System

Abstract

The system according to the embodiment aims to provide a fresh error-finding experience to the user.SOLUTION: A system according to an embodiment includes a selection unit, a generation unit, and a provision unit. The selection unit selects an appropriate pattern based on the preference of the user or the past play history. The generation unit automatically generates a mistake-finding question based on the picture selected by the selection unit. The providing unit provides the user with the mistake-finding question generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, the problem of finding the difference is fixed, making it difficult to provide users with a fresh experience.

[0005] The system according to the embodiment aims to provide the user with a constantly fresh spot-the-difference experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a generation unit, and a provision unit. The selection unit selects an appropriate image based on the user's preferences and past play history. The generation unit automatically generates a spot-the-difference puzzle based on the image selected by the selection unit. The provision unit provides the user with the spot-the-difference puzzle generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide the user with a constantly fresh spot-the-difference experience. [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 infinite spot-the-difference system according to an embodiment of the present invention uses a generation AI to automatically generate new images and designs of spot-the-difference puzzles, providing users with constantly fresh visual stimulation. The infinite spot-the-difference system incorporates an element that allows users to revisit spot-the-difference puzzles they have previously found, providing an experience that combines nostalgia with novelty. For example, in an infinite spot-the-difference system, a generation AI automatically generates new images and designs for users to begin their spot-the-difference puzzles. For example, images with various themes, such as landscapes and character illustrations, are generated. The generation AI selects the optimal image based on the user's preferences and past play history. Next, a spot-the-difference puzzle is automatically generated based on the generated images. The generation AI then modifies parts of the image to create mistakes for the user to find. For example, mistakes are created by changing the color of a character's clothing or adding parts of the scenery. Furthermore, the system incorporates an element that allows users to revisit spot-the-difference puzzles they have previously found. This allows users to enjoy new spot-the-difference puzzles while feeling nostalgic. For example, images from previous spot-the-difference puzzles may be redisplayed, allowing users to search for the mistakes again. This allows the user to continue searching for new differences indefinitely, expanding the variety of play while enjoying both newness and nostalgia. As a result, the infinite spot the difference system is always provided with new pictures and designs, allowing the user to enjoy searching for differences without getting bored.

[0029] An infinite spot the difference system according to an embodiment includes a selection unit, a generation unit, and a provision unit. The selection unit selects appropriate pictures based on a user's preferences and past play history. For example, the selection unit prioritizes themes (e.g., animals or nature) that the user has previously enjoyed playing. The selection unit can also reselect a theme with a picture that the user has previously scored highly on. The selection unit can also select new pictures based on a theme that the user has previously played for a long time. The generation unit automatically generates spot the difference puzzles based on the pictures selected by the selection unit. For example, the generation unit changes a part of the selected picture to create mistakes that the user needs to find. For example, mistakes are created by changing the color of a character's clothing or adding a part of the scenery. The generation unit can also use a generation AI to change a part of the picture to create mistakes that the user needs to find. For example, the generation AI receives a prompt to change a part of the picture and creates a mistake. The provision unit provides the spot the difference puzzles generated by the generation unit to the user. For example, the provision unit displays the generated spot the difference puzzles to the user. The providing unit may also provide an element that allows the user to search for the difference again by redisplaying images from a previously played spot-the-difference game. For example, the providing unit may redisplay images from a previously played spot-the-difference game, allowing the user to search for the difference again. As a result, the infinite spot-the-difference system according to the embodiment can select optimal images based on the user's preferences and past play history, automatically generate spot-the-difference problems, and provide them to the user, thereby constantly providing fresh visual stimulation.

[0030] The selection unit can analyze the user's past play history and select a theme with an appropriate design. For example, the selection unit prioritizes themes that the user has previously liked (e.g., animals or nature). The selection unit can also, for example, reselect a theme with a design on which the user has previously achieved a high score. The selection unit can also select a new design based on a theme on which the user has previously played for a long time. In this way, by analyzing the user's past play history, it is possible to select a theme with an optimal design for the user. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past play history data into the generation AI and cause the generation AI to select a theme with an optimal design.

[0031] When selecting images, the selection unit can perform appropriate filtering based on the user's current interests and concerns. For example, the selection unit selects images related to keywords recently searched by the user. For example, the selection unit can also select images based on the content of websites recently viewed by the user. For example, the selection unit can also select images related to events or activities in which the user recently participated. This makes it possible to provide more interesting images by filtering images based on the user's current interests and concerns. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's current interest and concern data into the generation AI and cause the generation AI to filter the images.

