System

Abstract

The system according to the embodiment is intended for elderly people to provide information about their memories and receive personalized memory services.SOLUTION: A system includes a call reception unit, an information collection unit, a generation unit, and a provision unit. The telephone reception unit receives a telephone call from the elderly person. The information collection unit collects the information on the memory of the elderly person received by the telephone reception unit. The generation unit generates a storage service based on the storage information collected by the information collection unit. The providing unit provides the elderly person with the storage service generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for elderly people to effectively utilize information about their memories and receive personalized services.

[0005] The system according to the embodiment aims to enable elderly people to provide information about their memory and receive personalized memory services. [Means for solving the problem]

[0006] The system according to the embodiment includes a telephone reception unit, an information collection unit, a generation unit, and a provision unit. The telephone reception unit receives telephone calls from elderly people. The information collection unit collects information about the elderly people's memories received by the telephone reception unit. The generation unit generates a memory service based on the memory information collected by the information collection unit. The provision unit provides the elderly with the memory service generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to provide information about their memory and receive personalized memory services. [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) In the memory service creation system according to an embodiment of the present invention, elderly people provide information about their memories via telephone, and a generation AI creates an original memory service based on that information. This allows elderly people to record their memories and experiences in a tangible form and share them with their family and friends.

[0029] A memory service creation system according to an embodiment includes a telephone reception unit, an information collection unit, a generation unit, and a provision unit. The telephone reception unit receives calls from elderly people. For example, when an elderly person calls, the telephone reception unit automatically answers the call and prompts the elderly person to provide information about their memory. The telephone reception unit can also have an operator answer the call directly and listen to the elderly person's story. The information collection unit collects information about the elderly person's memory received by the telephone reception unit. For example, the operator listens to the elderly person's story and takes notes of important points. The information collection unit can also automatically convert the elderly person's story into text using voice recognition technology. The generation unit generates a memory service based on the memory information collected by the information collection unit. For example, the generation AI analyzes the provided information and generates an individually customized memory service. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to create a story based on a specific episode, an album combining memorable photos, or the like. The provision unit provides the memory service generated by the generation unit to the elderly person. For example, the created story or album can be mailed as a printed matter or sent as digital data via email. As a result, the memory service creation system according to the embodiment allows elderly people to record their memories and experiences in a tangible form and share them with their family and friends.

[0030] The information collection unit can automatically convert information provided by the elderly into text using voice recognition technology and extract important keywords. For example, when the elderly makes a phone call, the information collection unit automatically converts what is being said into text using voice recognition technology and extracts important keywords. For example, specific episodes and names of people are automatically extracted. A system is also constructed that automatically converts information provided by the elderly into text using voice recognition technology and extracts important keywords. For example, words that appear frequently in the conversation are extracted and recorded as important points. Also, when the elderly makes a phone call, the information collection unit automatically converts what is being said into text using voice recognition technology and extracts important keywords. For example, names of specific events and places are automatically extracted. This makes it possible to efficiently convert information provided by the elderly into text and extract important keywords.

[0031] The information collection unit can record what the elderly say and build a voice database for later detailed analysis. For example, the information collection unit records what the elderly say when they make a phone call and builds a voice database for later detailed analysis. For example, the recorded voice data is analyzed and important episodes are extracted. A system is also developed that records information provided by the elderly and builds a voice database for later detailed analysis. For example, the recorded voice data is converted into text and important points are extracted. Also, a voice database is built that records what the elderly say when they make a phone call and builds a voice database for later detailed analysis. For example, the recorded voice data is analyzed and specific episodes or names of people are extracted. This makes it possible to record what the elderly say and later analyze it in detail.

[0032] The telephone reception unit can enable the elderly to simultaneously provide visual information using a video call instead of making a telephone call. The telephone reception unit, for example, enables the elderly to simultaneously provide visual information using a video call instead of making a telephone call. For example, by talking while showing photos or videos, more specific information can be provided. Furthermore, a system is constructed that enables the elderly to simultaneously provide visual information using a video call instead of making a telephone call. For example, by using a screen sharing function during a video call, photos or videos can be shown. Furthermore, the elderly can simultaneously provide visual information using a video call instead of making a telephone call. For example, by talking while showing an album during a video call, more specific episodes can be provided. This enables the elderly to simultaneously provide visual information.

[0033] The information collecting unit can build an online platform for sharing information provided by the elderly with family and friends. The information collecting unit, for example, builds an online platform for sharing information provided by the elderly with family and friends. For example, the provided information is shared with family as a digital album. Also, an online platform is developed for sharing information provided by the elderly with family and friends. For example, the provided information is stored on the cloud so that family members can access it. Also, an online platform is built for sharing information provided by the elderly with family and friends. For example, the provided information is shared in a social networking site format so that family members and friends can leave comments. This makes it possible for information provided by the elderly to be shared with family and friends.

[0034] The information collection unit can automatically summarize the collected information using natural language processing technology and extract important points. The information collection unit, for example, automatically summarizes information collected by an operator using natural language processing technology and extracts important points. For example, it extracts keywords that appear frequently in a conversation and generates a summary. A system is also constructed that automatically summarizes information collected by an operator using natural language processing technology and extracts important points. For example, it analyzes the content of a conversation and generates a summary. The information collection unit also automatically summarizes information collected by an operator using natural language processing technology and extracts important points. For example, it extracts important episodes and names of people in a conversation and generates a summary. This makes it possible to efficiently summarize the collected information and extract important points.

