System

Abstract

An object of the system according to the embodiment is to propose a unique experience on the basis of a user's interest or preference and store the unique experience as a digital time capsule.SOLUTION: A system includes a learning unit, a proposal unit, and a storage unit. The learning unit learns the user's interests, preferences, and dreams. The proposal unit proposes a unique experience or activity on the basis of the information learned by the learning unit. The storage unit stores the experience or activity proposed by the proposal unit as a digital time capsule.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 technologies have had the problem of not being able to adequately propose and preserve unique experiences based on a user's interests and preferences.

[0005] The system according to the embodiment aims to propose unique experiences based on the user's interests and preferences and preserve them as a digital time capsule. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a suggestion unit, and a storage unit. The learning unit learns a user's interests, preferences, and dreams. The suggestion unit suggests unique experiences and activities based on the information learned by the learning unit. The storage unit stores the experiences and activities suggested by the suggestion unit as a digital time capsule. [Effects of the Invention]

[0007] An embodiment of the system can suggest unique experiences based on a user's interests and preferences and preserve them as a digital time capsule. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The Memory Capsule AI system according to an embodiment of the present invention is a system that makes the most of precious moments with family and friends by using a generative AI to learn about the interests, preferences, and dreams of the user and their loved ones, suggesting unique experiences and activities based on that information, and preserving these experiences as a digital time capsule. This allows the Memory Capsule AI system to make the most of precious moments with family and friends and create unforgettable memories.

[0029] The memory capsule AI system according to the embodiment includes a learning unit, a suggestion unit, and a storage unit. The learning unit learns the user's interests, preferences, and dreams. For example, when a user inputs a prompt such as, "Our family loves nature and wants to enjoy outdoor activities," the generation AI learns based on that information. The learning unit can also analyze the user's behavioral history and social media data to extract the user's interests and preferences. The suggestion unit suggests unique experiences and activities based on the information learned by the learning unit. For example, the generation AI may make specific suggestions such as, "Go camping with the family this weekend," "Cook together," or "Start a new hobby." The suggestion unit can also suggest trips, events, and hobby activities based on the user's preferences. The storage unit stores the experiences and activities suggested by the suggestion unit as a digital time capsule. For example, photos and videos of family camping trips and texts detailing camping memories may be stored. The storage unit can also securely store the digital time capsule using cloud storage and encryption technology. This allows the memory capsule AI system according to the embodiment to suggest unique experiences and activities based on the user's interests, preferences, and dreams, and store them as a digital time capsule.

[0030] The learning unit can analyze a user's past SNS posts and message history to create a more detailed profile. For example, the learning unit uses a generation AI to analyze a user's past SNS posts and extract the user's interests and preferences from the content of the posts. For example, it can identify areas of interest based on the themes the user frequently posts on and the hashtags they use. The learning unit can also analyze message history to understand the user's interests and concerns. For example, it can analyze chat history and email history to extract topics that the user frequently discusses. This makes it possible to analyze a user's past SNS posts and message history and create a detailed profile.

[0031] The learning unit can analyze the user's past goal achievement history and unachieved goals to support future goal setting. For example, the learning unit uses a generation AI to analyze the user's past goal achievement history and learn patterns of achieved goals. For example, the learning unit can support future goal setting based on the types and frequency of goals the user has achieved in the past. The learning unit can also analyze unachieved goals to understand what goals the user is challenging. For example, it can analyze incomplete tasks and unachieved projects to identify the user's goal achievement trends. This makes it possible to analyze the user's past goal achievement history and unachieved goals and support future goal setting.

[0032] The learning unit can analyze a user's voice input and video input, and also create a profile from visual and auditory information. For example, the learning unit uses a generative AI to analyze a user's voice input and learn their interests and preferences from the tone and content of the voice. For example, it identifies topics that the user is interested in talking about and reflects that information in the profile. The learning unit can also analyze video input and learn the user's interests and preferences from their facial expressions and movements. For example, it analyzes how the user is enjoying themselves and reflects that information in the profile. This makes it possible to analyze a user's voice input and video input, and also create a profile from visual and auditory information.

[0033] The learning unit can learn about the interests of users from different cultural spheres and regions and make suggestions from a global perspective. For example, the generation AI of the learning unit learns about the interests of users from different cultural spheres and regions and makes suggestions from a global perspective based on that information. For example, it suggests activities based on the culture and customs of each region. The learning unit can also analyze the behavioral patterns of users from different cultural spheres and regions and make suggestions from a global perspective. For example, it suggests activities that users from different regions commonly enjoy. This makes it possible to learn about the interests of users from different cultural spheres and regions and make suggestions from a global perspective.

