System

Abstract

An object of a system according to an embodiment is to enable a user to easily and automatically generate a life history and receive the life history in various formats.SOLUTION: A system includes an information storage part, a generation part, and a provision part. The information storage part stores information through conversation with the ChatBot. The generation part automatically generates the life history by using a generation AI on the basis of the information stored by the information storage part. The provision unit provides the life history generated by the generation unit in the form of at least one of a story, an album, a moving image, and a sound.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, users had to spend time and money creating autobiographies or photo albums, and they were unable to fully express themselves due to fading memories or a lack of expressive skills.

[0005] The system according to the embodiment aims to enable users to easily and automatically generate life histories and receive them in a variety of formats. [Means for solving the problem]

[0006] The system according to the embodiment includes an information storage unit, a generation unit, and a provision unit. The information storage unit stores information through conversations with a ChatBot. The generation unit automatically generates a life history using a generation AI based on the information stored by the information storage unit. The provision unit provides the life history generated by the generation unit in at least one format of a story, an album, a video, or audio. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily automatically generate life histories and receive them in a variety of formats. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A life history generation system according to an embodiment of the present invention is a system that allows a user to compile their life experiences, events, and thoughts as a "life history" and receive it in various formats such as a story, an album, a video, an audio, etc. This allows the life history generation system to provide the user's life experiences and events in various formats, allowing them to deepen their bonds with family and friends by sharing them.

[0029] A life history generation system according to an embodiment includes an information storage unit, a generation unit, and a providing unit. The information storage unit accumulates information through conversations with a ChatBot. For example, when a user tells the ChatBot, "I went to the park with my family today and had a great time," the information is accumulated. The ChatBot uses natural language processing technology to understand the user's speech and stores it in a database in an appropriate format. The generation unit automatically generates a life history using a generation AI based on the information accumulated by the information storage unit. For example, the generation AI analyzes what the user said and creates a life history in the form of a story, album, video, audio, or the like. The generation AI generates the life history based on what the user said using a text generation AI (e.g., GPT-3) or a multimodal generation AI. The providing unit provides the life history generated by the generation unit in at least one of the following formats: a story, album, video, or audio. For example, the story created by the generation AI can be provided as an e-book, or the album can be displayed on a digital photo frame. Furthermore, by providing the life history as a video or audio file, the user can enjoy the life history visually and aurally. As a result, the life history generation system according to the embodiment can provide the user's life history in various formats.

[0030] The information accumulation unit analyzes the user's tone of voice and speaking style to determine their stress and fatigue levels and promote conversation at the appropriate time. For example, the information accumulation unit uses a ChatBot to analyze the user's tone of voice to determine their stress and fatigue levels. For example, if the user's voice gets lower, it will provide topics to help them relax. It also analyzes the user's speaking style to determine their fatigue level. For example, if the user is speaking slowly, it will ask short questions to make it easier to continue the conversation. The ChatBot also analyzes the user's tone of voice and speaking style in combination to promote conversation at the appropriate time. For example, if the user is speaking in a cheerful voice, it will ask proactive questions. This makes it possible to promote conversation at the appropriate time according to the user's stress and fatigue levels.

[0031] The information accumulation unit can refer to the user's past conversation history and automatically generate questions to draw out related events and memories. For example, the information accumulation unit allows a ChatBot to analyze the user's past conversation history and generate questions to draw out related events. For example, it may revisit a topic about travel that was previously discussed. The information accumulation unit also automatically generates questions to draw out related memories based on the user's past conversation history. For example, it may revisit a topic about family that was previously discussed. The ChatBot also refers to the past conversation history and automatically generates topics that the user is likely to want to talk about. For example, it may revisit a topic about hobbies that was previously discussed. In this way, it is possible to draw out related events and memories based on the user's past conversation history.

[0032] The information accumulation unit can analyze the user's photos and videos and progress the conversation based on them. For example, the information accumulation unit allows ChatBot to analyze photos uploaded by the user and progress the conversation based on their content. For example, it can look at travel photos and ask questions about the places. ChatBot can also analyze videos uploaded by the user and progress the conversation based on their content. For example, it can watch videos of family members and ask questions about events that occurred there. ChatBot can also analyze the user's photos and videos and provide related topics. For example, it can look at photos of pets and ask questions about the pets. In this way, the conversation can progress based on the user's photos and videos.

