system

Abstract

The system according to the embodiment aims to enable efficient learning and information gathering using cards. [Solution] The system according to the embodiment includes a reading unit, an analysis unit, a display unit, an addition unit, and a game unit. The reading unit reads a card. The analysis unit analyzes the information on the card read by the reading unit. The display unit displays detailed information based on the information analyzed by the analysis unit. The addition unit adds photos taken by the user to the picture book. The game unit is applied to a game format using cards.

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 do not allow for efficient learning or information gathering using cards, and there is room for improvement.

[0005] The system according to the embodiment aims to enable efficient learning and information gathering using cards. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading unit, an analysis unit, a display unit, an addition unit, and a game unit. The reading unit reads a card. The analysis unit analyzes the information on the card read by the reading unit. The display unit displays detailed information based on the information analyzed by the analysis unit. The addition unit adds photos taken by the user to the picture book. The game unit is applied to a game format using cards. [Effects of the Invention]

[0007] The system according to the embodiment allows efficient learning and information gathering using cards. [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 illustrated encyclopedia system according to an embodiment of the present invention uses playing card-sized cards for each category, such as animals, plants, celestial bodies, national flags, and world heritage sites. When the card is scanned with a smartphone or other device, detailed information about the subject written on the card is displayed. In this illustrated encyclopedia system, a user scans the card with their smartphone camera, and a smartphone application analyzes the card's information and displays the corresponding detailed information. Furthermore, if a user actually sees or visits a certain place, they can add a photo of that place to the illustrated encyclopedia, allowing them to create their own original illustrated encyclopedia. Furthermore, the card format can also be used as a game to improve knowledge. For example, a user scans a card with their smartphone camera. The card contains identification information, such as a two-dimensional code (e.g., a QR code (registered trademark)) or a barcode, which is read by the smartphone camera. For example, an animal card has an image of the animal and a two-dimensional code printed on it. When the user scans the two-dimensional code with their smartphone camera, detailed information about the animal is displayed. The smartphone application then analyzes the card's information and displays the corresponding detailed information. For example, when an animal card is scanned, detailed information about the animal's ecology, characteristics, distribution, and other aspects are displayed. This allows users to acquire a variety of knowledge through the cards. Furthermore, if a user actually sees or visits an animal, the user can add a photo of that animal to the encyclopedia. For example, if a user takes a photo of an animal they saw at the zoo and uploads it to the application, the photo will be added to the encyclopedia. This allows the user to create their own original encyclopedia. Also, by making it a card type, it can be applied to games that improve knowledge. For example, quiz games using cards or collection games where cards are collected can be considered. This allows users to deepen their knowledge while having fun. This allows the encyclopedia system to be applied to games, where the user can read a card, display detailed information, add photos, etc.

[0029] An illustrated encyclopedia system according to an embodiment includes a reading unit, an analysis unit, a display unit, an addition unit, and a game unit. The reading unit reads a card. The card includes identification information such as a two-dimensional code or a barcode, which the reading unit reads. For example, the reading unit may include a camera for reading the two-dimensional code, allowing a user to read the two-dimensional code by holding the card over the camera. The reading unit may also include a scanner for reading barcodes, allowing the reading unit to read barcodes. The analysis unit analyzes the information on the card read by the reading unit. For example, the analysis unit may analyze the information on the two-dimensional code or barcode to obtain corresponding detailed information. For example, the analysis unit may decode the data of the two-dimensional code and obtain corresponding information from a database. The analysis unit may also analyze the data of the barcode to obtain corresponding information. The display unit displays the detailed information based on the information analyzed by the analysis unit. The display unit displays the analyzed information using, for example, a smartphone display. For example, when the display unit reads an animal card, it displays detailed information about the animal, such as its ecology, characteristics, and distribution. The adding unit adds a photo taken by the user to the illustrated book. For example, the adding unit uploads a photo taken by the user with a smartphone camera to an application and adds the photo to the illustrated book. For example, the adding unit can add a photo of an animal the user saw at the zoo to the illustrated book by taking the photo and uploading it to an application. The game unit provides a quiz game or a collection game using cards. For example, the game unit provides a quiz game using cards, allowing the user to answer quizzes about the contents of the cards. The game unit also provides a collection game in which the user collects cards, allowing the user to enjoy collecting cards. As a result, the illustrated book system according to the embodiment allows the user to read cards, display detailed information, add photos, and be applied to a game format.

[0030] The reading unit can read two-dimensional codes or barcodes. For example, the reading unit has a camera for reading two-dimensional codes, and the user can read the two-dimensional code by holding the card over the camera. The reading unit also has a scanner for reading barcodes, and can also read barcodes. For example, when reading a two-dimensional code, the reading unit can automatically adjust the focus of the camera to accurately read the two-dimensional code. When reading a barcode, the reading unit can adjust the light of the scanner to accurately read the barcode. This makes it possible to accurately obtain card information by reading the two-dimensional code or barcode.

[0031] The analysis unit can analyze two-dimensional codes or barcodes. For example, the analysis unit decodes two-dimensional code data and obtains corresponding information from a database. For example, the analysis unit analyzes two-dimensional code data and obtains detailed information about animals based on the data. The analysis unit can also analyze barcode data and obtain corresponding information. For example, the analysis unit analyzes barcode data and obtains detailed information about plants based on the data. This allows detailed information to be accurately displayed by analyzing two-dimensional codes or barcodes.

[0032] The display unit can display detailed information based on the analyzed information. The display unit displays the analyzed information using, for example, a smartphone display. For example, when the display unit reads an animal card, it displays detailed information such as the animal's ecology, characteristics, and distribution. When the display unit reads a plant card, it can also display the plant's growth process and characteristics. Furthermore, when the display unit reads a celestial body card, it can also display the position and characteristics of the celestial body. In this way, detailed information can be displayed based on the analyzed information, allowing the user to gain knowledge.

[0033] The adding unit can add photos taken by the user to the illustrated book. For example, the adding unit uploads photos taken by the user with a smartphone camera to the application and adds the photos to the illustrated book. For example, the adding unit can take photos of animals the user saw at a zoo and upload them to the application to add the photos to the illustrated book. The adding unit can also take photos of plants the user saw at a botanical garden and upload them to the application to add the photos to the illustrated book. Furthermore, the adding unit can take photos of celestial bodies the user saw at an observatory and upload them to the application to add the photos to the illustrated book. In this way, an original illustrated book can be created by adding photos taken by the user to the illustrated book.

[0034] The game unit may provide a quiz game or a collection game using cards. For example, the game unit may provide a quiz game using cards, allowing the user to answer questions about the content of the cards. For example, the game unit may provide a quiz game using animal cards, allowing the user to answer questions about the ecology and characteristics of animals. The game unit may also provide a quiz game using plant cards, allowing the user to answer questions about the growth process and characteristics of plants. The game unit may also provide a quiz game using celestial body cards, allowing the user to answer questions about the positions and characteristics of celestial bodies. The game unit may also provide a collection game for collecting cards, allowing the user to enjoy collecting the cards. For example, the game unit may provide a collection game for collecting animal cards, allowing the user to enjoy collecting the animal cards. The game unit may also provide a collection game for collecting plant cards, allowing the user to enjoy collecting the plant cards. The game unit may also provide a collection game for collecting celestial body cards, allowing the user to enjoy collecting the celestial body cards. In this way, by providing quiz games and collection games using cards, users can deepen their knowledge while having fun.

[0035] The reading unit can detect the condition of the card and select an appropriate reading method. For example, if the card is dirty, the reading unit applies an algorithm that ignores the dirt when reading. For example, the reading unit photographs the surface of the card with a camera and detects dirt on the card using image analysis technology. If dirt is detected, the reading unit applies an algorithm that ignores the dirt when reading. Furthermore, if the card is damaged, the reading unit can apply an algorithm that complements the damaged portion when reading. For example, the reading unit scans the surface of the card with a scanner and detects damage to the card using image analysis technology. If damage is detected, the reading unit applies an algorithm that complements the damaged portion when reading. Furthermore, if the card is folded, the reading unit can apply an algorithm that ignores the folded portion when reading. For example, the reading unit photographs the surface of the card with a camera and detects a fold in the card using image analysis technology. If a fold is detected, the reading unit applies an algorithm that ignores the fold. This makes it possible to acquire accurate information by selecting the optimal reading method depending on the condition of the card.

