System

Abstract

To provide a system that selects photos according to a theme from a huge amount of photo data, and efficiently generates an album.SOLUTION: A system according to the embodiment comprises a reception unit, an analysis unit, a selection unit and a generation unit. The reception unit uploads photo data. The analysis unit analyzes the photo data uploaded by the reception unit. The selection unit selects photos based on the photo data analyzed by the analysis unit. The generation unit generates an album based on the photos selected by the selection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the task of selecting photos that fit a theme from vast amounts of photo data and creating albums was time-consuming and laborious.

[0005] The system according to the embodiment aims to select photos according to a theme from a huge amount of photo data and to efficiently generate an album. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a generation unit. The reception unit uploads photo data. The analysis unit analyzes the photo data uploaded by the reception unit. The selection unit selects photos based on the photo data analyzed by the analysis unit. The generation unit generates an album based on the photos selected by the selection unit. Effect of the Invention

[0007] The system according to the embodiment can select photos according to a theme from a huge amount of photo data and efficiently generate an album. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The album generation system according to the embodiment of the present invention is a system that uses AI to generate an album tailored to the user's preferences from a huge amount of photo data uploaded to the cloud. In this album generation system, a user uploads photo data to the cloud, and AI analyzes the photo data and selects photos based on the user's preferences. For example, photos are selected based on a specific event or a specific moment. Based on the selected photos, AI generates an innovative and unique album. Furthermore, as an optional service, albums for a specific organization can be contracted for a fee and created on behalf of the staff. In this case, the staff of the organization uploads photo data to the cloud, and AI analyzes the photo data to generate an album. This allows the album generation system to efficiently analyze, select, and generate albums from the user's photo data.

[0029] The album generating system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a generation unit. The reception unit is a unit for a user to upload photo data to the cloud. For example, the reception unit allows a user to upload photo data by dragging and dropping the photo data. The reception unit also allows a user to select a specific folder and upload all at once. The reception unit can also upload photo data through a smartphone app. The analysis unit is a unit that analyzes the photo data uploaded by the reception unit. For example, the analysis unit analyzes the content of the photo data using image recognition technology. The analysis unit can also identify people appearing in the photo using face recognition technology. The analysis unit can also analyze metadata such as the date and time of taking the photo and the location. The selection unit is a unit that selects photos based on the photo data analyzed by the analysis unit. For example, the selection unit selects photos based on a specific event or a specific moment. The selection unit can also select photos based on the user's preferences. The selection unit can also select photos based on the image quality of the photo and the type of subject. The generation unit is a unit that generates an album based on the photos selected by the selection unit. For example, the generation unit lays out the selected photos and generates a digital album. The generation unit can also generate the selected photos as a print album. Furthermore, the generation unit can generate an album from the selected photos based on a specific theme. This allows the album generation system according to the embodiment to efficiently analyze, select, and generate an album from user photo data.

[0030] The reception unit is a part for a user to upload photo data to the cloud. For example, the reception unit allows a user to upload photo data by dragging and dropping the photo data. The reception unit also allows a user to select a specific folder and upload all at once. Furthermore, the reception unit can upload photo data through a smartphone app. Specifically, a user accesses the cloud service through a web browser and easily completes the upload by dragging and dropping the photo data. Furthermore, the function of selecting a specific folder and uploading all at once makes it possible to upload a large amount of photo data at once, significantly reducing the user's efforts. When using a smartphone app, a user can upload photo data to the cloud simply by tapping the upload button in the app and selecting from the photo library in the device. Furthermore, the reception unit also has a check function for checking the consistency of the uploaded photo data and preventing data corruption or duplication. For example, if an error occurs during uploading, the reception unit notifies the user and prompts the user to re-upload. Furthermore, if duplicate photo data is detected, the reception unit checks the duplication for the user and suggests deleting unnecessary data. As a result, the reception unit provides an environment in which a user can easily and efficiently upload photo data to the cloud, and ensures the consistency and quality of the data.

[0031] The analysis unit is a part that analyzes the photo data uploaded by the reception unit. For example, the analysis unit analyzes the contents of the photo data using image recognition technology. The analysis unit can also identify people appearing in the photo using face recognition technology. Furthermore, the analysis unit can analyze metadata such as the date and time of taking the photo and the location. Specifically, the image recognition technology is used to identify objects and scenery appearing in the photo and tag them. For example, buildings, natural scenery, food, etc. appearing in the photo are automatically classified to make it easier for users to search later. Furthermore, face recognition technology can be used to identify people appearing in the photo and group photos in which the same person appears. This allows users to easily find photos in which a specific person appears. Furthermore, the analysis unit analyzes metadata such as the date and time of taking the photo and the location, and organizes the photos based on the chronological order and geographical information of the photos. For example, travel photos can be classified by the location where they were taken, and event photos can be arranged in order of the date and time they were taken. Based on these analysis results, the analysis unit provides users with a function to assist in organizing and searching photos. For example, when a user searches for photos of a particular event or place, the results of the analysis unit can be used to quickly find the desired photo. In this way, the analysis unit plays an important role in efficiently analyzing the user's photo data and supporting the organization and search of photos.

[0032] The selection unit is a part that selects photos based on the photo data analyzed by the analysis unit. For example, the selection unit selects photos based on a specific event or a specific moment. The selection unit can also select photos based on the user's preferences. Furthermore, the selection unit can also select photos based on the image quality of the photos and the type of subject. Specifically, the selection unit automatically selects photos of a specific event or trip based on event information, shooting date and time, and location data provided by the analysis unit. For example, event photos such as weddings and birthday parties can be selected collectively and compiled into an album. In addition, in order to select photos based on the user's preferences, past selection history and evaluation data are utilized. For example, if a user prefers a specific subject or scene, the tendency can be analyzed and similar photos can be preferentially selected. Furthermore, the selection unit also has a function of selecting based on the image quality of the photos and the type of subject. For example, out-of-focus and blurred photos are automatically excluded, and only photos with good image quality are selected. In addition, appropriate photos can be selected according to the type of subject, such as landscape photos and portrait photos. By combining these functions, the selection unit realizes optimal photo selection according to the user's needs and significantly improves the efficiency of album creation. As a result, the selection unit plays an important role in quickly creating the high-quality albums that users desire.

[0033] The generating unit is a part that generates an album based on the photos selected by the selecting unit. For example, the generating unit lays out the selected photos and generates a digital album. The generating unit can also generate the selected photos as a print album. Furthermore, the generating unit can generate an album based on a specific theme from the selected photos. Specifically, the selected photos are automatically laid out and optimally arranged for each page. For example, the photos are arranged while maintaining the balance of the entire page, taking into account the size and aspect ratio of the photos. Furthermore, an album with a story can be generated by adjusting the order and arrangement of the photos. In the case of a digital album, the user can view or share the generated album online. In the case of a print album, the generating unit uses high-resolution data to create a layout for printing and transmits it to a printing company. Furthermore, the generating unit also has a function of generating an album based on a specific theme. For example, the selection and layout of photos can be adjusted according to a theme such as travel, wedding, or birthday, and an album according to the theme can be generated. This allows the generating unit to quickly and efficiently generate a variety of albums according to the needs of the user. Furthermore, the generating unit can provide a template that can be customized by the user, and the user can edit the album according to his or her preferences. In this way, the generation unit plays an important role in providing high satisfaction to users and preserving their memories in a beautiful way.

