system

Abstract

The system according to this embodiment aims to alleviate feelings of loneliness among elderly people living alone and to support communication and health management in their daily lives. [Solution] The system according to the embodiment comprises a reception unit, a language generation unit, an action suggestion unit, and a health monitoring unit. The reception unit receives input from the user. The language generation unit engages in everyday conversation based on the information received by the reception unit. The action suggestion unit proposes actions based on the user's health condition and weather conditions, based on the conversation content generated by the language generation unit. The health monitoring unit monitors the user's health based on the actions proposed by the action suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately supported elderly people living alone in reducing their sense of loneliness while simultaneously supporting their daily communication and health management, and there is room for improvement.

[0005] The system according to this embodiment aims to alleviate feelings of loneliness among elderly people living alone and to support communication and health management in their daily lives. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a language generation unit, an action suggestion unit, and a health monitoring unit. The reception unit receives input from a user. The language generation unit conducts everyday conversations based on the information received by the reception unit. The action suggestion unit suggests actions based on the user's health status and weather conditions, based on the content of the conversation generated by the language generation unit. The health monitoring unit monitors the user's health based on the actions suggested by the action suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the sense of loneliness felt by elderly people living alone and support communication and health management in daily life. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A digital companion service according to an embodiment of the present invention is an AI-based system that alleviates loneliness in elderly people living alone and supports communication and health management in their daily lives. This system performs health checks, conversations, and suggests hobby activities through AI that can be customized by the user. It also has a safety feature that automatically notifies family and medical institutions when an abnormality occurs. For example, when the user speaks to the system, the AI ​​responds appropriately and continues the conversation. The system then suggests appropriate outdoor or indoor activities based on the user's health condition and weather conditions. For example, it suggests a walk on a sunny day and an indoor hobby activity on a rainy day. Furthermore, the system regularly checks the user's health, measures their biometric information, and immediately notifies family and medical institutions if an abnormality is detected. In this way, the system constantly monitors the user's health and provides a safe and secure living environment. This reduces loneliness in elderly people living alone and supports communication and health management in their daily lives. For example, users can enjoy conversations with the AI, which can alleviate loneliness. Furthermore, the AI ​​suggests appropriate activities, enriching the user's life and ensuring proper health management. Furthermore, the system can quickly respond to abnormalities, allowing users to live with peace of mind. This allows the digital companion service to reduce the sense of loneliness felt by elderly people living alone and assist with communication and health management in daily life.

[0029] A digital companion service according to an embodiment includes a reception unit, a language generation unit, an action suggestion unit, and a health monitoring unit. The reception unit accepts input from a user. The user input includes, but is not limited to, voice input and text input. The reception unit converts the user's voice input into text data using, for example, voice recognition technology. The reception unit can also directly accept text input. The reception unit can also analyze the user's input and provide information for performing appropriate processing. For example, the reception unit analyzes the user's voice input and provides information for generating an appropriate response. The language generation unit uses a generation AI to conduct everyday conversations based on the information accepted by the reception unit. The language generation unit generates conversations with the user using, for example, a text generation AI (e.g., LLM). The language generation unit can also generate conversations with the user using a multimodal generation AI. For example, the language generation unit can generate appropriate responses based on the user's input and continue the conversation. The action suggestion unit suggests actions based on the conversation content generated by the language generation unit, taking into account the user's health condition and weather conditions. The action suggestion unit, for example, monitors the user's health condition and suggests appropriate activities. The action suggestion unit can also suggest outdoor or indoor activities based on weather conditions. For example, the action suggestion unit suggests taking a walk on a sunny day and suggesting indoor hobby activities on a rainy day. The health monitoring unit monitors the user's health based on the actions suggested by the action suggestion unit. For example, the health monitoring unit periodically measures the user's biometric information and immediately notifies family or medical institutions if any abnormalities are detected. The health monitoring unit can also constantly monitor the user's health condition and provide an environment in which the user can live safely. For example, the health monitoring unit measures the user's biometric information, such as body temperature, blood pressure, and heart rate, and immediately notifies the user if any abnormalities are detected. As a result, the digital companion service according to the embodiment can accept user input, have daily conversations, suggest actions, and monitor health.

[0030] The health monitoring unit can periodically measure biometric information and notify family members or medical institutions if an abnormality occurs. For example, the health monitoring unit can periodically measure the user's biometric information such as body temperature, blood pressure, and heart rate. For example, the health monitoring unit can measure the user's body temperature daily and immediately notify if an abnormality is detected. It can also measure the user's blood pressure weekly and immediately notify if an abnormality is detected. Furthermore, the health monitoring unit can measure the user's heart rate monthly and immediately notify if an abnormality is detected. This allows the health of the user to be protected by regularly measuring biometric information and promptly notifying of any abnormalities. Some or all of the above processes in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's biometric information into a generating AI and have the generating AI perform abnormality detection.

[0031] The language generation unit can diversify the content of conversations with the user and reduce feelings of loneliness. For example, the language generation unit uses a generation AI to diversify the content of conversations with the user. For example, the language generation unit can provide a variety of topics such as the weather, health, and hobbies. The language generation unit can also adjust the tone of conversation to match the user's mood. For example, if the user is relaxed, the language generation unit will converse in a calm tone. If the user is excited, the language generation unit can converse in a lively tone. Furthermore, if the user is sad, the language generation unit can converse in a comforting tone. In this way, by diversifying the content of conversations, feelings of loneliness can be reduced for the user. Some or all of the above processing in the language generation unit may be performed using AI, for example, or without AI. For example, the language generation unit can input user input into a generation AI and have the generation AI generate appropriate conversation content.

[0032] The action suggestion unit can suggest activities based on the user's hobbies. The action suggestion unit can suggest appropriate activities based on the user's hobbies, for example. For example, if the user's hobby is sports, the action suggestion unit can suggest moderate exercise. Furthermore, if the user's hobby is reading, the action suggestion unit can also suggest time to read. Furthermore, if the user's hobby is music, the action suggestion unit can also suggest time to enjoy music. In this way, by suggesting hobby activities for the user, the quality of life can be improved. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the action suggestion unit can input information about the user's hobbies into the generation AI and cause the generation AI to suggest appropriate activities.

