system

Abstract

The system according to the embodiment aims to easily convert children's drawings into 3D and display them in real space. [Solution] A system according to an embodiment includes a reading unit, a generating unit, and a display unit. The reading unit reads a drawing. The generating unit analyzes the drawing read by the reading unit and generates a 3D model. The display unit displays the 3D model generated by the generating unit in real space.

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] With conventional technology, it was difficult to easily convert children's drawings into 3D and display them in real space.

[0005] The system according to the embodiment aims to easily convert children's drawings into 3D and display them in real space. [Means for solving the problem]

[0006] A system according to an embodiment includes a reading unit, a generating unit, and a display unit. The reading unit reads a drawing. The generating unit analyzes the drawing read by the reading unit and generates a 3D model. The display unit displays the 3D model generated by the generating unit in real space. [Effects of the Invention]

[0007] The system according to the embodiment can easily convert children's drawings into 3D and display them in real space. [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) A system according to an embodiment of the present invention reads a child's drawing, converts it into 3D using a generation AI, and displays it in real space using AR technology. This system reads the child's drawing into a device, analyzes the drawing using a generation AI, generates a 3D model, and displays it in real space using AR technology. For example, a user photographs the child's drawing with a device's camera and imports the image into an app. The generation AI then analyzes the image data and extracts characteristics of the drawing. For example, it analyzes the shape, color, and placement of characters and objects in the drawing. The generation AI then generates a 3D model based on the analyzed information. For example, it reproduces the characters in the drawing in three dimensions and applies colors and textures. The generated 3D model is then displayed in real space using AR technology. For example, the 3D model is displayed in real space using a device's camera. This allows a child's drawing to be displayed in three dimensions in the real world, allowing the child to experience their creation in real space. This allows the system to explore children's creativity in the real world. For example, parents and children can enjoy watching their child's drawing move around in real space. This will deepen family communication and stimulate children's creativity.

[0029] The system according to the embodiment includes a reading unit, a generating unit, and a display unit. The reading unit reads a child's drawing. For example, the drawing can be photographed using a device's camera. The reading unit can also directly read a drawing submitted in digital format. The generating unit uses a generation AI to analyze the drawing read by the reading unit and generate a 3D model. For example, the generation AI analyzes image data and extracts characteristics of the drawing. The generation AI generates a 3D model based on the analyzed information. For example, the generation AI may reproduce a character in the drawing in three dimensions and apply colors and textures. The display unit displays the 3D model generated by the generating unit in real space. For example, the 3D model is displayed in real space via the device's camera. This allows the system according to the embodiment to create a 3D version of the child's drawing and display it in real space. This allows children to experience their creativity in the real world.

[0030] The reading unit can capture a picture of the drawing using the device's camera. Examples of the device's camera include, but are not limited to, a smartphone camera, a webcam, etc. The reading unit can, for example, capture a picture of the drawing using the device's camera and import the image into the app. The reading unit can also capture a picture of the drawing in real time using the device's camera and analyze it immediately. This allows the drawing to be easily read by using the device's camera.

[0031] The generation unit can analyze the image data and extract features of the picture. The image data includes, but is not limited to, file formats such as JPEG and PNG. The generation unit can analyze the image data using, for example, image processing technology to extract features of the picture. For example, the generation unit can analyze the shape, color, and placement of characters and objects in the picture. The generation unit can also extract features of the picture using a feature extraction algorithm. For example, the generation unit can analyze color distribution and shape patterns to identify the features of the picture. This allows the image data to be analyzed to accurately extract the features of the picture.

[0032] The generation unit can generate a 3D model based on the analyzed information. The analyzed information includes, but is not limited to, feature vectors and segment information. The generation unit generates the 3D model based on the analyzed information. For example, the generation unit reproduces a character in a picture in three dimensions and applies color and texture. The generation unit can also generate the 3D model using a polygon model or a voxel model. For example, the generation unit reproduces the shape of a character using a polygon model and expresses the details of an object using a voxel model. This allows for the creation of an accurate 3D model by generating the 3D model based on the analyzed information.

[0033] The display unit can display a 3D model in real space through the device's camera. Methods for displaying through the device's camera include, but are not limited to, AR applications, camera feed overlays, etc. The display unit can display the 3D model in real space through the device's camera. For example, the display unit can display the 3D model in real space through the device's camera using AR technology. The display unit can also display the 3D model in real space using projection mapping technology. For example, the display unit can project the 3D model into real space using a projector. This allows the 3D model to be displayed three-dimensionally in real space by displaying it through the device's camera.

[0034] The display unit can display the character drawn by the child moving around in real space. Methods of expressing the movement include, but are not limited to, animation, physical simulation, and the like. For example, the display unit can display the character drawn by the child moving around in real space using animation. The display unit can also display the character moving naturally in real space using physical simulation. For example, the display unit can simulate the character's movement in real time and display it in real space. This further stimulates the child's creativity by displaying the character drawn by the child moving around in real space.

[0035] The reading unit can analyze the child's past drawing history and select the optimal reading method. For example, the reading unit prioritizes reading themes that the child has previously liked. The reading unit can also adjust the reading method to match the style of drawings that the child has previously drawn. The reading unit can also adjust the reading timing based on the frequency of drawings that the child has previously drawn. In this way, the optimal reading method can be selected by analyzing the child's past drawing history.