[0034] The selection unit can select photos based on a specific event or a specific moment. The selection unit selects photos based on a specific event, such as a birthday, a wedding, or a trip. The selection unit can also select photos based on a specific moment, such as a smile moment or a specific action. For example, the selection unit selects photos of a birthday party and organizes them into an album. The selection unit can also select photos of a wedding and organize them into an album. Furthermore, the selection unit can select photos of a trip and organize them into an album. In this way, an album that matches the user's preferences can be generated by selecting photos based on a specific event or moment. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can select photos using an AI model for selecting photos based on a specific event or moment.

[0035] The generating unit can generate a unique album based on the selected photos. For example, the generating unit arranges the selected photos in a customized layout to generate a unique album. The generating unit can also generate an album based on a specific theme. For example, the generating unit selects photos of a family trip and generates an album based on the theme of the trip. The generating unit can also generate an album based on the theme of children's growth. Furthermore, the generating unit can also select photos of a specific event (for example, a graduation ceremony) and generate an album based on the theme of the event. This allows a special album to be provided to the user by generating an epoch-making album based on the selected photos. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for generating an album based on the selected photos.

[0036] The reception unit can upload the photo data of the organization. The reception unit is a part for organizations such as a company, a school, and a club to upload photo data. For example, the reception unit can upload photo data taken by employees of the company. The reception unit can also upload photo data taken by school staff. Furthermore, the reception unit can also upload photo data taken by club members. In this way, an album of the organization can be generated by uploading photo data of a specific organization. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can upload photo data using an AI model for uploading photo data of the organization.

[0037] The analysis unit can analyze the photo data of the organization. The analysis unit can analyze the photo data of the organization using, for example, image recognition technology. The analysis unit can also identify people in the photo using face recognition technology. Furthermore, the analysis unit can analyze metadata such as the date and time of taking the photo and the location. For example, the analysis unit can analyze photo data taken by employees of a company and identify the faces of the employees. The analysis unit can also analyze photo data taken by school staff and identify the faces of the staff. Furthermore, the analysis unit can analyze photo data taken by club members and identify the faces of the members. In this way, by analyzing the photo data of a specific organization, information necessary for generating an album for the organization can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the photo data using an AI model for analyzing the photo data of the organization.

[0038] The generating unit can generate an album for an organization. The generating unit can generate an album for a company based on, for example, photo data taken by an employee of the company. The generating unit can also generate an album for a school based on photo data taken by a staff member of the school. The generating unit can also generate an album for a club based on photo data taken by a member of the club. For example, the generating unit lays out photo data taken by an employee of the company to generate an album for the company. The generating unit can also lay out photo data taken by a staff member of the school to generate an album for the school. The generating unit can also lay out photo data taken by a member of the club to generate an album for the club. This allows an album to be provided according to the needs of the organization by generating an album for a specific organization. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for generating an album for an organization.

[0039] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, the reception unit preferentially suggests an upload method (e.g., drag & drop) that the user has used frequently in the past. The reception unit can also suggest the optimal upload time based on the user's past upload history in a specific time period. The reception unit can also suggest the optimal upload method based on the device (smartphone, PC, etc.) that the user has used in the past. For example, the reception unit preferentially suggests an upload method that the user has used frequently in the past. The reception unit can also suggest the optimal upload time based on the user's past upload history in a specific time period. The reception unit can also suggest the optimal upload method based on the device that the user has used in the past. In this way, the optimal upload method for the user can be suggested by analyzing the past upload history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the optimal upload method using an AI model for analyzing the user's past upload history.

[0040] The reception unit can perform filtering based on the user's current event and field of interest when uploading photo data. The reception unit, for example, preferentially uploads related photo data based on an event (e.g., a wedding) in which the user is currently participating. The reception unit can also filter and upload related photo data based on the user's field of interest (e.g., sports). Furthermore, the reception unit can automatically select related photos when the user uploads photo data based on a specific theme (e.g., a family trip). For example, the reception unit preferentially uploads related photo data based on an event in which the user is currently participating. The reception unit can also filter and upload related photo data based on the user's field of interest. Furthermore, the reception unit can automatically select related photos when the user uploads photo data based on a specific theme. Thereby, by filtering based on the user's current event and field of interest, highly related photo data can be uploaded. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter photo data using an AI model for filtering photo data based on the user's current event and field of interest.

[0041] The reception unit can prioritize uploading highly relevant data based on the geographical location information of the user when uploading photo data. For example, when the user is traveling, the reception unit prioritizes uploading photo data related to the current location. In addition, when the user is at a specific event venue, the reception unit can also prioritize uploading photo data taken at the venue. Furthermore, when the user is at home, the reception unit can also prioritize uploading photo data taken at home. For example, when the user is traveling, the reception unit prioritizes uploading photo data related to the current location. In addition, when the user is at a specific event venue, the reception unit can also prioritize uploading photo data taken at the venue. Furthermore, when the user is at home, the reception unit can also prioritize uploading photo data taken at home. As a result, more appropriate photo data can be uploaded by preferentially uploading highly relevant data based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can upload photo data using an AI model for preferentially uploading highly relevant data based on the geographical location information of the user.

[0042] The reception unit can analyze the social media activity of the user and upload related data when uploading the photo data. For example, the reception unit preferentially uploads photo data shared by the user on social media. The reception unit can also preferentially upload photo data tagged by the user on social media. Furthermore, the reception unit can also preferentially upload photo data for which the user has received many "likes" or comments on social media. For example, the reception unit preferentially uploads photo data shared by the user on social media. The reception unit can also preferentially upload photo data tagged by the user on social media. Furthermore, the reception unit can also preferentially upload photo data for which the user has received many "likes" or comments on social media. In this way, by analyzing the social media activity of the user, it is possible to upload photo data with high relevance. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can upload related data using an AI model for analyzing the social media activity of the user.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the photo data during the analysis. For example, the analysis unit analyzes the photo data of an important event (for example, a wedding) in detail. The analysis unit can also analyze the photo data of everyday life simply. The analysis unit can also analyze the photo data of a specific theme (for example, a family trip) at a medium level of detail. For example, the analysis unit analyzes the photo data of an important event in detail. The analysis unit can also analyze the photo data of everyday life simply. The analysis unit can also analyze the photo data of a specific theme at a medium level of detail. This allows the level of detail of the analysis to be adjusted based on the importance of the photo data, thereby providing a more appropriate analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for adjusting the level of detail of the analysis based on the importance of the photo data.

[0044] The analysis unit can apply different analysis algorithms depending on the category of photo data during analysis. For example, the analysis unit applies a face recognition algorithm to family photos to identify family members. The analysis unit can also apply an image recognition algorithm to landscape photos to identify places and landscapes. The analysis unit can also apply a scene analysis algorithm to event photos to identify the type of event. For example, the analysis unit applies a face recognition algorithm to family photos to identify family members. The analysis unit can also apply an image recognition algorithm to landscape photos to identify places and landscapes. The analysis unit can also apply a scene analysis algorithm to event photos to identify the type of event. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the category of photo data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model for applying different analysis algorithms depending on the category of photo data.