[0033] The action suggestion unit can suggest outdoor or indoor activities depending on weather conditions. The action suggestion unit suggests appropriate activities based on weather conditions, for example. For example, the action suggestion unit suggests a walk on a sunny day. The action suggestion unit can also suggest indoor hobby activities on a rainy day. Furthermore, the action suggestion unit can also suggest activities in cool places on hot days and activities in warm places on cold days. This allows the user's health to be maintained by suggesting appropriate activities based on weather conditions. Some or all of the above-mentioned processing in the action suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the action suggestion unit can input information about weather conditions into the generation AI and cause the generation AI to suggest appropriate activities.

[0034] The health monitoring unit continuously monitors the user's health condition and can provide an environment in which the user can live safely. The health monitoring unit, for example, continuously monitors the user's health condition. For example, the health monitoring unit monitors the user's biological information, such as body temperature, blood pressure, and heart rate, in real time. The health monitoring unit can also periodically check the user's health and immediately notify the user if an abnormality is detected. Furthermore, the health monitoring unit can constantly monitor the user's health condition and provide an environment in which the user can live safely. For example, the health monitoring unit monitors the user's health condition and immediately notifies family members or medical institutions if an abnormality is detected. This allows the user's health condition to be constantly monitored, thereby providing an environment in which the user can live safely. Some or all of the above-described processing in the health monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the health monitoring unit can input the user's biological information into the generation AI and have the generation AI monitor the user's health condition.

[0035] The reception unit can analyze the user's past input history and select an appropriate reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can automatically complete similar input content by referring to content entered by the user in the past. In this way, the optimal reception method can be provided to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal reception method.

[0036] The reception unit can filter the input based on the user's current lifestyle or areas of interest. For example, the reception unit can filter the input based on the user's current lifestyle and areas of interest. For example, the reception unit can accept only information relevant to the user's current lifestyle. The reception unit can also filter out unnecessary information based on the user's areas of interest and accept only the necessary information. Furthermore, the reception unit can accept information at an appropriate time in accordance with the user's daily rhythm. This allows the reception unit to accept only the necessary information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0037] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving input. For example, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving input. For example, the reception unit can prioritize receiving nearby information based on the user's current location. The reception unit can also prioritize receiving information related to a specific region if the user is in that region. Furthermore, if the reception unit is on the move, it can prioritize receiving information about the user's destination. In this way, by considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of receiving highly relevant information.

[0038] The reception unit can analyze the user's social media usage status and receive related information when receiving input. For example, the reception unit can analyze the user's social media activity and receive related information when receiving input. For example, the reception unit can prioritize receiving topics of interest from the user's social media activity. The reception unit can also prioritize receiving information about accounts the user follows. Furthermore, the reception unit can analyze the content of the user's social media posts and receive related information. In this way, related information can be received by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to receive related information.

[0039] The language generation unit can generate appropriate conversation content by referring to the user's past conversation history when generating a conversation. For example, the language generation unit generates optimal conversation content by referring to the user's past conversation history when generating a conversation. For example, the language generation unit generates related conversation content based on topics the user has previously discussed. The language generation unit can also prioritize topics of interest from the user's past conversation history. Furthermore, the language generation unit can generate conversation content by referring to the user's preferred conversation style in the past. In this way, optimal conversation content for the user can be generated by referring to the past conversation history. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit can input the user's past conversation history data into the generation AI and cause the generation AI to generate appropriate conversation content.

[0040] The language generation unit can select a conversation topic based on the user's interests or concerns when generating a conversation. For example, the language generation unit selects a conversation topic based on the user's interests or concerns when generating a conversation. For example, the language generation unit selects a topic related to a hobby in which the user is interested. The language generation unit can also select a topic related to news or events in which the user is interested. Furthermore, the language generation unit can select a topic that the user has previously discussed. This enables more interesting conversations by selecting a conversation topic based on the user's interests or concerns. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit can input data related to the user's interests or concerns into the generation AI and have the generation AI select a conversation topic.

[0041] The language generation unit can adjust the timing of the conversation according to the user's lifestyle when generating a conversation. The language generation unit, for example, adjusts the timing of the conversation based on the user's lifestyle when generating a conversation. For example, the language generation unit may have a lively conversation when the user is in the morning. The language generation unit may also have a relaxed conversation when the user is in the evening. Furthermore, the language generation unit can start the conversation at an appropriate timing in accordance with the user's lifestyle. This allows for more appropriate conversation by adjusting the timing of the conversation based on the user's lifestyle. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit may input data regarding the user's lifestyle into the generation AI and cause the generation AI to adjust the timing of the conversation.

[0042] The language generation unit can customize the content of the conversation based on the user's cultural background when generating the conversation. For example, the language generation unit customizes the content of the conversation by taking the user's cultural background into consideration when generating the conversation. For example, the language generation unit uses appropriate greetings and expressions based on the user's cultural background. The language generation unit can also hold conversations about the user's cultural events and holidays. Furthermore, the language generation unit can select topics that match the user's cultural background. This makes it possible to provide more appropriate conversation content by taking the user's cultural background into consideration. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit can input data about the user's cultural background into the generation AI and cause the generation AI to customize the conversation content.

[0043] The action suggestion unit can make appropriate suggestions by referring to the user's past action history when suggesting an action. For example, the action suggestion unit can make optimal suggestions by referring to the user's past action history when suggesting an action. For example, the action suggestion unit makes relevant action suggestions based on activities the user has performed in the past. The action suggestion unit can also preferentially suggest activities that the user is interested in based on the user's past action history. Furthermore, the action suggestion unit can make action suggestions by referring to activities that the user has preferred in the past. In this way, optimal action suggestions can be made to the user by referring to the past action history. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the action suggestion unit can input the user's past action history data into a generation AI and cause the generation AI to generate appropriate action suggestions.

