System

Abstract

An object of the system according to the embodiment is to prevent an elderly person living alone from being disconnected from the society and to reduce a sense of loneliness.SOLUTION: A system includes a conversation generation unit, an image generation unit, a shopping agency unit, and a setting unit. The speech generation unit makes an appropriate response to a question or a topic from the elderly person. The image generation unit generates and transmits a photograph in response to a request from the elderly person. The shopping agent unit performs Internet shopping in response to a request from the elderly person. The setting unit performs a conversation based on the setting of the conversation partner selected by the elderly person.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 faced the challenge of lacking appropriate communication methods to prevent elderly people living alone from becoming isolated from society and alleviating feelings of loneliness.

[0005] The system according to the embodiment aims to prevent elderly people living alone from becoming isolated from society and to reduce their feelings of loneliness. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation generation unit, an image generation unit, a shopping agent unit, and a setting unit. The conversation generation unit provides appropriate responses to questions and topics from the elderly. The image generation unit generates and transmits photos in response to requests from the elderly. The shopping agent unit performs online shopping in response to requests from the elderly. The setting unit conducts conversations based on the settings of a conversation partner selected by the elderly. [Effects of the Invention]

[0007] The system according to the embodiment can prevent elderly people living alone from being isolated from society and reduce their feelings of loneliness. [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 conversation partner service system according to an embodiment of the present invention uses a smart speaker or robot equipped with a generative AI to serve as a conversation partner for elderly people. This system responds to elderly people's needs, such as "I want someone to talk to," "I want to ask for a quick consultation," and "I feel lonely when I'm alone." As a result, the conversation partner service system can reduce the elderly's sense of loneliness and maintain their connection with society. For example, by having the generative AI serve as a conversation partner for elderly people, it can relieve daily stress and promote brain activation. Furthermore, by handling online shopping on their behalf, it can improve the convenience of elderly people's lives. Furthermore, by setting a conversation partner, elderly people can enjoy communication that suits them. This is expected to extend the healthy life expectancy of elderly people.

[0029] A conversation partner service system according to an embodiment includes a conversation generation unit, an image generation unit, a shopping agent unit, and a setting unit. The conversation generation unit provides appropriate responses to questions and topics from the elderly. For example, in response to a topic such as "The weather is nice today, isn't it?", the conversation generation unit responds with "Yes, it's sunny and pleasant today." The conversation generation unit also uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to convert the elderly's speech into text using speech recognition technology and generates a response based on the text. The image generation unit sends a photo (generated image) in response to a request from the elderly. For example, in response to a request such as "Show me a photo of my grandchild," the image generation unit generates a photo of the grandchild using the generation AI and displays it on the display of a smart speaker or robot. The shopping agent unit performs online shopping on the elderly's behalf in response to a request such as "Order milk," the shopping agent unit processes the milk order from an online store. The setting unit conducts a conversation based on the settings of the conversation partner selected by the elderly. For example, the setting unit can set "real family (living far away)," "deceased family," "virtual friends," etc., and generates conversation content according to each setting. As a result, the conversation partner service system according to the embodiment can reduce the sense of loneliness felt by the elderly and maintain connections with society. For example, by having the generation AI become a conversation partner for the elderly, it can relieve everyday stress and promote brain activation. In addition, by handling online shopping on their behalf, the convenience of the elderly's lives can be improved. Furthermore, by setting a conversation partner, the elderly can enjoy communication that suits them. This is expected to extend the healthy life expectancy of the elderly.

[0030] The conversation generation unit can learn the elderly person's past conversation history and generate personalized responses tailored to each elderly person. For example, the conversation generation unit stores the content of past conversations that the elderly person has had in a database, and the generation AI learns from that data. For example, it learns the topics and language that a specific elderly person often talks about and generates responses based on that. The conversation generation unit also analyzes the elderly person's past conversation history and generates personalized responses for that specific elderly person. For example, it provides a response that includes information about the specific elderly person's favorite topics and hobbies. The conversation generation unit also builds a system in which the generation AI generates the optimal response for that elderly person based on the elderly person's past conversation history. For example, in response to a topic about family that a specific elderly person often talks about, it provides a response that includes the latest information about the family. This makes it possible to provide more appropriate responses to the elderly person.

[0031] The conversation generation unit can analyze the tone and speed of the elderly person's voice and generate a response that matches their mood and physical condition on that day. For example, the conversation generation unit analyzes the tone and speed of the elderly person's voice in real time and generates a response that matches their mood and physical condition on that day. For example, if the voice tone is lively, it will provide a cheerful topic, and if the voice tone is calm, it will provide a relaxing topic. The conversation generation unit also analyzes the tone and speed of the elderly person's voice and builds a system in which the generation AI adjusts the response based on that data. For example, if the voice rate is slow, it will provide a slow response, and if the voice rate is high, it will provide a cheerful response. The conversation generation unit also develops an algorithm that analyzes the tone and speed of the elderly person's voice and generates a response that matches their mood and physical condition on that day. For example, if the voice rate is low, it will provide a response that includes encouraging words, and if the voice rate is high, it will provide an interesting topic. This makes it possible to provide an appropriate response that matches the elderly person's mood and physical condition.

