System

Abstract

An object of the system according to the embodiment is to efficiently organize photographs and moving images in a smartphone and create a digital photo book.SOLUTION: A system according to an embodiment includes a photo / video analysis unit, a category classification unit, and a photo book generation unit. The photo / video analysis unit analyzes photos and videos in the smartphone. The category classification unit classifies the photograph or the moving image analyzed by the photograph and moving image analysis unit into an appropriate category. The photo book generation unit creates a digital photo book based on the photograph or the moving image classified by the category classification 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, it was difficult to efficiently organize the large number of photos and videos stored on a smartphone and create a digital photo book.

[0005] The system according to the embodiment aims to efficiently organize photos and videos stored in a smartphone and create a digital photo book. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo / video analysis unit, a category classification unit, and a photobook creation unit. The photo / video analysis unit analyzes photos and videos stored in a smartphone. The category classification unit classifies the photos and videos analyzed by the photo / video analysis unit into appropriate categories. The photobook creation unit creates a digital photobook based on the photos and videos classified by the category classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize photos and videos stored in a smartphone and create a digital photo book. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The photo organizing system according to an embodiment of the present invention efficiently organizes the ever-increasing number of photos and videos stored on smartphones and uses a generative AI to create a digital photo book. This significantly reduces the time it takes for users to organize their photos, and allows them to enjoy the completed photo book.

[0029] A photo organizing system according to an embodiment includes a photo / video analysis unit, a category classification unit, and a photobook generation unit. The photo / video analysis unit analyzes photos and videos stored on a smartphone. For example, the photo / video analysis unit analyzes the content of photos and videos using image recognition technology. The photo / video analysis unit can also analyze audio data from videos using audio analysis technology. The photo / video analysis unit can also analyze metadata to obtain location information and shooting dates and times. The category classification unit classifies photos and videos analyzed by the photo / video analysis unit into appropriate categories. For example, the category classification unit classifies photos and videos into categories such as travel, family, friends, and events. The category classification unit can also classify photos and videos based on emotions using an emotion estimation function. The category classification unit can also classify photos and videos based on events and locations using metadata. The photobook generation unit creates a digital photobook based on the photos and videos classified by the category classification unit. For example, the photobook generation unit creates a photobook by arranging travel photos in chronological order. The photobook generation unit can also create a photobook that compiles highlights from a specific event. The photo book generator can also learn the user's preferences and past photo book styles to suggest optimal layouts and designs. This allows the photo organizing system according to the embodiment to efficiently organize photos and videos stored on a smartphone and automatically create a digital photo book. For example, users can eliminate the time required for organizing photos and use the saved time to enjoy viewing the completed photo book.

[0030] The photo and video analysis unit can analyze the metadata of photos and videos and organize them based on related events and locations. For example, the generation AI in the photo and video analysis unit analyzes the metadata of photos and videos and organizes them based on location information. For example, photos taken at a specific travel destination may be classified into a "travel" category. The generation AI in the photo and video analysis unit also analyzes the date and time the photos and videos were taken and organizes them chronologically. For example, photos taken on the date of a specific event may be classified into an "event" category. The generation AI in the photo and video analysis unit also analyzes the metadata of photos and videos and automatically creates albums based on related events and locations. For example, it generates albums such as "Summer Vacation Trip" and "Christmas Party." This allows photos and videos to be organized based on metadata.

[0031] The photo and video analysis unit can analyze audio data from photos and videos and organize them based on the audio content. For example, the photo and video analysis unit uses a generation AI to analyze the audio data from videos and convert the content into text using voice recognition technology. For example, it can categorize videos based on audio containing specific keywords. The photo and video analysis unit also uses a generation AI to analyze the audio data from photos and videos and create albums based on the audio content. For example, it can generate albums such as "Family Conversations" and "Fun Times with Friends." The photo and video analysis unit also uses a generation AI to analyze audio data and organize photos and videos based on the audio content. For example, it can classify videos containing audio from specific events into the "Events" category. This allows photos and videos to be organized based on their audio content.

