System

Abstract

An object of a system according to an embodiment is to automatically generate a graduation album.SOLUTION: A system includes a collection unit, an analysis unit, and a generation unit. The collection unit collects photographs and data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an album based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating a yearbook requires a great deal of time and effort.

[0005] The system according to the embodiment aims to automatically generate a yearbook. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects photos and data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an album based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a yearbook. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic yearbook generation system according to an embodiment of the present invention collects and analyzes photos and data to generate an album. The automatic yearbook generation system utilizes AI to automatically generate personalized yearbook albums, reducing the workload for schools and graduates. For example, the automatic yearbook generation system collects data such as student photos, event records, and messages. The automatic yearbook generation system then analyzes the collected data to extract individual student characteristics and memories. Based on the analyzed data, the automatic yearbook generation system automatically determines the design and layout to generate a beautiful album. This saves schools and graduates the trouble of manually creating albums and allows them to easily create albums filled with individual memories. This allows the automatic yearbook generation system to automatically generate personalized yearbook albums, reducing the workload for schools and graduates. For example, by automatically generating albums that include event photos, group photos, individual messages, and more, each graduate's memories can be cherished. Furthermore, by incorporating information about students' involvement in specific events and activities, personalized albums tailored to individual students can be provided.

[0029] The automatic yearbook generation system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects photos and data. The collection unit collects data such as event photos, group photos, and individual messages. The collection unit can collect data in various formats, such as digital photos, video clips, and text data. For example, the collection unit automatically collects photos taken at school events. The collection unit can also collect individual messages provided by students. The collection unit can also collect related data from social media. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected photos and data to extract characteristics and memories of individual students. For example, the analysis unit extracts information about students who participated in a specific event. The analysis unit can also extract information about students who were involved in a specific activity. The analysis unit can also extract students' hobbies and special skills based on the collected data. The generation unit generates an album based on the data analyzed by the analysis unit. For example, the generation unit automatically determines a design and layout based on the analyzed data to generate a beautiful album. For example, the generation unit can generate a design based on a specific theme. The generation unit can also generate a layout that emphasizes the memories of individual students. The generation unit can also automatically determine the number of pages and layout based on the analyzed data. This allows the automatic yearbook generation system according to the embodiment to automate the collection and analysis of photos and data, and the generation of albums, thereby reducing the amount of work required.

[0030] The collection unit can collect data on event photos, group photos, and individual messages. The collection unit, for example, collects event photos. For example, the collection unit collects photos of graduation ceremonies, sporting events, and the like. The collection unit can also collect group photos. For example, the collection unit collects class photos and group photos of club activities. The collection unit can also collect individual messages. For example, the collection unit collects messages written by students and messages from teachers. In this way, by collecting data such as event photos, group photos, and individual messages, it is possible to enrich the memories of individual students. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect photos using an AI model that automatically collects event photos.

[0031] The analysis unit can analyze the collected data and extract characteristics or memories of individual students. The analysis unit, for example, analyzes the collected data and extracts characteristics of individual students. For example, the analysis unit extracts information about students who participated in a particular event. The analysis unit can also extract information about students who were involved in a particular activity. The analysis unit can also extract students' hobbies and special skills based on the collected data. For example, the analysis unit extracts information about club activities and special skills in which students participated. The analysis unit can also extract students' memories based on the collected data. For example, the analysis unit extracts memories based on messages and photos written by students. By extracting the characteristics and memories of individual students, a personalized album can be created. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the collected data using an AI model that inputs the data and extracts characteristics and memories of individual students.

[0032] The generation unit can automatically determine a design or layout based on the analyzed data and generate a beautiful album. The generation unit automatically determines a design based on, for example, the analyzed data. For example, the generation unit generates a design based on a specific theme. The generation unit can also generate a layout that emphasizes memories of an individual student. For example, the generation unit generates a layout that reflects the characteristics and memories of the student. The generation unit can also automatically determine the number of pages and layout based on the analyzed data. For example, the generation unit automatically determines the number of pages and layout using a design template. This makes it possible to easily create a beautiful album by automatically determining the design and layout. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate an album using an AI model that uses the analyzed data as input and automatically determines the design and layout.

[0033] The generation unit can generate a design based on a specific theme or a layout that emphasizes memories of an individual student. The generation unit, for example, generates a design based on a specific theme. For example, the generation unit generates designs based on seasonal themes or event themes. The generation unit can also generate a layout that emphasizes memories of an individual student. For example, the generation unit generates a layout that reflects the characteristics and memories of a student. The generation unit can also generate designs and layouts related to specific activities. For example, the generation unit generates designs and layouts related to club activities or sporting events. This makes it possible to provide a more personalized album by generating a design based on a specific theme or a layout that emphasizes memories of an individual student. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate an album using an AI model that automatically determines a design and layout based on a specific theme.

[0034] The collection unit can analyze the student's past event participation history and select an appropriate collection method. For example, the collection unit prioritizes collecting photos of events the student has participated in in the past. For example, the collection unit prioritizes collecting photos of events the student has participated in in the past. The collection unit can also collect many photos of events the student frequently participates in. For example, the collection unit collects many photos of events the student frequently participates in. The collection unit can also select the type of photos to collect based on the number of times the student has participated in a specific event. For example, the collection unit selects the type of photos to collect based on the number of times the student has participated in a specific event. This allows the analysis of the student's past event participation history to select an optimal collection method and collect more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select an appropriate collection method using an AI model that analyzes the student's past event participation history.