[0045] The analysis unit can determine the priority of the analysis based on the time when the photo data was taken during the analysis. For example, the analysis unit preferentially analyzes the photo data taken recently. The analysis unit can also preferentially analyze the photo data of a specific event (for example, a graduation ceremony). Furthermore, the analysis unit can also preferentially analyze the photo data within a period designated by the user. For example, the analysis unit preferentially analyzes the photo data taken recently. The analysis unit can also preferentially analyze the photo data of a specific event. Furthermore, the analysis unit can also preferentially analyze the photo data within a period designated by the user. This allows the analysis to be prioritized based on the time when the photo data was taken, thereby providing a more appropriate analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for determining the priority of the analysis based on the time when the photo data was taken.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the photo data during analysis. The analysis unit, for example, continuously analyzes photo data taken at the same event. The analysis unit can also continuously analyze photo data taken at the same location. Furthermore, the analysis unit can also continuously analyze photo data in which the same person appears. For example, the analysis unit continuously analyzes photo data taken at the same event. The analysis unit can also continuously analyze photo data taken at the same location. Furthermore, the analysis unit can also continuously analyze photo data in which the same person appears. This allows the order of analysis to be adjusted based on the relevance of the photo data, thereby providing a more appropriate analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model for adjusting the order of analysis based on the relevance of the photo data.

[0047] The selection unit can improve the accuracy of selection by considering the interrelationships of the photo data during selection. For example, the selection unit groups photos taken at the same event and selects photos with high relevance. The selection unit can also group photos taken at the same place and select photos with high relevance. Furthermore, the selection unit can group photos in which the same person appears and select photos with high relevance. For example, the selection unit groups photos taken at the same event and selects photos with high relevance. The selection unit can also group photos taken at the same place and select photos with high relevance. Furthermore, the selection unit can group photos in which the same person appears and select photos with high relevance. Thereby, the accuracy of selection is improved by considering the interrelationships of the photo data. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos using an AI model for improving the accuracy of selection by considering the interrelationships of the photo data.

[0048] The selection unit can select photos by considering attribute information of the photographer of the photo data. For example, the selection unit preferentially selects photos taken by a specific photographer. The selection unit can also select photos taken by multiple photographers in a balanced manner. Furthermore, the selection unit can select photos based on the professional level of the photographer. For example, the selection unit preferentially selects photos taken by a specific photographer. The selection unit can also select photos taken by multiple photographers in a balanced manner. Furthermore, the selection unit can select photos based on the professional level of the photographer. This allows more appropriate photos to be selected by considering attribute information of the photographer of the photo data. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos by using an AI model for performing selection by considering attribute information of the photographer of the photo data.

[0049] The selection unit can select photos by taking into consideration the geographical distribution of the photo data. For example, the selection unit preferentially selects photos taken in a specific area. The selection unit can also select photos taken in a plurality of areas in a balanced manner. Furthermore, the selection unit can group and select photos with high geographical relevance. For example, the selection unit preferentially selects photos taken in a specific area. The selection unit can also select photos taken in a plurality of areas in a balanced manner. Furthermore, the selection unit can group and select photos with high geographical relevance. This allows more appropriate photos to be selected by taking into consideration the geographical distribution of the photo data. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos by using an AI model for performing selection by taking into consideration the geographical distribution of the photo data.

[0050] The selection unit can improve the accuracy of selection by referring to the literature related to the photo data at the time of selection. The selection unit, for example, refers to the literature of an event related to the photo and selects photos with high relevance. The selection unit can also refer to the literature of a place related to the photo and select photos with high relevance. The selection unit can also refer to the literature of a person related to the photo and select photos with high relevance. For example, the selection unit refers to the literature of an event related to the photo and selects photos with high relevance. The selection unit can also refer to the literature of a place related to the photo and select photos with high relevance. The selection unit can also refer to the literature of a person related to the photo and select photos with high relevance. Thereby, by referring to the literature related to the photo data, the accuracy of selection is improved. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos using an AI model for improving the accuracy of selection by referring to the literature related to the photo data.

[0051] The generating unit can adjust the level of detail of the album based on the importance of the selected photo data at the time of generation. For example, the generating unit arranges photos of an important event (for example, a wedding) in detail and adds a description. The generating unit can also arrange everyday photos simply and omit the description. Furthermore, the generating unit can arrange photos of a specific theme (for example, a family trip) with a medium level of detail and add a simple description. For example, the generating unit arranges photos of an important event in detail and adds a description. The generating unit can also arrange everyday photos simply and omit the description. Furthermore, the generating unit can arrange photos of a specific theme with a medium level of detail and add a simple description. This allows a more appropriate album to be generated by adjusting the level of detail of the album based on the importance of the selected photo data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for adjusting the level of detail of the album based on the importance of the selected photo data.

[0052] The generating unit can apply different generating algorithms according to the category of the selected photo data at the time of generation. For example, the generating unit applies a face recognition algorithm to family photos to identify and arrange family members. The generating unit can also apply an image recognition algorithm to landscape photos to identify and arrange places and scenery. The generating unit can also apply a scene analysis algorithm to event photos to identify and arrange the type of event. For example, the generating unit applies a face recognition algorithm to family photos to identify and arrange family members. The generating unit can also apply an image recognition algorithm to landscape photos to identify and arrange places and scenery. The generating unit can also apply a scene analysis algorithm to event photos to identify and arrange the type of event. This allows a more appropriate album to be generated by applying different generating algorithms according to the category of the selected photo data. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for applying different generating algorithms according to the category of the selected photo data.

[0053] The generating unit can determine the page order of the album based on the shooting time of the selected photo data at the time of generation. The generating unit arranges the pages in chronological order based on, for example, the shooting time of the photos. The generating unit can also group photos of a specific event (for example, a graduation ceremony) into one section. Furthermore, the generating unit can also group photos within a period designated by the user into one section. For example, the generating unit arranges the pages in chronological order based on the shooting time of the photos. The generating unit can also group photos of a specific event into one section. Furthermore, the generating unit can also group photos within a period designated by the user into one section. In this way, a more appropriate album can be generated by determining the page order of the album based on the shooting time of the selected photo data. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can generate the album using an AI model for determining the page order of the album based on the shooting time of the selected photo data.

[0054] The generating unit can adjust the theme of the album based on the relevance of the selected photo data at the time of generation. The generating unit, for example, groups photos taken at the same event into one theme. The generating unit can also group photos taken at the same place into one theme. Furthermore, the generating unit can also group photos in which the same person appears into one theme. For example, the generating unit groups photos taken at the same event into one theme. The generating unit can also group photos taken at the same place into one theme. Furthermore, the generating unit can also group photos in which the same person appears into one theme. Thereby, a more appropriate album can be generated by adjusting the theme of the album based on the relevance of the selected photo data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for adjusting the theme of the album based on the relevance of the selected photo data.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past upload history and suggest the optimal upload method. For example, the reception unit preferentially suggests an upload method (e.g., drag and drop) that the user has used frequently in the past. The reception unit can also suggest the optimal upload time based on the user's past upload history during a specific time period. Furthermore, the reception unit can also suggest the optimal upload method based on the device (smartphone, PC, etc.) that the user has used in the past. In this way, the reception unit can suggest the optimal upload method for the user by analyzing the past upload history.

[0057] The analysis unit can determine the priority of the analysis based on the time when the photo data was taken. For example, the analysis unit gives priority to analyzing recently taken photo data. The analysis unit can also give priority to analyzing photo data of a specific event (for example, a graduation ceremony). Furthermore, the analysis unit can also give priority to analyzing photo data within a period designated by the user. In this way, by determining the priority of the analysis based on the time when the photo data was taken, more appropriate analysis results can be provided.

[0058] The generation unit can adjust the level of detail of the album based on the importance of the selected photo data. For example, photos of important events (e.g., weddings) can be arranged in detail and explanatory text can be added. Also, everyday photos can be arranged simply and explanatory text can be omitted. Furthermore, photos of a specific theme (e.g., family trips) can be arranged with a medium level of detail and simple explanatory text can be added. In this way, a more appropriate album can be generated by adjusting the level of detail of the album based on the importance of the selected photo data.