[0044] The action suggestion unit can customize the suggested content according to the user's health condition when suggesting an action. For example, the action suggestion unit customizes the suggested content based on the user's health condition when suggesting an action. For example, if the user is tired, the action suggestion unit can suggest a relaxing activity. Furthermore, if the user is seeking healthy exercise, the action suggestion unit can also suggest moderate exercise. Furthermore, if the user is feeling unwell, the action suggestion unit can also suggest rest. In this way, customizing the suggested content based on the user's health condition enables more appropriate action suggestions. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, or without, AI, for example. For example, the action suggestion unit can input data regarding the user's health condition into the generation AI and cause the generation AI to customize the suggested content.

[0045] The action suggestion unit can make appropriate suggestions based on the user's geographical location information when suggesting an action. For example, the action suggestion unit makes optimal suggestions based on the user's geographical location information when suggesting an action. For example, the action suggestion unit suggests nearby activities based on the user's current location. Furthermore, if the user is in a specific area, the action suggestion unit can also suggest activities related to that area. Furthermore, if the user is traveling, the action suggestion unit can also suggest activities at the user's destination. In this way, optimal action suggestions can be made by taking the user's geographical location information into consideration. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the action suggestion unit can input the user's geographical location information to a generation AI and cause the generation AI to generate appropriate suggestions.

[0046] The Action Suggestion Unit can analyze the user's social media usage and make relevant suggestions when making action suggestions. For example, the Action Suggestion Unit can analyze the user's social media activity and make relevant suggestions when making action suggestions. For example, the Action Suggestion Unit can suggest activities of interest based on the user's social media activity. The Action Suggestion Unit can also suggest activities of accounts that the user follows. Furthermore, the Action Suggestion Unit can analyze the content of the user's social media posts and suggest relevant activities. In this way, relevant action suggestions can be made by analyzing the user's social media activity. Some or all of the above processing in the Action Suggestion Unit may be performed using AI, for example, or without AI. For example, the Action Suggestion Unit can input the user's social media activity data into a generating AI and have the generating AI generate relevant suggestions.

[0047] The health monitoring unit can improve the accuracy of anomaly detection by referring to the user's past health data during health monitoring. For example, the health monitoring unit can improve the accuracy of anomaly detection by referring to the user's past health data during health monitoring. For example, the health monitoring unit can perform early detection of anomalies based on the user's past health data. The health monitoring unit can also analyze specific patterns from the user's past health data and detect anomalies. Furthermore, the health monitoring unit can optimize the anomaly detection algorithm by referring to the user's past health data. In this way, the accuracy of anomaly detection can be improved by referring to past health data. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without using AI. For example, the health monitoring unit can input the user's past health data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0048] The health monitoring unit can adjust the monitoring timing according to the user's lifestyle during health monitoring. For example, the health monitoring unit adjusts the monitoring timing based on the user's lifestyle. For example, if the user has a morning lifestyle, the health monitoring unit will perform health monitoring in the morning. Also, if the user has a night lifestyle, the health monitoring unit can perform health monitoring in the evening. Furthermore, the health monitoring unit can perform health monitoring at an appropriate time in accordance with the user's lifestyle. By adjusting the monitoring timing based on the user's lifestyle, more appropriate health monitoring becomes possible. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input data about the user's lifestyle into a generating AI and have the generating AI perform the adjustment of the monitoring timing.

[0049] The health monitoring unit can select an appropriate monitoring method based on the user's geographical location information during health monitoring. For example, the health monitoring unit can select the optimal monitoring method based on the user's geographical location information during health monitoring. For example, if the user is at home, the health monitoring unit can perform regular health checks. The health monitoring unit can also perform simple health checks if the user is out. Furthermore, if the user is in a specific area, the health monitoring unit can perform health monitoring tailored to the environment of that area. In this way, optimal health monitoring can be performed by considering the user's geographical location information. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's geographical location information into a generating AI and have the generating AI select an appropriate monitoring method.

[0050] The health monitoring unit can monitor a user's health status by analyzing their social media usage during health monitoring. For example, the health monitoring unit can analyze a user's social media activity to monitor their health status. For example, the health monitoring unit can estimate a user's stress level from their social media activity and reflect this in the health monitoring. The health monitoring unit can also analyze the content of a user's social media posts to monitor their health status. Furthermore, the health monitoring unit can adjust the frequency of health monitoring based on the user's frequency of social media activity. This allows for more accurate monitoring of a user's health status by analyzing their social media activity. Some or all of the above-described processes in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's social media activity data into a generating AI and have the generating AI perform health status monitoring.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze user input and refer to the user's past behavior history to generate more appropriate responses. For example, the reception desk can prioritize providing relevant information based on the user's frequently performed actions and statements in the past. It can also identify specific patterns in the user's past behavior history and prepare in advance for predicted actions. Furthermore, the reception desk can select a response style preferred by the user based on their past behavior history, providing more personalized responses. In this way, by leveraging the user's past behavior history, it is possible to provide more appropriate and personalized responses.

[0053] When monitoring the user's health status, the health monitoring unit can adjust the monitoring timing based on the user's lifestyle. For example, if the user has a morning-type lifestyle, the health monitoring unit can perform a health check in the morning. Alternatively, if the user has a nocturnal lifestyle, the health monitoring unit can perform a health check in the evening. Furthermore, the health monitoring unit can perform health monitoring at an appropriate timing in accordance with the user's lifestyle. This allows for more appropriate health monitoring by adjusting the monitoring timing based on the user's lifestyle.

[0054] The reception unit can preferentially receive highly relevant information based on the user's geographical location information. For example, the reception unit preferentially receives nearby information based on the user's current location. Furthermore, when the user is in a specific area, the reception unit can also preferentially receive information related to that area. Furthermore, when the user is traveling, the reception unit can also preferentially receive information about the user's destination. In this way, highly relevant information can be preferentially received by taking the user's geographical location information into consideration.

[0055] When generating a conversation, the language generation unit can generate appropriate conversation content by referring to the user's past conversation history. For example, the language generation unit generates related conversation content based on topics that the user has talked about in the past. The language generation unit can also prioritize topics of interest from the user's past conversation history. Furthermore, the language generation unit can generate conversation content by referring to the conversation style that the user has preferred in the past. In this way, by referring to the past conversation history, it is possible to generate conversation content that is optimal for the user.

