System

Abstract

To provide a system for providing a customized service matched with the individual needs or tastes of an aged person.SOLUTION: A customized service providing unit provides a customized service according to individual needs and preferences of an elderly person, a data analysis unit analyzes data for a service provided by the customized service providing unit, a voice command reception unit receives a voice command, an interaction response unit responds in a natural interaction format on the basis of the voice command received by the voice command reception unit, a memo creation unit creates a memo through voice input, a reminder setting unit sets a reminder through voice input, and a communication support unit supports communication with family members and friends through messages, moving image calls, and voice calls.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] Conventional technology has made it difficult to provide services tailored to the individual needs and preferences of elderly people, resulting in a lack of convenience.

[0005] The system according to the embodiment aims to provide customized services tailored to the individual needs and preferences of elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a customization service providing unit, a data analysis unit, a voice command receiving unit, a dialogue response unit, a memo creation unit, a reminder setting unit, and a communication support unit. The customization service providing unit provides customization services tailored to the individual needs and preferences of the elderly person. The data analysis unit analyzes data for the services provided by the customization service providing unit. The voice command receiving unit receives voice commands. The dialogue response unit responds in a natural dialogue format based on the voice commands received by the voice command receiving unit. The memo creation unit creates memos based on voice input. The reminder setting unit sets reminders based on voice input. The communication support unit supports communication with family and friends via messages, video calls, and voice calls. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized services that meet the individual needs and preferences of elderly people. [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 Life Enrich AI system according to an embodiment of the present invention provides customized services tailored to the individual needs and preferences of elderly people. This system realizes support suited to the preferences and circumstances of elderly people through AI learning and data analysis. As a result, the Life Enrich AI system provides customized services tailored to the individual needs and preferences of elderly people and can be easily used because it can be operated by voice commands.

[0029] A life enrichment AI system according to an embodiment includes a customization service providing unit, a data analysis unit, a voice command receiving unit, a dialogue response unit, a memo creation unit, a reminder setting unit, and a communication support unit. The customization service providing unit provides customization services tailored to the individual needs and preferences of elderly people. For example, it proposes appropriate meal plans based on dietary preferences and health status. It also proposes recreational activities based on hobbies and interests. The data analysis unit analyzes data for the services provided by the customization service providing unit. For example, it analyzes past behavioral history and health data to propose optimal exercise programs and health management methods for the user. The voice command receiving unit receives voice commands from the user. For example, by inputting a voice command such as "Tell me what's on my schedule for today," the generation AI provides the user's schedule by voice. The dialogue response unit responds in a natural dialogue format based on the voice command received by the voice command receiving unit. For example, in response to a question such as "What's the weather forecast?", the generation AI responds with "Today's weather is sunny and the temperature is 25 degrees." The memo creation unit creates memos based on voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10 AM tomorrow," the generation AI sets a reminder. The reminder setting unit sets a reminder through voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10 AM tomorrow," the generation AI sets a reminder. The communication support unit supports communication with family and friends through messages, video calls, and voice calls. For example, by inputting a voice command such as "Call my daughter," the generation AI starts a voice call. As a result, the life enriching AI system according to the embodiment provides customized services tailored to the individual needs and preferences of elderly people and is easy to use because it can be operated by voice commands.

[0030] The customization service providing unit can propose an appropriate meal plan based on the dietary preferences and health condition of the elderly person. The customization service providing unit proposes an appropriate meal plan based on, for example, the dietary preferences and health condition of the elderly person. For example, the customization service providing unit creates a meal plan based on favorite ingredients and allergy information as dietary preferences, and on medical history and current health indicators as health conditions. In this way, by proposing an individual meal plan for the elderly person, a healthy lifestyle is supported.

[0031] The customization service providing unit can suggest recreational activities that match the hobbies and interests. The customization service providing unit suggests recreational activities that match the hobbies and interests of the elderly, for example. For example, the customization service providing unit takes into account hobbies such as sports, music, and art and suggests recreational activities that match the interests. In this way, suggesting activities that match the hobbies and interests of the elderly improves their quality of life.

[0032] The data analysis unit can analyze past behavioral history and health data to propose optimal exercise programs and health management methods for users. The data analysis unit, for example, analyzes past behavioral history and health data to propose optimal exercise programs and health management methods for users. For example, the data analysis unit collects daily activity records and exercise history as past behavioral history, and analyzes blood pressure, heart rate, weight, and the like as health data. This supports health management for elderly people and proposes appropriate exercise programs.

[0033] The voice command receiving unit receives a voice command from the user, and the generation AI can generate a response based on the prompt. The voice command receiving unit, for example, receives a voice command from the user, and the generation AI generates a response based on the prompt. For example, by inputting a voice command such as "Tell me what's on my schedule for today," the generation AI provides the user's schedule by voice. This allows the generation AI to generate an appropriate response based on the user's voice command.

[0034] The dialogue response unit responds to the user's questions in a natural dialogue format and can also provide necessary information and supplementary information by voice. For example, in response to a question such as "What is the weather forecast?", the generation AI will respond with "Today's weather is sunny. The temperature is 25 degrees." This allows the system to respond to the user's questions in a natural dialogue format and provide necessary information.

[0035] The memo creation unit can create memos through voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the generation AI will set the reminder. This makes it easy to create memos through voice input.

[0036] The reminder setting unit can set reminders through voice input. For example, the reminder setting unit sets reminders through voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the generation AI sets the reminder. This makes it easy to set reminders through voice input.

[0037] The communication support unit can support communication with family and friends through messages, video calls, and voice calls. For example, the communication support unit supports communication with family and friends through messages, video calls, and voice calls. For example, by inputting a voice command such as "call my daughter," the generation AI starts a voice call. This supports communication with family and friends and reduces feelings of isolation.