[0036] The reading unit can apply different reading algorithms depending on the type of card. For example, in the case of an animal card, the reading unit applies an algorithm that prioritizes reading images of animals. For example, the reading unit photographs the animal card with a camera and uses image analysis technology to prioritize reading images of animals. In addition, in the case of a plant card, the reading unit can also apply an algorithm that prioritizes reading characteristics of plants. For example, the reading unit photographs the plant card with a camera and uses image analysis technology to prioritize reading characteristics of plants. In addition, in the case of a celestial body card, the reading unit can also apply an algorithm that prioritizes reading location information of the celestial body. For example, the reading unit photographs the celestial body card with a camera and uses image analysis technology to prioritize reading location information of the celestial body. In this way, accurate information can be obtained by applying an appropriate algorithm depending on the type of card.

[0037] The reading unit can prioritize reading highly relevant cards based on the user's geographical location information. For example, if the user is at a zoo, the reading unit prioritizes reading cards related to animals. For example, the reading unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at a zoo, it prioritizes reading cards related to animals. The reading unit can also prioritize reading cards related to plants if the user is at a botanical garden. For example, the reading unit acquires the user's geographical location information using Wi-Fi location information, and if it is determined that the user is at a botanical garden, it prioritizes reading cards related to plants. The reading unit can also prioritize reading cards related to celestial bodies if the user is at an astronomical observatory. For example, the reading unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at an astronomical observatory, it prioritizes reading cards related to celestial bodies. This enables more appropriate information to be provided by preferentially reading cards related to highly relevant cards based on the user's geographical location information.

[0038] The reading unit can analyze the user's social media activity and read related cards. For example, if the user has posted many posts about animals, the reading unit preferentially reads animal cards. For example, if the reading unit analyzes the user's social media activity and determines that the user has posted many posts about animals, the reading unit preferentially reads animal cards. Furthermore, if the user has posted many posts about plants, the reading unit can preferentially read plant cards. For example, if the reading unit analyzes the user's social media activity and determines that the user has posted many posts about plants, the reading unit preferentially reads plant cards. Furthermore, if the user has posted many posts about celestial bodies, the reading unit can preferentially read celestial body cards. For example, if the reading unit analyzes the user's social media activity and determines that the user has posted many posts about celestial bodies, the reading unit preferentially reads celestial body cards. This makes it possible to provide more appropriate information by reading related cards based on the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the card information. The analysis unit, for example, analyzes and displays information of high importance in detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of high importance in detail. The analysis unit can also simplify and analyze and display information of low importance. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of low importance in a simplified manner. The analysis unit can also analyze and display information of medium importance in moderate detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of medium importance in moderate detail. By adjusting the level of detail of the analysis based on the importance of the card information, more appropriate information can be provided.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the card. For example, in the case of an animal card, the analysis unit applies an algorithm that analyzes the ecology and characteristics of the animal. For example, the analysis unit analyzes the animal card and obtains information about the ecology and characteristics of the animal. In addition, in the case of a plant card, the analysis unit can apply an algorithm that analyzes the growth process and characteristics of the plant. For example, the analysis unit analyzes the plant card and obtains information about the growth process and characteristics of the plant. In addition, in the case of a celestial body card, the analysis unit can apply an algorithm that analyzes the position and characteristics of the celestial body. For example, the analysis unit analyzes the celestial body card and obtains information about the position and characteristics of the celestial body. In this way, accurate information can be provided by applying an appropriate algorithm depending on the category of the card.

[0041] The analysis unit can determine the priority of analysis based on the time when the card was read. The analysis unit, for example, prioritizes analyzing cards that were read most recently. For example, the analysis unit records the time when the card was read and prioritizes analyzing cards that were read most recently. The analysis unit can also prioritize analyzing cards that have not been read for a long time. For example, the analysis unit records the time when the card was read and prioritizes analyzing cards that have not been read for a long time. The analysis unit can also prioritize analyzing cards related to a specific event. For example, the analysis unit records the time when the card was read and prioritizes analyzing cards related to a specific event. In this way, by determining the priority of analysis based on the time when the card was read, more appropriate information can be provided.

[0042] The analysis unit can adjust the order of analysis based on the relevance of cards. The analysis unit, for example, prioritizes analysis of highly relevant cards. For example, the analysis unit evaluates the relevance of cards and prioritizes analysis of cards determined to be highly relevant. The analysis unit can also postpone analysis of cards with low relevance. For example, the analysis unit evaluates the relevance of cards and postpones analysis of cards determined to be low relevance. The analysis unit can also moderately analyze cards with medium relevance. For example, the analysis unit evaluates the relevance of cards and moderately analyzes cards determined to be medium relevance. In this way, by adjusting the order of analysis based on the relevance of cards, more appropriate information can be provided.

[0043] The display unit can adjust the level of detail of the display based on the importance of the information on the card. For example, the display unit displays information of high importance in detail. For example, the display unit evaluates the importance of the information on the card and displays information determined to be of high importance in detail. The display unit can also display information of low importance in a simplified manner. For example, the display unit evaluates the importance of the information on the card and displays information determined to be of low importance in a simplified manner. The display unit can also display information of medium importance in moderate detail. For example, the display unit evaluates the importance of the information on the card and displays information determined to be of medium importance in moderate detail. In this way, by adjusting the level of detail of the display based on the importance of the information on the card, more appropriate information can be provided.

[0044] The display unit can apply different display algorithms depending on the category of the card. For example, in the case of an animal card, the display unit applies an algorithm that displays the ecology and characteristics of the animal. For example, the display unit displays an animal card and displays information about the ecology and characteristics of the animal. In addition, in the case of a plant card, the display unit can apply an algorithm that displays the growth process and characteristics of the plant. For example, the display unit displays a plant card and displays information about the growth process and characteristics of the plant. In addition, in the case of a celestial body card, the display unit can apply an algorithm that displays the position and characteristics of the celestial body. For example, the display unit displays a celestial body card and displays information about the position and characteristics of the celestial body. In this way, accurate information can be provided by applying an appropriate display algorithm depending on the category of the card.

[0045] The display unit can determine the display priority based on the time when the card was read. The display unit, for example, prioritizes displaying the most recently read card. For example, the display unit records the time when the card was read and prioritizes displaying the most recently read card. The display unit can also prioritize displaying cards that have not been read for a long time. For example, the display unit records the time when the card was read and prioritizes displaying cards that have not been read for a long time. The display unit can also prioritize displaying cards related to a specific event. For example, the display unit records the time when the card was read and prioritizes displaying cards related to a specific event. In this way, by determining the display priority based on the time when the card was read, more appropriate information can be provided.

[0046] The display unit can adjust the display order based on the relevance of the cards. The display unit, for example, prioritizes displaying highly relevant cards. For example, the display unit evaluates the relevance of the cards and prioritizes displaying cards determined to be highly relevant. The display unit can also postpone displaying cards with low relevance. For example, the display unit evaluates the relevance of the cards and postpones displaying cards determined to be low relevance. The display unit can also moderately display cards with medium relevance. For example, the display unit evaluates the relevance of the cards and moderately display cards determined to be medium relevance. In this way, by adjusting the display order based on the relevance of the cards, more appropriate information can be provided.

[0047] The adding unit can analyze the user's past photo history and select an appropriate adding method. The adding unit, for example, suggests an optimal adding method based on photos taken by the user in the past. For example, the adding unit analyzes the user's past photo history and suggests an optimal adding method based on trends in photos taken in the past. The adding unit can also prioritize adding highly relevant photos from the user's past photo history. For example, the adding unit analyzes the user's past photo history and suggests highly relevant photos. The adding unit can also analyze the user's past photo history and suggest the most efficient adding method. For example, the adding unit analyzes the user's past photo history and suggests the most efficient adding method. This makes it possible to provide more appropriate information by selecting an optimal adding method based on the user's past photo history.