[0033] The information accumulation unit can develop a multilingual ChatBot that supports conversations in different languages ​​and can accommodate international users. The information accumulation unit, for example, allows the ChatBot to support conversations in different languages ​​and accommodate international users. For example, it can hold conversations in multiple languages, such as English, French, and Chinese. A multilingual ChatBot can also be developed to proceed with the conversation in the language selected by the user. For example, it can generate questions based on the language selected by the user. The ChatBot can also translate conversations in different languages ​​in real time and accommodate international users. For example, it can translate what the user says into another language and continue the conversation. This allows for conversations in different languages ​​to be supported and accommodate international users.

[0034] The generation unit can incorporate external data related to the user's life history to generate a more realistic story. For example, the generation unit's generation AI incorporates news data related to the user's life history to generate a story. For example, it can reflect the news behind an event the user talked about. It can also incorporate weather data related to the user's life history to generate a story. For example, it can reflect the weather on the day of an event the user talked about. It can also incorporate event data related to the user's life history to generate a story. For example, it can reflect the events behind an event the user talked about. In this way, by incorporating external data, it is possible to generate a more realistic story.

[0035] The generation unit can learn the user's past life history and generate a continuously improved story. In the generation unit, for example, the generation AI learns the user's past life history and generates a continuously improved story. For example, a more detailed story is generated based on past data. Also, the generation AI learns the user's past life history and generates a continuously improved story. For example, a story that reflects past events is generated. Also, the generation AI learns the user's past life history and generates a continuously improved story. For example, a more emotional story is generated based on past data. In this way, it is possible to learn the user's past life history and generate a continuously improved story.

[0036] The generation unit can animate the user's life history and generate a visually appealing video. In the generation unit, for example, a generation AI animates the user's life history and generates a visually appealing video. For example, events talked about by the user are recreated in animation. In addition, the user's life history is animated and the generation AI generates a visually appealing video. For example, the user's memories are expressed in animation. In addition, the generation AI animates the user's life history and generates a visually appealing video. For example, events talked about by the user are recreated in animation. In this way, the life history can be animated and a visually appealing video can be generated.

[0037] The generation unit can combine the user's life history with music and sound effects and provide it in audiobook format. For example, the generation AI can combine the user's life history with music and sound effects and provide it in audiobook format. For example, music that matches what the user has said is added. Also, the generation AI can combine the user's life history with music and sound effects and provide it in audiobook format. For example, sound effects that match the user's memories are added. Also, the generation AI can combine the user's life history with music and sound effects and provide it in audiobook format. For example, music that matches what the user has said is added. In this way, the life history can be combined with music and sound effects and provided in audiobook format.

[0038] The providing unit can provide the user's life history in an interactive format, allowing the user to progress through the story at their own pace. The providing unit, for example, provides the user's life history in an interactive format, allowing the user to progress through the story at their own pace. For example, proceeding to the next scene by clicking or tapping. Furthermore, an element that changes the development of the story depending on the options selected by the user is added to the life history provided in an interactive format. For example, a different ending is displayed depending on the option selected by the user. Furthermore, the life history is provided in an interactive format so that the user can progress through the story at their own pace. For example, the user selects a scene that interests them and plays it back. In this way, the life history can be provided in an interactive format that allows the user to progress through the story at their own pace.

[0039] The providing unit can provide the life history in a VR format, allowing the user to experience their past in a virtual space. The providing unit, for example, provides the life history in a VR format, allowing the user to experience their past in a virtual space. For example, events talked about by the user are recreated in VR. Furthermore, an element that allows the user to move freely within the virtual space is added to the life history provided in VR format. For example, the user can walk around the locations of past events. Furthermore, the life history is provided in VR format so that the user can experience their past in the virtual space. For example, events talked about by the user are recreated from a 360-degree perspective. In this way, the life history can be provided in a VR format that allows the user to experience their past in a virtual space.

