System

Abstract

An object of the system according to the embodiment is to reproduce memories in the mind of a user as an image.SOLUTION: A system includes a memory input unit, an image generation unit, and an output unit. The memory input unit inputs a memory of the user. The image generation unit generates an image based on the memory input by the memory input unit. The output unit outputs the image generated by the image generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to recreate memorable images.

[0005] The system according to the embodiment aims to recreate a user's memorable memories as an image. [Means for solving the problem]

[0006] The system according to the embodiment includes a memory input unit, an image generation unit, and an output unit. The memory input unit inputs a user's memories. The image generation unit generates an image based on the memories input by the memory input unit. The output unit outputs the image generated by the image generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the user's memorable memories as images. [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 restoration system according to an embodiment of the present invention is a system that restores memorable memories as images using generative AI, allowing users to visually recreate memorable memories and share them with others.

[0029] A memory restoration system according to an embodiment includes a memory input unit, an image generation unit, and an output unit. The memory input unit inputs a user's memories. For example, the user inputs memories in text format using a keyboard. The memory input unit can also convert what the user says into text using voice input. For example, it converts voice into text using voice recognition technology. The memory input unit can also input memories by handwriting on a tablet using touch input. For example, it recognizes handwritten characters using the tablet's touch screen. The image generation unit generates an image based on the memories input by the memory input unit. For example, the generation AI analyzes the input memories using a machine learning algorithm and generates an image. The generation AI can also recreate a memorable scene using image processing technology. For example, the generation AI depicts a landscape or characters based on the input text. The generation AI can also generate an image that reflects the user's emotions. For example, it can express the joy or sadness felt by the user. The output unit outputs the image generated by the image generation unit. For example, the output unit displays the generated image on a display. The output unit can also print the generated image using a printer. For example, the generated image is printed at high resolution. Furthermore, the output unit can also save the generated image in a digital format. For example, the generated image is saved in cloud storage. In this way, the memory restoration system according to the embodiment can visually recreate memorable memories and share them with others. For example, the user can share the generated image on social media and reminisce about memories with family and friends. The user can also print the generated image and save it in an album. Furthermore, the user can save the generated image in a digital format and access it at any time.

[0030] The memory input unit can analyze the user's brainwave data in real time and input memorable scenes directly to the generation AI as prompts. For example, when a user wears an EEG sensor and recalls a memory, the memory input unit analyzes the EEG data in real time and inputs the scene as a prompt to the generation AI. For example, it extracts keywords such as "hospital room," "grandmother," and "time spent together" from the EEG. The memory input unit can also acquire EEG data using an EEG sensor and generate prompts based on that data. For example, an EEG sensor measures EEGs with high accuracy and generates prompts based on that data. This allows prompts to be generated directly from the user's brainwave data.

[0031] The memory input unit can analyze photos and videos taken in the past and automatically generate memory prompts based on their metadata. For example, when a user uploads photos taken in the past, the memory input unit analyzes the metadata and automatically generates memory prompts. For example, keywords such as "summer beach" or "family trip" are extracted based on the location and date and time of the photo. The memory input unit can also analyze video metadata and generate prompts based on its content. For example, a prompt is generated based on the location and characters in the video. This makes it possible to automatically generate prompts based on the metadata of past photos and videos.

[0032] The memory input unit can make suggestions to complement the user's memory input by referring to similar memory prompts input by other users. For example, when a user inputs "time spent in the hospital room with grandmother," the memory input unit makes suggestions to complement the user's memory input by referring to similar memory prompts input by other users. For example, keywords such as "grandmother's smile" and "scenery from the hospital room" are added. The memory input unit can also analyze the input history of other users and make suggestions based on that history. For example, suggestions to complement the user's input are made based on prompts input by other users. This allows the memory input to be complemented by referring to the input of other users.

[0033] The image generation unit can automatically generate and add text or poetry related to the user's memories to the generated image. For example, when the generation AI generates an image of "time spent in the hospital room with grandmother," the image generation unit automatically generates and adds text or poetry related to the user's memories. For example, it adds a poem such as "my grandmother's smile remains in my heart." The image generation unit can also generate text or poetry related to memories using a natural language generation algorithm. For example, a natural language generation algorithm generates poetry based on the user's memories. This makes it possible to automatically generate and add text or poetry related to the user's memories.