[0032] The photo and video analysis unit can recognize the subjects of photos and videos and organize them by subject. For example, the generation AI in the photo and video analysis unit recognizes the subjects of photos and videos and organizes them by person. For example, photos of family members are classified into the "Family" category and photos of friends into the "Friends" category. The generation AI in the photo and video analysis unit also recognizes the subjects of photos and videos and organizes them by object. For example, photos of landscapes are classified into the "Landscape" category and photos of food are classified into the "Food" category. The generation AI in the photo and video analysis unit also recognizes the subjects and creates albums for each subject. For example, it generates albums such as "Photos of Pets" and "Scenery from Travel Destinations." This allows photos and videos to be organized by subject.

[0033] The category classification unit learns the user's past classification history and can perform more accurate category classification. For example, the generation AI in the category classification unit learns the user's past classification history and classifies new photos and videos based on similar patterns. For example, it automatically classifies new travel photos based on photos that were previously classified in the "travel" category. The category classification unit also analyzes the user's past classification history and learns specific trends. For example, it automatically classifies photos related to specific events or places into the same category. The category classification unit also performs more accurate category classification based on the user's past classification history. For example, it prioritizes the display of categories that the user frequently uses and automatically classifies new photos and videos. This allows the system to learn the user's past classification history and perform more accurate category classification.

[0034] The categorization unit can analyze the content of photos and videos and classify them based on specific themes. For example, the generation AI analyzes the content of photos and videos and classifies them based on specific themes. For example, photos are classified by season into "spring," "summer," "autumn," and "winter." The generation AI also analyzes the color tones of photos and videos and classifies them based on color themes. For example, photos of blue skies are classified into the "blue" category and photos of autumn leaves are classified into the "red" category. The generation AI also analyzes the content of photos and videos and creates albums based on specific themes. For example, it generates albums such as "Summer Memories" and "Autumn Scenery." This allows photos and videos to be classified based on specific themes.

[0035] The category classification unit can analyze the text data of photos and videos and classify them based on the text content. For example, the generation AI analyzes the captions and comments of photos and videos and classifies them based on the text content. For example, a photo with a caption that reads "Travel" is classified into the "Travel" category. The generation AI also analyzes the text data of photos and videos and classifies them based on specific keywords. For example, a photo with a comment that reads "Birthday" is classified into the "Event" category. The generation AI also analyzes the text data of photos and videos and creates albums based on the text content. For example, it generates albums such as "Family Memories" and "Fun Times with Friends." This allows photos and videos to be classified based on the text content.

[0036] The category classification unit can analyze the shooting device information of photos and videos and classify them by device. For example, the generation AI analyzes the shooting device information of photos and videos and classifies them by device. For example, photos taken with a smartphone are classified into the "smartphone" category, and photos taken with a digital camera are classified into the "digital camera" category. The category classification unit also analyzes the shooting device information of photos and videos and creates albums based on specific devices. For example, it generates albums such as "Travel memories taken with a smartphone" and "Events taken with a digital camera." The category classification unit also analyzes the shooting device information of photos and videos and organizes them by device. For example, it automatically classifies photos taken with a specific device into the same folder. This allows photos and videos to be classified by shooting device.

[0037] The photobook generation unit can analyze the storytelling elements of photos and videos to create a photobook with a narrative. For example, the photobook generation unit uses a generation AI to analyze the content of photos and videos and extract storytelling elements. For example, a photobook can be created by arranging travel photos in chronological order to tell the story of a trip. The photobook generation unit also uses a generation AI to analyze the content of photos and videos and create a narrative photobook based on a specific theme. For example, a photobook can be generated based on themes such as "family growth record" or "adventures with friends." The photobook generation unit also uses a generation AI to analyze the content of photos and videos and suggest layouts and designs to emphasize the storytelling elements. For example, it can enlarge important moments to visually express the flow of the story. This allows a photobook with a narrative to be created.

