system

Abstract

The system according to this embodiment aims to efficiently organize and classify photographs taken and automatically generate albums. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a classification unit, and a generation unit. The collection unit collects captured photographic data. The analysis unit performs face recognition or object detection on the photographs collected by the collection unit. The classification unit classifies the photographs by category or by person based on the information analyzed by the analysis unit. The generation unit generates albums based on events, locations, or dates, based on the photographs classified by the classification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was time-consuming to efficiently organize and classify the taken photos and generate an album.

[0005] The system according to the embodiment aims to efficiently organize and classify the taken photos and automatically generate an album.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a classification unit, and a generation unit. The collection unit collects captured photographic data. The analysis unit performs face recognition or object detection on the photographs collected by the collection unit. The classification unit classifies the photographs by category or by person based on the information analyzed by the analysis unit. The generation unit generates albums based on events, locations, or dates, using the photographs classified by the classification unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently organize and classify photographs taken and automatically generate albums. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI assistant for automatic organization of smartphone photo albums according to an embodiment of the present invention is a system that automatically organizes a smartphone's photo album. This system collects captured photo data, performs face recognition or object detection, and classifies the photos by category or person. Based on the classified photos, it generates albums based on events, locations, and dates. In this process, duplicate photos and photos of low quality can be excluded. It can also identify events based on the date and time of shooting and location information, and group photos related to those events. Furthermore, it can automatically detect and delete photos of low quality, such as blurry or underexposed photos. It can also estimate the user's emotions and adjust the timing of photo collection based on the estimated emotions. When collecting photos, filtering can be performed based on the user's current activities and areas of interest, and the priority of photos to be collected can be determined based on the user's emotions. As a result, the AI ​​assistant for automatic organization of smartphone photo albums can efficiently organize the user's photo album and provide it in a more user-friendly format.

[0029] The AI ​​assistant for automatically organizing smartphone photo albums according to this embodiment comprises a collection unit, an analysis unit, a classification unit, and a generation unit. The collection unit collects captured photo data. For example, the collection unit automatically collects photo data taken with a smartphone camera. The collection unit can also collect photo data from cloud storage or social media. For example, with the user's permission, the collection unit periodically collects photo data stored in cloud storage. The collection unit can also collect photo data posted by users using social media APIs. The analysis unit performs face recognition or object detection on the photos collected by the collection unit. For example, the analysis unit identifies people in the photos using face recognition technology with deep learning. The analysis unit can also identify objects in the photos using object detection technology with YOLO. For example, the analysis unit uses face recognition technology to detect the faces of people in the photos and extract their facial features. The analysis unit can also use object detection technology to identify the types of objects in the photos. The classification unit categorizes photos by category or person based on the information analyzed by the analysis unit. For example, the classification unit categorizes photos based on criteria such as age, gender, and areas of interest. The classification unit can also categorize photos by specific people. For example, it groups photos of the same person based on facial features identified by the analysis unit. The classification unit can also categorize photos based on specific events or locations. The generation unit generates albums based on events, locations, and dates, using the photos categorized by the classification unit. For example, it generates albums based on criteria such as specific event names, geographical coordinates, or calendar dates. The generation unit can also generate albums while excluding duplicate or low-quality photos. For example, it detects and excludes duplicate photos based on hash value matches or image similarity. It can also detect and exclude low-quality photos based on criteria such as resolution, noise level, and blur.As a result, the AI ​​assistant that supports automatic organization of smartphone photo albums according to this embodiment can efficiently organize the user's photo albums and present them in a more user-friendly format.

[0030] The collection unit collects captured photographic data. For example, the collection unit automatically collects photographic data taken with a smartphone camera. Specifically, when a smartphone camera app takes a picture, the collection unit immediately detects the photographic data and saves it to its internal storage. The collection unit can also collect photographic data from cloud storage and social media. For example, with the user's permission, the collection unit periodically collects photographic data stored in cloud storage. It uses the cloud storage API to access the user's account and automatically downloads newly uploaded photos. The collection unit can also use social media APIs to collect photographic data posted by users. For example, the collection unit periodically checks for photos posted by users on social media and collects the photographic data if there are new posts. This allows the collection unit to centrally manage not only photos on the smartphone but also photos stored in the cloud and on social media. Furthermore, the collection unit simultaneously collects metadata of the photographic data (date and time of shooting, location information, camera settings, etc.) which can be used for subsequent analysis and classification. This allows the collection unit to efficiently collect user photographic data and improve the overall system performance.

[0031] The analysis unit performs face recognition or object detection on the photographs collected by the collection unit. For example, the analysis unit uses deep learning-based face recognition technology to identify people in the photographs. Specifically, it uses a face recognition model to detect faces in the photographs and extract their facial features. This allows it to identify who the people in the photographs are. The analysis unit can also use YOLO-based object detection technology to identify objects in the photographs. For example, it uses an object detection model to detect objects in the photographs and identify their types. This allows it to identify what the objects in the photographs are. Furthermore, the analysis unit can combine face recognition and object detection technologies to analyze the relationship between people and objects in the photographs. For example, it can identify objects held by people in the photographs or the location where people are. This allows the analysis unit to analyze the content of the photographs in detail and use this information for subsequent classification and album generation. In addition, the analysis unit can save the analysis results to a database and reuse them in subsequent processing. This allows the analysis unit to analyze the collected data quickly and accurately, improving the overall system performance.

[0032] The classification unit categorizes photos by category or person based on the information analyzed by the analysis unit. For example, the classification unit categorizes photos based on criteria such as age, gender, and areas of interest. Specifically, it categorizes photos into specific categories based on facial features or object types extracted by the analysis unit. For instance, the classification unit groups photos of the same person based on facial features identified by the analysis unit. The classification unit can also categorize photos based on specific events or locations. For example, it groups photos taken at specific events or locations based on the photo's metadata (date and time taken, location information, etc.). This allows the classification unit to efficiently organize the user's photos and facilitate subsequent album creation. Furthermore, the classification unit can learn the user's preferences and past classification history to improve classification accuracy. For example, it can learn patterns from photos previously classified by the user and automatically categorize new photos with similar patterns. This reduces the user's effort and streamlines photo organization.

[0033] The generation unit generates albums based on events, locations, and dates, using photos classified by the classification unit. For example, it generates albums based on criteria such as specific event names, geographical coordinates, or calendar dates. Specifically, it automatically creates albums related to specific events or locations based on photos grouped by the classification unit. For instance, it generates an album of photos related to a trip based on the geographical coordinates of the travel destination. The generation unit can also exclude duplicate and low-quality photos when generating albums. For example, it detects and excludes duplicate photos based on hash value matches or image similarity. It can also detect and exclude low-quality photos based on criteria such as resolution, noise level, and blur. This allows the generation unit to provide users with albums that are easy to view and use. Furthermore, the generation unit can automatically adjust the album layout and design to create visually appealing albums. For example, it adjusts photo placement, size, and background color to produce professionally finished albums. This allows the generation unit to efficiently organize users' photo albums and present them in a more user-friendly format.