[0035] The information collection unit can assign metadata for linking the collected information with related photos and videos. The information collection unit, for example, assigns metadata for linking the information collected by an operator with related photos and videos. For example, it links photos and videos related to the content of the story. Also, a system is constructed for assigning metadata for linking the information collected by an operator with related photos and videos. For example, it automatically generates metadata related to the content of the story. Also, it assigns metadata for linking the information collected by the operator with related photos and videos. For example, it links photos and videos related to people and places mentioned in the story. In this way, the collected information can be linked with related photos and videos.

[0036] The information collection unit can automatically translate the collected information into different languages ​​and provide a multilingual memory service. The information collection unit, for example, automatically translates information collected by an operator into different languages ​​and provides a multilingual memory service. For example, it translates the information into English or Chinese and provides it. Also, a system is constructed that automatically translates information collected by an operator into different languages ​​and provides a multilingual memory service. For example, it generates a memory service based on the translated information. Also, it automatically translates information collected by an operator into different languages ​​and provides a multilingual memory service. For example, it translates the information into Spanish or French and provides it. In this way, it is possible to automatically translate the collected information into different languages ​​and provide a multilingual memory service.

[0037] The information collection unit can store the collected information in cloud storage and make it accessible at any time. The information collection unit, for example, stores information collected by an operator in cloud storage and makes it accessible at any time. For example, the information stored in the cloud can be accessed by family members. A system is also constructed in which information collected by an operator is stored in cloud storage and made accessible at any time. For example, the information stored in the cloud can be accessed by the elderly person themselves. Information collected by an operator is also stored in cloud storage and made accessible at any time. For example, the information stored in the cloud can be accessed by friends. In this way, the collected information can be stored in cloud storage and made accessible at any time.

[0038] The generation unit can analyze the provided information and create a memory service that incorporates relevant historical background and cultural elements. For example, the generation unit uses a generation AI to analyze the provided information and create a memory service that incorporates relevant historical background and cultural elements. For example, it generates a story that reflects the culture of a particular era or region. In addition, a system is constructed in which the generation AI analyzes the provided information and creates a memory service that incorporates relevant historical background and cultural elements. For example, it provides a service that reflects historical events and cultural customs. In addition, the generation AI analyzes the provided information and creates a memory service that incorporates relevant historical background and cultural elements. For example, it generates an album that reflects a particular culture or tradition. In this way, it is possible to analyze the provided information and create a memory service that incorporates relevant historical background and cultural elements.

[0039] The generation unit can generate interactive digital artworks based on what the elderly person has said. For example, the generation unit uses a generation AI to generate interactive digital artworks based on what the elderly person has said. For example, it creates animations and visual effects based on the content of the conversation. In addition, a system is constructed in which the generation AI generates interactive digital artworks based on what the elderly person has said. For example, it generates digital art that changes depending on the content of the conversation. In addition, the generation AI generates interactive digital artworks based on what the elderly person has said. For example, it creates an interactive picture book based on the content of the conversation. In this way, it is possible to generate interactive digital artworks based on what the elderly person has said.

[0040] The generation unit can make the created memory service experienceable in virtual reality (VR) or augmented reality (AR). For example, the generation unit can make the memory service created by the generation AI experienceable in virtual reality (VR) or augmented reality (AR). For example, it can recreate a memorable place using VR goggles. It can also build a system that allows the memory service created by the generation AI to be experienced in virtual reality (VR) or augmented reality (AR). For example, it can display memorable photos in 3D using an AR app. It can also make the memory service created by the generation AI experienceable in virtual reality (VR) or augmented reality (AR). For example, it can recreate a memorable episode in a VR environment. This makes it possible to experience the created memory service in virtual reality or augmented reality.

[0041] The generation unit can make the created memory service playable by voice through a voice assistant. The generation unit, for example, makes it possible to play a memory service created by the generation AI by voice through a voice assistant. For example, playing a memorable episode using a smart speaker. Also, a system is constructed that can play a memory service created by the generation AI by voice through a voice assistant. For example, playing a specific episode with a voice command. Also, it makes it possible to play a memory service created by the generation AI by voice through a voice assistant. For example, playing a memorable episode by speaking to the voice assistant. In this way, it is possible to play a created memory service by voice through a voice assistant.

[0042] The provision unit can periodically update the memory service created by the generation AI and add new episodes and information. The provision unit, for example, periodically updates the memory service created by the generation AI and adds new episodes and information. For example, adding a new episode every month. A system is also constructed that periodically updates the memory service created by the generation AI and adds new episodes and information. For example, the service is updated every time new information is provided. The memory service created by the generation AI can also be periodically updated and new episodes and information added. For example, adding a new episode for each season. This makes it possible to periodically update the memory service created by the generation AI and add new episodes and information.

[0043] The providing unit can develop a dedicated app for sharing the memory service created by the generating AI with family and friends. The providing unit, for example, develops a dedicated app for sharing the memory service created by the generating AI with family and friends. For example, sharing memorable episodes through the app. Also, a system is constructed to develop a dedicated app for sharing the memory service created by the generating AI with family and friends. For example, sharing photos and videos using the app. Also, a dedicated app is developed for sharing the memory service created by the generating AI with family and friends. For example, sharing an album of memories through the app. In this way, a dedicated app can be developed for sharing the memory service created by the generating AI with family and friends.

