evaluation.

[0089] The evaluation unit can improve the accuracy of the evaluation by referring to the elderly person's related medical data when evaluating the health condition. The evaluation unit can improve the accuracy of the evaluation by referring to the elderly person's related medical data when evaluating the health condition, for example. Specific types and collection methods of the related medical data include, for example, medical records, test results, etc., but are not limited to such examples. The evaluation unit can also improve the accuracy of the evaluation by analyzing the elderly person's related medical data using AI, for example. For example, the evaluation unit evaluates the health condition based on the elderly person's medical data. In addition, the evaluation unit can also correct the evaluation result by referring to the elderly person's medical data. Furthermore, the evaluation unit can analyze the elderly person's medical data and adjust the evaluation criteria. As a result, the accuracy of the evaluation is improved by referring to the related medical data. Part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can analyze the elderly person's related medical data and perform the evaluation using an AI model that improves the accuracy of the evaluation.

[0090] The management unit can estimate the user's emotion and adjust the data management method based on the estimated user's emotion. The management unit, for example, estimates the user's emotion and adjusts the data management method based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis and facial expression analysis, but are not limited to such examples. The management unit can also estimate the user's emotion using, for example, AI and adjust the data management method. For example, the management unit can analyze the tone of the user's voice using voice recognition technology and estimate the emotion. The management unit can also analyze the user's facial expression using image recognition technology and estimate the emotion. This enables more appropriate data management by adjusting the data management method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can manage data using an AI model that infers a user's emotions and adjusts the way data is managed based on the inferred emotions.

[0091] The management unit can select the optimal management method by referring to the past data management history during data management. For example, the management unit selects the optimal management method by referring to the past data management history during data management. Specific collection and use methods of the data management history include, for example, past data operation records, change history, etc., but are not limited to such examples. The management unit can also analyze the past data management history using AI to select the optimal management method. For example, the management unit selects the optimal management method based on the past data management history. In addition, the management unit can adjust the management method by referring to the past data management history. Furthermore, the management unit can analyze the past data management history and propose the optimal management method. As a result, the optimal management method can be selected by referring to the past data management history. Part or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the past data management history and selects the optimal management method.

[0092] The management unit can customize the data arrangement method based on the elderly person's living situation when managing data. The management unit, for example, customizes the data arrangement method based on the elderly person's living situation when managing data. Specific evaluation criteria and collection methods for the living situation include, for example, daily activity patterns and living environment, but are not limited to such examples. The management unit can also analyze the elderly person's living situation using AI and customize the data arrangement method. For example, the management unit customizes the data arrangement method based on the elderly person's living situation. In addition, the management unit can adjust the data arrangement method taking into account the elderly person's living situation. Furthermore, the management unit can analyze the elderly person's living situation and propose an optimal data arrangement method. As a result, more appropriate data management is possible by customizing the data arrangement method based on the living situation. Part or all of the above-mentioned processing in the management unit may be performed using AI, for example, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the elderly person's living situation and customizes the data arrangement method.

[0093] The management unit can estimate the user's emotion and determine the priority of data management based on the estimated user's emotion. The management unit, for example, estimates the user's emotion and determines the priority of data management based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis and facial expression analysis, but are not limited to such examples. The management unit can also estimate the user's emotion using, for example, AI and determine the priority of data management. For example, the management unit can analyze the tone of the user's voice using voice recognition technology and estimate the emotion. The management unit can also analyze the user's facial expression using image recognition technology and estimate the emotion. As a result, important data can be preferentially managed by determining the priority of data management based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can manage data using an AI model that estimates a user's emotions and determines priorities for data management based on the estimated emotions.

[0094] The management unit can select the optimal management method in consideration of the geographical location information of the elderly person at the time of data management. For example, the management unit selects the optimal management method in consideration of the geographical location information of the elderly person at the time of data management. Specific collection methods and usage methods of geographical location information include, for example, GPS data, address information, etc., but are not limited to such examples. The management unit can also analyze the geographical location information of the elderly person using AI, for example, and select the optimal management method. For example, the management unit selects the optimal data management method based on the geographical location information of the elderly person. In addition, the management unit can adjust the management method by referring to the geographical location information of the elderly person. Furthermore, the management unit can analyze the geographical location information of the elderly person and propose the optimal management method. In this way, the optimal data management method can be selected by considering the geographical location information. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the geographical location information of the elderly person and selects the optimal management method.