[0034] The output unit can automatically set the generated image as wallpaper on the user's device, providing an opportunity to reminisce on a daily basis. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit automatically sets the image as wallpaper on the user's smartphone or computer. This allows the user to reminisce on a daily basis. The output unit can also periodically change the wallpaper settings. For example, the wallpaper can be automatically changed based on a schedule set by the user. This allows the generated image to be set as wallpaper on the device, providing an opportunity to reminisce on a daily basis.

[0035] The output unit can automatically add the generated image to a calendar of places and events related to the user's memories. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit automatically adds the image to the user's calendar app and records related places and events. For example, it adds "time spent in the hospital room" to the calendar. The output unit can also set reminders for events added to the calendar. For example, it sends a notification the day before the event. This allows the generated image to be automatically added to the calendar and memories to be recorded.

[0036] The output unit can automatically generate the generated images as a photo book or album with a theme related to the user's memories. For example, after the generation AI generates images of "time spent in the hospital room with grandmother," the output unit automatically generates the images as a photo book with a theme related to the user's memories. For example, a "Memories with Grandmother" photo book can be created. The output unit can also automatically adjust the layout of the photo book or album. For example, it automatically positions images and adds text. This allows the generated images to be automatically generated as a photo book or album.

[0037] The output unit can output the generated image as a 3D printed object related to the user's memories. For example, after the generation AI generates an image of "time spent in a hospital room with grandmother," the output unit outputs a 3D printed object based on that image. For example, it could create a miniature model of the hospital room where the user spent time with their grandmother. The output unit can also automatically adjust the design of the 3D printed object. For example, it could change the size or shape according to the user's requests. This allows the generated image to be output as a 3D printed object.

[0038] The memory input unit allows users to input the elements they want to customize in natural language, and the generation AI can automatically regenerate an image based on that content. For example, if a user inputs "Make grandmother's smile brighter" in natural language, the memory input unit will regenerate an image based on that content. For example, it will express grandmother's smile as brighter. The memory input unit can also analyze the user's input using natural language processing technology and regenerate an image based on that content. For example, natural language processing technology can analyze the user's input with high accuracy and regenerate an image based on that content. This allows users to input the elements they want to customize in natural language, and the generation AI will automatically regenerate an image.

[0039] The memory input unit allows the user to hand-draw elements they want to customize, and the generation AI can regenerate an image based on that sketch. For example, when the user hand-draws a sketch of "grandmother's smile," the memory input unit regenerates an image based on that sketch. For example, the memory input unit recreates the grandmother's smile based on the user's sketch. The memory input unit can also obtain a hand-drawn sketch using tablet input and regenerate an image based on that sketch. For example, it can recognize a hand-drawn sketch using the tablet's touchscreen and regenerate an image based on that sketch. This allows the user to hand-draw elements they want to customize, and the generation AI can regenerate an image based on that sketch.

[0040] The memory input unit can suggest elements that the user wants to customize by referring to examples of customization made by other users. For example, when a user inputs "make grandmother's smile brighter," the memory input unit makes suggestions by referring to similar examples of customization made by other users. For example, it refers to images of smiles that other users have brightened. The memory input unit can also analyze the customization history of other users and make suggestions based on that data. For example, it suggests optimal customization based on the customization history of other users. This allows the memory input unit to suggest elements that the user wants to customize by referring to examples of customization made by other users.

[0041] The memory input unit can generate multiple versions with different art styles and filters to provide the user with options. For example, if a user inputs "Make my grandmother's smile brighter," the generation AI will generate multiple versions with different art styles and filters to provide options. For example, it can generate an oil painting-style version or a watercolor painting-style version. The memory input unit can also apply different filters to change the atmosphere of the image. For example, it can apply a monochrome filter or a sepia filter. This allows multiple versions with different art styles and filters to be generated to provide the user with options.

[0042] The output unit can save the generated images in an application that can be played in a story mode related to the user's memories. For example, after the generation AI generates images of "time spent in the hospital room with grandmother," the output unit saves the images in an application that can be played in story mode. For example, the images can be displayed in chronological order, allowing the user to look back on memories. The output unit can also provide an interactive story mode. For example, when the user taps an image, a related episode is displayed. This allows the generated images to be saved in an application that can be played in story mode.