[0035] When collecting photos and data, the collection unit can filter the data based on the student's current activities or areas of interest. For example, the collection unit prioritizes collecting photos of events in which the student is currently participating. For example, the collection unit prioritizes collecting photos of events in which the student is currently participating. The collection unit can also filter and collect photos related to the student's areas of interest. For example, the collection unit filters and collects photos related to the student's areas of interest. The collection unit can also select data to collect based on the student's current activities. For example, the collection unit selects data to collect based on the student's current activities. In this way, filtering based on the student's current activities and areas of interest allows for collection of more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can perform filtering using an AI model that analyzes the student's current activities and areas of interest.

[0036] When collecting photos and data, the collection unit can select an appropriate collection means depending on the student's input method. For example, if a student uses voice input, the collection unit prioritizes collecting voice data. For example, if a student uses voice input, the collection unit prioritizes collecting voice data. The collection unit can also prioritize collecting text data if a student uses text input. For example, if a student uses text input, the collection unit prioritizes collecting text data. The collection unit can also prioritize collecting image data if a student uses image input. For example, if a student uses image input, the collection unit prioritizes collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select an appropriate collection means using an AI model that analyzes the student's input method.

[0037] When collecting photos and data, the collection unit can prioritize collecting highly relevant data by taking into account the student's geographical location information. For example, when a student is in a specific location, the collection unit prioritizes collecting photos related to that location. For example, when a student is in a specific location, the collection unit prioritizes collecting photos related to that location. Furthermore, when a student is traveling, the collection unit can prioritize collecting photos of the travel destination. For example, when a student is traveling, the collection unit prioritizes collecting photos of the travel destination. Furthermore, when a student is in school, the collection unit can prioritize collecting photos related to events and activities within the school. For example, when a student is in school, the collection unit prioritizes collecting photos related to events and activities within the school. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can prioritize collecting highly relevant data using an AI model that analyzes the student's geographical location information.

[0038] The collection unit can analyze students' social media activities and collect specific data when collecting photos and data. For example, the collection unit collects photos shared by students on social media. The collection unit can also analyze the content of students' social media posts and collect related photos. For example, the collection unit analyzes the content of students' social media posts and collects related photos. The collection unit can also collect related photos by referring to the activities of the students' friends on social media. For example, the collection unit collects related photos by referring to the activities of the students' friends on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can collect specific data using an AI model that analyzes students' social media activities.

[0039] When collecting photos and data, the collection unit can adjust the collection method by reflecting students' past feedback. For example, the collection unit collects data by reflecting the student's previously preferred photo style. For example, the collection unit collects data by reflecting the student's previously preferred photo style. The collection unit can also adjust the type of data to collect based on feedback provided by the student in the past. For example, the collection unit adjusts the type of data to collect based on feedback provided by the student in the past. The collection unit can also collect data by reflecting features of photos previously rated by the student. For example, the collection unit collects data by reflecting features of photos previously rated by the student. In this way, by reflecting past feedback, the collection method can be customized and more appropriate data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection method using an AI model that analyzes students' past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit may analyze photos of important events in detail and analyze photos of less importance simply. For example, the analysis unit may analyze photos of important events in detail and analyze photos of less importance simply. The analysis unit may also analyze photos that highlight student characteristics in detail and analyze general photos simply. For example, the analysis unit may analyze photos that highlight student characteristics in detail and analyze general photos simply. The analysis unit may also analyze photos related to specific activities in detail and analyze other photos simply. For example, the analysis unit may analyze photos related to specific activities in detail and analyze other photos simply. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. Some 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 may adjust the level of detail of the analysis using an AI model that evaluates the importance of data.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an analysis algorithm dedicated to an event to photos of an event. For example, the analysis unit applies an analysis algorithm dedicated to an event to photos of an event. The analysis unit can also apply an analysis algorithm dedicated to group photos to group photos. For example, the analysis unit applies an analysis algorithm dedicated to group photos to group photos. The analysis unit can also apply a text analysis algorithm to individual messages. For example, the analysis unit applies a text analysis algorithm to individual messages. In this way, by applying different analysis algorithms depending on the category of data, more accurate analysis can be performed. Some 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 data using an AI model that applies different analysis algorithms depending on the category of data.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. The analysis unit, for example, adjusts the current analysis result based on the student's past analysis results. For example, the analysis unit adjusts the current analysis result based on the student's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to analysis results previously evaluated by the student. For example, the analysis unit improves the accuracy of the analysis by referring to analysis results previously evaluated by the student. The analysis unit can also adjust the analysis algorithm based on the student's past feedback. For example, the analysis unit adjusts the analysis algorithm based on the student's past feedback. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some 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 improve the accuracy of the analysis by using an AI model that analyzes the student's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing recently collected data. For example, the analysis unit prioritizes analyzing recently collected data. The analysis unit can also prioritize analyzing data collected during a specific event period. For example, the analysis unit prioritizes analyzing data collected during a specific event period. The analysis unit can also prioritize analyzing data collected during an important student activity period. For example, the analysis unit prioritizes analyzing data collected during an important student activity period. In this way, by determining the priority of analysis based on the time when the data was collected, analysis can be performed efficiently. Some 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 determine the priority of analysis using an AI model that analyzes the time when the data was collected.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit can also prioritize analyzing data related to student characteristics. For example, the analysis unit prioritizes analyzing data related to student characteristics. The analysis unit can also prioritize analyzing data related to a specific event. For example, the analysis unit prioritizes analyzing data related to a specific event. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can be performed efficiently. Some 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 adjust the order of analysis using an AI model that evaluates the relevance of data.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. For example, if the student's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terminology. For example, if the student's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terminology. Furthermore, if the student's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terminology. For example, if the student's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terminology. Furthermore, the analysis unit can provide an analysis result that uses appropriate technical terminology according to the student's level of expertise. For example, the analysis unit provides an analysis result that uses appropriate technical terminology according to the student's level of expertise. In this way, by adjusting the use of technical terminology according to the student's level of expertise, it is possible to provide an analysis result that is easier to understand. Some or all of the above-described 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 adjust the use of technical terminology using an AI model that evaluates the student's level of expertise.