[0048] The adding unit can customize the means for adding photos based on the user's current living situation. For example, if the user is traveling, the adding unit prioritizes adding photos of the travel destination. For example, the adding unit evaluates the user's current living situation and, if it is determined that the user is traveling, prioritizes adding photos of the travel destination. Furthermore, if the user is at home, the adding unit can prioritize adding photos of the area around the user's home. For example, the adding unit evaluates the user's current living situation and, if it is determined that the user is at home, prioritizes adding photos of the area around the user's home. Furthermore, if the user is participating in a specific event, the adding unit can prioritize adding photos related to the event. For example, the adding unit evaluates the user's current living situation and, if it is determined that the user is participating in a specific event, prioritizes adding photos related to the event. This allows for customizing the means for adding photos based on the user's current living situation, making it possible to provide more appropriate information.

[0049] The adding unit can select an appropriate photo adding method based on the user's geographical location information. For example, if the user is at a zoo, the adding unit prioritizes adding photos taken at the zoo. For example, the adding unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at a zoo, it prioritizes adding photos taken at the zoo. Furthermore, if the user is at a botanical garden, the adding unit can also prioritize adding photos taken at the botanical garden. For example, the adding unit acquires the user's geographical location information using Wi-Fi location information, and if it is determined that the user is at a botanical garden, it prioritizes adding photos taken at the botanical garden. Furthermore, if the user is at an astronomical observatory, the adding unit can also prioritize adding photos taken at the observatory. For example, the adding unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at an astronomical observatory, it prioritizes adding photos taken at the observatory. This allows for more appropriate information to be provided by selecting an optimal photo adding method based on the user's geographical location information.

[0050] The adding unit can analyze the user's social media activity and suggest a means for adding photos. For example, if the user posts many times about animals, the adding unit prioritizes adding photos of animals. For example, if the adding unit analyzes the user's social media activity and determines that there are many posts about animals, it prioritizes adding photos of animals. Furthermore, if the user posts many times about plants, the adding unit can also prioritize adding photos of plants. For example, if the adding unit analyzes the user's social media activity and determines that there are many posts about plants, it prioritizes adding photos of plants. Furthermore, if the user posts many times about celestial bodies, it can also prioritize adding photos of celestial bodies. For example, if the adding unit analyzes the user's social media activity and determines that there are many posts about celestial bodies, it prioritizes adding photos of celestial bodies. This makes it possible to provide more appropriate information by suggesting a means for adding photos based on the user's social media activity.

[0051] The game unit can analyze the user's past game history and select optimal game content. The game unit, for example, suggests optimal game content based on the user's history of games played in the past. For example, the game unit analyzes the user's past game history and suggests optimal game content based on the user's tendencies in games played in the past. The game unit can also prioritize providing games with interesting content based on the user's past game history. For example, the game unit analyzes the user's past game history and suggests games with interesting content based on the user's past game history. The game unit can also analyze the user's past game history and suggest the most enjoyable game content. For example, the game unit analyzes the user's past game history and suggests the most enjoyable game content. This allows for more appropriate information to be provided by selecting optimal game content based on the user's past game history.

[0052] The game unit can apply different game algorithms depending on the category of the cards. For example, in the case of animal cards, the game unit provides a quiz game about animals. For example, the game unit provides a quiz game about animals using animal cards. Furthermore, in the case of plant cards, the game unit can provide a quiz game about plants. For example, the game unit provides a quiz game about plants using plant cards. Furthermore, in the case of celestial body cards, the game unit can provide a quiz game about celestial bodies. For example, the game unit provides a quiz game about celestial bodies using celestial body cards. This makes it possible to provide accurate information by applying an appropriate game algorithm depending on the category of the cards.

[0053] The game unit can select appropriate game content based on the user's geographical location information. For example, if the user is at a zoo, the game unit provides a game related to animals. For example, the game unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at a zoo, it provides a game related to animals. Furthermore, if the user is at a botanical garden, it can provide a game related to plants. For example, the game unit acquires the user's geographical location information using Wi-Fi location information, and if it is determined that the user is at a botanical garden, it provides a game related to plants. Furthermore, if the user is at an astronomical observatory, it can provide a game related to celestial bodies. For example, the game unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at an astronomical observatory, it provides a game related to celestial bodies. This allows for more appropriate information to be provided by selecting optimal game content based on the user's geographical location information.

[0054] The game module can analyze the user's social media activity to suggest game content. For example, if the user posts a lot about animals, the game module can provide a game related to animals. For example, if the game module analyzes the user's social media activity and determines that the user posts a lot about animals, the game module can provide a game related to animals. Furthermore, if the user posts a lot about plants, the game module can provide a game related to plants. For example, if the game module analyzes the user's social media activity and determines that the user posts a lot about plants, the game module can provide a game related to plants. Furthermore, if the user posts a lot about celestial bodies, the game module can provide a game related to celestial bodies. For example, if the game module analyzes the user's social media activity and determines that the user posts a lot about celestial bodies, the game module can provide a game related to celestial bodies. This makes it possible to provide more appropriate information by suggesting game content based on the user's social media activity.

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

[0056] The analysis unit can analyze the user's past learning history and adjust the content of the analysis according to the user's learning progress. For example, the analysis unit can suggest the next content to be learned based on the content the user has learned in the past. For example, the analysis unit can analyze the user's past learning history and provide new information according to the user's learning progress. The analysis unit can also provide information for the user to review content that the user has learned in the past. For example, the analysis unit can analyze the user's past learning history and suggest content that needs to be reviewed. The analysis unit can also adjust the difficulty of learning according to the user's learning progress. For example, the analysis unit can evaluate the user's learning progress and adjust the difficulty and provide information. This allows for more effective learning by adjusting the content of the analysis according to the user's learning progress.

[0057] The display unit can customize the display method according to the user's learning style. For example, the display unit provides a display method that makes extensive use of images and videos to a user who prefers visual learning. For example, when the display unit reads an animal card, it displays a video of the animal's ecology and characteristics. The display unit can also provide a display method that makes extensive use of audio commentary to a user who prefers auditory learning. For example, when the display unit reads a plant card, it provides an audio commentary on the plant's growth process. The display unit can also provide an interactive display method to a user who prefers tactile learning. For example, when the display unit reads a celestial body card, it interactively displays the position of the celestial body so that the user can operate it. This allows for more effective learning by customizing the display method according to the user's learning style.

[0058] The reading unit can detect the condition of the card and select an appropriate reading method. For example, if the card is dirty, the reading unit applies an algorithm that ignores the dirt when reading. For example, the reading unit photographs the surface of the card with a camera and detects dirt on the card using image analysis technology. If dirt is detected, the reading unit applies an algorithm that ignores the dirt when reading. Furthermore, if the card is damaged, the reading unit can apply an algorithm that complements the damaged portion when reading. For example, the reading unit scans the surface of the card with a scanner and detects damage to the card using image analysis technology. If damage is detected, the reading unit applies an algorithm that complements the damaged portion when reading. Furthermore, if the card is folded, the reading unit can apply an algorithm that ignores the folded portion when reading. For example, the reading unit photographs the surface of the card with a camera and detects folds in the card using image analysis technology. If a fold is detected, the reading unit applies an algorithm that ignores the fold. This makes it possible to select the optimal reading method depending on the condition of the card, thereby enabling accurate information acquisition.

[0059] The analysis unit can adjust the level of detail of the analysis based on the importance of the card information. For example, the analysis unit analyzes and displays information of high importance in detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of high importance in detail. The analysis unit can also simplify and analyze and display information of low importance. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of low importance in a simplified manner. The analysis unit can also analyze and display information of medium importance in moderate detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of medium importance in moderate detail. By adjusting the level of detail of the analysis based on the importance of the card information, more appropriate information can be provided.

[0060] The display unit can apply different display algorithms depending on the category of the card. For example, in the case of an animal card, the display unit applies an algorithm that displays the ecology and characteristics of the animal. For example, the display unit displays an animal card and displays information about the ecology and characteristics of the animal. In addition, in the case of a plant card, the display unit can apply an algorithm that displays the growth process and characteristics of the plant. For example, the display unit displays a plant card and displays information about the growth process and characteristics of the plant. In addition, in the case of a celestial body card, the display unit can apply an algorithm that displays the position and characteristics of the celestial body. For example, the display unit displays a celestial body card and displays information about the position and characteristics of the celestial body. In this way, accurate information can be provided by applying an appropriate display algorithm depending on the category of the card.