[0032] When selecting a design, the selection unit can select an appropriate selection means according to the user's input method. For example, if the user uses voice input, the selection unit selects a design using voice recognition technology. For example, if the user uses text input, the selection unit can also select a design based on the input keyword. For example, if the user uses image input, the selection unit can also select a related design using image analysis technology. This makes it possible to provide a more appropriate design by selecting the optimal selection means according to the user's input method. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's input data into a generation AI and cause the generation AI to select the optimal selection means.

[0033] When selecting a design, the selection unit can prioritize selecting a highly relevant design by taking into consideration the user's geographical location information. The selection unit, for example, selects a design related to the user's current location. The selection unit can also select a design depicting local specialties or scenery based on the user's geographical location. The selection unit can also select a design depicting nearby tourist spots or famous places based on the user's location information. This makes it possible to provide a highly relevant design by taking the user's geographical location information into consideration. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the user's geographical location information to the generation AI and cause the generation AI to select a highly relevant design.

[0034] When selecting an image, the selection unit can analyze the user's social media activity and select a relevant image. For example, the selection unit selects an image related to a post that the user has "liked" on social media. For example, the selection unit can also select an image based on content shared by the user on social media. For example, the selection unit can also select a relevant image by referring to the activity of the user's friends on social media. In this way, it is possible to provide a relevant image by analyzing the user's social media activity. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media activity data into the generation AI and cause the generation AI to select a relevant image.

[0035] When selecting a design, the selection unit can customize the selection method by reflecting the user's past feedback. For example, the selection unit preferentially selects themes with designs that the user has previously rated highly. For example, the selection unit can also avoid themes with designs that the user has previously rated poorly. For example, the selection unit can adjust the design selection algorithm based on the user's feedback. This makes it possible to provide more appropriate designs by reflecting the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the selection method.

[0036] The generation unit can adjust the level of detail of the errors based on the importance of the image during generation. For example, the generation unit can create small errors in important parts of the image. For example, the generation unit can also create large errors in unimportant parts of the image. For example, the generation unit can create detailed errors in the central part of the image and simple errors in the peripheral parts. By adjusting the level of detail of the errors based on the importance of the image, a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input image importance data into the generation AI and cause the generation AI to adjust the level of detail of the errors.

[0037] The generation unit can apply different generation algorithms depending on the category of the picture during generation. For example, in the case of a landscape painting, the generation unit can apply an algorithm that changes natural elements. For example, in the case of a character illustration, the generation unit can also apply an algorithm that changes the character's clothing or facial expression. For example, in the case of an abstract painting, the generation unit can also apply an algorithm that changes the color or shape. In this way, by applying different generation algorithms depending on the category of the picture, a more appropriate spot the difference game can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input picture category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0038] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, analyzes patterns of mistakes that the user has found in the past and generates a similar pattern. The generation unit can also analyze patterns of mistakes that the user has overlooked in the past and generate a different pattern. The generation unit can also adjust the optimal method of generating mistakes based on the user's past play history, for example. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] The generation unit can determine the priority of mistakes based on the time of submission of the picture at the time of generation. For example, the generation unit prioritizes creating the most recent mistakes for a new picture. For example, the generation unit can also recreate past mistakes for an old picture. The generation unit can also adjust the priority of mistakes based on the time of submission, for example. This makes it possible to provide a more appropriate spot-the-difference game by determining the priority of mistakes based on the time of submission of the picture. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input picture submission time data into the generation AI and have the generation AI determine the priority of mistakes.

[0040] The generation unit can adjust the order of mistakes based on the relevance of the pictures during generation. For example, the generation unit prioritizes creating mistakes for pictures with high relevance. For example, the generation unit can postpone creating mistakes for pictures with low relevance. The generation unit can also adjust the order of mistakes based on the relevance of the pictures. This makes it possible to provide a more appropriate spot-the-difference game by adjusting the order of mistakes based on the relevance of the pictures. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input picture relevance data into the generation AI and have the generation AI adjust the order of mistakes.