[0044] The providing unit can make the memory service created by the generation AI displayable on a digital photo frame or e-book reader. The providing unit, for example, makes the memory service created by the generation AI displayable on a digital photo frame or e-book reader. For example, it displays memorable photos on a digital photo frame. Also, a system is constructed that can display the memory service created by the generation AI on a digital photo frame or e-book reader. For example, it displays memorable episodes on an e-book reader. Also, it makes the memory service created by the generation AI displayable on a digital photo frame or e-book reader. For example, it displays an album of memories on a digital photo frame. In this way, the memory service created by the generation AI can be displayed on a digital photo frame or e-book reader.

[0045] The providing unit can convert the memory service created by the generating AI into a format that can be shared on social media. The providing unit, for example, converts the memory service created by the generating AI into a format that can be shared on social media. For example, posting a memorable episode on a social media platform. A system is also constructed that converts the memory service created by the generating AI into a format that can be shared on social media. For example, posting memorable photos and videos on a social media platform. The memory service created by the generating AI is also converted into a format that can be shared on social media. For example, posting an album of memories on a social media platform. In this way, the memory service created by the generating AI can be converted into a format that can be shared on social media.

[0046] The generation unit can provide a customized memory service based on the hobbies and interests of the elderly. For example, the generation AI provides a customized memory service based on the hobbies and interests of the elderly. For example, it highlights episodes related to a hobby. Furthermore, a system is constructed in which the generation AI provides a customized memory service based on the hobbies and interests of the elderly. For example, it combines photos and videos related to a hobby. Furthermore, the generation AI provides a customized memory service based on the hobbies and interests of the elderly. For example, it incorporates episodes related to a hobby into a story. In this way, it is possible to provide a customized memory service based on the hobbies and interests of the elderly.

[0047] The generation unit can incorporate information about the elderly person's family and friends to create a more personalized memory service. For example, the generation AI can incorporate information about the elderly person's family and friends to create a more personalized memory service. For example, it can highlight episodes with family and friends. A system can also be built in which the generation AI incorporates information about the elderly person's family and friends to create a more personalized memory service. For example, it can combine photos and videos of family and friends. The generation AI can also incorporate information about the elderly person's family and friends to create a more personalized memory service. For example, it can incorporate episodes with family and friends into a story. This makes it possible to incorporate information about the elderly person's family and friends to create a more personalized memory service.

[0048] The generation unit can create multiple versions of the elderly memory service with different themes and styles. For example, the generation unit has a generation AI that creates multiple versions of the elderly memory service with different themes and styles. For example, it creates a moving version and a humorous version. Also, a system is constructed in which the generation AI creates multiple versions of the elderly memory service with different themes and styles. For example, it creates a historical theme and a modern theme. Also, the generation AI creates multiple versions of the elderly memory service with different themes and styles. For example, it creates a version for family and a version for friends. In this way, it is possible to create multiple versions of the elderly memory service with different themes and styles.

[0049] The generation unit can provide the elderly memory service in a multimedia format by adding music and narration. For example, the generation AI provides the elderly memory service in a multimedia format by adding music and narration. For example, music is added to a memorable episode. Also, a system is constructed in which the generation AI provides the elderly memory service in a multimedia format by adding music and narration. For example, narration is added to an episode. Also, the generation AI provides the elderly memory service in a multimedia format by adding music and narration. For example, music and narration are added to a memorable episode. This makes it possible to provide the elderly memory service in a multimedia format by adding music and narration.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The memory service creation system can further include a health management unit that monitors the health status of the elderly. For example, the health status of the elderly can be estimated from the tone of voice and speaking style of the elderly during a phone call, and the health management unit can prompt the elderly to contact a medical institution if necessary. The health management unit can also periodically check the elderly's health status and provide health advice. Furthermore, the health management unit can adjust the content of the memory service based on the elderly's health status to provide more appropriate services.

[0052] The memory service creation system can further include a hobby estimation unit that asks customized questions based on the elderly person's hobbies and interests. For example, the hobby estimation unit estimates the elderly person's hobbies and interests from what the elderly person says and asks questions related to them. The hobby estimation unit can also customize the content of the memory service based on the elderly person's hobbies to provide a more interesting service. Furthermore, the hobby estimation unit can also suggest events and activities related to the elderly person's hobbies.

[0053] The memory service creation system can further include a community linkage module to strengthen social connections among the elderly. For example, it can plan events that connect elderly people living in the same area. The community linkage module can also introduce local events and activities that the elderly can participate in, promoting social connections. Furthermore, the community linkage module can provide common topics that the elderly can share with other participants.

[0054] The memory service creation system can further include a historical information providing unit to supplement the elderly person's memories. For example, it can provide historical events and background information related to the episodes the elderly person talks about. The historical information providing unit can also provide related photos and videos to further enrich the elderly person's memories. Furthermore, the historical information providing unit can search for and provide related historical materials based on what the elderly person talks about.