[0046] During generation, the generation unit can adjust the level of detail of the design and layout based on the importance of the data. For example, the generation unit applies a detailed design and layout to photos of important events. For example, the generation unit applies a detailed design and layout to photos of important events. The generation unit can also apply a detailed design and layout to photos that highlight student features. For example, the generation unit applies a detailed design and layout to photos that highlight student features. The generation unit can also apply a detailed design and layout to photos related to a specific activity. For example, the generation unit applies a detailed design and layout to photos related to a specific activity. In this way, by adjusting the level of detail of the design and layout based on the importance of the data, more important data can be emphasized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the design and layout using an AI model that evaluates the importance of data.

[0047] The generation unit can apply different design algorithms depending on the data category during generation. For example, the generation unit applies a design algorithm dedicated to an event to photos of an event. For example, the generation unit applies a design algorithm dedicated to an event to photos of an event. The generation unit can also apply a design algorithm dedicated to group photos to group photos. For example, the generation unit applies a design algorithm dedicated to group photos to group photos. The generation unit can also apply a text design algorithm to individual messages. For example, the generation unit applies a text design algorithm to individual messages. In this way, by applying different design algorithms depending on the data category, more appropriate designs can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate designs using an AI model that applies different design algorithms depending on the data category.

[0048] During generation, the generation unit can improve the accuracy of generation by referring to the student's past generation results. The generation unit, for example, adjusts the current generation result based on the student's past generation results. For example, the generation unit adjusts the current generation result based on the student's past generation results. The generation unit can also improve the accuracy of generation by referring to generation results previously evaluated by the student. For example, the generation unit improves the accuracy of generation by referring to generation results previously evaluated by the student. The generation unit can also adjust the generation algorithm based on the student's past feedback. For example, the generation unit adjusts the generation algorithm based on the student's past feedback. In this way, the accuracy of generation can be improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can improve the accuracy of generation by using an AI model that analyzes the student's past generation results.

[0049] During generation, the generation unit can determine the priority of the design and layout based on the time when the data was collected. For example, the generation unit prioritizes reflecting recently collected data in the design and layout. For example, the generation unit prioritizes reflecting recently collected data in the design and layout. The generation unit can also prioritize reflecting data collected during a specific event period in the design and layout. For example, the generation unit prioritizes reflecting data collected during a specific event period in the design and layout. The generation unit can also prioritize reflecting data collected during important student activity periods in the design and layout. For example, the generation unit prioritizes reflecting data collected during important student activity periods in the design and layout. In this way, by prioritizing the design and layout based on the time when the data was collected, a more relevant design can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of the design and layout using an AI model that analyzes the time when the data was collected.

[0050] The generation unit can adjust the order of the design and layout based on the relevance of the data during generation. For example, the generation unit prioritizes reflecting highly relevant data in the design and layout. For example, the generation unit prioritizes reflecting highly relevant data in the design and layout. The generation unit can also prioritize reflecting data related to student characteristics in the design and layout. For example, the generation unit prioritizes reflecting data related to student characteristics in the design and layout. The generation unit can also prioritize reflecting data related to a specific event in the design and layout. For example, the generation unit prioritizes reflecting data related to a specific event in the design and layout. By adjusting the order of the design and layout based on the relevance of the data, it is possible to provide a more relevant album. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of the design and layout using an AI model that evaluates the relevance of data.

[0051] During generation, the generation unit can adjust the use of technical terminology in the design and layout according to the student's level of expertise. For example, if the student's level of expertise is high, the generation unit provides a design and layout that uses a lot of technical terminology. For example, if the student's level of expertise is high, the generation unit provides a design and layout that uses a lot of technical terminology. Furthermore, if the student's level of expertise is low, the generation unit can provide a design and layout that avoids technical terminology. For example, if the student's level of expertise is low, the generation unit can provide a design and layout that avoids technical terminology. Furthermore, the generation unit can provide a design and layout that uses appropriate technical terminology according to the student's level of expertise. For example, the generation unit provides a design and layout that uses appropriate technical terminology according to the student's level of expertise. This makes it possible to provide an album that is easier to understand by adjusting the use of technical terminology according to the student's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can adjust the use of technical terminology using an AI model that evaluates the student's level of expertise.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection department can collect data on students' academic performance and extracurricular activities and use this data to enrich the content of the album. For example, the collection department can collect students' report cards and awards. The collection department can also collect records of club activities and volunteer activities in which students participated. Furthermore, the collection department can collect essays and project results written by students. This allows the creation of an album that reflects the results of academic and extracurricular activities and records the students' efforts and growth.

[0054] The analysis unit can extract students' future goals and dreams based on the collected data and reflect them in the album. For example, the analysis unit can analyze essays written by students and survey responses to extract future goals. The analysis unit can also analyze records of career counseling sessions attended by students to extract information about their future careers. Furthermore, the analysis unit can analyze the fields and activities in which students are interested and reflect their future dreams. This makes it possible to incorporate students' future visions into the album, providing more personalized content.

[0055] The generator can add interactive elements to each page of the album. For example, the generator can generate a two-dimensional code (e.g., a QR code) and link it to a student's video message or digital content. The generator can also use augmented reality (AR) technology to display 3D objects or animations related to the photos. Furthermore, the generator can add a function to play audio guides or background music when students view the album. This can make the album viewing experience richer and more interactive.