[0038] The photobook generation unit can learn the style of a user's past photobooks and suggest designs that suit the user's preferences. For example, the generation AI of the photobook generation unit learns the style of a user's past photobooks and suggests similar designs. For example, it creates a new photobook based on a specific layout and color scheme. The generation AI of the photobook generation unit also analyzes the user's preferences and suggests customized designs. For example, it automatically applies the user's preferred fonts and background designs. The generation AI of the photobook generation unit also learns the style of a user's past photobooks and suggests themes and layouts that suit the user's preferences. For example, it suggests designs such as "simple and modern design" or "colorful and fun design." This makes it possible to suggest designs that suit the user's preferences.

[0039] The photobook generation unit can analyze the music data of photos and videos and create a photobook that matches the music. For example, the generation AI in the photobook generation unit analyzes the music data of a video and creates a photobook that matches the rhythm and tempo of the music. For example, the photobook generation unit switches photos in time with the beat of the music. The generation AI in the photobook generation unit also analyzes the music data of photos and videos and suggests a design that matches the atmosphere of the music. For example, it uses bright colors for happy music and calm colors for moving music. The generation AI in the photobook generation unit also analyzes the music data and creates a photobook based on a musical theme. For example, it generates a photobook that matches "wedding music" or a photobook that matches "travel soundtrack." This makes it possible to create a photobook that matches music.

[0040] The photobook generation unit can analyze location information of photos and videos and create a photobook arranged on a map. In the photobook generation unit, for example, a generation AI analyzes location information of photos and videos and creates a photobook arranged on a map. For example, travel photos are arranged at places visited on a map. In addition, the photobook generation unit analyzes location information of photos and videos and creates albums based on specific places. For example, it generates albums such as "Travel in Europe" or "Tourist Spots in Japan." In addition, the photobook generation unit analyzes location information and creates a photobook arranged on a map to visually display places visited by the user. For example, photos are displayed by placing pins on the map. This makes it possible to create a photobook arranged on a map.

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

[0042] The photo organizing system may further include an audio guide unit. The audio guide unit can provide related information and memories by voice when the user is viewing photos and videos. For example, when viewing travel photos, the audio guide unit can provide audio information about the history and tourist attractions of the location. Also, when viewing family photos, the audio guide unit can play audio stories about specific events or happenings. Furthermore, the audio guide unit can play music or sound effects that evoke emotions when the user is viewing photos and videos. This allows the user to enjoy the photos and videos not only visually but also aurally.

[0043] The photo organizing system may further include an interactive map unit. The interactive map unit can arrange photos and videos taken by the user on a map based on location information. For example, travel photos can be displayed by placing pins at locations visited on the map. The interactive map unit can also display photos and videos related to a location when the user clicks on a pin on the map. Furthermore, the interactive map unit can allow the user to set a specific route on the map and display photos and videos along that route in chronological order. This allows the user to use the map to organize photos and videos and enjoy them visually.

[0044] The photo organizing system may further include a social sharing unit. The social sharing unit allows users to easily share organized photos and videos on social media. For example, a photo book created by a user can be posted to Facebook® or Instagram® with one click. The social sharing unit can also generate links for users to select specific photos and videos and share them with friends and family. The social sharing unit can also display comments and reactions to photos and videos shared by users in real time. This allows users to easily share photos and videos and enjoy them with others.

[0045] The photo organization system may further include a custom filter section. The custom filter section allows a user to apply custom filters to photos and videos. For example, the user may edit photos by adding specific colors or effects. The custom filter section also allows a user to create their own filters and apply them to other photos and videos. The custom filter section also allows a user to select a filter based on a specific theme and apply it to the entire photo book. This allows a user to edit photos and videos to their liking and create a more attractive photo book.

[0046] The photo organization system may further include an automatic backup unit. The automatic backup unit may periodically back up a user's photos and videos to cloud storage. For example, automatic backups may be performed based on a daily or weekly schedule. The automatic backup unit may also allow a user to select specific folders or albums and prioritize their contents for backup. The automatic backup unit may also provide the user with notifications about the progress and completion of the backup. This allows the user to safely store photo and video data and reduce the risk of loss or damage.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The photo and video analysis unit analyzes the photos and videos stored on the smartphone. For example, it can use image recognition technology to analyze the content of photos and videos, and audio analysis technology to analyze the audio data of videos. It can also analyze metadata to obtain location information and the date and time of shooting. Step 2: The categorization unit categorizes the photos and videos analyzed by the photo and video analysis unit into appropriate categories. For example, they can be classified into categories such as travel, family, friends, and events. It can also classify photos and videos based on their emotions using emotion estimation. It can also classify them based on events and locations using metadata. Step 3: The photobook generator creates a digital photobook based on the photos and videos categorized by the category classifier. For example, it can create a photobook by arranging travel photos in chronological order, or by compiling highlights from a particular event. It can also learn the user's preferences and the style of past photobooks, and suggest optimal layouts and designs.