[0055] The memory service creation system may further include a visual effect generation unit for visually complementing the elderly person's memories. For example, the visual effect generation unit may generate and provide related visual effects based on the stories told by the elderly person. The visual effect generation unit may also add effects to photos and videos to make the elderly person's memories more vivid. Furthermore, the visual effect generation unit may generate and provide interactive visual content based on what the elderly person is saying.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The call reception unit accepts calls from elderly people. For example, when an elderly person calls, the call reception unit automatically answers and prompts them to provide information about their memory. Alternatively, the call reception unit can have an operator answer the call directly and listen to what the elderly person has to say. Step 2: The information collection unit collects information about the elderly person's memory received by the telephone reception unit. For example, an operator listens to what the elderly person says and takes notes of important points. The information collection unit can also automatically convert what the elderly person says into text using voice recognition technology. Step 3: The generation unit generates a memory service based on the memory information collected by the information collection unit. For example, the generation AI analyzes the provided information and generates an individually customized memory service. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to create a story based on a specific episode or an album combining memorable photos. Step 4: The providing unit provides the memory service generated by the generating unit to the elderly. For example, the created story or album may be mailed as a printed matter or sent as digital data by email.

[0058] (Example 2) In the memory service creation system according to an embodiment of the present invention, elderly people provide information about their memories via telephone, and a generation AI creates an original memory service based on that information. This allows elderly people to record their memories and experiences in a tangible form and share them with their family and friends.

[0059] A memory service creation system according to an embodiment includes a telephone reception unit, an information collection unit, a generation unit, and a provision unit. The telephone reception unit receives calls from elderly people. For example, when an elderly person calls, the telephone reception unit automatically answers the call and prompts the elderly person to provide information about their memory. The telephone reception unit can also have an operator answer the call directly and listen to the elderly person's story. The information collection unit collects information about the elderly person's memory received by the telephone reception unit. For example, the operator listens to the elderly person's story and takes notes of important points. The information collection unit can also automatically convert the elderly person's story into text using voice recognition technology. The generation unit generates a memory service based on the memory information collected by the information collection unit. For example, the generation AI analyzes the provided information and generates an individually customized memory service. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to create a story based on a specific episode, an album combining memorable photos, or the like. The provision unit provides the memory service generated by the generation unit to the elderly person. For example, the created story or album can be mailed as a printed matter or sent as digital data via email. As a result, the memory service creation system according to the embodiment allows elderly people to record their memories and experiences in a tangible form and share them with their family and friends.

[0060] The call reception unit uses the emotion estimation function to analyze the emotion of what is being said in real time, allowing the operator to ask questions based on that emotion. For example, when an elderly person makes a call, the call reception unit uses the emotion estimation function to analyze the emotion of what is being said in real time, allowing the operator to ask questions based on that emotion. For example, if the person is talking about something sad, the operator will speak to them in a kind manner. The emotion estimation function is also used to analyze the emotion of what is being said in real time, allowing the operator to ask questions based on that emotion. For example, if the person is expressing a strong sense of joy, the operator will ask for more details about that episode. The emotion estimation function is also used to analyze the emotion of what is being said in real time, allowing the operator to ask questions based on that emotion. For example, if the person is expressing anger, the operator will respond calmly. This makes it possible to respond according to the elderly person's emotions and ask more appropriate questions.

[0061] The information collection unit can automatically convert information provided by the elderly into text using voice recognition technology and extract important keywords. For example, when the elderly makes a phone call, the information collection unit automatically converts what is being said into text using voice recognition technology and extracts important keywords. For example, specific episodes and names of people are automatically extracted. A system is also constructed that automatically converts information provided by the elderly into text using voice recognition technology and extracts important keywords. For example, words that appear frequently in the conversation are extracted and recorded as important points. Also, when the elderly makes a phone call, the information collection unit automatically converts what is being said into text using voice recognition technology and extracts important keywords. For example, names of specific events and places are automatically extracted. This makes it possible to efficiently convert information provided by the elderly into text and extract important keywords.

[0062] The information collection unit can record what the elderly say and build a voice database for later detailed analysis. For example, the information collection unit records what the elderly say when they make a phone call and builds a voice database for later detailed analysis. For example, the recorded voice data is analyzed and important episodes are extracted. A system is also developed that records information provided by the elderly and builds a voice database for later detailed analysis. For example, the recorded voice data is converted into text and important points are extracted. Also, a voice database is built that records what the elderly say when they make a phone call and builds a voice database for later detailed analysis. For example, the recorded voice data is analyzed and specific episodes or names of people are extracted. This makes it possible to record what the elderly say and later analyze it in detail.

[0063] The telephone reception unit can enable the elderly to simultaneously provide visual information using a video call instead of making a telephone call. The telephone reception unit, for example, enables the elderly to simultaneously provide visual information using a video call instead of making a telephone call. For example, by talking while showing photos or videos, more specific information can be provided. Furthermore, a system is constructed that enables the elderly to simultaneously provide visual information using a video call instead of making a telephone call. For example, by using a screen sharing function during a video call, photos or videos can be shown. Furthermore, the elderly can simultaneously provide visual information using a video call instead of making a telephone call. For example, by talking while showing an album during a video call, more specific episodes can be provided. This enables the elderly to simultaneously provide visual information.

[0064] The information collecting unit can build an online platform for sharing information provided by the elderly with family and friends. The information collecting unit, for example, builds an online platform for sharing information provided by the elderly with family and friends. For example, the provided information is shared with family as a digital album. Also, an online platform is developed for sharing information provided by the elderly with family and friends. For example, the provided information is stored on the cloud so that family members can access it. Also, an online platform is built for sharing information provided by the elderly with family and friends. For example, the provided information is shared in a social networking site format so that family members and friends can leave comments. This makes it possible for information provided by the elderly to be shared with family and friends.