[0056] The collection section can collect messages and photos from the student's family and friends and reflect them in the album. For example, the collection section can collect congratulatory messages and memorable photos from the student's family. The collection section can also collect messages and group photos from the student's friends. Furthermore, the collection section can collect comments and letters of recommendation from teachers and coaches. This allows the album to incorporate warm messages and memories from people around the student, providing more moving content.

[0057] The collection unit can collect students' health and fitness data and reflect it in the album. For example, the collection unit can collect students' exercise records and health checkup results. The collection unit can also collect data on sports events and fitness activities in which students participated. Furthermore, the collection unit can collect students' meal records and sleep data. This allows students' health and fitness achievements to be incorporated into the album, providing a more comprehensive record.

[0058] The collection department can collect data based on students' cultural backgrounds and languages ​​and reflect it in the album. For example, the collection department can collect messages and poems written in students' native languages. The collection department can also collect photos of students' cultural events and traditional ceremonies. Furthermore, the collection department can collect cultural messages and photos from students' families and communities. This allows the album to reflect students' cultural backgrounds and provide content that respects diversity.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects photos and data. For example, the collection unit collects data such as event photos, group photos, and individual messages. The collection unit can also collect data in various formats, such as digital photos, video clips, and text data. For example, the collection unit can automatically collect photos taken at school events. The collection unit can also collect individual messages provided by students. The collection unit can also collect related data from social media. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected photos and data to extract characteristics and memories of individual students. For example, the analysis unit extracts information about students who participated in a specific event. The analysis unit can also extract information about students who were involved in a specific activity. The analysis unit can also extract students' hobbies and special skills based on the collected data. Step 3: The generation unit generates an album based on the data analyzed by the analysis unit. For example, the generation unit automatically determines the design and layout based on the analyzed data to generate a beautiful album. For example, the generation unit generates a design based on a specific theme. The generation unit can also generate a layout that emphasizes the memories of individual students. The generation unit can also automatically determine the number of pages and layout based on the analyzed data.

[0061] (Example 2) An automatic yearbook generation system according to an embodiment of the present invention collects and analyzes photos and data to generate an album. The automatic yearbook generation system utilizes AI to automatically generate personalized yearbook albums, reducing the workload for schools and graduates. For example, the automatic yearbook generation system collects data such as student photos, event records, and messages. The automatic yearbook generation system then analyzes the collected data to extract individual student characteristics and memories. Based on the analyzed data, the automatic yearbook generation system automatically determines the design and layout to generate a beautiful album. This saves schools and graduates the trouble of manually creating albums and allows them to easily create albums filled with individual memories. This allows the automatic yearbook generation system to automatically generate personalized yearbook albums, reducing the workload for schools and graduates. For example, by automatically generating albums that include event photos, group photos, individual messages, and more, each graduate's memories can be cherished. Furthermore, by incorporating information about students' involvement in specific events and activities, personalized albums tailored to individual students can be provided.

[0062] The automatic yearbook generation system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects photos and data. The collection unit collects data such as event photos, group photos, and individual messages. The collection unit can collect data in various formats, such as digital photos, video clips, and text data. For example, the collection unit automatically collects photos taken at school events. The collection unit can also collect individual messages provided by students. The collection unit can also collect related data from social media. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected photos and data to extract characteristics and memories of individual students. For example, the analysis unit extracts information about students who participated in a specific event. The analysis unit can also extract information about students who were involved in a specific activity. The analysis unit can also extract students' hobbies and special skills based on the collected data. The generation unit generates an album based on the data analyzed by the analysis unit. For example, the generation unit automatically determines a design and layout based on the analyzed data to generate a beautiful album. For example, the generation unit can generate a design based on a specific theme. The generation unit can also generate a layout that emphasizes the memories of individual students. The generation unit can also automatically determine the number of pages and layout based on the analyzed data. This allows the automatic yearbook generation system according to the embodiment to automate the collection and analysis of photos and data, and the generation of albums, thereby reducing the amount of work required.

[0063] The collection unit can collect data on event photos, group photos, and individual messages. The collection unit, for example, collects event photos. For example, the collection unit collects photos of graduation ceremonies, sporting events, and the like. The collection unit can also collect group photos. For example, the collection unit collects class photos and group photos of club activities. The collection unit can also collect individual messages. For example, the collection unit collects messages written by students and messages from teachers. In this way, by collecting data such as event photos, group photos, and individual messages, it is possible to enrich the memories of individual students. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect photos using an AI model that automatically collects event photos.

[0064] The analysis unit can analyze the collected data and extract characteristics or memories of individual students. The analysis unit, for example, analyzes the collected data and extracts characteristics of individual students. For example, the analysis unit extracts information about students who participated in a particular event. The analysis unit can also extract information about students who were involved in a particular activity. The analysis unit can also extract students' hobbies and special skills based on the collected data. For example, the analysis unit extracts information about club activities and special skills in which students participated. The analysis unit can also extract students' memories based on the collected data. For example, the analysis unit extracts memories based on messages and photos written by students. By extracting the characteristics and memories of individual students, a personalized album can be created. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the collected data using an AI model that inputs the data and extracts characteristics and memories of individual students.

[0065] The generation unit can automatically determine a design or layout based on the analyzed data and generate a beautiful album. The generation unit automatically determines a design based on, for example, the analyzed data. For example, the generation unit generates a design based on a specific theme. The generation unit can also generate a layout that emphasizes memories of an individual student. For example, the generation unit generates a layout that reflects the characteristics and memories of the student. The generation unit can also automatically determine the number of pages and layout based on the analyzed data. For example, the generation unit automatically determines the number of pages and layout using a design template. This makes it possible to easily create a beautiful album by automatically determining the design and layout. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate an album using an AI model that uses the analyzed data as input and automatically determines the design and layout.

