System

Abstract

An object of a system according to an embodiment is to enable an elderly person to easily operate the system, an application, and an electronic device.SOLUTION: A system includes an interface part, a cooperation part, and a generation AI part. The interface unit provides a simple interface. The cooperation unit cooperates with the system, the application, and the electronic device. The generation AI unit transmits an instruction via the generation AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, it is difficult for elderly people to master systems, apps, and electronic devices, and there is room for improvement in usability.

[0005] The system according to the embodiment aims to enable elderly people to easily operate systems, apps, and electronic devices. [Means for solving the problem]

[0006] The system according to the embodiment includes an interface unit, a linking unit, and a generation AI unit. The interface unit provides a simple interface. The linking unit links with the system, the app, and the electronic device. The generation AI unit transmits instructions via the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can enable elderly people to easily operate systems, apps, and electronic devices. [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) The tablet system according to an embodiment of the present invention is designed to enable elderly people to easily perform advanced operations simply by communicating instructions. This system provides a simple interface, works in conjunction with systems, apps, and electronic devices, and can communicate instructions via a generation AI. This allows elderly people to easily perform advanced operations simply by communicating instructions, enriching communication and daily life.

[0029] The tablet system according to the embodiment includes an interface unit, a linking unit, and a generation AI unit. The interface unit provides a simple interface. For example, the interface unit provides large icons, text, and a simple menu structure. The interface unit also supports voice control, making it easy to use for elderly people with limited mobility. The linking unit links with systems, apps, and electronic devices. For example, the linking unit links with a smart home system to control lighting and air conditioning. The linking unit can also link with a health management app to monitor blood pressure and heart rate. The linking unit can also use a video calling app to enjoy communication with family and friends. The generation AI unit transmits instructions via the generation AI. For example, the generation AI unit analyzes instructions from the elderly person and performs appropriate operations based on the content of the instructions. The generation AI unit analyzes instructions and executes operations using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the generation AI unit receives an instruction such as "Turn on the living room lights," it analyzes the instruction and instructs the smart home system to operate the lights. As a result, the tablet system according to the embodiment allows elderly people to easily perform advanced operations simply by giving instructions. For example, by simply saying "Tell me my schedule for today," the AI ​​generation unit opens a calendar app and reads out the schedule. Similarly, by simply saying "Call my family," a video calling app can be launched and a call can be made to a family member.

[0030] The interface unit can provide large icons, text, and a simple menu structure. For example, the interface unit can learn the user's operation history and automatically generate a menu structure optimized for each elderly person. For example, it can prioritize the display of frequently used apps and functions and hide unnecessary menu items. The interface unit can also dynamically change the interface layout based on the user's operation history. For example, it can automatically place apps used during specific times of the day in the foreground, improving operation efficiency. The interface unit can also analyze the operation patterns of elderly people and build a system that proposes the optimal menu structure. For example, it can automatically adjust the layout of menu items depending on the frequency of operation and the time of day. This makes it easy for even elderly people with poor eyesight to operate the device.

[0031] The linking unit can link with the smart home system to operate lighting and air conditioning. For example, the linking unit's generating AI learns the elderly person's lifestyle patterns and automatically launches the systems and apps they use on a daily basis. For example, it could automatically open a news app every morning or a relaxing music app in the evening. The linking unit also builds a system that automatically launches necessary apps and systems in line with the elderly person's lifestyle rhythm. For example, it could open a recipe app at mealtimes. The linking unit's generating AI also analyzes the elderly person's behavioral patterns and launches apps and systems at the optimal times. For example, it could automatically open a fitness app when it's time to exercise. This allows the elderly to easily control lighting and air conditioning.

[0032] The generation AI unit can analyze the instructions given by the elderly person and instruct the smart home system to operate the lighting. For example, when the generation AI unit analyzes the instructions given by the elderly person, it refers to the past instruction history to suggest the optimal operation. For example, it may prioritize operations that have been performed frequently in the past. The generation AI unit also builds a system that suggests the optimal operation based on the instruction history of the elderly person. For example, it may analyze past operation patterns and automatically select the optimal operation. The generation AI unit also learns the instruction history of the elderly person and suggests the optimal operation. For example, it may automatically suggest operations to be performed at specific times of the day. This allows the elderly person to operate the lighting with voice commands.

[0033] The generation AI unit can perform operations in conjunction with gesture recognition technology. For example, when the generation AI receives instructions from an elderly person, it performs operations in conjunction with gesture recognition technology. For example, it analyzes hand movements and facial expressions to perform operations. The generation AI unit also builds a system that allows elderly people to give instructions using gestures. For example, it can perform specific operations by simply waving a hand. The generation AI unit also uses gesture recognition technology to analyze instructions from elderly people and perform the most appropriate operation. For example, it analyzes facial expressions to perform operations. This allows elderly people to perform operations using gestures.