[0034] The suggestion unit can take into account the user's past experience history and make new suggestions based on successful experiences in the past. For example, the suggestion unit uses a generation AI to analyze the user's past experience history and make new suggestions based on successful experiences. For example, the suggestion unit can suggest similar experiences based on activities the user has enjoyed in the past. The suggestion unit can also take into account the user's past experience history and make suggestions from a different perspective. For example, the suggestion unit can suggest new activities based on successful experiences in the past. This makes it possible to take into account the user's past experience history and make new suggestions based on successful experiences in the past.

[0035] The suggestion unit can select an appropriate activity by taking into account the user's health condition and fitness level. For example, the suggestion unit uses a generation AI to analyze the user's health condition and fitness level and suggest an appropriate activity based on that. For example, it can suggest exercise or recreation that suits the user's physical strength and health condition. The suggestion unit can also suggest activities for rehabilitation or health promotion by taking into account the user's health condition and fitness level. For example, it can suggest light exercise or stretching. This makes it possible to select an appropriate activity by taking into account the user's health condition and fitness level.

[0036] The suggestion unit can include activities in which the user's pets or animals as family members can also participate. For example, the suggestion unit uses a generation AI to suggest activities in which the user's pets or animals as family members can also participate. For example, the suggestion unit can suggest outdoor activities or indoor games that can be enjoyed with pets. The suggestion unit can also suggest appropriate activities taking into account the characteristics of the user's pets or animals. For example, the suggestion unit can suggest walking routes with dogs or games to play with cats. This makes it possible to include activities in which the user's pets or animals as family members can also participate.

[0037] The suggestion unit can suggest experiences according to different seasons and weather conditions, and provide activities that can be enjoyed throughout the year. For example, the generation AI in the suggestion unit suggests experiences according to different seasons and weather conditions. For example, it suggests beach activities in the summer, and skiing and snowboarding in the winter. The suggestion unit can also suggest activities that can be enjoyed indoors and outdoors depending on the weather conditions. For example, it suggests games and activities that can be enjoyed indoors on rainy days. In this way, it is possible to suggest experiences according to different seasons and weather conditions, and provide activities that can be enjoyed throughout the year.

[0038] The storage unit can include a user's voice message or video message in the digital time capsule to create a more personal record. The storage unit can, for example, include a user's voice message in the digital time capsule to create a more personal record. For example, a message to family or friends can be recorded and saved. The storage unit can also include a video message to record the user's facial expressions and voice. For example, a message for a special event or anniversary can be recorded and saved. This allows the user's voice message or video message to be included in the digital time capsule to create a more personal record.

[0039] The storage unit can include the user's biometric information in the digital time capsule and record the emotional state during the experience. The storage unit can include, for example, the user's biometric information such as heart rate and body temperature in the digital time capsule and record the emotional state during the experience. For example, the heart rate during a special event can be recorded and stored. The storage unit can also analyze the user's biometric information and evaluate the emotional state during the experience. For example, the stress level and relaxation level can be analyzed and recorded. This allows the user's biometric information to be included in the digital time capsule and record the emotional state during the experience.

[0040] The storage unit can create a detailed record of the user's experience by including geographic information and weather information of the places the user visited in the digital time capsule. The storage unit, for example, can create a detailed record of the user's experience by including geographic information of the places the user visited in the digital time capsule. For example, the storage unit can record and store the places visited based on GPS data. The storage unit can also include weather information of the places the user visited. For example, the storage unit can record and store the weather and temperature on a specific day. This allows the user to create a detailed record of the user's experience by including geographic information and weather information of the places visited in the digital time capsule.

[0041] The storage unit can store data in different formats so that it can be re-experienced with future technology. For example, the storage unit can include 3D models in a digital time capsule so that it can be re-experienced with future technology. For example, a 3D model of a particular place or object can be created and stored. The storage unit can also include VR content so that a user can re-live past experiences in virtual reality. For example, a particular event or occurrence can be recreated and stored in VR so that it can be stored in different formats so that it can be re-experienced with future technology.