[0034] The generation unit can generate albums by excluding duplicate photos or photos with image quality below a threshold. For example, the generation unit can automatically detect and exclude duplicate photos from albums. For example, the generation unit can detect duplicate photos based on matching hash values. The generation unit can also detect duplicate photos based on image similarity. For example, the generation unit can extract image features and detect duplicate photos based on the degree of matching of those features. The generation unit can also automatically detect and exclude photos with image quality below a threshold. For example, the generation unit can detect and exclude photos with low resolution from albums. For example, the generation unit can detect photos with resolution below a specific threshold. The generation unit can also detect and exclude photos with high noise levels from albums. For example, the generation unit can detect photos with noise levels exceeding a specific threshold. The generation unit can also detect and exclude photos with a high degree of blur from albums. For example, the generation unit can detect photos with a degree of blur exceeding a specific threshold. This improves the quality of albums by excluding duplicate photos and photos with low image quality. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate albums using an AI model that detects duplicate photos and evaluates image quality.

[0035] The generation unit can identify events based on the date and time of shooting and location information, and group photos related to those events. For example, the generation unit can group photos taken at similar times and put them into albums as part of the same event. For example, the generation unit can group photos taken at specific times within a certain range. The generation unit can also group photos taken at similar locations and put them into albums as events from the same location. For example, the generation unit can group photos taken at specific locations within a certain range. The generation unit can also combine the date and time of shooting and location information to identify specific events and group photos related to those events into albums. For example, the generation unit can group photos taken at specific times and location information that meet certain conditions. This allows for the identification of events based on the date and time of shooting and location information, and the grouping of related photos. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the date and time of shooting and location information as input and use an AI model to identify events and group photos related to those events.

[0036] The generation unit can automatically detect and delete out-of-focus or underexposed photos, or photos with poor image quality. For example, the generation unit can automatically detect out-of-focus photos and remove them from the album. For example, the generation unit can detect out-of-focus photos based on the degree of focus match. The generation unit can also automatically detect and delete underexposed photos. For example, the generation unit can detect underexposed photos based on the exposure value range. The generation unit can also automatically detect and delete photos with poor image quality. For example, the generation unit can detect photos with low resolution and delete them from the album. For example, the generation unit can detect photos with a resolution below a certain threshold. The generation unit can also detect photos with a high noise level and delete them from the album. For example, the generation unit can detect photos with a noise level exceeding a certain threshold. The generation unit can also detect photos with a high degree of blur and delete them from the album. For example, the generation unit can detect photos with a high degree of blur exceeding a certain threshold. This improves the quality of the album by automatically detecting and deleting photos with poor image quality. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate an album using an AI model that detects out-of-focus or underexposed photos.

[0037] The photo collection unit can filter photos based on the user's current activities and areas of interest. For example, if the user is playing sports, the unit can prioritize collecting photos of movement. If the user is participating in a sporting event, the unit can prioritize collecting photos related to that event. The unit can also prioritize collecting photos of tourist attractions and landscapes if the user is traveling. For example, if the user is at a specific tourist destination, the unit can prioritize collecting photos of landmarks in that destination. If the user is eating, the unit can also prioritize collecting photos of food. For example, if the user is eating at a restaurant, the unit can prioritize collecting photos of the restaurant's dishes. By filtering photos based on the user's current activities and areas of interest, more relevant photos can be collected. Some or all of the above processing in the photo collection unit may be performed using AI, for example, or without AI. For example, the photo collection unit can collect photos using an AI model that takes user activity data and information on areas of interest as input and performs filtering.

[0038] The collection unit can analyze the user's past photo collection history and select the optimal collection method. For example, if the user has taken many photos at a particular event in the past, the collection unit can adjust the collection frequency to match that event. For example, if the user has taken many photos at a particular event in the past, the collection unit can prioritize collecting photos related to that event. The collection unit can also increase the collection frequency at a particular location if the user frequently takes photos there. For example, if the user has taken many photos at a particular tourist destination in the past, the collection unit can prioritize collecting photos from that tourist destination. The collection unit can also adjust the collection timing to match a particular time of day if the user has taken many photos during that time period. For example, if the collection unit has taken many photos during a particular time period in the past, the collection unit can prioritize collecting photos related to that time period. This allows the collection unit to select the optimal collection method by analyzing the user's past photo collection history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes the user's past photo collection history as input and selects the optimal collection method.

[0039] The collection unit can prioritize collecting highly relevant photos by considering the user's geographical location information when collecting photos. For example, if the user is in a tourist destination, the collection unit can prioritize collecting photos of landmarks in that tourist destination. The collection unit can also prioritize collecting photos related to an event if the user is at an event venue. The collection unit can also prioritize collecting photos related to an event if the user is at a specific event venue. The collection unit can also prioritize collecting photos of family and pets if the user is at home. The collection unit can prioritize collecting photos of family and pets if the user is at home. By considering the user's geographical location information, the collection unit can prioritize collecting highly relevant photos. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes the user's geographical location information as input and prioritizes collecting highly relevant photos.

[0040] The collection unit can analyze a user's social media activity when collecting photos and collect relevant photos. For example, if a user posts about a specific event on social media, the collection unit can prioritize collecting photos related to that event. The collection unit can also prioritize collecting photos related to a specific location if a user posts about that location on social media. The collection unit can also prioritize collecting photos related to a specific person if a user posts about that person on social media. This allows for the collection of relevant photos by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes a user's social media activity as input and collects relevant photos.

[0041] The analysis unit can optimize its analysis algorithm by considering the shooting conditions of the photograph (e.g., light intensity and angle) during analysis. For example, if the light intensity is strong, the analysis unit can perform exposure compensation to improve analysis accuracy. The analysis unit can also perform image correction to improve analysis accuracy if the angle is oblique. The analysis unit can also perform noise reduction to improve analysis accuracy if the light intensity is weak. In this way, by considering the shooting conditions of the photograph, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the shooting conditions of the photograph as input and perform photograph analysis using an AI model that optimizes the analysis algorithm.

[0042] The analysis unit can improve the accuracy of the analysis by considering the background information of the photograph during the analysis. For example, if there is a specific landmark in the background, the analysis unit can improve the accuracy of the analysis based on that landmark. The analysis unit can also improve the accuracy of the analysis based on the event if a specific event is visible in the background. The analysis unit can also improve the accuracy of the analysis based on the location if a specific place is visible in the background. In this way, the accuracy of the analysis can be improved by considering the background information of the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the background information of the photograph as input and perform the analysis of the photograph using an AI model that improves the accuracy of the analysis.

