System

Abstract

An object of the system according to the embodiment is to store a large number of photographs on a cloud and to easily organize, extract, and process the photographs.SOLUTION: A system according to an embodiment includes a cloud storage part, an access authority setting part, and a generation AI part. The cloud storage unit stores the photograph on the cloud. The access right setting unit sets an access right to the photograph stored by the cloud storage unit. The generation / AI unit organizes, extracts, and processes photos based on the access rights set by the access rights setting 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] With conventional technology, even if a large number of photos were stored on the cloud, organizing, retrieving, and processing them was complicated, making them difficult to utilize.

[0005] The system according to the embodiment aims to store a large number of photos on the cloud and to easily organize, retrieve, and process them. [Means for solving the problem]

[0006] The system according to the embodiment includes a cloud storage unit, an access right setting unit, and a generation AI unit. The cloud storage unit stores photos on the cloud. The access right setting unit sets access rights for the photos stored by the cloud storage unit. The generation AI unit organizes, retrieves, and processes the photos based on the access rights set by the access right setting unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a large number of photos to be stored on the cloud and easily organized, retrieved, and processed. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) A photo management system according to an embodiment of the present invention stores photos on the cloud and allows users to easily organize, retrieve, and edit photos using a platform app with built-in generative AI. This allows the photo management system to efficiently manage large amounts of photos and utilize them without stress.

[0029] A photo management system according to an embodiment includes a cloud storage unit, an access right setting unit, and a generation AI unit. The cloud storage unit stores photos on the cloud. For example, it uploads photos taken by a user or digitized photos to cloud storage. The cloud storage unit can also analyze photo metadata and apply storage formats and security measures. The access right setting unit sets access rights for photos stored by the cloud storage unit. For example, it grants access rights to photos that users want to share with family and friends and restricts access to private photos. The access right setting unit can also learn user behavior patterns and automatically suggest optimal access right settings. The generation AI unit organizes, retrieves, and processes photos based on the access rights set by the access right setting unit. For example, the generation AI automatically generates albums based on information such as the date and location of photo capture and facial recognition of the subject. The generation AI can also search for and display photos based on user instructions. The generation AI can also process photos based on user instructions. This allows the photo management system according to an embodiment to efficiently manage large numbers of photos and utilize them without stress. For example, you can easily share memories with family and friends, quickly find photos from a specific event, and easily edit photos, making it easy to post them to social media or create albums.

[0030] The cloud storage unit can analyze photo metadata, learn user behavior patterns, and automatically suggest optimal access right settings. The cloud storage unit, for example, analyzes photo metadata and learns user behavior patterns. For example, it prioritizes analysis of frequently shared photos or photos from specific events to understand sharing trends. The cloud storage unit also automatically suggests optimal access right settings based on the user's behavior patterns. For example, it grants access rights to all family members for family photos and restricts access to private photos. This makes it possible to automatically suggest optimal access right settings based on the user's behavior patterns.

[0031] The access right setting unit allows the generation AI to automatically determine the privacy level based on the content of the photo and set appropriate access rights. For example, the access right setting unit uses the generation AI to analyze the content of the photo and automatically determine the privacy level. For example, facial recognition technology is used to set a high privacy level for photos that can identify individuals. The access right setting unit also sets appropriate access rights based on the privacy level. For example, it restricts access to photos with a high privacy level and grants shared access rights to photos with a low privacy level. This makes it possible to automatically determine the privacy level based on the content of the photo and set appropriate access rights.

[0032] The cloud storage unit can link photos with other cloud services to achieve integrated media management. For example, the cloud storage unit can link photos stored in the cloud with other cloud services. For example, the cloud storage unit can link with a music streaming service to add music to a photo slideshow. The cloud storage unit can also link with a video streaming service to manage photos and videos in an integrated manner. This allows photos to be linked with other cloud services to achieve integrated media management.

[0033] The access right setting unit manages the access rights to photos using blockchain technology and can record the access history transparently and securely. The access right setting unit, for example, manages the access rights to photos using blockchain technology. For example, the access right change history is recorded on the blockchain to ensure transparency. The access right setting unit also records the access history transparently and securely. For example, the access history is recorded on the blockchain to prevent unauthorized access. This allows the access rights to photos to be managed using blockchain technology and the access history to be recorded transparently and securely.