[0034] The generation AI unit can process multiple instructions simultaneously and perform operations efficiently. For example, when the generation AI receives instructions from an elderly person, it processes multiple instructions simultaneously and performs operations efficiently. For example, it executes multiple instructions such as "Turn on the TV and turn on the air conditioner" simultaneously. The generation AI unit also builds a system that allows elderly people to give multiple instructions at once. For example, it processes instructions such as "Play music and turn on the lights" simultaneously. The generation AI unit also analyzes multiple instructions simultaneously and performs operations efficiently. For example, it executes instructions such as "Close the curtains and lock the door" simultaneously. This allows elderly people to give multiple instructions simultaneously.

[0035] The generation AI unit can monitor the operations of the elderly in real time and automatically correct any incorrect operations. For example, the generation AI unit can monitor the operations of the elderly in real time and automatically correct any incorrect operations. For example, it can automatically restart an app if it is accidentally closed. The generation AI unit can also constantly monitor the operations of the elderly and create a system that immediately corrects any incorrect operations that occur. For example, it can automatically restore data that has been accidentally deleted. The generation AI unit can also analyze the operations of the elderly and automatically correct any incorrect operations. For example, it can automatically correct characters that have been entered incorrectly. This makes it possible to automatically correct any incorrect operations made by the elderly.

[0036] The generation AI unit can provide step-by-step guidance when performing advanced operations. For example, when performing advanced operations, the generation AI unit provides step-by-step guidance. For example, when changing app settings, each step is explained in order. The generation AI unit also builds a system that provides real-time guidance when elderly people perform advanced operations. For example, when performing the initial setup of a device, each step is explained in order. The generation AI unit also supports elderly people in their operations and provides step-by-step guidance. For example, when installing a new app, each step is explained in order. This makes it easy for elderly people to perform advanced operations.

[0037] The generation AI unit can provide visual feedback when performing advanced operations, allowing the progress of the operation to be visually confirmed. For example, when performing advanced operations, the generation AI unit provides visual feedback, allowing the progress of the operation to be visually confirmed. For example, each step of the operation is displayed graphically. The generation AI unit also builds a system that provides visual feedback when elderly people perform advanced operations. For example, the progress of the operation is displayed using bar graphs or icons. The generation AI unit also supports the elderly people's operations and provides visual feedback. For example, each step of the operation is visually displayed, allowing the elderly people to check the progress. This allows the elderly people to visually check the progress of the operation.

[0038] The generation AI unit can provide voice feedback when performing advanced operations, allowing the progress of the operation to be confirmed by voice. For example, when performing advanced operations, the generation AI unit provides voice feedback, allowing the progress of the operation to be confirmed by voice. For example, it notifies the user by voice when each step is completed. The generation AI unit also builds a system that provides voice feedback when elderly people perform advanced operations. For example, it provides voice guidance on the progress of the operation. The generation AI unit also supports the elderly in their operations and provides voice feedback. For example, it explains each step of the operation by voice, allowing the user to confirm the progress. This allows the elderly to confirm the progress of the operation by voice.

[0039] The generation AI unit can learn the communication history of the elderly person and suggest appropriate topics and messages. For example, the generation AI unit learns the communication history of the elderly person and suggests appropriate topics and messages. For example, it suggests new topics based on the content of past conversations. The generation AI unit also builds a system in which the generation AI suggests appropriate messages based on the communication history of the elderly person. For example, it analyzes the content of past messages and suggests appropriate replies. The generation AI unit also learns the communication history of the elderly person and suggests optimal topics and messages. For example, it suggests new topics based on the conversation history with a specific person. In this way, the elderly person is suggested appropriate topics and messages.

[0040] The generation AI unit generates subtitles in real time during video calls, allowing even elderly people with hearing impairments to enjoy communication. For example, the generation AI unit generates subtitles in real time during video calls, allowing even elderly people with hearing impairments to enjoy communication. For example, it converts the audio during a call into text and displays it on the screen. The generation AI unit also builds a system that generates subtitles in real time when elderly people make video calls. For example, it automatically converts the content of the conversation during a call into subtitles and displays them on the screen. The generation AI unit also analyzes the audio during a video call and generates subtitles in real time. For example, it converts the audio during a call into text, allowing even elderly people with hearing impairments to understand the conversation. This allows even elderly people with hearing impairments to enjoy communication.

[0041] The generation AI unit can provide a translation function during communication, enabling smooth communication with family and friends who speak different languages. For example, the generation AI unit can translate audio during a call in real time and display it as subtitles. The generation AI unit can also build a system that provides real-time translation when elderly people communicate with people who speak different languages. For example, it can automatically translate when messages are sent and received. The generation AI unit can also analyze audio and text during communication and translate it in real time. For example, it can automatically translate conversations during video calls and display them as subtitles. This allows smooth communication with family and friends who speak different languages.