[0049] (Example 2) The photo organizing system according to an embodiment of the present invention efficiently organizes the ever-increasing number of photos and videos stored on smartphones and uses a generative AI to create a digital photo book. This significantly reduces the time it takes for users to organize their photos, and allows them to enjoy the completed photo book.

[0050] A photo organizing system according to an embodiment includes a photo / video analysis unit, a category classification unit, and a photobook generation unit. The photo / video analysis unit analyzes photos and videos stored on a smartphone. For example, the photo / video analysis unit analyzes the content of photos and videos using image recognition technology. The photo / video analysis unit can also analyze audio data from videos using audio analysis technology. The photo / video analysis unit can also analyze metadata to obtain location information and shooting dates and times. The category classification unit classifies photos and videos analyzed by the photo / video analysis unit into appropriate categories. For example, the category classification unit classifies photos and videos into categories such as travel, family, friends, and events. The category classification unit can also classify photos and videos based on emotions using an emotion estimation function. The category classification unit can also classify photos and videos based on events and locations using metadata. The photobook generation unit creates a digital photobook based on the photos and videos classified by the category classification unit. For example, the photobook generation unit creates a photobook by arranging travel photos in chronological order. The photobook generation unit can also create a photobook that compiles highlights from a specific event. The photo book generator can also learn the user's preferences and past photo book styles to suggest optimal layouts and designs. This allows the photo organizing system according to the embodiment to efficiently organize photos and videos stored on a smartphone and automatically create a digital photo book. For example, users can eliminate the time required for organizing photos and use the saved time to enjoy viewing the completed photo book.

[0051] The photo and video analysis unit can analyze the content of photos and videos and classify them based on specific emotions. For example, the generation AI analyzes the content of photos and videos and uses image recognition technology to infer specific emotions. For example, it classifies photos of smiling faces as "joy" and photos of crying faces as "sadness." The generation AI also analyzes the content of photos and videos and calculates an emotion score using an emotion analysis algorithm. For example, it prioritizes photos with high emotion scores and classifies them into the "positive" category. The generation AI also analyzes the content of photos and videos and automatically creates albums based on the type of emotion. For example, it generates albums such as "joyful family moments" and "fun times with friends." This allows photos and videos to be classified based on emotion.

[0052] The photo and video analysis unit can analyze the metadata of photos and videos and organize them based on related events and locations. For example, the generation AI in the photo and video analysis unit analyzes the metadata of photos and videos and organizes them based on location information. For example, photos taken at a specific travel destination may be classified into a "travel" category. The generation AI in the photo and video analysis unit also analyzes the date and time the photos and videos were taken and organizes them chronologically. For example, photos taken on the date of a specific event may be classified into an "event" category. The generation AI in the photo and video analysis unit also analyzes the metadata of photos and videos and automatically creates albums based on related events and locations. For example, it generates albums such as "Summer Vacation Trip" and "Christmas Party." This allows photos and videos to be organized based on metadata.

[0053] The photo and video analysis unit can use the emotion estimation function to organize photos and videos based on the user's emotions and arrange them to elicit positive emotions. For example, the photo and video analysis unit uses a generation AI to analyze the content of photos and videos and organize them based on the user's emotions using the emotion estimation function. For example, to elicit positive emotions, photos of smiling faces are preferentially displayed. The photo and video analysis unit also uses a generation AI to analyze the content of photos and videos and create albums based on the user's emotions using the emotion estimation function. For example, albums such as "happy moments" and "fun memories" are generated. The photo and video analysis unit also uses a generation AI to analyze the content of photos and videos and arrange photos and videos based on the user's emotions using the emotion estimation function. For example, to elicit positive emotions, photos of fun events are displayed first. This allows the photo and video analysis unit to organize photos and videos based on the user's emotions and elicit positive emotions.