[0061] The adding unit can analyze the user's past photo history and select an appropriate adding method. For example, the adding unit suggests an optimal adding method based on photos taken by the user in the past. For example, the adding unit analyzes the user's past photo history and suggests an optimal adding method based on trends in photos taken in the past. The adding unit can also prioritize adding highly relevant photos from the user's past photo history. For example, the adding unit analyzes the user's past photo history and suggests highly relevant photos. The adding unit can also analyze the user's past photo history and suggest the most efficient adding method. For example, the adding unit analyzes the user's past photo history and suggests the most efficient adding method. This makes it possible to provide more appropriate information by selecting an optimal adding method based on the user's past photo history.

[0062] The game unit can analyze the user's past game history and select optimal game content. For example, the game unit can suggest optimal game content based on the user's history of games played in the past. For example, the game unit can analyze the user's past game history and suggest optimal game content based on the user's tendencies in games played in the past. The game unit can also prioritize providing games with content that pique the user's interest based on the user's past game history. For example, the game unit can analyze the user's past game history and suggest games with content that pique the user's interest. The game unit can also analyze the user's past game history and suggest the most enjoyable game content. For example, the game unit can analyze the user's past game history and suggest the most enjoyable game content. This allows for more appropriate information to be provided by selecting optimal game content based on the user's past game history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reading unit reads the card. The card contains identification information such as a two-dimensional code or barcode, which the reading unit reads. For example, the reading unit may be equipped with a camera for reading two-dimensional codes, and the user can read the two-dimensional code by holding the card over the camera. The reading unit may also be equipped with a scanner for reading barcodes, and can also read barcodes. Step 2: The analysis unit analyzes the information on the card read by the reading unit. The analysis unit analyzes, for example, the information in the two-dimensional code or barcode and obtains corresponding detailed information. For example, the analysis unit decodes the two-dimensional code data and obtains the corresponding information from a database. The analysis unit can also analyze the barcode data and obtain the corresponding information. Step 3: The display unit displays detailed information based on the information analyzed by the analysis unit. The display unit displays the analyzed information using, for example, a smartphone display. For example, when the display unit reads an animal card, it displays detailed information about the animal's ecology, characteristics, distribution, etc. Step 4: The adding unit adds the photo taken by the user to the illustrated book. For example, the adding unit uploads a photo taken by the user with a smartphone camera to the application and adds the photo to the illustrated book. For example, the adding unit can add a photo of an animal the user saw at the zoo to the illustrated book by taking the photo and uploading it to the application. Step 5: The game section provides a quiz game or a collection game using cards. For example, the game section provides a quiz game using cards, allowing users to answer questions about the contents of the cards. The game section also provides a collection game for collecting cards, allowing users to enjoy collecting cards.

[0065] (Example 2) An illustrated encyclopedia system according to an embodiment of the present invention uses playing card-sized cards for each category, such as animals, plants, celestial bodies, national flags, and world heritage sites. When the card is scanned with a smartphone or other device, detailed information about the subject written on the card is displayed. In this illustrated encyclopedia system, a user scans the card with their smartphone camera, and a smartphone application analyzes the card's information and displays the corresponding detailed information. Furthermore, if a user actually sees or visits a certain place, they can add a photo of that place to the illustrated encyclopedia, allowing them to create their own original illustrated encyclopedia. Furthermore, the card format can also be used as a game to improve knowledge. For example, a user scans a card with their smartphone camera. The card contains identification information, such as a two-dimensional code (e.g., a QR code) or a barcode, which is read by the smartphone camera. For example, an animal card has an image of the animal and a two-dimensional code printed on it. When the user scans the two-dimensional code with their smartphone camera, detailed information about the animal is displayed. The smartphone application then analyzes the card's information and displays the corresponding detailed information. For example, when an animal card is scanned, detailed information about the animal's ecology, characteristics, distribution, and other aspects are displayed. This allows users to acquire a variety of knowledge through the cards. Furthermore, if a user actually sees or visits an animal, the user can add a photo of that animal to the encyclopedia. For example, if a user takes a photo of an animal they saw at the zoo and uploads it to the application, the photo will be added to the encyclopedia. This allows the user to create their own original encyclopedia. Also, by making it a card type, it can be applied to games that improve knowledge. For example, quiz games using cards or collection games where cards are collected can be considered. This allows users to deepen their knowledge while having fun. This allows the encyclopedia system to be applied to games, where the user can read a card, display detailed information, add photos, etc.

[0066] An illustrated encyclopedia system according to an embodiment includes a reading unit, an analysis unit, a display unit, an addition unit, and a game unit. The reading unit reads a card. The card includes identification information such as a two-dimensional code or a barcode, which the reading unit reads. For example, the reading unit may include a camera for reading the two-dimensional code, allowing a user to read the two-dimensional code by holding the card over the camera. The reading unit may also include a scanner for reading barcodes, allowing the reading unit to read barcodes. The analysis unit analyzes the information on the card read by the reading unit. For example, the analysis unit may analyze the information on the two-dimensional code or barcode to obtain corresponding detailed information. For example, the analysis unit may decode the data of the two-dimensional code and obtain corresponding information from a database. The analysis unit may also analyze the data of the barcode to obtain corresponding information. The display unit displays the detailed information based on the information analyzed by the analysis unit. The display unit displays the analyzed information using, for example, a smartphone display. For example, when the display unit reads an animal card, it displays detailed information about the animal, such as its ecology, characteristics, and distribution. The adding unit adds a photo taken by the user to the illustrated book. For example, the adding unit uploads a photo taken by the user with a smartphone camera to an application and adds the photo to the illustrated book. For example, the adding unit can add a photo of an animal the user saw at the zoo to the illustrated book by taking the photo and uploading it to an application. The game unit provides a quiz game or a collection game using cards. For example, the game unit provides a quiz game using cards, allowing the user to answer quizzes about the contents of the cards. The game unit also provides a collection game in which the user collects cards, allowing the user to enjoy collecting cards. As a result, the illustrated book system according to the embodiment allows the user to read cards, display detailed information, add photos, and be applied to a game format.

[0067] The reading unit can read two-dimensional codes or barcodes. For example, the reading unit has a camera for reading two-dimensional codes, and the user can read the two-dimensional code by holding the card over the camera. The reading unit also has a scanner for reading barcodes, and can also read barcodes. For example, when reading a two-dimensional code, the reading unit can automatically adjust the focus of the camera to accurately read the two-dimensional code. When reading a barcode, the reading unit can adjust the light of the scanner to accurately read the barcode. This makes it possible to accurately obtain card information by reading the two-dimensional code or barcode.

[0068] The analysis unit can analyze two-dimensional codes or barcodes. For example, the analysis unit decodes two-dimensional code data and obtains corresponding information from a database. For example, the analysis unit analyzes two-dimensional code data and obtains detailed information about animals based on the data. The analysis unit can also analyze barcode data and obtain corresponding information. For example, the analysis unit analyzes barcode data and obtains detailed information about plants based on the data. This allows detailed information to be accurately displayed by analyzing two-dimensional codes or barcodes.

[0069] The display unit can display detailed information based on the analyzed information. The display unit displays the analyzed information using, for example, a smartphone display. For example, when the display unit reads an animal card, it displays detailed information such as the animal's ecology, characteristics, and distribution. When the display unit reads a plant card, it can also display the plant's growth process and characteristics. Furthermore, when the display unit reads a celestial body card, it can also display the position and characteristics of the celestial body. In this way, detailed information can be displayed based on the analyzed information, allowing the user to gain knowledge.

[0070] The adding unit can add photos taken by the user to the illustrated book. For example, the adding unit uploads photos taken by the user with a smartphone camera to the application and adds the photos to the illustrated book. For example, the adding unit can take photos of animals the user saw at a zoo and upload them to the application to add the photos to the illustrated book. The adding unit can also take photos of plants the user saw at a botanical garden and upload them to the application to add the photos to the illustrated book. Furthermore, the adding unit can take photos of celestial bodies the user saw at an observatory and upload them to the application to add the photos to the illustrated book. In this way, an original illustrated book can be created by adding photos taken by the user to the illustrated book.