[0038] The customized service providing unit can analyze the lifestyle rhythm of the elderly person and provide services at the optimal timing. The customized service providing unit, for example, analyzes the lifestyle rhythm of the elderly person and provides services at the optimal timing. For example, it collects daily activity data and identifies sleeping times and meal times. Based on the lifestyle rhythm, it provides services at the optimal timing. For example, it performs a health check after breakfast and plays relaxing music before dinner. This allows it to provide services that are tailored to the lifestyle rhythm of the elderly person, thereby achieving more effective support.

[0039] The customization service providing unit can provide customization services tailored to the needs of the elderly person's pet. For example, the customization service providing unit monitors the health condition and activity level of the elderly person's pet and provides services tailored to the pet's needs. For example, if it detects that the pet is not getting enough exercise, it will suggest taking the pet for a walk. To support the pet's dietary management, it analyzes the pet's diet history and proposes an optimal meal plan. For example, it adjusts the amount and type of food based on the pet's weight and health condition. In this way, the health and happiness of the pet can be supported by providing services tailored to the needs of the elderly person's pet.

[0040] The customization service providing unit can support elderly people's travel plans and propose travel plans tailored to their individual preferences. For example, the customization service providing unit analyzes the preferences and health status of elderly people and proposes optimal travel plans. For example, it can propose a trip to a natural park to an elderly person who loves nature, and a tour of museums to an elderly person who loves culture. To support travel plans, it provides information on travel destinations and transportation options. For example, it provides weather forecasts and traffic conditions at the travel destination in real time. This increases the enjoyment of travel by proposing travel plans tailored to the preferences of elderly people.

[0041] The data analysis unit can analyze the health data of the elderly and make suggestions for preventive medical care. The data analysis unit, for example, collects and analyzes the health data of the elderly to make suggestions for preventive medical care. For example, it analyzes blood pressure and heart rate data and suggests visiting a medical institution if abnormalities are detected. An individual preventive medical care plan is created based on the health data. For example, if a lack of exercise is detected, an appropriate exercise program is suggested. In this way, health management is supported by analyzing the health data of the elderly and making suggestions for preventive medical care.

[0042] The data analysis unit can analyze the social activity data of the elderly person and make suggestions to prevent isolation. The data analysis unit, for example, collects and analyzes the social activity data of the elderly person and makes suggestions to prevent isolation. For example, if the elderly person has little interaction with friends, it may suggest participating in social events. An individual interaction plan is created based on the social activity data. For example, it may suggest participating in club activities based on hobbies or interests. In this way, the social activity data of the elderly person is analyzed and suggestions to prevent isolation are made, thereby maintaining social connections.

[0043] The data analysis unit can analyze the hobby data of the elderly and suggest new hobbies. The data analysis unit, for example, collects and analyzes the hobby data of the elderly to suggest new hobbies. For example, it suggests new related hobbies based on past hobby data. It creates individual hobby plans based on the hobby data. For example, it suggests new craft kits to elderly people who like handicrafts. In this way, analyzing the hobby data of the elderly and suggesting new hobbies improves their quality of life.

[0044] The data analysis unit can analyze the purchasing history of elderly people and suggest the most suitable products and services. The data analysis unit, for example, collects and analyzes the purchasing history of elderly people to suggest the most suitable products and services. For example, it suggests related products based on past purchasing data. It creates an individual purchasing plan based on the purchasing history. For example, it suggests new health foods to elderly people who purchase health foods. In this way, the purchasing experience is improved by analyzing the purchasing history of elderly people and suggesting the most suitable products and services.

[0045] The voice command receiving unit can analyze the tone and speed of the elderly person's voice and respond with an optimal response speed and tone. For example, the voice command receiving unit collects and analyzes voice data using voice recognition technology to analyze the tone and speed of the elderly person's voice. For example, the response speed is adjusted based on the pitch of the voice and the speaking speed. The voice command receiving unit responds with an optimal response speed and tone based on the tone and speed of the voice. For example, a slower response is provided to an elderly person who speaks slowly. This allows for a more natural dialogue by providing an optimal response based on the tone and speed of the elderly person's voice.

[0046] The voice command receiving unit can analyze the history of voice commands from the elderly person and prioritize recognition of frequently used commands. The voice command receiving unit, for example, identifies frequently used commands by collecting and analyzing the history of voice commands from the elderly person. For example, it lists frequently used commands based on past voice command data. The voice recognition system is adjusted to prioritize recognition of frequently used commands. For example, it improves recognition accuracy for frequently used commands. In this way, the efficiency of operation is improved by analyzing the history of voice commands from the elderly person and prioritize recognition of frequently used commands.

[0047] The voice command receiving unit can recognize the elderly person's gestures and perform operations in combination with the voice commands. For example, the voice command receiving unit collects and analyzes gesture data using a camera or sensor to recognize the elderly person's gestures. For example, gestures are recognized based on hand movements and facial orientation. Operations are performed by combining gestures and voice commands. For example, music is played by combining the gesture of raising a hand with the voice command "play music." This improves the flexibility of operations by performing operations by combining the elderly person's gestures with voice commands.

[0048] The voice command receiving unit can track the gaze of the elderly person and perform operations by combining gaze and voice commands. For example, the voice command receiving unit uses gaze tracking technology to collect and analyze gaze data in order to track the gaze of the elderly person. For example, the gaze is recognized based on the direction of gaze and the duration of gaze. Operations are performed by combining gaze and voice commands. For example, by directing the gaze at a specific icon and inputting the voice command "select," the icon can be selected. In this way, the accuracy of operations is improved by performing operations by combining the gaze of the elderly person and voice commands.

[0049] The dialogue response unit can analyze the dialogue history of the elderly person and provide a response that matches the individual dialogue style. The dialogue response unit, for example, identifies the individual dialogue style by collecting and analyzing the dialogue history of the elderly person. For example, it analyzes frequently used phrases and speaking styles based on past dialogue data. It provides a response that matches the individual dialogue style based on the dialogue history. For example, it generates a response that includes frequently used phrases. In this way, by analyzing the dialogue history of the elderly person and providing a response that matches the individual dialogue style, a more natural dialogue can be achieved.