[0054] The photo and video analysis unit can analyze audio data from photos and videos and organize them based on the audio content. For example, the photo and video analysis unit uses a generation AI to analyze the audio data from videos and convert the content into text using voice recognition technology. For example, it can categorize videos based on audio containing specific keywords. The photo and video analysis unit also uses a generation AI to analyze the audio data from photos and videos and create albums based on the audio content. For example, it can generate albums such as "Family Conversations" and "Fun Times with Friends." The photo and video analysis unit also uses a generation AI to analyze audio data and organize photos and videos based on the audio content. For example, it can classify videos containing audio from specific events into the "Events" category. This allows photos and videos to be organized based on their audio content.

[0055] The photo and video analysis unit can recognize the subjects of photos and videos and organize them by subject. For example, the generation AI in the photo and video analysis unit recognizes the subjects of photos and videos and organizes them by person. For example, photos of family members are classified into the "Family" category and photos of friends into the "Friends" category. The generation AI in the photo and video analysis unit also recognizes the subjects of photos and videos and organizes them by object. For example, photos of landscapes are classified into the "Landscape" category and photos of food are classified into the "Food" category. The generation AI in the photo and video analysis unit also recognizes the subjects and creates albums for each subject. For example, it generates albums such as "Photos of Pets" and "Scenery from Travel Destinations." This allows photos and videos to be organized by subject.

[0056] The photo and video analysis unit uses an emotion estimation function to monitor the user's emotions in real time as they view photos and videos, and display them in the optimal order. For example, the photo and video analysis unit uses a generation AI to analyze the content of photos and videos and monitor the user's emotions in real time using the emotion estimation function. For example, photos that evoke positive emotions in the user are displayed preferentially. The photo and video analysis unit also uses a generation AI to analyze the content of photos and videos and display them in the optimal order based on the user's emotions using the emotion estimation function. For example, photos that evoke positive emotions are displayed first so that the user can reminisce about happy memories. The photo and video analysis unit also uses a generation AI to analyze the content of photos and videos and monitor the user's emotions in real time using the emotion estimation function, and create albums in the optimal order. For example, albums such as "happy memories" and "touching moments" are generated. This allows photos and videos to be displayed in the optimal order based on the user's emotions.

[0057] The category classification unit learns the user's past classification history and can perform more accurate category classification. For example, the generation AI in the category classification unit learns the user's past classification history and classifies new photos and videos based on similar patterns. For example, it automatically classifies new travel photos based on photos that were previously classified in the "travel" category. The category classification unit also analyzes the user's past classification history and learns specific trends. For example, it automatically classifies photos related to specific events or places into the same category. The category classification unit also performs more accurate category classification based on the user's past classification history. For example, it prioritizes the display of categories that the user frequently uses and automatically classifies new photos and videos. This allows the system to learn the user's past classification history and perform more accurate category classification.

[0058] The categorization unit can analyze the content of photos and videos and classify them based on specific themes. For example, the generation AI analyzes the content of photos and videos and classifies them based on specific themes. For example, photos are classified by season into "spring," "summer," "autumn," and "winter." The generation AI also analyzes the color tones of photos and videos and classifies them based on color themes. For example, photos of blue skies are classified into the "blue" category and photos of autumn leaves are classified into the "red" category. The generation AI also analyzes the content of photos and videos and creates albums based on specific themes. For example, it generates albums such as "Summer Memories" and "Autumn Scenery." This allows photos and videos to be classified based on specific themes.

[0059] The category classification unit uses the emotion estimation function to dynamically change categories based on the user's emotions, allowing classification to match the user's mood. For example, the generation AI analyzes the content of photos and videos and dynamically changes categories based on the user's emotions using the emotion estimation function. For example, photos in which the user has positive emotions are classified into a "happy memories" category. The category classification unit also analyzes the content of photos and videos and uses the emotion estimation function to create albums based on the user's emotions. For example, albums such as "happy moments" and "moving events" are generated. The category classification unit also analyzes the content of photos and videos and dynamically changes categories based on the user's emotions using the emotion estimation function, allowing classification to match the user's mood. For example, when a user wants to relax, the "soothing scenery" category is displayed. This allows categories to be dynamically changed based on the user's emotions, allowing classification to match the user's mood.