[0043] The analysis unit can improve the accuracy of its analysis by considering information about the location where the photograph was taken. For example, if the location where the photograph was taken is a specific tourist spot, the analysis unit can improve the accuracy of its analysis based on information about that tourist spot. The analysis unit can also improve the accuracy of its analysis based on information about an event if the location where the photograph was taken is a specific event venue. The analysis unit can also improve the accuracy of its analysis based on information about a city if the location where the photograph was taken is a specific city. In this way, the accuracy of the analysis can be improved by considering information about the location where the photograph was taken. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take information about the location where the photograph was taken as input and perform the analysis of the photograph using an AI model that improves the accuracy of the analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the photograph during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to literature on objects depicted in the photograph. The analysis unit can also improve the accuracy of its analysis by referring to literature on locations depicted in the photograph. The analysis unit can also improve the accuracy of its analysis by referring to literature on people depicted in the photograph. In this way, the accuracy of the analysis can be improved by referring to relevant literature on the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take relevant literature on the photograph as input and perform the analysis of the photograph using an AI model that improves the accuracy of the analysis.

[0045] The classification unit can improve the accuracy of classification by considering the interrelationships between photographs during the classification process. For example, the classification unit can group and classify photographs taken at the same event. The classification unit can also group and classify photographs taken at the same location. The classification unit can also group and classify photographs that feature the same person. By considering the interrelationships between photographs, the accuracy of classification can be improved. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can use an AI model that improves classification accuracy and takes the interrelationships between photographs as input to classify the photographs.

[0046] The classification unit can classify photos while considering the attribute information of the photographer. For example, the classification unit can prioritize the classification of photos taken by a specific photographer. The classification unit can also classify photos based on the photographer's age and gender. The classification unit can also classify photos based on the photographer's shooting style. This allows for more appropriate classification by considering the attribute information of the photographer. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can classify photos using an AI model that takes the photographer's attribute information as input.

[0047] The classification unit can perform classification while considering the geographical distribution of the photographs. For example, the classification unit can group and classify photographs taken in the same city. The classification unit can also group and classify photographs taken in the same country. The classification unit can also group and classify photographs taken in the same region. This allows for more appropriate classification by considering the geographical distribution of the photographs. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can classify photographs using an AI model that takes the geographical distribution of photographs as input.

[0048] The classification unit can improve the accuracy of classification by referring to relevant literature for the photographs during the classification process. For example, the classification unit can improve the accuracy of classification by referring to literature about objects depicted in the photographs. The classification unit can also improve the accuracy of classification by referring to literature about places depicted in the photographs. The classification unit can also improve the accuracy of classification by referring to literature about people depicted in the photographs. In this way, the accuracy of classification can be improved by referring to relevant literature for the photographs. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can take relevant literature for the photographs as input and classify the photographs using an AI model that improves classification accuracy.

[0049] The generation unit can generate albums while excluding duplicate photos or photos with image quality below a threshold. For example, the generation unit can automatically detect and exclude duplicate photos from albums. For example, the generation unit can detect duplicate photos based on matching hash values. The generation unit can also detect duplicate photos based on image similarity. For example, the generation unit can extract image features and detect duplicate photos based on the degree of matching of those features. The generation unit can also automatically detect and exclude photos with image quality below a threshold. For example, the generation unit can detect and exclude photos with low resolution. For example, the generation unit can detect photos with resolution below a specific threshold. The generation unit can also detect and exclude photos with high noise levels. For example, the generation unit can detect photos with noise levels exceeding a specific threshold. The generation unit can also detect and exclude photos with a high degree of blur. For example, the generation unit can detect photos with a degree of blur exceeding a specific threshold. This improves the quality of albums by excluding duplicate photos and photos with low image quality. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate albums using an AI model that detects duplicate photos and evaluates image quality.

[0050] The generation unit can identify events based on shooting date and location information when generating albums, and group photos related to those events. For example, the generation unit can group photos taken at similar times and put them together in an album as part of the same event. For example, the generation unit can group photos taken at specific times within a certain range. The generation unit can also group photos taken at similar locations and put them together in an album as part of an event at the same location. For example, the generation unit can group photos taken at specific locations within a certain range. The generation unit can also combine shooting date and location information to identify specific events and group photos related to those events in an album. For example, the generation unit can group photos taken at specific times and location information that meet certain conditions. This allows the generation unit to identify events based on shooting date and location information and group related photos together. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take shooting date and location information as input and use an AI model to identify events and group photos related to those events.

[0051] The generation unit can automatically detect and delete low-quality photos, such as out-of-focus or underexposed photos, when generating an album. For example, the generation unit can automatically detect out-of-focus photos and exclude them from the album. For example, the generation unit can detect out-of-focus photos based on the degree of focus match. The generation unit can also automatically detect and delete underexposed photos. For example, the generation unit can detect underexposed photos based on the exposure value range. The generation unit can also automatically detect and delete low-quality photos from the album. For example, the generation unit can detect low-resolution photos and delete them from the album. For example, the generation unit can detect photos with a resolution below a certain threshold. The generation unit can also detect photos with a high noise level and delete them from the album. For example, the generation unit can detect photos with a noise level exceeding a certain threshold. The generation unit can also detect photos with a high degree of blur and delete them from the album. For example, the generation unit can detect photos with a high degree of blur exceeding a certain threshold. This improves the quality of the album by automatically detecting and deleting low-quality photos. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate an album using an AI model that detects out-of-focus or underexposed photos.

[0052] The generation unit can analyze the user's social media activity when generating an album and add relevant photos to the album. For example, if the user has posted about a specific event on social media, the generation unit can add photos related to that event to the album. The generation unit can also add photos related to a specific place if the user has posted about that place on social media. The generation unit can also add photos related to a specific person if the user has posted about that person on social media. This allows the generation unit to add relevant photos to the album by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate albums using an AI model that takes the user's social media activity as input and adds relevant photos to the album.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the user's past photo collection history and select the optimal collection method. For example, if a user has taken many photos at a particular event in the past, the collection frequency can be adjusted to match that event. Similarly, if a user frequently takes photos at a specific location, the collection frequency at that location can be increased. Furthermore, if a user takes many photos during a specific time period, the collection timing can be adjusted to match that time period. In this way, the optimal collection method can be selected by analyzing the user's past photo collection history.

[0055] The analysis unit can optimize the analysis algorithm by considering the shooting conditions of the photograph (e.g., light intensity and angle). For example, if the light intensity is strong, exposure compensation can be performed to improve analysis accuracy. Also, if the angle is oblique, image correction can be performed to improve analysis accuracy. Furthermore, if the light intensity is weak, noise reduction can be performed to improve analysis accuracy. In this way, by considering the shooting conditions of the photograph, the analysis algorithm can be optimized and analysis accuracy can be improved.

[0056] The classification unit can improve the accuracy of classification by considering the interrelationships between photographs. For example, photographs taken at the same event can be grouped and classified. Similarly, photographs taken in the same location can be grouped and classified. Furthermore, photographs featuring the same person can be grouped and classified. This allows for improved classification accuracy by considering the interrelationships between photographs.