[0065] The information collection unit can use the emotion estimation function to analyze the emotions felt by the elderly when they speak and play music and videos in the background to elicit positive emotions. The information collection unit, for example, uses the emotion estimation function to analyze the emotions felt by the elderly when they speak and play music and videos in the background to elicit positive emotions. For example, playing nostalgic music creates an atmosphere that makes it easy to talk. Furthermore, a system is constructed that uses the emotion estimation function to analyze the emotions felt by the elderly when they speak and play music and videos in the background to elicit positive emotions. For example, videos that match the content of the conversation are played. Furthermore, the emotion estimation function is used to analyze the emotions felt by the elderly when they speak and play music and videos in the background to elicit positive emotions. For example, playing videos related to the content of the conversation creates an atmosphere that makes it easy to talk. In this way, it is possible to analyze the emotions felt by the elderly when they speak and elicit positive emotions.

[0066] The information collection unit can automatically summarize the collected information using natural language processing technology and extract important points. The information collection unit, for example, automatically summarizes information collected by an operator using natural language processing technology and extracts important points. For example, it extracts keywords that appear frequently in a conversation and generates a summary. A system is also constructed that automatically summarizes information collected by an operator using natural language processing technology and extracts important points. For example, it analyzes the content of a conversation and generates a summary. The information collection unit also automatically summarizes information collected by an operator using natural language processing technology and extracts important points. For example, it extracts important episodes and names of people in a conversation and generates a summary. This makes it possible to efficiently summarize the collected information and extract important points.

[0067] The information collection unit can assign metadata for linking the collected information with related photos and videos. The information collection unit, for example, assigns metadata for linking the information collected by an operator with related photos and videos. For example, it links photos and videos related to the content of the story. Also, a system is constructed for assigning metadata for linking the information collected by an operator with related photos and videos. For example, it automatically generates metadata related to the content of the story. Also, it assigns metadata for linking the information collected by the operator with related photos and videos. For example, it links photos and videos related to people and places mentioned in the story. In this way, the collected information can be linked with related photos and videos.

[0068] The information collection unit can use the emotion estimation function to evaluate the emotional value of the collected information and prioritize organizing positive episodes. The information collection unit, for example, uses the emotion estimation function to evaluate the emotional value of the collected information and prioritize organizing positive episodes. For example, joyful or moving episodes are preferentially recorded. Furthermore, a system is constructed that uses the emotion estimation function to evaluate the emotional value of the collected information and prioritize organizing positive episodes. For example, episodes with high emotion scores are preferentially recorded. Furthermore, the emotion estimation function is used to evaluate the emotional value of the collected information and prioritize organizing positive episodes. For example, moving episodes are preferentially recorded. In this way, the emotional value of the collected information can be evaluated and positive episodes can be prioritized.

[0069] The information collection unit can automatically translate the collected information into different languages ​​and provide a multilingual memory service. The information collection unit, for example, automatically translates information collected by an operator into different languages ​​and provides a multilingual memory service. For example, it translates the information into English or Chinese and provides it. Also, a system is constructed that automatically translates information collected by an operator into different languages ​​and provides a multilingual memory service. For example, it generates a memory service based on the translated information. Also, it automatically translates information collected by an operator into different languages ​​and provides a multilingual memory service. For example, it translates the information into Spanish or French and provides it. In this way, it is possible to automatically translate the collected information into different languages ​​and provide a multilingual memory service.

[0070] The information collection unit can store the collected information in cloud storage and make it accessible at any time. The information collection unit, for example, stores information collected by an operator in cloud storage and makes it accessible at any time. For example, the information stored in the cloud can be accessed by family members. A system is also constructed in which information collected by an operator is stored in cloud storage and made accessible at any time. For example, the information stored in the cloud can be accessed by the elderly person themselves. Information collected by an operator is also stored in cloud storage and made accessible at any time. For example, the information stored in the cloud can be accessed by friends. In this way, the collected information can be stored in cloud storage and made accessible at any time.

[0071] The information collection unit can use the emotion estimation function to analyze the emotional reactions of the operators to the collected information and utilize the results for operator training. The information collection unit, for example, uses the emotion estimation function to analyze the emotional reactions of the operators to the collected information and utilize the results for operator training. For example, it analyzes the emotions with which the operators respond and finds areas for improvement. It also uses the emotion estimation function to analyze the emotional reactions of the operators to the collected information and builds a system that utilizes the results for operator training. For example, it creates a training program based on the emotional reaction data. It also uses the emotion estimation function to analyze the emotional reactions of the operators to the collected information and utilizes the results for operator training. For example, it performs training to elicit positive emotional reactions. This makes it possible to analyze the emotional reactions of the operators to the collected information and utilize the results for training.

[0072] When creating a memory service, the generation unit can use the emotion estimation function to perform storytelling based on the emotions of the elderly. For example, when the generation AI creates a memory service, the generation unit uses the emotion estimation function to perform storytelling based on the emotions of the elderly. For example, it emphasizes moving episodes. Also, when the generation AI creates a memory service, it uses the emotion estimation function to build a system that performs storytelling based on the emotions of the elderly. For example, it generates stories that reflect emotions of joy and sadness. Also, when the generation AI creates a memory service, it uses the emotion estimation function to perform storytelling based on the emotions of the elderly. For example, it selects episodes that elicit specific emotions. This makes it possible to perform storytelling based on the emotions of the elderly.