[0056] When suggesting an action, the action suggestion unit can make appropriate suggestions by referring to the user's past action history. For example, the action suggestion unit makes relevant action suggestions based on activities the user has performed in the past. The action suggestion unit can also preferentially suggest activities that the user is interested in based on the user's past action history. Furthermore, the action suggestion unit can also make action suggestions by referring to activities that the user has preferred in the past. In this way, by referring to the past action history, it is possible to make optimal action suggestions for the user.

[0057] During health monitoring, the health monitoring unit can improve the accuracy of anomaly detection by referring to the user's past health data. For example, the health monitoring unit can perform early detection of anomalies based on the user's past health data. The health monitoring unit can also analyze specific patterns from the user's past health data to detect anomalies. Furthermore, the health monitoring unit can also optimize the anomaly detection algorithm by referring to the user's past health data. In this way, the accuracy of anomaly detection can be improved by referring to past health data.

[0058] When suggesting an action, the action suggestion unit can analyze the user's social media usage status and make related suggestions. For example, the action suggestion unit can suggest activities of interest based on the user's social media activity. The action suggestion unit can also suggest activities of accounts the user follows. Furthermore, the action suggestion unit can analyze the content of the user's comments on social media and suggest related activities. In this way, it is possible to make related action suggestions by analyzing the user's social media activity.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives input from the user. User input includes voice input and text input. The reception unit can also convert voice input into text data using voice recognition technology and directly receive text input. The reception unit also analyzes the user's input and provides information for appropriate processing. Step 2: The language generation unit conducts everyday conversations based on the information received by the reception unit. The language generation unit uses generation AI, such as text generation AI (LLM) or multimodal generation AI, to generate a conversation with the user. This allows the unit to generate appropriate responses based on the user's input and continue the conversation. Step 3: The action suggestion unit suggests actions based on the conversation content generated by the language generation unit, taking into account the user's health condition and weather conditions. The action suggestion unit monitors the user's health condition and suggests appropriate activities. It can also suggest outdoor or indoor activities based on weather conditions. For example, it suggests taking a walk on a nice day and indoor hobby activities on a rainy day. Step 4: The health monitoring unit monitors the user's health based on the actions suggested by the action suggestion unit. The health monitoring unit periodically measures the user's biometric information and immediately notifies family members or medical institutions if any abnormalities are detected. The health monitoring unit also measures the user's biometric information, such as body temperature, blood pressure, and heart rate, and immediately notifies any abnormalities.

[0061] (Example 2) A digital companion service according to an embodiment of the present invention is an AI-based system that alleviates loneliness in elderly people living alone and supports communication and health management in their daily lives. This system performs health checks, conversations, and suggests hobby activities through AI that can be customized by the user. It also has a safety feature that automatically notifies family and medical institutions when an abnormality occurs. For example, when the user speaks to the system, the AI ​​responds appropriately and continues the conversation. The system then suggests appropriate outdoor or indoor activities based on the user's health condition and weather conditions. For example, it suggests a walk on a sunny day and an indoor hobby activity on a rainy day. Furthermore, the system regularly checks the user's health, measures their biometric information, and immediately notifies family and medical institutions if an abnormality is detected. In this way, the system constantly monitors the user's health and provides a safe and secure living environment. This reduces loneliness in elderly people living alone and supports communication and health management in their daily lives. For example, users can enjoy conversations with the AI, which can alleviate loneliness. Furthermore, the AI ​​suggests appropriate activities, enriching the user's life and ensuring proper health management. Furthermore, the system can quickly respond to abnormalities, allowing users to live with peace of mind. This allows the digital companion service to reduce the sense of loneliness felt by elderly people living alone and assist with communication and health management in daily life.

[0062] A digital companion service according to an embodiment includes a reception unit, a language generation unit, an action suggestion unit, and a health monitoring unit. The reception unit accepts input from a user. The user input includes, but is not limited to, voice input and text input. The reception unit converts the user's voice input into text data using, for example, voice recognition technology. The reception unit can also directly accept text input. The reception unit can also analyze the user's input and provide information for performing appropriate processing. For example, the reception unit analyzes the user's voice input and provides information for generating an appropriate response. The language generation unit uses a generation AI to conduct everyday conversations based on the information accepted by the reception unit. The language generation unit generates conversations with the user using, for example, a text generation AI (e.g., LLM). The language generation unit can also generate conversations with the user using a multimodal generation AI. For example, the language generation unit can generate appropriate responses based on the user's input and continue the conversation. The action suggestion unit suggests actions based on the conversation content generated by the language generation unit, taking into account the user's health condition and weather conditions. The action suggestion unit, for example, monitors the user's health condition and suggests appropriate activities. The action suggestion unit can also suggest outdoor or indoor activities based on weather conditions. For example, the action suggestion unit suggests taking a walk on a sunny day and suggesting indoor hobby activities on a rainy day. The health monitoring unit monitors the user's health based on the actions suggested by the action suggestion unit. For example, the health monitoring unit periodically measures the user's biometric information and immediately notifies family or medical institutions if any abnormalities are detected. The health monitoring unit can also constantly monitor the user's health condition and provide an environment in which the user can live safely. For example, the health monitoring unit measures the user's biometric information, such as body temperature, blood pressure, and heart rate, and immediately notifies the user if any abnormalities are detected. As a result, the digital companion service according to the embodiment can accept user input, have daily conversations, suggest actions, and monitor health.

[0063] The health monitoring unit can periodically measure biometric information and notify family members or medical institutions if an abnormality occurs. For example, the health monitoring unit can periodically measure the user's biometric information such as body temperature, blood pressure, and heart rate. For example, the health monitoring unit can measure the user's body temperature daily and immediately notify if an abnormality is detected. It can also measure the user's blood pressure weekly and immediately notify if an abnormality is detected. Furthermore, the health monitoring unit can measure the user's heart rate monthly and immediately notify if an abnormality is detected. This allows the health of the user to be protected by regularly measuring biometric information and promptly notifying of any abnormalities. Some or all of the above processes in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's biometric information into a generating AI and have the generating AI perform abnormality detection.