[0043] The output unit can automatically generate and print the generated images as a photo book or album with a theme related to the user's memories. For example, after the generation AI generates images of "time spent in the hospital room with grandmother," the output unit automatically generates and prints the images as a photo book with a theme related to the user's memories. For example, a "Memories with Grandmother" photo book can be created. The output unit can also automatically adjust the layout of the photo book or album. For example, it automatically positions images and adds text. This allows the generated images to be automatically generated and printed as a photo book or album.

[0044] The output unit can save the generated image as a 3D printed object related to the user's memories. For example, after the generation AI generates an image of "time spent in a hospital room with grandmother," the output unit saves the image as a 3D printed object. For example, it creates a miniature model of the hospital room where the user spent time with their grandmother. The output unit can also automatically adjust the design of the 3D printed object. For example, it can change the size or shape according to the user's requests. This allows the generated image to be saved as a 3D printed object.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The memory input unit can analyze the user's gesture input and input memorable scenes as prompts to the generation AI. For example, if a user expresses "a walk by the sea" with hand movements, the gesture is analyzed and a prompt is generated. The memory input unit can also use gesture recognition technology to analyze the user's movements with high accuracy and generate prompts based on those movements. This allows prompts to be generated from the user's gesture input.

[0047] The memory input unit can analyze the user's biometric data and input memorable scenes as prompts into the generation AI. For example, the user can wear a device that measures their heart rate or skin galvanic response, and the data can be analyzed to generate prompts. The memory input unit can also estimate the user's emotional state based on the biometric data and generate prompts based on that state. This allows prompts to be generated from the user's biometric data.

[0048] The memory input unit can analyze a user's past SNS posts and automatically generate memory prompts based on their content. For example, it can analyze photos and comments posted by the user in the past and extract keywords such as "summer trip" or "reunion with friends." The memory input unit can also generate prompts based on the metadata of SNS posts. This allows for the automatic generation of prompts based on the user's past SNS posts.

[0049] The memory input unit can analyze the user's past emails and messages and automatically generate memory prompts based on their content. For example, it can analyze emails and messages sent and received by the user in the past and extract keywords such as "conversations with family" and "interactions with friends." The memory input unit can also generate prompts based on the metadata of emails and messages. This allows for the automatic generation of prompts based on the user's past emails and messages.

[0050] The memory input unit can analyze a user's past blogs and diaries and automatically generate memory prompts based on their contents. For example, it can analyze a user's past blogs and diaries and extract keywords such as "travel memories" and "everyday events." The memory input unit can also generate prompts based on the metadata of the blogs and diaries. This allows for the automatic generation of prompts based on the user's past blogs and diaries.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The memory input unit inputs the user's memories. For example, the user can input memories in text format using a keyboard. Alternatively, the user can use voice input to convert what the user says into text. Furthermore, the user can use touch input to input memories by handwriting on the tablet. Step 2: The image generation unit generates an image based on the memories input by the memory input unit. For example, the generation AI uses a machine learning algorithm to analyze the input memories and generate an image. It can also use image processing technology to recreate memorable scenes. It can also generate images that reflect the user's emotions. Step 3: The output unit outputs the image generated by the image generation unit. For example, the generated image is displayed on a display. The generated image can also be printed by a printer. Furthermore, the generated image can also be saved in a digital format.

[0053] (Example 2) The memory restoration system according to an embodiment of the present invention is a system that restores memorable memories as images using generative AI, allowing users to visually recreate memorable memories and share them with others.

[0054] A memory restoration system according to an embodiment includes a memory input unit, an image generation unit, and an output unit. The memory input unit inputs a user's memories. For example, the user inputs memories in text format using a keyboard. The memory input unit can also convert what the user says into text using voice input. For example, it converts voice into text using voice recognition technology. The memory input unit can also input memories by handwriting on a tablet using touch input. For example, it recognizes handwritten characters using the tablet's touch screen. The image generation unit generates an image based on the memories input by the memory input unit. For example, the generation AI analyzes the input memories using a machine learning algorithm and generates an image. The generation AI can also recreate a memorable scene using image processing technology. For example, the generation AI depicts a landscape or characters based on the input text. The generation AI can also generate an image that reflects the user's emotions. For example, it can express the joy or sadness felt by the user. The output unit outputs the image generated by the image generation unit. For example, the output unit displays the generated image on a display. The output unit can also print the generated image using a printer. For example, the generated image is printed at high resolution. Furthermore, the output unit can also save the generated image in a digital format. For example, the generated image is saved in cloud storage. In this way, the memory restoration system according to the embodiment can visually recreate memorable memories and share them with others. For example, the user can share the generated image on social media and reminisce about memories with family and friends. The user can also print the generated image and save it in an album. Furthermore, the user can save the generated image in a digital format and access it at any time.