[0042] The generation AI unit uses facial recognition technology to analyze the facial expressions of the other person during communication and can suggest an appropriate response. For example, when communicating, the generation AI unit uses facial recognition technology to analyze the other person's facial expressions and suggest an appropriate response. For example, if the other person is smiling, it will suggest a positive response. The generation AI unit also builds a system that analyzes the facial expressions of the other person when elderly people communicate and suggests an appropriate response. For example, it will suggest helping if the other person is in trouble. The generation AI unit also analyzes the facial expressions of the other person during communication and suggests an appropriate response. For example, if the other person is surprised, it will suggest a response to surprise. This makes it possible to suggest an appropriate response according to the other person's facial expression.

[0043] The generation AI unit can learn the daily life patterns of the elderly and provide appropriate advice and reminders. For example, the generation AI unit learns the daily life patterns of the elderly and provides appropriate advice and reminders. For example, it may remind them when to take their medicine every day. The generation AI unit also builds a system in which the generation AI provides appropriate advice and reminders based on the daily life patterns of the elderly. For example, it may remind them when to exercise regularly. The generation AI unit also learns the daily life patterns of the elderly and provides optimal advice and reminders. For example, it may remind them when to eat meals. This can support the daily lives of the elderly.

[0044] The generation AI unit works in conjunction with a health management app to analyze the health data of the elderly and provide necessary advice. For example, the generation AI unit works in conjunction with a health management app, and the generation AI analyzes the health data of the elderly and provides necessary advice. For example, health advice is provided based on blood pressure and heart rate data. The generation AI unit also builds a system in which the generation AI provides appropriate advice based on the elderly's health data. For example, it analyzes weight and dietary data and suggests healthy lifestyle habits. The generation AI unit also works in conjunction with a health management app to analyze the elderly's health data and provide necessary advice. For example, it detects lack of exercise and suggests exercise. This can support the health management of the elderly.

[0045] The generative AI unit can suggest new activities based on the hobbies and interests of the elderly. For example, to support daily life, the generative AI unit suggests new activities based on the hobbies and interests of the elderly. For example, it suggests hobbies such as gardening and handicrafts. The generative AI unit also builds a system in which the generative AI suggests new activities based on the hobbies and interests of the elderly. For example, it suggests activities such as listening to music or reading. The generative AI unit also learns the hobbies and interests of the elderly and suggests the most suitable activity. For example, it suggests new hobbies such as traveling or cooking. This makes it possible to suggest new activities based on the hobbies and interests of the elderly.

[0046] The generative AI unit can suggest recipes to support the diet and nutritional management of the elderly. For example, as support for daily life, the generative AI unit suggests recipes to support the diet and nutritional management of the elderly. For example, it provides recipes for healthy meals. The generative AI unit also builds a system in which the generative AI suggests appropriate recipes based on the diet and nutritional management of the elderly. For example, it provides recipes for meals containing specific nutrients. The generative AI unit also supports the diet and nutritional management of the elderly and suggests optimal recipes. For example, it provides recipes for dieting and maintaining health. This makes it possible to support the diet and nutritional management of the elderly.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The tablet system may further include a health management unit that monitors the user's health condition. The health management unit periodically acquires vital data, such as the user's blood pressure, heart rate, and body temperature, and issues an alert if an abnormality is detected. The health management unit can also provide appropriate health advice based on the user's health data. For example, if a lack of exercise is detected, the health management unit can send a notification encouraging exercise. The health management unit can also store the user's health data in the cloud and share the data in cooperation with medical institutions. This allows the user's health condition to be constantly monitored and appropriate health management to be carried out.

[0049] The tablet system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit may use, for example, GPS to identify the user's current location and notify family members or caregivers. The location information acquisition unit may also issue an alert if the user leaves a specific area. For example, if an elderly person with dementia leaves their home, the family may be notified. The location information acquisition unit may also record the user's movement history and analyze their daily behavioral patterns. This helps ensure the user's safety and provide appropriate support.

[0050] The tablet system can further include a diet management unit that supports the user's dietary management. The diet management unit, for example, records the user's dietary content and analyzes nutritional balance. The diet management unit can also suggest meals based on the user's health condition. For example, it can suggest low-carbohydrate recipes to a diabetic user. The diet management unit can also notify the user of appropriate meal times based on the user's diet history. This allows the user to maintain their health and perform appropriate dietary management.

[0051] The tablet system may further include a sleep management unit that monitors the user's sleep state. The sleep management unit, for example, records and analyzes the user's sleep time and sleep quality. The sleep management unit can also provide appropriate sleep advice based on the user's sleep patterns. For example, if insufficient sleep is detected, the sleep management unit can send a notification encouraging the user to go to bed early. The sleep management unit can also suggest an appropriate sleeping environment based on the user's sleep data. This can improve the user's sleep quality and maintain their health.