[0034] The generation AI unit can analyze the content of photos and automatically generate albums based on the user's life events. For example, the generation AI unit can analyze the content of photos and automatically generate albums based on the user's life events. For example, an album containing birthday photos can be automatically created. The generation AI unit can also automatically create an album containing wedding photos. This makes it possible to automatically generate albums based on the user's life events.

[0035] The generation AI unit can learn the user's past organizing patterns when organizing photos and suggest albums that suit individual preferences. For example, the generation AI unit can learn the user's past organizing patterns and suggest albums that suit individual preferences. For example, it can suggest a new album based on the patterns of albums the user has created in the past. The generation AI unit can also suggest album layouts and designs based on the user's preferences. This makes it possible to suggest albums that suit individual preferences based on the user's past organizing patterns.

[0036] The generation AI unit can link the organization of photos with other digital content to generate an integrated life log. The generation AI unit, for example, links the organization of photos with other digital content. For example, it links with text messages and SNS posts to generate an integrated life log. The generation AI unit can also integrate photos with other digital content to record the user's life events. This makes it possible to generate a life log by integrating photos with other digital content.

[0037] When organizing photos, the generation AI unit can analyze the user's voice instructions in real time and generate an album based on the voice command. For example, the generation AI unit can analyze the user's voice instructions in real time and generate an album based on the voice command. For example, an album can be created in response to an instruction such as "Put all my travel photos together in an album." The generation AI unit can also change the layout and design of the album based on the voice command. This allows albums to be generated in real time based on the user's voice command.

[0038] The generation AI unit can analyze the content of the photo and learn the user's past search history to automatically suggest the most suitable search results. The generation AI unit, for example, analyzes the content of the photo and learns the user's past search history. For example, it can suggest the most suitable search results based on the keywords and dates and times of past searches. The generation AI unit can also prioritize the display of related photos based on the user's search history. This makes it possible to automatically suggest the most suitable search results based on the user's past search history.

[0039] When retrieving photos, the generation AI unit can analyze the user's voice instructions in real time and search for photos based on the voice commands. For example, the generation AI unit can analyze the user's voice instructions in real time and search for photos based on the voice commands. For example, it can search for photos in response to an instruction such as "Show me photos of my trip last summer." The generation AI unit can also filter search results based on voice commands. For example, it can narrow down search results in response to an instruction such as "Show me only photos of my family." This allows for real-time photo search based on the user's voice instructions.

[0040] The generation AI unit can link the retrieval of photos with other digital content to realize an integrated media search. The generation AI unit, for example, links the retrieval of photos with other digital content. For example, it can link with videos and music to realize an integrated media search. The generation AI unit can also integrate photos with other digital content to record the user's life events. This makes it possible to integrate photos with other digital content to realize a media search.

[0041] When retrieving photos, the generation AI unit can analyze the user's gestures in real time and search for photos based on the gesture commands. For example, the generation AI unit can analyze the user's gestures in real time and search for photos based on the gesture commands. For example, a specific gesture can open a specific album. The generation AI unit can also filter search results based on the gesture commands. For example, a specific gesture can display photos in a specific category. This allows photos to be searched for in real time based on the user's gestures.

[0042] The generation AI unit can analyze the content of a photo, learn the user's past editing history, and automatically suggest the optimal editing method. The generation AI unit, for example, analyzes the content of a photo and learns the user's past editing history. For example, it can suggest the optimal editing method based on past editing patterns. The generation AI unit can also suggest editing methods based on the user's preferences. For example, it can suggest filters and effects that the user prefers. This makes it possible to automatically suggest the optimal editing method based on the user's past editing history.

[0043] When processing photos, the generation AI unit can analyze the user's voice instructions in real time and process the photo based on the voice command. For example, the generation AI unit analyzes the user's voice instructions in real time and processes the photo based on the voice command. For example, it processes the photo in response to an instruction such as "brighten this photo." The generation AI unit can also adjust the type and intensity of processing based on the voice command. For example, it adjusts the processing in response to an instruction such as "make it a little brighter." This allows photos to be processed in real time based on the user's voice instructions.