[0055] The memory input unit can analyze the user's voice input, extract memorable scenes and emotions from the voice, and automatically generate prompts. For example, if the user says, "The time I spent in the hospital room with my grandmother," the memory input unit analyzes the voice, extracts the scenes and emotions, and automatically generates a prompt. For example, keywords such as "hospital room," "grandmother," and "time spent together" are extracted from the voice and input into the generation AI. The memory input unit can also convert voice to text using voice recognition technology and generate prompts based on that text. For example, voice recognition technology converts voice to text with high accuracy, and generates prompts based on that text. This allows prompts to be automatically generated from the user's voice input.

[0056] The memory input unit can analyze the user's brainwave data in real time and input memorable scenes directly to the generation AI as prompts. For example, when a user wears an EEG sensor and recalls a memory, the memory input unit analyzes the EEG data in real time and inputs the scene as a prompt to the generation AI. For example, it extracts keywords such as "hospital room," "grandmother," and "time spent together" from the EEG. The memory input unit can also acquire EEG data using an EEG sensor and generate prompts based on that data. For example, an EEG sensor measures EEGs with high accuracy and generates prompts based on that data. This allows prompts to be generated directly from the user's brainwave data.

[0057] The memory input unit can use the emotion estimation function to analyze the emotion a user has when entering a memory and generate a prompt that emphasizes positive emotions. For example, when a user enters "time spent in the hospital room with my grandmother," the emotion estimation function analyzes the emotion at the time of entry and generates a prompt that emphasizes positive emotions. For example, keywords such as "fun time" and "smiling grandmother" are added. The memory input unit can also use facial expression recognition technology to analyze the user's facial expression and estimate emotions from the facial expression. For example, the facial expression recognition technology detects the user's smile and emphasizes positive emotions based on the smile. This allows the user's emotions to be analyzed and a prompt that emphasizes positive emotions to be generated.

[0058] The memory input unit can analyze photos and videos taken in the past and automatically generate memory prompts based on their metadata. For example, when a user uploads photos taken in the past, the memory input unit analyzes the metadata and automatically generates memory prompts. For example, keywords such as "summer beach" or "family trip" are extracted based on the location and date and time of the photo. The memory input unit can also analyze video metadata and generate prompts based on its content. For example, a prompt is generated based on the location and characters in the video. This makes it possible to automatically generate prompts based on the metadata of past photos and videos.

[0059] The memory input unit can make suggestions to complement the user's memory input by referring to similar memory prompts input by other users. For example, when a user inputs "time spent in the hospital room with grandmother," the memory input unit makes suggestions to complement the user's memory input by referring to similar memory prompts input by other users. For example, keywords such as "grandmother's smile" and "scenery from the hospital room" are added. The memory input unit can also analyze the input history of other users and make suggestions based on that history. For example, suggestions to complement the user's input are made based on prompts input by other users. This allows the memory input to be complemented by referring to the input of other users.

[0060] The memory input unit can use the emotion estimation function to collect other users' emotional responses to memories entered by the user and generate a prompt that evokes empathy. For example, when a user enters "time spent in the hospital room with my grandmother," the memory input unit's emotion estimation function collects other users' emotional responses and generates a prompt that evokes empathy. For example, keywords such as "grandmother's smile" and "warm time" are added. The memory input unit can also analyze other users' emotional responses and generate a prompt based on those responses. For example, a prompt that evokes empathy is generated based on the emotion scores of other users. This makes it possible to generate a prompt that evokes empathy based on the emotional responses of other users.