[0095] The management unit can analyze the social media activity of the elderly person at the time of data management and propose a means of data management. For example, the management unit can analyze the social media activity of the elderly person at the time of data management and propose a means of data management. Specific analysis methods and target data of social media activity include, for example, the content of posts, comments, the number of likes, etc., but are not limited to such examples. The management unit can also analyze the social media activity of the elderly person using AI and propose a means of data management. For example, the management unit proposes an optimal data management means based on the social media activity of the elderly person. In addition, the management unit can adjust the management means by referring to the social media activity of the elderly person. Furthermore, the management unit can analyze the social media activity of the elderly person and select an optimal data management means. In this way, the optimal data management means can be proposed by analyzing the social media activity. Part or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can manage data using an AI model that analyzes the social media activity of the elderly person and proposes a means of data management.

[0096] The calling unit can estimate the user's emotion and adjust the content of the call based on the estimated user's emotion. The calling unit, for example, estimates the user's emotion and adjusts the content of the call based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis, facial expression analysis, and the like, but are not limited to such examples. The calling unit can also estimate the user's emotion and adjust the content of the call using, for example, AI. For example, the calling unit can analyze the tone of the user's voice using voice recognition technology to estimate the emotion. The calling unit can also analyze the user's facial expression using image recognition technology to estimate the emotion. This makes it possible to adjust the content of the call based on the user's emotion, thereby making it possible to make a more appropriate call. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can make a call using an AI model that estimates the user's emotions and adjusts the content of the call based on the estimated emotions.

[0097] The calling unit can select the optimal calling method by referring to the elderly person's past response history when calling. For example, the calling unit selects the optimal calling method by referring to the elderly person's past response history when calling. Specific collection and use methods of the response history include, for example, past response content, response time, etc., but are not limited to such examples. The calling unit can also analyze the elderly person's past response history using AI to select the optimal calling method. For example, the calling unit selects the optimal calling method based on the elderly person's past response history. In addition, the calling unit can adjust the calling content by referring to the elderly person's past response history. Furthermore, the calling unit can analyze the elderly person's past response history and determine the optimal calling timing. As a result, the optimal calling method can be selected by referring to the past response history. Part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can perform a call using an AI model that analyzes the elderly person's past response history and selects the optimal calling method.

[0098] The calling unit can adjust the timing of the call based on the current living situation of the elderly person when making a call. For example, the calling unit adjusts the timing of the call based on the current living situation of the elderly person when making a call. Specific evaluation criteria and collection methods for the living situation include, for example, daily activity patterns and living environment, but are not limited to such examples. The calling unit can also adjust the timing of the call by analyzing the living situation of the elderly person using AI, for example. For example, the calling unit can make a call during a time period when the elderly person is relaxing. In addition, the calling unit can also make a call by avoiding a time period when the elderly person is busy. Furthermore, the calling unit can make a call at an optimal timing in accordance with the elderly person's life rhythm. As a result, by adjusting the timing of the call based on the living situation, it is possible to make a call at a more appropriate timing. Part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can make a call using an AI model that analyzes the living situation of the elderly person and adjusts the timing of the call.

[0099] The calling unit can estimate the user's emotion and determine the priority of the calls based on the estimated user's emotion. The calling unit, for example, estimates the user's emotion and determines the priority of the calls based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis and facial expression analysis, but are not limited to such examples. The calling unit can also estimate the user's emotion and determine the priority of the calls using, for example, AI. For example, the calling unit analyzes the tone of the user's voice using voice recognition technology to estimate the emotion. The calling unit can also analyze the user's facial expression using image recognition technology to estimate the emotion. As a result, important calls can be made preferentially by determining the priority of the calls based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calling unit may be performed using, for example, AI, or may be performed without using AI. For example, the call unit can make calls using an AI model that estimates the user's emotions and prioritizes calls based on the estimated emotions.