[0052] The tablet system can further include an exercise management unit that supports the user's exercise. The exercise management unit, for example, records the user's exercise volume and provides appropriate exercise advice. The exercise management unit can also suggest exercise programs based on the user's health condition. For example, it can suggest low-impact exercises to a user with arthritis. The exercise management unit can also notify the user of the timing of exercise based on the user's exercise history. This allows the user to maintain their health and perform appropriate exercise management.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The interface section provides a simple interface. For example, it provides large icons and text, a simple menu structure, and also allows voice control. This makes the interface easy to use even for elderly people with limited mobility. Step 2: The connectivity unit connects with systems, apps, and electronic devices. For example, it can connect with a smart home system to control lighting and air conditioning, connect with a health management app to monitor blood pressure and heart rate, and use a video chat app to enjoy communication with family and friends. Step 3: The generation AI unit transmits instructions via the generation AI. For example, it analyzes instructions from the elderly person and performs appropriate operations based on the content. The generation AI unit analyzes instructions using text generation AI (e.g., LLM) or multimodal generation AI and executes the operation. For example, if it receives the instruction "Turn on the lights in the living room," it analyzes the instruction and instructs the smart home system to operate the lights. Also, simply by instructing "Tell me what's on my schedule for today," it will open the calendar app and read out the schedule. Simply by instructing "Call my family," it can launch a video calling app and call family members.

[0055] (Example 2) The tablet system according to an embodiment of the present invention is designed to enable elderly people to easily perform advanced operations simply by communicating instructions. This system provides a simple interface, works in conjunction with systems, apps, and electronic devices, and can communicate instructions via a generation AI. This allows elderly people to easily perform advanced operations simply by communicating instructions, enriching communication and daily life.

[0056] The tablet system according to the embodiment includes an interface unit, a linking unit, and a generation AI unit. The interface unit provides a simple interface. For example, the interface unit provides large icons, text, and a simple menu structure. The interface unit also supports voice control, making it easy to use for elderly people with limited mobility. The linking unit links with systems, apps, and electronic devices. For example, the linking unit links with a smart home system to control lighting and air conditioning. The linking unit can also link with a health management app to monitor blood pressure and heart rate. The linking unit can also use a video calling app to enjoy communication with family and friends. The generation AI unit transmits instructions via the generation AI. For example, the generation AI unit analyzes instructions from the elderly person and performs appropriate operations based on the content of the instructions. The generation AI unit analyzes instructions and executes operations using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the generation AI unit receives an instruction such as "Turn on the living room lights," it analyzes the instruction and instructs the smart home system to operate the lights. As a result, the tablet system according to the embodiment allows elderly people to easily perform advanced operations simply by giving instructions. For example, by simply saying "Tell me my schedule for today," the AI ​​generation unit opens a calendar app and reads out the schedule. Similarly, by simply saying "Call my family," a video calling app can be launched and a call can be made to a family member.

[0057] The interface unit can provide large icons, text, and a simple menu structure. For example, the interface unit can learn the user's operation history and automatically generate a menu structure optimized for each elderly person. For example, it can prioritize the display of frequently used apps and functions and hide unnecessary menu items. The interface unit can also dynamically change the interface layout based on the user's operation history. For example, it can automatically place apps used during specific times of the day in the foreground, improving operation efficiency. The interface unit can also analyze the operation patterns of elderly people and build a system that proposes the optimal menu structure. For example, it can automatically adjust the layout of menu items depending on the frequency of operation and the time of day. This makes it easy for even elderly people with poor eyesight to operate the device.

[0058] The linking unit can link with the smart home system to operate lighting and air conditioning. For example, the linking unit's generating AI learns the elderly person's lifestyle patterns and automatically launches the systems and apps they use on a daily basis. For example, it could automatically open a news app every morning or a relaxing music app in the evening. The linking unit also builds a system that automatically launches necessary apps and systems in line with the elderly person's lifestyle rhythm. For example, it could open a recipe app at mealtimes. The linking unit's generating AI also analyzes the elderly person's behavioral patterns and launches apps and systems at the optimal times. For example, it could automatically open a fitness app when it's time to exercise. This allows the elderly to easily control lighting and air conditioning.

[0059] The generation AI unit can analyze the instructions given by the elderly person and instruct the smart home system to operate the lighting. For example, when the generation AI unit analyzes the instructions given by the elderly person, it refers to the past instruction history to suggest the optimal operation. For example, it may prioritize operations that have been performed frequently in the past. The generation AI unit also builds a system that suggests the optimal operation based on the instruction history of the elderly person. For example, it may analyze past operation patterns and automatically select the optimal operation. The generation AI unit also learns the instruction history of the elderly person and suggests the optimal operation. For example, it may automatically suggest operations to be performed at specific times of the day. This allows the elderly person to operate the lighting with voice commands.

[0060] The generation AI unit can generate appropriate feedback according to the emotional state of the elderly person. For example, the generation AI unit uses an emotion estimation function to generate appropriate feedback according to the elderly person's emotional state. For example, if the elderly person is feeling stressed, it provides feedback that helps them relax. The generation AI unit also builds a system that analyzes the elderly person's emotional state in real time and generates appropriate feedback. For example, if the elderly person is feeling positive, it provides encouraging feedback. The generation AI unit also dynamically generates feedback that matches the elderly person's emotional state based on the emotion estimation data. For example, if the elderly person is tired, it provides feedback that encourages them to rest. This makes it possible to provide feedback that matches the elderly person's emotional state.