[0032] The image generation unit can analyze the elderly person's past photo albums and generate and send memorable photos. For example, the image generation unit stores the elderly person's past photo albums in a database, and the generation AI generates memorable photos based on that data. For example, new photos are generated based on photos taken in the past by a specific elderly person. The image generation unit also analyzes the elderly person's past photo albums and builds a system in which the generation AI generates memorable photos based on that data. For example, new photos are generated based on photos taken in the past by a specific elderly person and sent. The image generation unit also develops an algorithm in which the generation AI generates memorable photos based on the elderly person's past photo albums. For example, new photos are generated based on photos taken in the past by a specific elderly person and sent. This makes it possible to provide the elderly with memorable photos.

[0033] The image generation unit generates landscape photos of places that the elderly have visited, giving them a sense of nostalgia. For example, the image generation unit collects data on places that the elderly have visited in the past, and the generation AI generates landscape photos based on that data. For example, landscape photos of places that a specific elderly person has visited in the past are generated and transmitted. The image generation unit also analyzes the elderly's past travel history, and builds a system in which the generation AI generates landscape photos based on that data. For example, landscape photos of places that a specific elderly person has visited in the past are generated and transmitted. The image generation unit also develops an algorithm that generates landscape photos of places that the elderly have visited in the past, giving them a sense of nostalgia. For example, landscape photos of places that a specific elderly person has visited in the past are generated and transmitted. This makes it possible to provide elderly people with landscape photos that evoke a sense of nostalgia.

[0034] The shopping agent unit can learn the elderly person's past purchase history and automatically list and suggest necessary products. For example, the shopping agent unit stores the elderly person's past purchase history in a database, and the generation AI automatically lists necessary products based on that data. For example, it lists products that a specific elderly person purchases regularly. The shopping agent unit also analyzes the elderly person's past purchase history and builds a system where the generation AI automatically lists necessary products based on that data. For example, it lists products that a specific elderly person often purchases. The shopping agent unit also develops an algorithm where the generation AI automatically lists necessary products based on the elderly person's past purchase history. For example, it lists and suggests products that a specific elderly person purchases regularly. This makes it possible to automatically list and suggest necessary products to the elderly.

[0035] The shopping agent unit is able to suggest healthy products taking into account the health condition of the elderly person. For example, the shopping agent unit stores the health condition of the elderly person in a database, and the generation AI suggests healthy products based on that data. For example, it suggests foods and supplements that are healthy for a specific elderly person. The shopping agent unit also builds a system that analyzes the health condition of the elderly person, and the generation AI suggests healthy products based on that data. For example, it lists and suggests products that are healthy for a specific elderly person. The shopping agent unit also develops an algorithm that allows the generation AI to suggest healthy products based on the health condition of the elderly person. For example, it suggests foods and supplements that are healthy for a specific elderly person. This makes it possible to suggest healthy products to the elderly.

[0036] The setting unit can learn the content of past conversations of the elderly person and generate responses that match the character of the selected conversation partner. For example, the setting unit stores the content of past conversations of the elderly person in a database, and the generation AI generates responses that match the character of the selected conversation partner based on that data. For example, a response that matches the character selected by a specific elderly person. The setting unit also builds a system that analyzes the content of past conversations of the elderly person and the generation AI generates responses that match the character of the selected conversation partner based on that data. For example, a response that matches the character selected by a specific elderly person. The setting unit also develops an algorithm that generates responses that match the character of the selected conversation partner based on the content of past conversations of the elderly person. For example, a response that matches the character selected by a specific elderly person. This makes it possible to provide responses that match the character of the selected conversation partner to the elderly person.

[0037] The setting unit allows the conversation partner character to start a conversation at an appropriate time, in line with the elderly person's lifestyle rhythm. For example, the setting unit stores the elderly person's lifestyle rhythm in a database, and the generation AI builds a system in which the conversation partner character starts a conversation at an appropriate time based on that data. For example, a specific elderly person starts a conversation after breakfast. The setting unit also analyzes the elderly person's lifestyle rhythm, and the generation AI develops an algorithm in which the conversation partner character starts a conversation at an appropriate time based on that data. For example, a specific elderly person starts a conversation after dinner. The setting unit also develops a system in which the generation AI allows the conversation partner character to start a conversation at an appropriate time, in line with the elderly person's lifestyle rhythm. For example, a specific elderly person starts a conversation before going to bed. This makes it possible to provide conversations to the elderly at appropriate times that match their lifestyle rhythm.