[0059] The analysis unit can apply different analysis algorithms depending on the category of photo data. For example, a face recognition algorithm can be applied to family photos to identify family members. An image recognition algorithm can also be applied to landscape photos to identify places and scenery. Furthermore, a scene analysis algorithm can be applied to event photos to identify the type of event. In this way, by applying different analysis algorithms depending on the category of photo data, more appropriate analysis results can be provided.

[0060] The selection unit can improve the accuracy of selection by taking into account the interrelationships of the photo data. For example, photos taken at the same event can be grouped and highly related photos can be selected. Photos taken at the same place can also be grouped and highly related photos can be selected. Furthermore, photos that include the same person can also be grouped and highly related photos can be selected. In this way, the accuracy of selection can be improved by taking into account the interrelationships of the photo data.

[0061] The reception unit can prioritize uploading highly relevant data based on the geographical location information of the user. For example, when the user is traveling, photo data related to the current location is prioritized for upload. Also, when the user is at a specific event venue, photo data taken at the venue can be prioritized for upload. Furthermore, when the user is at home, photo data taken at home can be prioritized for upload. In this way, by uploading highly relevant data based on the geographical location information of the user, more appropriate photo data can be uploaded.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The reception section is a section where the user uploads photo data to the cloud. For example, the user can upload photo data by dragging and dropping it. In addition, the user can select a specific folder and upload all at once. In addition, the user can upload photo data via a smartphone app. Step 2: The analysis unit is a part that analyzes the photo data uploaded by the reception unit. For example, it can analyze the contents of the photo data using image recognition technology and identify people appearing in the photo using face recognition technology. It can also analyze metadata such as the date and time the photo was taken and the location. Step 3: The selection unit is a unit that selects photos based on the photo data analyzed by the analysis unit. For example, photos can be selected based on a specific event or a specific moment, and photos can also be selected based on user preferences. In addition, photos can also be selected based on the image quality and the type of subject. Step 4: The generating unit generates an album based on the photos selected by the selecting unit. For example, the generating unit lays out the selected photos and generates a digital album. The generating unit can also generate a print album of the selected photos. Furthermore, the generating unit can generate an album based on a specific theme.

[0064] (Example 2) The album generation system according to the embodiment of the present invention is a system that uses AI to generate an album tailored to the user's preferences from a huge amount of photo data uploaded to the cloud. In this album generation system, a user uploads photo data to the cloud, and AI analyzes the photo data and selects photos based on the user's preferences. For example, photos are selected based on a specific event or a specific moment. Based on the selected photos, AI generates an innovative and unique album. Furthermore, as an optional service, albums for a specific organization can be contracted for a fee and created on behalf of the staff. In this case, the staff of the organization uploads photo data to the cloud, and AI analyzes the photo data to generate an album. This allows the album generation system to efficiently analyze, select, and generate albums from the user's photo data.

[0065] The album generating system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a generation unit. The reception unit is a unit for a user to upload photo data to the cloud. For example, the reception unit allows a user to upload photo data by dragging and dropping the photo data. The reception unit also allows a user to select a specific folder and upload all at once. The reception unit can also upload photo data through a smartphone app. The analysis unit is a unit that analyzes the photo data uploaded by the reception unit. For example, the analysis unit analyzes the content of the photo data using image recognition technology. The analysis unit can also identify people appearing in the photo using face recognition technology. The analysis unit can also analyze metadata such as the date and time of taking the photo and the location. The selection unit is a unit that selects photos based on the photo data analyzed by the analysis unit. For example, the selection unit selects photos based on a specific event or a specific moment. The selection unit can also select photos based on the user's preferences. The selection unit can also select photos based on the image quality of the photo and the type of subject. The generation unit is a unit that generates an album based on the photos selected by the selection unit. For example, the generation unit lays out the selected photos and generates a digital album. The generation unit can also generate the selected photos as a print album. Furthermore, the generation unit can generate an album from the selected photos based on a specific theme. This allows the album generation system according to the embodiment to efficiently analyze, select, and generate an album from user photo data.

[0066] The reception unit is a part for a user to upload photo data to the cloud. For example, the reception unit allows a user to upload photo data by dragging and dropping the photo data. The reception unit also allows a user to select a specific folder and upload all at once. Furthermore, the reception unit can upload photo data through a smartphone app. Specifically, a user accesses the cloud service through a web browser and easily completes the upload by dragging and dropping the photo data. Furthermore, the function of selecting a specific folder and uploading all at once makes it possible to upload a large amount of photo data at once, significantly reducing the user's efforts. When using a smartphone app, a user can upload photo data to the cloud simply by tapping the upload button in the app and selecting from the photo library in the device. Furthermore, the reception unit also has a check function for checking the consistency of the uploaded photo data and preventing data corruption or duplication. For example, if an error occurs during uploading, the reception unit notifies the user and prompts the user to re-upload. Furthermore, if duplicate photo data is detected, the reception unit checks the duplication for the user and suggests deleting unnecessary data. As a result, the reception unit provides an environment in which a user can easily and efficiently upload photo data to the cloud, and ensures the consistency and quality of the data.

[0067] The analysis unit is a part that analyzes the photo data uploaded by the reception unit. For example, the analysis unit analyzes the contents of the photo data using image recognition technology. The analysis unit can also identify people appearing in the photo using face recognition technology. Furthermore, the analysis unit can analyze metadata such as the date and time of taking the photo and the location. Specifically, the image recognition technology is used to identify objects and scenery appearing in the photo and tag them. For example, buildings, natural scenery, food, etc. appearing in the photo are automatically classified to make it easier for users to search later. Furthermore, face recognition technology can be used to identify people appearing in the photo and group photos in which the same person appears. This allows users to easily find photos in which a specific person appears. Furthermore, the analysis unit analyzes metadata such as the date and time of taking the photo and the location, and organizes the photos based on the chronological order and geographical information of the photos. For example, travel photos can be classified by the location where they were taken, and event photos can be arranged in order of the date and time they were taken. Based on these analysis results, the analysis unit provides users with a function to assist in organizing and searching photos. For example, when a user searches for photos of a particular event or place, the results of the analysis unit can be used to quickly find the desired photo. In this way, the analysis unit plays an important role in efficiently analyzing the user's photo data and supporting the organization and search of photos.

[0068] The selection unit is a part that selects photos based on the photo data analyzed by the analysis unit. For example, the selection unit selects photos based on a specific event or a specific moment. The selection unit can also select photos based on the user's preferences. Furthermore, the selection unit can also select photos based on the image quality of the photos and the type of subject. Specifically, the selection unit automatically selects photos of a specific event or trip based on event information, shooting date and time, and location data provided by the analysis unit. For example, event photos such as weddings and birthday parties can be selected collectively and compiled into an album. In addition, in order to select photos based on the user's preferences, past selection history and evaluation data are utilized. For example, if a user prefers a specific subject or scene, the tendency can be analyzed and similar photos can be preferentially selected. Furthermore, the selection unit also has a function of selecting based on the image quality of the photos and the type of subject. For example, out-of-focus and blurred photos are automatically excluded, and only photos with good image quality are selected. In addition, appropriate photos can be selected according to the type of subject, such as landscape photos and portrait photos. By combining these functions, the selection unit realizes optimal photo selection according to the user's needs and significantly improves the efficiency of album creation. As a result, the selection unit plays an important role in quickly creating the high-quality albums that users desire.