[0061] The generation AI unit can perform operations in conjunction with gesture recognition technology. For example, when the generation AI receives instructions from an elderly person, it performs operations in conjunction with gesture recognition technology. For example, it analyzes hand movements and facial expressions to perform operations. The generation AI unit also builds a system that allows elderly people to give instructions using gestures. For example, it can perform specific operations by simply waving a hand. The generation AI unit also uses gesture recognition technology to analyze instructions from elderly people and perform the most appropriate operation. For example, it analyzes facial expressions to perform operations. This allows elderly people to perform operations using gestures.

[0062] The generation AI unit can process multiple instructions simultaneously and perform operations efficiently. For example, when the generation AI receives instructions from an elderly person, it processes multiple instructions simultaneously and performs operations efficiently. For example, it executes multiple instructions such as "Turn on the TV and turn on the air conditioner" simultaneously. The generation AI unit also builds a system that allows elderly people to give multiple instructions at once. For example, it processes instructions such as "Play music and turn on the lights" simultaneously. The generation AI unit also analyzes multiple instructions simultaneously and performs operations efficiently. For example, it executes instructions such as "Close the curtains and lock the door" simultaneously. This allows elderly people to give multiple instructions simultaneously.

[0063] The generation AI unit can use the emotion estimation function to suggest appropriate actions when an elderly person is in a specific emotional state. For example, using the emotion estimation function, the generation AI unit can suggest appropriate actions when an elderly person is in a specific emotional state. For example, if the elderly person is feeling stressed, it will suggest an action that will help them relax. The generation AI unit also analyzes the emotional state of the elderly person in real time and builds a system that suggests appropriate actions. For example, if the elderly person is feeling positive, it will suggest an active action. The generation AI unit also dynamically suggests actions that match the elderly person's emotional state based on the emotion estimation data. For example, if the elderly person is tired, it will suggest an action that encourages them to rest. This makes it possible to suggest actions that match the elderly person's emotional state.

[0064] The generation AI unit can monitor the operations of the elderly in real time and automatically correct any incorrect operations. For example, the generation AI unit can monitor the operations of the elderly in real time and automatically correct any incorrect operations. For example, it can automatically restart an app if it is accidentally closed. The generation AI unit can also constantly monitor the operations of the elderly and create a system that immediately corrects any incorrect operations that occur. For example, it can automatically restore data that has been accidentally deleted. The generation AI unit can also analyze the operations of the elderly and automatically correct any incorrect operations. For example, it can automatically correct characters that have been entered incorrectly. This makes it possible to automatically correct any incorrect operations made by the elderly.

[0065] The generation AI unit can provide step-by-step guidance when performing advanced operations. For example, when performing advanced operations, the generation AI unit provides step-by-step guidance. For example, when changing app settings, each step is explained in order. The generation AI unit also builds a system that provides real-time guidance when elderly people perform advanced operations. For example, when performing the initial setup of a device, each step is explained in order. The generation AI unit also supports elderly people in their operations and provides step-by-step guidance. For example, when installing a new app, each step is explained in order. This makes it easy for elderly people to perform advanced operations.

[0066] The generation AI unit can use the emotion estimation function to provide support to reduce the anxiety and stress that elderly people feel while operating the device. For example, the generation AI unit uses the emotion estimation function to provide support to reduce the anxiety and stress that elderly people feel while operating the device. For example, if they find the operation difficult, it will suggest a simpler method of operation. The generation AI unit also analyzes the emotional state of elderly people in real time and builds a system that provides support to reduce anxiety and stress. For example, if the operation is complicated, it will suggest a simpler method of operation. The generation AI unit also dynamically provides support tailored to the emotional state of the elderly person based on the emotion estimation data. For example, if they find the operation difficult, it will provide a step-by-step guide. This can reduce the anxiety and stress that elderly people feel while operating the device.

[0067] The generation AI unit can provide visual feedback when performing advanced operations, allowing the progress of the operation to be visually confirmed. For example, when performing advanced operations, the generation AI unit provides visual feedback, allowing the progress of the operation to be visually confirmed. For example, each step of the operation is displayed graphically. The generation AI unit also builds a system that provides visual feedback when elderly people perform advanced operations. For example, the progress of the operation is displayed using bar graphs or icons. The generation AI unit also supports the elderly people's operations and provides visual feedback. For example, each step of the operation is visually displayed, allowing the elderly people to check the progress. This allows the elderly people to visually check the progress of the operation.

[0068] The generation AI unit can provide voice feedback when performing advanced operations, allowing the progress of the operation to be confirmed by voice. For example, when performing advanced operations, the generation AI unit provides voice feedback, allowing the progress of the operation to be confirmed by voice. For example, it notifies the user by voice when each step is completed. The generation AI unit also builds a system that provides voice feedback when elderly people perform advanced operations. For example, it provides voice guidance on the progress of the operation. The generation AI unit also supports the elderly in their operations and provides voice feedback. For example, it explains each step of the operation by voice, allowing the user to confirm the progress. This allows the elderly to confirm the progress of the operation by voice.