[0066] The generation unit can generate a design based on a specific theme or a layout that emphasizes memories of an individual student. The generation unit, for example, generates a design based on a specific theme. For example, the generation unit generates designs based on seasonal themes or event themes. The generation unit can also generate a layout that emphasizes memories of an individual student. For example, the generation unit generates a layout that reflects the characteristics and memories of a student. The generation unit can also generate designs and layouts related to specific activities. For example, the generation unit generates designs and layouts related to club activities or sporting events. This makes it possible to provide a more personalized album by generating a design based on a specific theme or a layout that emphasizes memories of an individual student. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate an album using an AI model that automatically determines a design and layout based on a specific theme.

[0067] The collection unit can estimate the user's emotions and adjust the timing of collecting photos or data based on the user's emotions. The collection unit, for example, estimates the user's emotions and increases the frequency of photo collection to capture emotional moments. For example, when the user is emotional, the collection unit increases the frequency of photo collection to capture emotional moments. The collection unit can also adjust the timing of photo collection to capture natural expressions when the user is relaxed. For example, when the user is relaxed, the collection unit adjusts the timing of photo collection to capture natural expressions. The collection unit can also collect photos when the user is excited. For example, when the user is excited, the collection unit collects photos at the peak of excitement. In this way, by adjusting the collection timing according to the user's emotions, more emotional moments can be captured. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can use an AI model that estimates a user's emotions and adjust collection timing based on the emotions.

[0068] The collection unit can analyze the student's past event participation history and select an appropriate collection method. For example, the collection unit prioritizes collecting photos of events the student has participated in in the past. For example, the collection unit prioritizes collecting photos of events the student has participated in in the past. The collection unit can also collect many photos of events the student frequently participates in. For example, the collection unit collects many photos of events the student frequently participates in. The collection unit can also select the type of photos to collect based on the number of times the student has participated in a specific event. For example, the collection unit selects the type of photos to collect based on the number of times the student has participated in a specific event. This allows the analysis of the student's past event participation history to select an optimal collection method and collect more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select an appropriate collection method using an AI model that analyzes the student's past event participation history.

[0069] When collecting photos and data, the collection unit can filter the data based on the student's current activities or areas of interest. For example, the collection unit prioritizes collecting photos of events in which the student is currently participating. For example, the collection unit prioritizes collecting photos of events in which the student is currently participating. The collection unit can also filter and collect photos related to the student's areas of interest. For example, the collection unit filters and collects photos related to the student's areas of interest. The collection unit can also select data to collect based on the student's current activities. For example, the collection unit selects data to collect based on the student's current activities. In this way, filtering based on the student's current activities and areas of interest allows for collection of more relevant data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can perform filtering using an AI model that analyzes the student's current activities and areas of interest.

[0070] When collecting photos and data, the collection unit can select an appropriate collection means depending on the student's input method. For example, if a student uses voice input, the collection unit prioritizes collecting voice data. For example, if a student uses voice input, the collection unit prioritizes collecting voice data. The collection unit can also prioritize collecting text data if a student uses text input. For example, if a student uses text input, the collection unit prioritizes collecting text data. The collection unit can also prioritize collecting image data if a student uses image input. For example, if a student uses image input, the collection unit prioritizes collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can select an appropriate collection means using an AI model that analyzes the student's input method.

[0071] The collection unit can estimate the user's emotions and prioritize data to be collected based on the user's emotions. For example, if the user is emotional, the collection unit can prioritize photos to capture emotional moments. For example, if the user is emotional, the collection unit can prioritize photos to capture emotional moments. Furthermore, if the user is relaxed, the collection unit can adjust the priority of photos to capture natural expressions. For example, if the user is relaxed, the collection unit can adjust the priority of photos to capture natural expressions. Furthermore, if the user is excited, the collection unit can prioritize photos at the peak of excitement. For example, if the user is excited, the collection unit can prioritize photos at the peak of excitement. Thus, by prioritizing data based on the user's emotions, more important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can use an AI model that estimates a user's emotions to prioritize the data to collect based on emotions.

[0072] When collecting photos and data, the collection unit can prioritize collecting highly relevant data by taking into account the student's geographical location information. For example, when a student is in a specific location, the collection unit prioritizes collecting photos related to that location. For example, when a student is in a specific location, the collection unit prioritizes collecting photos related to that location. Furthermore, when a student is traveling, the collection unit can prioritize collecting photos of the travel destination. For example, when a student is traveling, the collection unit prioritizes collecting photos of the travel destination. Furthermore, when a student is in school, the collection unit can prioritize collecting photos related to events and activities within the school. For example, when a student is in school, the collection unit prioritizes collecting photos related to events and activities within the school. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can prioritize collecting highly relevant data using an AI model that analyzes the student's geographical location information.

[0073] The collection unit can analyze students' social media activities and collect specific data when collecting photos and data. For example, the collection unit collects photos shared by students on social media. The collection unit can also analyze the content of students' social media posts and collect related photos. For example, the collection unit analyzes the content of students' social media posts and collects related photos. The collection unit can also collect related photos by referring to the activities of the students' friends on social media. For example, the collection unit collects related photos by referring to the activities of the students' friends on social media. In this way, related data can be efficiently collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can collect specific data using an AI model that analyzes students' social media activities.