[0057] The collection unit can filter photos based on the user's current activities and areas of interest. For example, if the user is playing sports, it can prioritize collecting photos of movement. If the user is traveling, it can prioritize collecting photos of tourist attractions and landscapes. Furthermore, if the user is eating, it can prioritize collecting photos of food. By filtering photos based on the user's current activities and areas of interest, it can collect more relevant photos.

[0058] The classification unit can classify photos while considering the attributes of the photographer. For example, it can prioritize the classification of photos taken by a specific photographer. It can also classify photos based on the photographer's age and gender. Furthermore, it can classify photos based on the photographer's shooting style. This allows for more appropriate classification by considering the attributes of the photographer.

[0059] The collection unit can prioritize collecting highly relevant photos by considering the user's geographical location. For example, if the user is in a tourist destination, it can prioritize collecting photos of landmarks in that destination. Similarly, if the user is at an event venue, it can prioritize collecting photos related to that event. Furthermore, if the user is at home, it can prioritize collecting photos of family and pets. In this way, by considering the user's geographical location, it can prioritize collecting highly relevant photos.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects captured photo data. The collection unit automatically collects photo data taken with, for example, a smartphone camera. The collection unit can also collect photo data from cloud storage and social media. For example, with the user's permission, the collection unit periodically collects photo data stored in cloud storage. The collection unit can also collect photo data posted by users using social media APIs. Step 2: The analysis unit performs face recognition or object detection on the photographs collected by the collection unit. The analysis unit can, for example, use deep learning-based face recognition technology to identify people in the photographs. Alternatively, the analysis unit can use YOLO-based object detection technology to identify objects in the photographs. For example, the analysis unit can use face recognition technology to detect the faces of people in the photographs and extract their facial features. Alternatively, the analysis unit can use object detection technology to identify the types of objects in the photographs. Step 3: The classification unit categorizes the photos by category or person based on the information analyzed by the analysis unit. The classification unit categorizes photos based on criteria such as age, gender, and areas of interest. The classification unit can also categorize photos by specific people. For example, the classification unit groups photos of the same person based on the facial features of that person identified by the analysis unit. The classification unit can also categorize photos based on specific events or locations. Step 4: The generation unit generates albums based on events, locations, and dates, using the photos classified by the classification unit. The generation unit generates albums based on criteria such as specific event names, geographical coordinates, or calendar dates. The generation unit can also generate albums while excluding duplicate or low-quality photos. For example, the generation unit can detect and exclude duplicate photos based on hash value matches or image similarity. The generation unit can also detect and exclude low-quality photos based on criteria such as resolution, noise level, and blur level.

[0062] (Example of form 2) An AI assistant for automatic organization of smartphone photo albums according to an embodiment of the present invention is a system that automatically organizes a smartphone's photo album. This system collects captured photo data, performs face recognition or object detection, and classifies the photos by category or person. Based on the classified photos, it generates albums based on events, locations, and dates. In this process, duplicate photos and photos of low quality can be excluded. It can also identify events based on the date and time of shooting and location information, and group photos related to those events. Furthermore, it can automatically detect and delete photos of low quality, such as blurry or underexposed photos. It can also estimate the user's emotions and adjust the timing of photo collection based on the estimated emotions. When collecting photos, filtering can be performed based on the user's current activities and areas of interest, and the priority of photos to be collected can be determined based on the user's emotions. As a result, the AI ​​assistant for automatic organization of smartphone photo albums can efficiently organize the user's photo album and provide it in a more user-friendly format.

[0063] The AI ​​assistant for automatically organizing smartphone photo albums according to this embodiment comprises a collection unit, an analysis unit, a classification unit, and a generation unit. The collection unit collects captured photo data. For example, the collection unit automatically collects photo data taken with a smartphone camera. The collection unit can also collect photo data from cloud storage or social media. For example, with the user's permission, the collection unit periodically collects photo data stored in cloud storage. The collection unit can also collect photo data posted by users using social media APIs. The analysis unit performs face recognition or object detection on the photos collected by the collection unit. For example, the analysis unit identifies people in the photos using face recognition technology with deep learning. The analysis unit can also identify objects in the photos using object detection technology with YOLO. For example, the analysis unit uses face recognition technology to detect the faces of people in the photos and extract their facial features. The analysis unit can also use object detection technology to identify the types of objects in the photos. The classification unit categorizes photos by category or person based on the information analyzed by the analysis unit. For example, the classification unit categorizes photos based on criteria such as age, gender, and areas of interest. The classification unit can also categorize photos by specific people. For example, it groups photos of the same person based on facial features identified by the analysis unit. The classification unit can also categorize photos based on specific events or locations. The generation unit generates albums based on events, locations, and dates, using the photos categorized by the classification unit. For example, it generates albums based on criteria such as specific event names, geographical coordinates, or calendar dates. The generation unit can also generate albums while excluding duplicate or low-quality photos. For example, it detects and excludes duplicate photos based on hash value matches or image similarity. It can also detect and exclude low-quality photos based on criteria such as resolution, noise level, and blur.As a result, the AI ​​assistant that supports automatic organization of smartphone photo albums according to this embodiment can efficiently organize the user's photo albums and present them in a more user-friendly format.

[0064] The collection unit collects captured photographic data. For example, the collection unit automatically collects photographic data taken with a smartphone camera. Specifically, when a smartphone camera app takes a picture, the collection unit immediately detects the photographic data and saves it to its internal storage. The collection unit can also collect photographic data from cloud storage and social media. For example, with the user's permission, the collection unit periodically collects photographic data stored in cloud storage. It uses the cloud storage API to access the user's account and automatically downloads newly uploaded photos. The collection unit can also use social media APIs to collect photographic data posted by users. For example, the collection unit periodically checks for photos posted by users on social media and collects the photographic data if there are new posts. This allows the collection unit to centrally manage not only photos on the smartphone but also photos stored in the cloud and on social media. Furthermore, the collection unit simultaneously collects metadata of the photographic data (date and time of shooting, location information, camera settings, etc.) which can be used for subsequent analysis and classification. This allows the collection unit to efficiently collect user photographic data and improve the overall system performance.

[0065] The analysis unit performs face recognition or object detection on the photographs collected by the collection unit. For example, the analysis unit uses deep learning-based face recognition technology to identify people in the photographs. Specifically, it uses a face recognition model to detect faces in the photographs and extract their facial features. This allows it to identify who the people in the photographs are. The analysis unit can also use YOLO-based object detection technology to identify objects in the photographs. For example, it uses an object detection model to detect objects in the photographs and identify their types. This allows it to identify what the objects in the photographs are. Furthermore, the analysis unit can combine face recognition and object detection technologies to analyze the relationship between people and objects in the photographs. For example, it can identify objects held by people in the photographs or the location where people are. This allows the analysis unit to analyze the content of the photographs in detail and use this information for subsequent classification and album generation. In addition, the analysis unit can save the analysis results to a database and reuse them in subsequent processing. This allows the analysis unit to analyze the collected data quickly and accurately, improving the overall system performance.

