system

Abstract

The system according to the embodiment aims to operate home appliances and monitor health conditions through voice instructions and conversations. [Solution] A system according to an embodiment includes a reception unit, an operation unit, a conversation unit, a monitoring unit, and a notification unit. The reception unit receives voice instructions. The operation unit operates a home appliance based on the voice instructions received by the reception unit. The conversation unit converses with a user based on the voice instructions received by the reception unit. The monitoring unit monitors the user's health condition based on the content of the conversation held by the conversation unit. The notification unit notifies the user of an abnormality detected by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately addressed the operation of home appliances and monitoring of health status through voice instructions and conversations in elderly care, and there is room for improvement.

[0005] The system according to the embodiment aims to operate home appliances and monitor health conditions through voice instructions and conversations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an operation unit, a conversation unit, a monitoring unit, and a notification unit. The reception unit receives voice instructions. The operation unit operates a home appliance based on the voice instructions received by the reception unit. The conversation unit converses with a user based on the voice instructions received by the reception unit. The monitoring unit monitors the user's health condition based on the content of the conversation held by the conversation unit. The notification unit notifies the user of an abnormality detected by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can operate home appliances and monitor health conditions through voice instructions and conversations. [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 smart home system according to an embodiment of the present invention utilizes a generative AI to reduce the mental burden on caregivers and improve the quality of life of elderly people. In this smart home system, the generative AI recognizes voice commands and controls home appliances, lighting, curtains, and other devices. The generative AI also acts as a conversation partner for the elderly, responding to repeated remarks or questions. Furthermore, the generative AI monitors the elderly's health and notifies caregivers and medical institutions if an abnormality is detected. For example, the generative AI recognizes voice commands such as "Turn on the lights" or "Close the curtains" and executes the corresponding action. This eliminates the need for the elderly to operate home appliances themselves and reduces the burden on caregivers. Next, the generative AI acts as a conversation partner for the elderly. Elderly people often repeat the same remarks or questions, and the generative AI responds to this, reducing caregiver stress. For example, if an elderly person repeatedly asks, "What day is it today?", the generative AI can accurately respond each time. The generative AI can also provide conversations based on the elderly's preferences and interests. This can reduce the elderly's sense of loneliness and promote mental stability. Furthermore, generative AI has the ability to monitor the health of elderly people and notify caregivers and medical institutions if it detects any abnormalities. For example, generative AI can detect changes in the tone and manner of an elderly person's voice and detect signs of poor health. This allows appropriate action to be taken early. In this way, smart home systems utilizing generative AI are expected to reduce the mental burden on caregivers and improve the quality of life of elderly people. As a result, smart home systems are expected to reduce the mental burden on caregivers and improve the quality of life of elderly people.

[0029] A smart home system according to an embodiment includes a reception unit, an operation unit, a conversation unit, a monitoring unit, and a notification unit. The reception unit receives voice instructions. Examples of voice instructions include, but are not limited to, "Turn on the lights" and "Close the curtains." The operation unit operates home appliances based on the voice instructions received by the reception unit. For example, the operation unit turns lights on and off based on the voice instructions. The operation unit can also open and close curtains based on the voice instructions. The conversation unit engages in conversation with a user based on the voice instructions received by the reception unit. For example, the conversation unit provides conversation based on the preferences and interests of the elderly person. The conversation unit can also respond to cases where the elderly person repeatedly makes the same statement or asks the same question. The monitoring unit monitors the user's health condition based on the content of the conversation conducted by the conversation unit. For example, the monitoring unit detects changes in the elderly person's tone of voice or speaking style. The monitoring unit also notifies the notification unit if an abnormality is detected. The notification unit notifies a caregiver or a medical institution of the abnormality detected by the monitoring unit. For example, when an abnormality is detected, the notification unit sends a notification to the registered contacts. As a result, the smart home system according to the embodiment can reduce the mental burden on caregivers and improve the quality of life of elderly people.

[0030] The operation unit can turn the lights on and off based on voice instructions. Turning the lights on and off includes, but is not limited to, the type of voice command and the timing of the operation, for example. The operation unit turns the lights on and off based on, for example, voice instructions. For example, the operation unit turns the lights on when it receives a voice instruction to "turn on the lights." The operation unit can also turn the lights off when it receives a voice instruction to "turn off the lights." This makes it possible to control the lights based on voice instructions. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input voice instructions to a generation AI and have the generation AI perform an on / off operation of the lights.

[0031] The operation unit can open and close the curtains based on voice instructions. Opening and closing the curtains includes, for example, the type of voice command and the timing of the operation, but is not limited to these examples. The operation unit opens and closes the curtains based on, for example, voice instructions. For example, the operation unit opens the curtains when it receives a voice instruction to "open the curtains." The operation unit can also close the curtains when it receives a voice instruction to "close the curtains." This makes it possible to operate the curtains based on voice instructions. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input voice instructions to a generation AI and cause the generation AI to perform the operation of opening and closing the curtains.

[0032] The conversation unit can provide a conversation based on the user's preferences and interests. The user's preferences and interests include, but are not limited to, past conversation history and survey results. For example, the conversation unit can analyze the user's past conversation history and provide a conversation based on the user's preferences and interests. For example, the conversation unit can prioritize topics in which the user has shown interest in the past. The conversation unit can also provide a conversation that matches the user's preferences and interests based on the user's survey results. This can reduce the elderly's sense of loneliness by providing a conversation based on their preferences and interests. Some or all of the above-described processing in the conversation unit can be performed using, or without, a generation AI. For example, the conversation unit can input conversation content based on the user's preferences and interests into the generation AI and have the generation AI generate the conversation.

[0033] The monitoring unit can detect changes in the user's tone of voice or speaking style. Changes in tone of voice or speaking style can be, for example, but are not limited to, voice analysis technology or a threshold value for change. The monitoring unit detects changes in the user's tone of voice or speaking style using, for example, voice analysis technology. For example, the monitoring unit detects changes in the user's tone of voice and detects abnormalities. The monitoring unit can also detect changes in the user's speaking style and detect abnormalities. This allows for early detection of changes in the health condition of elderly people. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input changes in the user's tone of voice or speaking style to the generation AI and cause the generation AI to detect abnormalities.