[0073] The generation unit can analyze the provided information and create a memory service that incorporates relevant historical background and cultural elements. For example, the generation unit uses a generation AI to analyze the provided information and create a memory service that incorporates relevant historical background and cultural elements. For example, it generates a story that reflects the culture of a particular era or region. In addition, a system is constructed in which the generation AI analyzes the provided information and creates a memory service that incorporates relevant historical background and cultural elements. For example, it provides a service that reflects historical events and cultural customs. In addition, the generation AI analyzes the provided information and creates a memory service that incorporates relevant historical background and cultural elements. For example, it generates an album that reflects a particular culture or tradition. In this way, it is possible to analyze the provided information and create a memory service that incorporates relevant historical background and cultural elements.

[0074] The generation unit can generate interactive digital artworks based on what the elderly person has said. For example, the generation unit uses a generation AI to generate interactive digital artworks based on what the elderly person has said. For example, it creates animations and visual effects based on the content of the conversation. In addition, a system is constructed in which the generation AI generates interactive digital artworks based on what the elderly person has said. For example, it generates digital art that changes depending on the content of the conversation. In addition, the generation AI generates interactive digital artworks based on what the elderly person has said. For example, it creates an interactive picture book based on the content of the conversation. In this way, it is possible to generate interactive digital artworks based on what the elderly person has said.

[0075] The generation unit can make the created memory service experienceable in virtual reality (VR) or augmented reality (AR). For example, the generation unit can make the memory service created by the generation AI experienceable in virtual reality (VR) or augmented reality (AR). For example, it can recreate a memorable place using VR goggles. It can also build a system that allows the memory service created by the generation AI to be experienced in virtual reality (VR) or augmented reality (AR). For example, it can display memorable photos in 3D using an AR app. It can also make the memory service created by the generation AI experienceable in virtual reality (VR) or augmented reality (AR). For example, it can recreate a memorable episode in a VR environment. This makes it possible to experience the created memory service in virtual reality or augmented reality.

[0076] The generation unit can make the created memory service playable by voice through a voice assistant. The generation unit, for example, makes it possible to play a memory service created by the generation AI by voice through a voice assistant. For example, playing a memorable episode using a smart speaker. Also, a system is constructed that can play a memory service created by the generation AI by voice through a voice assistant. For example, playing a specific episode with a voice command. Also, it makes it possible to play a memory service created by the generation AI by voice through a voice assistant. For example, playing a memorable episode by speaking to the voice assistant. In this way, it is possible to play a created memory service by voice through a voice assistant.

[0077] The generation unit can use the emotion estimation function to collect users' emotional reactions to the memory service created by the generation AI and use the collected data to improve the service. The generation unit, for example, uses the emotion estimation function to collect users' emotional reactions to the memory service created by the generation AI and use the collected data to improve the service. For example, it emphasizes parts with a high number of positive emotional reactions. In addition, the emotion estimation function is used to collect users' emotional reactions to the memory service created by the generation AI and use the collected data to improve the service. For example, it improves the service based on the emotional reaction data. In addition, the emotion estimation function is used to collect users' emotional reactions to the memory service created by the generation AI and use the collected data to improve the service. For example, it emphasizes parts with a low number of negative emotional reactions. In this way, users' emotional reactions to the memory service created by the generation AI can be collected and used to improve the service.

[0078] The provision unit can provide the memory service created by the generation AI in a format that matches the emotions of the elderly using an emotion estimation function. For example, the provision unit uses the emotion estimation function to provide the memory service created by the generation AI in a format that matches the emotions of the elderly. For example, it provides a story that emphasizes moving episodes. Furthermore, a system is constructed that uses the emotion estimation function to provide the memory service created by the generation AI in a format that matches the emotions of the elderly. For example, it provides a service that reflects emotions of joy and emotion. Furthermore, it uses the emotion estimation function to provide the memory service created by the generation AI in a format that matches the emotions of the elderly. For example, it selects episodes that elicit specific emotions. This makes it possible to provide the memory service created by the generation AI in a format that matches the emotions of the elderly.

[0079] The provision unit can periodically update the memory service created by the generation AI and add new episodes and information. The provision unit, for example, periodically updates the memory service created by the generation AI and adds new episodes and information. For example, adding a new episode every month. A system is also constructed that periodically updates the memory service created by the generation AI and adds new episodes and information. For example, the service is updated every time new information is provided. The memory service created by the generation AI can also be periodically updated and new episodes and information added. For example, adding a new episode for each season. This makes it possible to periodically update the memory service created by the generation AI and add new episodes and information.

[0080] The providing unit can develop a dedicated app for sharing the memory service created by the generating AI with family and friends. The providing unit, for example, develops a dedicated app for sharing the memory service created by the generating AI with family and friends. For example, sharing memorable episodes through the app. Also, a system is constructed to develop a dedicated app for sharing the memory service created by the generating AI with family and friends. For example, sharing photos and videos using the app. Also, a dedicated app is developed for sharing the memory service created by the generating AI with family and friends. For example, sharing an album of memories through the app. In this way, a dedicated app can be developed for sharing the memory service created by the generating AI with family and friends.