[0064] The language generation unit can diversify the content of conversations with the user and reduce feelings of loneliness. For example, the language generation unit uses a generation AI to diversify the content of conversations with the user. For example, the language generation unit can provide a variety of topics such as the weather, health, and hobbies. The language generation unit can also adjust the tone of conversation to match the user's mood. For example, if the user is relaxed, the language generation unit will converse in a calm tone. If the user is excited, the language generation unit can converse in a lively tone. Furthermore, if the user is sad, the language generation unit can converse in a comforting tone. In this way, by diversifying the content of conversations, feelings of loneliness can be reduced for the user. Some or all of the above processing in the language generation unit may be performed using AI, for example, or without AI. For example, the language generation unit can input user input into a generation AI and have the generation AI generate appropriate conversation content.

[0065] The action suggestion unit can suggest activities based on the user's hobbies. The action suggestion unit can suggest appropriate activities based on the user's hobbies, for example. For example, if the user's hobby is sports, the action suggestion unit can suggest moderate exercise. Furthermore, if the user's hobby is reading, the action suggestion unit can also suggest time to read. Furthermore, if the user's hobby is music, the action suggestion unit can also suggest time to enjoy music. In this way, by suggesting hobby activities for the user, the quality of life can be improved. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the action suggestion unit can input information about the user's hobbies into the generation AI and cause the generation AI to suggest appropriate activities.

[0066] The action suggestion unit can suggest outdoor or indoor activities depending on weather conditions. The action suggestion unit suggests appropriate activities based on weather conditions, for example. For example, the action suggestion unit suggests a walk on a sunny day. The action suggestion unit can also suggest indoor hobby activities on a rainy day. Furthermore, the action suggestion unit can also suggest activities in cool places on hot days and activities in warm places on cold days. This allows the user's health to be maintained by suggesting appropriate activities based on weather conditions. Some or all of the above-mentioned processing in the action suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the action suggestion unit can input information about weather conditions into the generation AI and cause the generation AI to suggest appropriate activities.

[0067] The health monitoring unit continuously monitors the user's health condition and can provide an environment in which the user can live safely. The health monitoring unit, for example, continuously monitors the user's health condition. For example, the health monitoring unit monitors the user's biological information, such as body temperature, blood pressure, and heart rate, in real time. The health monitoring unit can also periodically check the user's health and immediately notify the user if an abnormality is detected. Furthermore, the health monitoring unit can constantly monitor the user's health condition and provide an environment in which the user can live safely. For example, the health monitoring unit monitors the user's health condition and immediately notifies family members or medical institutions if an abnormality is detected. This allows the user's health condition to be constantly monitored, thereby providing an environment in which the user can live safely. Some or all of the above-described processing in the health monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the health monitoring unit can input the user's biological information into the generation AI and have the generation AI monitor the user's health condition.

[0068] The reception unit can estimate the user's emotion and adjust the timing of input reception according to the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of input reception. For example, if the user is feeling stressed, the reception unit delays the timing of input reception to provide time for relaxation. Furthermore, if the user is relaxed, the reception unit can immediately accept input to promote smooth operation. Furthermore, if the user is in a hurry, the reception unit can accelerate the timing of input reception to enable a quick response. This enables more appropriate input reception by adjusting the timing of input reception according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute a process of adjusting the timing of input reception.

[0069] The reception unit can analyze the user's past input history and select an appropriate reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can automatically complete similar input content by referring to content entered by the user in the past. In this way, the optimal reception method can be provided to the user by analyzing the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal reception method.

[0070] The reception unit can filter the input based on the user's current lifestyle or areas of interest. For example, the reception unit can filter the input based on the user's current lifestyle and areas of interest. For example, the reception unit can accept only information relevant to the user's current lifestyle. The reception unit can also filter out unnecessary information based on the user's areas of interest and accept only the necessary information. Furthermore, the reception unit can accept information at an appropriate time in accordance with the user's daily rhythm. This allows the reception unit to accept only the necessary information by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0071] The reception unit can estimate the user's emotions and determine the priority of the information to receive based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize receiving important information. If the user is relaxed, the reception unit may also prioritize receiving interesting information. Furthermore, if the user is in a hurry, the reception unit may prioritize receiving urgent information. This allows for the reception of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0072] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving input. For example, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving input. For example, the reception unit can prioritize receiving nearby information based on the user's current location. The reception unit can also prioritize receiving information related to a specific region if the user is in that region. Furthermore, if the reception unit is on the move, it can prioritize receiving information about the user's destination. In this way, by considering the user's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of receiving highly relevant information.

[0073] The reception unit can analyze the user's social media usage status and receive related information when receiving input. For example, the reception unit can analyze the user's social media activity and receive related information when receiving input. For example, the reception unit can prioritize receiving topics of interest from the user's social media activity. The reception unit can also prioritize receiving information about accounts the user follows. Furthermore, the reception unit can analyze the content of the user's social media posts and receive related information. In this way, related information can be received by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to receive related information.

[0074] The language generation unit can estimate the user's emotions and adjust the way the conversation is expressed depending on the estimated user's emotions. The language generation unit, for example, estimates the user's emotions and adjusts the way the conversation is expressed. For example, if the user is relaxed, the language generation unit can use a calm tone when speaking. If the user is excited, the language generation unit can also use a lively tone when speaking. Furthermore, if the user is sad, the language generation unit can also use a comforting tone when speaking. This allows for more appropriate conversation by adjusting the way the conversation is expressed depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the language generation unit may be performed using an AI, or may be performed without an AI. For example, the language generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the conversation is expressed.

[0075] The language generation unit can generate appropriate conversation content by referring to the user's past conversation history when generating a conversation. For example, the language generation unit generates optimal conversation content by referring to the user's past conversation history when generating a conversation. For example, the language generation unit generates related conversation content based on topics the user has previously discussed. The language generation unit can also prioritize topics of interest from the user's past conversation history. Furthermore, the language generation unit can generate conversation content by referring to the user's preferred conversation style in the past. In this way, optimal conversation content for the user can be generated by referring to the past conversation history. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit can input the user's past conversation history data into the generation AI and cause the generation AI to generate appropriate conversation content.