[0034] The notification unit can notify a caregiver or a medical institution when an abnormality is detected. The caregiver or medical institution may include, but is not limited to, a registered contact or an emergency contact. For example, the notification unit can send a notification to a registered contact when an abnormality is detected. For example, the notification unit can send a notification to a caregiver when an abnormality is detected. The notification unit can also send a notification to a medical institution when an abnormality is detected. This enables early notification of the abnormality to enable a prompt response. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the abnormality detection result to a generation AI and have the generation AI generate a notification.

[0035] When receiving a voice instruction, the reception unit can analyze the user's past instruction history and select the optimal reception method. Examples of past instruction history include, but are not limited to, the use of a database or a machine learning algorithm. The reception unit, for example, stores the user's past instruction history in a database and analyzes it. For example, the reception unit prioritizes receiving voice instructions that the user has frequently used in the past. The reception unit can also predict and receive voice instructions to be used in a specific time period based on the user's past instruction history. This allows the optimal reception method to be selected by analyzing the past instruction history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past instruction history into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving a voice instruction, the reception unit may perform filtering taking into account the user's current situation or environmental sounds. Examples of the current situation and environmental sounds include, but are not limited to, using a microphone or noise filtering technology. The reception unit may, for example, detect the user's current situation and environmental sounds using a microphone. For example, when the user is in a quiet environment, the reception unit may clearly receive the voice instruction. Furthermore, when the user is in a noisy environment, the reception unit may filter the environmental sounds before receiving the voice instruction. This improves the accuracy of receiving the voice instruction by taking the current situation and environmental sounds into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's current situation and environmental sounds into the generation AI and have the generation AI perform filtering.

[0037] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. The reception unit acquires the user's geographical location information by, for example, GPS data. For example, when the user is at home, the reception unit prioritizes receiving voice instructions related to operating home appliances. Furthermore, when the user is out, the reception unit can also prioritize receiving voice instructions received while the user is away from home. This makes it possible to prioritize receiving highly relevant instructions by taking the geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant instructions.

[0038] When receiving a voice instruction, the reception unit can analyze the user's social media activity and receive related instructions. Social media activity includes, but is not limited to, the use of APIs and data mining techniques. The reception unit, for example, collects and analyzes the user's social media activity using an API. For example, the reception unit receives related voice instructions based on information shared by the user on social media. The reception unit can also receive voice instructions related to specific events from the user's social media activity. This allows related instructions to be received by analyzing the social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity into the generation AI and cause the generation AI to select related instructions.

[0039] When operating a home appliance, the operation unit can analyze the user's past operation history and select the optimal operation method. Examples of past operation history include, but are not limited to, the use of a database or a machine learning algorithm. The operation unit, for example, stores the user's past operation history in a database and analyzes it. For example, the operation unit prioritizes and provides operation methods that the user has frequently used in the past. The operation unit can also predict and provide an operation method to be used during a specific time period based on the user's past operation history. This allows the optimal operation method to be selected by analyzing the past operation history. Some or all of the above-described processing in the operation unit may be performed using, or without, a generation AI. For example, the operation unit can input the user's past operation history into the generation AI and have the generation AI select the optimal operation method.

[0040] When operating a home appliance, the operation unit can perform the operation while taking into consideration the user's current living situation and environment. Examples of the current living situation and environment include, but are not limited to, the use of sensors and environmental monitoring technology. The operation unit, for example, detects the user's current living situation and environment using a sensor. For example, when the user is in the living room, the operation unit prioritizes operating home appliances in the living room. Also, when the user is in the bedroom, the operation unit can prioritize operating home appliances in the bedroom. This enables more appropriate operation by taking the current living situation and environment into consideration. Some or all of the above-mentioned processing in the operation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the operation unit can input the user's current living situation and environment into the generation AI and have the generation AI adjust the operation.

[0041] When operating a home appliance, the operation unit can perform optimal operations by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. The operation unit acquires the user's geographical location information using, for example, GPS data. For example, when the user is at home, the operation unit prioritizes home appliance operations within the home. Furthermore, when the user is out, the operation unit can also provide home appliance operations while the user is away from home. This enables optimal operations by taking into account the geographical location information. Some or all of the above-described processing in the operation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's geographical location information to the generation AI and have the generation AI select the optimal operation.

[0042] The operation unit can analyze the user's social media activity and perform related operations when operating the home appliance. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The operation unit, for example, uses an API to collect and analyze the user's social media activity. For example, the operation unit performs related home appliance operations based on information shared by the user on social media. The operation unit can also perform home appliance operations related to specific events based on the user's social media activity. This makes it possible to perform related operations by analyzing social media activity. Some or all of the above-described processing in the operation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's social media activity into the generation AI and have the generation AI select related operations.

[0043] During a conversation, the conversation unit can analyze the elderly person's past conversation history and select the optimal conversation content. Examples of past conversation history include, but are not limited to, the use of a database or a machine learning algorithm. For example, the conversation unit stores the user's past conversation history in a database and analyzes it. For example, the conversation unit prioritizes providing topics that the user has shown interest in in the past. The conversation unit can also provide topics appropriate for a specific time period from the user's past conversation history. This allows the optimal conversation content to be selected by analyzing the past conversation history. Some or all of the above-described processing in the conversation unit may be performed using, or without, a generation AI. For example, the conversation unit can input the user's past conversation history into the generation AI and have the generation AI select the optimal conversation content.

[0044] The conversation unit can conduct a conversation while taking into consideration the user's current interests. Current interests include, but are not limited to, survey results and past conversation history. The conversation unit, for example, identifies the user's current interests based on the user's survey results. For example, the conversation unit prioritizes providing topics in which the user is currently interested. The conversation unit can also provide topics related to events in which the user is currently interested. This allows for more appropriate conversation by taking into consideration the user's current interests. Some or all of the above-described processing in the conversation unit may be performed using, or without, a generation AI. For example, the conversation unit can input the user's current interests into the generation AI and have the generation AI select the content of the conversation.

[0045] The conversation unit can have a highly relevant conversation by taking into account the user's geographical location information during the conversation. Geographical location information includes, but is not limited to, GPS data and location information services, for example. The conversation unit acquires the user's geographical location information using, for example, GPS data. For example, when the user is at home, the conversation unit can provide topics related to the user's home. Furthermore, when the user is out, the conversation unit can also provide topics related to the user's destination. This enables a highly relevant conversation by taking into account the geographical location information. Some or all of the above-described processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's geographical location information into the generation AI and cause the generation AI to select highly relevant conversation content.