[0040] The providing unit can link the life history with an SNS platform to enable users to easily share it. The providing unit, for example, links the life history with an SNS platform to enable users to easily share it. For example, the generated life history can be posted to an SNS with one click. The providing unit can also link with an SNS platform to provide a function that enables users to share their life history with friends and family. For example, the generated life history can be sent using a messenger app. The providing unit can also link the life history with an SNS platform to enable users to easily share it. For example, the generated life history can be published using the story function of the SNS. In this way, the providing unit can link the life history with an SNS platform to enable users to easily share it.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The information storage unit can collect the user's health data and add health information to the life history. For example, it can record the user's number of steps and heart rate to reflect the user's daily health condition. In addition, by the user inputting information about diet and exercise, the health management history can be incorporated into the life history. Furthermore, it is possible to provide health advice and reminders based on the user's health data. This allows the user's health condition to be reflected in the life history, providing a more comprehensive record.

[0043] The information accumulation unit can provide information on related events and activities based on the user's hobbies and interests. For example, if the user is interested in music, information on nearby concerts and music events can be provided. If the user is interested in sports, information on local sporting events and matches can be provided. Furthermore, it is possible to provide information on related online events and workshops based on the user's hobbies and interests. This allows the user to receive information on events and activities that match their hobbies and interests, further enriching their life history.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The information storage unit accumulates information through conversations with ChatBot. For example, if a user tells ChatBot, "I had a great time at the park with my family today," that information is accumulated. ChatBot uses natural language processing technology to understand what the user is saying and stores it in a database in an appropriate format. Step 2: The generation unit uses a generation AI to automatically generate a life history based on the information accumulated by the information accumulation unit. For example, the generation AI analyzes what the user has said and creates a life history in the form of a story, album, video, audio, or other format. The generation AI uses a text generation AI (e.g., GPT-3) or a multimodal generation AI to generate a life history based on what the user has said. Step 3: The providing unit provides the life history generated by the generating unit in at least one format of a story, an album, a video, or audio. For example, the story created by the generating AI can be provided as an e-book, or the album can be displayed on a digital photo frame. Also, by providing it as a video or audio file, the user can enjoy the life history visually or aurally.

[0046] (Example 2) A life history generation system according to an embodiment of the present invention is a system that allows a user to compile their life experiences, events, and thoughts as a "life history" and receive it in various formats such as a story, an album, a video, an audio, etc. This allows the life history generation system to provide the user's life experiences and events in various formats, allowing them to deepen their bonds with family and friends by sharing them.

[0047] A life history generation system according to an embodiment includes an information storage unit, a generation unit, and a providing unit. The information storage unit accumulates information through conversations with a ChatBot. For example, when a user tells the ChatBot, "I went to the park with my family today and had a great time," the information is accumulated. The ChatBot uses natural language processing technology to understand the user's speech and stores it in a database in an appropriate format. The generation unit automatically generates a life history using a generation AI based on the information accumulated by the information storage unit. For example, the generation AI analyzes what the user said and creates a life history in the form of a story, album, video, audio, or the like. The generation AI generates the life history based on what the user said using a text generation AI (e.g., GPT-3) or a multimodal generation AI. The providing unit provides the life history generated by the generation unit in at least one of the following formats: a story, album, video, or audio. For example, the story created by the generation AI can be provided as an e-book, or the album can be displayed on a digital photo frame. Furthermore, by providing the life history as a video or audio file, the user can enjoy the life history visually and aurally. As a result, the life history generation system according to the embodiment can provide the user's life history in various formats.

[0048] The information accumulation unit estimates the user's emotions in real time and asks questions that correspond to those emotions, thereby extracting deeper information. For example, the information accumulation unit uses ChatBot to analyze the user's facial expressions and voice and estimate emotions in real time. For example, if the user seems to be having a good time, it will ask questions to get more details about the event. The ChatBot also generates appropriate questions based on the user's emotions. For example, if the user seems sad, it will ask questions to find out the cause of that emotion. The ChatBot also detects changes in the user's emotions and asks follow-up questions that correspond to those emotions. For example, if the user is excited, it will ask questions to get more details about the reason for their excitement. In this way, by asking questions that correspond to the user's emotions, it is possible to extract deeper information.