[0060] The category classification unit can analyze the text data of photos and videos and classify them based on the text content. For example, the generation AI analyzes the captions and comments of photos and videos and classifies them based on the text content. For example, a photo with a caption that reads "Travel" is classified into the "Travel" category. The generation AI also analyzes the text data of photos and videos and classifies them based on specific keywords. For example, a photo with a comment that reads "Birthday" is classified into the "Event" category. The generation AI also analyzes the text data of photos and videos and creates albums based on the text content. For example, it generates albums such as "Family Memories" and "Fun Times with Friends." This allows photos and videos to be classified based on the text content.

[0061] The category classification unit can analyze the shooting device information of photos and videos and classify them by device. For example, the generation AI analyzes the shooting device information of photos and videos and classifies them by device. For example, photos taken with a smartphone are classified into the "smartphone" category, and photos taken with a digital camera are classified into the "digital camera" category. The category classification unit also analyzes the shooting device information of photos and videos and creates albums based on specific devices. For example, it generates albums such as "Travel memories taken with a smartphone" and "Events taken with a digital camera." The category classification unit also analyzes the shooting device information of photos and videos and organizes them by device. For example, it automatically classifies photos taken with a specific device into the same folder. This allows photos and videos to be classified by shooting device.

[0062] The photobook generation unit can analyze the storytelling elements of photos and videos to create a photobook with a narrative. For example, the photobook generation unit uses a generation AI to analyze the content of photos and videos and extract storytelling elements. For example, a photobook can be created by arranging travel photos in chronological order to tell the story of a trip. The photobook generation unit also uses a generation AI to analyze the content of photos and videos and create a narrative photobook based on a specific theme. For example, a photobook can be generated based on themes such as "family growth record" or "adventures with friends." The photobook generation unit also uses a generation AI to analyze the content of photos and videos and suggest layouts and designs to emphasize the storytelling elements. For example, it can enlarge important moments to visually express the flow of the story. This allows a photobook with a narrative to be created.

[0063] The photobook generation unit can learn the style of a user's past photobooks and suggest designs that suit the user's preferences. For example, the generation AI of the photobook generation unit learns the style of a user's past photobooks and suggests similar designs. For example, it creates a new photobook based on a specific layout and color scheme. The generation AI of the photobook generation unit also analyzes the user's preferences and suggests customized designs. For example, it automatically applies the user's preferred fonts and background designs. The generation AI of the photobook generation unit also learns the style of a user's past photobooks and suggests themes and layouts that suit the user's preferences. For example, it suggests designs such as "simple and modern design" or "colorful and fun design." This makes it possible to suggest designs that suit the user's preferences.

[0064] The photobook generation unit can dynamically change the layout and design of the photobook based on the user's emotions using an emotion estimation function. For example, the photobook generation unit uses a generation AI to analyze the content of photos and videos and dynamically change the layout of the photobook based on the user's emotions using the emotion estimation function. For example, bright colors are used to elicit positive emotions. The photobook generation unit also analyzes the content of photos and videos and dynamically changes the design based on the user's emotions using the emotion estimation function. For example, specific effects are added to emphasize moving moments. The photobook generation unit also analyzes the content of photos and videos and dynamically changes the layout and design of the photobook based on the user's emotions using the emotion estimation function, creating a photobook that matches the user's mood. For example, a photobook based on themes such as "happy memories" or "touching moments" is generated. This allows the layout and design of the photobook to be dynamically changed based on the user's emotions.