[0042] The storage unit can compare past experiences with the current situation to look back at a certain point in the future, and visualize the user's growth and change. For example, the generative AI can compare past experiences with the current situation and visualize the user's growth and change. For example, it can display past photos and videos side by side with current photos and videos. The storage unit can also visualize the user's growth and change in graphs and charts. For example, it can visually display skill improvements and knowledge gains. This makes it possible to compare past experiences with the current situation and visualize the user's growth and change.

[0043] The storage unit can suggest new goal setting and plans based on past experiences to look back on at some point in the future. For example, the generation AI in the storage unit suggests new goal setting and plans based on past experiences. For example, the next goal is set based on past successful experiences. The storage unit can also analyze the user's past experiences and assist in making future plans. For example, a new project plan is made based on the results of a past project. This makes it possible to suggest new goal setting and plans based on past experiences.

[0044] The storage unit can provide interactive storytelling based on past experiences to be looked back on at some point in the future. For example, the storage unit provides interactive storytelling based on past experiences using a generative AI. For example, a user selects a past memory, and a story based on that memory is generated. The storage unit can also develop a story in accordance with the user's selection. For example, a different story is generated based on an experience selected by the user. This makes it possible to provide interactive storytelling based on past experiences.

[0045] The storage unit can enable recollection on different devices in order to look back at a certain point in the future. The storage unit, for example, enables the generation AI to look back on different devices. For example, it provides an app that allows users to look back on past memories on a smartphone or tablet. The storage unit can also recreate past experiences in virtual reality using a VR headset. For example, specific events or occurrences can be recreated and reminisced about in VR. This allows recollection on different devices.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The storage unit can organize events experienced by the user in chronological order, making it visually easy to understand when looking back on past experiences. For example, the storage unit can display experiences in a calendar format, and clicking on a specific date will display details for that day. The storage unit can also allow the user to select a specific period and display all experiences within that period. This allows the user to easily look back on past experiences.

[0048] The storage unit can display events experienced by the user on a map, allowing the user to visually confirm the places visited. For example, travel destinations and event venues can be displayed as pins on the map, and clicking on them displays details of the experiences at those locations. The storage unit can also provide surrounding information for the places visited by the user, allowing the user to look back on past experiences geographically.

[0049] The storage unit can organize the events experienced by the user by category and display experiences belonging to a specific category together. For example, it can display experiences by category such as travel, events, and hobbies, and when the user selects a category of interest, details of experiences belonging to that category are displayed. The storage unit also allows the user to add new categories and customize and organize experiences. This allows the user to efficiently organize and review their experiences.

[0050] The storage unit can store the events the user has experienced not only as photos and videos, but also as text and voice memos. For example, the storage unit can record and store the user's thoughts and memories about the events they have experienced in text. The storage unit can also record and store voice memos about the events they have experienced. This allows the user to record and look back on their experiences in various formats.

[0051] The storage unit may provide a function for the user to share the events that the user has experienced with other users. For example, the storage unit may select a specific experience and generate a link to share with family and friends. The storage unit may also customize privacy settings when the user shares an experience. For example, the storage unit may set the experience to be accessible only by specific people. This allows the user to safely share their experiences with other users.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The learning unit learns the user's interests, preferences, and dreams. For example, when a user inputs a prompt such as, "Our family loves nature and enjoys outdoor activities," the generative AI learns based on that information. The learning unit can also analyze the user's behavioral history and social media data to extract interests and preferences. Step 2: The suggestion unit suggests unique experiences and activities based on the information learned by the learning unit. For example, the generation AI makes specific suggestions such as "go camping with the family this weekend," "cook together," or "take up a new hobby." The suggestion unit can also suggest trips, events, and hobby activities based on the user's preferences. Step 3: The storage unit stores the experiences and activities suggested by the suggestion unit as a digital time capsule. For example, photos and videos from a family camping trip, as well as text describing camping memories, may be stored. The storage unit can also securely store the digital time capsule using cloud storage and encryption technology.

[0054] (Example 2) The Memory Capsule AI system according to an embodiment of the present invention is a system that makes the most of precious moments with family and friends by using a generative AI to learn about the interests, preferences, and dreams of the user and their loved ones, suggesting unique experiences and activities based on that information, and preserving these experiences as a digital time capsule. This allows the Memory Capsule AI system to make the most of precious moments with family and friends and create unforgettable memories.