[0069] The generating unit is a part that generates an album based on the photos selected by the selecting unit. For example, the generating unit lays out the selected photos and generates a digital album. The generating unit can also generate the selected photos as a print album. Furthermore, the generating unit can generate an album based on a specific theme from the selected photos. Specifically, the selected photos are automatically laid out and optimally arranged for each page. For example, the photos are arranged while maintaining the balance of the entire page, taking into account the size and aspect ratio of the photos. Furthermore, an album with a story can be generated by adjusting the order and arrangement of the photos. In the case of a digital album, the user can view or share the generated album online. In the case of a print album, the generating unit uses high-resolution data to create a layout for printing and transmits it to a printing company. Furthermore, the generating unit also has a function of generating an album based on a specific theme. For example, the selection and layout of photos can be adjusted according to a theme such as travel, wedding, or birthday, and an album according to the theme can be generated. This allows the generating unit to quickly and efficiently generate a variety of albums according to the needs of the user. Furthermore, the generating unit can provide a template that can be customized by the user, and the user can edit the album according to his or her preferences. In this way, the generation unit plays an important role in providing high satisfaction to users and preserving their memories in a beautiful way.

[0070] The selection unit can select photos based on a specific event or a specific moment. The selection unit selects photos based on a specific event, such as a birthday, a wedding, or a trip. The selection unit can also select photos based on a specific moment, such as a smile moment or a specific action. For example, the selection unit selects photos of a birthday party and organizes them into an album. The selection unit can also select photos of a wedding and organize them into an album. Furthermore, the selection unit can select photos of a trip and organize them into an album. In this way, an album that matches the user's preferences can be generated by selecting photos based on a specific event or moment. Some or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can select photos using an AI model for selecting photos based on a specific event or moment.

[0071] The generating unit can generate a unique album based on the selected photos. For example, the generating unit arranges the selected photos in a customized layout to generate a unique album. The generating unit can also generate an album based on a specific theme. For example, the generating unit selects photos of a family trip and generates an album based on the theme of the trip. The generating unit can also generate an album based on the theme of children's growth. Furthermore, the generating unit can also select photos of a specific event (for example, a graduation ceremony) and generate an album based on the theme of the event. This allows a special album to be provided to the user by generating an epoch-making album based on the selected photos. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for generating an album based on the selected photos.

[0072] The reception unit can upload the photo data of the organization. The reception unit is a part for organizations such as a company, a school, and a club to upload photo data. For example, the reception unit can upload photo data taken by employees of the company. The reception unit can also upload photo data taken by school staff. Furthermore, the reception unit can also upload photo data taken by club members. In this way, an album of the organization can be generated by uploading photo data of a specific organization. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can upload photo data using an AI model for uploading photo data of the organization.

[0073] The analysis unit can analyze the photo data of the organization. The analysis unit can analyze the photo data of the organization using, for example, image recognition technology. The analysis unit can also identify people in the photo using face recognition technology. Furthermore, the analysis unit can analyze metadata such as the date and time of taking the photo and the location. For example, the analysis unit can analyze photo data taken by employees of a company and identify the faces of the employees. The analysis unit can also analyze photo data taken by school staff and identify the faces of the staff. Furthermore, the analysis unit can analyze photo data taken by club members and identify the faces of the members. In this way, by analyzing the photo data of a specific organization, information necessary for generating an album for the organization can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the photo data using an AI model for analyzing the photo data of the organization.

[0074] The generating unit can generate an album for an organization. The generating unit can generate an album for a company based on, for example, photo data taken by an employee of the company. The generating unit can also generate an album for a school based on photo data taken by a staff member of the school. The generating unit can also generate an album for a club based on photo data taken by a member of the club. For example, the generating unit lays out photo data taken by an employee of the company to generate an album for the company. The generating unit can also lay out photo data taken by a staff member of the school to generate an album for the school. The generating unit can also lay out photo data taken by a member of the club to generate an album for the club. This allows an album to be provided according to the needs of the organization by generating an album for a specific organization. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for generating an album for an organization.

[0075] The reception unit can estimate the user's emotion and adjust the upload timing of the photo data based on the estimated emotion of the user. For example, when the user is excited, the reception unit immediately transmits a notification urging the user to upload the photo data. In addition, when the user is relaxed, the reception unit can flexibly adjust the upload timing according to the user's convenience. Furthermore, when the user is busy, the reception unit can set a reminder for uploading later. For example, when the user is excited, the reception unit transmits a notification urging the user to upload the photo data. In addition, when the user is relaxed, the reception unit can adjust the upload timing according to the user's convenience. Furthermore, when the user is busy, the reception unit can set a reminder for uploading later. In this way, by adjusting the upload timing according to the user's emotion, the photo data can be uploaded at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can estimate the user's emotions using an AI model for estimating the user's emotions and adjust the upload timing.

[0076] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, the reception unit preferentially suggests an upload method (e.g., drag & drop) that the user has used frequently in the past. The reception unit can also suggest the optimal upload time based on the user's past upload history in a specific time period. The reception unit can also suggest the optimal upload method based on the device (smartphone, PC, etc.) that the user has used in the past. For example, the reception unit preferentially suggests an upload method that the user has used frequently in the past. The reception unit can also suggest the optimal upload time based on the user's past upload history in a specific time period. The reception unit can also suggest the optimal upload method based on the device that the user has used in the past. In this way, the optimal upload method for the user can be suggested by analyzing the past upload history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the optimal upload method using an AI model for analyzing the user's past upload history.

[0077] The reception unit can perform filtering based on the user's current event and field of interest when uploading photo data. The reception unit, for example, preferentially uploads related photo data based on an event (e.g., a wedding) in which the user is currently participating. The reception unit can also filter and upload related photo data based on the user's field of interest (e.g., sports). Furthermore, the reception unit can automatically select related photos when the user uploads photo data based on a specific theme (e.g., a family trip). For example, the reception unit preferentially uploads related photo data based on an event in which the user is currently participating. The reception unit can also filter and upload related photo data based on the user's field of interest. Furthermore, the reception unit can automatically select related photos when the user uploads photo data based on a specific theme. Thereby, by filtering based on the user's current event and field of interest, highly related photo data can be uploaded. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter photo data using an AI model for filtering photo data based on the user's current event and field of interest.

[0078] The reception unit can estimate the user's emotion and determine the priority of the photo data to be uploaded based on the estimated user's emotion. For example, when the user is moved, the reception unit preferentially uploads photo data capturing an emotional moment. Also, when the user is having fun, the reception unit can also preferentially upload photo data with a happy expression. Furthermore, when the user is tired, the reception unit can also preferentially upload photo data with a relaxed moment. For example, when the user is moved, the reception unit preferentially uploads photo data capturing an emotional moment. Also, when the user is having fun, the reception unit can also preferentially upload photo data with a happy expression. Furthermore, when the user is tired, the reception unit can also preferentially upload photo data with a relaxed moment. In this way, by determining the priority of the photo data according to the user's emotion, more appropriate photo data can be uploaded. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model for estimating a user's emotion to estimate the emotion and determine the priority order of the photo data.

[0079] The reception unit can prioritize uploading highly relevant data based on the geographical location information of the user when uploading photo data. For example, when the user is traveling, the reception unit prioritizes uploading photo data related to the current location. In addition, when the user is at a specific event venue, the reception unit can also prioritize uploading photo data taken at the venue. Furthermore, when the user is at home, the reception unit can also prioritize uploading photo data taken at home. For example, when the user is traveling, the reception unit prioritizes uploading photo data related to the current location. In addition, when the user is at a specific event venue, the reception unit can also prioritize uploading photo data taken at the venue. Furthermore, when the user is at home, the reception unit can also prioritize uploading photo data taken at home. As a result, more appropriate photo data can be uploaded by preferentially uploading highly relevant data based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can upload photo data using an AI model for preferentially uploading highly relevant data based on the geographical location information of the user.