[0038] The setting unit can provide a function that enables elderly people to set their favorite celebrities or historical figures as conversation partners. For example, the setting unit provides a function that enables elderly people to register their favorite celebrities or historical figures in a database and the generation AI to set them as conversation partners based on that information. For example, a celebrity that a specific elderly person likes is set as a conversation partner. The setting unit also builds a system that analyzes the interests and concerns of elderly people and enables the generation AI to set celebrities or historical figures as conversation partners based on that information. For example, a celebrity that a specific elderly person is interested in is set as a conversation partner. The setting unit also develops an algorithm that provides a function that enables elderly people to set their favorite celebrities or historical figures as conversation partners. For example, a celebrity that a specific elderly person likes is set as a conversation partner and information about that celebrity is provided. This makes it possible to set a favorite celebrity or historical figure as a conversation partner for an elderly person.

[0039] The setting unit can provide a function that allows an elderly person to set a pet that they previously owned as a conversation partner. The setting unit, for example, stores information about the elderly person's past pets in a database, and provides a function that allows the generation AI to set a pet as a conversation partner based on that data. For example, a pet that a specific elderly person previously owned is set as a conversation partner. The setting unit also builds a system that analyzes information about the elderly person's past pets, and allows the generation AI to set a pet as a conversation partner based on that data. For example, a pet that a specific elderly person previously owned is set as a conversation partner. The setting unit also develops an algorithm that provides a function that allows the generation AI to set a pet as a conversation partner based on information about the elderly person's past pets. For example, a pet that a specific elderly person previously owned is set as a conversation partner, and memories related to that pet are shared. This makes it possible to set a pet that a specific elderly person previously owned as a conversation partner for the elderly.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The image generation unit generates landscape photos of places the user has visited, giving the user a sense of nostalgia. For example, data on places the user has visited in the past is collected, and the generation AI generates landscape photos based on that data. For example, landscape photos of places a specific user has visited in the past are generated and transmitted. The image generation unit also analyzes the user's past travel history, and builds a system in which the generation AI generates landscape photos based on that data. For example, landscape photos of places a specific user has visited in the past are generated and transmitted. The image generation unit also develops an algorithm that generates landscape photos of places the user has visited, giving the user a sense of nostalgia. For example, landscape photos of places a specific user has visited in the past are generated and transmitted. This makes it possible to provide the user with landscape photos that evoke a sense of nostalgia.

[0042] The shopping agent unit can take the user's health condition into consideration and suggest healthy products. For example, the user's health condition is stored in a database, and the generation AI suggests healthy products based on that data. For example, a specific user might suggest healthy foods or supplements. The shopping agent unit also builds a system that analyzes the user's health condition, and the generation AI suggests healthy products based on that data. For example, a specific user might list and suggest healthy products. The shopping agent unit also develops an algorithm that allows the generation AI to suggest healthy products based on the user's health condition. For example, a specific user might suggest healthy foods or supplements. This makes it possible to suggest healthy products to the user.

[0043] The setting unit can provide a function that allows a user to set a favorite celebrity or historical figure as a conversation partner. For example, a function is provided that allows a user to register a favorite celebrity or historical figure in a database and the generation AI to set the celebrity or historical figure as a conversation partner based on that information. For example, a favorite celebrity of a specific user is set as a conversation partner. The setting unit also builds a system that analyzes a user's interests and concerns and allows the generation AI to set a celebrity or historical figure as a conversation partner based on that information. For example, a celebrity in which a specific user is interested is set as a conversation partner. The setting unit also develops an algorithm that provides a function that allows a user to set a favorite celebrity or historical figure as a conversation partner. For example, a favorite celebrity of a specific user is set as a conversation partner and information about that celebrity is provided. This allows a favorite celebrity or historical figure to be set as a conversation partner for the user.

[0044] The conversation generation unit can learn a user's past conversation history and generate personalized responses tailored to each individual user. For example, the content of a user's past conversations is stored in a database, and the generation AI learns from that data. For example, it can learn the topics and language that a particular user often talks about and generate responses based on that. The conversation generation unit can also analyze a user's past conversation history and generate personalized responses for a particular user. For example, it can provide a response that includes information about a particular user's favorite topics and hobbies. The conversation generation unit can also build a system in which the generation AI generates optimal responses for a user based on the user's past conversation history. For example, in response to a topic about family that a particular user often talks about, it can provide a response that includes the latest information about the family. This makes it possible to provide more appropriate responses to the user.

[0045] The shopping agent unit can learn the user's past purchase history and automatically list and suggest necessary products. For example, the user's past purchase history is stored in a database, and the generation AI automatically lists necessary products based on that data. For example, it can list products that a specific user purchases regularly. The shopping agent unit also analyzes the user's past purchase history and builds a system where the generation AI automatically lists necessary products based on that data. For example, it can list products that a specific user frequently purchases. The shopping agent unit also develops an algorithm where the generation AI automatically lists necessary products based on the user's past purchase history. For example, it can list and suggest products that a specific user purchases regularly. This makes it possible to automatically list and suggest necessary products to the user.