[0069] The generation AI unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the elderly while operating the device. The generation AI unit, for example, uses the emotion estimation function to provide feedback to reinforce the positive emotions felt by the elderly while operating the device. For example, it may display a praising message when the operation is successful. The generation AI unit also analyzes the emotional state of the elderly in real time and builds a system that provides feedback to reinforce the positive emotions. For example, it may display an encouraging message when the operation is successful. The generation AI unit also dynamically provides feedback that matches the emotional state of the elderly based on the emotion estimation data. For example, it may provide positive feedback when the operation is successful. This makes it possible to reinforce the positive emotions felt by the elderly while operating the device.

[0070] The generation AI unit can learn the communication history of the elderly person and suggest appropriate topics and messages. For example, the generation AI unit learns the communication history of the elderly person and suggests appropriate topics and messages. For example, it suggests new topics based on the content of past conversations. The generation AI unit also builds a system in which the generation AI suggests appropriate messages based on the communication history of the elderly person. For example, it analyzes the content of past messages and suggests appropriate replies. The generation AI unit also learns the communication history of the elderly person and suggests optimal topics and messages. For example, it suggests new topics based on the conversation history with a specific person. In this way, the elderly person is suggested appropriate topics and messages.

[0071] The generation AI unit generates subtitles in real time during video calls, allowing even elderly people with hearing impairments to enjoy communication. For example, the generation AI unit generates subtitles in real time during video calls, allowing even elderly people with hearing impairments to enjoy communication. For example, it converts the audio during a call into text and displays it on the screen. The generation AI unit also builds a system that generates subtitles in real time when elderly people make video calls. For example, it automatically converts the content of the conversation during a call into subtitles and displays them on the screen. The generation AI unit also analyzes the audio during a video call and generates subtitles in real time. For example, it converts the audio during a call into text, allowing even elderly people with hearing impairments to understand the conversation. This allows even elderly people with hearing impairments to enjoy communication.

[0072] The generation AI unit can use the emotion estimation function to suggest appropriate communication methods according to the emotional state of the elderly person. For example, the generation AI unit uses the emotion estimation function to suggest appropriate communication methods according to the emotional state of the elderly person. For example, if the elderly person is feeling stressed, it will suggest topics that will help them relax. The generation AI unit also analyzes the emotional state of the elderly person in real time and builds a system that suggests appropriate communication methods. For example, if the elderly person is feeling positive, it will suggest fun topics. The generation AI unit also dynamically suggests communication methods that match the elderly person's emotional state based on the emotion estimation data. For example, if the elderly person is tired, it will suggest light-hearted topics. This makes it possible to suggest communication methods that match the elderly person's emotional state.

[0073] The generation AI unit can provide a translation function during communication, enabling smooth communication with family and friends who speak different languages. For example, the generation AI unit can translate audio during a call in real time and display it as subtitles. The generation AI unit can also build a system that provides real-time translation when elderly people communicate with people who speak different languages. For example, it can automatically translate when messages are sent and received. The generation AI unit can also analyze audio and text during communication and translate it in real time. For example, it can automatically translate conversations during video calls and display them as subtitles. This allows smooth communication with family and friends who speak different languages.

[0074] The generation AI unit uses facial recognition technology to analyze the facial expressions of the other person during communication and can suggest an appropriate response. For example, when communicating, the generation AI unit uses facial recognition technology to analyze the other person's facial expressions and suggest an appropriate response. For example, if the other person is smiling, it will suggest a positive response. The generation AI unit also builds a system that analyzes the facial expressions of the other person when elderly people communicate and suggests an appropriate response. For example, it will suggest helping if the other person is in trouble. The generation AI unit also analyzes the facial expressions of the other person during communication and suggests an appropriate response. For example, if the other person is surprised, it will suggest a response to surprise. This makes it possible to suggest an appropriate response according to the other person's facial expression.

[0075] The generation AI unit can use the emotion estimation function to suggest appropriate communication methods when an elderly person is in a specific emotional state. For example, using the emotion estimation function, the generation AI unit can suggest appropriate communication methods when an elderly person is in a specific emotional state. For example, if the elderly person is feeling stressed, it will suggest topics that will help them relax. The generation AI unit also analyzes the elderly person's emotional state in real time and builds a system that suggests appropriate communication methods. For example, if the elderly person is feeling positive, it will suggest fun topics. The generation AI unit also dynamically suggests communication methods that match the elderly person's emotional state based on the emotion estimation data. For example, if the elderly person is tired, it will suggest light-hearted topics. This makes it possible to suggest communication methods that suit the elderly person's emotional state.