[0050] The dialogue response unit can use appropriate language and expressions, taking into account the cultural background of the elderly person. The dialogue response unit, for example, identifies appropriate language and expressions by collecting and analyzing the cultural background of the elderly person. For example, it analyzes commonly used words and expressions based on the place of origin and age. It responds using appropriate language and expressions based on the cultural background. For example, it generates a response that includes dialects and idioms specific to the region. In this way, by taking into account the cultural background of the elderly person and using appropriate language and expressions, it is possible to achieve a more friendly dialogue.

[0051] The dialogue response unit can record the dialogue content of the elderly person and play it back later. For example, the dialogue response unit will develop a system that records the dialogue content of the elderly person as voice data and allows it to be played back later. For example, important conversations and instructions can be recorded and played back when needed. The dialogue content can be recorded as text data and made searchable and playable. For example, a conversation containing a specific keyword can be searched and played back. In this way, by recording the dialogue content of the elderly person and allowing it to be played back later, important information will not be missed.

[0052] The dialogue response unit can convert the content of the elderly person's dialogue into text and share it with family members and medical professionals. The dialogue response unit will develop a system that converts the content of the elderly person's dialogue into text using voice recognition technology and shares it with family members and medical professionals. For example, important conversations and instructions will be converted into text and shared. The converted dialogue content will be stored on the cloud and made accessible to family members and medical professionals. For example, it will be shared through a dedicated app or website. This will make it easier to share important information by converting the content of the elderly person's dialogue into text and sharing it with family members and medical professionals.

[0053] The memo creation unit can analyze the contents of notes taken by the elderly person and automatically extract and highlight important information. The memo creation unit will develop a system that analyzes the contents of notes taken by the elderly person through voice input, for example, and automatically extracts important information. For example, important information such as date, time, and location will be highlighted. The memo contents will be analyzed, and important information will be automatically extracted and highlighted. For example, the name of medication and the time to take it will be highlighted. In this way, by analyzing the contents of notes taken by the elderly person and automatically extracting and highlighting important information, important information will not be overlooked.

[0054] The reminder setting unit can analyze the elderly person's reminder history and suggest optimal reminder settings. The reminder setting unit, for example, collects and analyzes the elderly person's reminder history to develop a system that suggests optimal reminder settings. For example, it suggests optimal settings based on past reminder setting data. It creates an individual reminder plan based on the reminder history. For example, it sets reminders based on medication times or important appointments. In this way, the reminder history of the elderly person can be analyzed and optimal reminder settings suggested, improving the effectiveness of reminders.

[0055] The memo creation unit can digitize handwritten notes taken by the elderly in combination with voice input. The memo creation unit develops a system that, for example, photographs handwritten notes taken by the elderly with a camera and digitizes them using image recognition technology. For example, the handwritten characters are converted into text data. The handwritten notes are digitized in combination with voice input. For example, the contents of the handwritten notes are supplemented with voice and saved as digital notes. In this way, digitizing handwritten notes taken by the elderly in combination with voice input makes it easier to manage the notes.

[0056] The reminder setting unit can share the elderly person's reminders with family members and caregivers to enhance support. The reminder setting unit may, for example, develop a system that stores the elderly person's reminders on the cloud and allows family members and caregivers to access them. For example, the reminders may be shared through a dedicated app or website. Reminder notifications may also be sent to family members and caregivers to enhance support. For example, important reminder notifications may be sent to family members and caregivers. In this way, sharing the elderly person's reminders with family members and caregivers improves the effectiveness of support.

[0057] The communication support unit can analyze the elderly person's communication history and encourage them to contact them at the optimal time. For example, the communication support unit will develop a system that encourages contact at the optimal time by collecting and analyzing the elderly person's communication history. For example, it will suggest the optimal time to contact them based on past frequency of contact and time of day. It will create an individual contact plan based on the communication history. For example, it will set a reminder to encourage contact on a specific day of the week or at a specific time of day. This will improve the quality of communication by analyzing the elderly person's communication history and encouraging them to contact them at the optimal time.

[0058] The communication support unit can share the elderly person's communication history with their family members to strengthen support for them. For example, the communication support unit develops a system that stores the elderly person's communication history on the cloud and allows family members to access it. For example, the communication history can be shared through a dedicated app or website. Sharing the communication history with family members strengthens support for them. For example, important conversations or instructions can be notified to family members. In this way, sharing the elderly person's communication history with family members strengthens support for them.

[0059] The communication support unit will enable elderly people to communicate not only through video calls but also through AR and VR. The communication support unit will develop a system that will enable elderly people to communicate not only through video calls but also through AR and VR. For example, it will provide a virtual joint experience with family members. It will provide an environment where elderly people and their families can engage in activities together using AR and VR. For example, it will promote communication through virtual trips and games. In this way, elderly people will be able to communicate using AR and VR, providing a richer communication experience.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The Life Enrich AI system can also be equipped with an exercise monitoring unit that monitors the physical abilities of elderly people and suggests appropriate exercise programs. For example, it measures walking speed and step count and records daily exercise volume. The exercise monitoring unit then suggests individual exercise programs based on this data. For example, if walking speed slows, it suggests strength training, and if step count is low, it suggests taking more frequent walks. This helps maintain and improve the physical abilities of elderly people.

[0062] The customized service provider can also suggest online learning programs based on the elderly's hobbies and interests. For example, it can suggest online classes in painting, music, cooking, etc. Furthermore, it can strengthen social connections by encouraging participation in communities and forums related to hobbies. This can satisfy the elderly's intellectual curiosity and promote social interaction.

[0063] The data analysis unit can analyze the sleep data of elderly people and make suggestions to improve their sleep quality. For example, it can monitor sleep duration and depth and suggest an appropriate sleep environment. Specifically, it can provide advice on adjusting the temperature, lighting, and sound environment in the bedroom. It can also improve sleep quality by suggesting relaxation methods before sleep and appropriate bedtimes.

[0064] The dialogue response unit can also use appropriate language and expressions based on the elderly person's cultural background and language preferences. For example, using regional dialects and idiomatic expressions can create a more friendly dialogue. For elderly people who speak a different language, the system can also provide responses in their native language. This allows for dialogue tailored to the elderly person's cultural background and language preferences, improving the quality of communication.