[0066] The classification unit categorizes photos by category or person based on the information analyzed by the analysis unit. For example, the classification unit categorizes photos based on criteria such as age, gender, and areas of interest. Specifically, it categorizes photos into specific categories based on facial features or object types extracted by the analysis unit. For instance, the classification unit groups photos of the same person based on facial features identified by the analysis unit. The classification unit can also categorize photos based on specific events or locations. For example, it groups photos taken at specific events or locations based on the photo's metadata (date and time taken, location information, etc.). This allows the classification unit to efficiently organize the user's photos and facilitate subsequent album creation. Furthermore, the classification unit can learn the user's preferences and past classification history to improve classification accuracy. For example, it can learn patterns from photos previously classified by the user and automatically categorize new photos with similar patterns. This reduces the user's effort and streamlines photo organization.

[0067] The generation unit generates albums based on events, locations, and dates, using photos classified by the classification unit. For example, it generates albums based on criteria such as specific event names, geographical coordinates, or calendar dates. Specifically, it automatically creates albums related to specific events or locations based on photos grouped by the classification unit. For instance, it generates an album of photos related to a trip based on the geographical coordinates of the travel destination. The generation unit can also exclude duplicate and low-quality photos when generating albums. For example, it detects and excludes duplicate photos based on hash value matches or image similarity. It can also detect and exclude low-quality photos based on criteria such as resolution, noise level, and blur. This allows the generation unit to provide users with albums that are easy to view and use. Furthermore, the generation unit can automatically adjust the album layout and design to create visually appealing albums. For example, it adjusts photo placement, size, and background color to produce professionally finished albums. This allows the generation unit to efficiently organize users' photo albums and present them in a more user-friendly format.

[0068] The generation unit can generate albums by excluding duplicate photos or photos with image quality below a threshold. For example, the generation unit can automatically detect and exclude duplicate photos from albums. For example, the generation unit can detect duplicate photos based on matching hash values. The generation unit can also detect duplicate photos based on image similarity. For example, the generation unit can extract image features and detect duplicate photos based on the degree of matching of those features. The generation unit can also automatically detect and exclude photos with image quality below a threshold. For example, the generation unit can detect and exclude photos with low resolution from albums. For example, the generation unit can detect photos with resolution below a specific threshold. The generation unit can also detect and exclude photos with high noise levels from albums. For example, the generation unit can detect photos with noise levels exceeding a specific threshold. The generation unit can also detect and exclude photos with a high degree of blur from albums. For example, the generation unit can detect photos with a degree of blur exceeding a specific threshold. This improves the quality of albums by excluding duplicate photos and photos with low image quality. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate albums using an AI model that detects duplicate photos and evaluates image quality.

[0069] The generation unit can identify events based on the date and time of shooting and location information, and group photos related to those events. For example, the generation unit can group photos taken at similar times and put them into albums as part of the same event. For example, the generation unit can group photos taken at specific times within a certain range. The generation unit can also group photos taken at similar locations and put them into albums as events from the same location. For example, the generation unit can group photos taken at specific locations within a certain range. The generation unit can also combine the date and time of shooting and location information to identify specific events and group photos related to those events into albums. For example, the generation unit can group photos taken at specific times and location information that meet certain conditions. This allows for the identification of events based on the date and time of shooting and location information, and the grouping of related photos. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take the date and time of shooting and location information as input and use an AI model to identify events and group photos related to those events.

[0070] The generation unit can automatically detect and delete out-of-focus or underexposed photos, or photos with poor image quality. For example, the generation unit can automatically detect out-of-focus photos and remove them from the album. For example, the generation unit can detect out-of-focus photos based on the degree of focus match. The generation unit can also automatically detect and delete underexposed photos. For example, the generation unit can detect underexposed photos based on the exposure value range. The generation unit can also automatically detect and delete photos with poor image quality. For example, the generation unit can detect photos with low resolution and delete them from the album. For example, the generation unit can detect photos with a resolution below a certain threshold. The generation unit can also detect photos with a high noise level and delete them from the album. For example, the generation unit can detect photos with a noise level exceeding a certain threshold. The generation unit can also detect photos with a high degree of blur and delete them from the album. For example, the generation unit can detect photos with a high degree of blur exceeding a certain threshold. This improves the quality of the album by automatically detecting and deleting photos with poor image quality. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate an album using an AI model that detects out-of-focus or underexposed photos.

[0071] The generation unit can estimate the user's emotions and adjust the timing of photo collection based on the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate timing of photo collection by adjusting the timing of photo collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0072] The photo collection unit can filter photos based on the user's current activities and areas of interest. For example, if the user is playing sports, the unit can prioritize collecting photos of movement. If the user is participating in a sporting event, the unit can prioritize collecting photos related to that event. The unit can also prioritize collecting photos of tourist attractions and landscapes if the user is traveling. For example, if the user is at a specific tourist destination, the unit can prioritize collecting photos of landmarks in that destination. If the user is eating, the unit can also prioritize collecting photos of food. For example, if the user is eating at a restaurant, the unit can prioritize collecting photos of the restaurant's dishes. By filtering photos based on the user's current activities and areas of interest, more relevant photos can be collected. Some or all of the above processing in the photo collection unit may be performed using AI, for example, or without AI. For example, the photo collection unit can collect photos using an AI model that takes user activity data and information on areas of interest as input and performs filtering.

[0073] The photo collection unit can estimate the user's emotions and determine the priority of photos to collect based on the estimated emotions. For example, if the user is having fun, the unit will prioritize collecting photos of them smiling. For example, if the user is attending an event, the unit will prioritize collecting photos of them smiling at that event. The unit can also prioritize collecting photos of landscapes and nature if the user is relaxed. For example, if the user is relaxing in a park, the unit will prioritize collecting photos of the park's scenery. The unit can also prioritize collecting photos of people in motion if the user is excited. For example, if the user is attending a sporting event, the unit will prioritize collecting photos of people in motion at that event. By prioritizing photos based on the user's emotions, the system can collect more appropriate photos. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes user emotion data as input and determines the priority of photos.

[0074] The collection unit can analyze the user's past photo collection history and select the optimal collection method. For example, if the user has taken many photos at a particular event in the past, the collection unit can adjust the collection frequency to match that event. For example, if the user has taken many photos at a particular event in the past, the collection unit can prioritize collecting photos related to that event. The collection unit can also increase the collection frequency at a particular location if the user frequently takes photos there. For example, if the user has taken many photos at a particular tourist destination in the past, the collection unit can prioritize collecting photos from that tourist destination. The collection unit can also adjust the collection timing to match a particular time of day if the user has taken many photos during that time period. For example, if the collection unit has taken many photos during a particular time period in the past, the collection unit can prioritize collecting photos related to that time period. This allows the collection unit to select the optimal collection method by analyzing the user's past photo collection history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes the user's past photo collection history as input and selects the optimal collection method.