[0081] The providing unit can make the memory service created by the generation AI displayable on a digital photo frame or e-book reader. The providing unit, for example, makes the memory service created by the generation AI displayable on a digital photo frame or e-book reader. For example, it displays memorable photos on a digital photo frame. Also, a system is constructed that can display the memory service created by the generation AI on a digital photo frame or e-book reader. For example, it displays memorable episodes on an e-book reader. Also, it makes the memory service created by the generation AI displayable on a digital photo frame or e-book reader. For example, it displays an album of memories on a digital photo frame. In this way, the memory service created by the generation AI can be displayed on a digital photo frame or e-book reader.

[0082] The providing unit can convert the memory service created by the generating AI into a format that can be shared on social media. The providing unit, for example, converts the memory service created by the generating AI into a format that can be shared on social media. For example, posting a memorable episode on a social media platform. A system is also constructed that converts the memory service created by the generating AI into a format that can be shared on social media. For example, posting memorable photos and videos on a social media platform. The memory service created by the generating AI is also converted into a format that can be shared on social media. For example, posting an album of memories on a social media platform. In this way, the memory service created by the generating AI can be converted into a format that can be shared on social media.

[0083] The provision unit can use the emotion estimation function to monitor the emotional reactions of the elderly to the memory service created by the generation AI and optimize the method of providing the service. The provision unit, for example, uses the emotion estimation function to monitor the emotional reactions of the elderly to the memory service created by the generation AI and optimize the method of providing the service. For example, it provides the service in a format that encourages a lot of positive emotional reactions. Furthermore, the emotion estimation function is used to monitor the emotional reactions of the elderly to the memory service created by the generation AI and build a system that optimizes the method of providing the service. For example, it adjusts the method of providing the service based on the emotional reaction data. Furthermore, the emotion estimation function is used to monitor the emotional reactions of the elderly to the memory service created by the generation AI and optimize the method of providing the service. For example, it provides the service in a format that encourages a little negative emotional reactions. In this way, the emotional reactions of the elderly to the memory service created by the generation AI can be monitored and the method of providing the service can be optimized.

[0084] The generation unit can emphasize specific episodes or add emotional elements based on the emotions of the elderly. For example, the generation AI of the generation unit emphasizes specific episodes or adds emotional elements based on the emotions of the elderly. For example, it emphasizes moving episodes. Furthermore, a system is constructed in which the generation AI emphasizes specific episodes or adds emotional elements based on the emotions of the elderly. For example, a service that reflects emotions of joy or emotion is provided. Furthermore, the generation AI emphasizes specific episodes or adds emotional elements based on the emotions of the elderly. For example, it selects episodes that elicit specific emotions. This makes it possible to emphasize specific episodes or add emotional elements based on the emotions of the elderly.

[0085] The generation unit can provide a customized memory service based on the hobbies and interests of the elderly. For example, the generation AI provides a customized memory service based on the hobbies and interests of the elderly. For example, it highlights episodes related to a hobby. Furthermore, a system is constructed in which the generation AI provides a customized memory service based on the hobbies and interests of the elderly. For example, it combines photos and videos related to a hobby. Furthermore, the generation AI provides a customized memory service based on the hobbies and interests of the elderly. For example, it incorporates episodes related to a hobby into a story. In this way, it is possible to provide a customized memory service based on the hobbies and interests of the elderly.

[0086] The generation unit can incorporate information about the elderly person's family and friends to create a more personalized memory service. For example, the generation AI can incorporate information about the elderly person's family and friends to create a more personalized memory service. For example, it can highlight episodes with family and friends. A system can also be built in which the generation AI incorporates information about the elderly person's family and friends to create a more personalized memory service. For example, it can combine photos and videos of family and friends. The generation AI can also incorporate information about the elderly person's family and friends to create a more personalized memory service. For example, it can incorporate episodes with family and friends into a story. This makes it possible to incorporate information about the elderly person's family and friends to create a more personalized memory service.

[0087] The generation unit can create multiple versions of the elderly memory service with different themes and styles. For example, the generation unit has a generation AI that creates multiple versions of the elderly memory service with different themes and styles. For example, it creates a moving version and a humorous version. Also, a system is constructed in which the generation AI creates multiple versions of the elderly memory service with different themes and styles. For example, it creates a historical theme and a modern theme. Also, the generation AI creates multiple versions of the elderly memory service with different themes and styles. For example, it creates a version for family and a version for friends. In this way, it is possible to create multiple versions of the elderly memory service with different themes and styles.

[0088] The generation unit can provide the elderly memory service in a multimedia format by adding music and narration. For example, the generation AI provides the elderly memory service in a multimedia format by adding music and narration. For example, music is added to a memorable episode. Also, a system is constructed in which the generation AI provides the elderly memory service in a multimedia format by adding music and narration. For example, narration is added to an episode. Also, the generation AI provides the elderly memory service in a multimedia format by adding music and narration. For example, music and narration are added to a memorable episode. This makes it possible to provide the elderly memory service in a multimedia format by adding music and narration.

[0089] The generation unit uses the emotion estimation function to customize based on the emotions of the elderly and provide an optimal memory service. For example, the generation unit uses the emotion estimation function so that the generation AI customizes based on the emotions of the elderly and provides an optimal memory service. For example, it emphasizes moving episodes. Furthermore, a system is constructed in which the generation AI customizes based on the emotions of the elderly and provides an optimal memory service using the emotion estimation function. For example, it provides a service that reflects emotions of joy and emotion. Furthermore, the generation AI uses the emotion estimation function to customize based on the emotions of the elderly and provide an optimal memory service. For example, it selects episodes that elicit specific emotions. This allows customization based on the emotions of the elderly and provides an optimal memory service.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The memory service creation system can further include a health management unit that monitors the health status of the elderly. For example, the health status of the elderly can be estimated from the tone of voice and speaking style of the elderly during a phone call, and the health management unit can prompt the elderly to contact a medical institution if necessary. The health management unit can also periodically check the elderly's health status and provide health advice. Furthermore, the health management unit can adjust the content of the memory service based on the elderly's health status to provide more appropriate services.