[0044] The generation AI unit can link photo processing with other digital content to achieve integrated media editing. The generation AI unit, for example, links photo processing with other digital content. For example, it can link with videos and music to achieve integrated media editing. The generation AI unit can also integrate photos with other digital content to record the user's life events. This allows media editing to be achieved by integrating photos with other digital content.

[0045] When processing a photo, the generation AI unit can analyze the user's gestures in real time and process the photo based on the gesture commands. For example, the generation AI unit can analyze the user's gestures in real time and process the photo based on the gesture commands. For example, a specific effect can be applied with a specific gesture. The generation AI unit can also adjust the type and intensity of processing based on the gesture commands. For example, the intensity of an effect can be changed with a specific gesture. This allows photos to be processed in real time based on the user's gestures.

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

[0047] The photo management system further includes a photo backup unit. The backup unit can periodically create backups of photos stored in the cloud storage unit and store them in different cloud storage services. For example, the backup unit can automatically create backups at a time specified by the user and store them in different cloud storage services. The backup unit can also allow the user to manually create backups. This reduces the risk of losing photo data and allows for worry-free photo management.

[0048] When analyzing photo metadata, the cloud storage can automatically assign geographic tags based on the location where the photo was taken. For example, photos taken while traveling can be geotagged with the location, making them easier to search for later. The cloud storage can also organize photos based on the geographic tags and automatically generate albums related to specific locations, allowing users to easily relive their travel memories.

[0049] The access rights setting unit allows the generation AI to automatically determine the photo category based on the content of the photo and set different access rights for each category. For example, it can classify photos into categories such as family photos, landscape photos, and event photos, and set appropriate access rights for each. The access rights setting unit also allows users to manually change categories. This allows for efficient access rights management based on the content of the photo.

[0050] The cloud storage unit can link photos with other cloud services and provide an automatic photo tagging function. For example, it can recognize people or objects in photos and automatically tag them. The cloud storage unit also allows users to manually add tags. This makes it easier to search for photos and allows for efficient photo management.

[0051] The access right setting unit can manage the access rights to photos using blockchain technology and record the change history of access rights transparently and securely. For example, the change history of access rights can be recorded on the blockchain to ensure transparency. The access right setting unit also records the access history transparently and securely. For example, the access history can be recorded on the blockchain to prevent unauthorized access. This allows the access rights to photos to be managed using blockchain technology and the access history can be recorded transparently and securely.

[0052] The generation AI unit can analyze the content of photos and automatically generate albums based on the user's life events. For example, it can automatically create an album of birthday photos. The generation AI unit can also automatically create an album of wedding photos. This makes it possible to automatically generate albums based on the user's life events.

[0053] The generation AI unit can learn the user's past organizing patterns when organizing photos and suggest albums that suit individual preferences. For example, it can suggest new albums based on the patterns of albums the user has created in the past. The generation AI unit can also suggest album layouts and designs based on the user's preferences. This makes it possible to suggest albums that suit individual preferences based on the user's past organizing patterns.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The cloud storage unit stores photos on the cloud. For example, a user uploads a photo they have taken or digitized to the cloud storage. The cloud storage unit can also analyze the photo's metadata and apply storage formats and security measures. Step 2: The access rights setting unit sets access rights for photos stored by the cloud storage unit. For example, it grants access rights to photos that you want to share with family and friends, and restricts access to private photos. The access rights setting unit can also learn user behavior patterns and automatically suggest optimal access rights settings. Step 3: The generation AI unit organizes, extracts, and processes photos based on the access rights set by the access rights setting unit. For example, the generation AI automatically generates an album based on information such as the date and location of the photo's capture, and facial recognition of the subject. The generation AI can also search for and display photos based on user instructions. Furthermore, the generation AI can also process photos based on user instructions.

[0056] (Example 2) A photo management system according to an embodiment of the present invention stores photos on the cloud and allows users to easily organize, retrieve, and edit photos using a platform app with built-in generative AI. This allows the photo management system to efficiently manage large amounts of photos and utilize them without stress.