[0080] The reception unit can analyze the social media activity of the user and upload related data when uploading the photo data. For example, the reception unit preferentially uploads photo data shared by the user on social media. The reception unit can also preferentially upload photo data tagged by the user on social media. Furthermore, the reception unit can also preferentially upload photo data for which the user has received many "likes" or comments on social media. For example, the reception unit preferentially uploads photo data shared by the user on social media. The reception unit can also preferentially upload photo data tagged by the user on social media. Furthermore, the reception unit can also preferentially upload photo data for which the user has received many "likes" or comments on social media. In this way, by analyzing the social media activity of the user, it is possible to upload photo data with high relevance. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can upload related data using an AI model for analyzing the social media activity of the user.

[0081] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is moved, the analysis unit provides an analysis result that emphasizes an emotional moment. Furthermore, when the user is enjoying, the analysis unit can also provide an analysis result that emphasizes a fun expression or moment. Furthermore, when the user is relaxed, the analysis unit can also provide an analysis result that emphasizes a relaxing moment. For example, when the user is moved, the analysis unit provides an analysis result that emphasizes an emotional moment. Furthermore, when the user is enjoying, the analysis unit can also provide an analysis result that emphasizes a fun expression or moment. Furthermore, when the user is relaxed, the analysis unit can also provide an analysis result that emphasizes a relaxing moment. In this way, by adjusting the expression method of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can use an AI model for inferring a user's emotions to infer emotions and adjust how the analysis is presented.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the photo data during the analysis. For example, the analysis unit analyzes the photo data of an important event (for example, a wedding) in detail. The analysis unit can also analyze the photo data of everyday life simply. The analysis unit can also analyze the photo data of a specific theme (for example, a family trip) at a medium level of detail. For example, the analysis unit analyzes the photo data of an important event in detail. The analysis unit can also analyze the photo data of everyday life simply. The analysis unit can also analyze the photo data of a specific theme at a medium level of detail. This allows the level of detail of the analysis to be adjusted based on the importance of the photo data, thereby providing a more appropriate analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for adjusting the level of detail of the analysis based on the importance of the photo data.

[0083] The analysis unit can apply different analysis algorithms depending on the category of photo data during analysis. For example, the analysis unit applies a face recognition algorithm to family photos to identify family members. The analysis unit can also apply an image recognition algorithm to landscape photos to identify places and landscapes. The analysis unit can also apply a scene analysis algorithm to event photos to identify the type of event. For example, the analysis unit applies a face recognition algorithm to family photos to identify family members. The analysis unit can also apply an image recognition algorithm to landscape photos to identify places and landscapes. The analysis unit can also apply a scene analysis algorithm to event photos to identify the type of event. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the category of photo data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model for applying different analysis algorithms depending on the category of photo data.

[0084] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. In addition, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. In addition, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis according to the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can use an AI model for estimating a user's emotions to estimate the emotions and adjust the length of the analysis.

[0085] The analysis unit can determine the priority of the analysis based on the time when the photo data was taken during the analysis. For example, the analysis unit preferentially analyzes the photo data taken recently. The analysis unit can also preferentially analyze the photo data of a specific event (for example, a graduation ceremony). Furthermore, the analysis unit can also preferentially analyze the photo data within a period designated by the user. For example, the analysis unit preferentially analyzes the photo data taken recently. The analysis unit can also preferentially analyze the photo data of a specific event. Furthermore, the analysis unit can also preferentially analyze the photo data within a period designated by the user. This allows the analysis to be prioritized based on the time when the photo data was taken, thereby providing a more appropriate analysis result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform the analysis using an AI model for determining the priority of the analysis based on the time when the photo data was taken.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the photo data during analysis. The analysis unit, for example, continuously analyzes photo data taken at the same event. The analysis unit can also continuously analyze photo data taken at the same location. Furthermore, the analysis unit can also continuously analyze photo data in which the same person appears. For example, the analysis unit continuously analyzes photo data taken at the same event. The analysis unit can also continuously analyze photo data taken at the same location. Furthermore, the analysis unit can also continuously analyze photo data in which the same person appears. This allows the order of analysis to be adjusted based on the relevance of the photo data, thereby providing a more appropriate analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model for adjusting the order of analysis based on the relevance of the photo data.

[0087] The selection unit can estimate the user's emotion and adjust the selection criteria based on the estimated user's emotion. For example, when the user is moved, the selection unit preferentially selects photos capturing an emotional moment. Also, when the user is having fun, the selection unit can also preferentially select photos with a happy expression. Furthermore, when the user is relaxed, the selection unit can also preferentially select photos of a relaxed moment. For example, when the user is moved, the selection unit preferentially selects photos capturing an emotional moment. Also, when the user is having fun, the selection unit can also preferentially select photos with a happy expression. Furthermore, when the user is relaxed, the selection unit can also preferentially select photos of a relaxed moment. In this way, by adjusting the selection criteria according to the user's emotion, more appropriate photos can be selected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may estimate emotions and adjust the selection criteria using an AI model for estimating user emotions.

[0088] The selection unit can improve the accuracy of selection by considering the interrelationships of the photo data during selection. For example, the selection unit groups photos taken at the same event and selects photos with high relevance. The selection unit can also group photos taken at the same place and select photos with high relevance. Furthermore, the selection unit can group photos in which the same person appears and select photos with high relevance. For example, the selection unit groups photos taken at the same event and selects photos with high relevance. The selection unit can also group photos taken at the same place and select photos with high relevance. Furthermore, the selection unit can group photos in which the same person appears and select photos with high relevance. Thereby, the accuracy of selection is improved by considering the interrelationships of the photo data. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos using an AI model for improving the accuracy of selection by considering the interrelationships of the photo data.

[0089] The selection unit can select photos by considering attribute information of the photographer of the photo data. For example, the selection unit preferentially selects photos taken by a specific photographer. The selection unit can also select photos taken by multiple photographers in a balanced manner. Furthermore, the selection unit can select photos based on the professional level of the photographer. For example, the selection unit preferentially selects photos taken by a specific photographer. The selection unit can also select photos taken by multiple photographers in a balanced manner. Furthermore, the selection unit can select photos based on the professional level of the photographer. This allows more appropriate photos to be selected by considering attribute information of the photographer of the photo data. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos by using an AI model for performing selection by considering attribute information of the photographer of the photo data.

[0090] The selection unit can estimate the user's emotion and adjust the order of displaying the selection results based on the estimated user's emotion. For example, when the user is moved, the selection unit displays an emotional photo first. Also, when the user is having fun, the selection unit can also display a fun-looking photo first. Furthermore, when the user is relaxed, the selection unit can also display a relaxing photo first. For example, when the user is moved, the selection unit displays an emotional photo first. Also, when the user is having fun, the selection unit can also display a fun-looking photo first. Furthermore, when the user is relaxed, the selection unit can also display a relaxing photo first. This allows for more appropriate display by adjusting the display order of the selection results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can use an AI model for estimating a user's emotions to estimate the emotions and adjust the display order of the selection results.