[0075] The collection unit can prioritize collecting highly relevant photos by considering the user's geographical location information when collecting photos. For example, if the user is in a tourist destination, the collection unit can prioritize collecting photos of landmarks in that tourist destination. The collection unit can also prioritize collecting photos related to an event if the user is at an event venue. The collection unit can also prioritize collecting photos related to an event if the user is at a specific event venue. The collection unit can also prioritize collecting photos of family and pets if the user is at home. The collection unit can prioritize collecting photos of family and pets if the user is at home. By considering the user's geographical location information, the collection unit can prioritize collecting highly relevant photos. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes the user's geographical location information as input and prioritizes collecting highly relevant photos.

[0076] The collection unit can analyze a user's social media activity when collecting photos and collect relevant photos. For example, if a user posts about a specific event on social media, the collection unit can prioritize collecting photos related to that event. The collection unit can also prioritize collecting photos related to a specific location if a user posts about that location on social media. The collection unit can also prioritize collecting photos related to a specific person if a user posts about that person on social media. This allows for the collection of relevant photos by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect photos using an AI model that takes a user's social media activity as input and collects relevant photos.

[0077] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is having fun, the analysis unit can analyze smiling photos with high accuracy. For example, if the user is participating in an event, the analysis unit can analyze smiling photos from that event with high accuracy. The analysis unit can also analyze landscape and nature photos with high accuracy if the user is relaxed. For example, if the user is relaxing in a park, the analysis unit can analyze landscape photos of that park with high accuracy. The analysis unit can also analyze moving photos with high accuracy if the user is excited. For example, if the user is participating in a sporting event, the analysis unit can analyze moving photos from that event with high accuracy. By adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take user emotion data as input and perform photo analysis using an AI model that adjusts the accuracy of the analysis.

[0078] The analysis unit can optimize its analysis algorithm by considering the shooting conditions of the photograph (e.g., light intensity and angle) during analysis. For example, if the light intensity is strong, the analysis unit can perform exposure compensation to improve analysis accuracy. The analysis unit can also perform image correction to improve analysis accuracy if the angle is oblique. The analysis unit can also perform noise reduction to improve analysis accuracy if the light intensity is weak. In this way, by considering the shooting conditions of the photograph, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the shooting conditions of the photograph as input and perform photograph analysis using an AI model that optimizes the analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis by considering the background information of the photograph during the analysis. For example, if there is a specific landmark in the background, the analysis unit can improve the accuracy of the analysis based on that landmark. The analysis unit can also improve the accuracy of the analysis based on the event if a specific event is visible in the background. The analysis unit can also improve the accuracy of the analysis based on the location if a specific place is visible in the background. In this way, the accuracy of the analysis can be improved by considering the background information of the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the background information of the photograph as input and perform the analysis of the photograph using an AI model that improves the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is enjoying themselves, the analysis unit can display the analysis results in bright colors. For example, if the user is participating in an event, the analysis unit can display the analysis results for that event in bright colors. The analysis unit can also display the analysis results in calm colors if the user is relaxed. For example, if the user is relaxing in a park, the analysis unit can display the analysis results for that park in calm colors. If the user is excited, the analysis unit can display the analysis results with visually stimulating effects. For example, if the user is participating in a sports event, the analysis unit can display the analysis results for that event with visually stimulating effects. This allows for a more appropriate display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform photo analysis using an AI model that takes user emotion data as input and adjusts how the analysis results are displayed.

[0081] The analysis unit can improve the accuracy of its analysis by considering information about the location where the photograph was taken. For example, if the location where the photograph was taken is a specific tourist spot, the analysis unit can improve the accuracy of its analysis based on information about that tourist spot. The analysis unit can also improve the accuracy of its analysis based on information about an event if the location where the photograph was taken is a specific event venue. The analysis unit can also improve the accuracy of its analysis based on information about a city if the location where the photograph was taken is a specific city. In this way, the accuracy of the analysis can be improved by considering information about the location where the photograph was taken. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take information about the location where the photograph was taken as input and perform the analysis of the photograph using an AI model that improves the accuracy of the analysis.

[0082] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the photograph during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to literature on objects depicted in the photograph. The analysis unit can also improve the accuracy of its analysis by referring to literature on locations depicted in the photograph. The analysis unit can also improve the accuracy of its analysis by referring to literature on people depicted in the photograph. In this way, the accuracy of the analysis can be improved by referring to relevant literature on the photograph. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take relevant literature on the photograph as input and perform the analysis of the photograph using an AI model that improves the accuracy of the analysis.

[0083] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated emotions. For example, if the user is having fun, the classification unit may prioritize classifying photos of smiling faces. For example, if the user is participating in an event, the classification unit may prioritize classifying photos of smiling faces from that event. The classification unit may also prioritize classifying photos of landscapes or nature if the user is relaxed. For example, if the user is relaxing in a park, the classification unit may prioritize classifying photos of the park's scenery. The classification unit may also prioritize classifying photos of movement if the user is excited. For example, if the user is participating in a sporting event, the classification unit may prioritize classifying photos of movement from that event. By adjusting the classification criteria based on the user's emotions, more appropriate classification becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can take user emotion data as input and use an AI model that adjusts the classification criteria to classify photographs.

[0084] The classification unit can improve the accuracy of classification by considering the interrelationships between photographs during the classification process. For example, the classification unit can group and classify photographs taken at the same event. The classification unit can also group and classify photographs taken at the same location. The classification unit can also group and classify photographs that feature the same person. By considering the interrelationships between photographs, the accuracy of classification can be improved. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can use an AI model that improves classification accuracy and takes the interrelationships between photographs as input to classify the photographs.

[0085] The classification unit can classify photos while considering the attribute information of the photographer. For example, the classification unit can prioritize the classification of photos taken by a specific photographer. The classification unit can also classify photos based on the photographer's age and gender. The classification unit can also classify photos based on the photographer's shooting style. This allows for more appropriate classification by considering the attribute information of the photographer. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can classify photos using an AI model that takes the photographer's attribute information as input.

[0086] The classification unit can perform classification while considering the geographical distribution of the photographs. For example, the classification unit can group and classify photographs taken in the same city. The classification unit can also group and classify photographs taken in the same country. The classification unit can also group and classify photographs taken in the same region. This allows for more appropriate classification by considering the geographical distribution of the photographs. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can classify photographs using an AI model that takes the geographical distribution of photographs as input.