[0071] The game unit may provide a quiz game or a collection game using cards. For example, the game unit may provide a quiz game using cards, allowing the user to answer questions about the content of the cards. For example, the game unit may provide a quiz game using animal cards, allowing the user to answer questions about the ecology and characteristics of animals. The game unit may also provide a quiz game using plant cards, allowing the user to answer questions about the growth process and characteristics of plants. The game unit may also provide a quiz game using celestial body cards, allowing the user to answer questions about the positions and characteristics of celestial bodies. The game unit may also provide a collection game for collecting cards, allowing the user to enjoy collecting the cards. For example, the game unit may provide a collection game for collecting animal cards, allowing the user to enjoy collecting the animal cards. The game unit may also provide a collection game for collecting plant cards, allowing the user to enjoy collecting the plant cards. The game unit may also provide a collection game for collecting celestial body cards, allowing the user to enjoy collecting the celestial body cards. In this way, by providing quiz games and collection games using cards, users can deepen their knowledge while having fun.

[0072] The reading unit can estimate the user's emotions and adjust the timing of reading the card based on the estimated user emotions. For example, if the user is excited, the reading unit advances the timing of reading to provide information quickly. For example, the reading unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the reading unit advances the timing of reading the card to provide information quickly. Furthermore, if the user is relaxed, the reading unit can delay the timing of reading to provide information more slowly. For example, the reading unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the reading unit delays the timing of reading the card to provide information more slowly. Furthermore, if the user is feeling stressed, the reading unit can adjust the timing of reading to provide information more smoothly. For example, the reading unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is feeling stressed, the reading unit adjusts the timing of reading the card to provide information more smoothly. This allows for more appropriate information to be provided by adjusting the timing of card reading according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0073] The reading unit can detect the condition of the card and select an appropriate reading method. For example, if the card is dirty, the reading unit applies an algorithm that ignores the dirt when reading. For example, the reading unit photographs the surface of the card with a camera and detects dirt on the card using image analysis technology. If dirt is detected, the reading unit applies an algorithm that ignores the dirt when reading. Furthermore, if the card is damaged, the reading unit can apply an algorithm that complements the damaged portion when reading. For example, the reading unit scans the surface of the card with a scanner and detects damage to the card using image analysis technology. If damage is detected, the reading unit applies an algorithm that complements the damaged portion when reading. Furthermore, if the card is folded, the reading unit can apply an algorithm that ignores the folded portion when reading. For example, the reading unit photographs the surface of the card with a camera and detects a fold in the card using image analysis technology. If a fold is detected, the reading unit applies an algorithm that ignores the fold. This makes it possible to acquire accurate information by selecting the optimal reading method depending on the condition of the card.

[0074] The reading unit can apply different reading algorithms depending on the type of card. For example, in the case of an animal card, the reading unit applies an algorithm that prioritizes reading images of animals. For example, the reading unit photographs the animal card with a camera and uses image analysis technology to prioritize reading images of animals. In addition, in the case of a plant card, the reading unit can also apply an algorithm that prioritizes reading characteristics of plants. For example, the reading unit photographs the plant card with a camera and uses image analysis technology to prioritize reading characteristics of plants. In addition, in the case of a celestial body card, the reading unit can also apply an algorithm that prioritizes reading location information of the celestial body. For example, the reading unit photographs the celestial body card with a camera and uses image analysis technology to prioritize reading location information of the celestial body. In this way, accurate information can be obtained by applying an appropriate algorithm depending on the type of card.

[0075] The reading unit can estimate the user's emotions and determine the priority of cards to read based on the estimated user emotions. For example, if the user is excited, the reading unit prioritizes reading cards that pique the user's interest. For example, the reading unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the reading unit prioritizes reading cards that pique the user's interest. Furthermore, if the user is relaxed, the reading unit can prioritize reading cards with relaxing content. For example, the reading unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the reading unit prioritizes reading cards with relaxing content. Furthermore, if the user is feeling stressed, the reading unit can prioritize reading cards with stress-relieving content. For example, the reading unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is feeling stressed, the reading unit prioritizes reading cards with stress-relieving content. This allows for more appropriate information provision by prioritizing cards according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reading unit can prioritize reading highly relevant cards based on the user's geographical location information. For example, if the user is at a zoo, the reading unit prioritizes reading cards related to animals. For example, the reading unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at a zoo, it prioritizes reading cards related to animals. The reading unit can also prioritize reading cards related to plants if the user is at a botanical garden. For example, the reading unit acquires the user's geographical location information using Wi-Fi location information, and if it is determined that the user is at a botanical garden, it prioritizes reading cards related to plants. The reading unit can also prioritize reading cards related to celestial bodies if the user is at an astronomical observatory. For example, the reading unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at an astronomical observatory, it prioritizes reading cards related to celestial bodies. This enables more appropriate information to be provided by preferentially reading cards related to highly relevant cards based on the user's geographical location information.

[0077] The reading unit can analyze the user's social media activity and read related cards. For example, if the user has posted many posts about animals, the reading unit preferentially reads animal cards. For example, if the reading unit analyzes the user's social media activity and determines that the user has posted many posts about animals, the reading unit preferentially reads animal cards. Furthermore, if the user has posted many posts about plants, the reading unit can preferentially read plant cards. For example, if the reading unit analyzes the user's social media activity and determines that the user has posted many posts about plants, the reading unit preferentially reads plant cards. Furthermore, if the user has posted many posts about celestial bodies, the reading unit can preferentially read celestial body cards. For example, if the reading unit analyzes the user's social media activity and determines that the user has posted many posts about celestial bodies, the reading unit preferentially reads celestial body cards. This makes it possible to provide more appropriate information by reading related cards based on the user's social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the method of expression of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit applies a visually stimulating expression. For example, the analysis unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the analysis unit applies a visually stimulating expression. Furthermore, if the user is relaxed, the analysis unit can apply a calm expression. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the analysis unit applies a calm expression. Furthermore, if the user is feeling stressed, the analysis unit can apply a simple and easy-to-understand expression. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is feeling stressed, the analysis unit applies a simple and easy-to-understand expression. This makes it possible to provide more appropriate information by adjusting the method of expression of the analysis according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the card information. The analysis unit, for example, analyzes and displays information of high importance in detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of high importance in detail. The analysis unit can also simplify and analyze and display information of low importance. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of low importance in a simplified manner. The analysis unit can also analyze and display information of medium importance in moderate detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of medium importance in moderate detail. By adjusting the level of detail of the analysis based on the importance of the card information, more appropriate information can be provided.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the card. For example, in the case of an animal card, the analysis unit applies an algorithm that analyzes the ecology and characteristics of the animal. For example, the analysis unit analyzes the animal card and obtains information about the ecology and characteristics of the animal. In addition, in the case of a plant card, the analysis unit can apply an algorithm that analyzes the growth process and characteristics of the plant. For example, the analysis unit analyzes the plant card and obtains information about the growth process and characteristics of the plant. In addition, in the case of a celestial body card, the analysis unit can apply an algorithm that analyzes the position and characteristics of the celestial body. For example, the analysis unit analyzes the celestial body card and obtains information about the position and characteristics of the celestial body. In this way, accurate information can be provided by applying an appropriate algorithm depending on the category of the card.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit performs a short and to-the-point analysis. For example, the analysis unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is in a hurry, the analysis unit performs a short and to-the-point analysis. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the analysis unit performs a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the analysis unit performs a visually stimulating analysis. This allows the length of the analysis to be adjusted according to the user's emotions, making it possible to provide more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can determine the priority of analysis based on the time when the card was read. The analysis unit, for example, prioritizes analyzing cards that were read most recently. For example, the analysis unit records the time when the card was read and prioritizes analyzing cards that were read most recently. The analysis unit can also prioritize analyzing cards that have not been read for a long time. For example, the analysis unit records the time when the card was read and prioritizes analyzing cards that have not been read for a long time. The analysis unit can also prioritize analyzing cards related to a specific event. For example, the analysis unit records the time when the card was read and prioritizes analyzing cards related to a specific event. In this way, by determining the priority of analysis based on the time when the card was read, more appropriate information can be provided.