[0055] The memory capsule AI system according to the embodiment includes a learning unit, a suggestion unit, and a storage unit. The learning unit learns the user's interests, preferences, and dreams. For example, when a user inputs a prompt such as, "Our family loves nature and wants to enjoy outdoor activities," the generation AI learns based on that information. The learning unit can also analyze the user's behavioral history and social media data to extract the user's interests and preferences. The suggestion unit suggests unique experiences and activities based on the information learned by the learning unit. For example, the generation AI may make specific suggestions such as, "Go camping with the family this weekend," "Cook together," or "Start a new hobby." The suggestion unit can also suggest trips, events, and hobby activities based on the user's preferences. The storage unit stores the experiences and activities suggested by the suggestion unit as a digital time capsule. For example, photos and videos of family camping trips and texts detailing camping memories may be stored. The storage unit can also securely store the digital time capsule using cloud storage and encryption technology. This allows the memory capsule AI system according to the embodiment to suggest unique experiences and activities based on the user's interests, preferences, and dreams, and store them as a digital time capsule.

[0056] The learning unit can analyze a user's past SNS posts and message history to create a more detailed profile. For example, the learning unit uses a generation AI to analyze a user's past SNS posts and extract the user's interests and preferences from the content of the posts. For example, it can identify areas of interest based on the themes the user frequently posts on and the hashtags they use. The learning unit can also analyze message history to understand the user's interests and concerns. For example, it can analyze chat history and email history to extract topics that the user frequently discusses. This makes it possible to analyze a user's past SNS posts and message history and create a detailed profile.

[0057] The learning unit can analyze the user's past goal achievement history and unachieved goals to support future goal setting. For example, the learning unit uses a generation AI to analyze the user's past goal achievement history and learn patterns of achieved goals. For example, the learning unit can support future goal setting based on the types and frequency of goals the user has achieved in the past. The learning unit can also analyze unachieved goals to understand what goals the user is challenging. For example, it can analyze incomplete tasks and unachieved projects to identify the user's goal achievement trends. This makes it possible to analyze the user's past goal achievement history and unachieved goals and support future goal setting.

[0058] The learning unit uses the emotion estimation function to analyze the emotional nuances of the prompts entered by the user and can make more personalized suggestions. The learning unit, for example, uses the emotion estimation function to analyze the emotional nuances of the prompts entered by the user. For example, a positive suggestion is made for a prompt that makes the user feel happy or excited. The learning unit can also analyze the emotional nuances of the prompts entered by the user and make suggestions to reduce negative emotions. For example, if the user is feeling stressed, the learning unit can suggest a relaxing activity. This allows the emotional nuances of the prompts entered by the user to be analyzed and more personalized suggestions to be made.

[0059] The learning unit can analyze a user's voice input and video input, and also create a profile from visual and auditory information. For example, the learning unit uses a generative AI to analyze a user's voice input and learn their interests and preferences from the tone and content of the voice. For example, it identifies topics that the user is interested in talking about and reflects that information in the profile. The learning unit can also analyze video input and learn the user's interests and preferences from their facial expressions and movements. For example, it analyzes how the user is enjoying themselves and reflects that information in the profile. This makes it possible to analyze a user's voice input and video input, and also create a profile from visual and auditory information.

[0060] The learning unit can learn about the interests of users from different cultural spheres and regions and make suggestions from a global perspective. For example, the generation AI of the learning unit learns about the interests of users from different cultural spheres and regions and makes suggestions from a global perspective based on that information. For example, it suggests activities based on the culture and customs of each region. The learning unit can also analyze the behavioral patterns of users from different cultural spheres and regions and make suggestions from a global perspective. For example, it suggests activities that users from different regions commonly enjoy. This makes it possible to learn about the interests of users from different cultural spheres and regions and make suggestions from a global perspective.

[0061] The suggestion unit can take into account the user's past experience history and make new suggestions based on successful experiences in the past. For example, the suggestion unit uses a generation AI to analyze the user's past experience history and make new suggestions based on successful experiences. For example, the suggestion unit can suggest similar experiences based on activities the user has enjoyed in the past. The suggestion unit can also take into account the user's past experience history and make suggestions from a different perspective. For example, the suggestion unit can suggest new activities based on successful experiences in the past. This makes it possible to take into account the user's past experience history and make new suggestions based on successful experiences in the past.