[0049] The information accumulation unit analyzes the user's tone of voice and speaking style to determine their stress and fatigue levels and promote conversation at the appropriate time. For example, the information accumulation unit uses a ChatBot to analyze the user's tone of voice to determine their stress and fatigue levels. For example, if the user's voice gets lower, it will provide topics to help them relax. It also analyzes the user's speaking style to determine their fatigue level. For example, if the user is speaking slowly, it will ask short questions to make it easier to continue the conversation. The ChatBot also analyzes the user's tone of voice and speaking style in combination to promote conversation at the appropriate time. For example, if the user is speaking in a cheerful voice, it will ask proactive questions. This makes it possible to promote conversation at the appropriate time according to the user's stress and fatigue levels.

[0050] The information accumulation unit can refer to the user's past conversation history and automatically generate questions to draw out related events and memories. For example, the information accumulation unit allows a ChatBot to analyze the user's past conversation history and generate questions to draw out related events. For example, it may revisit a topic about travel that was previously discussed. The information accumulation unit also automatically generates questions to draw out related memories based on the user's past conversation history. For example, it may revisit a topic about family that was previously discussed. The ChatBot also refers to the past conversation history and automatically generates topics that the user is likely to want to talk about. For example, it may revisit a topic about hobbies that was previously discussed. In this way, it is possible to draw out related events and memories based on the user's past conversation history.

[0051] The information accumulation unit can analyze the user's photos and videos and progress the conversation based on them. For example, the information accumulation unit allows ChatBot to analyze photos uploaded by the user and progress the conversation based on their content. For example, it can look at travel photos and ask questions about the places. ChatBot can also analyze videos uploaded by the user and progress the conversation based on their content. For example, it can watch videos of family members and ask questions about events that occurred there. ChatBot can also analyze the user's photos and videos and provide related topics. For example, it can look at photos of pets and ask questions about the pets. In this way, the conversation can progress based on the user's photos and videos.

[0052] The information accumulation unit can develop a multilingual ChatBot that supports conversations in different languages ​​and can accommodate international users. The information accumulation unit, for example, allows the ChatBot to support conversations in different languages ​​and accommodate international users. For example, it can hold conversations in multiple languages, such as English, French, and Chinese. A multilingual ChatBot can also be developed to proceed with the conversation in the language selected by the user. For example, it can generate questions based on the language selected by the user. The ChatBot can also translate conversations in different languages ​​in real time and accommodate international users. For example, it can translate what the user says into another language and continue the conversation. This allows for conversations in different languages ​​to be supported and accommodate international users.

[0053] The information accumulation unit can use the emotion estimation function to identify topics to which the user responds particularly emotionally and promote conversations based on those topics. For example, the information accumulation unit uses the emotion estimation function in a ChatBot to identify topics to which the user responds particularly emotionally. For example, it finds topics that the user talks about with excitement. It also uses the emotion estimation function to promote conversations based on topics to which the user responds emotionally. For example, it asks the user in detail about an event that moved them. It also uses the emotion estimation function to identify topics to which the user responds particularly emotionally and generates questions based on those topics. For example, it brings up topics that the user enjoys talking about. This makes it possible to promote conversations based on topics to which the user responds particularly emotionally.

[0054] The generation unit can analyze the user's emotions and perform storytelling according to changes in emotions. For example, the generation AI in the generation unit analyzes the user's emotions and performs storytelling according to changes in emotions. For example, if the user talks about a sad event, it generates a story that reflects those emotions. It can also analyze the user's emotional changes and perform storytelling that matches the peak of the emotion. For example, if the user talks about an exciting event, it generates a story that emphasizes that emotion. It can also analyze the user's emotions and perform storytelling according to changes in emotions. For example, if the user talks about a moving event, it generates a story that reflects those emotions. This makes it possible to perform storytelling according to changes in the user's emotions.