[0046] The conversation unit can analyze the user's social media activity during a conversation and hold a related conversation. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The conversation unit, for example, uses an API to collect and analyze the user's social media activity. For example, the conversation unit can provide related topics based on information shared by the user on social media. The conversation unit can also provide topics related to specific events from the user's social media activity. This enables related conversations by analyzing social media activity. Some or all of the above-described processing in the conversation unit can be performed using, or without, a generation AI. For example, the conversation unit can input the user's social media activity into the generation AI and have the generation AI select related conversation content.

[0047] During monitoring, the monitoring unit can analyze the user's past health data and select the optimal monitoring method. Examples of past health data include, but are not limited to, electronic medical records and health management apps. For example, the monitoring unit stores the user's past health data in the electronic medical record and analyzes it. For example, the monitoring unit prioritizes monitoring of specific health conditions from the user's past health data. The monitoring unit can also analyze the user's past health data and select the most efficient monitoring method. This allows the optimal monitoring method to be selected by analyzing the past health data. Some or all of the above-described processing in the monitoring unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input the user's past health data into the generation AI and have the generation AI select the optimal monitoring method.

[0048] The monitoring unit may perform monitoring while taking into account the user's current living situation or environment. Examples of the current living situation or environment include, but are not limited to, the use of sensors and environmental monitoring technology. The monitoring unit may, for example, use a sensor to detect the user's current living situation or environment. For example, when the user is at home, the monitoring unit may prioritize monitoring the user's health status at home. Furthermore, when the user is out, the monitoring unit may also monitor the user's health status while away from home. This enables more appropriate monitoring by taking into account the current living situation and environment. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit may input the user's current living situation or environment into the generation AI and have the generation AI adjust the monitoring method.

[0049] During monitoring, the monitoring unit can perform optimal monitoring by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. The monitoring unit, for example, acquires the user's geographical location information using GPS data. For example, when the user is at home, the monitoring unit prioritizes monitoring the user's health status at home. Furthermore, when the user is out, the monitoring unit can also monitor the user's health status while away from home. This enables optimal monitoring by taking into account the geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal monitoring method.

[0050] During monitoring, the monitoring unit can analyze the user's social media activity and perform related monitoring. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The monitoring unit, for example, uses an API to collect and analyze the user's social media activity. For example, the monitoring unit monitors related health conditions based on information shared by the user on social media. The monitoring unit can also monitor health conditions related to specific events from the user's social media activity. This enables related monitoring by analyzing the social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the user's social media activity into the generation AI and have the generation AI select a related monitoring method.

[0051] The notification unit can analyze the user's past health data and select the optimal notification method when notifying the user. Examples of past health data include, but are not limited to, electronic medical records and health management apps. The notification unit, for example, stores the user's past health data in the electronic medical record and analyzes it. For example, the notification unit can prioritize notifications regarding specific health conditions based on the user's past health data. The notification unit can also analyze the user's past health data and select the most efficient notification method. This allows the optimal notification method to be selected by analyzing the past health data. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's past health data into the generation AI and have the generation AI select the optimal notification method.

[0052] The notification unit may take into consideration the user's current living situation or environment when providing a notification. Examples of the current living situation or environment include, but are not limited to, the use of sensors and environmental monitoring technology. The notification unit may, for example, use a sensor to detect the user's current living situation or environment. For example, when the user is at home, the notification unit may prioritize notifications related to the user's health status at home. Furthermore, when the user is out, the notification unit may also provide notifications related to the user's health status while away from home. This allows for more appropriate notifications by taking the current living situation and environment into consideration. Some or all of the above-described processing in the notification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the notification unit may input the user's current living situation or environment into the generation AI and have the generation AI adjust the notification content.

[0053] The notification unit can provide optimal notification by taking into account the user's geographical location information when providing notification. Examples of geographical location information include, but are not limited to, GPS data and location information services. The notification unit acquires the user's geographical location information using, for example, GPS data. For example, when the user is at home, the notification unit prioritizes notifications related to the user's health status at home. Furthermore, when the user is out, the notification unit can also provide notifications related to the user's health status while away from home. This enables optimal notification by taking into account the geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal notification method.

[0054] The notification unit may analyze the user's social media activity and provide relevant notifications at the time of notification. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The notification unit may, for example, use an API to collect and analyze the user's social media activity. For example, the notification unit may provide notifications about relevant health conditions based on information shared by the user on social media. The notification unit may also provide notifications about health conditions related to specific events from the user's social media activity. This enables relevant notifications to be provided by analyzing social media activity. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit may input the user's social media activity into the generation AI and have the generation AI select relevant notification content.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The smart home system can further include an energy management unit. The energy management unit monitors the usage of home appliances and optimizes energy consumption. For example, the energy management unit can automatically turn off appliances that are used infrequently. The energy management unit can also suggest a schedule for operating home appliances during times when electricity rates are low. Furthermore, the energy management unit can link with a solar power generation system and efficiently utilize the energy generated. This enables optimization of energy consumption and cost reduction.

[0057] The operation unit can also change the operation mode of the home appliance based on a voice instruction. For example, when the operation unit receives a voice instruction such as "switch to eco mode," it switches the home appliance to energy-saving mode. Also, when the operation unit receives a voice instruction such as "switch to night mode," it can switch the home appliance to a mode that reduces the operating noise. Furthermore, when the operation unit receives a voice instruction such as "switch to party mode," it can switch the home appliance to a mode that operates at maximum output. This makes it possible to change the operation mode of the home appliance according to the user's needs.

[0058] The control unit can also set operation schedules for home appliances based on voice instructions. For example, if the control unit receives a voice instruction such as "Open the curtains at 7 o'clock every morning," it can set a schedule to automatically open the curtains at 7 o'clock every morning. Also, if the control unit receives a voice instruction such as "Turn off the lights at 10 o'clock every night," it can set a schedule to automatically turn off the lights at 10 o'clock every night. Furthermore, if the control unit receives a voice instruction such as "Turn on the air conditioner only on weekends," it can set a schedule to automatically turn on the air conditioner only on weekends. This makes it possible to set operation schedules for home appliances that suit the user's lifestyle.

[0059] When operating a home appliance, the operation unit can analyze the user's past operation history and select the optimal operation method. For example, the operation unit can prioritize and provide operation methods that the user has frequently used in the past. The operation unit can also predict and provide an operation method to be used during a specific time period based on the user's past operation history. Furthermore, the operation unit can analyze the user's past operation history and suggest the most efficient operation method. This makes it possible to select the optimal operation method by analyzing the past operation history.