[0041] The generation unit can adjust the difficulty of the mistakes according to the user's level of expertise during generation. For example, the generation unit creates easy mistakes for a beginner user. For example, the generation unit can also create mistakes of moderate difficulty for an intermediate user. For example, the generation unit can also create mistakes of high difficulty for an advanced user. This allows for adjusting the difficulty of the mistakes according to the user's level of expertise, thereby providing a more appropriate spot-the-difference game. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the difficulty of the mistakes.

[0042] When providing the display method, the providing unit can select the optimal display method by referring to the user's past play history. For example, the providing unit can again provide a display method that the user has previously used favorably. For example, the providing unit can also preferentially provide a display method that the user has previously given a high rating. For example, the providing unit can also select the optimal display method based on the user's past play history. In this way, the optimal display method can be provided by referring to the user's past play history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past play history data into the generating AI and cause the generating AI to select the optimal display method.

[0043] The providing unit can customize the display content according to the user's current task when providing the display content. For example, if the user wants to play for a short time, the providing unit can provide a simple spot-the-difference game. For example, if the user wants to play for a long time, the providing unit can also provide a detailed spot-the-difference game. For example, the providing unit can also customize the display content according to the user's current task. In this way, by customizing the display content according to the user's current task, a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generating AI and cause the generating AI to customize the display content.

[0044] The providing unit can improve the providing method by reflecting user feedback when providing the game. For example, if a user provides feedback on a previously provided spot-the-difference game, the providing unit can improve the providing method based on that feedback. For example, if a user gives a high rating to a specific picture or theme, the providing unit can preferentially provide similar pictures or themes. For example, the providing unit can analyze user feedback and adjust the algorithm of the providing method. In this way, by reflecting user feedback, the providing method can be improved and a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0045] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing the information. The providing unit, for example, provides a spot-the-difference game with a design related to the user's current location. The providing unit can also provide a spot-the-difference game depicting local specialties or scenery based on the user's geographical location. The providing unit can also provide a spot-the-difference game depicting nearby tourist spots or famous places based on the user's location information. This makes it possible to provide the optimal providing method by taking into consideration the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0046] The providing unit can customize the content to be provided by analyzing the user's social media activity at the time of providing. For example, the providing unit provides a spot-the-difference game related to a post that the user has "liked" on social media. For example, the providing unit can also provide a spot-the-difference game based on content shared by the user on social media. For example, the providing unit can also provide a related spot-the-difference game by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, the content to be provided can be customized and more appropriate spot-the-difference games can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the content to be provided.

[0047] The providing unit can customize the providing method by reflecting the user's past feedback when providing the game. For example, the providing unit can prioritize providing spot-the-difference themes that the user has previously rated highly. For example, the providing unit can also avoid spot-the-difference themes that the user has previously rated poorly. For example, the providing unit can adjust the algorithm of the providing method based on the user's feedback. In this way, the providing method can be customized by reflecting the user's past feedback, and a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the providing unit can be performed using, or without, AI, for example. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the providing method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The generator can dynamically adjust the difficulty of the mistakes based on the user's past play history. For example, it can avoid patterns of mistakes that the user found easily in the past and generate more challenging mistakes. Also, if the user tends to overlook a particular mistake, it can emphasize that pattern. Furthermore, the generator can provide a good balance of mistakes that can be solved in a short time and mistakes that take a long time to solve based on the user's play time. This makes it possible to provide an optimal spot-the-difference game that suits the user's play style.

[0050] The selection unit can analyze the user's social media activity and prioritize the selection of images played by friends. For example, it can select images that the user's friends have highly rated and provide them to the user. It can also generate new spot the difference games based on images shared by friends. It can also incorporate a competitive element with friends and add a function to compare scores with the same images. This makes it possible to utilize the user's social media activity to provide more interesting images.

[0051] The providing unit can provide a region-specific spot the difference event by taking into consideration the user's geographical location information. For example, a user in a specific region can be provided with a spot the difference event themed around the tourist spots and famous places of interest in that region. It can also select and provide images related to local events and festivals to the user. Furthermore, it is possible to attract the user's interest by providing region-specific rewards and benefits. In this way, a more personalized spot the difference event can be provided by utilizing the user's geographical location information.

[0052] The generation unit can diversify the types of mistakes based on the user's past feedback. For example, it can analyze the mistake patterns that the user liked in the past and generate similar patterns. It can also avoid mistake patterns that the user avoided in the past. Furthermore, it can try out new mistake patterns based on the user's feedback. This makes it possible to provide a more diverse spot-the-difference game that reflects the user's feedback.