[0055] The generation unit can incorporate external data related to the user's life history to generate a more realistic story. For example, the generation unit's generation AI incorporates news data related to the user's life history to generate a story. For example, it can reflect the news behind an event the user talked about. It can also incorporate weather data related to the user's life history to generate a story. For example, it can reflect the weather on the day of an event the user talked about. It can also incorporate event data related to the user's life history to generate a story. For example, it can reflect the events behind an event the user talked about. In this way, by incorporating external data, it is possible to generate a more realistic story.

[0056] The generation unit can learn the user's past life history and generate a continuously improved story. In the generation unit, for example, the generation AI learns the user's past life history and generates a continuously improved story. For example, a more detailed story is generated based on past data. Also, the generation AI learns the user's past life history and generates a continuously improved story. For example, a story that reflects past events is generated. Also, the generation AI learns the user's past life history and generates a continuously improved story. For example, a more emotional story is generated based on past data. In this way, it is possible to learn the user's past life history and generate a continuously improved story.

[0057] The generation unit can animate the user's life history and generate a visually appealing video. In the generation unit, for example, a generation AI animates the user's life history and generates a visually appealing video. For example, events talked about by the user are recreated in animation. In addition, the user's life history is animated and the generation AI generates a visually appealing video. For example, the user's memories are expressed in animation. In addition, the generation AI animates the user's life history and generates a visually appealing video. For example, events talked about by the user are recreated in animation. In this way, the life history can be animated and a visually appealing video can be generated.

[0058] The generation unit can combine the user's life history with music and sound effects and provide it in audiobook format. For example, the generation AI can combine the user's life history with music and sound effects and provide it in audiobook format. For example, music that matches what the user has said is added. Also, the generation AI can combine the user's life history with music and sound effects and provide it in audiobook format. For example, sound effects that match the user's memories are added. Also, the generation AI can combine the user's life history with music and sound effects and provide it in audiobook format. For example, music that matches what the user has said is added. In this way, the life history can be combined with music and sound effects and provided in audiobook format.

[0059] The generation unit can use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. In the generation unit, for example, the generation AI uses the emotion estimation function to identify the scene that moves the user most. For example, it emphasizes a moving event that the user talked about. In addition, the generation unit can use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. For example, it can construct a story around an event that moved the user. In addition, the generation AI can use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. For example, it can emphasize a moving event that the user talked about. In this way, it is possible to generate a life history that emphasizes the scene that moves the user most.

[0060] The providing unit can provide a life history that combines music and sound effects to elicit specific emotions based on the user's emotions. The providing unit, for example, provides a life history that combines music to elicit specific emotions based on the user's emotions. For example, moving music is added to moving scenes. Also, a life history that combines sound effects to elicit specific emotions is provided. For example, happy sound effects are added to happy scenes. Also, a life history that combines music and sound effects to elicit specific emotions based on the user's emotions is provided. For example, moving music is added to moving scenes. In this way, a life history that combines music and sound effects to elicit specific emotions can be provided.

[0061] The providing unit can provide the user's life history in an interactive format, allowing the user to progress through the story at their own pace. The providing unit, for example, provides the user's life history in an interactive format, allowing the user to progress through the story at their own pace. For example, proceeding to the next scene by clicking or tapping. Furthermore, an element that changes the development of the story depending on the options selected by the user is added to the life history provided in an interactive format. For example, a different ending is displayed depending on the option selected by the user. Furthermore, the life history is provided in an interactive format so that the user can progress through the story at their own pace. For example, the user selects a scene that interests them and plays it back. In this way, the life history can be provided in an interactive format that allows the user to progress through the story at their own pace.

[0062] The providing unit can provide the life history in a VR format, allowing the user to experience their past in a virtual space. The providing unit, for example, provides the life history in a VR format, allowing the user to experience their past in a virtual space. For example, events talked about by the user are recreated in VR. Furthermore, an element that allows the user to move freely within the virtual space is added to the life history provided in VR format. For example, the user can walk around the locations of past events. Furthermore, the life history is provided in VR format so that the user can experience their past in the virtual space. For example, events talked about by the user are recreated from a 360-degree perspective. In this way, the life history can be provided in a VR format that allows the user to experience their past in a virtual space.