[0076] The language generation unit can select a conversation topic based on the user's interests or concerns when generating a conversation. For example, the language generation unit selects a conversation topic based on the user's interests or concerns when generating a conversation. For example, the language generation unit selects a topic related to a hobby in which the user is interested. The language generation unit can also select a topic related to news or events in which the user is interested. Furthermore, the language generation unit can select a topic that the user has previously discussed. This enables more interesting conversations by selecting a conversation topic based on the user's interests or concerns. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit can input data related to the user's interests or concerns into the generation AI and have the generation AI select a conversation topic.

[0077] The language generation unit can estimate the user's emotions and adjust the length of the conversation according to the estimated user emotions. The language generation unit, for example, estimates the user's emotions and adjusts the length of the conversation. For example, the language generation unit may hold a longer conversation when the user is relaxed. Furthermore, the language generation unit may hold a short, to-the-point conversation when the user is in a hurry. Furthermore, the language generation unit may hold a short, concise conversation when the user is tired. This allows for more appropriate conversation by adjusting the length of the conversation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the language generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the language generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the conversation.

[0078] The language generation unit can adjust the timing of the conversation according to the user's lifestyle when generating a conversation. The language generation unit, for example, adjusts the timing of the conversation based on the user's lifestyle when generating a conversation. For example, the language generation unit may have a lively conversation when the user is in the morning. The language generation unit may also have a relaxed conversation when the user is in the evening. Furthermore, the language generation unit can start the conversation at an appropriate timing in accordance with the user's lifestyle. This allows for more appropriate conversation by adjusting the timing of the conversation based on the user's lifestyle. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit may input data regarding the user's lifestyle into the generation AI and cause the generation AI to adjust the timing of the conversation.

[0079] The language generation unit can customize the content of the conversation based on the user's cultural background when generating the conversation. For example, the language generation unit customizes the content of the conversation by taking the user's cultural background into consideration when generating the conversation. For example, the language generation unit uses appropriate greetings and expressions based on the user's cultural background. The language generation unit can also hold conversations about the user's cultural events and holidays. Furthermore, the language generation unit can select topics that match the user's cultural background. This makes it possible to provide more appropriate conversation content by taking the user's cultural background into consideration. Some or all of the above-described processing in the language generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the language generation unit can input data about the user's cultural background into the generation AI and cause the generation AI to customize the conversation content.

[0080] The action suggestion unit can estimate the user's emotions and adjust the manner in which action suggestions are expressed according to the estimated user emotions. The action suggestion unit, for example, estimates the user's emotions and adjusts the manner in which action suggestions are expressed. For example, if the user is relaxed, the action suggestion unit can make action suggestions in a calm tone. Furthermore, if the user is excited, the action suggestion unit can make action suggestions in a lively tone. Furthermore, if the user is sad, the action suggestion unit can make action suggestions in a comforting tone. This enables more appropriate action suggestions by adjusting the manner in which action suggestions are expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the action suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the manner in which action suggestions are expressed.

[0081] The action suggestion unit can make appropriate suggestions by referring to the user's past action history when suggesting an action. For example, the action suggestion unit can make optimal suggestions by referring to the user's past action history when suggesting an action. For example, the action suggestion unit makes relevant action suggestions based on activities the user has performed in the past. The action suggestion unit can also preferentially suggest activities that the user is interested in based on the user's past action history. Furthermore, the action suggestion unit can make action suggestions by referring to activities that the user has preferred in the past. In this way, optimal action suggestions can be made to the user by referring to the past action history. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the action suggestion unit can input the user's past action history data into a generation AI and cause the generation AI to generate appropriate action suggestions.

[0082] The action suggestion unit can customize the suggested content according to the user's health condition when suggesting an action. For example, the action suggestion unit customizes the suggested content based on the user's health condition when suggesting an action. For example, if the user is tired, the action suggestion unit can suggest a relaxing activity. Furthermore, if the user is seeking healthy exercise, the action suggestion unit can also suggest moderate exercise. Furthermore, if the user is feeling unwell, the action suggestion unit can also suggest rest. In this way, customizing the suggested content based on the user's health condition enables more appropriate action suggestions. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, or without, AI, for example. For example, the action suggestion unit can input data regarding the user's health condition into the generation AI and cause the generation AI to customize the suggested content.

[0083] The action suggestion unit can estimate the user's emotions and determine the priority of action suggestions based on the estimated emotions. For example, if the user is feeling stressed, the action suggestion unit will prioritize suggesting relaxing activities. If the user is relaxed, the action suggestion unit can also prioritize suggesting interesting activities. Furthermore, if the user is in a hurry, the action suggestion unit can prioritize suggesting activities that can be done quickly. By prioritizing action suggestions according to the user's emotions, more appropriate action suggestions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the action suggestion unit may be performed using AI or not. For example, the action suggestion unit can input user emotion data into the generative AI and have the generative AI determine the priority of action suggestions.

[0084] The action suggestion unit can make appropriate suggestions based on the user's geographical location information when suggesting an action. For example, the action suggestion unit makes optimal suggestions based on the user's geographical location information when suggesting an action. For example, the action suggestion unit suggests nearby activities based on the user's current location. Furthermore, if the user is in a specific area, the action suggestion unit can also suggest activities related to that area. Furthermore, if the user is traveling, the action suggestion unit can also suggest activities at the user's destination. In this way, optimal action suggestions can be made by taking the user's geographical location information into consideration. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the action suggestion unit can input the user's geographical location information to a generation AI and cause the generation AI to generate appropriate suggestions.

[0085] The Action Suggestion Unit can analyze the user's social media usage and make relevant suggestions when making action suggestions. For example, the Action Suggestion Unit can analyze the user's social media activity and make relevant suggestions when making action suggestions. For example, the Action Suggestion Unit can suggest activities of interest based on the user's social media activity. The Action Suggestion Unit can also suggest activities of accounts that the user follows. Furthermore, the Action Suggestion Unit can analyze the content of the user's social media posts and suggest relevant activities. In this way, relevant action suggestions can be made by analyzing the user's social media activity. Some or all of the above processing in the Action Suggestion Unit may be performed using AI, for example, or without AI. For example, the Action Suggestion Unit can input the user's social media activity data into a generating AI and have the generating AI generate relevant suggestions.