[0053] The providing unit can select the optimal display method depending on the user's current device. For example, if the user is using a smartphone, a spot-the-difference game optimized for portrait display can be provided. If the user is using a tablet, a spot-the-difference game optimized for landscape display can be provided. Also, if the user is using a desktop, a spot-the-difference game optimized for a large screen can be provided. This makes it possible to provide the optimal display method depending on the user's device.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The selection unit selects appropriate images based on the user's preferences and past play history. For example, it selects new images based on themes that the user has previously liked (animals or nature), themes that have earned high scores, or themes that have been played for a long time. Step 2: The generator automatically generates spot-the-difference puzzles based on the images selected by the selector. For example, it can create mistakes by changing parts of the image. Specifically, it can change the color of a character's clothing or add a part of the scenery. It is also possible to use the generator AI to change parts of the image to create mistakes. Step 3: The providing unit provides the user with the spot-the-difference puzzle generated by the generating unit. For example, the generated spot-the-difference puzzle is displayed to the user. In addition, the providing unit may display images of a spot-the-difference game previously played again, allowing the user to search for the difference again.

[0056] (Example 2) An infinite spot-the-difference system according to an embodiment of the present invention uses a generation AI to automatically generate new images and designs of spot-the-difference puzzles, providing users with constantly fresh visual stimulation. The infinite spot-the-difference system incorporates an element that allows users to revisit spot-the-difference puzzles they have previously found, providing an experience that combines nostalgia with novelty. For example, in an infinite spot-the-difference system, a generation AI automatically generates new images and designs for users to begin their spot-the-difference puzzles. For example, images with various themes, such as landscapes and character illustrations, are generated. The generation AI selects the optimal image based on the user's preferences and past play history. Next, a spot-the-difference puzzle is automatically generated based on the generated images. The generation AI then modifies parts of the image to create mistakes for the user to find. For example, mistakes are created by changing the color of a character's clothing or adding parts of the scenery. Furthermore, the system incorporates an element that allows users to revisit spot-the-difference puzzles they have previously found. This allows users to enjoy new spot-the-difference puzzles while feeling nostalgic. For example, images from previous spot-the-difference puzzles may be redisplayed, allowing users to search for the mistakes again. This allows the user to continue searching for new differences indefinitely, expanding the variety of play while enjoying both newness and nostalgia. As a result, the infinite spot the difference system is always provided with new pictures and designs, allowing the user to enjoy searching for differences without getting bored.

[0057] An infinite spot the difference system according to an embodiment includes a selection unit, a generation unit, and a provision unit. The selection unit selects appropriate pictures based on a user's preferences and past play history. For example, the selection unit prioritizes themes (e.g., animals or nature) that the user has previously enjoyed playing. The selection unit can also reselect a theme with a picture that the user has previously scored highly on. The selection unit can also select new pictures based on a theme that the user has previously played for a long time. The generation unit automatically generates spot the difference puzzles based on the pictures selected by the selection unit. For example, the generation unit changes a part of the selected picture to create mistakes that the user needs to find. For example, mistakes are created by changing the color of a character's clothing or adding a part of the scenery. The generation unit can also use a generation AI to change a part of the picture to create mistakes that the user needs to find. For example, the generation AI receives a prompt to change a part of the picture and creates a mistake. The provision unit provides the spot the difference puzzles generated by the generation unit to the user. For example, the provision unit displays the generated spot the difference puzzles to the user. The providing unit may also provide an element that allows the user to search for the difference again by redisplaying images from a previously played spot-the-difference game. For example, the providing unit may redisplay images from a previously played spot-the-difference game, allowing the user to search for the difference again. As a result, the infinite spot-the-difference system according to the embodiment can select optimal images based on the user's preferences and past play history, automatically generate spot-the-difference problems, and provide them to the user, thereby constantly providing fresh visual stimulation.

[0058] The selection unit can estimate the user's emotions and adjust the image selection criteria based on the estimated user emotions. For example, if the user is relaxed, the selection unit selects images with calm landscapes and soft colors. For example, if the user is excited, the selection unit can select images with vivid colors and movement. For example, if the user is stressed, the selection unit can select images that are simple and visually calming. This allows the image selection criteria to be adjusted according to the user's emotions, thereby providing more appropriate images. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the image selection criteria based on the emotion.