[0087] The classification unit can improve the accuracy of classification by referring to relevant literature for the photographs during the classification process. For example, the classification unit can improve the accuracy of classification by referring to literature about objects depicted in the photographs. The classification unit can also improve the accuracy of classification by referring to literature about places depicted in the photographs. The classification unit can also improve the accuracy of classification by referring to literature about people depicted in the photographs. In this way, the accuracy of classification can be improved by referring to relevant literature for the photographs. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can take relevant literature for the photographs as input and classify the photographs using an AI model that improves classification accuracy.

[0088] The generation unit can estimate the user's emotions and adjust the album generation method based on the estimated user emotions. For example, if the user is having fun, the generation unit can generate an album with bright colors. For example, if the user is participating in an event, the generation unit can generate an album of that event with bright colors. The generation unit can also generate an album with calm colors if the user is relaxed. For example, if the user is relaxing in a park, the generation unit can generate an album of that park with calm colors. The generation unit can also generate an album with visually stimulating effects if the user is excited. For example, if the user is participating in a sporting event, the generation unit can generate an album of that event with visually stimulating effects. In this way, by adjusting the album generation method based on the user's emotions, a more appropriate album can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take user emotion data as input and generate albums using an AI model that adjusts the album generation method.

[0089] The generation unit can generate albums while excluding duplicate photos or photos with image quality below a threshold. For example, the generation unit can automatically detect and exclude duplicate photos from albums. For example, the generation unit can detect duplicate photos based on matching hash values. The generation unit can also detect duplicate photos based on image similarity. For example, the generation unit can extract image features and detect duplicate photos based on the degree of matching of those features. The generation unit can also automatically detect and exclude photos with image quality below a threshold. For example, the generation unit can detect and exclude photos with low resolution. For example, the generation unit can detect photos with resolution below a specific threshold. The generation unit can also detect and exclude photos with high noise levels. For example, the generation unit can detect photos with noise levels exceeding a specific threshold. The generation unit can also detect and exclude photos with a high degree of blur. For example, the generation unit can detect photos with a degree of blur exceeding a specific threshold. This improves the quality of albums by excluding duplicate photos and photos with low image quality. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate albums using an AI model that detects duplicate photos and evaluates image quality.

[0090] The generation unit can identify events based on shooting date and location information when generating albums, and group photos related to those events. For example, the generation unit can group photos taken at similar times and put them together in an album as part of the same event. For example, the generation unit can group photos taken at specific times within a certain range. The generation unit can also group photos taken at similar locations and put them together in an album as part of an event at the same location. For example, the generation unit can group photos taken at specific locations within a certain range. The generation unit can also combine shooting date and location information to identify specific events and group photos related to those events in an album. For example, the generation unit can group photos taken at specific times and location information that meet certain conditions. This allows the generation unit to identify events based on shooting date and location information and group related photos together. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take shooting date and location information as input and use an AI model to identify events and group photos related to those events.

[0091] The generation unit can estimate the user's emotions and adjust how the album is displayed based on the estimated emotions. For example, if the user is having fun, the generation unit can display the album in bright colors. For example, if the user is attending an event, the generation unit can display the album from that event in bright colors. The generation unit can also display the album in calm colors if the user is relaxed. For example, if the user is relaxing in a park, the generation unit can display the album from that park in calm colors. The generation unit can also display the album with visually stimulating effects if the user is excited. For example, if the user is attending a sporting event, the generation unit can display the album from that event with visually stimulating effects. This allows for a more appropriate display by adjusting how the album is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can take user emotion data as input and use an AI model to adjust how the album is displayed to display the album.

[0092] The generation unit can automatically detect and delete low-quality photos, such as out-of-focus or underexposed photos, when generating an album. For example, the generation unit can automatically detect out-of-focus photos and exclude them from the album. For example, the generation unit can detect out-of-focus photos based on the degree of focus match. The generation unit can also automatically detect and delete underexposed photos. For example, the generation unit can detect underexposed photos based on the exposure value range. The generation unit can also automatically detect and delete low-quality photos from the album. For example, the generation unit can detect low-resolution photos and delete them from the album. For example, the generation unit can detect photos with a resolution below a certain threshold. The generation unit can also detect photos with a high noise level and delete them from the album. For example, the generation unit can detect photos with a noise level exceeding a certain threshold. The generation unit can also detect photos with a high degree of blur and delete them from the album. For example, the generation unit can detect photos with a high degree of blur exceeding a certain threshold. This improves the quality of the album by automatically detecting and deleting low-quality photos. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate an album using an AI model that detects out-of-focus or underexposed photos.

[0093] The generation unit can analyze the user's social media activity when generating an album and add relevant photos to the album. For example, if the user has posted about a specific event on social media, the generation unit can add photos related to that event to the album. The generation unit can also add photos related to a specific place if the user has posted about that place on social media. The generation unit can also add photos related to a specific person if the user has posted about that person on social media. This allows the generation unit to add relevant photos to the album by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate albums using an AI model that takes the user's social media activity as input and adds relevant photos to the album.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The collection unit can analyze the user's past photo collection history and select the optimal collection method. For example, if a user has taken many photos at a particular event in the past, the collection frequency can be adjusted to match that event. Similarly, if a user frequently takes photos at a specific location, the collection frequency at that location can be increased. Furthermore, if a user takes many photos during a specific time period, the collection timing can be adjusted to match that time period. In this way, the optimal collection method can be selected by analyzing the user's past photo collection history.

[0096] The analysis unit can optimize the analysis algorithm by considering the shooting conditions of the photograph (e.g., light intensity and angle). For example, if the light intensity is strong, exposure compensation can be performed to improve analysis accuracy. Also, if the angle is oblique, image correction can be performed to improve analysis accuracy. Furthermore, if the light intensity is weak, noise reduction can be performed to improve analysis accuracy. In this way, by considering the shooting conditions of the photograph, the analysis algorithm can be optimized and analysis accuracy can be improved.

[0097] The classification unit can improve the accuracy of classification by considering the interrelationships between photographs. For example, photographs taken at the same event can be grouped and classified. Similarly, photographs taken in the same location can be grouped and classified. Furthermore, photographs featuring the same person can be grouped and classified. This allows for improved classification accuracy by considering the interrelationships between photographs.

[0098] The generation unit can estimate the user's emotions and adjust the album generation method based on those emotions. For example, if the user is having fun, it can generate an album with bright colors. If the user is relaxed, it can generate an album with calm colors. Furthermore, if the user is excited, it can generate an album with visually stimulating effects. By adjusting the album generation method based on the user's emotions, it is possible to generate a more appropriate album.

[0099] The collection unit can filter photos based on the user's current activities and areas of interest. For example, if the user is playing sports, it can prioritize collecting photos of movement. If the user is traveling, it can prioritize collecting photos of tourist attractions and landscapes. Furthermore, if the user is eating, it can prioritize collecting photos of food. By filtering photos based on the user's current activities and areas of interest, it can collect more relevant photos.