[0046] The setting unit can provide a function that allows a user to set a pet that the user previously owned as a conversation partner. For example, a function is provided that stores information about the user's past pets in a database and allows the generation AI to set the pet as a conversation partner based on that data. For example, a pet that a specific user previously owned is set as a conversation partner. The setting unit also builds a system that analyzes information about the user's past pets and allows the generation AI to set the pet as a conversation partner based on that data. For example, a pet that a specific user previously owned is set as a conversation partner. The setting unit also develops an algorithm that provides a function that allows the generation AI to set the pet as a conversation partner based on information about the user's past pet. For example, a pet that a specific user previously owned is set as a conversation partner and memories related to that pet are shared. This makes it possible to set the pet that the user previously owned as a conversation partner.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The conversation generation unit responds appropriately to questions and topics posed by the elderly person. For example, in response to a question like, "The weather is nice today, isn't it?", the unit responds with, "Yes, it's sunny and pleasant today." The conversation generation unit uses generation AI (for example, text generation AI or multimodal generation AI) to convert the elderly person's speech into text using speech recognition technology, and then generates a response based on that text. Step 2: The image generation unit sends a photo (generated image) in response to the elderly person's request. For example, in response to a request such as "Show me a photo of my grandchild," the AI ​​will generate a photo of the grandchild and display it on the smart speaker or robot's display. Step 3: The shopping agent performs online shopping on behalf of the elderly person in response to their request. For example, if the request is "order milk," the AI ​​generator will carry out the process of ordering milk from an online store. Step 4: The setting unit conducts a conversation based on the settings of the conversation partner selected by the elderly person. For example, settings such as "real family (living far away)," "deceased family," and "virtual friends" are possible, and conversation content is generated according to each setting.

[0049] (Example 2) The conversation partner service system according to an embodiment of the present invention uses a smart speaker or robot equipped with a generative AI to serve as a conversation partner for elderly people. This system responds to elderly people's needs, such as "I want someone to talk to," "I want to ask for a quick consultation," and "I feel lonely when I'm alone." As a result, the conversation partner service system can reduce the elderly's sense of loneliness and maintain their connection with society. For example, by having the generative AI serve as a conversation partner for elderly people, it can relieve daily stress and promote brain activation. Furthermore, by handling online shopping on their behalf, it can improve the convenience of elderly people's lives. Furthermore, by setting a conversation partner, elderly people can enjoy communication that suits them. This is expected to extend the healthy life expectancy of elderly people.

[0050] A conversation partner service system according to an embodiment includes a conversation generation unit, an image generation unit, a shopping agent unit, and a setting unit. The conversation generation unit provides appropriate responses to questions and topics from the elderly. For example, in response to a topic such as "The weather is nice today, isn't it?", the conversation generation unit responds with "Yes, it's sunny and pleasant today." The conversation generation unit also uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to convert the elderly's speech into text using speech recognition technology and generates a response based on the text. The image generation unit sends a photo (generated image) in response to a request from the elderly. For example, in response to a request such as "Show me a photo of my grandchild," the image generation unit generates a photo of the grandchild using the generation AI and displays it on the display of a smart speaker or robot. The shopping agent unit performs online shopping on the elderly's behalf in response to a request such as "Order milk," the shopping agent unit processes the milk order from an online store. The setting unit conducts a conversation based on the settings of the conversation partner selected by the elderly. For example, the setting unit can set "real family (living far away)," "deceased family," "virtual friends," etc., and generates conversation content according to each setting. As a result, the conversation partner service system according to the embodiment can reduce the sense of loneliness felt by the elderly and maintain connections with society. For example, by having the generation AI become a conversation partner for the elderly, it can relieve everyday stress and promote brain activation. In addition, by handling online shopping on their behalf, the convenience of the elderly's lives can be improved. Furthermore, by setting a conversation partner, the elderly can enjoy communication that suits them. This is expected to extend the healthy life expectancy of the elderly.

[0051] The conversation generation unit can learn the elderly person's past conversation history and generate personalized responses tailored to each elderly person. For example, the conversation generation unit stores the content of past conversations that the elderly person has had in a database, and the generation AI learns from that data. For example, it learns the topics and language that a specific elderly person often talks about and generates responses based on that. The conversation generation unit also analyzes the elderly person's past conversation history and generates personalized responses for that specific elderly person. For example, it provides a response that includes information about the specific elderly person's favorite topics and hobbies. The conversation generation unit also builds a system in which the generation AI generates the optimal response for that elderly person based on the elderly person's past conversation history. For example, in response to a topic about family that a specific elderly person often talks about, it provides a response that includes the latest information about the family. This makes it possible to provide more appropriate responses to the elderly person.

[0052] The conversation generation unit can analyze the tone and speed of the elderly person's voice and generate a response that matches their mood and physical condition on that day. For example, the conversation generation unit analyzes the tone and speed of the elderly person's voice in real time and generates a response that matches their mood and physical condition on that day. For example, if the voice tone is lively, it will provide a cheerful topic, and if the voice tone is calm, it will provide a relaxing topic. The conversation generation unit also analyzes the tone and speed of the elderly person's voice and builds a system in which the generation AI adjusts the response based on that data. For example, if the voice rate is slow, it will provide a slow response, and if the voice rate is high, it will provide a cheerful response. The conversation generation unit also develops an algorithm that analyzes the tone and speed of the elderly person's voice and generates a response that matches their mood and physical condition on that day. For example, if the voice rate is low, it will provide a response that includes encouraging words, and if the voice rate is high, it will provide an interesting topic. This makes it possible to provide an appropriate response that matches the elderly person's mood and physical condition.