[0091] The selection unit can select photos by taking into consideration the geographical distribution of the photo data. For example, the selection unit preferentially selects photos taken in a specific area. The selection unit can also select photos taken in a plurality of areas in a balanced manner. Furthermore, the selection unit can group and select photos with high geographical relevance. For example, the selection unit preferentially selects photos taken in a specific area. The selection unit can also select photos taken in a plurality of areas in a balanced manner. Furthermore, the selection unit can group and select photos with high geographical relevance. This allows more appropriate photos to be selected by taking into consideration the geographical distribution of the photo data. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos by using an AI model for performing selection by taking into consideration the geographical distribution of the photo data.

[0092] The selection unit can improve the accuracy of selection by referring to the literature related to the photo data at the time of selection. The selection unit, for example, refers to the literature of an event related to the photo and selects photos with high relevance. The selection unit can also refer to the literature of a place related to the photo and select photos with high relevance. The selection unit can also refer to the literature of a person related to the photo and select photos with high relevance. For example, the selection unit refers to the literature of an event related to the photo and selects photos with high relevance. The selection unit can also refer to the literature of a place related to the photo and select photos with high relevance. The selection unit can also refer to the literature of a person related to the photo and select photos with high relevance. Thereby, by referring to the literature related to the photo data, the accuracy of selection is improved. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can select photos using an AI model for improving the accuracy of selection by referring to the literature related to the photo data.

[0093] The generation unit can estimate the user's emotion and adjust the generation method of the album based on the estimated user's emotion. For example, when the user is moved, the generation unit generates an album that emphasizes moving moments. Also, when the user is enjoying, the generation unit can also generate an album that emphasizes a happy expression. Furthermore, when the user is relaxed, the generation unit can also generate an album that emphasizes a relaxing moment. For example, when the user is moved, the generation unit generates an album that emphasizes moving moments. Also, when the user is enjoying, the generation unit can also generate an album that emphasizes a happy expression. Furthermore, when the user is relaxed, the generation unit can also generate an album that emphasizes a relaxing moment. In this way, by adjusting the album generation method according to the user's emotion, a more appropriate album can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generator can use an AI model for estimating user emotions to estimate emotions and adjust how the album is generated.

[0094] The generating unit can adjust the level of detail of the album based on the importance of the selected photo data at the time of generation. For example, the generating unit arranges photos of an important event (for example, a wedding) in detail and adds a description. The generating unit can also arrange everyday photos simply and omit the description. Furthermore, the generating unit can arrange photos of a specific theme (for example, a family trip) with a medium level of detail and add a simple description. For example, the generating unit arranges photos of an important event in detail and adds a description. The generating unit can also arrange everyday photos simply and omit the description. Furthermore, the generating unit can arrange photos of a specific theme with a medium level of detail and add a simple description. This allows a more appropriate album to be generated by adjusting the level of detail of the album based on the importance of the selected photo data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for adjusting the level of detail of the album based on the importance of the selected photo data.

[0095] The generating unit can apply different generating algorithms according to the category of the selected photo data at the time of generation. For example, the generating unit applies a face recognition algorithm to family photos to identify and arrange family members. The generating unit can also apply an image recognition algorithm to landscape photos to identify and arrange places and scenery. The generating unit can also apply a scene analysis algorithm to event photos to identify and arrange the type of event. For example, the generating unit applies a face recognition algorithm to family photos to identify and arrange family members. The generating unit can also apply an image recognition algorithm to landscape photos to identify and arrange places and scenery. The generating unit can also apply a scene analysis algorithm to event photos to identify and arrange the type of event. This allows a more appropriate album to be generated by applying different generating algorithms according to the category of the selected photo data. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for applying different generating algorithms according to the category of the selected photo data.

[0096] The generation unit can estimate the user's emotion and adjust the layout of the album based on the estimated emotion of the user. For example, when the user is moved, the generation unit generates a layout in which moving moments are arranged prominently. Furthermore, when the user is enjoying, the generation unit can also generate a layout in which the happy facial expression is emphasized. Furthermore, when the user is relaxed, the generation unit can also generate a layout in which the relaxing moment is arranged at the center. For example, when the user is moved, the generation unit generates a layout in which moving moments are arranged prominently. Furthermore, when the user is enjoying, the generation unit can also generate a layout in which the happy facial expression is emphasized. Furthermore, when the user is relaxed, the generation unit can also generate a layout in which the relaxing moment is arranged at the center. In this way, by adjusting the album layout according to the user's emotion, a more appropriate album can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generator can use an AI model for estimating a user's emotions to estimate emotions and adjust the layout of the album.

[0097] The generating unit can determine the page order of the album based on the shooting time of the selected photo data at the time of generation. The generating unit arranges the pages in chronological order based on, for example, the shooting time of the photos. The generating unit can also group photos of a specific event (for example, a graduation ceremony) into one section. Furthermore, the generating unit can also group photos within a period designated by the user into one section. For example, the generating unit arranges the pages in chronological order based on the shooting time of the photos. The generating unit can also group photos of a specific event into one section. Furthermore, the generating unit can also group photos within a period designated by the user into one section. In this way, a more appropriate album can be generated by determining the page order of the album based on the shooting time of the selected photo data. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can generate the album using an AI model for determining the page order of the album based on the shooting time of the selected photo data.

[0098] The generating unit can adjust the theme of the album based on the relevance of the selected photo data at the time of generation. The generating unit, for example, groups photos taken at the same event into one theme. The generating unit can also group photos taken at the same place into one theme. Furthermore, the generating unit can also group photos in which the same person appears into one theme. For example, the generating unit groups photos taken at the same event into one theme. The generating unit can also group photos taken at the same place into one theme. Furthermore, the generating unit can also group photos in which the same person appears into one theme. Thereby, a more appropriate album can be generated by adjusting the theme of the album based on the relevance of the selected photo data. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can generate an album using an AI model for adjusting the theme of the album based on the relevance of the selected photo data.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The reception unit can analyze the user's past upload history and suggest the optimal upload method. For example, the reception unit preferentially suggests an upload method (e.g., drag and drop) that the user has used frequently in the past. The reception unit can also suggest the optimal upload time based on the user's past upload history during a specific time period. Furthermore, the reception unit can also suggest the optimal upload method based on the device (smartphone, PC, etc.) that the user has used in the past. In this way, the reception unit can suggest the optimal upload method for the user by analyzing the past upload history.

[0101] The analysis unit can determine the priority of the analysis based on the time when the photo data was taken. For example, the analysis unit gives priority to analyzing recently taken photo data. The analysis unit can also give priority to analyzing photo data of a specific event (for example, a graduation ceremony). Furthermore, the analysis unit can also give priority to analyzing photo data within a period designated by the user. In this way, by determining the priority of the analysis based on the time when the photo data was taken, more appropriate analysis results can be provided.

[0102] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated user's emotions. For example, if the user is moved, photos capturing moving moments can be preferentially selected. Also, if the user is having fun, photos showing happy expressions can be preferentially selected. Furthermore, if the user is relaxed, photos of moments when the user is relaxed can be preferentially selected. In this way, by adjusting the selection criteria according to the user's emotions, more appropriate photos can be selected.

[0103] The generation unit can adjust the level of detail of the album based on the importance of the selected photo data. For example, photos of important events (e.g., weddings) can be arranged in detail and explanatory text can be added. Also, everyday photos can be arranged simply and explanatory text can be omitted. Furthermore, photos of a specific theme (e.g., family trips) can be arranged with a medium level of detail and simple explanatory text can be added. In this way, a more appropriate album can be generated by adjusting the level of detail of the album based on the importance of the selected photo data.