[0059] The selection unit can analyze the user's past play history and select a theme with an appropriate design. For example, the selection unit prioritizes themes that the user has previously liked (e.g., animals or nature). The selection unit can also, for example, reselect a theme with a design on which the user has previously achieved a high score. The selection unit can also select a new design based on a theme on which the user has previously played for a long time. In this way, by analyzing the user's past play history, it is possible to select a theme with an optimal design for the user. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past play history data into the generation AI and cause the generation AI to select a theme with an optimal design.

[0060] When selecting images, the selection unit can perform appropriate filtering based on the user's current interests and concerns. For example, the selection unit selects images related to keywords recently searched by the user. For example, the selection unit can also select images based on the content of websites recently viewed by the user. For example, the selection unit can also select images related to events or activities in which the user recently participated. This makes it possible to provide more interesting images by filtering images based on the user's current interests and concerns. Some or all of the above-described processing by the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's current interest and concern data into the generation AI and cause the generation AI to filter the images.

[0061] When selecting a design, the selection unit can select an appropriate selection means according to the user's input method. For example, if the user uses voice input, the selection unit selects a design using voice recognition technology. For example, if the user uses text input, the selection unit can also select a design based on the input keyword. For example, if the user uses image input, the selection unit can also select a related design using image analysis technology. This makes it possible to provide a more appropriate design by selecting the optimal selection means according to the user's input method. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's input data into a generation AI and cause the generation AI to select the optimal selection means.

[0062] The selection unit can estimate the user's emotions and determine the priority of the images to be selected based on the estimated user's emotions. For example, if the user is relaxed, the selection unit can preferentially select calm images. For example, if the user is excited, the selection unit can preferentially select dynamic images. For example, if the user is stressed, the selection unit can preferentially select visually soothing images. This allows for more appropriate images to be provided by determining the priority of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, an AI, or without an AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of images based on the emotion.

[0063] When selecting a design, the selection unit can prioritize selecting a highly relevant design by taking into consideration the user's geographical location information. The selection unit, for example, selects a design related to the user's current location. The selection unit can also select a design depicting local specialties or scenery based on the user's geographical location. The selection unit can also select a design depicting nearby tourist spots or famous places based on the user's location information. This makes it possible to provide a highly relevant design by taking the user's geographical location information into consideration. Some or all of the above-described processing by the selection unit may be performed using, or without, AI. For example, the selection unit can input the user's geographical location information to the generation AI and cause the generation AI to select a highly relevant design.

[0064] When selecting an image, the selection unit can analyze the user's social media activity and select a relevant image. For example, the selection unit selects an image related to a post that the user has "liked" on social media. For example, the selection unit can also select an image based on content shared by the user on social media. For example, the selection unit can also select a relevant image by referring to the activity of the user's friends on social media. In this way, it is possible to provide a relevant image by analyzing the user's social media activity. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media activity data into the generation AI and cause the generation AI to select a relevant image.

[0065] When selecting a design, the selection unit can customize the selection method by reflecting the user's past feedback. For example, the selection unit preferentially selects themes with designs that the user has previously rated highly. For example, the selection unit can also avoid themes with designs that the user has previously rated poorly. For example, the selection unit can adjust the design selection algorithm based on the user's feedback. This makes it possible to provide more appropriate designs by reflecting the user's past feedback. Some or all of the above-described processing in the selection unit may be performed using AI, for example, or may be performed without using AI. For example, the selection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the selection method.

[0066] The generation unit can estimate the user's emotions and adjust the error creation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can create easy errors. For example, if the user is excited, the generation unit can create visually stimulating errors. For example, if the user is stressed, the generation unit can create simple, visually calming errors. This allows for adjusting the error creation method according to the user's emotions to provide a more appropriate spot-the-difference game. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the error creation method based on the emotion.

[0067] The generation unit can adjust the level of detail of the errors based on the importance of the image during generation. For example, the generation unit can create small errors in important parts of the image. For example, the generation unit can also create large errors in unimportant parts of the image. For example, the generation unit can create detailed errors in the central part of the image and simple errors in the peripheral parts. By adjusting the level of detail of the errors based on the importance of the image, a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input image importance data into the generation AI and cause the generation AI to adjust the level of detail of the errors.