[0100] The calling unit can select the optimal calling method in consideration of the geographical location information of the elderly person when calling. For example, the calling unit selects the optimal calling method in consideration of the geographical location information of the elderly person when calling. Specific methods of collecting and using the geographical location information include, for example, GPS data, address information, and the like, but are not limited to such examples. The calling unit can also analyze the geographical location information of the elderly person using AI, for example, and select the optimal calling method. For example, the calling unit selects the optimal calling method based on the geographical location information of the elderly person. In addition, the calling unit can adjust the calling method by referring to the geographical location information of the elderly person. Furthermore, the calling unit can analyze the geographical location information of the elderly person and propose the optimal calling method. In this way, the optimal calling method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can perform a call using an AI model that analyzes the geographical location information of the elderly person and selects the optimal calling method.

[0101] The calling unit can analyze the social media activity of the elderly person at the time of the call and propose the content of the call. For example, the calling unit analyzes the social media activity of the elderly person at the time of the call and proposes the content of the call. Specific analysis methods and target data of social media activities include, for example, the content of posts, the number of comments, and likes, but are not limited to such examples. The calling unit can also analyze the social media activity of the elderly person using AI and propose the content of the call. For example, the calling unit proposes the optimal content of the call based on the social media activity of the elderly person. In addition, the calling unit can adjust the content of the call by referring to the social media activity of the elderly person. Furthermore, the calling unit can analyze the social media activity of the elderly person and select the optimal content of the call. In this way, the optimal content of the call can be proposed by analyzing the social media activity. Part or all of the above-mentioned processing in the calling unit may be performed using AI, for example, or may be performed without using AI. For example, the calling unit can make a call using an AI model that analyzes the social media activity of the elderly person and proposes the content of the call.

[0102] The matching unit can estimate the user's emotion and adjust the matching criteria based on the estimated user's emotion. The matching unit, for example, estimates the user's emotion and adjusts the matching criteria based on the estimated user's emotion. Specific estimation methods and criteria for emotion include, for example, voice analysis, facial expression analysis, etc., but are not limited to such examples. The matching unit can also estimate the user's emotion and adjust the matching criteria using, for example, AI. For example, the matching unit analyzes the tone of the user's voice using voice recognition technology to estimate the emotion. The matching unit can also analyze the user's facial expression using image recognition technology to estimate the emotion. This enables more appropriate matching by adjusting the matching criteria based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that estimates a user's emotions and adjusts matching criteria based on the estimated emotions.

[0103] The matching unit can select the optimal matching method by referring to the elderly person's past interaction history at the time of matching. For example, the matching unit selects the optimal matching method by referring to the elderly person's past interaction history at the time of matching. Specific collection methods and usage methods of the interaction history include, for example, past dialogue content, interaction frequency, etc., but are not limited to such examples. The matching unit can also analyze the elderly person's past interaction history using AI, for example, and select the optimal matching method. For example, the matching unit selects the optimal matching method based on the elderly person's past interaction history. In addition, the matching unit can adjust the matching method by referring to the elderly person's past interaction history. Furthermore, the matching unit can analyze the elderly person's past interaction history and propose the optimal matching method. As a result, the optimal matching method can be selected by referring to the past interaction history. A part or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that analyzes the elderly person's past interaction history and selects the optimal matching method.

[0104] The matching unit can perform matching based on the hobbies and fields of interest of the elderly person at the time of matching. For example, the matching unit performs matching based on the hobbies and fields of interest of the elderly person at the time of matching. Specific definitions and collection methods of hobbies and fields of interest include, for example, sports, music, reading, etc., but are not limited to such examples. The matching unit can also perform matching by analyzing the hobbies and fields of interest of the elderly person using AI, for example. For example, the matching unit performs optimal matching based on the hobbies of the elderly person. In addition, the matching unit can also adjust the matching by referring to the fields of interest of the elderly person. Furthermore, the matching unit can analyze the hobbies and fields of interest of the elderly person and propose optimal matching. As a result, more appropriate matching is possible by performing matching based on the hobbies and fields of interest. Part or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can perform matching by analyzing the hobbies and fields of interest of the elderly person and using an AI model that performs matching.