[0076] The generation AI unit can learn the daily life patterns of the elderly and provide appropriate advice and reminders. For example, the generation AI unit learns the daily life patterns of the elderly and provides appropriate advice and reminders. For example, it may remind them when to take their medicine every day. The generation AI unit also builds a system in which the generation AI provides appropriate advice and reminders based on the daily life patterns of the elderly. For example, it may remind them when to exercise regularly. The generation AI unit also learns the daily life patterns of the elderly and provides optimal advice and reminders. For example, it may remind them when to eat meals. This can support the daily lives of the elderly.

[0077] The generation AI unit works in conjunction with a health management app to analyze the health data of the elderly and provide necessary advice. For example, the generation AI unit works in conjunction with a health management app, and the generation AI analyzes the health data of the elderly and provides necessary advice. For example, health advice is provided based on blood pressure and heart rate data. The generation AI unit also builds a system in which the generation AI provides appropriate advice based on the elderly's health data. For example, it analyzes weight and dietary data and suggests healthy lifestyle habits. The generation AI unit also works in conjunction with a health management app to analyze the elderly's health data and provide necessary advice. For example, it detects lack of exercise and suggests exercise. This can support the health management of the elderly.

[0078] The generation AI unit can use the emotion estimation function to provide appropriate daily life support according to the emotional state of the elderly. The generation AI unit, for example, uses the emotion estimation function to provide appropriate daily life support according to the emotional state of the elderly. For example, if the elderly is feeling stressed, it will suggest a relaxing activity. The generation AI unit also analyzes the emotional state of the elderly in real time and builds a system that provides appropriate daily life support. For example, if the elderly is feeling positive, it will suggest an active activity. The generation AI unit also dynamically provides daily life support according to the emotional state of the elderly based on the emotion estimation data. For example, if the elderly is tired, it will suggest an activity that encourages rest. This makes it possible to provide daily life support according to the emotional state of the elderly.

[0079] The generative AI unit can suggest new activities based on the hobbies and interests of the elderly. For example, to support daily life, the generative AI unit suggests new activities based on the hobbies and interests of the elderly. For example, it suggests hobbies such as gardening and handicrafts. The generative AI unit also builds a system in which the generative AI suggests new activities based on the hobbies and interests of the elderly. For example, it suggests activities such as listening to music or reading. The generative AI unit also learns the hobbies and interests of the elderly and suggests the most suitable activity. For example, it suggests new hobbies such as traveling or cooking. This makes it possible to suggest new activities based on the hobbies and interests of the elderly.

[0080] The generative AI unit can suggest recipes to support the diet and nutritional management of the elderly. For example, as support for daily life, the generative AI unit suggests recipes to support the diet and nutritional management of the elderly. For example, it provides recipes for healthy meals. The generative AI unit also builds a system in which the generative AI suggests appropriate recipes based on the diet and nutritional management of the elderly. For example, it provides recipes for meals containing specific nutrients. The generative AI unit also supports the diet and nutritional management of the elderly and suggests optimal recipes. For example, it provides recipes for dieting and maintaining health. This makes it possible to support the diet and nutritional management of the elderly.

[0081] The generation AI unit can use the emotion estimation function to provide appropriate daily living support when an elderly person is in a specific emotional state. For example, using the emotion estimation function, the generation AI unit can provide appropriate daily living support when an elderly person is in a specific emotional state. For example, if the elderly person is feeling stressed, it can suggest relaxing activities. The generation AI unit also analyzes the emotional state of the elderly person in real time and builds a system that provides appropriate daily living support. For example, if the elderly person is feeling positive, it can suggest active activities. The generation AI unit also dynamically provides daily living support that matches the elderly person's emotional state based on the emotion estimation data. For example, if the elderly person is tired, it can suggest activities that encourage rest. This makes it possible to provide daily living support that matches the elderly person's emotional state.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The tablet system may further include a health management unit that monitors the user's health condition. The health management unit periodically acquires vital data, such as the user's blood pressure, heart rate, and body temperature, and issues an alert if an abnormality is detected. The health management unit can also provide appropriate health advice based on the user's health data. For example, if a lack of exercise is detected, the health management unit can send a notification encouraging exercise. The health management unit can also store the user's health data in the cloud and share the data in cooperation with medical institutions. This allows the user's health condition to be constantly monitored and appropriate health management to be carried out.

[0084] The tablet system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit may use, for example, GPS to identify the user's current location and notify family members or caregivers. The location information acquisition unit may also issue an alert if the user leaves a specific area. For example, if an elderly person with dementia leaves their home, the family may be notified. The location information acquisition unit may also record the user's movement history and analyze their daily behavioral patterns. This helps ensure the user's safety and provide appropriate support.

[0085] The tablet system can further include a diet management unit that supports the user's dietary management. The diet management unit, for example, records the user's dietary content and analyzes nutritional balance. The diet management unit can also suggest meals based on the user's health condition. For example, it can suggest low-carbohydrate recipes to a diabetic user. The diet management unit can also notify the user of appropriate meal times based on the user's diet history. This allows the user to maintain their health and perform appropriate dietary management.