[0065] The reminder setting unit can also analyze the elderly person's reminder history and suggest optimal reminder settings. For example, it can suggest optimal settings based on past reminder setting data. It can also create an individual reminder plan based on the reminder history. For example, it can set reminders based on medication times or important appointments. This allows the reminder history of the elderly person to be analyzed and optimal reminder settings to be suggested, thereby improving the effectiveness of reminders.

[0066] The communication support unit can also analyze the elderly person's communication history and encourage them to contact them at the optimal time. For example, it can suggest the optimal time to contact them based on past contact frequency and time of day. It can also create an individual contact plan based on the communication history. For example, it can set reminders to encourage contact on specific days of the week or at specific times. This allows the quality of communication to be improved by analyzing the elderly person's communication history and encouraging them to contact them at the optimal time.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The Customized Service Department provides customized services tailored to the individual needs and preferences of seniors, such as proposing appropriate meal plans based on dietary preferences and health conditions, and suggesting recreational activities based on hobbies and interests. Step 2: The data analysis unit analyzes the data for the services provided by the customization service provider. For example, it analyzes past behavioral history and health data and proposes optimal exercise programs and health management methods for the user. Step 3: The voice command receiver accepts voice commands from the user. For example, by inputting a voice command such as "Tell me my schedule for today," the generation AI will provide the user's schedule via voice. Step 4: The dialogue response unit responds in a natural dialogue format based on the voice command received by the voice command reception unit. For example, in response to a question such as "What is the weather forecast?", the generation AI responds with "Today's weather is sunny. The temperature is 25 degrees." Step 5: The memo creation unit creates memos through voice input. For example, by entering a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the AI ​​generator sets the reminder. Step 6: The reminder setting unit sets a reminder through voice input. For example, by entering a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the generation AI sets the reminder. Step 7: The communication support section supports communication with family and friends via messages, video calls, and voice calls. For example, by entering a voice command such as "Call my daughter," the generation AI will start a voice call.

[0069] (Example 2) The Life Enrich AI system according to an embodiment of the present invention provides customized services tailored to the individual needs and preferences of elderly people. This system realizes support suited to the preferences and circumstances of elderly people through AI learning and data analysis. As a result, the Life Enrich AI system provides customized services tailored to the individual needs and preferences of elderly people and can be easily used because it can be operated by voice commands.

[0070] A life enrichment AI system according to an embodiment includes a customization service providing unit, a data analysis unit, a voice command receiving unit, a dialogue response unit, a memo creation unit, a reminder setting unit, and a communication support unit. The customization service providing unit provides customization services tailored to the individual needs and preferences of elderly people. For example, it proposes appropriate meal plans based on dietary preferences and health status. It also proposes recreational activities based on hobbies and interests. The data analysis unit analyzes data for the services provided by the customization service providing unit. For example, it analyzes past behavioral history and health data to propose optimal exercise programs and health management methods for the user. The voice command receiving unit receives voice commands from the user. For example, by inputting a voice command such as "Tell me what's on my schedule for today," the generation AI provides the user's schedule by voice. The dialogue response unit responds in a natural dialogue format based on the voice command received by the voice command receiving unit. For example, in response to a question such as "What's the weather forecast?", the generation AI responds with "Today's weather is sunny and the temperature is 25 degrees." The memo creation unit creates memos based on voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10 AM tomorrow," the generation AI sets a reminder. The reminder setting unit sets a reminder through voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10 AM tomorrow," the generation AI sets a reminder. The communication support unit supports communication with family and friends through messages, video calls, and voice calls. For example, by inputting a voice command such as "Call my daughter," the generation AI starts a voice call. As a result, the life enriching AI system according to the embodiment provides customized services tailored to the individual needs and preferences of elderly people and is easy to use because it can be operated by voice commands.

[0071] The customization service providing unit can propose an appropriate meal plan based on the dietary preferences and health condition of the elderly person. The customization service providing unit proposes an appropriate meal plan based on, for example, the dietary preferences and health condition of the elderly person. For example, the customization service providing unit creates a meal plan based on favorite ingredients and allergy information as dietary preferences, and on medical history and current health indicators as health conditions. In this way, by proposing an individual meal plan for the elderly person, a healthy lifestyle is supported.

[0072] The customization service providing unit can suggest recreational activities that match the hobbies and interests. The customization service providing unit suggests recreational activities that match the hobbies and interests of the elderly, for example. For example, the customization service providing unit takes into account hobbies such as sports, music, and art and suggests recreational activities that match the interests. In this way, suggesting activities that match the hobbies and interests of the elderly improves their quality of life.

[0073] The data analysis unit can analyze past behavioral history and health data to propose optimal exercise programs and health management methods for users. The data analysis unit, for example, analyzes past behavioral history and health data to propose optimal exercise programs and health management methods for users. For example, the data analysis unit collects daily activity records and exercise history as past behavioral history, and analyzes blood pressure, heart rate, weight, and the like as health data. This supports health management for elderly people and proposes appropriate exercise programs.

[0074] The voice command receiving unit receives a voice command from the user, and the generation AI can generate a response based on the prompt. The voice command receiving unit, for example, receives a voice command from the user, and the generation AI generates a response based on the prompt. For example, by inputting a voice command such as "Tell me what's on my schedule for today," the generation AI provides the user's schedule by voice. This allows the generation AI to generate an appropriate response based on the user's voice command.

[0075] The dialogue response unit responds to the user's questions in a natural dialogue format and can also provide necessary information and supplementary information by voice. For example, in response to a question such as "What is the weather forecast?", the generation AI will respond with "Today's weather is sunny. The temperature is 25 degrees." This allows the system to respond to the user's questions in a natural dialogue format and provide necessary information.

[0076] The memo creation unit can create memos through voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the generation AI will set the reminder. This makes it easy to create memos through voice input.