[0105] The matching unit can estimate the user's emotion and determine the priority of matching based on the estimated user's emotion. The matching unit, for example, estimates the user's emotion and determines the priority of matching based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis, facial expression analysis, etc., but are not limited to such examples. The matching unit can also estimate the user's emotion and determine the priority of matching using, for example, AI. For example, the matching unit analyzes the tone of the user's voice using voice recognition technology to estimate the emotion. The matching unit can also analyze the user's facial expression using image recognition technology to estimate the emotion. Thereby, by determining the priority of matching based on the user's emotion, important matching can be performed preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that estimates a user's emotions and determines matching priorities based on the estimated emotions.

[0106] The matching unit can select the optimal matching method in consideration of the geographical location information of the elderly person at the time of matching. For example, the matching unit selects the optimal matching method in consideration of the geographical location information of the elderly person at the time of matching. Specific collection methods and usage methods of the geographical location information include, for example, GPS data, address information, etc., but are not limited to such examples. For example, the matching unit can analyze the geographical location information of the elderly person using AI and select the optimal matching method. For example, the matching unit selects the optimal matching method based on the geographical location information of the elderly person. In addition, the matching unit can adjust the matching method by referring to the geographical location information of the elderly person. Furthermore, the matching unit can analyze the geographical location information of the elderly person and propose the optimal matching method. In this way, the optimal matching method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that analyzes the geographical location information of the elderly person and selects the optimal matching method.

[0107] The matching unit can analyze the social media activity of the elderly person at the time of matching and propose a matching means. The matching unit, for example, analyzes the social media activity of the elderly person at the time of matching and proposes a matching means. Specific analysis methods and target data of social media activity include, for example, the content of posts, comments, the number of likes, etc., but are not limited to such examples. The matching unit can also analyze the social media activity of the elderly person using AI and propose a matching means. For example, the matching unit proposes an optimal matching means based on the social media activity of the elderly person. In addition, the matching unit can adjust the matching means by referring to the social media activity of the elderly person. Furthermore, the matching unit can analyze the social media activity of the elderly person and select an optimal matching means. In this way, the optimal matching means can be proposed by analyzing the social media activity. A part or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can perform matching using an AI model that analyzes the social media activity of the elderly person and proposes a matching means.

[0108] The dialogue support unit can estimate the user's emotion and adjust the content of the dialogue support based on the estimated user's emotion. The dialogue support unit, for example, estimates the user's emotion and adjusts the content of the dialogue support based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis, facial expression analysis, and the like, but are not limited to such examples. The dialogue support unit can also estimate the user's emotion using, for example, AI and adjust the content of the dialogue support. For example, the dialogue support unit analyzes the tone of the user's voice using voice recognition technology and estimates the emotion. The dialogue support unit can also analyze the user's facial expression using image recognition technology and estimate the emotion. This enables more appropriate dialogue support by adjusting the content of the dialogue support based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue support unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue support unit can provide dialogue support using an AI model that estimates the user's emotions and adjusts the content of the dialogue support based on the estimated emotions.

[0109] The dialogue support unit can select the optimal support method by referring to the elderly person's past dialogue history during dialogue support. For example, the dialogue support unit selects the optimal support method by referring to the elderly person's past dialogue history during dialogue support. Specific collection and use methods of the dialogue history include, for example, past dialogue content, dialogue frequency, etc., but are not limited to such examples. The dialogue support unit can also analyze the elderly person's past dialogue history using AI to select the optimal support method. For example, the dialogue support unit selects the optimal support method based on the elderly person's past dialogue history. In addition, the dialogue support unit can adjust the support method by referring to the elderly person's past dialogue history. Furthermore, the dialogue support unit can analyze the elderly person's past dialogue history and propose the optimal support method. As a result, the optimal support method can be selected by referring to the past dialogue history. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the elderly person's past dialogue history and selects the optimal support method.

[0110] The dialogue support unit can adjust the timing of dialogue support based on the current living situation of the elderly person during dialogue support. The dialogue support unit, for example, adjusts the timing of dialogue support based on the current living situation of the elderly person during dialogue support. Specific evaluation criteria and collection methods for the living situation include, for example, daily activity patterns and living environment, but are not limited to such examples. The dialogue support unit can also analyze the living situation of the elderly person using AI and adjust the timing of dialogue support. For example, the dialogue support unit performs dialogue support during a time period when the elderly person is relaxed. In addition, the dialogue support unit can also perform dialogue support by avoiding a time period when the elderly person is busy. Furthermore, the dialogue support unit can perform dialogue support at an optimal timing in accordance with the elderly person's life rhythm. As a result, dialogue support can be performed at a more appropriate timing by adjusting the timing of dialogue support based on the living situation. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the living situation of the elderly person and adjusts the timing of dialogue support.