[0060] The conversation unit can have a highly relevant conversation by taking into account the user's geographical location information. For example, if the user is at home, the conversation unit can provide topics related to the user's home. If the user is out, the conversation unit can also provide topics related to the user's destination. Furthermore, if the user is traveling, the conversation unit can provide tourist information and weather information for the user's travel destination. This makes it possible to have a highly relevant conversation by taking into account the geographical location information.

[0061] During monitoring, the monitoring unit can analyze the user's social media activity and perform related monitoring. For example, the monitoring unit can monitor related health conditions based on information shared by the user on social media. The monitoring unit can also monitor health conditions related to specific events from the user's social media activity. Furthermore, the monitoring unit can analyze the user's social media activity and detect changes in the health condition early. This makes it possible to perform related monitoring by analyzing social media activity.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives a voice command, such as, but not limited to, "Turn on the lights" or "Close the curtains." Step 2: The operation unit operates the home appliance based on the voice instruction received by the reception unit. For example, the operation unit turns on and off the lights based on the voice instruction. The operation unit can also open and close curtains based on the voice instruction. Step 3: The conversation unit engages in a conversation with the user based on the voice instructions received by the reception unit. For example, the conversation unit provides a conversation based on the elderly person's preferences and interests. The conversation unit can also respond to cases where the elderly person repeatedly makes the same remarks or asks the same questions. Step 4: The monitoring unit monitors the user's health condition based on the content of the conversation conducted by the conversation unit. For example, the monitoring unit detects changes in the elderly person's tone of voice or speaking style. If the monitoring unit detects an abnormality, it notifies the notification unit. Step 5: The notification unit notifies a caregiver or a medical institution of the abnormality detected by the monitoring unit. For example, when the notification unit detects an abnormality, it sends a notification to a registered contact.

[0064] (Example 2) A smart home system according to an embodiment of the present invention utilizes a generative AI to reduce the mental burden on caregivers and improve the quality of life of elderly people. In this smart home system, the generative AI recognizes voice commands and controls home appliances, lighting, curtains, and other devices. The generative AI also acts as a conversation partner for the elderly, responding to repeated remarks or questions. Furthermore, the generative AI monitors the elderly's health and notifies caregivers and medical institutions if an abnormality is detected. For example, the generative AI recognizes voice commands such as "Turn on the lights" or "Close the curtains" and executes the corresponding action. This eliminates the need for the elderly to operate home appliances themselves and reduces the burden on caregivers. Next, the generative AI acts as a conversation partner for the elderly. Elderly people often repeat the same remarks or questions, and the generative AI responds to this, reducing caregiver stress. For example, if an elderly person repeatedly asks, "What day is it today?", the generative AI can accurately respond each time. The generative AI can also provide conversations based on the elderly's preferences and interests. This can reduce the elderly's sense of loneliness and promote mental stability. Furthermore, generative AI has the ability to monitor the health of elderly people and notify caregivers and medical institutions if it detects any abnormalities. For example, generative AI can detect changes in the tone and manner of an elderly person's voice and detect signs of poor health. This allows appropriate action to be taken early. In this way, smart home systems utilizing generative AI are expected to reduce the mental burden on caregivers and improve the quality of life of elderly people. As a result, smart home systems are expected to reduce the mental burden on caregivers and improve the quality of life of elderly people.

[0065] A smart home system according to an embodiment includes a reception unit, an operation unit, a conversation unit, a monitoring unit, and a notification unit. The reception unit receives voice instructions. Examples of voice instructions include, but are not limited to, "Turn on the lights" and "Close the curtains." The operation unit operates home appliances based on the voice instructions received by the reception unit. For example, the operation unit turns lights on and off based on the voice instructions. The operation unit can also open and close curtains based on the voice instructions. The conversation unit engages in conversation with a user based on the voice instructions received by the reception unit. For example, the conversation unit provides conversation based on the preferences and interests of the elderly person. The conversation unit can also respond to cases where the elderly person repeatedly makes the same statement or asks the same question. The monitoring unit monitors the user's health condition based on the content of the conversation conducted by the conversation unit. For example, the monitoring unit detects changes in the elderly person's tone of voice or speaking style. The monitoring unit also notifies the notification unit if an abnormality is detected. The notification unit notifies a caregiver or a medical institution of the abnormality detected by the monitoring unit. For example, when an abnormality is detected, the notification unit sends a notification to the registered contacts. As a result, the smart home system according to the embodiment can reduce the mental burden on caregivers and improve the quality of life of elderly people.

[0066] The operation unit can turn the lights on and off based on voice instructions. Turning the lights on and off includes, but is not limited to, the type of voice command and the timing of the operation, for example. The operation unit turns the lights on and off based on, for example, voice instructions. For example, the operation unit turns the lights on when it receives a voice instruction to "turn on the lights." The operation unit can also turn the lights off when it receives a voice instruction to "turn off the lights." This makes it possible to control the lights based on voice instructions. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input voice instructions to a generation AI and have the generation AI perform an on / off operation of the lights.

[0067] The operation unit can open and close the curtains based on voice instructions. Opening and closing the curtains includes, for example, the type of voice command and the timing of the operation, but is not limited to these examples. The operation unit opens and closes the curtains based on, for example, voice instructions. For example, the operation unit opens the curtains when it receives a voice instruction to "open the curtains." The operation unit can also close the curtains when it receives a voice instruction to "close the curtains." This makes it possible to operate the curtains based on voice instructions. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input voice instructions to a generation AI and cause the generation AI to perform the operation of opening and closing the curtains.

[0068] The conversation unit can provide a conversation based on the user's preferences and interests. The user's preferences and interests include, but are not limited to, past conversation history and survey results. For example, the conversation unit can analyze the user's past conversation history and provide a conversation based on the user's preferences and interests. For example, the conversation unit can prioritize topics in which the user has shown interest in the past. The conversation unit can also provide a conversation that matches the user's preferences and interests based on the user's survey results. This can reduce the elderly's sense of loneliness by providing a conversation based on their preferences and interests. Some or all of the above-described processing in the conversation unit can be performed using, or without, a generation AI. For example, the conversation unit can input conversation content based on the user's preferences and interests into the generation AI and have the generation AI generate the conversation.

[0069] The monitoring unit can detect changes in the user's tone of voice or speaking style. Changes in tone of voice or speaking style can be, for example, but are not limited to, voice analysis technology or a threshold value for change. The monitoring unit detects changes in the user's tone of voice or speaking style using, for example, voice analysis technology. For example, the monitoring unit detects changes in the user's tone of voice and detects abnormalities. The monitoring unit can also detect changes in the user's speaking style and detect abnormalities. This allows for early detection of changes in the health condition of elderly people. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input changes in the user's tone of voice or speaking style to the generation AI and cause the generation AI to detect abnormalities.