[0057] A photo management system according to an embodiment includes a cloud storage unit, an access right setting unit, and a generation AI unit. The cloud storage unit stores photos on the cloud. For example, it uploads photos taken by a user or digitized photos to cloud storage. The cloud storage unit can also analyze photo metadata and apply storage formats and security measures. The access right setting unit sets access rights for photos stored by the cloud storage unit. For example, it grants access rights to photos that users want to share with family and friends and restricts access to private photos. The access right setting unit can also learn user behavior patterns and automatically suggest optimal access right settings. The generation AI unit organizes, retrieves, and processes photos based on the access rights set by the access right setting unit. For example, the generation AI automatically generates albums based on information such as the date and location of photo capture and facial recognition of the subject. The generation AI can also search for and display photos based on user instructions. The generation AI can also process photos based on user instructions. This allows the photo management system according to an embodiment to efficiently manage large numbers of photos and utilize them without stress. For example, you can easily share memories with family and friends, quickly find photos from a specific event, and easily edit photos, making it easy to post them to social media or create albums.

[0058] The cloud storage unit can analyze photo metadata, learn user behavior patterns, and automatically suggest optimal access right settings. The cloud storage unit, for example, analyzes photo metadata and learns user behavior patterns. For example, it prioritizes analysis of frequently shared photos or photos from specific events to understand sharing trends. The cloud storage unit also automatically suggests optimal access right settings based on the user's behavior patterns. For example, it grants access rights to all family members for family photos and restricts access to private photos. This makes it possible to automatically suggest optimal access right settings based on the user's behavior patterns.

[0059] The access right setting unit allows the generation AI to automatically determine the privacy level based on the content of the photo and set appropriate access rights. For example, the access right setting unit uses the generation AI to analyze the content of the photo and automatically determine the privacy level. For example, facial recognition technology is used to set a high privacy level for photos that can identify individuals. The access right setting unit also sets appropriate access rights based on the privacy level. For example, it restricts access to photos with a high privacy level and grants shared access rights to photos with a low privacy level. This makes it possible to automatically determine the privacy level based on the content of the photo and set appropriate access rights.

[0060] The generative AI unit can use the emotion estimation function to analyze the emotions of people in photos and set special access rights for emotionally significant photos. The generative AI unit, for example, uses the emotion estimation function to analyze the emotions of people in photos. For example, it identifies photos of smiling people or photos of moving moments and classifies them as emotionally significant photos. The generative AI unit also sets special access rights for emotionally significant photos. For example, it can grant access rights to all family members to emotionally significant photos and restrict access to private photos. This makes it possible to set special access rights for emotionally significant photos.

[0061] The cloud storage unit can link photos with other cloud services to achieve integrated media management. For example, the cloud storage unit can link photos stored in the cloud with other cloud services. For example, the cloud storage unit can link with a music streaming service to add music to a photo slideshow. The cloud storage unit can also link with a video streaming service to manage photos and videos in an integrated manner. This allows photos to be linked with other cloud services to achieve integrated media management.

[0062] The access right setting unit manages the access rights to photos using blockchain technology and can record the access history transparently and securely. The access right setting unit, for example, manages the access rights to photos using blockchain technology. For example, the access right change history is recorded on the blockchain to ensure transparency. The access right setting unit also records the access history transparently and securely. For example, the access history is recorded on the blockchain to prevent unauthorized access. This allows the access rights to photos to be managed using blockchain technology and the access history to be recorded transparently and securely.

[0063] The generation AI unit uses the emotion estimation function to analyze the emotions of a user when viewing photos in real time and can suggest changes to access rights according to the emotions. The generation AI unit, for example, uses the emotion estimation function to analyze the emotions of a user when viewing photos in real time. For example, if the user is moved, it suggests sharing the photo with a specific group. The generation AI unit also suggests changes to access rights according to the emotions. For example, if the user is having fun, it suggests sharing the photo with friends. This makes it possible to suggest changes to access rights according to the user's emotions.

[0064] The generation AI unit can analyze the content of photos and automatically generate albums based on the user's life events. For example, the generation AI unit can analyze the content of photos and automatically generate albums based on the user's life events. For example, an album containing birthday photos can be automatically created. The generation AI unit can also automatically create an album containing wedding photos. This makes it possible to automatically generate albums based on the user's life events.