[0111] The dialogue support unit can estimate the user's emotion and determine the priority of dialogue support based on the estimated user's emotion. The dialogue support unit, for example, estimates the user's emotion and determines the priority of dialogue support based on the estimated user's emotion. Specific estimation methods and standards for emotion include, for example, voice analysis and facial expression analysis, but are not limited to such examples. The dialogue support unit can also estimate the user's emotion using, for example, AI and determine the priority of dialogue support. For example, the dialogue support unit analyzes the tone of the user's voice using voice recognition technology and estimates the emotion. The dialogue support unit can also analyze the user's facial expression using image recognition technology and estimate the emotion. Thereby, important dialogue support can be given priority by determining the priority of dialogue support based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue support unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue support unit can provide dialogue support using an AI model that estimates the user's emotions and determines the priority of dialogue support based on the estimated emotions.

[0112] The dialogue support unit can select the optimal support method in consideration of the geographical location information of the elderly person at the time of dialogue support. For example, the dialogue support unit selects the optimal support method in consideration of the geographical location information of the elderly person at the time of dialogue support. Specific collection methods and usage methods of the geographical location information include, for example, GPS data, address information, etc., but are not limited to such examples. The dialogue support unit can also analyze the geographical location information of the elderly person using AI, for example, and select the optimal support method. For example, the dialogue support unit selects the optimal support method based on the geographical location information of the elderly person. In addition, the dialogue support unit can also adjust the support method by referring to the geographical location information of the elderly person. Furthermore, the dialogue support unit can analyze the geographical location information of the elderly person and propose the optimal support method. As a result, the optimal support method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the geographical location information of the elderly person and selects the optimal support method.

[0113] The dialogue support unit can analyze the social media activity of the elderly person at the time of dialogue support and propose the content of the dialogue support. For example, the dialogue support unit analyzes the social media activity of the elderly person at the time of dialogue support and proposes the content of the dialogue support. Specific analysis methods and target data of social media activity include, for example, the content of posts, the number of comments, and the like, but are not limited to such examples. The dialogue support unit can also analyze the social media activity of the elderly person using AI and propose the content of the dialogue support. For example, the dialogue support unit proposes optimal dialogue support content based on the social media activity of the elderly person. In addition, the dialogue support unit can adjust the dialogue support content by referring to the social media activity of the elderly person. Furthermore, the dialogue support unit can analyze the social media activity of the elderly person and select optimal dialogue support content. In this way, the optimal dialogue support content can be proposed by analyzing the social media activity. A part or all of the above-mentioned processing in the dialogue support unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue support unit can perform dialogue support using an AI model that analyzes the social media activity of the elderly person and proposes the content of dialogue support.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The health management system may further include a nutritional evaluation unit that records the dietary contents of the elderly person and evaluates the nutritional balance. For example, the collection unit collects images of the elderly person eating and analyzes the dietary contents using image recognition technology. The evaluation unit can evaluate the nutritional balance based on the analyzed dietary contents and recommend the intake of necessary nutrients. In addition, the management unit can store the nutritional evaluation results in a database and compare them with past dietary history to track changes in nutritional balance. This makes it possible to manage the dietary contents of the elderly person and help maintain their health.

[0116] The health management system may further include an exercise evaluation unit that records the amount of exercise of the elderly person and warns them of lack of exercise. For example, the collection unit collects the number of steps and exercise time of the elderly person, and the exercise evaluation unit analyzes the data. The evaluation unit can warn them of lack of exercise based on the analysis results and recommend appropriate exercise. In addition, the management unit can store the exercise evaluation results in a database and track changes in the amount of exercise by comparing them with past exercise history. This makes it possible to manage the exercise habits of the elderly person and help them maintain their health.