[0070] The notification unit can notify a caregiver or a medical institution when an abnormality is detected. The caregiver or medical institution may include, but is not limited to, a registered contact or an emergency contact. For example, the notification unit can send a notification to a registered contact when an abnormality is detected. For example, the notification unit can send a notification to a caregiver when an abnormality is detected. The notification unit can also send a notification to a medical institution when an abnormality is detected. This enables early notification of the abnormality to enable a prompt response. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the abnormality detection result to a generation AI and have the generation AI generate a notification.

[0071] The reception unit can estimate the user's emotion and adjust the timing of receiving a voice instruction based on the estimated user's emotion. Emotion estimation includes, but is not limited to, voice analysis and facial expression recognition. The reception unit can estimate the user's emotion using, for example, voice analysis technology. For example, the reception unit can analyze changes in the user's tone of voice or speaking style to estimate the emotion. The reception unit can also estimate the user's emotion using facial expression recognition technology. For example, the reception unit can analyze changes in the user's facial expression to estimate the emotion. This makes it possible to adjust the timing of receiving a voice instruction based on the user's emotion. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the user's emotion into the generation AI and have the generation AI execute emotion estimation.

[0072] When receiving a voice instruction, the reception unit can analyze the user's past instruction history and select the optimal reception method. Examples of past instruction history include, but are not limited to, the use of a database or a machine learning algorithm. The reception unit, for example, stores the user's past instruction history in a database and analyzes it. For example, the reception unit prioritizes receiving voice instructions that the user has frequently used in the past. The reception unit can also predict and receive voice instructions to be used in a specific time period based on the user's past instruction history. This allows the optimal reception method to be selected by analyzing the past instruction history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past instruction history into the generation AI and have the generation AI select the optimal reception method.

[0073] When receiving a voice instruction, the reception unit may perform filtering taking into account the user's current situation or environmental sounds. Examples of the current situation and environmental sounds include, but are not limited to, using a microphone or noise filtering technology. The reception unit may, for example, detect the user's current situation and environmental sounds using a microphone. For example, when the user is in a quiet environment, the reception unit may clearly receive the voice instruction. Furthermore, when the user is in a noisy environment, the reception unit may filter the environmental sounds before receiving the voice instruction. This improves the accuracy of receiving the voice instruction by taking the current situation and environmental sounds into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's current situation and environmental sounds into the generation AI and have the generation AI perform filtering.

[0074] The reception unit can estimate the user's emotions and determine the priority of voice instructions to be received based on the estimated user's emotions. The priority of voice instructions includes, but is not limited to, urgency and importance, for example. The reception unit can estimate the user's emotions and determine the priority of voice instructions based on the estimated emotions. For example, when the user is stressed, the reception unit can prioritize important voice instructions. Furthermore, when the user is relaxed, the reception unit can prioritize normal voice instructions. This enables more appropriate responses by determining the priority of voice instructions according to the user's emotions. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's emotions into the generation AI and have the generation AI determine the priority of voice instructions.

[0075] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. The reception unit acquires the user's geographical location information by, for example, GPS data. For example, when the user is at home, the reception unit prioritizes receiving voice instructions related to operating home appliances. Furthermore, when the user is out, the reception unit can also prioritize receiving voice instructions received while the user is away from home. This makes it possible to prioritize receiving highly relevant instructions by taking the geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant instructions.

[0076] When receiving a voice instruction, the reception unit can analyze the user's social media activity and receive related instructions. Social media activity includes, but is not limited to, the use of APIs and data mining techniques. The reception unit, for example, collects and analyzes the user's social media activity using an API. For example, the reception unit receives related voice instructions based on information shared by the user on social media. The reception unit can also receive voice instructions related to specific events from the user's social media activity. This allows related instructions to be received by analyzing the social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity into the generation AI and cause the generation AI to select related instructions.

[0077] The operation unit can estimate the user's emotions and adjust the operation method of the home appliance based on the estimated user's emotions. Examples of the operation method of the home appliance include, but are not limited to, remote control operation and voice commands. For example, the operation unit can estimate the user's emotions and adjust the operation method of the home appliance based on the estimated emotions. For example, if the user is feeling stressed, the operation unit can simplify the operation of the home appliance to reduce stress. Furthermore, if the user is relaxed, the operation unit can make the operation of the home appliance more detailed to provide enjoyment. This allows for more appropriate operation by adjusting the operation method of the home appliance according to the user's emotions. Some or all of the above-described processing in the operation unit may be performed using, or without, a generation AI. For example, the operation unit can input the user's emotions into the generation AI and have the generation AI adjust the operation method of the home appliance.

[0078] When operating a home appliance, the operation unit can analyze the user's past operation history and select the optimal operation method. Examples of past operation history include, but are not limited to, the use of a database or a machine learning algorithm. The operation unit, for example, stores the user's past operation history in a database and analyzes it. For example, the operation unit prioritizes and provides operation methods that the user has frequently used in the past. The operation unit can also predict and provide an operation method to be used during a specific time period based on the user's past operation history. This allows the optimal operation method to be selected by analyzing the past operation history. Some or all of the above-described processing in the operation unit may be performed using, or without, a generation AI. For example, the operation unit can input the user's past operation history into the generation AI and have the generation AI select the optimal operation method.

[0079] When operating a home appliance, the operation unit can perform the operation while taking into consideration the user's current living situation and environment. Examples of the current living situation and environment include, but are not limited to, the use of sensors and environmental monitoring technology. The operation unit, for example, detects the user's current living situation and environment using a sensor. For example, when the user is in the living room, the operation unit prioritizes operating home appliances in the living room. Also, when the user is in the bedroom, the operation unit can prioritize operating home appliances in the bedroom. This enables more appropriate operation by taking the current living situation and environment into consideration. Some or all of the above-mentioned processing in the operation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the operation unit can input the user's current living situation and environment into the generation AI and have the generation AI adjust the operation.