[0068] The generation unit can apply different generation algorithms depending on the category of the picture during generation. For example, in the case of a landscape painting, the generation unit can apply an algorithm that changes natural elements. For example, in the case of a character illustration, the generation unit can also apply an algorithm that changes the character's clothing or facial expression. For example, in the case of an abstract painting, the generation unit can also apply an algorithm that changes the color or shape. In this way, by applying different generation algorithms depending on the category of the picture, a more appropriate spot the difference game can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input picture category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0069] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, analyzes patterns of mistakes that the user has found in the past and generates a similar pattern. The generation unit can also analyze patterns of mistakes that the user has overlooked in the past and generate a different pattern. The generation unit can also adjust the optimal method of generating mistakes based on the user's past play history, for example. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0070] The generation unit can estimate the user's emotions and adjust the number of mistakes based on the estimated user emotions. For example, if the user is relaxed, the generation unit can create fewer mistakes. For example, if the user is excited, the generation unit can also create more mistakes. For example, if the user is stressed, the generation unit can also create an appropriate number of mistakes. This allows the number of mistakes to be adjusted according to the user's emotions, thereby providing a more appropriate spot-the-difference game. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the number of mistakes based on the emotion.

[0071] The generation unit can determine the priority of mistakes based on the time of submission of the picture at the time of generation. For example, the generation unit prioritizes creating the most recent mistakes for a new picture. For example, the generation unit can also recreate past mistakes for an old picture. The generation unit can also adjust the priority of mistakes based on the time of submission, for example. This makes it possible to provide a more appropriate spot-the-difference game by determining the priority of mistakes based on the time of submission of the picture. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input picture submission time data into the generation AI and have the generation AI determine the priority of mistakes.

[0072] The generation unit can adjust the order of mistakes based on the relevance of the pictures during generation. For example, the generation unit prioritizes creating mistakes for pictures with high relevance. For example, the generation unit can postpone creating mistakes for pictures with low relevance. The generation unit can also adjust the order of mistakes based on the relevance of the pictures. This makes it possible to provide a more appropriate spot-the-difference game by adjusting the order of mistakes based on the relevance of the pictures. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input picture relevance data into the generation AI and have the generation AI adjust the order of mistakes.

[0073] The generation unit can adjust the difficulty of the mistakes according to the user's level of expertise during generation. For example, the generation unit creates easy mistakes for a beginner user. For example, the generation unit can also create mistakes of moderate difficulty for an intermediate user. For example, the generation unit can also create mistakes of high difficulty for an advanced user. This allows for adjusting the difficulty of the mistakes according to the user's level of expertise, thereby providing a more appropriate spot-the-difference game. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the difficulty of the mistakes.

[0074] The providing unit can estimate the user's emotions and adjust the display method of the spot-the-difference game to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display the game in calm colors. For example, if the user is excited, the providing unit can display the game in vivid colors. For example, if the user is stressed, the providing unit can provide a simple, visually soothing display method. This allows the display method to be adjusted according to the user's emotions, thereby providing a more appropriate spot-the-difference game. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0075] When providing the display method, the providing unit can select the optimal display method by referring to the user's past play history. For example, the providing unit can again provide a display method that the user has previously used favorably. For example, the providing unit can also preferentially provide a display method that the user has previously given a high rating. For example, the providing unit can also select the optimal display method based on the user's past play history. In this way, the optimal display method can be provided by referring to the user's past play history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past play history data into the generating AI and cause the generating AI to select the optimal display method.

[0076] The providing unit can customize the display content according to the user's current task when providing the display content. For example, if the user wants to play for a short time, the providing unit can provide a simple spot-the-difference game. For example, if the user wants to play for a long time, the providing unit can also provide a detailed spot-the-difference game. For example, the providing unit can also customize the display content according to the user's current task. In this way, by customizing the display content according to the user's current task, a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current task data into the generating AI and cause the generating AI to customize the display content.

[0077] The providing unit can improve the providing method by reflecting user feedback when providing the game. For example, if a user provides feedback on a previously provided spot-the-difference game, the providing unit can improve the providing method based on that feedback. For example, if a user gives a high rating to a specific picture or theme, the providing unit can preferentially provide similar pictures or themes. For example, the providing unit can analyze user feedback and adjust the algorithm of the providing method. In this way, by reflecting user feedback, the providing method can be improved and a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generating AI and cause the generating AI to improve the providing method.