[0117] The health management system may further include a sleep evaluation unit that records the sleep patterns of the elderly and evaluates the quality of their sleep. For example, the collection unit collects the movements and sounds of the elderly while they are sleeping, and the sleep evaluation unit analyzes the data. The evaluation unit can evaluate the quality of sleep based on the analysis results and suggest necessary improvements. The management unit can also store the sleep evaluation results in a database and compare them with past sleep histories to track changes in sleep patterns. This can help manage the sleeping habits of the elderly and maintain their health.

[0118] The health management system may further include a stress assessment unit that assesses the elderly person's stress level and suggests relaxation methods. For example, the collection unit collects the elderly person's heart rate and breathing patterns, and the stress assessment unit analyzes the data. The assessment unit can assess the stress level based on the analysis results and suggest relaxation methods. The management unit can also store the stress assessment results in a database and compare them with past stress levels to track changes. This allows for stress management for the elderly person and helps maintain their health.

[0119] The health management system may further include a social activity evaluation unit for recording the social activities of the elderly and preventing isolation. For example, the collection unit collects the frequency of the elderly's going out and the duration of conversation, and the social activity evaluation unit analyzes the data. The evaluation unit can evaluate the level of social activity based on the analysis results and recommend necessary social activities. The management unit can also store the social activity evaluation results in a database and compare them with past social activity history to track changes. This makes it possible to manage the social activities of the elderly and prevent isolation.

[0120] The health management system may further include a music providing unit that estimates the elderly person's emotions and provides appropriate music based on the estimated emotions. For example, the collection unit collects the elderly person's tone of voice and facial expressions to estimate emotions. The music providing unit may provide relaxing music or uplifting music based on the estimated emotions. The management unit may also store the music provision results in a database and compare them with past music provision history to track the effects. This allows music to be provided according to the elderly person's emotions to support their mental health.

[0121] The health management system may further include an exercise suggestion unit that estimates the elderly person's emotions and suggests appropriate exercises based on the estimated emotions. For example, the collection unit collects the elderly person's tone of voice and facial expressions to estimate emotions. The exercise suggestion unit may suggest relaxing yoga or invigorating walking based on the estimated emotions. The management unit may also store the exercise suggestion results in a database and track the effects by comparing them with past exercise suggestion history. This makes it possible to suggest exercises according to the elderly person's emotions and support their physical and mental health.

[0122] The health management system may further include a meal suggestion unit that estimates the elderly person's emotions and suggests an appropriate meal based on the estimated emotions. For example, the collection unit collects the elderly person's tone of voice and facial expressions to estimate the emotions. The meal suggestion unit may suggest a relaxing meal or an invigorating meal based on the estimated emotions. The management unit may also store the meal suggestion results in a database and compare them with the history of past meal suggestions to track the effects. This makes it possible to suggest meals according to the elderly person's emotions and support their physical and mental health.

[0123] The health management system may further include a relaxation suggestion unit that estimates the elderly person's emotions and suggests an appropriate relaxation method based on the estimated emotions. For example, the collection unit collects the elderly person's tone of voice and facial expressions to estimate the emotions. The relaxation suggestion unit may suggest relaxation methods such as meditation and deep breathing based on the estimated emotions. The management unit may also store the relaxation suggestion results in a database and track the effects by comparing them with past relaxation suggestion history. This makes it possible to suggest relaxation methods according to the elderly person's emotions and support their mental health.

[0124] The health management system may further include a dialogue suggestion unit that estimates the emotions of the elderly person and suggests appropriate dialogue content based on the estimated emotions. For example, the collection unit collects the tone of voice and facial expressions of the elderly person to estimate the emotions. The dialogue suggestion unit may suggest relaxing or uplifting topics based on the estimated emotions. The management unit may also store the dialogue suggestion results in a database and track their effectiveness by comparing them with the history of past dialogue suggestions. This makes it possible to suggest dialogue content according to the emotions of the elderly person and support their mental health.

[0125] The process flow of the second embodiment will be briefly described below.