[0077] The reminder setting unit can set reminders through voice input. For example, the reminder setting unit sets reminders through voice input. For example, by inputting a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the generation AI sets the reminder. This makes it easy to set reminders through voice input.

[0078] The communication support unit can support communication with family and friends through messages, video calls, and voice calls. For example, the communication support unit supports communication with family and friends through messages, video calls, and voice calls. For example, by inputting a voice command such as "call my daughter," the generation AI starts a voice call. This supports communication with family and friends and reduces feelings of isolation.

[0079] The customized service providing unit can monitor the emotional state of the elderly person in real time and provide services according to the emotion. The customized service providing unit, for example, monitors the emotional state of the elderly person in real time and provides services according to the emotion. For example, using an emotion estimation function, facial recognition technology is used to analyze facial expressions and calculate an emotion score. If the emotion score is low, relaxing music is played, and if the emotion score is high, active activities are suggested. In this way, more appropriate support can be achieved by providing services according to the emotional state of the elderly person.

[0080] The customized service providing unit can analyze the lifestyle rhythm of the elderly person and provide services at the optimal timing. The customized service providing unit, for example, analyzes the lifestyle rhythm of the elderly person and provides services at the optimal timing. For example, it collects daily activity data and identifies sleeping times and meal times. Based on the lifestyle rhythm, it provides services at the optimal timing. For example, it performs a health check after breakfast and plays relaxing music before dinner. This allows it to provide services that are tailored to the lifestyle rhythm of the elderly person, thereby achieving more effective support.

[0081] The customization service providing unit can use the emotion estimation function to suggest recreational activities based on the emotions of the elderly person. For example, the customization service providing unit uses the emotion estimation function to analyze the emotional state of the elderly person and suggest recreational activities based on the emotions. For example, if the emotion score is high, it suggests going outside or exercising, and if the emotion score is low, it suggests indoor relaxation activities. In this way, more appropriate support can be achieved by suggesting recreational activities based on the emotions of the elderly person.

[0082] The customization service providing unit can provide customization services tailored to the needs of the elderly person's pet. For example, the customization service providing unit monitors the health condition and activity level of the elderly person's pet and provides services tailored to the pet's needs. For example, if it detects that the pet is not getting enough exercise, it will suggest taking the pet for a walk. To support the pet's dietary management, it analyzes the pet's diet history and proposes an optimal meal plan. For example, it adjusts the amount and type of food based on the pet's weight and health condition. In this way, the health and happiness of the pet can be supported by providing services tailored to the needs of the elderly person's pet.

[0083] The customization service providing unit can support elderly people's travel plans and propose travel plans tailored to their individual preferences. For example, the customization service providing unit analyzes the preferences and health status of elderly people and proposes optimal travel plans. For example, it can propose a trip to a natural park to an elderly person who loves nature, and a tour of museums to an elderly person who loves culture. To support travel plans, it provides information on travel destinations and transportation options. For example, it provides weather forecasts and traffic conditions at the travel destination in real time. This increases the enjoyment of travel by proposing travel plans tailored to the preferences of elderly people.

[0084] The customization service providing unit can use the emotion estimation function to propose a meal plan based on the emotions of the elderly person. For example, the customization service providing unit uses the emotion estimation function to analyze the emotional state of the elderly person and propose a meal plan based on the emotions. For example, if the emotion score is low, a meal including ingredients to lift the mood is proposed. The emotional state of the elderly person is monitored and the meal plan based on the emotions is automatically adjusted. For example, if the emotion score is high, a meal to replenish energy is proposed. In this way, by proposing a meal plan based on the emotions of the elderly person, meal satisfaction is improved.

[0085] The data analysis unit can analyze the health data of the elderly and make suggestions for preventive medical care. The data analysis unit, for example, collects and analyzes the health data of the elderly to make suggestions for preventive medical care. For example, it analyzes blood pressure and heart rate data and suggests visiting a medical institution if abnormalities are detected. An individual preventive medical care plan is created based on the health data. For example, if a lack of exercise is detected, an appropriate exercise program is suggested. In this way, health management is supported by analyzing the health data of the elderly and making suggestions for preventive medical care.

[0086] The data analysis unit can analyze the social activity data of the elderly person and make suggestions to prevent isolation. The data analysis unit, for example, collects and analyzes the social activity data of the elderly person and makes suggestions to prevent isolation. For example, if the elderly person has little interaction with friends, it may suggest participating in social events. An individual interaction plan is created based on the social activity data. For example, it may suggest participating in club activities based on hobbies or interests. In this way, the social activity data of the elderly person is analyzed and suggestions to prevent isolation are made, thereby maintaining social connections.

[0087] The data analysis unit can use the emotion estimation function to analyze the emotion data of the elderly person and provide support according to their emotional fluctuations. The data analysis unit, for example, uses the emotion estimation function to collect and analyze the emotion data of the elderly person and provide support according to their emotional fluctuations. For example, if the emotion score is low, relaxing music is played. An individual emotion support plan is created based on the emotion data. For example, if the emotion score is high, active activities are suggested. In this way, psychological stability is supported by analyzing the emotion data of the elderly person and providing support according to their emotional fluctuations.

[0088] The data analysis unit can analyze the hobby data of the elderly and suggest new hobbies. The data analysis unit, for example, collects and analyzes the hobby data of the elderly to suggest new hobbies. For example, it suggests new related hobbies based on past hobby data. It creates individual hobby plans based on the hobby data. For example, it suggests new craft kits to elderly people who like handicrafts. In this way, analyzing the hobby data of the elderly and suggesting new hobbies improves their quality of life.

[0089] The data analysis unit can analyze the purchasing history of elderly people and suggest the most suitable products and services. The data analysis unit, for example, collects and analyzes the purchasing history of elderly people to suggest the most suitable products and services. For example, it suggests related products based on past purchasing data. It creates an individual purchasing plan based on the purchasing history. For example, it suggests new health foods to elderly people who purchase health foods. In this way, the purchasing experience is improved by analyzing the purchasing history of elderly people and suggesting the most suitable products and services.