[0062] The suggestion unit can select an appropriate activity by taking into account the user's health condition and fitness level. For example, the suggestion unit uses a generation AI to analyze the user's health condition and fitness level and suggest an appropriate activity based on that. For example, it can suggest exercise or recreation that suits the user's physical strength and health condition. The suggestion unit can also suggest activities for rehabilitation or health promotion by taking into account the user's health condition and fitness level. For example, it can suggest light exercise or stretching. This makes it possible to select an appropriate activity by taking into account the user's health condition and fitness level.

[0063] The suggestion unit can use the emotion estimation function to predict the emotional impact that a proposed experience will have on the user and make suggestions that will elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to predict the emotional impact that a proposed experience will have on the user. For example, the suggestion unit can preferentially suggest experiences that will make the user feel joyful or excited. The suggestion unit can also use the emotion estimation function to take into account the user's emotional state and make suggestions that will reduce negative emotions. For example, if the user is feeling stressed, the suggestion unit can suggest activities that will help them relax. This makes it possible to predict the emotional impact that a proposed experience will have on the user and make suggestions that will elicit positive emotions.

[0064] The suggestion unit can include activities in which the user's pets or animals as family members can also participate. For example, the suggestion unit uses a generation AI to suggest activities in which the user's pets or animals as family members can also participate. For example, the suggestion unit can suggest outdoor activities or indoor games that can be enjoyed with pets. The suggestion unit can also suggest appropriate activities taking into account the characteristics of the user's pets or animals. For example, the suggestion unit can suggest walking routes with dogs or games to play with cats. This makes it possible to include activities in which the user's pets or animals as family members can also participate.

[0065] The suggestion unit can suggest experiences according to different seasons and weather conditions, and provide activities that can be enjoyed throughout the year. For example, the generation AI in the suggestion unit suggests experiences according to different seasons and weather conditions. For example, it suggests beach activities in the summer, and skiing and snowboarding in the winter. The suggestion unit can also suggest activities that can be enjoyed indoors and outdoors depending on the weather conditions. For example, it suggests games and activities that can be enjoyed indoors on rainy days. In this way, it is possible to suggest experiences according to different seasons and weather conditions, and provide activities that can be enjoyed throughout the year.

[0066] The storage unit can include a user's voice message or video message in the digital time capsule to create a more personal record. The storage unit can, for example, include a user's voice message in the digital time capsule to create a more personal record. For example, a message to family or friends can be recorded and saved. The storage unit can also include a video message to record the user's facial expressions and voice. For example, a message for a special event or anniversary can be recorded and saved. This allows the user's voice message or video message to be included in the digital time capsule to create a more personal record.

[0067] The storage unit can include the user's biometric information in the digital time capsule and record the emotional state during the experience. The storage unit can include, for example, the user's biometric information such as heart rate and body temperature in the digital time capsule and record the emotional state during the experience. For example, the heart rate during a special event can be recorded and stored. The storage unit can also analyze the user's biometric information and evaluate the emotional state during the experience. For example, the stress level and relaxation level can be analyzed and recorded. This allows the user's biometric information to be included in the digital time capsule and record the emotional state during the experience.

[0068] The storage unit can use the emotion estimation function to analyze the emotional value of the content to be stored and highlight particularly emotionally significant moments. The storage unit, for example, uses the emotion estimation function to analyze the emotional value of the content to be stored. For example, it identifies and highlights moments that made the user feel particularly emotional. The storage unit can also highlight emotionally significant moments to make them easier for the user to look back on. For example, it can highlight specific events or happenings. In this way, it is possible to analyze the emotional value of the content to be stored and highlight particularly emotionally significant moments.

[0069] The storage unit can create a detailed record of the user's experience by including geographic information and weather information of the places the user visited in the digital time capsule. The storage unit, for example, can create a detailed record of the user's experience by including geographic information of the places the user visited in the digital time capsule. For example, the storage unit can record and store the places visited based on GPS data. The storage unit can also include weather information of the places the user visited. For example, the storage unit can record and store the weather and temperature on a specific day. This allows the user to create a detailed record of the user's experience by including geographic information and weather information of the places visited in the digital time capsule.

[0070] The storage unit can store data in different formats so that it can be re-experienced with future technology. For example, the storage unit can include 3D models in a digital time capsule so that it can be re-experienced with future technology. For example, a 3D model of a particular place or object can be created and stored. The storage unit can also include VR content so that a user can re-live past experiences in virtual reality. For example, a particular event or occurrence can be recreated and stored in VR so that it can be stored in different formats so that it can be re-experienced with future technology.