[0083] The analysis unit can adjust the order of analysis based on the relevance of cards. The analysis unit, for example, prioritizes analysis of highly relevant cards. For example, the analysis unit evaluates the relevance of cards and prioritizes analysis of cards determined to be highly relevant. The analysis unit can also postpone analysis of cards with low relevance. For example, the analysis unit evaluates the relevance of cards and postpones analysis of cards determined to be low relevance. The analysis unit can also moderately analyze cards with medium relevance. For example, the analysis unit evaluates the relevance of cards and moderately analyzes cards determined to be medium relevance. In this way, by adjusting the order of analysis based on the relevance of cards, more appropriate information can be provided.

[0084] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, if the user is excited, the display unit applies a visually stimulating display method. For example, the display unit captures the user's facial expression with a camera and estimates the user's emotion using an emotion estimation algorithm. If the user is estimated to be excited, the display unit applies a visually stimulating display method. Furthermore, the display unit can also apply a calming display method if the user is relaxed. For example, the display unit records the user's voice and estimates the user's emotion using voice analysis technology. If the user is estimated to be relaxed, the display unit applies a calming display method. Furthermore, the display unit can also apply a simple and easy-to-understand display method if the user is feeling stressed. For example, the display unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotion using an emotion estimation algorithm. If the user is estimated to be stressed, the display unit applies a simple and easy-to-understand display method. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The display unit can adjust the level of detail of the display based on the importance of the information on the card. For example, the display unit displays information of high importance in detail. For example, the display unit evaluates the importance of the information on the card and displays information determined to be of high importance in detail. The display unit can also display information of low importance in a simplified manner. For example, the display unit evaluates the importance of the information on the card and displays information determined to be of low importance in a simplified manner. The display unit can also display information of medium importance in moderate detail. For example, the display unit evaluates the importance of the information on the card and displays information determined to be of medium importance in moderate detail. In this way, by adjusting the level of detail of the display based on the importance of the information on the card, more appropriate information can be provided.

[0086] The display unit can apply different display algorithms depending on the category of the card. For example, in the case of an animal card, the display unit applies an algorithm that displays the ecology and characteristics of the animal. For example, the display unit displays an animal card and displays information about the ecology and characteristics of the animal. In addition, in the case of a plant card, the display unit can apply an algorithm that displays the growth process and characteristics of the plant. For example, the display unit displays a plant card and displays information about the growth process and characteristics of the plant. In addition, in the case of a celestial body card, the display unit can apply an algorithm that displays the position and characteristics of the celestial body. For example, the display unit displays a celestial body card and displays information about the position and characteristics of the celestial body. In this way, accurate information can be provided by applying an appropriate display algorithm depending on the category of the card.

[0087] The display unit can estimate the user's emotion and adjust the length of the display based on the estimated user's emotion. For example, if the user is in a hurry, the display unit displays a short, concise display. For example, the display unit captures the user's facial expression with a camera and estimates the user's emotion using an emotion estimation algorithm. If it is estimated that the user is in a hurry, the display unit displays a short, concise display. Furthermore, if the user is relaxed, the display unit can display a detailed display. For example, the display unit records the user's voice and estimates the user's emotion using voice analysis technology. If it is estimated that the user is relaxed, the display unit displays a detailed display. Furthermore, if the user is excited, the display unit can display a visually stimulating display. For example, the display unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotion using an emotion estimation algorithm. If it is estimated that the user is excited, the display unit displays a visually stimulating display. This makes it possible to provide more appropriate information by adjusting the length of the display according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The display unit can determine the display priority based on the time when the card was read. The display unit, for example, prioritizes displaying the most recently read card. For example, the display unit records the time when the card was read and prioritizes displaying the most recently read card. The display unit can also prioritize displaying cards that have not been read for a long time. For example, the display unit records the time when the card was read and prioritizes displaying cards that have not been read for a long time. The display unit can also prioritize displaying cards related to a specific event. For example, the display unit records the time when the card was read and prioritizes displaying cards related to a specific event. In this way, by determining the display priority based on the time when the card was read, more appropriate information can be provided.

[0089] The display unit can adjust the display order based on the relevance of the cards. The display unit, for example, prioritizes displaying highly relevant cards. For example, the display unit evaluates the relevance of the cards and prioritizes displaying cards determined to be highly relevant. The display unit can also postpone displaying cards with low relevance. For example, the display unit evaluates the relevance of the cards and postpones displaying cards determined to be low relevance. The display unit can also moderately display cards with medium relevance. For example, the display unit evaluates the relevance of the cards and moderately display cards determined to be medium relevance. In this way, by adjusting the display order based on the relevance of the cards, more appropriate information can be provided.

[0090] The adding unit can estimate the user's emotion and adjust the photo adding method based on the estimated user emotion. For example, if the user is excited, the adding unit provides a method that allows the user to quickly add a photo. For example, the adding unit captures the user's facial expression with a camera and estimates the user's emotion using an emotion estimation algorithm. If it is estimated that the user is excited, the adding unit provides a method that allows the user to quickly add a photo. Furthermore, if the user is relaxed, the adding unit can provide a method that includes a detailed explanation. For example, the adding unit records the user's voice and estimates the user's emotion using voice analysis technology. If it is estimated that the user is relaxed, the adding unit provides a method that includes a detailed explanation. Furthermore, if the user is stressed, the adding unit can provide a simple and easy-to-understand method. For example, the adding unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotion using an emotion estimation algorithm. If it is estimated that the user is stressed, the adding unit provides a simple and easy-to-understand method. This makes it possible to provide more appropriate information by adjusting the photo adding method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The adding unit can analyze the user's past photo history and select an appropriate adding method. The adding unit, for example, suggests an optimal adding method based on photos taken by the user in the past. For example, the adding unit analyzes the user's past photo history and suggests an optimal adding method based on trends in photos taken in the past. The adding unit can also prioritize adding highly relevant photos from the user's past photo history. For example, the adding unit analyzes the user's past photo history and suggests highly relevant photos. The adding unit can also analyze the user's past photo history and suggest the most efficient adding method. For example, the adding unit analyzes the user's past photo history and suggests the most efficient adding method. This makes it possible to provide more appropriate information by selecting an optimal adding method based on the user's past photo history.

[0092] The adding unit can customize the means for adding photos based on the user's current living situation. For example, if the user is traveling, the adding unit prioritizes adding photos of the travel destination. For example, the adding unit evaluates the user's current living situation and, if it is determined that the user is traveling, prioritizes adding photos of the travel destination. Furthermore, if the user is at home, the adding unit can prioritize adding photos of the area around the user's home. For example, the adding unit evaluates the user's current living situation and, if it is determined that the user is at home, prioritizes adding photos of the area around the user's home. Furthermore, if the user is participating in a specific event, the adding unit can prioritize adding photos related to the event. For example, the adding unit evaluates the user's current living situation and, if it is determined that the user is participating in a specific event, prioritizes adding photos related to the event. This allows for customizing the means for adding photos based on the user's current living situation, making it possible to provide more appropriate information.

[0093] The adding unit can estimate the user's emotions and determine the priority of adding photos based on the estimated user emotions. For example, if the user is excited, the adding unit prioritizes adding photos that interest them. For example, the adding unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the adding unit prioritizes adding photos that interest them. Furthermore, if the user is relaxed, the adding unit can prioritize adding photos with relaxing content. For example, the adding unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the adding unit prioritizes adding photos with relaxing content. Furthermore, if the user is feeling stressed, the adding unit can prioritize adding photos with stress-relieving content. For example, the adding unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is feeling stressed, the adding unit prioritizes adding photos with stress-relieving content. This allows for more appropriate information provision by determining the priority of adding photos according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The adding unit can select an appropriate photo adding method based on the user's geographical location information. For example, if the user is at a zoo, the adding unit prioritizes adding photos taken at the zoo. For example, the adding unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at a zoo, it prioritizes adding photos taken at the zoo. Furthermore, if the user is at a botanical garden, the adding unit can also prioritize adding photos taken at the botanical garden. For example, the adding unit acquires the user's geographical location information using Wi-Fi location information, and if it is determined that the user is at a botanical garden, it prioritizes adding photos taken at the botanical garden. Furthermore, if the user is at an astronomical observatory, the adding unit can also prioritize adding photos taken at the observatory. For example, the adding unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at an astronomical observatory, it prioritizes adding photos taken at the observatory. This allows for more appropriate information to be provided by selecting an optimal photo adding method based on the user's geographical location information.