[0080] The operation unit can estimate the user's emotions and determine the operation order of the home appliances based on the estimated user emotions. The operation order of the home appliances includes, but is not limited to, priority and efficiency, for example. The operation unit can estimate the user's emotions and determine the operation order of the home appliances based on the estimated emotions, for example. For example, when the user is stressed, the operation unit prioritizes the operation of important home appliances. Furthermore, when the user is relaxed, the operation unit can prioritize the operation of ordinary home appliances. This enables more appropriate operation by determining the operation order of the home appliances according to the user's emotions. Some or all of the above-described processing in the operation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's emotions into the generation AI and have the generation AI determine the operation order of the home appliances.

[0081] When operating a home appliance, the operation unit can perform optimal operations by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. The operation unit acquires the user's geographical location information using, for example, GPS data. For example, when the user is at home, the operation unit prioritizes home appliance operations within the home. Furthermore, when the user is out, the operation unit can also provide home appliance operations while the user is away from home. This enables optimal operations by taking into account the geographical location information. Some or all of the above-described processing in the operation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's geographical location information to the generation AI and have the generation AI select the optimal operation.

[0082] The operation unit can analyze the user's social media activity and perform related operations when operating the home appliance. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The operation unit, for example, uses an API to collect and analyze the user's social media activity. For example, the operation unit performs related home appliance operations based on information shared by the user on social media. The operation unit can also perform home appliance operations related to specific events based on the user's social media activity. This makes it possible to perform related operations by analyzing social media activity. Some or all of the above-described processing in the operation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the operation unit can input the user's social media activity into the generation AI and have the generation AI select related operations.

[0083] The conversation unit can estimate the user's emotions and adjust the content and tone of the conversation based on the estimated user's emotions. Examples of the content and tone of the conversation include, but are not limited to, emotion analysis and dialogue system settings. The conversation unit estimates the user's emotions using, for example, emotion analysis technology. For example, the conversation unit analyzes changes in the user's tone of voice and speaking style to estimate emotions. The conversation unit can also analyze changes in the user's facial expressions to estimate emotions. This allows the content and tone of the conversation to be adjusted according to the user's emotions. Some or all of the above-described processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversation unit can input the user's emotions into the generation AI and have the generation AI adjust the content and tone of the conversation.

[0084] During a conversation, the conversation unit can analyze the elderly person's past conversation history and select the optimal conversation content. Examples of past conversation history include, but are not limited to, the use of a database or a machine learning algorithm. For example, the conversation unit stores the user's past conversation history in a database and analyzes it. For example, the conversation unit prioritizes providing topics that the user has shown interest in in the past. The conversation unit can also provide topics appropriate for a specific time period from the user's past conversation history. This allows the optimal conversation content to be selected by analyzing the past conversation history. Some or all of the above-described processing in the conversation unit may be performed using, or without, a generation AI. For example, the conversation unit can input the user's past conversation history into the generation AI and have the generation AI select the optimal conversation content.

[0085] The conversation unit can conduct a conversation while taking into consideration the user's current interests. Current interests include, but are not limited to, survey results and past conversation history. The conversation unit, for example, identifies the user's current interests based on the user's survey results. For example, the conversation unit prioritizes providing topics in which the user is currently interested. The conversation unit can also provide topics related to events in which the user is currently interested. This allows for more appropriate conversation by taking into consideration the user's current interests. Some or all of the above-described processing in the conversation unit may be performed using, or without, a generation AI. For example, the conversation unit can input the user's current interests into the generation AI and have the generation AI select the content of the conversation.

[0086] The conversation unit can estimate the user's emotions and determine the priority of conversations based on the estimated user emotions. Conversation priorities include, but are not limited to, urgency and importance, for example. The conversation unit can estimate the user's emotions and determine the priority of conversations based on the estimated emotions. For example, if the user is feeling stressed, the conversation unit can prioritize important conversations. Furthermore, if the user is relaxed, the conversation unit can prioritize normal conversations. This enables more appropriate conversations by determining the priority of conversations based on the user's emotions. Some or all of the above-described processing in the conversation unit can be performed using, or without, a generation AI. For example, the conversation unit can input the user's emotions into the generation AI and have the generation AI determine the priority of conversations.

[0087] The conversation unit can have a highly relevant conversation by taking into account the user's geographical location information during the conversation. Geographical location information includes, but is not limited to, GPS data and location information services, for example. The conversation unit acquires the user's geographical location information using, for example, GPS data. For example, when the user is at home, the conversation unit can provide topics related to the user's home. Furthermore, when the user is out, the conversation unit can also provide topics related to the user's destination. This enables a highly relevant conversation by taking into account the geographical location information. Some or all of the above-described processing in the conversation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the conversation unit can input the user's geographical location information into the generation AI and cause the generation AI to select highly relevant conversation content.

[0088] The conversation unit can analyze the user's social media activity during a conversation and hold a related conversation. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The conversation unit, for example, uses an API to collect and analyze the user's social media activity. For example, the conversation unit can provide related topics based on information shared by the user on social media. The conversation unit can also provide topics related to specific events from the user's social media activity. This enables related conversations by analyzing social media activity. Some or all of the above-described processing in the conversation unit can be performed using, or without, a generation AI. For example, the conversation unit can input the user's social media activity into the generation AI and have the generation AI select related conversation content.

[0089] The monitoring unit can estimate the user's emotions and adjust the health status monitoring method based on the estimated user emotions. Health status monitoring methods include, but are not limited to, the use of sensors and data analysis techniques. For example, the monitoring unit can estimate the user's emotions and adjust the health status monitoring method based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can perform monitoring to reduce stress. Furthermore, if the user is relaxed, the monitoring unit can perform normal health status monitoring. This allows for more appropriate health status monitoring by adjusting the monitoring method according to the user's emotions. Some or all of the above-described processing in the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input the user's emotions into the generation AI and have the generation AI adjust the monitoring method.

[0090] During monitoring, the monitoring unit can analyze the user's past health data and select the optimal monitoring method. Examples of past health data include, but are not limited to, electronic medical records and health management apps. For example, the monitoring unit stores the user's past health data in the electronic medical record and analyzes it. For example, the monitoring unit prioritizes monitoring of specific health conditions from the user's past health data. The monitoring unit can also analyze the user's past health data and select the most efficient monitoring method. This allows the optimal monitoring method to be selected by analyzing the past health data. Some or all of the above-described processing in the monitoring unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input the user's past health data into the generation AI and have the generation AI select the optimal monitoring method.