[0065] The generation AI unit can learn the user's past organizing patterns when organizing photos and suggest albums that suit individual preferences. For example, the generation AI unit can learn the user's past organizing patterns and suggest albums that suit individual preferences. For example, it can suggest a new album based on the patterns of albums the user has created in the past. The generation AI unit can also suggest album layouts and designs based on the user's preferences. This makes it possible to suggest albums that suit individual preferences based on the user's past organizing patterns.

[0066] The generation AI unit can use the emotion estimation function to analyze the emotions of people in photos and prioritize and organize emotionally significant photos. The generation AI unit, for example, uses the emotion estimation function to analyze the emotions of people in photos. For example, it identifies photos of smiling people or photos of moving moments and classifies them as emotionally significant photos. The generation AI unit also prioritizes and organizes emotionally significant photos. For example, it places emotionally significant photos at the top of an album. This allows emotionally significant photos to be prioritized and organized.

[0067] The generation AI unit can link the organization of photos with other digital content to generate an integrated life log. The generation AI unit, for example, links the organization of photos with other digital content. For example, it links with text messages and SNS posts to generate an integrated life log. The generation AI unit can also integrate photos with other digital content to record the user's life events. This makes it possible to generate a life log by integrating photos with other digital content.

[0068] When organizing photos, the generation AI unit can analyze the user's voice instructions in real time and generate an album based on the voice command. For example, the generation AI unit can analyze the user's voice instructions in real time and generate an album based on the voice command. For example, an album can be created in response to an instruction such as "Put all my travel photos together in an album." The generation AI unit can also change the layout and design of the album based on the voice command. This allows albums to be generated in real time based on the user's voice command.

[0069] The generation AI unit can use the emotion estimation function to analyze the emotions of the user when organizing photos and suggest an organizing method that matches the emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions of the user when organizing photos. For example, if the user is enjoying themselves, the generation AI unit can suggest an organizing method that matches the emotions. The generation AI unit can also suggest album layouts and designs based on the user's emotions. This makes it possible to suggest organizing methods that match the user's emotions.

[0070] The generation AI unit can analyze the content of the photo and learn the user's past search history to automatically suggest the most suitable search results. The generation AI unit, for example, analyzes the content of the photo and learns the user's past search history. For example, it can suggest the most suitable search results based on the keywords and dates and times of past searches. The generation AI unit can also prioritize the display of related photos based on the user's search history. This makes it possible to automatically suggest the most suitable search results based on the user's past search history.

[0071] When retrieving photos, the generation AI unit can analyze the user's voice instructions in real time and search for photos based on the voice commands. For example, the generation AI unit can analyze the user's voice instructions in real time and search for photos based on the voice commands. For example, it can search for photos in response to an instruction such as "Show me photos of my trip last summer." The generation AI unit can also filter search results based on voice commands. For example, it can narrow down search results in response to an instruction such as "Show me only photos of my family." This allows for real-time photo search based on the user's voice instructions.

[0072] The generation AI unit uses the emotion estimation function to analyze the emotion a user feels when searching for photos and can suggest search results that match that emotion. The generation AI unit, for example, uses the emotion estimation function to analyze the emotion a user feels when searching for photos. For example, if the user is having fun, the generation AI unit suggests search results that match that emotion. The generation AI unit can also filter search results based on the user's emotion. For example, if the user is moved, photos that match that emotion are preferentially displayed. This makes it possible to suggest search results that match the user's emotion.

[0073] The generation AI unit can link the retrieval of photos with other digital content to realize an integrated media search. The generation AI unit, for example, links the retrieval of photos with other digital content. For example, it can link with videos and music to realize an integrated media search. The generation AI unit can also integrate photos with other digital content to record the user's life events. This makes it possible to integrate photos with other digital content to realize a media search.

[0074] When retrieving photos, the generation AI unit can analyze the user's gestures in real time and search for photos based on the gesture commands. For example, the generation AI unit can analyze the user's gestures in real time and search for photos based on the gesture commands. For example, a specific gesture can open a specific album. The generation AI unit can also filter search results based on the gesture commands. For example, a specific gesture can display photos in a specific category. This allows photos to be searched for in real time based on the user's gestures.