[0100] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is enjoying themselves, it can analyze photos of smiles with high accuracy. If the user is relaxed, it can also analyze photos of landscapes or nature with high accuracy. Furthermore, if the user is excited, it can analyze photos with movement with high accuracy. By adjusting the accuracy of the analysis based on the user's emotions, more appropriate analysis results can be obtained.

[0101] The classification unit can classify photos while considering the attributes of the photographer. For example, it can prioritize the classification of photos taken by a specific photographer. It can also classify photos based on the photographer's age and gender. Furthermore, it can classify photos based on the photographer's shooting style. This allows for more appropriate classification by considering the attributes of the photographer.

[0102] The generation unit can estimate the user's emotions and adjust how the album is displayed based on those emotions. For example, if the user is enjoying themselves, the album can be displayed in bright colors. If the user is relaxed, the album can be displayed in calm colors. Furthermore, if the user is excited, the album can be displayed with visually stimulating effects. By adjusting how the album is displayed based on the user's emotions, a more appropriate display becomes possible.

[0103] The collection unit can prioritize collecting highly relevant photos by considering the user's geographical location. For example, if the user is in a tourist destination, it can prioritize collecting photos of landmarks in that destination. Similarly, if the user is at an event venue, it can prioritize collecting photos related to that event. Furthermore, if the user is at home, it can prioritize collecting photos of family and pets. In this way, by considering the user's geographical location, it can prioritize collecting highly relevant photos.

[0104] The classification unit can estimate the user's emotions and adjust the classification criteria based on those emotions. For example, if the user is having fun, photos of smiles can be prioritized. If the user is relaxed, photos of landscapes and nature can be prioritized. Furthermore, if the user is excited, photos with movement can be prioritized. By adjusting the classification criteria based on the user's emotions, more appropriate classification becomes possible.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit collects captured photo data. The collection unit automatically collects photo data taken with, for example, a smartphone camera. The collection unit can also collect photo data from cloud storage and social media. For example, with the user's permission, the collection unit periodically collects photo data stored in cloud storage. The collection unit can also collect photo data posted by users using social media APIs. Step 2: The analysis unit performs face recognition or object detection on the photographs collected by the collection unit. The analysis unit can, for example, use deep learning-based face recognition technology to identify people in the photographs. Alternatively, the analysis unit can use YOLO-based object detection technology to identify objects in the photographs. For example, the analysis unit can use face recognition technology to detect the faces of people in the photographs and extract their facial features. Alternatively, the analysis unit can use object detection technology to identify the types of objects in the photographs. Step 3: The classification unit categorizes the photos by category or person based on the information analyzed by the analysis unit. The classification unit categorizes photos based on criteria such as age, gender, and areas of interest. The classification unit can also categorize photos by specific people. For example, the classification unit groups photos of the same person based on the facial features of that person identified by the analysis unit. The classification unit can also categorize photos based on specific events or locations. Step 4: The generation unit generates albums based on events, locations, and dates, using the photos classified by the classification unit. The generation unit generates albums based on criteria such as specific event names, geographical coordinates, or calendar dates. The generation unit can also generate albums while excluding duplicate or low-quality photos. For example, the generation unit can detect and exclude duplicate photos based on hash value matches or image similarity. The generation unit can also detect and exclude low-quality photos based on criteria such as resolution, noise level, and blur level.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] For example, the collection unit can collect photographic data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes photographs using deep learning-based face recognition technology or YOLO-based object detection technology. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and classifies photographs by category or person based on the analyzed information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and generates albums based on events, locations, and dates based on the classified photographs. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] For example, the data collection unit can collect photographic data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes photographs using deep learning-based face recognition technology or YOLO-based object detection technology. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and classifies photographs by category or person based on the analyzed information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and generates albums based on events, locations, and dates based on the classified photographs. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] For example, the data collection unit can collect photographic data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes photographs using deep learning-based face recognition technology or YOLO-based object detection technology. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and classifies photographs by category or person based on the analyzed information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and generates albums based on events, locations, and dates based on the classified photographs. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] For example, the collection unit can collect photographic data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the photographs using deep learning-based face recognition technology or YOLO-based object detection technology. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and classifies the photographs by category or person based on the analyzed information. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and generates albums based on events, locations, and dates based on the classified photographs. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A collection unit that collects the captured photographic data, An analysis unit performs face recognition or object detection on the photographs collected by the aforementioned collection unit, A classification unit that classifies the photographs by category or by person based on the information analyzed by the analysis unit, The system includes a generation unit that generates albums based on events, locations, and dates, using photographs classified by the classification unit. A system characterized by the following features. (Note 2) The generating unit is The album is generated excluding duplicate photos or photos whose image quality falls below a certain threshold. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Identify events based on the date, time, and location information of the photos taken, and then compile photos related to those events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Automatically detects and deletes blurry, underexposed, or poorly quality photos. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It estimates the user's emotions and adjusts the timing of photo collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting photos, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and determines the priority of photos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past photo collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting photos, the system prioritizes collecting highly relevant photos by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting photos, the system analyzes the user's social media activity and collects relevant photos. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by taking into account the conditions under which the photographs were taken. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, background information of the photograph is taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, information about the location where the photos were taken is taken to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we refer to relevant literature related to the photographs to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is When classifying, consider the interrelationships between photos to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned classification unit is When classifying, the attributes of the photographer are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned classification unit is When classifying, the geographical distribution of the photographs is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned classification unit is When classifying, we refer to related literature for the photographs to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the album generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When creating an album, duplicate photos or photos with image quality below a certain threshold will be excluded from the album. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When creating an album, the system identifies an event based on the date and location information of the photos taken, and then groups together photos related to that event. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts how albums are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When creating an album, it automatically detects and deletes low-quality photos, such as those that are out of focus or underexposed. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When creating an album, the system analyzes the user's social media activity and adds relevant photos to the album. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the captured photographic data, An analysis unit performs face recognition or object detection on the photographs collected by the aforementioned collection unit, A classification unit that classifies the photographs by category or by person based on the information analyzed by the analysis unit, The system includes a generation unit that generates albums based on events, locations, and dates, using photographs classified by the classification unit. A system characterized by the following features.

2. The generating unit is The album is generated excluding duplicate photos or photos whose image quality falls below a certain threshold. The system according to feature 1.

3. The generating unit is Identify events based on the date, time, and location information of the photos taken, and then compile photos related to those events. The system according to feature 1.

4. The generating unit is Automatically detects and deletes blurry, underexposed, or poorly quality photos. The system according to feature 1.

5. The generating unit is It estimates the user's emotions and adjusts the timing of photo collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting photos, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and determines the priority of photos to collect based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past photo collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting photos, the system prioritizes collecting highly relevant photos by considering the user's geographical location. The system according to feature 1.

Images