[0095] The adding unit can analyze the user's social media activity and suggest a means for adding photos. For example, if the user posts many times about animals, the adding unit prioritizes adding photos of animals. For example, if the adding unit analyzes the user's social media activity and determines that there are many posts about animals, it prioritizes adding photos of animals. Furthermore, if the user posts many times about plants, the adding unit can also prioritize adding photos of plants. For example, if the adding unit analyzes the user's social media activity and determines that there are many posts about plants, it prioritizes adding photos of plants. Furthermore, if the user posts many times about celestial bodies, it can also prioritize adding photos of celestial bodies. For example, if the adding unit analyzes the user's social media activity and determines that there are many posts about celestial bodies, it prioritizes adding photos of celestial bodies. This makes it possible to provide more appropriate information by suggesting a means for adding photos based on the user's social media activity.

[0096] The game unit can estimate the user's emotions and adjust the difficulty of the game based on the estimated user emotions. For example, if the user is excited, the game unit increases the difficulty level to provide a more challenging game. For example, the game unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the game unit increases the difficulty level to provide a more challenging game. Furthermore, if the user is relaxed, the game unit can lower the difficulty level to provide a relaxing game. For example, the game unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the game unit lowers the difficulty level to provide a relaxing game. Furthermore, if the user is feeling stressed, the game unit can adjust the difficulty level to provide a game that reduces stress. For example, the game unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is feeling stressed, the game unit adjusts the difficulty level to provide a game that reduces stress. This allows for more appropriate information provision by adjusting the game difficulty according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0097] The game unit can analyze the user's past game history and select optimal game content. The game unit, for example, suggests optimal game content based on the user's history of games played in the past. For example, the game unit analyzes the user's past game history and suggests optimal game content based on the user's tendencies in games played in the past. The game unit can also prioritize providing games with interesting content based on the user's past game history. For example, the game unit analyzes the user's past game history and suggests games with interesting content based on the user's past game history. The game unit can also analyze the user's past game history and suggest the most enjoyable game content. For example, the game unit analyzes the user's past game history and suggests the most enjoyable game content. This allows for more appropriate information to be provided by selecting optimal game content based on the user's past game history.

[0098] The game unit can apply different game algorithms depending on the category of the cards. For example, in the case of animal cards, the game unit provides a quiz game about animals. For example, the game unit provides a quiz game about animals using animal cards. Furthermore, in the case of plant cards, the game unit can provide a quiz game about plants. For example, the game unit provides a quiz game about plants using plant cards. Furthermore, in the case of celestial body cards, the game unit can provide a quiz game about celestial bodies. For example, the game unit provides a quiz game about celestial bodies using celestial body cards. This makes it possible to provide accurate information by applying an appropriate game algorithm depending on the category of the cards.

[0099] The game unit can estimate the user's emotions and adjust the game display method based on the estimated user's emotions. For example, if the user is excited, the game unit applies a visually stimulating display method. For example, the game unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If the user is estimated to be excited, the game unit applies a visually stimulating display method. Furthermore, if the user is relaxed, the game unit can apply a calm display method. For example, the game unit records the user's voice and estimates the user's emotions using voice analysis technology. If the user is estimated to be relaxed, the game unit applies a calm display method. Furthermore, if the user is feeling stressed, the game unit can apply a simple and easy-to-understand display method. For example, the game unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If the user is estimated to be stressed, the game unit applies a simple and easy-to-understand display method. This makes it possible to provide more appropriate information by adjusting the game display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The game unit can select appropriate game content based on the user's geographical location information. For example, if the user is at a zoo, the game unit provides a game related to animals. For example, the game unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at a zoo, it provides a game related to animals. Furthermore, if the user is at a botanical garden, it can provide a game related to plants. For example, the game unit acquires the user's geographical location information using Wi-Fi location information, and if it is determined that the user is at a botanical garden, it provides a game related to plants. Furthermore, if the user is at an astronomical observatory, it can provide a game related to celestial bodies. For example, the game unit acquires the user's geographical location information using GPS data, and if it is determined that the user is at an astronomical observatory, it provides a game related to celestial bodies. This allows for more appropriate information to be provided by selecting optimal game content based on the user's geographical location information.

[0101] The game module can analyze the user's social media activity to suggest game content. For example, if the user posts a lot about animals, the game module can provide a game related to animals. For example, if the game module analyzes the user's social media activity and determines that the user posts a lot about animals, the game module can provide a game related to animals. Furthermore, if the user posts a lot about plants, the game module can provide a game related to plants. For example, if the game module analyzes the user's social media activity and determines that the user posts a lot about plants, the game module can provide a game related to plants. Furthermore, if the user posts a lot about celestial bodies, the game module can provide a game related to celestial bodies. For example, if the game module analyzes the user's social media activity and determines that the user posts a lot about celestial bodies, the game module can provide a game related to celestial bodies. This makes it possible to provide more appropriate information by suggesting game content based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the reading unit, analysis unit, display unit, adding unit, and game unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit reads a two-dimensional code or a barcode on a card using the camera 42 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read information to obtain detailed information. The display unit displays the analyzed information using the display 40A of the smart device 14. The adding unit uploads photos taken by the camera 42 of the smart device 14 to an application and adds them to the picture book. The game unit can provide a quiz game or a collection game via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reading unit, analysis unit, display unit, adding unit, and game unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit reads a two-dimensional code or a barcode on a card using the camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read information to obtain detailed information. The display unit displays the analyzed information using the display of the smart glasses 214. The adding unit uploads photos taken by the camera 42 of the smart glasses 214 to an application and adds them to an illustrated book. The game unit can provide a quiz game or a collection game via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the reading unit, analysis unit, display unit, adding unit, and game unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reading unit reads a two-dimensional code or a barcode on a card using the camera 42 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read information to obtain detailed information. The display unit displays the analyzed information using the display 343 of the headset terminal 314. The adding unit uploads photos taken by the camera 42 of the headset terminal 314 to an application and adds them to the picture book. The game unit can provide a quiz game or a collection game via the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reading unit, analysis unit, display unit, adding unit, and game unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit reads a two-dimensional code or a barcode on a card using the camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read information to obtain detailed information. The display unit displays the analyzed information using the display of the robot 414. The adding unit uploads photos taken by the camera 42 of the robot 414 to an application and adds them to the picture book. The game unit can provide a quiz game or a collection game via the control unit 46A of the robot 414.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The analysis unit can analyze the user's past learning history and adjust the content of the analysis according to the user's learning progress. For example, the analysis unit can suggest the next content to be learned based on the content the user has learned in the past. For example, the analysis unit can analyze the user's past learning history and provide new information according to the user's learning progress. The analysis unit can also provide information for the user to review content that the user has learned in the past. For example, the analysis unit can analyze the user's past learning history and suggest content that needs to be reviewed. The analysis unit can also adjust the difficulty of learning according to the user's learning progress. For example, the analysis unit can evaluate the user's learning progress and adjust the difficulty and provide information. This allows for more effective learning by adjusting the content of the analysis according to the user's learning progress.

[0104] The display unit can customize the display method according to the user's learning style. For example, the display unit provides a display method that makes extensive use of images and videos to a user who prefers visual learning. For example, when the display unit reads an animal card, it displays a video of the animal's ecology and characteristics. The display unit can also provide a display method that makes extensive use of audio commentary to a user who prefers auditory learning. For example, when the display unit reads a plant card, it provides an audio commentary on the plant's growth process. The display unit can also provide an interactive display method to a user who prefers tactile learning. For example, when the display unit reads a celestial body card, it interactively displays the position of the celestial body so that the user can operate it. This allows for more effective learning by customizing the display method according to the user's learning style.