[0065] The photobook generation unit can analyze the music data of photos and videos and create a photobook that matches the music. For example, the generation AI in the photobook generation unit analyzes the music data of a video and creates a photobook that matches the rhythm and tempo of the music. For example, the photobook generation unit switches photos in time with the beat of the music. The generation AI in the photobook generation unit also analyzes the music data of photos and videos and suggests a design that matches the atmosphere of the music. For example, it uses bright colors for happy music and calm colors for moving music. The generation AI in the photobook generation unit also analyzes the music data and creates a photobook based on a musical theme. For example, it generates a photobook that matches "wedding music" or a photobook that matches "travel soundtrack." This makes it possible to create a photobook that matches music.

[0066] The photobook generation unit can analyze location information of photos and videos and create a photobook arranged on a map. In the photobook generation unit, for example, a generation AI analyzes location information of photos and videos and creates a photobook arranged on a map. For example, travel photos are arranged at places visited on a map. In addition, the photobook generation unit analyzes location information of photos and videos and creates albums based on specific places. For example, it generates albums such as "Travel in Europe" or "Tourist Spots in Japan." In addition, the photobook generation unit analyzes location information and creates a photobook arranged on a map to visually display places visited by the user. For example, photos are displayed by placing pins on the map. This makes it possible to create a photobook arranged on a map.

[0067] The photobook generation unit uses an emotion estimation function to monitor the user's emotions in real time as they view the photobook, and can transition pages in accordance with their emotions. For example, the photobook generation unit uses a generation AI to analyze the content of photos and videos and monitor the user's emotions in real time using the emotion estimation function. For example, pages that the user feels positive about are preferentially displayed. The photobook generation unit also uses a generation AI to analyze the content of photos and videos and transition pages in accordance with the user's emotions using the emotion estimation function. For example, pages that elicit emotions are displayed first so that the user can look back on a moving moment. The photobook generation unit also uses a generation AI to analyze the content of photos and videos and monitor the user's emotions in real time using the emotion estimation function, and transition pages in accordance with the emotions. For example, page transitions are performed based on themes such as "happy memories" and "moving moments." This enables page transitions in accordance with the user's emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The photo organizing system may further include an audio guide unit. The audio guide unit can provide related information and memories by voice when the user is viewing photos and videos. For example, when viewing travel photos, the audio guide unit can provide audio information about the history and tourist attractions of the location. Also, when viewing family photos, the audio guide unit can play audio stories about specific events or happenings. Furthermore, the audio guide unit can play music or sound effects that evoke emotions when the user is viewing photos and videos. This allows the user to enjoy the photos and videos not only visually but also aurally.

[0070] The photo organizing system may further include an interactive map unit. The interactive map unit can arrange photos and videos taken by the user on a map based on location information. For example, travel photos can be displayed by placing pins at locations visited on the map. The interactive map unit can also display photos and videos related to a location when the user clicks on a pin on the map. Furthermore, the interactive map unit can allow the user to set a specific route on the map and display photos and videos along that route in chronological order. This allows the user to use the map to organize photos and videos and enjoy them visually.

[0071] The photo organizing system may further include a social sharing unit. The social sharing unit allows users to easily share organized photos and videos on social media. For example, a photo book created by a user can be posted to Facebook® or Instagram® with one click. The social sharing unit can also generate links for users to select specific photos and videos and share them with friends and family. The social sharing unit can also display comments and reactions to photos and videos shared by users in real time. This allows users to easily share photos and videos and enjoy them with others.

[0072] The photo organization system may further include a custom filter section. The custom filter section allows a user to apply custom filters to photos and videos. For example, the user may edit photos by adding specific colors or effects. The custom filter section also allows a user to create their own filters and apply them to other photos and videos. The custom filter section also allows a user to select a filter based on a specific theme and apply it to the entire photo book. This allows a user to edit photos and videos to their liking and create a more attractive photo book.

[0073] The photo organization system may further include an automatic backup unit. The automatic backup unit may periodically back up a user's photos and videos to cloud storage. For example, automatic backups may be performed based on a daily or weekly schedule. The automatic backup unit may also allow a user to select specific folders or albums and prioritize their contents for backup. The automatic backup unit may also provide the user with notifications about the progress and completion of the backup. This allows the user to safely store photo and video data and reduce the risk of loss or damage.