[0074] When collecting photos and data, the collection unit can adjust the collection method by reflecting students' past feedback. For example, the collection unit collects data by reflecting the student's previously preferred photo style. For example, the collection unit collects data by reflecting the student's previously preferred photo style. The collection unit can also adjust the type of data to collect based on feedback provided by the student in the past. For example, the collection unit adjusts the type of data to collect based on feedback provided by the student in the past. The collection unit can also collect data by reflecting features of photos previously rated by the student. For example, the collection unit collects data by reflecting features of photos previously rated by the student. In this way, by reflecting past feedback, the collection method can be customized and more appropriate data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can adjust the collection method using an AI model that analyzes students' past feedback.

[0075] The analysis unit can estimate the user's emotions and adjust the way the data analysis is presented based on the estimated user's emotions. For example, if the user is emotional, the analysis unit provides an analysis result that emphasizes the emotional moment. For example, if the user is emotional, the analysis unit provides an analysis result that emphasizes the emotional moment. Furthermore, if the user is relaxed, the analysis unit can provide an analysis result that emphasizes a natural facial expression. For example, if the user is relaxed, the analysis unit provides an analysis result that emphasizes a natural facial expression. Furthermore, if the user is excited, the analysis unit can provide an analysis result that emphasizes the peak of the excitement. For example, if the user is excited, the analysis unit provides an analysis result that emphasizes the peak of the excitement. In this way, by adjusting the way the data analysis is presented based on the user's emotions, a more emotional analysis result can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can use an AI model that estimates a user's emotions and adjust how the data analysis is presented based on the emotions.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit may analyze photos of important events in detail and analyze photos of less importance simply. For example, the analysis unit may analyze photos of important events in detail and analyze photos of less importance simply. The analysis unit may also analyze photos that highlight student characteristics in detail and analyze general photos simply. For example, the analysis unit may analyze photos that highlight student characteristics in detail and analyze general photos simply. The analysis unit may also analyze photos related to specific activities in detail and analyze other photos simply. For example, the analysis unit may analyze photos related to specific activities in detail and analyze other photos simply. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. Some 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 may adjust the level of detail of the analysis using an AI model that evaluates the importance of data.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an analysis algorithm dedicated to an event to photos of an event. For example, the analysis unit applies an analysis algorithm dedicated to an event to photos of an event. The analysis unit can also apply an analysis algorithm dedicated to group photos to group photos. For example, the analysis unit applies an analysis algorithm dedicated to group photos to group photos. The analysis unit can also apply a text analysis algorithm to individual messages. For example, the analysis unit applies a text analysis algorithm to individual messages. In this way, by applying different analysis algorithms depending on the category of data, more accurate analysis can be performed. Some 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 data using an AI model that applies different analysis algorithms depending on the category of data.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results. The analysis unit, for example, adjusts the current analysis result based on the student's past analysis results. For example, the analysis unit adjusts the current analysis result based on the student's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to analysis results previously evaluated by the student. For example, the analysis unit improves the accuracy of the analysis by referring to analysis results previously evaluated by the student. The analysis unit can also adjust the analysis algorithm based on the student's past feedback. For example, the analysis unit adjusts the analysis algorithm based on the student's past feedback. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some 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 improve the accuracy of the analysis by using an AI model that analyzes the student's past analysis results.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can use an AI model to estimate a user's emotions and adjust the length of the analysis based on the emotions.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing recently collected data. For example, the analysis unit prioritizes analyzing recently collected data. The analysis unit can also prioritize analyzing data collected during a specific event period. For example, the analysis unit prioritizes analyzing data collected during a specific event period. The analysis unit can also prioritize analyzing data collected during an important student activity period. For example, the analysis unit prioritizes analyzing data collected during an important student activity period. In this way, by determining the priority of analysis based on the time when the data was collected, analysis can be performed efficiently. Some 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 determine the priority of analysis using an AI model that analyzes the time when the data was collected.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data. For example, the analysis unit prioritizes analyzing highly relevant data. The analysis unit can also prioritize analyzing data related to student characteristics. For example, the analysis unit prioritizes analyzing data related to student characteristics. The analysis unit can also prioritize analyzing data related to a specific event. For example, the analysis unit prioritizes analyzing data related to a specific event. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can be performed efficiently. Some 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 adjust the order of analysis using an AI model that evaluates the relevance of data.

[0082] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise. For example, if the student's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terminology. For example, if the student's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terminology. Furthermore, if the student's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terminology. For example, if the student's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terminology. Furthermore, the analysis unit can provide an analysis result that uses appropriate technical terminology according to the student's level of expertise. For example, the analysis unit provides an analysis result that uses appropriate technical terminology according to the student's level of expertise. In this way, by adjusting the use of technical terminology according to the student's level of expertise, it is possible to provide an analysis result that is easier to understand. Some or all of the above-described 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 adjust the use of technical terminology using an AI model that evaluates the student's level of expertise.

[0083] The generation unit can estimate the user's emotions and adjust the album generation method based on the user's emotions. For example, if the user is emotional, the generation unit generates an album that emphasizes emotional moments. For example, if the user is emotional, the generation unit generates an album that emphasizes emotional moments. Furthermore, if the user is relaxed, the generation unit can generate an album that emphasizes natural expressions. For example, if the user is relaxed, the generation unit generates an album that emphasizes natural expressions. Furthermore, if the user is excited, the generation unit can generate an album that emphasizes peak moments of excitement. For example, if the user is excited, the generation unit generates an album that emphasizes peak moments of excitement. In this way, by adjusting the album generation method based on the user's emotions, a more moving album can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generator can use an AI model that estimates a user's emotions and adjust how the album is generated based on those emotions.