[0086] The tablet system may further include a sleep management unit that monitors the user's sleep state. The sleep management unit, for example, records and analyzes the user's sleep time and sleep quality. The sleep management unit can also provide appropriate sleep advice based on the user's sleep patterns. For example, if insufficient sleep is detected, the sleep management unit can send a notification encouraging the user to go to bed early. The sleep management unit can also suggest an appropriate sleeping environment based on the user's sleep data. This can improve the user's sleep quality and maintain their health.

[0087] The tablet system can further include an exercise management unit that supports the user's exercise. The exercise management unit, for example, records the user's exercise volume and provides appropriate exercise advice. The exercise management unit can also suggest exercise programs based on the user's health condition. For example, it can suggest low-impact exercises to a user with arthritis. The exercise management unit can also notify the user of the timing of exercise based on the user's exercise history. This allows the user to maintain their health and perform appropriate exercise management.

[0088] The tablet system may further include an entertainment provider that analyzes the user's emotional state and provides appropriate entertainment. The entertainment provider may, for example, analyze the user's emotional state and suggest relaxing music or movies. The entertainment provider may also suggest games or activities that correspond to the user's emotional state. For example, if the user is feeling stressed, the entertainment provider may suggest relaxing games. The entertainment provider may also analyze the user's emotional state in real time and dynamically provide appropriate entertainment. This allows entertainment to be provided that corresponds to the user's emotional state, enriching daily life.

[0089] The tablet system can further include a communication suggestion unit that analyzes the user's emotional state and suggests an appropriate communication method. The communication suggestion unit, for example, analyzes the user's emotional state and suggests topics or messages that will help the user relax. The communication suggestion unit can also suggest a communication method according to the user's emotional state. For example, if the user has positive emotions, it can suggest a fun topic. The communication suggestion unit can also analyze the user's emotional state in real time and dynamically suggest an appropriate communication method. This makes it possible to suggest a communication method according to the user's emotional state and support smooth communication.

[0090] The tablet system may further include a relaxation suggestion unit that analyzes the user's emotional state and suggests appropriate relaxation methods. The relaxation suggestion unit may, for example, analyze the user's emotional state and suggest relaxing activities or environments. The relaxation suggestion unit may also suggest relaxation methods according to the user's emotional state. For example, if the user is feeling stressed, it may suggest relaxing music or scents. The relaxation suggestion unit may also analyze the user's emotional state in real time and dynamically suggest appropriate relaxation methods. This allows the system to suggest relaxation methods according to the user's emotional state, thereby reducing stress in daily life.

[0091] The tablet system may further include a feedback providing unit that analyzes the user's emotional state and provides appropriate feedback. The feedback providing unit may, for example, analyze the user's emotional state and provide positive feedback. The feedback providing unit may also provide feedback according to the user's emotional state. For example, a praising message may be displayed when an operation is successful. The feedback providing unit may also analyze the user's emotional state in real time and dynamically provide appropriate feedback. This may provide feedback according to the user's emotional state, thereby improving motivation for operation.

[0092] The tablet system may further include an activity suggestion unit that analyzes the user's emotional state and suggests appropriate activities. The activity suggestion unit may, for example, analyze the user's emotional state and suggest relaxing activities or active activities. The activity suggestion unit may also suggest activities according to the user's emotional state. For example, if the user has positive emotions, an active activity may be suggested. The activity suggestion unit may also analyze the user's emotional state in real time and dynamically suggest appropriate activities. This may suggest activities according to the user's emotional state, enriching daily life.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The interface section provides a simple interface. For example, it provides large icons and text, a simple menu structure, and also allows voice control. This makes the interface easy to use even for elderly people with limited mobility. Step 2: The connectivity unit connects with systems, apps, and electronic devices. For example, it can connect with a smart home system to control lighting and air conditioning, connect with a health management app to monitor blood pressure and heart rate, and use a video chat app to enjoy communication with family and friends. Step 3: The generation AI unit transmits instructions via the generation AI. For example, it analyzes instructions from the elderly person and performs appropriate operations based on the content. The generation AI unit analyzes instructions using text generation AI (e.g., LLM) or multimodal generation AI and executes the operation. For example, if it receives the instruction "Turn on the lights in the living room," it analyzes the instruction and instructs the smart home system to operate the lights. Also, simply by instructing "Tell me what's on my schedule for today," it will open the calendar app and read out the schedule. Simply by instructing "Call my family," it can launch a video calling app and call family members.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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. [Explanation of symbols]

[0162] 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. an interface section that provides a simple interface; A linking section that links with systems, apps, and electronic devices, a generation AI unit that transmits instructions via the generation AI; A system characterized by:

2. The interface unit Provide large icons and text, and simple menu structure 2. The system of claim 1.

3. The linking unit is Link with smart home systems to control lighting and air conditioning 2. The system of claim 1.

4. The generation AI unit The instructions of the elderly person are analyzed, and instructions are given to operate the lighting in the smart home system.

2. The system of claim 1.

5. The generation AI unit Providing appropriate feedback according to the emotional state of the elderly 2. The system of claim 1.

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