[0086] The health monitoring unit can estimate the user's emotions and adjust the frequency of health monitoring according to the estimated emotions. For example, if the user is stressed, the health monitoring unit can increase the frequency of health monitoring to detect abnormalities early. Conversely, if the user is relaxed, the health monitoring unit can decrease the frequency of health monitoring to reduce the user's burden. Furthermore, if the user is in a hurry, the health monitoring unit can adjust the frequency of health monitoring to enable a quick response. In this way, adjusting the frequency of health monitoring according to the user's emotions enables more appropriate health monitoring. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of health monitoring.

[0087] The health monitoring unit can improve the accuracy of anomaly detection by referring to the user's past health data during health monitoring. For example, the health monitoring unit can improve the accuracy of anomaly detection by referring to the user's past health data during health monitoring. For example, the health monitoring unit can perform early detection of anomalies based on the user's past health data. The health monitoring unit can also analyze specific patterns from the user's past health data and detect anomalies. Furthermore, the health monitoring unit can optimize the anomaly detection algorithm by referring to the user's past health data. In this way, the accuracy of anomaly detection can be improved by referring to past health data. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without using AI. For example, the health monitoring unit can input the user's past health data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0088] The health monitoring unit can adjust the monitoring timing according to the user's lifestyle during health monitoring. For example, the health monitoring unit adjusts the monitoring timing based on the user's lifestyle. For example, if the user has a morning lifestyle, the health monitoring unit will perform health monitoring in the morning. Also, if the user has a night lifestyle, the health monitoring unit can perform health monitoring in the evening. Furthermore, the health monitoring unit can perform health monitoring at an appropriate time in accordance with the user's lifestyle. By adjusting the monitoring timing based on the user's lifestyle, more appropriate health monitoring becomes possible. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input data about the user's lifestyle into a generating AI and have the generating AI perform the adjustment of the monitoring timing.

[0089] The health monitoring unit can estimate the user's emotions and determine the priority of health monitoring based on the estimated emotions. For example, if the user is stressed, the health monitoring unit will prioritize monitoring important health indicators. If the user is relaxed, the health monitoring unit can also monitor the overall health status. Furthermore, if the user is in a hurry, the health monitoring unit can prioritize monitoring high-urgency health indicators. This allows for more appropriate health monitoring by determining the priority of health monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the health monitoring unit may be performed using AI or not. For example, the health monitoring unit can input user emotion data into a generative AI and have the generative AI determine the priority of health monitoring.

[0090] The health monitoring unit can select an appropriate monitoring method based on the user's geographical location information during health monitoring. For example, the health monitoring unit can select the optimal monitoring method based on the user's geographical location information during health monitoring. For example, if the user is at home, the health monitoring unit can perform regular health checks. The health monitoring unit can also perform simple health checks if the user is out. Furthermore, if the user is in a specific area, the health monitoring unit can perform health monitoring tailored to the environment of that area. In this way, optimal health monitoring can be performed by considering the user's geographical location information. Some or all of the above processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's geographical location information into a generating AI and have the generating AI select an appropriate monitoring method.

[0091] The health monitoring unit can monitor a user's health status by analyzing their social media usage during health monitoring. For example, the health monitoring unit can analyze a user's social media activity to monitor their health status. For example, the health monitoring unit can estimate a user's stress level from their social media activity and reflect this in the health monitoring. The health monitoring unit can also analyze the content of a user's social media posts to monitor their health status. Furthermore, the health monitoring unit can adjust the frequency of health monitoring based on the user's frequency of social media activity. This allows for more accurate monitoring of a user's health status by analyzing their social media activity. Some or all of the above-described processes in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's social media activity data into a generating AI and have the generating AI perform health status monitoring. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, language generation unit, action suggestion unit, and health monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives the user's voice or text input using the microphone 38B or touch panel 38A of the smart device 14, and analyzes it using the control unit 46A. The language generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates conversations with the user using a generation AI. The action suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and suggests appropriate activities considering the user's health status and weather conditions. The health monitoring unit measures the user's biometric information using the camera 42 or sensors of the smart device 14, and detects and notifies of abnormalities using the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, language generation unit, action suggestion unit, and health monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the smart glasses 214, which is analyzed by the control unit 46A. The language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation with the user using a generation AI. The action suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate activities taking into account the user's health condition and weather conditions. The health monitoring unit measures the user's biometric information using, for example, the camera 42 or sensor of the smart glasses 214, and detects and notifies the user of abnormalities using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, language generation unit, action suggestion unit, and health monitoring unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the headset-type terminal 314, and the voice input is analyzed by the control unit 46A. The language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation with the user using a generation AI. The action suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate activities taking into account the user's health condition and weather conditions. The health monitoring unit measures the user's biometric information using, for example, the camera 42 or sensor of the headset-type terminal 314, and detects and notifies the user of abnormalities using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, language generation unit, action suggestion unit, and health monitoring unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the robot 414, which is analyzed by the control unit 46A. The language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a conversation with the user using a generation AI. The action suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate activities taking into account the user's health condition and weather conditions. The health monitoring unit measures the user's biometric information using, for example, the camera 42 and sensors of the robot 414, and detects and notifies the user of abnormalities using the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception desk can analyze user input and refer to the user's past behavior history to generate more appropriate responses. For example, the reception desk can prioritize providing relevant information based on the user's frequently performed actions and statements in the past. It can also identify specific patterns in the user's past behavior history and prepare in advance for predicted actions. Furthermore, the reception desk can select a response style preferred by the user based on their past behavior history, providing more personalized responses. In this way, by leveraging the user's past behavior history, it is possible to provide more appropriate and personalized responses.