[0090] The data analysis unit can use the emotion estimation function to analyze the emotion data of the elderly person and suggest an exercise program based on the emotion. The data analysis unit, for example, uses the emotion estimation function to collect and analyze the emotion data of the elderly person and suggest an exercise program based on the emotion. For example, if the emotion score is low, relaxing yoga is suggested. An individual exercise program is created based on the emotion data. For example, if the emotion score is high, energetic exercise is suggested. In this way, by analyzing the emotion data of the elderly person and suggesting an exercise program based on the emotion, the effectiveness of exercise is improved.

[0091] The voice command receiving unit can analyze the tone and speed of the elderly person's voice and respond with an optimal response speed and tone. For example, the voice command receiving unit collects and analyzes voice data using voice recognition technology to analyze the tone and speed of the elderly person's voice. For example, the response speed is adjusted based on the pitch of the voice and the speaking speed. The voice command receiving unit responds with an optimal response speed and tone based on the tone and speed of the voice. For example, a slower response is provided to an elderly person who speaks slowly. This allows for a more natural dialogue by providing an optimal response based on the tone and speed of the elderly person's voice.

[0092] The voice command receiving unit can analyze the history of voice commands from the elderly person and prioritize recognition of frequently used commands. The voice command receiving unit, for example, identifies frequently used commands by collecting and analyzing the history of voice commands from the elderly person. For example, it lists frequently used commands based on past voice command data. The voice recognition system is adjusted to prioritize recognition of frequently used commands. For example, it improves recognition accuracy for frequently used commands. In this way, the efficiency of operation is improved by analyzing the history of voice commands from the elderly person and prioritize recognition of frequently used commands.

[0093] The voice command receiving unit can use the emotion estimation function to suggest voice commands according to the emotions of the elderly person. The voice command receiving unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person and suggest voice commands according to the emotions. For example, if the emotion score is low, it suggests playing relaxing music. The voice command receiving unit monitors the emotional state of the elderly person and automatically suggests voice commands according to the emotions. For example, if the emotion score is high, it suggests active activities. In this way, by suggesting voice commands according to the emotions of the elderly person, more appropriate operation can be achieved.

[0094] The voice command receiving unit can recognize the elderly person's gestures and perform operations in combination with the voice commands. For example, the voice command receiving unit collects and analyzes gesture data using a camera or sensor to recognize the elderly person's gestures. For example, gestures are recognized based on hand movements and facial orientation. Operations are performed by combining gestures and voice commands. For example, music is played by combining the gesture of raising a hand with the voice command "play music." This improves the flexibility of operations by performing operations by combining the elderly person's gestures with voice commands.

[0095] The voice command receiving unit can track the gaze of the elderly person and perform operations by combining gaze and voice commands. For example, the voice command receiving unit uses gaze tracking technology to collect and analyze gaze data in order to track the gaze of the elderly person. For example, the gaze is recognized based on the direction of gaze and the duration of gaze. Operations are performed by combining gaze and voice commands. For example, by directing the gaze at a specific icon and inputting the voice command "select," the icon can be selected. In this way, the accuracy of operations is improved by performing operations by combining the gaze of the elderly person and voice commands.

[0096] The voice command receiving unit can customize voice commands according to the emotions of the elderly person using the emotion estimation function. The voice command receiving unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person and customize voice commands according to the emotions. For example, if the emotion score is low, a command to play relaxing music is set preferentially. The emotional state of the elderly person is monitored and voice commands according to the emotions are automatically customized. For example, if the emotion score is high, a command for active activities is set preferentially. In this way, by customizing voice commands according to the emotions of the elderly person, convenience of operation is improved.

[0097] The dialogue response unit can analyze the dialogue history of the elderly person and provide a response that matches the individual dialogue style. The dialogue response unit, for example, identifies the individual dialogue style by collecting and analyzing the dialogue history of the elderly person. For example, it analyzes frequently used phrases and speaking styles based on past dialogue data. It provides a response that matches the individual dialogue style based on the dialogue history. For example, it generates a response that includes frequently used phrases. In this way, by analyzing the dialogue history of the elderly person and providing a response that matches the individual dialogue style, a more natural dialogue can be achieved.

[0098] The dialogue response unit can use appropriate language and expressions, taking into account the cultural background of the elderly person. The dialogue response unit, for example, identifies appropriate language and expressions by collecting and analyzing the cultural background of the elderly person. For example, it analyzes commonly used words and expressions based on the place of origin and age. It responds using appropriate language and expressions based on the cultural background. For example, it generates a response that includes dialects and idioms specific to the region. In this way, by taking into account the cultural background of the elderly person and using appropriate language and expressions, it is possible to achieve a more friendly dialogue.

[0099] The dialogue response unit can use the emotion estimation function to select a dialogue style according to the emotion of the elderly person. The dialogue response unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person and select a dialogue style according to the emotion. For example, if the emotion score is low, the dialogue response unit responds in a gentle tone. The dialogue response unit monitors the emotional state of the elderly person and automatically selects a dialogue style according to the emotion. For example, if the emotion score is high, the dialogue response unit responds in a bright tone. In this way, by selecting a dialogue style according to the emotion of the elderly person, more appropriate dialogue can be achieved.

[0100] The dialogue response unit can record the dialogue content of the elderly person and play it back later. For example, the dialogue response unit will develop a system that records the dialogue content of the elderly person as voice data and allows it to be played back later. For example, important conversations and instructions can be recorded and played back when needed. The dialogue content can be recorded as text data and made searchable and playable. For example, a conversation containing a specific keyword can be searched and played back. In this way, by recording the dialogue content of the elderly person and allowing it to be played back later, important information will not be missed.

[0101] The dialogue response unit can convert the content of the elderly person's dialogue into text and share it with family members and medical professionals. The dialogue response unit will develop a system that converts the content of the elderly person's dialogue into text using voice recognition technology and shares it with family members and medical professionals. For example, important conversations and instructions will be converted into text and shared. The converted dialogue content will be stored on the cloud and made accessible to family members and medical professionals. For example, it will be shared through a dedicated app or website. This will make it easier to share important information by converting the content of the elderly person's dialogue into text and sharing it with family members and medical professionals.