[0061] The image generation unit reflects the user's past emotional data in the image it generates, enabling it to generate an image that is more emotionally relatable. For example, when the generation AI generates an image of "time spent in the hospital room with grandmother," the image generation unit reflects the user's past emotional data to generate an image that is more emotionally relatable. For example, it expresses the warmth and sense of security felt by the user. The image generation unit can also analyze past emotional records and generate images based on that data. For example, it generates an image that reflects the user's emotions based on past emotional records. This allows it to reflect the user's past emotional data and generate an image that is more emotionally relatable.

[0062] The image generation unit can combine audio and music related to the user's memories with the generated image and output it in multimedia format. For example, when the generation AI generates an image of "time spent in the hospital room with grandmother," the image generation unit combines audio and music related to the user's memories and outputs it in multimedia format. For example, the grandmother's voice or favorite music can be added. The image generation unit can also select audio and music related to memories using recorded audio and a music library. For example, it can analyze recorded audio and extract parts related to memories. This allows audio and music related to the user's memories to be combined and output in multimedia format.

[0063] The image generation unit uses the emotion estimation function to provide real-time feedback on the user's emotional response to the generated image, thereby optimizing the image. For example, when the generation AI generates an image of "time spent in the hospital room with grandmother," the image generation unit uses the emotion estimation function to provide real-time feedback on the user's emotional response and optimize the image. For example, it can emphasize the warmth felt by the user. The image generation unit can also analyze the user's emotions in real time and adjust the image based on that data. For example, it can adjust the color tone and brightness of the image based on the user's emotion score. This allows the user's emotional response to be provided as feedback in real time and the image to be optimized.

[0064] The image generation unit can automatically generate and add text or poetry related to the user's memories to the generated image. For example, when the generation AI generates an image of "time spent in the hospital room with grandmother," the image generation unit automatically generates and adds text or poetry related to the user's memories. For example, it adds a poem such as "my grandmother's smile remains in my heart." The image generation unit can also generate text or poetry related to memories using a natural language generation algorithm. For example, a natural language generation algorithm generates poetry based on the user's memories. This makes it possible to automatically generate and add text or poetry related to the user's memories.

[0065] The image generation unit can use the emotion estimation function to collect other users' emotional reactions to the generated image and identify the most empathetic version. For example, when the generation AI generates an image of "time spent in the hospital room with grandmother," the image generation unit can use the emotion estimation function to collect other users' emotional reactions and identify the most empathetic version. For example, the optimal version is selected based on the user's emotion score. The image generation unit can also analyze other users' emotional reactions and identify a version based on that data. For example, the version that is most empathetic is selected based on the emotion scores of other users. This makes it possible to identify the most empathetic version based on the emotional reactions of other users.

[0066] The output unit can automatically set the generated image as wallpaper on the user's device, providing an opportunity to reminisce on a daily basis. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit automatically sets the image as wallpaper on the user's smartphone or computer. This allows the user to reminisce on a daily basis. The output unit can also periodically change the wallpaper settings. For example, the wallpaper can be automatically changed based on a schedule set by the user. This allows the generated image to be set as wallpaper on the device, providing an opportunity to reminisce on a daily basis.

[0067] The output unit can automatically add the generated image to a calendar of places and events related to the user's memories. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit automatically adds the image to the user's calendar app and records related places and events. For example, it adds "time spent in the hospital room" to the calendar. The output unit can also set reminders for events added to the calendar. For example, it sends a notification the day before the event. This allows the generated image to be automatically added to the calendar and memories to be recorded.

[0068] The output unit can use the emotion estimation function to record the user's emotions when viewing the generated image and evaluate the value of the memory. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit can use the emotion estimation function to record the user's emotions when viewing the image and evaluate the value of the memory. For example, the output unit calculates the value based on the user's emotion score. The output unit can also analyze the user's emotion record and evaluate the value of the memory based on that data. For example, the value can be evaluated based on the intensity and frequency of the emotion. This allows the user's emotions when viewing the generated image to be recorded and the value of the memory to be evaluated.

[0069] The output unit can automatically generate the generated images as a photo book or album with a theme related to the user's memories. For example, after the generation AI generates images of "time spent in the hospital room with grandmother," the output unit automatically generates the images as a photo book with a theme related to the user's memories. For example, a "Memories with Grandmother" photo book can be created. The output unit can also automatically adjust the layout of the photo book or album. For example, it automatically positions images and adds text. This allows the generated images to be automatically generated as a photo book or album.