[0075] The generation AI unit uses the emotion estimation function to analyze the emotion a user has when retrieving a photo, and can suggest search results that correspond to that emotion. The generation AI unit, for example, uses the emotion estimation function to analyze the emotion a user has when retrieving a photo. For example, if the user is having fun, the generation AI unit suggests search results that correspond to that emotion. The generation AI unit can also filter search results based on the user's emotion. For example, if the user is moved, photos that correspond to that emotion are preferentially displayed. This makes it possible to suggest search results that correspond to the user's emotion.

[0076] The generation AI unit can analyze the content of a photo, learn the user's past editing history, and automatically suggest the optimal editing method. The generation AI unit, for example, analyzes the content of a photo and learns the user's past editing history. For example, it can suggest the optimal editing method based on past editing patterns. The generation AI unit can also suggest editing methods based on the user's preferences. For example, it can suggest filters and effects that the user prefers. This makes it possible to automatically suggest the optimal editing method based on the user's past editing history.

[0077] When processing photos, the generation AI unit can analyze the user's voice instructions in real time and process the photo based on the voice command. For example, the generation AI unit analyzes the user's voice instructions in real time and processes the photo based on the voice command. For example, it processes the photo in response to an instruction such as "brighten this photo." The generation AI unit can also adjust the type and intensity of processing based on the voice command. For example, it adjusts the processing in response to an instruction such as "make it a little brighter." This allows photos to be processed in real time based on the user's voice instructions.

[0078] The generation AI unit uses the emotion estimation function to analyze the emotions of the user when editing a photo and can suggest editing methods that match the emotions. The generation AI unit, for example, uses the emotion estimation function to analyze the emotions of the user when editing a photo. For example, if the user is enjoying themselves, it will suggest editing methods that match their emotions. The generation AI unit can also suggest the type and intensity of editing based on the user's emotions. For example, if the user is moved, it will suggest effects that match their emotions. This makes it possible to suggest editing methods that match the user's emotions.

[0079] The generation AI unit can link photo processing with other digital content to achieve integrated media editing. The generation AI unit, for example, links photo processing with other digital content. For example, it can link with videos and music to achieve integrated media editing. The generation AI unit can also integrate photos with other digital content to record the user's life events. This allows media editing to be achieved by integrating photos with other digital content.

[0080] When processing a photo, the generation AI unit can analyze the user's gestures in real time and process the photo based on the gesture commands. For example, the generation AI unit can analyze the user's gestures in real time and process the photo based on the gesture commands. For example, a specific effect can be applied with a specific gesture. The generation AI unit can also adjust the type and intensity of processing based on the gesture commands. For example, the intensity of an effect can be changed with a specific gesture. This allows photos to be processed in real time based on the user's gestures.

[0081] The generation AI unit uses the emotion estimation function to analyze the emotions of the user when editing a photo and can suggest editing methods that match the emotions. The generation AI unit, for example, uses the emotion estimation function to analyze the emotions of the user when editing a photo. For example, if the user is enjoying themselves, it will suggest editing methods that match their emotions. The generation AI unit can also suggest the type and intensity of editing based on the user's emotions. For example, if the user is moved, it will suggest effects that match their emotions. This makes it possible to suggest editing methods that match the user's emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The photo management system further includes a photo backup unit. The backup unit can periodically create backups of photos stored in the cloud storage unit and store them in different cloud storage services. For example, the backup unit can automatically create backups at a time specified by the user and store them in different cloud storage services. The backup unit can also allow the user to manually create backups. This reduces the risk of losing photo data and allows for worry-free photo management.

[0084] When analyzing photo metadata, the cloud storage can automatically assign geographic tags based on the location where the photo was taken. For example, photos taken while traveling can be geotagged with the location, making them easier to search for later. The cloud storage can also organize photos based on the geographic tags and automatically generate albums related to specific locations, allowing users to easily relive their travel memories.