[0091] The monitoring unit may perform monitoring while taking into account the user's current living situation or environment. Examples of the current living situation or environment include, but are not limited to, the use of sensors and environmental monitoring technology. The monitoring unit may, for example, use a sensor to detect the user's current living situation or environment. For example, when the user is at home, the monitoring unit may prioritize monitoring the user's health status at home. Furthermore, when the user is out, the monitoring unit may also monitor the user's health status while away from home. This enables more appropriate monitoring by taking into account the current living situation and environment. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit may input the user's current living situation or environment into the generation AI and have the generation AI adjust the monitoring method.

[0092] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated user emotions. Examples of monitoring priorities include, but are not limited to, urgency and importance. The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize monitoring stress-related health conditions. Furthermore, if the user is relaxed, the monitoring unit can prioritize monitoring normal health conditions. This enables more appropriate monitoring by determining monitoring priorities based on the user's emotions. Some or all of the above-described processing in the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input the user's emotions into the generation AI and have the generation AI determine the monitoring priorities.

[0093] During monitoring, the monitoring unit can perform optimal monitoring by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. The monitoring unit, for example, acquires the user's geographical location information using GPS data. For example, when the user is at home, the monitoring unit prioritizes monitoring the user's health status at home. Furthermore, when the user is out, the monitoring unit can also monitor the user's health status while away from home. This enables optimal monitoring by taking into account the geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal monitoring method.

[0094] During monitoring, the monitoring unit can analyze the user's social media activity and perform related monitoring. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The monitoring unit, for example, uses an API to collect and analyze the user's social media activity. For example, the monitoring unit monitors related health conditions based on information shared by the user on social media. The monitoring unit can also monitor health conditions related to specific events from the user's social media activity. This enables related monitoring by analyzing the social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the user's social media activity into the generation AI and have the generation AI select a related monitoring method.

[0095] The notification unit can estimate the user's emotions and adjust the content and timing of the notification based on the estimated user's emotions. The content and timing of the notification can include, but are not limited to, emotion analysis and notification system settings. The notification unit can estimate the user's emotions using emotion analysis technology, for example. For example, the notification unit can analyze changes in the user's tone of voice or speaking style to estimate emotions. The notification unit can also analyze changes in the user's facial expressions to estimate emotions. This allows the content and timing of the notification to be adjusted according to the user's emotions. Some or all of the above-described processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the notification unit can input the user's emotions into the generation AI and have the generation AI adjust the content and timing of the notification.

[0096] The notification unit can analyze the user's past health data and select the optimal notification method when notifying the user. Examples of past health data include, but are not limited to, electronic medical records and health management apps. The notification unit, for example, stores the user's past health data in the electronic medical record and analyzes it. For example, the notification unit can prioritize notifications regarding specific health conditions based on the user's past health data. The notification unit can also analyze the user's past health data and select the most efficient notification method. This allows the optimal notification method to be selected by analyzing the past health data. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's past health data into the generation AI and have the generation AI select the optimal notification method.

[0097] The notification unit may take into consideration the user's current living situation or environment when providing a notification. Examples of the current living situation or environment include, but are not limited to, the use of sensors and environmental monitoring technology. The notification unit may, for example, use a sensor to detect the user's current living situation or environment. For example, when the user is at home, the notification unit may prioritize notifications related to the user's health status at home. Furthermore, when the user is out, the notification unit may also provide notifications related to the user's health status while away from home. This allows for more appropriate notifications by taking the current living situation and environment into consideration. Some or all of the above-described processing in the notification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the notification unit may input the user's current living situation or environment into the generation AI and have the generation AI adjust the notification content.

[0098] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. Notification priorities include, but are not limited to, urgency and importance, for example. The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can prioritize important notifications. Furthermore, if the user is relaxed, the notification unit can prioritize regular notifications. This enables more appropriate notifications by determining the priority of notifications based on the user's emotions. Some or all of the above-described processing in the notification unit can be performed using, or without, a generation AI. For example, the notification unit can input the user's emotions into the generation AI and have the generation AI determine the priority of notifications.

[0099] The notification unit can provide optimal notification by taking into account the user's geographical location information when providing notification. Examples of geographical location information include, but are not limited to, GPS data and location information services. The notification unit acquires the user's geographical location information using, for example, GPS data. For example, when the user is at home, the notification unit prioritizes notifications related to the user's health status at home. Furthermore, when the user is out, the notification unit can also provide notifications related to the user's health status while away from home. This enables optimal notification by taking into account the geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the notification unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal notification method.

[0100] The notification unit may analyze the user's social media activity and provide relevant notifications at the time of notification. Examples of social media activity include, but are not limited to, the use of APIs and data mining techniques. The notification unit may, for example, use an API to collect and analyze the user's social media activity. For example, the notification unit may provide notifications about relevant health conditions based on information shared by the user on social media. The notification unit may also provide notifications about health conditions related to specific events from the user's social media activity. This enables relevant notifications to be provided by analyzing social media activity. Some or all of the above-described processing in the notification unit may be performed using, or without, a generation AI. For example, the notification unit may input the user's social media activity into the generation AI and have the generation AI select relevant notification content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, operation unit, conversation unit, monitoring unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice instructions using the microphone 38B of the smart device 14. The operation unit operates home appliances via the control unit 46A of the smart device 14. The conversation unit converses with the user via the control unit 46A of the smart device 14. The monitoring unit monitors the user's health condition via the specific processing unit 290 of the data processing device 12. The notification unit detects an abnormality via the specific processing unit 290 of the data processing device 12 and notifies a caregiver or a medical institution. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, operation unit, conversation unit, monitoring unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice instructions using the microphone 238 of the smart glasses 214. The operation unit operates home appliances via the control unit 46A of the smart glasses 214. The conversation unit converses with the user via the control unit 46A of the smart glasses 214. The monitoring unit monitors the user's health condition via the specific processing unit 290 of the data processing device 12. The notification unit detects an abnormality via the specific processing unit 290 of the data processing device 12 and notifies a caregiver or a medical institution. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, operation unit, conversation unit, monitoring unit, and notification unit 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 voice instructions using the microphone 238 of the headset type terminal 314. The operation unit operates a home appliance via the control unit 46A of the headset type terminal 314. The conversation unit converses with the user via the control unit 46A of the headset type terminal 314. The monitoring unit monitors the user's health condition via the specific processing unit 290 of the data processing device 12. The notification unit detects an abnormality via the specific processing unit 290 of the data processing device 12 and notifies a caregiver or a medical institution. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, operation unit, conversation unit, monitoring unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice instructions using the microphone 238 of the robot 414. The operation unit operates home appliances via the control unit 46A of the robot 414. The conversation unit converses with the user via the control unit 46A of the robot 414. The monitoring unit monitors the user's health condition via the specific processing unit 290 of the data processing device 12. The notification unit detects an abnormality via the specific processing unit 290 of the data processing device 12 and notifies a caregiver or a medical institution.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The smart home system can further include an energy management unit. The energy management unit monitors the usage of home appliances and optimizes energy consumption. For example, the energy management unit can automatically turn off appliances that are used infrequently. The energy management unit can also suggest a schedule for operating home appliances during times when electricity rates are low. Furthermore, the energy management unit can link with a solar power generation system and efficiently utilize the energy generated. This enables optimization of energy consumption and cost reduction.