[0102] The dialogue response unit can use the emotion estimation function to provide a summary of the dialogue content according to the emotion of the elderly person. The dialogue response unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person and provide a summary of the dialogue content according to the emotion. For example, if the emotion score is low, a summary that emphasizes important points is provided. The dialogue response unit monitors the emotional state of the elderly person and automatically generates a summary of the dialogue content according to the emotion. For example, if the emotion score is high, a detailed summary is provided. In this way, by providing a summary of the dialogue content according to the emotion of the elderly person, important information can be grasped concisely.

[0103] The memo creation unit can analyze the contents of notes taken by the elderly person and automatically extract and highlight important information. The memo creation unit will develop a system that analyzes the contents of notes taken by the elderly person through voice input, for example, and automatically extracts important information. For example, important information such as date, time, and location will be highlighted. The memo contents will be analyzed, and important information will be automatically extracted and highlighted. For example, the name of medication and the time to take it will be highlighted. In this way, by analyzing the contents of notes taken by the elderly person and automatically extracting and highlighting important information, important information will not be overlooked.

[0104] The reminder setting unit can analyze the elderly person's reminder history and suggest optimal reminder settings. The reminder setting unit, for example, collects and analyzes the elderly person's reminder history to develop a system that suggests optimal reminder settings. For example, it suggests optimal settings based on past reminder setting data. It creates an individual reminder plan based on the reminder history. For example, it sets reminders based on medication times or important appointments. In this way, the reminder history of the elderly person can be analyzed and optimal reminder settings suggested, improving the effectiveness of reminders.

[0105] The memo creation unit can digitize handwritten notes taken by the elderly in combination with voice input. The memo creation unit develops a system that, for example, photographs handwritten notes taken by the elderly with a camera and digitizes them using image recognition technology. For example, the handwritten characters are converted into text data. The handwritten notes are digitized in combination with voice input. For example, the contents of the handwritten notes are supplemented with voice and saved as digital notes. In this way, digitizing handwritten notes taken by the elderly in combination with voice input makes it easier to manage the notes.

[0106] The reminder setting unit can share the elderly person's reminders with family members and caregivers to enhance support. The reminder setting unit may, for example, develop a system that stores the elderly person's reminders on the cloud and allows family members and caregivers to access them. For example, the reminders may be shared through a dedicated app or website. Reminder notifications may also be sent to family members and caregivers to enhance support. For example, important reminder notifications may be sent to family members and caregivers. In this way, sharing the elderly person's reminders with family members and caregivers improves the effectiveness of support.

[0107] The memo creation unit can use the emotion estimation function to automatically classify the content of notes according to the emotions of the elderly. The memo creation unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly and automatically classify the content of notes according to the emotions. For example, if the emotion score is high, the note is classified as having positive content. A system is developed that monitors the emotional state of the elderly and automatically classifies the content of notes according to the emotions. For example, if the emotion score is low, the note is classified as having negative content. In this way, automatic classification of the content of notes according to the emotions of the elderly makes note management more efficient.

[0108] The communication support unit can analyze the elderly person's communication history and encourage them to contact them at the optimal time. For example, the communication support unit will develop a system that encourages contact at the optimal time by collecting and analyzing the elderly person's communication history. For example, it will suggest the optimal time to contact them based on past frequency of contact and time of day. It will create an individual contact plan based on the communication history. For example, it will set a reminder to encourage contact on a specific day of the week or at a specific time of day. This will improve the quality of communication by analyzing the elderly person's communication history and encouraging them to contact them at the optimal time.

[0109] The communication support unit can monitor the emotional state of the elderly person and suggest a communication method according to the emotion. The communication support unit will develop a system that monitors the emotional state of the elderly person and suggests a communication method according to the emotion, for example. For example, if the emotion score is low, it will suggest a relaxing conversation. An individual communication plan will be created based on the emotional state. For example, if the emotion score is high, it will suggest a conversation that includes active activities. In this way, by monitoring the emotional state of the elderly person and suggesting a communication method according to the emotion, more appropriate communication will be achieved.

[0110] The communication support unit can use the emotion estimation function to suggest message content based on the emotions of the elderly. The communication support unit develops a system that, for example, uses the emotion estimation function to analyze the emotional state of the elderly and suggest message content based on the emotions. For example, if the emotion score is low, an encouraging message is suggested. The emotional state of the elderly is monitored and message content based on the emotions is automatically suggested. For example, if the emotion score is high, a congratulatory message is suggested. In this way, more appropriate communication is achieved by suggesting message content based on the emotions of the elderly.

[0111] The communication support unit can share the elderly person's communication history with their family members to strengthen support for them. For example, the communication support unit develops a system that stores the elderly person's communication history on the cloud and allows family members to access it. For example, the communication history can be shared through a dedicated app or website. Sharing the communication history with family members strengthens support for them. For example, important conversations or instructions can be notified to family members. In this way, sharing the elderly person's communication history with family members strengthens support for them.

[0112] The communication support unit will enable elderly people to communicate not only through video calls but also through AR and VR. The communication support unit will develop a system that will enable elderly people to communicate not only through video calls but also through AR and VR. For example, it will provide a virtual joint experience with family members. It will provide an environment where elderly people and their families can engage in activities together using AR and VR. For example, it will promote communication through virtual trips and games. In this way, elderly people will be able to communicate using AR and VR, providing a richer communication experience.

[0113] The communication support unit can use the emotion estimation function to adjust the frequency of communication according to the emotions of the elderly person. For example, the communication support unit will develop a system that uses the emotion estimation function to analyze the emotional state of the elderly person and adjust the frequency of communication according to the emotions. For example, if the emotion score is low, the system will encourage frequent contact. The system will monitor the emotional state of the elderly person and automatically adjust the frequency of communication according to the emotions. For example, if the emotion score is high, the frequency of contact will be reduced. In this way, more appropriate communication can be achieved by adjusting the frequency of communication according to the emotions of the elderly person.