[0063] The providing unit can link the life history with an SNS platform to enable users to easily share it. The providing unit, for example, links the life history with an SNS platform to enable users to easily share it. For example, the generated life history can be posted to an SNS with one click. The providing unit can also link with an SNS platform to provide a function that enables users to share their life history with friends and family. For example, the generated life history can be sent using a messenger app. The providing unit can also link the life history with an SNS platform to enable users to easily share it. For example, the generated life history can be published using the story function of the SNS. In this way, the providing unit can link the life history with an SNS platform to enable users to easily share it.

[0064] The providing unit can use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. The providing unit can, for example, use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. For example, by emphasizing a moving event that the user talked about. Furthermore, the generation AI can use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. For example, by constructing a story centered around an event that moved the user. Furthermore, the providing unit can use the emotion estimation function to identify the scene that moves the user most and generate a life history that emphasizes that scene. For example, by emphasizing a moving event that the user talked about. This makes it possible to generate a life history that emphasizes the scene that moves the user most.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The information storage unit can collect the user's health data and add health information to the life history. For example, it can record the user's number of steps and heart rate to reflect the user's daily health condition. In addition, by the user inputting information about diet and exercise, the health management history can be incorporated into the life history. Furthermore, it is possible to provide health advice and reminders based on the user's health data. This allows the user's health condition to be reflected in the life history, providing a more comprehensive record.

[0067] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0068] The information accumulation unit can provide information on related events and activities based on the user's hobbies and interests. For example, if the user is interested in music, information on nearby concerts and music events can be provided. If the user is interested in sports, information on local sporting events and matches can be provided. Furthermore, it is possible to provide information on related online events and workshops based on the user's hobbies and interests. This allows the user to receive information on events and activities that match their hobbies and interests, further enriching their life history.

[0069] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0070] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0071] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0072] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0073] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0074] The information storage unit can estimate the user's emotions and provide music and environmental sounds that help the user relax based on the estimated emotions. For example, if the user is feeling stressed, music with a relaxing effect can be played. Also, if the user wants to concentrate, environmental sounds that will help improve concentration can be provided. Furthermore, it is also possible to automatically select and play appropriate music and environmental sounds according to the user's emotions. This makes it possible to provide music and environmental sounds that match the user's emotions and support relaxation and concentration.

[0075] The processing flow of the second embodiment will be briefly explained below.

[0076] Step 1: The information storage unit accumulates information through conversations with ChatBot. For example, if a user tells ChatBot, "I had a great time at the park with my family today," that information is accumulated. ChatBot uses natural language processing technology to understand what the user is saying and stores it in a database in an appropriate format. Step 2: The generation unit uses a generation AI to automatically generate a life history based on the information accumulated by the information accumulation unit. For example, the generation AI analyzes what the user has said and creates a life history in the form of a story, album, video, audio, or other format. The generation AI uses a text generation AI (e.g., GPT-3) or a multimodal generation AI to generate a life history based on what the user has said. Step 3: The providing unit provides the life history generated by the generating unit in at least one format of a story, an album, a video, or audio. For example, the story created by the generating AI can be provided as an e-book, or the album can be displayed on a digital photo frame. Also, by providing it as a video or audio file, the user can enjoy the life history visually or aurally.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0096] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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."

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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]

[0144] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information storage unit that accumulates information through conversations with ChatBot, a generation unit that automatically generates a life history using a generation AI based on the information accumulated by the information accumulation unit; a providing unit that provides the life history generated by the generating unit in at least one format of a story, an album, a video, or an audio. A system characterized by:

2. The information storage unit Extract deeper information by estimating the user's emotions in real time and asking questions based on those emotions.

2. The system of claim 1.

3. The information storage unit Analyzes the user's tone of voice and speaking style, determines stress and fatigue levels, and promotes the conversation at the appropriate time 2. The system of claim 1.

4. The information storage unit It references the user's past conversation history and automatically generates questions to elicit relevant events and memories.

2. The system of claim 1.

5. The information storage unit Analyze the user's photos and videos and progress the conversation based on them 2. The system of claim 1.

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