[0103] The operation unit can also change the operation mode of the home appliance based on a voice instruction. For example, when the operation unit receives a voice instruction such as "switch to eco mode," it switches the home appliance to energy-saving mode. Also, when the operation unit receives a voice instruction such as "switch to night mode," it can switch the home appliance to a mode that reduces the operating noise. Furthermore, when the operation unit receives a voice instruction such as "switch to party mode," it can switch the home appliance to a mode that operates at maximum output. This makes it possible to change the operation mode of the home appliance according to the user's needs.

[0104] The control unit can also set operation schedules for home appliances based on voice instructions. For example, if the control unit receives a voice instruction such as "Open the curtains at 7 o'clock every morning," it can set a schedule to automatically open the curtains at 7 o'clock every morning. Also, if the control unit receives a voice instruction such as "Turn off the lights at 10 o'clock every night," it can set a schedule to automatically turn off the lights at 10 o'clock every night. Furthermore, if the control unit receives a voice instruction such as "Turn on the air conditioner only on weekends," it can set a schedule to automatically turn on the air conditioner only on weekends. This makes it possible to set operation schedules for home appliances that suit the user's lifestyle.

[0105] The conversation unit can estimate the user's emotions and select a conversation topic based on the estimated user's emotions. For example, if the user is feeling stressed, the conversation unit can provide a topic that will help the user relax. If the user is excited, the conversation unit can provide an interesting topic. Furthermore, if the user is sad, the conversation unit can provide a comforting topic. This allows for better communication by selecting an appropriate conversation topic according to the user's emotions.

[0106] The monitoring unit can estimate the user's emotions and adjust the frequency of monitoring the health condition based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can increase the monitoring frequency and collect more detailed data. Also, if the user is relaxed, the monitoring unit can decrease the monitoring frequency to reduce the burden on the user. Furthermore, if the user is excited, monitoring can be focused on specific health indicators. This enables appropriate monitoring according to the user's emotions.

[0107] The notification unit can estimate the user's emotions and customize the content of the notification based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can send a simple and easy-to-understand notification. If the user is relaxed, the notification unit can also send a notification containing detailed information. Furthermore, if the user is excited, the notification unit can prioritize sending notifications with high urgency. This makes it possible to provide appropriate notifications according to the user's emotions.

[0108] The reception unit can estimate the user's emotions and adjust the feedback of the voice instructions based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide quick and concise feedback. If the user is feeling relaxed, the reception unit can provide detailed feedback. Furthermore, if the user is excited, the reception unit can provide more emphatic feedback. This allows for appropriate feedback according to the user's emotions.

[0109] When operating a home appliance, the operation unit can analyze the user's past operation history and select the optimal operation method. For example, the operation unit can prioritize and provide operation methods that the user has frequently used in the past. The operation unit can also predict and provide an operation method to be used during a specific time period based on the user's past operation history. Furthermore, the operation unit can analyze the user's past operation history and suggest the most efficient operation method. This makes it possible to select the optimal operation method by analyzing the past operation history.

[0110] The conversation unit can have a highly relevant conversation by taking into account the user's geographical location information. For example, if the user is at home, the conversation unit can provide topics related to the user's home. If the user is out, the conversation unit can also provide topics related to the user's destination. Furthermore, if the user is traveling, the conversation unit can provide tourist information and weather information for the user's travel destination. This makes it possible to have a highly relevant conversation by taking into account the geographical location information.

[0111] During monitoring, the monitoring unit can analyze the user's social media activity and perform related monitoring. For example, the monitoring unit can monitor related health conditions based on information shared by the user on social media. The monitoring unit can also monitor health conditions related to specific events from the user's social media activity. Furthermore, the monitoring unit can analyze the user's social media activity and detect changes in the health condition early. This makes it possible to perform related monitoring by analyzing social media activity.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives a voice command, such as, but not limited to, "Turn on the lights" or "Close the curtains." Step 2: The operation unit operates the home appliance based on the voice instruction received by the reception unit. For example, the operation unit turns on and off the lights based on the voice instruction. The operation unit can also open and close curtains based on the voice instruction. Step 3: The conversation unit engages in a conversation with the user based on the voice instructions received by the reception unit. For example, the conversation unit provides a conversation based on the elderly person's preferences and interests. The conversation unit can also respond to cases where the elderly person repeatedly makes the same remarks or asks the same questions. Step 4: The monitoring unit monitors the user's health condition based on the content of the conversation conducted by the conversation unit. For example, the monitoring unit detects changes in the elderly person's tone of voice or speaking style. If the monitoring unit detects an abnormality, it notifies the notification unit. Step 5: The notification unit notifies a caregiver or a medical institution of the abnormality detected by the monitoring unit. For example, when the notification unit detects an abnormality, it sends a notification to a registered contact.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0144] 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.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 voice instructions; an operation unit that operates the home appliance based on the voice instruction received by the reception unit; a conversation unit that converses with a user based on the voice instruction received by the reception unit; a monitoring unit that monitors the health state of the user based on the content of the conversation carried out by the conversation unit; a notification unit that notifies the abnormality detected by the monitoring unit. A system characterized by:

2. The operation unit includes: Turn lights on and off based on voice commands The system of claim 1 .

3. The operation unit includes: Opens and closes curtains based on voice commands The system of claim 1 .

4. The conversation unit is Providing conversations based on user preferences and interests The system of claim 1 .

5. The monitoring unit Detect changes in the user's tone of voice and speaking style The system of claim 1 .

6. The notification unit Notify caregivers or medical institutions if an abnormality is detected The system of claim 1 .

7. The reception unit The system estimates the user's emotions and adjusts the timing of accepting voice instructions based on the estimated user emotions. The system of claim 1 .

8. The reception unit When receiving voice instructions, the system analyzes the user's past instruction history and selects the optimal reception method. The system of claim 1 .

Images