[0053] The conversation generation unit can use the emotion estimation function to estimate the emotions of the elderly person in real time and respond according to those emotions. For example, the conversation generation unit uses the emotion estimation function to analyze the emotions of the elderly person in real time and generate a response according to those emotions. For example, if the elderly person has a sad expression, a response including words of comfort is provided. The conversation generation unit also estimates the emotions of the elderly person in real time, and builds a system in which the generation AI adjusts the response based on that data. For example, if the elderly person has a happy expression, a response including words of empathy is provided. The conversation generation unit also uses the emotion estimation function to develop an algorithm that analyzes the emotions of the elderly person in real time and generates a response according to those emotions. For example, if the elderly person has an angry expression, a calm response is provided, and if the elderly person has a relaxed expression, a relaxing topic is offered. This makes it possible to provide an appropriate response according to the elderly person's emotions.

[0054] The image generation unit can analyze the elderly person's past photo albums and generate and send memorable photos. For example, the image generation unit stores the elderly person's past photo albums in a database, and the generation AI generates memorable photos based on that data. For example, new photos are generated based on photos taken in the past by a specific elderly person. The image generation unit also analyzes the elderly person's past photo albums and builds a system in which the generation AI generates memorable photos based on that data. For example, new photos are generated based on photos taken in the past by a specific elderly person and sent. The image generation unit also develops an algorithm in which the generation AI generates memorable photos based on the elderly person's past photo albums. For example, new photos are generated based on photos taken in the past by a specific elderly person and sent. This makes it possible to provide the elderly with memorable photos.

[0055] The image generation unit generates landscape photos of places that the elderly have visited, giving them a sense of nostalgia. For example, the image generation unit collects data on places that the elderly have visited in the past, and the generation AI generates landscape photos based on that data. For example, landscape photos of places that a specific elderly person has visited in the past are generated and transmitted. The image generation unit also analyzes the elderly's past travel history, and builds a system in which the generation AI generates landscape photos based on that data. For example, landscape photos of places that a specific elderly person has visited in the past are generated and transmitted. The image generation unit also develops an algorithm that generates landscape photos of places that the elderly have visited in the past, giving them a sense of nostalgia. For example, landscape photos of places that a specific elderly person has visited in the past are generated and transmitted. This makes it possible to provide elderly people with landscape photos that evoke a sense of nostalgia.

[0056] The image generation unit can use the emotion estimation function to estimate photos that the elderly would like to see and send them at the appropriate time. The image generation unit, for example, uses the emotion estimation function to analyze the emotions of the elderly in real time and build a system that estimates photos that the elderly would like to see. For example, if the elderly has a relaxed expression, a photo of a relaxing landscape is sent. The image generation unit also estimates the emotions of the elderly in real time and, based on that data, develops an algorithm that allows the generation AI to estimate photos that the elderly would like to see. For example, if the elderly has a relaxed expression, a photo of a relaxing landscape is sent. The image generation unit also uses the emotion estimation function to analyze the emotions of the elderly in real time and develops a system that estimates photos that the elderly would like to see and send them at the appropriate time. For example, if the elderly has a relaxed expression, a photo of a relaxing landscape is sent. This makes it possible to provide the elderly with photos that they would like to see at the appropriate time.

[0057] The shopping agent unit can learn the elderly person's past purchase history and automatically list and suggest necessary products. For example, the shopping agent unit stores the elderly person's past purchase history in a database, and the generation AI automatically lists necessary products based on that data. For example, it lists products that a specific elderly person purchases regularly. The shopping agent unit also analyzes the elderly person's past purchase history and builds a system where the generation AI automatically lists necessary products based on that data. For example, it lists products that a specific elderly person often purchases. The shopping agent unit also develops an algorithm where the generation AI automatically lists necessary products based on the elderly person's past purchase history. For example, it lists and suggests products that a specific elderly person purchases regularly. This makes it possible to automatically list and suggest necessary products to the elderly.

[0058] The shopping agent unit is able to suggest healthy products taking into account the health condition of the elderly person. For example, the shopping agent unit stores the health condition of the elderly person in a database, and the generation AI suggests healthy products based on that data. For example, it suggests foods and supplements that are healthy for a specific elderly person. The shopping agent unit also builds a system that analyzes the health condition of the elderly person, and the generation AI suggests healthy products based on that data. For example, it lists and suggests products that are healthy for a specific elderly person. The shopping agent unit also develops an algorithm that allows the generation AI to suggest healthy products based on the health condition of the elderly person. For example, it suggests foods and supplements that are healthy for a specific elderly person. This makes it possible to suggest healthy products to the elderly.