[0078] The providing unit can estimate the user's emotions and determine the priority of the spot-the-difference games to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can prioritize providing spot-the-difference games with calm images. For example, if the user is excited, the providing unit can prioritize providing spot-the-difference games with dynamic images. For example, if the user is stressed, the providing unit can prioritize providing spot-the-difference games with visually calming images. This allows for more appropriate spot-the-difference games to be provided by determining the priority of the spot-the-difference games according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of the spot-the-difference games based on the emotion.

[0079] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing the information. The providing unit, for example, provides a spot-the-difference game with a design related to the user's current location. The providing unit can also provide a spot-the-difference game depicting local specialties or scenery based on the user's geographical location. The providing unit can also provide a spot-the-difference game depicting nearby tourist spots or famous places based on the user's location information. This makes it possible to provide the optimal providing method by taking into consideration the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0080] The providing unit can customize the content to be provided by analyzing the user's social media activity at the time of providing. For example, the providing unit provides a spot-the-difference game related to a post that the user has "liked" on social media. For example, the providing unit can also provide a spot-the-difference game based on content shared by the user on social media. For example, the providing unit can also provide a related spot-the-difference game by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, the content to be provided can be customized and more appropriate spot-the-difference games can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the content to be provided.

[0081] The providing unit can customize the providing method by reflecting the user's past feedback when providing the game. For example, the providing unit can prioritize providing spot-the-difference themes that the user has previously rated highly. For example, the providing unit can also avoid spot-the-difference themes that the user has previously rated poorly. For example, the providing unit can adjust the algorithm of the providing method based on the user's feedback. In this way, the providing method can be customized by reflecting the user's past feedback, and a more appropriate spot-the-difference game can be provided. Some or all of the above-described processing in the providing unit can be performed using, or without, AI, for example. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements including the selection unit, generation unit, and provision 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 selection unit is realized by the control unit 46A of the smart device 14 and selects an appropriate image based on the user's preferences and past play history. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates a spot-the-difference problem based on the selected image. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated spot-the-difference problem to the user. Furthermore, some or all of the selection unit, generation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described selection unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and selects an appropriate image based on the user's preferences and past play history. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates a spot-the-difference problem based on the selected image. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated spot-the-difference problem to the user. Furthermore, some or all of the selection unit, generation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned selection unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 and selects an appropriate image based on the user's preferences and past play history. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a spot the difference problem based on the selected image. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated spot the difference problem to the user. Furthermore, some or all of the selection unit, generation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned selection unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and selects an appropriate image based on the user's preferences and past play history. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a spot-the-difference problem based on the selected image. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated spot-the-difference problem to the user. Furthermore, some or all of the selection unit, generation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The selection unit can also monitor the user's current health condition and select an appropriate image. For example, the selection unit can measure the user's heart rate and stress level and select a calming landscape image if the user needs relaxation. Conversely, the selection unit can select an image with vibrant colors if the user needs energy. The selection unit can also analyze the user's sleep patterns and select an image with colors that are easy on the eyes if the user is sleep-deprived. This makes it possible to provide the optimal image according to the user's health condition.

[0084] The generator can dynamically adjust the difficulty of the mistakes based on the user's past play history. For example, it can avoid patterns of mistakes that the user found easily in the past and generate more challenging mistakes. Also, if the user tends to overlook a particular mistake, it can emphasize that pattern. Furthermore, the generator can provide a good balance of mistakes that can be solved in a short time and mistakes that take a long time to solve based on the user's play time. This makes it possible to provide an optimal spot-the-difference game that suits the user's play style.

[0085] The providing unit can estimate the user's emotions and adjust the reward system based on the estimated emotions. For example, if the user is relaxed, calm music or visual effects can be provided as a reward. If the user is excited, more flashy effects or music can be provided. Also, if the user is stressed, a simple, calming reward can be provided. This makes it possible to provide an optimal reward system according to the user's emotions.

[0086] The selection unit can analyze the user's social media activity and prioritize the selection of images played by friends. For example, it can select images that the user's friends have highly rated and provide them to the user. It can also generate new spot the difference games based on images shared by friends. It can also incorporate a competitive element with friends and add a function to compare scores with the same images. This makes it possible to utilize the user's social media activity to provide more interesting images.