[0126] Step 1: The collection unit collects voices and images of the elderly. The voices and images of the elderly include everyday conversations, specific actions, and facial expressions. The collection unit collects the elderly's tone of voice using voice recognition technology and collects the elderly's appearance using image recognition technology. AI can also be used to collect voices and images. Step 2: The evaluation unit evaluates the health condition of the elderly person from the tone of voice or appearance based on the information collected by the collection unit. The evaluation unit uses AI to analyze voice and images, analyzes the tone of voice of the elderly person using voice recognition technology, and analyzes the appearance of the elderly person using image recognition technology. Step 3: The management department collects and manages the evaluation results by the evaluation department. The management department uses AI to manage the evaluation results, stores the evaluation results in a database, and refers to the evaluation results as needed.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] For example, the collection unit can collect voices and images of the elderly person using the camera 42 and microphone 38B of the smart device 14. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voices and images to evaluate the health condition. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the evaluation results in the database 24 and refers to them as necessary. The calling unit is realized, for example, by the control unit 46A of the smart device 14, and calls the user by generating a voice message or a text message. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] For example, the collection unit can collect voices and images of the elderly person using the camera 42 and microphone 238 of the smart glasses 214. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voices and images to evaluate the health condition. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the evaluation results in the database 24 and refers to them as necessary. The calling unit is realized, for example, by the control unit 46A of the smart glasses 214, and calls the user by generating a voice message or a text message. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] For example, the collecting unit can collect voices and images of the elderly person using the camera 42 and microphone 238 of the headset type terminal 314. The evaluating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voices and images to evaluate the health condition. The managing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the evaluation results in the database 24 and refers to them as necessary. The calling unit is realized, for example, by the control unit 46A of the headset type terminal 314, and calls the user by generating a voice message or a text message. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] For example, the collecting unit can collect voices and images of the elderly person using the camera 42 and microphone 238 of the robot 414. The evaluating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected voices and images to evaluate the health condition. The managing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the evaluation results in the database 24 and refers to them as necessary. The calling unit is realized, for example, by the control unit 46A of the robot 414, and calls the user by generating a voice message or a text message. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Appendix 1) A collection unit that collects voices and images of the elderly person; an evaluation unit that evaluates a health condition of a user from a tone of voice or an appearance of an elderly person based on the information collected by the collection unit; A management unit that collects and manages the evaluation results by the evaluation unit. A system characterized by: (Appendix 2) A calling unit is provided to call the user. 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collecting voices and images showing the user's response to the call made by the call unit 3. The system of claim 2, (Appendix 4) The system described in Appendix 1, characterized in that the management unit has a matching unit that matches elderly people who have the same hobbies. (Appendix 5) The management unit Equipping the elderly with a dialogue support unit to assist in dialogue between them 2. The system of claim 1, (Appendix 6) The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting voice and images based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The system described in Appendix 1, characterized in that the collection unit analyzes the elderly person's past health data and selects an appropriate collection method. (Appendix 8) The collecting unit includes: When collecting audio and images, filtering is performed based on the senior's current living situation and areas of interest. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting audio and images, the geographic location of the senior is taken into account to prioritize the collection of more relevant data. 2. The system of claim 1, (Appendix 11) The collecting unit includes: During audio and image collection, social media activity of seniors will be analyzed to collect relevant data. 2. The system of claim 1, (Appendix 12) The evaluation unit is Estimating a user's emotion and adjusting a health condition evaluation criterion based on the estimated user's emotion 2. The system of claim 1, (Appendix 13) The evaluation unit is When assessing health status, refer to past health data to improve the accuracy of the assessment 2. The system of claim 1, (Appendix 14) The evaluation unit is When assessing the health status of an elderly person, the lifestyle and environmental factors are taken into consideration. 2. The system of claim 1, (Appendix 15) The evaluation unit is Inferring user emotion and adjusting the way rating results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 16) The evaluation unit is When assessing health status, referencing an older adult's activity history helps prioritize assessments 2. The system of claim 1, (Appendix 17) The evaluation unit is When assessing health status, referencing relevant medical data of the elderly person will improve the accuracy of the assessment. 