[0084] During generation, the generation unit can adjust the level of detail of the design and layout based on the importance of the data. For example, the generation unit applies a detailed design and layout to photos of important events. For example, the generation unit applies a detailed design and layout to photos of important events. The generation unit can also apply a detailed design and layout to photos that highlight student features. For example, the generation unit applies a detailed design and layout to photos that highlight student features. The generation unit can also apply a detailed design and layout to photos related to a specific activity. For example, the generation unit applies a detailed design and layout to photos related to a specific activity. In this way, by adjusting the level of detail of the design and layout based on the importance of the data, more important data can be emphasized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the design and layout using an AI model that evaluates the importance of data.

[0085] The generation unit can apply different design algorithms depending on the data category during generation. For example, the generation unit applies a design algorithm dedicated to an event to photos of an event. For example, the generation unit applies a design algorithm dedicated to an event to photos of an event. The generation unit can also apply a design algorithm dedicated to group photos to group photos. For example, the generation unit applies a design algorithm dedicated to group photos to group photos. The generation unit can also apply a text design algorithm to individual messages. For example, the generation unit applies a text design algorithm to individual messages. In this way, by applying different design algorithms depending on the data category, more appropriate designs can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate designs using an AI model that applies different design algorithms depending on the data category.

[0086] During generation, the generation unit can improve the accuracy of generation by referring to the student's past generation results. The generation unit, for example, adjusts the current generation result based on the student's past generation results. For example, the generation unit adjusts the current generation result based on the student's past generation results. The generation unit can also improve the accuracy of generation by referring to generation results previously evaluated by the student. For example, the generation unit improves the accuracy of generation by referring to generation results previously evaluated by the student. The generation unit can also adjust the generation algorithm based on the student's past feedback. For example, the generation unit adjusts the generation algorithm based on the student's past feedback. In this way, the accuracy of generation can be improved by referring to the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can improve the accuracy of generation by using an AI model that analyzes the student's past generation results.

[0087] The generation unit can estimate the user's emotions and adjust the length of the album based on the user's emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point album. For example, if the user is in a hurry, the generation unit generates a short, to-the-point album. The generation unit can also generate a longer album with detailed explanations if the user is relaxed. For example, if the user is relaxed, the generation unit generates a longer album with detailed explanations. The generation unit can also generate an album with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit generates an album with visually stimulating effects. This allows the user to adjust the length of the album based on the user's emotions, thereby providing a more appropriate album. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generator can use an AI model to estimate a user's emotions and adjust the length of the album based on the emotions.

[0088] During generation, the generation unit can determine the priority of the design and layout based on the time when the data was collected. For example, the generation unit prioritizes reflecting recently collected data in the design and layout. For example, the generation unit prioritizes reflecting recently collected data in the design and layout. The generation unit can also prioritize reflecting data collected during a specific event period in the design and layout. For example, the generation unit prioritizes reflecting data collected during a specific event period in the design and layout. The generation unit can also prioritize reflecting data collected during important student activity periods in the design and layout. For example, the generation unit prioritizes reflecting data collected during important student activity periods in the design and layout. In this way, by prioritizing the design and layout based on the time when the data was collected, a more relevant design can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can determine the priority of the design and layout using an AI model that analyzes the time when the data was collected.

[0089] The generation unit can adjust the order of the design and layout based on the relevance of the data during generation. For example, the generation unit prioritizes reflecting highly relevant data in the design and layout. For example, the generation unit prioritizes reflecting highly relevant data in the design and layout. The generation unit can also prioritize reflecting data related to student characteristics in the design and layout. For example, the generation unit prioritizes reflecting data related to student characteristics in the design and layout. The generation unit can also prioritize reflecting data related to a specific event in the design and layout. For example, the generation unit prioritizes reflecting data related to a specific event in the design and layout. By adjusting the order of the design and layout based on the relevance of the data, it is possible to provide a more relevant album. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of the design and layout using an AI model that evaluates the relevance of data.

[0090] During generation, the generation unit can adjust the use of technical terminology in the design and layout according to the student's level of expertise. For example, if the student's level of expertise is high, the generation unit provides a design and layout that uses a lot of technical terminology. For example, if the student's level of expertise is high, the generation unit provides a design and layout that uses a lot of technical terminology. Furthermore, if the student's level of expertise is low, the generation unit can provide a design and layout that avoids technical terminology. For example, if the student's level of expertise is low, the generation unit can provide a design and layout that avoids technical terminology. Furthermore, the generation unit can provide a design and layout that uses appropriate technical terminology according to the student's level of expertise. For example, the generation unit provides a design and layout that uses appropriate technical terminology according to the student's level of expertise. This makes it possible to provide an album that is easier to understand by adjusting the use of technical terminology according to the student's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can adjust the use of technical terminology using an AI model that evaluates the student's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect photos and data using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and extract characteristics and memories of individual students. For example, the generation unit can generate an album based on the data analyzed by the specific processing unit 290 of the data processing device 12 and automatically determine the design and layout using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect photos and data using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and extract characteristics and memories of individual students. For example, the generation unit can generate an album based on the data analyzed by the specific processing unit 290 of the data processing device 12 and automatically determine the design and layout using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and generation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect photos and data using the camera 42 and microphone 238 of the headset type terminal 314. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and extract characteristics and memories of individual students. For example, the generation unit can generate an album based on the data analyzed by the specific processing unit 290 of the data processing device 12 and automatically determine the design and layout using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect photos and data using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 and extract characteristics and memories of individual students. For example, the generation unit can generate an album based on the data analyzed by the specific processing unit 290 of the data processing device 12 and automatically determine the design and layout using the control unit 46A of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection department can collect data on students' academic performance and extracurricular activities and use this data to enrich the content of the album. For example, the collection department can collect students' report cards and awards. The collection department can also collect records of club activities and volunteer activities in which students participated. Furthermore, the collection department can collect essays and project results written by students. This allows the creation of an album that reflects the results of academic and extracurricular activities and records the students' efforts and growth.