[0059] The shopping agent unit can use the emotion estimation function to provide a simple and smooth purchasing procedure so that the elderly do not feel stressed. For example, the shopping agent unit uses the emotion estimation function to analyze the emotions of the elderly in real time and build a system that provides a purchasing procedure that does not cause stress. For example, if the elderly have a relaxed facial expression, a simple purchasing procedure is provided. The shopping agent unit also estimates the emotions of the elderly in real time and, based on that data, develops an algorithm that uses a generation AI to provide a purchasing procedure that does not cause stress. For example, if the elderly have a relaxed facial expression, a simple purchasing procedure is provided. The shopping agent unit also uses the emotion estimation function to analyze the emotions of the elderly in real time and develops a system that provides a purchasing procedure that does not cause stress. For example, if the elderly have a relaxed facial expression, a simple purchasing procedure is provided. This makes it possible to provide a simple and smooth purchasing procedure so that the elderly do not feel stressed.

[0060] The setting unit can learn the content of past conversations of the elderly person and generate responses that match the character of the selected conversation partner. For example, the setting unit stores the content of past conversations of the elderly person in a database, and the generation AI generates responses that match the character of the selected conversation partner based on that data. For example, a response that matches the character selected by a specific elderly person. The setting unit also builds a system that analyzes the content of past conversations of the elderly person and the generation AI generates responses that match the character of the selected conversation partner based on that data. For example, a response that matches the character selected by a specific elderly person. The setting unit also develops an algorithm that generates responses that match the character of the selected conversation partner based on the content of past conversations of the elderly person. For example, a response that matches the character selected by a specific elderly person. This makes it possible to provide responses that match the character of the selected conversation partner to the elderly person.

[0061] The setting unit allows the conversation partner character to start a conversation at an appropriate time, in line with the elderly person's lifestyle rhythm. For example, the setting unit stores the elderly person's lifestyle rhythm in a database, and the generation AI builds a system in which the conversation partner character starts a conversation at an appropriate time based on that data. For example, a specific elderly person starts a conversation after breakfast. The setting unit also analyzes the elderly person's lifestyle rhythm, and the generation AI develops an algorithm in which the conversation partner character starts a conversation at an appropriate time based on that data. For example, a specific elderly person starts a conversation after dinner. The setting unit also develops a system in which the generation AI allows the conversation partner character to start a conversation at an appropriate time, in line with the elderly person's lifestyle rhythm. For example, a specific elderly person starts a conversation before going to bed. This makes it possible to provide conversations to the elderly at appropriate times that match their lifestyle rhythm.

[0062] The setting unit can use the emotion estimation function to suggest a character that the elderly person can most relax with. For example, the setting unit uses the emotion estimation function to analyze the emotions of the elderly person in real time and build a system that suggests a character that the elderly person can most relax with. For example, if the person has a relaxed facial expression, a character that is relaxing is suggested. The setting unit also estimates the emotions of the elderly person in real time and, based on that data, develops an algorithm that allows the generation AI to suggest a character that the elderly person can most relax with. For example, if the person has a relaxed facial expression, a character that is relaxing is suggested. The setting unit also uses the emotion estimation function to develop a system that analyzes the emotions of the elderly person in real time and suggests a character that the elderly person can most relax with. For example, if the person has a relaxed facial expression, a character that is relaxing is suggested. This makes it possible to suggest a character that the elderly person can most relax with.

[0063] The setting unit can provide a function that enables elderly people to set their favorite celebrities or historical figures as conversation partners. For example, the setting unit provides a function that enables elderly people to register their favorite celebrities or historical figures in a database and the generation AI to set them as conversation partners based on that information. For example, a celebrity that a specific elderly person likes is set as a conversation partner. The setting unit also builds a system that analyzes the interests and concerns of elderly people and enables the generation AI to set celebrities or historical figures as conversation partners based on that information. For example, a celebrity that a specific elderly person is interested in is set as a conversation partner. The setting unit also develops an algorithm that provides a function that enables elderly people to set their favorite celebrities or historical figures as conversation partners. For example, a celebrity that a specific elderly person likes is set as a conversation partner and information about that celebrity is provided. This makes it possible to set a favorite celebrity or historical figure as a conversation partner for an elderly person.

[0064] The setting unit can provide a function that allows an elderly person to set a pet that they previously owned as a conversation partner. The setting unit, for example, stores information about the elderly person's past pets in a database, and provides a function that allows the generation AI to set a pet as a conversation partner based on that data. For example, a pet that a specific elderly person previously owned is set as a conversation partner. The setting unit also builds a system that analyzes information about the elderly person's past pets, and allows the generation AI to set a pet as a conversation partner based on that data. For example, a pet that a specific elderly person previously owned is set as a conversation partner. The setting unit also develops an algorithm that provides a function that allows the generation AI to set a pet as a conversation partner based on information about the elderly person's past pets. For example, a pet that a specific elderly person previously owned is set as a conversation partner, and memories related to that pet are shared. This makes it possible to set a pet that a specific elderly person previously owned as a conversation partner for the elderly.