[0087] The generation unit can estimate the user's emotions and adjust the visual difficulty of the mistakes based on the estimated emotions. For example, if the user is relaxed, the color contrast can be lowered to make the mistakes easier to find. If the user is excited, the color contrast can be increased to create visually stimulating mistakes. Also, if the user is stressed, simple, visually calming mistakes can be created. This makes it possible to provide an optimal spot-the-difference game that suits the user's emotions.

[0088] The providing unit can provide a region-specific spot the difference event by taking into consideration the user's geographical location information. For example, a user in a specific region can be provided with a spot the difference event themed around the tourist spots and famous places of interest in that region. It can also select and provide images related to local events and festivals to the user. Furthermore, it is possible to attract the user's interest by providing region-specific rewards and benefits. In this way, a more personalized spot the difference event can be provided by utilizing the user's geographical location information.

[0089] The selection unit can estimate the user's emotion and adjust the color tone of the image based on the estimated emotion. For example, if the user is relaxed, a calm pastel color image can be selected. If the user is excited, a vivid color image can be selected. Also, if the user is stressed, a visually soothing monotone image can be selected. This makes it possible to provide the optimal image according to the user's emotion.

[0090] The generation unit can diversify the types of mistakes based on the user's past feedback. For example, it can analyze the mistake patterns that the user liked in the past and generate similar patterns. It can also avoid mistake patterns that the user avoided in the past. Furthermore, it can try out new mistake patterns based on the user's feedback. This makes it possible to provide a more diverse spot-the-difference game that reflects the user's feedback.

[0091] The providing unit can estimate the user's emotions and provide hints for finding the difference based on the estimated emotions. For example, if the user is relaxed, a simple hint can be provided. If the user is excited, a challenging hint can be provided. Also, if the user is stressed, a detailed hint can be provided. In this way, it is possible to provide optimal hints according to the user's emotions.

[0092] The providing unit can select the optimal display method depending on the user's current device. For example, if the user is using a smartphone, a spot-the-difference game optimized for portrait display can be provided. If the user is using a tablet, a spot-the-difference game optimized for landscape display can be provided. Also, if the user is using a desktop, a spot-the-difference game optimized for a large screen can be provided. This makes it possible to provide the optimal display method depending on the user's device.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The selection unit selects appropriate images based on the user's preferences and past play history. For example, it selects new images based on themes that the user has previously liked (animals or nature), themes that have earned high scores, or themes that have been played for a long time. Step 2: The generator automatically generates spot-the-difference puzzles based on the images selected by the selector. For example, it can create mistakes by changing parts of the image. Specifically, it can change the color of a character's clothing or add a part of the scenery. It is also possible to use the generator AI to change parts of the image to create mistakes. Step 3: The providing unit provides the user with the spot-the-difference puzzle generated by the generating unit. For example, the generated spot-the-difference puzzle is displayed to the user. In addition, the providing unit may display images of a spot-the-difference game previously played again, allowing the user to search for the difference again.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The 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.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 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.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the 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.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0125] In the 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.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

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

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0142] 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.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] 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.

[0166] [Explanation of symbols]

[0167] 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 selection unit that selects an appropriate image based on the user's preferences and past play history; a generation unit that automatically generates spot-the-difference questions based on the pictures selected by the selection unit; a providing unit that provides the user with the spot the difference problem generated by the generating unit. A system characterized by:

2. The selection unit Estimate the user's emotions and adjust the selection criteria for the images based on the estimated user emotions.

2. The system of claim 1.

3. The selection unit Analyze the user's past play history and select an appropriate theme 2. The system of claim 1.

4. The selection unit When selecting images, appropriate filtering is performed based on the user's current interests.

2. The system of claim 1.

5. The selection unit When selecting a design, select an appropriate selection method according to the user's input method.

2. The system of claim 1.

6. The selection unit The user's emotions are estimated, and the priority of the images to be selected is determined based on the estimated user's emotions.

2. The system of claim 1.

7. The selection unit When selecting images, the system prioritizes images that are highly relevant to the user based on their geographic location.

2. The system of claim 1.

8. The selection unit When selecting images, we analyze users' social media activity and select relevant images.

2. The system of claim 1.

Images