[0085] The access rights setting unit allows the generation AI to automatically determine the photo category based on the content of the photo and set different access rights for each category. For example, it can classify photos into categories such as family photos, landscape photos, and event photos, and set appropriate access rights for each. The access rights setting unit also allows users to manually change categories. This allows for efficient access rights management based on the content of the photo.

[0086] The generative AI unit uses emotion estimation to analyze the emotions of people in photos and apply special effects to emotionally significant photos. For example, it can apply a bright effect to photos of smiling people and a warm effect to photos of moving moments. The generative AI unit can also automatically add emotionally significant photos to specific albums, making them look more appealing.

[0087] The cloud storage unit can link photos with other cloud services and provide an automatic photo tagging function. For example, it can recognize people or objects in photos and automatically tag them. The cloud storage unit also allows users to manually add tags. This makes it easier to search for photos and allows for efficient photo management.

[0088] The access right setting unit can manage the access rights to photos using blockchain technology and record the change history of access rights transparently and securely. For example, the change history of access rights can be recorded on the blockchain to ensure transparency. The access right setting unit also records the access history transparently and securely. For example, the access history can be recorded on the blockchain to prevent unauthorized access. This allows the access rights to photos to be managed using blockchain technology and the access history can be recorded transparently and securely.

[0089] The generative AI unit uses its emotion estimation function to analyze the user's emotions in real time as they view photos, and can suggest photo layouts that match their emotions. For example, if the user is having fun, it will suggest a bright layout, and if they are moved, it will suggest a warm layout. The generative AI unit can also filter photos according to their emotions, allowing it to provide the optimal photo layout that matches the user's emotions.

[0090] The generation AI unit can analyze the content of photos and automatically generate albums based on the user's life events. For example, it can automatically create an album of birthday photos. The generation AI unit can also automatically create an album of wedding photos. This makes it possible to automatically generate albums based on the user's life events.

[0091] The generation AI unit can learn the user's past organizing patterns when organizing photos and suggest albums that suit individual preferences. For example, it can suggest new albums based on the patterns of albums the user has created in the past. The generation AI unit can also suggest album layouts and designs based on the user's preferences. This makes it possible to suggest albums that suit individual preferences based on the user's past organizing patterns.

[0092] The generation AI unit uses the emotion estimation function to analyze the user's emotions when organizing photos and can suggest organizing methods that match their emotions. For example, if the user is having fun, it can suggest organizing methods that match their emotions. The generation AI unit can also suggest album layouts and designs based on the user's emotions. This makes it possible to suggest organizing methods that match the user's emotions.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The cloud storage unit stores photos on the cloud. For example, a user uploads a photo they have taken or digitized to the cloud storage. The cloud storage unit can also analyze the photo's metadata and apply storage formats and security measures. Step 2: The access rights setting unit sets access rights for photos stored by the cloud storage unit. For example, it grants access rights to photos that you want to share with family and friends, and restricts access to private photos. The access rights setting unit can also learn user behavior patterns and automatically suggest optimal access rights settings. Step 3: The generation AI unit organizes, extracts, and processes photos based on the access rights set by the access rights setting unit. For example, the generation AI automatically generates an album based on information such as the date and location of the photo's capture, and facial recognition of the subject. The generation AI can also search for and display photos based on user instructions. Furthermore, the generation AI can also process photos based on user instructions.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] 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.

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

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

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

[0127] The data processing system 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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, in order to avoid confusion and to 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.

[0161] 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]

[0162] 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 cloud storage unit that stores photos on the cloud; an access right setting unit that sets access rights to the photos stored by the cloud storage unit; a generation AI unit that organizes, extracts, and processes the photos based on the access rights set by the access right setting unit. A system characterized by:

2. The cloud storage unit: Analyzes the metadata of the photos, learns user behavior patterns, and automatically suggests optimal access rights settings.

2. The system of claim 1.

3. The cloud storage unit: Link the photos with other cloud services to achieve integrated media management.

2. The system of claim 1.

4. The generation AI unit The content of the photos is analyzed, and an album is automatically generated based on the user's life events.

2. The system of claim 1.

5. The generation AI unit Analyzing the emotions of users when searching for photos and suggesting search results according to the emotions 2. The system of claim 1.

Images