[0065] The setting unit can use the emotion estimation function to automatically select a character that the elderly person will enjoy talking to the most. For example, the setting unit uses the emotion estimation function to analyze the emotions of the elderly person in real time and build a system that automatically selects the most enjoyable character to talk to. For example, if the person has a happy expression, a happy character is suggested. The setting unit also develops an algorithm that estimates the emotions of the elderly person in real time and, based on that data, allows the generation AI to automatically select the most enjoyable character to talk to. For example, if the person has a happy expression, a happy character is suggested. The setting unit also develops a system that uses the emotion estimation function to analyze the emotions of the elderly person in real time and automatically selects the most enjoyable character to talk to. For example, if the person has a happy expression, a happy character is suggested and information about that character is provided. This makes it possible to automatically select the most enjoyable character to talk to for the elderly.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The conversation generation unit can analyze the tone and speed of the user's voice and generate a response that matches the user's mood and physical condition on that day. For example, if the user's voice tone is lively, it will provide a cheerful topic, and if the voice tone is calm, it will provide a relaxing topic. The conversation generation unit also analyzes the user's voice tone and speed, and builds a system in which the generation AI adjusts the response based on that data. For example, if the voice tone is slow, it will provide a slow response, and if the voice tone is high, it will provide a cheerful response. The conversation generation unit also analyzes the user's voice tone and speed, and develops an algorithm that generates a response that matches the user's mood and physical condition on that day. For example, if the voice tone is low, it will provide a response that includes encouraging words, and if the voice tone is fast, it will provide an interesting topic. This makes it possible to provide an appropriate response that matches the user's mood and physical condition.

[0068] The image generation unit generates landscape photos of places the user has visited, giving the user a sense of nostalgia. For example, data on places the user has visited in the past is collected, and the generation AI generates landscape photos based on that data. For example, landscape photos of places a specific user has visited in the past are generated and transmitted. The image generation unit also analyzes the user's past travel history, and builds a system in which the generation AI generates landscape photos based on that data. For example, landscape photos of places a specific user has visited in the past are generated and transmitted. The image generation unit also develops an algorithm that generates landscape photos of places the user has visited, giving the user a sense of nostalgia. For example, landscape photos of places a specific user has visited in the past are generated and transmitted. This makes it possible to provide the user with landscape photos that evoke a sense of nostalgia.

[0069] The shopping agent unit can take the user's health condition into consideration and suggest healthy products. For example, the user's health condition is stored in a database, and the generation AI suggests healthy products based on that data. For example, a specific user might suggest healthy foods or supplements. The shopping agent unit also builds a system that analyzes the user's health condition, and the generation AI suggests healthy products based on that data. For example, a specific user might list and suggest healthy products. The shopping agent unit also develops an algorithm that allows the generation AI to suggest healthy products based on the user's health condition. For example, a specific user might suggest healthy foods or supplements. This makes it possible to suggest healthy products to the user.

[0070] The setting unit can provide a function that allows a user to set a favorite celebrity or historical figure as a conversation partner. For example, a function is provided that allows a user to register a favorite celebrity or historical figure in a database and the generation AI to set the celebrity or historical figure as a conversation partner based on that information. For example, a favorite celebrity of a specific user is set as a conversation partner. The setting unit also builds a system that analyzes a user's interests and concerns and allows the generation AI to set a celebrity or historical figure as a conversation partner based on that information. For example, a celebrity in which a specific user is interested is set as a conversation partner. The setting unit also develops an algorithm that provides a function that allows a user to set a favorite celebrity or historical figure as a conversation partner. For example, a favorite celebrity of a specific user is set as a conversation partner and information about that celebrity is provided. This allows a favorite celebrity or historical figure to be set as a conversation partner for the user.

[0071] The setting unit can use the emotion estimation function to suggest a character that the user can most relax with as a conversation partner. For example, a system is constructed using the emotion estimation function to analyze the user's emotions in real time and suggest a character that the user can most relax with as a conversation partner. For example, if the user has a relaxed facial expression, a character that is relaxing is suggested. The setting unit also develops an algorithm that estimates the user's emotions in real time and, based on that data, a generation AI suggests a character that the user can most relax with as a conversation partner. For example, if the user has a relaxed facial expression, a character that is relaxing is suggested. The setting unit also develops a system that uses the emotion estimation function to analyze the user's emotions in real time and suggest a character that the user can most relax with as a conversation partner. For example, if the user has a relaxed facial expression, a character that is relaxing is suggested. This makes it possible to suggest a character that the user can most relax with as a conversation partner.

[0072] The conversation generation unit can learn a user's past conversation history and generate personalized responses tailored to each individual user. For example, the content of a user's past conversations is stored in a database, and the generation AI learns from that data. For example, it can learn the topics and language that a particular user often talks about and generate responses based on that. The conversation generation unit can also analyze a user's past conversation history and generate personalized responses for a particular user. For example, it can provide a response that includes information about a particular user's favorite topics and hobbies. The conversation generation unit can also build a system in which the generation AI generates optimal responses for a user based on the user's past conversation history. For example, in response to a topic about family that a particular user often talks about, it can provide a response that includes the latest information about the family. This makes it possible to provide more appropriate responses to the user.