[0074] The photo organizing system can further use an emotion estimation function to dynamically change the display order of photos and videos based on the user's emotions. For example, photos that the user feels positive about can be preferentially displayed. The emotion estimation function can also be used to dynamically change the theme of an album based on the user's emotions. For example, a "Relaxing Scenery" category can be displayed when the user wants to relax. The emotion estimation function can also be used to dynamically change the layout and design of a photo book based on the user's emotions. This makes it possible to display photos and videos that match the user's emotions.

[0075] The photo organization system can also use an emotion estimation function to monitor the user's emotions in real time as they browse photos and videos, and display them in the optimal order. For example, photos in which the user has positive emotions can be displayed preferentially. The emotion estimation function can also be used to dynamically change the album theme based on the user's emotions. For example, a "Relaxing Scenery" category can be displayed when the user wants to relax. The emotion estimation function can also be used to dynamically change the layout and design of the photo book based on the user's emotions. This makes it possible to display photos and videos that match the user's emotions.

[0076] The photo organizing system can further use an emotion estimation function to dynamically change the display order of photos and videos based on the user's emotions. For example, photos that the user feels positive about can be preferentially displayed. The emotion estimation function can also be used to dynamically change the theme of an album based on the user's emotions. For example, a "Relaxing Scenery" category can be displayed when the user wants to relax. The emotion estimation function can also be used to dynamically change the layout and design of a photo book based on the user's emotions. This makes it possible to display photos and videos that match the user's emotions.

[0077] The photo organization system can also use an emotion estimation function to monitor the user's emotions in real time as they browse photos and videos, and display them in the optimal order. For example, photos in which the user has positive emotions can be displayed preferentially. The emotion estimation function can also be used to dynamically change the album theme based on the user's emotions. For example, a "Relaxing Scenery" category can be displayed when the user wants to relax. The emotion estimation function can also be used to dynamically change the layout and design of the photo book based on the user's emotions. This makes it possible to display photos and videos that match the user's emotions.

[0078] The photo organizing system can further use an emotion estimation function to dynamically change the display order of photos and videos based on the user's emotions. For example, photos that the user feels positive about can be preferentially displayed. The emotion estimation function can also be used to dynamically change the theme of an album based on the user's emotions. For example, a "Relaxing Scenery" category can be displayed when the user wants to relax. The emotion estimation function can also be used to dynamically change the layout and design of a photo book based on the user's emotions. This makes it possible to display photos and videos that match the user's emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The photo and video analysis unit analyzes the photos and videos stored on the smartphone. For example, it can use image recognition technology to analyze the content of photos and videos, and audio analysis technology to analyze the audio data of videos. It can also analyze metadata to obtain location information and the date and time of shooting. Step 2: The categorization unit categorizes the photos and videos analyzed by the photo and video analysis unit into appropriate categories. For example, they can be classified into categories such as travel, family, friends, and events. It can also classify photos and videos based on their emotions using emotion estimation. It can also classify them based on events and locations using metadata. Step 3: The photobook generator creates a digital photobook based on the photos and videos categorized by the category classifier. For example, it can create a photobook by arranging travel photos in chronological order, or by compiling highlights from a particular event. It can also learn the user's preferences and the style of past photobooks, and suggest optimal layouts and designs.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a 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.

[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 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.

[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 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).

[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] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 photo and video analysis unit that analyzes photos and videos stored on the smartphone, a category classification unit that classifies the photos and videos analyzed by the photo / video analysis unit into appropriate categories; a photo book creation unit that creates a digital photo book based on the photos and videos classified by the category classification unit. A system characterized by:

2. The photo / video analysis unit Analyze the content of the photos and videos and classify them based on specific emotions 2. The system of claim 1.

3. The photo / video analysis unit Analyze the metadata of the photos and videos and organize them based on related events and locations 2. The system of claim 1.

4. The photo / video analysis unit Organize the photos and videos based on the user's emotions and arrange them to elicit positive emotions 2. The system of claim 1.

5. The photo / video analysis unit Analyze the audio data of the photos and videos and organize them based on the audio content 2. The system of claim 1.

6. The photo / video analysis unit Recognize the subject of the photo or video and organize it by subject 2. The system of claim 1.

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