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

[0115] The Life Enrich AI system can also be equipped with an exercise monitoring unit that monitors the physical abilities of elderly people and suggests appropriate exercise programs. For example, it measures walking speed and step count and records daily exercise volume. The exercise monitoring unit then suggests individual exercise programs based on this data. For example, if walking speed slows, it suggests strength training, and if step count is low, it suggests taking more frequent walks. This helps maintain and improve the physical abilities of elderly people.

[0116] The customized service provider can also suggest online learning programs based on the elderly's hobbies and interests. For example, it can suggest online classes in painting, music, cooking, etc. Furthermore, it can strengthen social connections by encouraging participation in communities and forums related to hobbies. This can satisfy the elderly's intellectual curiosity and promote social interaction.

[0117] The customization service providing unit can also use the emotion estimation function to suggest relaxation programs based on the elderly person's emotions. For example, if the emotion score is low, it can suggest meditation or deep breathing exercises. If the emotion score is high, it can play relaxing music or natural sounds. This allows the system to provide relaxation programs tailored to the elderly person's emotional state and support their psychological stability.

[0118] The data analysis unit can analyze the sleep data of elderly people and make suggestions to improve their sleep quality. For example, it can monitor sleep duration and depth and suggest an appropriate sleep environment. Specifically, it can provide advice on adjusting the temperature, lighting, and sound environment in the bedroom. It can also improve sleep quality by suggesting relaxation methods before sleep and appropriate bedtimes.

[0119] The voice command receiver can analyze the tone and speed of the elderly person's voice and use the emotion estimation function to generate a response that matches their emotion. For example, if the voice tone is low and the speed is slow, the voice command receiver will respond slowly and in a gentle tone. Conversely, if the voice tone is high and the speed is fast, the voice command receiver will respond quickly and in a bright tone. This allows for a response that matches the elderly person's emotional state, resulting in a more natural dialogue.

[0120] The dialogue response unit can also use appropriate language and expressions based on the elderly person's cultural background and language preferences. For example, using regional dialects and idiomatic expressions can create a more friendly dialogue. For elderly people who speak a different language, the system can also provide responses in their native language. This allows for dialogue tailored to the elderly person's cultural background and language preferences, improving the quality of communication.

[0121] The memo creation unit can also use the emotion estimation function to automatically classify the content of notes according to the elderly person's emotions. For example, if the emotion score is high, the note is classified as a note with positive content, and if the emotion score is low, the note is classified as a note with negative content. This makes it possible to efficiently manage notes according to the elderly person's emotions and prevent important information from being overlooked.

[0122] The reminder setting unit can also analyze the elderly person's reminder history and suggest optimal reminder settings. For example, it can suggest optimal settings based on past reminder setting data. It can also create an individual reminder plan based on the reminder history. For example, it can set reminders based on medication times or important appointments. This allows the reminder history of the elderly person to be analyzed and optimal reminder settings to be suggested, thereby improving the effectiveness of reminders.

[0123] The communication support unit can also analyze the elderly person's communication history and encourage them to contact them at the optimal time. For example, it can suggest the optimal time to contact them based on past contact frequency and time of day. It can also create an individual contact plan based on the communication history. For example, it can set reminders to encourage contact on specific days of the week or at specific times. This allows the quality of communication to be improved by analyzing the elderly person's communication history and encouraging them to contact them at the optimal time.

[0124] The customization service providing unit can also use the emotion estimation function to propose a meal plan based on the emotions of the elderly person. For example, if the emotion score is low, it will propose a meal that includes ingredients to lift the mood. It monitors the elderly person's emotional state and automatically adjusts the meal plan based on the emotions. For example, if the emotion score is high, it will propose a meal that will replenish energy. In this way, by proposing a meal plan based on the emotions of the elderly person, it is possible to improve meal satisfaction.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The Customized Service Department provides customized services tailored to the individual needs and preferences of seniors, such as proposing appropriate meal plans based on dietary preferences and health conditions, and suggesting recreational activities based on hobbies and interests. Step 2: The data analysis unit analyzes the data for the services provided by the customization service provider. For example, it analyzes past behavioral history and health data and proposes optimal exercise programs and health management methods for the user. Step 3: The voice command receiver accepts voice commands from the user. For example, by inputting a voice command such as "Tell me my schedule for today," the generation AI will provide the user's schedule via voice. Step 4: The dialogue response unit responds in a natural dialogue format based on the voice command received by the voice command reception unit. For example, in response to a question such as "What is the weather forecast?", the generation AI responds with "Today's weather is sunny. The temperature is 25 degrees." Step 5: The memo creation unit creates memos through voice input. For example, by entering a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the AI ​​generator sets the reminder. Step 6: The reminder setting unit sets a reminder through voice input. For example, by entering a voice command such as "Set a reminder to take my medicine at 10:00 AM tomorrow," the generation AI sets the reminder. Step 7: The communication support section supports communication with family and friends via messages, video calls, and voice calls. For example, by entering a voice command such as "Call my daughter," the generation AI will start a voice call.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0128] 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.

[0129] 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.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A customized service department that provides customized services tailored to the individual needs and preferences of seniors; a data analysis unit that analyzes data for the service provided by the customization service providing unit; a voice command receiving unit that receives a voice command; a dialogue response unit that responds in a natural dialogue format based on the voice command received by the voice command reception unit; a memo creation unit that creates memos by voice input; a reminder setting unit for setting a reminder by voice input; a communication support unit that supports communication with family and friends through messages, video calls, and voice calls; A system characterized by:

2. The customization service providing unit Recommend appropriate meal plans based on your dietary preferences and health status 2. The system of claim 1.

3. The customization service providing unit Suggest recreational activities based on hobbies and interests 2. The system of claim 1.

4. The data analysis unit Analyzes past behavioral history and health data to suggest optimal exercise programs and health management methods for users 2. The system of claim 1.

5. The voice command receiving unit It accepts user voice commands and generates responses based on the prompts.

2. The system of claim 1.

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