[0073] The image generation unit can use the emotion estimation function to estimate photos that the user wants to see and send them at the appropriate time. For example, a system can be constructed using the emotion estimation function to analyze a user's emotions in real time and estimate photos that the user wants to see. For example, if the user has a relaxed expression, a photo of a relaxing landscape can be sent. The image generation unit can also estimate a user's emotions in real time, and an algorithm can be developed based on that data to enable the generation AI to estimate photos that the user wants to see. For example, if the user has a relaxed expression, a photo of a relaxing landscape can be sent. The image generation unit can also use the emotion estimation function to analyze a user's emotions in real time, and a system can be developed to estimate photos that the user wants to see and send them at the appropriate time. For example, if the user has a relaxed expression, a photo of a relaxing landscape can be sent. This makes it possible to provide users with photos that they want to see at the appropriate time.

[0074] The shopping agent unit can learn the user's past purchase history and automatically list and suggest necessary products. For example, the user's past purchase history is stored in a database, and the generation AI automatically lists necessary products based on that data. For example, it can list products that a specific user purchases regularly. The shopping agent unit also analyzes the user's past purchase history and builds a system where the generation AI automatically lists necessary products based on that data. For example, it can list products that a specific user frequently purchases. The shopping agent unit also develops an algorithm where the generation AI automatically lists necessary products based on the user's past purchase history. For example, it can list and suggest products that a specific user purchases regularly. This makes it possible to automatically list and suggest necessary products to the user.

[0075] The shopping agent unit can use the emotion estimation function to provide a simple and smooth purchasing procedure so that the user does not feel stressed. For example, a system can be constructed using the emotion estimation function to analyze a user's emotions in real time and provide a purchasing procedure that does not cause stress. For example, if the user has a relaxed facial expression, a simple purchasing procedure can be provided. The shopping agent unit can also develop an algorithm that estimates a user's emotions in real time and uses that data to allow the generation AI to provide a purchasing procedure that does not cause stress. For example, if the user has a relaxed facial expression, a simple purchasing procedure can be provided. The shopping agent unit can also develop a system that uses the emotion estimation function to analyze a user's emotions in real time and provide a purchasing procedure that does not cause stress. For example, if the user has a relaxed facial expression, a simple purchasing procedure can be provided. This makes it possible to provide a simple and smooth purchasing procedure so that the user does not feel stressed.

[0076] The setting unit can provide a function that allows a user to set a pet that the user previously owned as a conversation partner. For example, a function is provided that stores information about the user's past pets in a database and allows the generation AI to set the pet as a conversation partner based on that data. For example, a pet that a specific user previously owned is set as a conversation partner. The setting unit also builds a system that analyzes information about the user's past pets and allows the generation AI to set the pet as a conversation partner based on that data. For example, a pet that a specific user previously owned is set as a conversation partner. The setting unit also develops an algorithm that provides a function that allows the generation AI to set the pet as a conversation partner based on information about the user's past pet. For example, a pet that a specific user previously owned is set as a conversation partner and memories related to that pet are shared. This makes it possible to set the pet that the user previously owned as a conversation partner.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The conversation generation unit responds appropriately to questions and topics posed by the elderly person. For example, in response to a question like, "The weather is nice today, isn't it?", the unit responds with, "Yes, it's sunny and pleasant today." The conversation generation unit uses generation AI (for example, text generation AI or multimodal generation AI) to convert the elderly person's speech into text using speech recognition technology, and then generates a response based on that text. Step 2: The image generation unit sends a photo (generated image) in response to the elderly person's request. For example, in response to a request such as "Show me a photo of my grandchild," the AI ​​will generate a photo of the grandchild and display it on the smart speaker or robot's display. Step 3: The shopping agent performs online shopping on behalf of the elderly person in response to their request. For example, if the request is "order milk," the AI ​​generator will carry out the process of ordering milk from an online store. Step 4: The setting unit conducts a conversation based on the settings of the conversation partner selected by the elderly person. For example, settings such as "real family (living far away)," "deceased family," and "virtual friends" are possible, and conversation content is generated according to each setting.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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. Equipped with a smart speaker or robot equipped with generative AI, The generated AI is a conversation generation unit that responds appropriately to questions and topics posed by the elderly; an image generation unit that generates and transmits a photograph in response to a request from the elderly person; a shopping agent unit that performs online shopping on behalf of the elderly person in response to a request from the elderly person; a setting unit that carries out a conversation based on the setting of a conversation partner selected by the elderly person; A system characterized by:

2. The conversation generation unit Learns the senior's past conversation history and generates personalized responses tailored to each senior.

2. The system of claim 1.

3. The conversation generation unit Analyze the tone and speed of the elderly person's voice and generate a response that matches their mood and physical condition on that day.

2. The system of claim 1.

4. The conversation generation unit Estimating the elderly person's emotions in real time and responding in accordance with the emotions 2. The system of claim 1.

5. The image generation unit Analyzing the elderly person's past photo albums, generating and transmitting the memorable photos 2. The system of claim 1.

Images