[0071] The storage unit can use the emotion estimation function to analyze the user's emotional response to the content to be stored and suggest an optimal storage format. The storage unit, for example, uses the emotion estimation function to analyze the user's emotional response to the content to be stored. For example, it identifies moments when the user was particularly emotional and suggests an optimal storage format. The storage unit can also select a storage format based on the user's emotional response. For example, it suggests saving a moving moment in video format. In this way, the user's emotional response to the content to be stored can be analyzed and an optimal storage format can be suggested.

[0072] The storage unit can compare past experiences with the current situation to look back at a certain point in the future, and visualize the user's growth and change. For example, the generative AI can compare past experiences with the current situation and visualize the user's growth and change. For example, it can display past photos and videos side by side with current photos and videos. The storage unit can also visualize the user's growth and change in graphs and charts. For example, it can visually display skill improvements and knowledge gains. This makes it possible to compare past experiences with the current situation and visualize the user's growth and change.

[0073] The storage unit can suggest new goal setting and plans based on past experiences to look back on at some point in the future. For example, the generation AI in the storage unit suggests new goal setting and plans based on past experiences. For example, the next goal is set based on past successful experiences. The storage unit can also analyze the user's past experiences and assist in making future plans. For example, a new project plan is made based on the results of a past project. This makes it possible to suggest new goal setting and plans based on past experiences.

[0074] The storage unit can use the emotion estimation function to analyze the user's emotional reaction when reflecting and suggest reflection at the optimal timing. The storage unit, for example, uses the emotion estimation function to analyze the user's emotional reaction when reflecting. For example, it can identify moments when the user is particularly emotional and suggest reflection at the optimal timing. The storage unit can also take the user's emotional state into consideration and provide reflection at an appropriate timing. For example, it can suggest reflection when the user is relaxed. This makes it possible to analyze the user's emotional reaction when reflecting and suggest reflection at the optimal timing.

[0075] The storage unit can provide interactive storytelling based on past experiences to be looked back on at some point in the future. For example, the storage unit provides interactive storytelling based on past experiences using a generative AI. For example, a user selects a past memory, and a story based on that memory is generated. The storage unit can also develop a story in accordance with the user's selection. For example, a different story is generated based on an experience selected by the user. This makes it possible to provide interactive storytelling based on past experiences.

[0076] The storage unit can enable recollection on different devices in order to look back at a certain point in the future. The storage unit, for example, enables the generation AI to look back on different devices. For example, it provides an app that allows users to look back on past memories on a smartphone or tablet. The storage unit can also recreate past experiences in virtual reality using a VR headset. For example, specific events or occurrences can be recreated and reminisced about in VR. This allows recollection on different devices.

[0077] The storage unit can use the emotion estimation function to analyze the user's emotional response during reflection in real time and suggest an optimal reflection method. The storage unit, for example, uses the emotion estimation function to analyze the user's emotional response during reflection in real time. For example, it identifies a moment when the user is particularly emotional and suggests an optimal reflection method for that moment. The storage unit can also analyze the user's emotional state in real time and suggest an appropriate reflection method. For example, it suggests a visual-based reflection when the user is relaxed. This makes it possible to analyze the user's emotional response during reflection in real time and suggest an optimal reflection method.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The suggestion unit can estimate the user's emotions and, based on the estimated emotions, preferentially suggest experiences that elicit particularly positive emotions from among the user's past experiences. For example, the suggestion unit can suggest trips or events that the user enjoyed in the past. Furthermore, if the user is feeling stressed, the suggestion unit can suggest relaxing activities. For example, the suggestion unit can suggest meditation or yoga sessions. This makes it possible to suggest more personalized experiences based on the user's emotions.

[0080] The storage unit can organize events experienced by the user in chronological order, making it visually easy to understand when looking back on past experiences. For example, the storage unit can display experiences in a calendar format, and clicking on a specific date will display details for that day. The storage unit can also allow the user to select a specific period and display all experiences within that period. This allows the user to easily look back on past experiences.

[0081] The suggestion unit can estimate the user's emotions and, based on the estimated emotions, support the user in preparing to enjoy a new experience. For example, if the user feels anxious, the suggestion unit can provide necessary information and preparations in advance. Furthermore, if the user feels excited, the suggestion unit can also suggest additional information or activities to further increase the user's excitement. In this way, support can be provided according to the user's emotions.