[0105] The adding unit can estimate the user's emotion and adjust the photo adding method based on the estimated user emotion. For example, the adding unit provides a method for quickly adding a photo when the user is excited. For example, the adding unit captures the user's facial expression with a camera and estimates the user's emotion using an emotion estimation algorithm. When the user is estimated to be excited, the adding unit provides a method for quickly adding a photo. Furthermore, the adding unit can provide a method including a detailed explanation when the user is relaxed. For example, the adding unit records the user's voice and estimates the user's emotion using voice analysis technology. When the user is estimated to be relaxed, the adding unit provides a method including a detailed explanation. Furthermore, the adding unit can provide a simple and easy-to-understand method when the user is stressed. For example, the adding unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotion using an emotion estimation algorithm. When the user is estimated to be stressed, the adding unit provides a simple and easy-to-understand method. This makes it possible to provide more appropriate information by adjusting the photo adding method according to the user's emotion.

[0106] The game unit can estimate the user's emotions and adjust the difficulty of the game based on the estimated user's emotions. For example, if the user is excited, the game unit increases the difficulty level to provide a more challenging game. For example, the game unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If the user is estimated to be excited, the game unit increases the difficulty level to provide a more challenging game. Furthermore, if the user is relaxed, the game unit can lower the difficulty level to provide a relaxing game. For example, the game unit records the user's voice and estimates the user's emotions using voice analysis technology. If the user is estimated to be relaxed, the game unit lowers the difficulty level to provide a relaxing game. Furthermore, if the user is feeling stressed, the game unit can adjust the difficulty level to provide a game that reduces stress. For example, the game unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If the user is estimated to be stressed, the game unit adjusts the difficulty level to provide a game that reduces stress. This allows the game difficulty to be adjusted according to the user's emotions, making it possible to provide more appropriate information.

[0107] The analysis unit can estimate the user's emotions and adjust the method of expression of the analysis based on the estimated user's emotions. For example, if the user is excited, the analysis unit applies a visually stimulating method of expression. For example, the analysis unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is excited, the analysis unit applies a visually stimulating method of expression. Furthermore, if the user is relaxed, the analysis unit can apply a calm method of expression. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. If it is estimated that the user is relaxed, the analysis unit applies a calm method of expression. Furthermore, if the user is feeling stressed, the analysis unit can apply a simple and easy-to-understand method of expression. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. If it is estimated that the user is feeling stressed, the analysis unit applies a simple and easy-to-understand method of expression. This makes it possible to provide more appropriate information by adjusting the method of expression of the analysis according to the user's emotions.

[0108] The reading unit can detect the condition of the card and select an appropriate reading method. For example, if the card is dirty, the reading unit applies an algorithm that ignores the dirt when reading. For example, the reading unit photographs the surface of the card with a camera and detects dirt on the card using image analysis technology. If dirt is detected, the reading unit applies an algorithm that ignores the dirt when reading. Furthermore, if the card is damaged, the reading unit can apply an algorithm that complements the damaged portion when reading. For example, the reading unit scans the surface of the card with a scanner and detects damage to the card using image analysis technology. If damage is detected, the reading unit applies an algorithm that complements the damaged portion when reading. Furthermore, if the card is folded, the reading unit can apply an algorithm that ignores the folded portion when reading. For example, the reading unit photographs the surface of the card with a camera and detects folds in the card using image analysis technology. If a fold is detected, the reading unit applies an algorithm that ignores the fold. This makes it possible to select the optimal reading method depending on the condition of the card, thereby enabling accurate information acquisition.

[0109] The analysis unit can adjust the level of detail of the analysis based on the importance of the card information. For example, the analysis unit analyzes and displays information of high importance in detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of high importance in detail. The analysis unit can also simplify and analyze and display information of low importance. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of low importance in a simplified manner. The analysis unit can also analyze and display information of medium importance in moderate detail. For example, the analysis unit evaluates the importance of the card information, and analyzes and displays information determined to be of medium importance in moderate detail. By adjusting the level of detail of the analysis based on the importance of the card information, more appropriate information can be provided.

[0110] The display unit can apply different display algorithms depending on the category of the card. For example, in the case of an animal card, the display unit applies an algorithm that displays the ecology and characteristics of the animal. For example, the display unit displays an animal card and displays information about the ecology and characteristics of the animal. In addition, in the case of a plant card, the display unit can apply an algorithm that displays the growth process and characteristics of the plant. For example, the display unit displays a plant card and displays information about the growth process and characteristics of the plant. In addition, in the case of a celestial body card, the display unit can apply an algorithm that displays the position and characteristics of the celestial body. For example, the display unit displays a celestial body card and displays information about the position and characteristics of the celestial body. In this way, accurate information can be provided by applying an appropriate display algorithm depending on the category of the card.

[0111] The adding unit can analyze the user's past photo history and select an appropriate adding method. For example, the adding unit suggests an optimal adding method based on photos taken by the user in the past. For example, the adding unit analyzes the user's past photo history and suggests an optimal adding method based on trends in photos taken in the past. The adding unit can also prioritize adding highly relevant photos from the user's past photo history. For example, the adding unit analyzes the user's past photo history and suggests highly relevant photos. The adding unit can also analyze the user's past photo history and suggest the most efficient adding method. For example, the adding unit analyzes the user's past photo history and suggests the most efficient adding method. This makes it possible to provide more appropriate information by selecting an optimal adding method based on the user's past photo history.

[0112] The game unit can analyze the user's past game history and select optimal game content. For example, the game unit can suggest optimal game content based on the user's history of games played in the past. For example, the game unit can analyze the user's past game history and suggest optimal game content based on the user's tendencies in games played in the past. The game unit can also prioritize providing games with content that pique the user's interest based on the user's past game history. For example, the game unit can analyze the user's past game history and suggest games with content that pique the user's interest. The game unit can also analyze the user's past game history and suggest the most enjoyable game content. For example, the game unit can analyze the user's past game history and suggest the most enjoyable game content. This allows for more appropriate information to be provided by selecting optimal game content based on the user's past game history.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reading unit reads the card. The card contains identification information such as a two-dimensional code or barcode, which the reading unit reads. For example, the reading unit may be equipped with a camera for reading two-dimensional codes, and the user can read the two-dimensional code by holding the card over the camera. The reading unit may also be equipped with a scanner for reading barcodes, and can also read barcodes. Step 2: The analysis unit analyzes the information on the card read by the reading unit. The analysis unit analyzes, for example, the information in the two-dimensional code or barcode and obtains corresponding detailed information. For example, the analysis unit decodes the two-dimensional code data and obtains the corresponding information from a database. The analysis unit can also analyze the barcode data and obtain the corresponding information. Step 3: The display unit displays detailed information based on the information analyzed by the analysis unit. The display unit displays the analyzed information using, for example, a smartphone display. For example, when the display unit reads an animal card, it displays detailed information about the animal's ecology, characteristics, distribution, etc. Step 4: The adding unit adds the photo taken by the user to the illustrated book. For example, the adding unit uploads a photo taken by the user with a smartphone camera to the application and adds the photo to the illustrated book. For example, the adding unit can add a photo of an animal the user saw at the zoo to the illustrated book by taking the photo and uploading it to the application. Step 5: The game section provides a quiz game or a collection game using cards. For example, the game section provides a quiz game using cards, allowing users to answer questions about the contents of the cards. The game section also provides a collection game for collecting cards, allowing users to enjoy collecting cards.

[0115] 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.

[0116] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 reader for reading a card; an analysis unit that analyzes the information on the card read by the reading unit; a display unit that displays detailed information based on the information analyzed by the analysis unit; an adding unit for adding a photo taken by a user to the illustrated book; A game unit that applies the game format using cards. A system characterized by:

2. The reading unit Scan a 2D code or barcode 2. The system of claim 1.

3. The analysis unit Analyze 2D codes or barcodes 2. The system of claim 1.

4. The display unit Display detailed information based on the analyzed information 2. The system of claim 1.

5. The adding unit Adding photos taken by users to the picture book 2. The system of claim 1.

6. The game section Offer a card trivia or collection game 2. The system of claim 1.

7. The reading unit Estimate the user's emotions and adjust the timing of card reading based on the estimated user emotions.

2. The system of claim 1.

8. The reading unit Detects the card status and selects the appropriate reading method 2. The system of claim 1.

Images