[0070] The output unit can output the generated image as a 3D printed object related to the user's memories. For example, after the generation AI generates an image of "time spent in a hospital room with grandmother," the output unit outputs a 3D printed object based on that image. For example, it could create a miniature model of the hospital room where the user spent time with their grandmother. The output unit can also automatically adjust the design of the 3D printed object. For example, it could change the size or shape according to the user's requests. This allows the generated image to be output as a 3D printed object.

[0071] The output unit can use the emotion estimation function to collect the emotional reactions of other users who view the generated image and suggest the optimal timing for sharing. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit can use the emotion estimation function to collect the emotional reactions of other users and suggest the optimal timing for sharing. For example, it can suggest a time period with a high emotional score. The output unit can also analyze the emotional reactions of other users and suggest the optimal timing based on that data. For example, it can suggest the optimal sharing timing based on the emotional scores of other users. This makes it possible to suggest the optimal timing for sharing based on the emotional reactions of other users.

[0072] The memory input unit allows users to input the elements they want to customize in natural language, and the generation AI can automatically regenerate an image based on that content. For example, if a user inputs "Make grandmother's smile brighter" in natural language, the memory input unit will regenerate an image based on that content. For example, it will express grandmother's smile as brighter. The memory input unit can also analyze the user's input using natural language processing technology and regenerate an image based on that content. For example, natural language processing technology can analyze the user's input with high accuracy and regenerate an image based on that content. This allows users to input the elements they want to customize in natural language, and the generation AI will automatically regenerate an image.

[0073] The memory input unit allows the user to hand-draw elements they want to customize, and the generation AI can regenerate an image based on that sketch. For example, when the user hand-draws a sketch of "grandmother's smile," the memory input unit regenerates an image based on that sketch. For example, the memory input unit recreates the grandmother's smile based on the user's sketch. The memory input unit can also obtain a hand-drawn sketch using tablet input and regenerate an image based on that sketch. For example, it can recognize a hand-drawn sketch using the tablet's touchscreen and regenerate an image based on that sketch. This allows the user to hand-draw elements they want to customize, and the generation AI can regenerate an image based on that sketch.

[0074] The memory input unit can use the emotion estimation function to analyze the emotion the user has regarding the element they wish to customize and make customization suggestions based on the emotion. For example, when the user inputs "make grandmother's smile brighter," the emotion estimation function analyzes the emotion at the time of input and makes customization suggestions based on the emotion. For example, the memory input unit can express grandmother's smile as brighter, reflecting the user's emotion. The memory input unit can also analyze the intensity and type of emotion and make customization suggestions based on that data. For example, it can suggest optimal customization based on the intensity of the emotion. This makes it possible to analyze the user's emotion and make customization suggestions based on the emotion.

[0075] The memory input unit can suggest elements that the user wants to customize by referring to examples of customization made by other users. For example, when a user inputs "make grandmother's smile brighter," the memory input unit makes suggestions by referring to similar examples of customization made by other users. For example, it refers to images of smiles that other users have brightened. The memory input unit can also analyze the customization history of other users and make suggestions based on that data. For example, it suggests optimal customization based on the customization history of other users. This allows the memory input unit to suggest elements that the user wants to customize by referring to examples of customization made by other users.

[0076] The memory input unit can generate multiple versions with different art styles and filters to provide the user with options. For example, if a user inputs "Make my grandmother's smile brighter," the generation AI will generate multiple versions with different art styles and filters to provide options. For example, it can generate an oil painting-style version or a watercolor painting-style version. The memory input unit can also apply different filters to change the atmosphere of the image. For example, it can apply a monochrome filter or a sepia filter. This allows multiple versions with different art styles and filters to be generated to provide the user with options.

[0077] The memory input unit can use the emotion estimation function to collect other users' emotional reactions to the customized image and suggest optimal customization. For example, when a user inputs "make grandmother's smile brighter," the emotion estimation function collects other users' emotional reactions and suggests optimal customization. For example, it suggests optimal brightness based on the emotion scores of other users. The memory input unit can also analyze other users' emotional reactions and suggest customization based on that data. For example, it suggests optimal customization based on the emotion scores of other users. This makes it possible to suggest optimal customization based on the emotional reactions of other users.