[0092] The memory service creation system can further include a hobby estimation unit that asks customized questions based on the elderly person's hobbies and interests. For example, the hobby estimation unit estimates the elderly person's hobbies and interests from what the elderly person says and asks questions related to them. The hobby estimation unit can also customize the content of the memory service based on the elderly person's hobbies to provide a more interesting service. Furthermore, the hobby estimation unit can also suggest events and activities related to the elderly person's hobbies.

[0093] The memory service creation system can further include a community linkage module to strengthen social connections among the elderly. For example, it can plan events that connect elderly people living in the same area. The community linkage module can also introduce local events and activities that the elderly can participate in, promoting social connections. Furthermore, the community linkage module can provide common topics that the elderly can share with other participants.

[0094] The memory service creation system can further include a historical information providing unit to supplement the elderly person's memories. For example, it can provide historical events and background information related to the episodes the elderly person talks about. The historical information providing unit can also provide related photos and videos to further enrich the elderly person's memories. Furthermore, the historical information providing unit can search for and provide related historical materials based on what the elderly person talks about.

[0095] The memory service creation system may further include a visual effect generation unit for visually complementing the elderly person's memories. For example, the visual effect generation unit may generate and provide related visual effects based on the stories told by the elderly person. The visual effect generation unit may also add effects to photos and videos to make the elderly person's memories more vivid. Furthermore, the visual effect generation unit may generate and provide interactive visual content based on what the elderly person is saying.

[0096] The memory service creation system can also estimate the elderly person's emotions and play relaxing music based on the estimated emotions. For example, if the elderly person is feeling stressed, relaxing music can be played in the background. The emotion estimation function can also be used to play environmental sounds that will help the elderly person relax. Furthermore, it can also provide relaxing videos based on the elderly person's emotions.

[0097] The memory service creation system can further estimate the emotions of the elderly person and provide positive feedback based on the estimated emotions. For example, the emotion estimation function can be used to automatically generate positive feedback in response to what the elderly person is saying. The emotion estimation function can also be used to provide words of encouragement in response to what the elderly person is saying. Furthermore, words of gratitude or praise can be provided based on the elderly person's emotions.

[0098] The memory service creation system can also estimate the emotions of the elderly and provide appropriate advice based on the estimated emotions. For example, if an elderly person is in trouble, the emotion estimation function can be used to provide appropriate advice. The emotion estimation function can also be used to suggest specific solutions to what the elderly person is saying. Furthermore, advice on how to relax can be provided based on the elderly person's emotions.

[0099] The memory service creation system can also estimate the emotions of the elderly and suggest appropriate exercises based on the estimated emotions. For example, if the elderly person is feeling stressed, it can suggest exercises that have a relaxing effect. The emotion estimation function can also be used to suggest appropriate stretches and exercises based on what the elderly person is saying. Furthermore, it can also suggest exercises that have a refreshing effect based on the elderly person's emotions.

[0100] The memory service creation system can also estimate the elderly person's emotions and suggest appropriate relaxation techniques based on the estimated emotions. For example, if the elderly person is tense, it can suggest breathing techniques that have a relaxing effect. The emotion estimation function can also be used to suggest appropriate meditation and mindfulness techniques based on what the elderly person is saying. It can also suggest relaxing massage techniques based on the elderly person's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The call reception unit accepts calls from elderly people. For example, when an elderly person calls, the call reception unit automatically answers and prompts them to provide information about their memory. Alternatively, the call reception unit can have an operator answer the call directly and listen to what the elderly person has to say. Step 2: The information collection unit collects information about the elderly person's memory received by the telephone reception unit. For example, an operator listens to what the elderly person says and takes notes of important points. The information collection unit can also automatically convert what the elderly person says into text using voice recognition technology. Step 3: The generation unit generates a memory service based on the memory information collected by the information collection unit. For example, the generation AI analyzes the provided information and generates an individually customized memory service. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to create a story based on a specific episode or an album combining memorable photos. Step 4: The providing unit provides the memory service generated by the generating unit to the elderly. For example, the created story or album may be mailed as a printed matter or sent as digital data by email.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a telephone reception department that receives calls from elderly people; an information collection unit that collects information about the elderly person's memory received by the telephone reception unit; a generation unit that generates a storage service based on the storage information collected by the information collection unit; a providing unit that provides the memory service generated by the generating unit to the elderly person. A system characterized by:

2. The telephone reception unit Analyzes the emotions of what is being said in real time, and the operator asks questions based on those emotions.

2. The system of claim 1.

3. The information collecting unit Using voice recognition technology, information provided by seniors is automatically converted into text and important keywords are extracted.

2. The system of claim 1.

4. The information collecting unit Record what the elderly say and build a voice database for later detailed analysis 2. The system of claim 1.

5. The telephone reception unit Instead of making phone calls, seniors can use video calls to provide visual information at the same time.

2. The system of claim 1.

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