[0094] When monitoring the user's health status, the health monitoring unit can adjust the monitoring timing based on the user's lifestyle. For example, if the user has a morning-type lifestyle, the health monitoring unit can perform a health check in the morning. Alternatively, if the user has a nocturnal lifestyle, the health monitoring unit can perform a health check in the evening. Furthermore, the health monitoring unit can perform health monitoring at an appropriate timing in accordance with the user's lifestyle. This allows for more appropriate health monitoring by adjusting the monitoring timing based on the user's lifestyle.

[0095] The language generation unit can estimate the user's emotions and select a conversation topic according to the estimated user's emotions. For example, if the user is relaxed, the language generation unit can select a calm topic. If the user is excited, the language generation unit can also select a lively topic. Furthermore, if the user is sad, the language generation unit can also select a comforting topic. In this way, selecting a conversation topic according to the user's emotions enables more appropriate conversation.

[0096] The action suggestion unit can estimate the user's emotions and customize the content of the action suggestion according to the estimated user's emotions. For example, if the user is feeling stressed, the action suggestion unit can suggest a relaxing activity. Also, if the user is relaxed, the action suggestion unit can suggest an interesting activity. Furthermore, if the user is in a hurry, the action suggestion unit can suggest an activity that can be done quickly. In this way, by customizing the content of the action suggestion according to the user's emotions, more appropriate action suggestions can be made.

[0097] The health monitoring unit can estimate the user's emotions and adjust the frequency of health monitoring according to the estimated user emotions. For example, if the user is feeling stressed, the health monitoring unit can increase the frequency of health monitoring to detect abnormalities early. Also, if the user is relaxed, the health monitoring unit can reduce the frequency of health monitoring to reduce the burden on the user. Furthermore, if the user is in a hurry, the health monitoring unit can adjust the frequency of health monitoring to enable a quick response. Thus, by adjusting the frequency of health monitoring according to the user's emotions, more appropriate health monitoring is possible.

[0098] The reception unit can preferentially receive highly relevant information based on the user's geographical location information. For example, the reception unit preferentially receives nearby information based on the user's current location. Furthermore, when the user is in a specific area, the reception unit can also preferentially receive information related to that area. Furthermore, when the user is traveling, the reception unit can also preferentially receive information about the user's destination. In this way, highly relevant information can be preferentially received by taking the user's geographical location information into consideration.

[0099] When generating a conversation, the language generation unit can generate appropriate conversation content by referring to the user's past conversation history. For example, the language generation unit generates related conversation content based on topics that the user has talked about in the past. The language generation unit can also prioritize topics of interest from the user's past conversation history. Furthermore, the language generation unit can generate conversation content by referring to the conversation style that the user has preferred in the past. In this way, by referring to the past conversation history, it is possible to generate conversation content that is optimal for the user.

[0100] When suggesting an action, the action suggestion unit can make appropriate suggestions by referring to the user's past action history. For example, the action suggestion unit makes relevant action suggestions based on activities the user has performed in the past. The action suggestion unit can also preferentially suggest activities that the user is interested in based on the user's past action history. Furthermore, the action suggestion unit can also make action suggestions by referring to activities that the user has preferred in the past. In this way, by referring to the past action history, it is possible to make optimal action suggestions for the user.

[0101] During health monitoring, the health monitoring unit can improve the accuracy of anomaly detection by referring to the user's past health data. For example, the health monitoring unit can perform early detection of anomalies based on the user's past health data. The health monitoring unit can also analyze specific patterns from the user's past health data to detect anomalies. Furthermore, the health monitoring unit can also optimize the anomaly detection algorithm by referring to the user's past health data. In this way, the accuracy of anomaly detection can be improved by referring to past health data.

[0102] When suggesting an action, the action suggestion unit can analyze the user's social media usage status and make related suggestions. For example, the action suggestion unit can suggest activities of interest based on the user's social media activity. The action suggestion unit can also suggest activities of accounts the user follows. Furthermore, the action suggestion unit can analyze the content of the user's comments on social media and suggest related activities. In this way, it is possible to make related action suggestions by analyzing the user's social media activity.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives input from the user. User input includes voice input and text input. The reception unit can also convert voice input into text data using voice recognition technology and directly receive text input. The reception unit also analyzes the user's input and provides information for appropriate processing. Step 2: The language generation unit conducts everyday conversations based on the information received by the reception unit. The language generation unit uses generation AI, such as text generation AI (LLM) or multimodal generation AI, to generate a conversation with the user. This allows the unit to generate appropriate responses based on the user's input and continue the conversation. Step 3: The action suggestion unit suggests actions based on the conversation content generated by the language generation unit, taking into account the user's health condition and weather conditions. The action suggestion unit monitors the user's health condition and suggests appropriate activities. It can also suggest outdoor or indoor activities based on weather conditions. For example, it suggests taking a walk on a nice day and indoor hobby activities on a rainy day. Step 4: The health monitoring unit monitors the user's health based on the actions suggested by the action suggestion unit. The health monitoring unit periodically measures the user's biometric information and immediately notifies family members or medical institutions if any abnormalities are detected. The health monitoring unit also measures the user's biometric information, such as body temperature, blood pressure, and heart rate, and immediately notifies any abnormalities.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives input from a user; a language generation unit that conducts everyday conversations based on the information received by the reception unit; an action suggestion unit that suggests an action based on the user's health condition and weather conditions, based on the conversation content generated by the language generation unit; a health monitoring unit that monitors the health of the user based on the action suggested by the action suggestion unit. A system characterized by:

2. The health monitoring unit Periodically measure vital signs and notify family members or medical institutions if any abnormalities occur.

2. The system of claim 1.

3. The language generation unit Diversify conversations with users and reduce feelings of loneliness 2. The system of claim 1.

4. The action suggestion unit Suggest activities based on the user's hobbies 2. The system of claim 1.

5. The action suggestion unit Suggest outdoor or indoor activities depending on weather conditions 2. The system of claim 1.

6. The health monitoring unit Continuously monitor the user's health and provide a safe and secure living environment 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input reception according to the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1.

9. The reception unit As input is received, filtering is performed based on the user's current life situation or areas of interest.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions.

2. The system of claim 1.

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