[0082] The storage unit can display events experienced by the user on a map, allowing the user to visually confirm the places visited. For example, travel destinations and event venues can be displayed as pins on the map, and clicking on them displays details of the experiences at those locations. The storage unit can also provide surrounding information for the places visited by the user, allowing the user to look back on past experiences geographically.

[0083] The suggestion unit can estimate the user's emotions and, based on the estimated emotions, make suggestions to recreate particularly moving moments from experiences the user has had in the past. For example, the suggestion unit can re-suggest movies or music that moved the user. The suggestion unit can also suggest new experiences based on events that moved the user. For example, the suggestion unit can suggest moving events or art exhibitions. In this way, suggestions to recreate particularly moving experiences can be made based on the user's emotions.

[0084] The storage unit can organize the events experienced by the user by category and display experiences belonging to a specific category together. For example, it can display experiences by category such as travel, events, and hobbies, and when the user selects a category of interest, details of experiences belonging to that category are displayed. The storage unit also allows the user to add new categories and customize and organize experiences. This allows the user to efficiently organize and review their experiences.

[0085] The suggestion unit can estimate the user's emotions and, based on the estimated emotions, suggest particularly relaxing experiences from among the experiences the user has had in the past. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing music or natural scenery. The suggestion unit can also suggest activities to provide the user with a relaxing environment. For example, the suggestion unit can suggest a spa or massage session. In this way, relaxing experiences can be suggested based on the user's emotions.

[0086] The storage unit can store the events the user has experienced not only as photos and videos, but also as text and voice memos. For example, the storage unit can record and store the user's thoughts and memories about the events they have experienced in text. The storage unit can also record and store voice memos about the events they have experienced. This allows the user to record and look back on their experiences in various formats.

[0087] The suggestion unit can estimate the user's emotions and, based on the estimated emotions, suggest particularly enjoyable experiences from among the experiences the user has had in the past. For example, it can suggest a party or event that the user enjoyed again. The suggestion unit can also suggest new experiences based on events the user enjoyed. For example, it can suggest fun activities or games. In this way, it is possible to suggest particularly enjoyable experiences based on the user's emotions.

[0088] The storage unit may provide a function for the user to share the events that the user has experienced with other users. For example, the storage unit may select a specific experience and generate a link to share with family and friends. The storage unit may also customize privacy settings when the user shares an experience. For example, the storage unit may set the experience to be accessible only by specific people. This allows the user to safely share their experiences with other users.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The learning unit learns the user's interests, preferences, and dreams. For example, when a user inputs a prompt such as, "Our family loves nature and enjoys outdoor activities," the generative AI learns based on that information. The learning unit can also analyze the user's behavioral history and social media data to extract interests and preferences. Step 2: The suggestion unit suggests unique experiences and activities based on the information learned by the learning unit. For example, the generation AI makes specific suggestions such as "go camping with the family this weekend," "cook together," or "take up a new hobby." The suggestion unit can also suggest trips, events, and hobby activities based on the user's preferences. Step 3: The storage unit stores the experiences and activities suggested by the suggestion unit as a digital time capsule. For example, photos and videos from a family camping trip, as well as text describing camping memories, may be stored. The storage unit can also securely store the digital time capsule using cloud storage and encryption technology.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 learning unit that learns the user's interests, preferences, and dreams; a suggestion unit that suggests unique experiences and activities based on the information learned by the learning unit; a storage unit that stores the experiences and activities suggested by the suggestion unit as a digital time capsule. A system characterized by:

2. The learning unit Analyze the user's past social media posts and message history to create a more detailed profile The system of claim 1 .

3. The learning unit Analyze the user's voice and video inputs and create a profile based on visual and auditory information. The system of claim 1 .

4. The proposal unit Consider the user's past experience history and make new suggestions based on past successful experiences The system of claim 1 .

5. The storage unit The digital time capsule may include the user's voice and video messages, creating a more personal record The system of claim 1 .

6. The learning unit Emotion estimation capabilities are used to analyze the emotional nuances of the user's input prompts to provide more personalized suggestions. The system of claim 1 .

7. The proposal unit Using emotion estimation capabilities, the system predicts the emotional impact of the proposed experience on the user and makes suggestions that elicit positive emotions. The system of claim 1 .

8. The storage unit Using emotion estimation, the app analyzes the emotional value of stored content and highlights particularly emotionally significant moments. The system of claim 1 .

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