[0104] The reception unit can estimate the user's emotion and adjust the timing of uploading the photo data based on the estimated emotion of the user. For example, if the user is excited, a notification is sent immediately to prompt the user to upload the photo data. Also, if the user is relaxed, the timing of uploading can be flexibly adjusted to suit the user's convenience. Furthermore, if the user is busy, a reminder can be set to upload later. In this way, the upload timing can be adjusted according to the user's emotion, so that the photo data can be uploaded at a more appropriate timing.

[0105] The analysis unit can apply different analysis algorithms depending on the category of photo data. For example, a face recognition algorithm can be applied to family photos to identify family members. An image recognition algorithm can also be applied to landscape photos to identify places and scenery. Furthermore, a scene analysis algorithm can be applied to event photos to identify the type of event. In this way, by applying different analysis algorithms depending on the category of photo data, more appropriate analysis results can be provided.

[0106] The selection unit can improve the accuracy of selection by taking into account the interrelationships of the photo data. For example, photos taken at the same event can be grouped and highly related photos can be selected. Photos taken at the same place can also be grouped and highly related photos can be selected. Furthermore, photos that include the same person can also be grouped and highly related photos can be selected. In this way, the accuracy of selection can be improved by taking into account the interrelationships of the photo data.

[0107] The generation unit can estimate the user's emotions and adjust the album layout based on the estimated user's emotions. For example, if the user is moved, a layout is generated in which moving moments are prominently displayed. If the user is having fun, a layout can be generated that emphasizes a happy expression. Furthermore, if the user is relaxed, a layout can be generated in which relaxing moments are centrally displayed. In this way, a more appropriate album can be generated by adjusting the album layout according to the user's emotions.

[0108] The reception unit can prioritize uploading highly relevant data based on the geographical location information of the user. For example, when the user is traveling, photo data related to the current location is prioritized for upload. Also, when the user is at a specific event venue, photo data taken at the venue can be prioritized for upload. Furthermore, when the user is at home, photo data taken at home can be prioritized for upload. In this way, by uploading highly relevant data based on the geographical location information of the user, more appropriate photo data can be uploaded.

[0109] The generation unit can estimate the user's emotions and adjust the album generation method based on the estimated user's emotions. For example, if the user is moved, an album that emphasizes moving moments can be generated. Also, if the user is having fun, an album that emphasizes happy expressions can be generated. Furthermore, if the user is relaxed, an album that emphasizes relaxing moments can be generated. In this way, a more appropriate album can be generated by adjusting the album generation method according to the user's emotions.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The reception section is a section where the user uploads photo data to the cloud. For example, the user can upload photo data by dragging and dropping it. In addition, the user can select a specific folder and upload all at once. In addition, the user can upload photo data via a smartphone app. Step 2: The analysis unit is a part that analyzes the photo data uploaded by the reception unit. For example, it can analyze the contents of the photo data using image recognition technology and identify people appearing in the photo using face recognition technology. It can also analyze metadata such as the date and time the photo was taken and the location. Step 3: The selection unit is a unit that selects photos based on the photo data analyzed by the analysis unit. For example, photos can be selected based on a specific event or a specific moment, and photos can also be selected based on user preferences. In addition, photos can also be selected based on the image quality and the type of subject. Step 4: The generating unit generates an album based on the photos selected by the selecting unit. For example, the generating unit lays out the selected photos and generates a digital album. The generating unit can also generate a print album of the selected photos. Furthermore, the generating unit can generate an album based on a specific theme.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the elements including the above-mentioned reception unit, analysis unit, selection unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and provides an interface for a user to upload photo data to the cloud. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photo data. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects photos based on the analyzed photo data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an album based on the selected photos. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and provides an interface for a user to upload photo data to the cloud. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photo data. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects photos based on the analyzed photo data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an album based on the selected photos. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the elements including the above-mentioned reception unit, analysis unit, selection unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and provides an interface for a user to upload photo data to the cloud. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photo data. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects photos based on the analyzed photo data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an album based on the selected photos. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and provides an interface for a user to upload photo data to the cloud. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the uploaded photo data. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects photos based on the analyzed photo data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an album based on the selected photos. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, 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 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) A reception section for uploading photo data; an analysis unit that analyzes the photograph data uploaded by the reception unit; a selection unit that selects photographs based on the photograph data analyzed by the analysis unit; a generation unit that generates an album based on the photos selected by the selection unit. A system characterized by: (Appendix 2) The sorting unit includes: Filter photos based on a specific event or a specific moment 2. The system of claim 1, (Appendix 3) The generation unit is Generate a unique album based on selected photos 2. The system of claim 1, (Appendix 4) The reception unit is Upload your organization's photo data 2. The system of claim 1, (Appendix 5) The analysis unit is Analyzing tissue photo data 2. The system of claim 1, (Appendix 6) The generation unit is Generate an album for your organization 2. The system of claim 1, (Appendix 7) The reception unit is The system estimates the user's emotions and adjusts the timing of uploading photo data based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past upload history and select the optimal upload method 2. The system of claim 1, (Appendix 9) The reception unit is Filtering photo data as it is uploaded based on the user's current events and interests 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize the photo data to be uploaded based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The reception unit is When uploading photo data, the most relevant data is uploaded based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The reception unit is When uploading photo data, analyze the user's social media activity and upload related data. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of analysis detail based on the importance of the photo data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of photo data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the photo data was taken 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of the photo data 2. The system of claim 1, (Appendix 19) The sorting unit includes: Inferring a user's emotions and adjusting selection criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The sorting unit includes: Improve sorting accuracy by considering correlations between photo data during sorting 2. The system of claim 1, (Appendix 21) The sorting unit includes: When selecting photos, the photo data is selected taking into consideration the photographer's attributes. 2. The system of claim 1, (Appendix 22) The sorting unit includes: Inferring user emotion and adjusting the order in which the results of the selection are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 23) The sorting unit includes: When selecting, the geographical distribution of photo data is taken into consideration. 2. The system of claim 1, (Appendix 24) The sorting unit includes: When sorting, refer to related literature on the photo data to improve the accuracy of sorting. 2. The system of claim 1, (Appendix 25) The generation unit is To estimate a user's emotion and adjust an album generation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The generation unit is At generation time, the album detail level is adjusted based on the importance of the selected photo data. 2. The system of claim 1, (Appendix 27) The generation unit is During generation, different generation algorithms are applied depending on the category of selected photo data. 2. The system of claim 1, (Appendix 28) The generation unit is Inferring a user's emotion and adjusting the album layout based on the estimated user's emotion 2. The system of claim 1, (Appendix 29) The generation unit is At generation time, the order of pages in the album is determined based on the shooting dates of the selected photos. 2. The system of claim 1, (Appendix 30) The generation unit is At generation time, the album theme is adjusted based on the relevance of the curated photo data 2. The system of claim 1, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for uploading photo data; an analysis unit that analyzes the photograph data uploaded by the reception unit; a selection unit that selects photographs based on the photograph data analyzed by the analysis unit; a generation unit that generates an album based on the photos selected by the selection unit. A system characterized by:

2. The sorting unit includes: Filter photos based on a specific event or a specific moment 2. The system of claim 1 .

3. The generation unit is Generate a unique album based on selected photos 2. The system of claim 1 .

4. The reception unit is Upload your organization's photo data 2. The system of claim 1 .

5. The analysis unit is Analyzing tissue photo data 5. The system of claim 4.

6. The generation unit is Generate an album for your organization 6. The system of claim 5.

7. The reception unit is The system estimates the user's emotions and adjusts the timing of uploading photo data based on the estimated user emotions.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past upload history and select the optimal upload method 2. The system of claim 1 .

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