[0078] The output unit can save the generated images in an application that can be played in a story mode related to the user's memories. For example, after the generation AI generates images of "time spent in the hospital room with grandmother," the output unit saves the images in an application that can be played in story mode. For example, the images can be displayed in chronological order, allowing the user to look back on memories. The output unit can also provide an interactive story mode. For example, when the user taps an image, a related episode is displayed. This allows the generated images to be saved in an application that can be played in story mode.

[0079] The output unit can use the emotion estimation function to record the user's emotions toward the saved image and evaluate the value of the memory. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit can use the emotion estimation function to record the emotions the user felt when viewing the image and evaluate the value of the memory. For example, the output unit can calculate the value based on the user's emotion score. The output unit can also analyze the user's emotion record and evaluate the value of the memory based on that data. For example, the value can be evaluated based on the intensity and frequency of the emotion. This allows the user's emotions toward the saved image to be recorded and the value of the memory to be evaluated.

[0080] The output unit can automatically generate and print the generated images as a photo book or album with a theme related to the user's memories. For example, after the generation AI generates images of "time spent in the hospital room with grandmother," the output unit automatically generates and prints the images as a photo book with a theme related to the user's memories. For example, a "Memories with Grandmother" photo book can be created. The output unit can also automatically adjust the layout of the photo book or album. For example, it automatically positions images and adds text. This allows the generated images to be automatically generated and printed as a photo book or album.

[0081] The output unit can save the generated image as a 3D printed object related to the user's memories. For example, after the generation AI generates an image of "time spent in a hospital room with grandmother," the output unit saves the image as a 3D printed object. For example, it creates a miniature model of the hospital room where the user spent time with their grandmother. The output unit can also automatically adjust the design of the 3D printed object. For example, it can change the size or shape according to the user's requests. This allows the generated image to be saved as a 3D printed object.

[0082] The output unit can use the emotion estimation function to collect the emotional reactions of other users who have viewed the saved image and suggest the optimal timing for sharing. For example, after the generation AI generates an image of "time spent in the hospital room with grandmother," the output unit can use the emotion estimation function to collect the emotional reactions of other users and suggest the optimal timing for sharing. For example, it can suggest a time period with a high emotional score. The output unit can also analyze the emotional reactions of other users and suggest timing based on that data. For example, it can suggest the optimal sharing timing based on the emotional scores of other users. This makes it possible to suggest the optimal timing for sharing based on the emotional reactions of other users.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The memory input unit can analyze the user's gesture input and input memorable scenes as prompts to the generation AI. For example, if a user expresses "a walk by the sea" with hand movements, the gesture is analyzed and a prompt is generated. The memory input unit can also use gesture recognition technology to analyze the user's movements with high accuracy and generate prompts based on those movements. This allows prompts to be generated from the user's gesture input.

[0085] The memory input unit uses the user's emotion estimation function to analyze the emotions the user feels when entering memories and generate prompts that alleviate negative emotions. For example, if a user enters "sad memories," the emotion estimation function analyzes the emotions felt at the time of entry and generates prompts that alleviate negative emotions. For example, keywords such as "warm memories" and "encouraging words" can be added. This allows the user's emotions to be analyzed and prompts that alleviate negative emotions to be generated.

[0086] The memory input unit can analyze the user's biometric data and input memorable scenes as prompts into the generation AI. For example, the user can wear a device that measures their heart rate or skin galvanic response, and the data can be analyzed to generate prompts. The memory input unit can also estimate the user's emotional state based on the biometric data and generate prompts based on that state. This allows prompts to be generated from the user's biometric data.

[0087] The memory input unit can use the emotion estimation function to collect other users' emotional reactions to memories entered by the user and generate a prompt that evokes empathy. For example, if a user enters "time spent in the hospital room with my grandmother," the emotion estimation function collects other users' emotional reactions and generates a prompt that evokes empathy. For example, keywords such as "grandmother's smile" and "warm time" can be added. The memory input unit can also analyze other users' emotional reactions and generate a prompt based on those reactions. This allows for the generation of a prompt that evokes empathy based on the emotional reactions of other users.

[0088] The memory input unit can analyze a user's past SNS posts and automatically generate memory prompts based on their content. For example, it can analyze photos and comments posted by the user in the past and extract keywords such as "summer trip" or "reunion with friends." The memory input unit can also generate prompts based on the metadata of SNS posts. This allows for the automatic generation of prompts based on the user's past SNS posts.