2. The system of claim 1, (Appendix 18) The management unit To estimate a user's emotion and adjust a data management method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The management unit When managing data, refer to past data management history to select the optimal management method 2. The system of claim 1, (Appendix 20) The management unit During data management, customize how data is organized based on the elderly person's living situation 2. The system of claim 1, (Appendix 21) The management unit To estimate a user's sentiment and determine a priority of data management based on the estimated user's sentiment. 2. The system of claim 1, (Appendix 22) The management unit When managing data, select the optimal management method taking into account the geographic location information of the elderly. 2. The system of claim 1, (Appendix 23) The management unit When managing data, analyze the social media activities of seniors and suggest ways to manage data. 2. The system of claim 1, (Appendix 24) The calling unit is Estimate the user's emotions and adjust the content of the call based on the estimated user's emotions. 3. The system of claim 2, (Appendix 25) The calling unit is When calling out to a senior, the system selects the most appropriate method of calling by referring to the senior's past response history. 3. The system of claim 2, (Appendix 26) The calling unit is When reaching out, adjust the timing of the call based on the senior's current living situation 3. The system of claim 2, (Appendix 27) The calling unit is Estimating a user's emotions and prioritizing calls based on the estimated user's emotions 3. The system of claim 2, (Appendix 28) The calling unit is When calling, the optimal calling method is selected taking into account the elderly person's geographic location information. 3. The system of claim 2, (Appendix 29) The calling unit is When making a call, the social media activity of seniors is analyzed to suggest the content of the call. 3. The system of claim 2, (Appendix 30) The matching unit is Estimate the user's emotions and adjust the matching criteria based on the estimated user's emotions. 4. The system of claim 3. (Appendix 31) The matching unit is When matching, the most appropriate matching method is selected by referring to the elderly person's past interaction history. 4. The system of claim 3. (Appendix 32) The matching unit is When matching, matching is done based on the hobbies and interests of seniors. 4. The system of claim 3. (Appendix 33) The matching unit is Estimate the user's emotions and determine matching priorities based on the estimated user emotions. 4. The system of claim 3. (Appendix 34) The matching unit is When matching, the optimal matching method is selected taking into account the geographic location information of the elderly person. 4. The system of claim 3. (Appendix 35) The matching unit is When matching, the social media activity of seniors is analyzed to suggest matching methods. 4. The system of claim 3. (Appendix 36) The dialogue support unit includes: To estimate a user's emotion and adjust the content of dialogue support based on the estimated user's emotion 5. The system of claim 4. (Appendix 37) The dialogue support unit includes: When providing dialogue support, the system selects the most appropriate support method by referring to the elderly person's past dialogue history. 5. The system of claim 4. (Appendix 38) The dialogue support unit includes: When providing dialogue support, the timing of the support is adjusted based on the elderly person's current living situation. 5. The system of claim 4. (Appendix 39) The dialogue support unit includes: To estimate a user's emotion and determine a priority of dialogue support based on the estimated user's emotion. 5. The system of claim 4. (Appendix 40) The dialogue support unit includes: When providing dialogue support, the system selects the most appropriate support method by taking into account the elderly person's geographic location information. 5. The system of claim 4. (Appendix 41) The dialogue support unit includes: When providing dialogue support, the system analyzes the social media activity of elderly people and suggests content for dialogue support. 5. The system of claim 4. [Explanation of symbols]

[0199] 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 voices and images of the elderly person; an evaluation unit that evaluates a health condition of a user from a tone of voice or an appearance of an elderly person based on the information collected by the collection unit; A management unit that collects and manages the evaluation results by the evaluation unit. A system characterized by:

2. A calling unit is provided to call the user.

2. The system of claim 1 .

3. The collecting unit includes: Collecting voices and images showing the user's response to the call made by the call unit 3. The system of claim 2.

4. The system according to claim 1 , wherein the management unit includes a matching unit that matches elderly people who have the same hobby with each other.

5. The management unit Equipping the elderly with a dialogue support unit to assist in dialogue between them 2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotion and adjust the timing of collecting voice and images based on the estimated user's emotion.

2. The system of claim 1 .

7. The system according to claim 1 , wherein the collection unit analyzes the elderly person's past health data and selects an appropriate collection method.

8. The collecting unit includes: When collecting audio and images, filtering is performed based on the senior's current living situation and areas of interest.

2. The system of claim 1 .

9. The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1 .

10. The collecting unit includes: When collecting audio and images, the geographic location of the senior is taken into account to prioritize the collection of more relevant data.

2. The system of claim 1 .

Images