[0093] The analysis unit can extract students' future goals and dreams based on the collected data and reflect them in the album. For example, the analysis unit can analyze essays written by students and survey responses to extract future goals. The analysis unit can also analyze records of career counseling sessions attended by students to extract information about their future careers. Furthermore, the analysis unit can analyze the fields and activities in which students are interested and reflect their future dreams. This makes it possible to incorporate students' future visions into the album, providing more personalized content.

[0094] The generator can add interactive elements to each page of the album. For example, the generator can generate a two-dimensional code and link it to a student's video message or digital content. The generator can also use augmented reality (AR) technology to display 3D objects or animations related to the photos. Furthermore, the generator can add a function to play audio guides or background music when students view the album. This makes the album viewing experience richer and more interactive.

[0095] The collection section can collect messages and photos from the student's family and friends and reflect them in the album. For example, the collection section can collect congratulatory messages and memorable photos from the student's family. The collection section can also collect messages and group photos from the student's friends. Furthermore, the collection section can collect comments and letters of recommendation from teachers and coaches. This allows the album to incorporate warm messages and memories from people around the student, providing more moving content.

[0096] The analysis unit can estimate the user's emotions and adjust the priority of data analysis based on the estimated user's emotions. For example, if the user is emotional, the analysis unit can prioritize analyzing emotional moments. Also, if the user is relaxed, the analysis unit can prioritize analyzing natural facial expressions. Furthermore, if the user is excited, the analysis unit can prioritize analyzing peak moments of excitement. In this way, by adjusting the priority of data analysis based on the user's emotions, it is possible to provide more emotional analysis results.

[0097] The generation unit can estimate the user's emotions and select a design theme for the album based on the estimated user's emotions. For example, if the user is emotional, the generation unit can select an emotional theme. If the user is relaxed, the generation unit can select a calm theme. If the user is excited, the generation unit can select a lively theme. In this way, by selecting a design theme for the album based on the user's emotions, it is possible to provide an album that appeals to the user's emotions more.

[0098] The collection unit can collect students' health and fitness data and reflect it in the album. For example, the collection unit can collect students' exercise records and health checkup results. The collection unit can also collect data on sports events and fitness activities in which students participated. Furthermore, the collection unit can collect students' meal records and sleep data. This allows students' health and fitness achievements to be incorporated into the album, providing a more comprehensive record.

[0099] The analysis unit can estimate the user's emotions and adjust the visual representation of the data analysis based on the estimated user's emotions. For example, the analysis unit can use emotional colors and effects when the user is emotional. The analysis unit can also use calm colors and effects when the user is relaxed. Furthermore, the analysis unit can use vivid colors and dynamic effects when the user is excited. In this way, by adjusting the visual representation of the data analysis based on the user's emotions, it is possible to provide analysis results that appeal more emotionally.

[0100] The generation unit can estimate the user's emotions and adjust the page order of the album based on the estimated user's emotions. For example, if the user is emotional, the generation unit can arrange emotional moments first. Also, if the user is relaxed, the generation unit can arrange calm moments first. Furthermore, if the user is excited, the generation unit can arrange the peak of excitement first. In this way, by adjusting the page order of the album based on the user's emotions, it is possible to provide an album that appeals to the user's emotions more.

[0101] The collection department can collect data based on students' cultural backgrounds and languages ​​and reflect it in the album. For example, the collection department can collect messages and poems written in students' native languages. The collection department can also collect photos of students' cultural events and traditional ceremonies. Furthermore, the collection department can collect cultural messages and photos from students' families and communities. This allows the album to reflect students' cultural backgrounds and provide content that respects diversity.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects photos and data. For example, the collection unit collects data such as event photos, group photos, and individual messages. The collection unit can also collect data in various formats, such as digital photos, video clips, and text data. For example, the collection unit can automatically collect photos taken at school events. The collection unit can also collect individual messages provided by students. The collection unit can also collect related data from social media. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected photos and data to extract characteristics and memories of individual students. For example, the analysis unit extracts information about students who participated in a specific event. The analysis unit can also extract information about students who were involved in a specific activity. The analysis unit can also extract students' hobbies and special skills based on the collected data. Step 3: The generation unit generates an album based on the data analyzed by the analysis unit. For example, the generation unit automatically determines the design and layout based on the analyzed data to generate a beautiful album. For example, the generation unit generates a design based on a specific theme. The generation unit can also generate a layout that emphasizes the memories of individual students. The generation unit can also automatically determine the number of pages and layout based on the analyzed data.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] 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.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] 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.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 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 collection department that collects photos and data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates an album based on the data analyzed by the analysis unit. A system characterized by:

2. The collecting unit Collect event photos or group photos, individual messages, and other data.

2. The system of claim 1.

3. The analysis unit Analyze the collected data to extract individual student characteristics or memories 2. The system of claim 1.

4. The generation unit Based on the analyzed data, the design or layout is automatically determined and a beautiful album is generated.

2. The system of claim 1.

5. The generation unit Generate layouts based on a specific theme or highlighting individual student memories 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of photo or data collection based on the user's emotions 2. The system of claim 1.

7. The collecting unit Analyze students' past event participation history and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit Filter photos and data collection based on students' current activities or interests 2. The system of claim 1.

Images