[0089] The memory input unit can use the emotion estimation function to collect other users' emotional reactions to memories entered by the user and generate a prompt that evokes empathy. For example, if a user enters "time spent in the hospital room with my grandmother," the emotion estimation function collects other users' emotional reactions and generates a prompt that evokes empathy. For example, keywords such as "grandmother's smile" and "warm time" can be added. The memory input unit can also analyze other users' emotional reactions and generate a prompt based on those reactions. This allows for the generation of a prompt that evokes empathy based on the emotional reactions of other users.

[0090] The memory input unit can analyze the user's past emails and messages and automatically generate memory prompts based on their content. For example, it can analyze emails and messages sent and received by the user in the past and extract keywords such as "conversations with family" and "interactions with friends." The memory input unit can also generate prompts based on the metadata of emails and messages. This allows for the automatic generation of prompts based on the user's past emails and messages.

[0091] The memory input unit can use the emotion estimation function to collect other users' emotional reactions to memories entered by the user and generate a prompt that evokes empathy. For example, if a user enters "time spent in the hospital room with my grandmother," the emotion estimation function collects other users' emotional reactions and generates a prompt that evokes empathy. For example, keywords such as "grandmother's smile" and "warm time" can be added. The memory input unit can also analyze other users' emotional reactions and generate a prompt based on those reactions. This allows for the generation of a prompt that evokes empathy based on the emotional reactions of other users.

[0092] The memory input unit can analyze a user's past blogs and diaries and automatically generate memory prompts based on their contents. For example, it can analyze a user's past blogs and diaries and extract keywords such as "travel memories" and "everyday events." The memory input unit can also generate prompts based on the metadata of the blogs and diaries. This allows for the automatic generation of prompts based on the user's past blogs and diaries.

[0093] The memory input unit can use the emotion estimation function to collect other users' emotional reactions to memories entered by the user and generate a prompt that evokes empathy. For example, if a user enters "time spent in the hospital room with my grandmother," the emotion estimation function collects other users' emotional reactions and generates a prompt that evokes empathy. For example, keywords such as "grandmother's smile" and "warm time" can be added. The memory input unit can also analyze other users' emotional reactions and generate a prompt based on those reactions. This allows for the generation of a prompt that evokes empathy based on the emotional reactions of other users.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The memory input unit inputs the user's memories. For example, the user can input memories in text format using a keyboard. Alternatively, the user can use voice input to convert what the user says into text. Furthermore, the user can use touch input to input memories by handwriting on the tablet. Step 2: The image generation unit generates an image based on the memories input by the memory input unit. For example, the generation AI uses a machine learning algorithm to analyze the input memories and generate an image. It can also use image processing technology to recreate memorable scenes. It can also generate images that reflect the user's emotions. Step 3: The output unit outputs the image generated by the image generation unit. For example, the generated image is displayed on a display. The generated image can also be printed by a printer. Furthermore, the generated image can also be saved in a digital format.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] 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.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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 memory input unit for inputting user memories; an image generation unit that generates an image based on the memories input by the memory input unit; an output unit that outputs the image generated by the image generation unit; A system characterized by:

2. The memory input unit The user's brainwave data is analyzed in real time, and memorable scenes are input directly as prompts into the generation AI.

2. The system of claim 1.

3. The image generation unit The generated images are based on the user's past emotional data, creating images that are more emotionally relatable.

2. The system of claim 1.

4. The output unit The generated image is automatically set as wallpaper on the user's device, providing an opportunity to reminisce on memories on a daily basis.

2. The system of claim 1.

5. The memory input unit Using emotion estimation, the system analyzes the user's feelings about the elements they want to customize and makes customization suggestions based on their emotions.

2. The system of claim 1.

6. The output unit Emotion estimation function records the user's emotions towards the saved images and evaluates the value of the memories.

2. The system of claim 1.

7. The memory input unit Using emotion estimation, the system analyzes the emotions expressed when users input their memories and generates prompts that emphasize positive emotions.

2. The system of claim 1.

8. The image generation unit Using emotion estimation, the system provides real-time feedback on the user's emotional reactions to the generated images, optimizing them.

2. The system of claim 1.

Images