[0036] When loading drawings, the loading unit can filter them based on the child's current interests and concerns. For example, the loading unit prioritizes loading characters that the child is currently interested in. The loading unit can also filter based on the themes of anime or movies that the child has recently watched. The loading unit can also prioritize loading drawings related to topics that the child has recently been talking about. In this way, by filtering based on the child's current interests and concerns, it is possible to load drawings that are more interesting.

[0037] When reading a drawing, the reading unit can select the optimal reading means depending on the child's input method. For example, if the child gives instructions by voice, the reading unit reads the drawing using voice recognition. Also, if the child gives instructions by text, the reading unit can read the drawing using text analysis. Also, if the child provides an image, the reading unit can read the drawing using image recognition. In this way, by selecting the optimal reading means depending on the child's input method, it is possible to efficiently read drawings.

[0038] When loading drawings, the loading unit can prioritize loading highly relevant drawings in consideration of the child's geographical location information. For example, the loading unit can prioritize loading drawings with a theme related to the child's current location. The loading unit can also prioritize loading drawings with a theme related to places the child has visited in the past. The loading unit can also prioritize loading drawings with a theme related to places the child plans to visit in the future. In this way, highly relevant drawings can be prioritized by taking the geographical location information into consideration.

[0039] When loading a drawing, the loading unit can analyze the child's social media activity and load related drawings. For example, the loading unit can prioritize loading drawings that the child has shared on social media. The loading unit can also prioritize loading drawings that the child has "liked" on social media. The loading unit can also load drawings based on the style of artists the child follows on social media. This makes it possible to efficiently load related drawings by analyzing social media activity.

[0040] When reading a drawing, the reading unit can customize the reading method by reflecting the child's past feedback. For example, the reading unit can preferentially use a reading method that the child has previously preferred. The reading unit can also avoid a reading method that the child has previously dissatisfied with. The reading unit can also adjust the reading method based on feedback that the child has previously provided. In this way, the optimal reading method can be provided by reflecting the past feedback.

[0041] When generating a 3D model, the generation unit can adjust the level of detail of the 3D model based on the importance of the drawing. For example, if a drawing is particularly popular with a child, the generation unit can generate a detailed 3D model. Alternatively, if a drawing is less interesting to a child, the generation unit can generate a simplified 3D model. Alternatively, if a child is paying attention to a particular part, the generation unit can reproduce that part in detail. In this way, by adjusting the level of detail of the 3D model based on the importance of the drawing, a 3D model with an appropriate level of detail can be generated.

[0042] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the drawing. For example, the generation unit applies a generation algorithm dedicated to characters to a character drawing. The generation unit can also apply a generation algorithm dedicated to landscapes to a landscape drawing. The generation unit can also apply a generation algorithm dedicated to abstract paintings to an abstract painting. In this way, by applying a generation algorithm depending on the category of the drawing, a more appropriate 3D model can be generated.

[0043] When generating a 3D model, the generation unit can improve the accuracy of the generation by referring to the child's past generation results. For example, the generation unit optimizes the generation algorithm based on data of 3D models generated by the child in the past. The generation unit can also generate a 3D model by referring to the style of 3D models that the child has preferred in the past. The generation unit can also improve the accuracy of the generation based on feedback provided by the child in the past. In this way, the accuracy of the generation can be improved by referring to the past generation results.

[0044] When generating a 3D model, the generation unit can determine the generation priority based on the time of submission of the drawing. For example, the generation unit can prioritize the creation of a 3D model from a drawing that was submitted most recently. The generation unit can also postpone drawings that were submitted earlier. The generation unit can also dynamically adjust the generation priority based on the time of submission. In this way, by determining the generation priority based on the time of submission, the most recent drawing can be prioritized as a 3D model.

[0045] When generating 3D models, the generation unit can adjust the order of generation based on the relevance of the drawings. For example, the generation unit can prioritize creating 3D models of drawings related to a theme that the child is currently interested in. The generation unit can also prioritize creating 3D models of drawings that are highly relevant to drawings that the child has drawn in the past. The generation unit can also prioritize creating 3D models of drawings that are related to drawings that the child plans to draw in the future. In this way, by adjusting the order of generation based on the relevance, it is possible to prioritize creating 3D models that are more relevant.

[0046] When generating a 3D model, the generation unit can adjust the use of technical terminology in accordance with the child's level of expertise. For example, if the child is a beginner, the generation unit can generate a 3D model using simple technical terminology. If the child is an intermediate learner, the generation unit can also generate a 3D model using standard technical terminology. If the child is an advanced learner, the generation unit can also generate a 3D model using detailed technical terminology. In this way, by adjusting the use of technical terminology according to the child's level of expertise, it is possible to generate a 3D model that is easy for children to understand.

[0047] When displaying the 3D model, the display unit can select the optimal display method by referring to the child's past display history. For example, the display unit can preferentially use a display method that the child has previously preferred. The display unit can also avoid a display method that the child has previously dissatisfied with. The display unit can also adjust the display method based on feedback provided by the child in the past. In this way, the optimal display method can be selected by referring to the past display history.

[0048] When displaying the 3D model, the display unit can customize the display content according to the child's current task. For example, if the child is studying, the display unit can provide display content including educational elements. If the child is playing, the display unit can also provide display content including fun elements. If the child is taking a break, the display unit can also provide display content that helps the child relax. In this way, by customizing the display content according to the current task, more appropriate display content can be provided.

[0049] The display unit can improve the display method by reflecting the child's feedback when displaying the 3D model. For example, the display unit adjusts the display method based on feedback provided by the child in the past. The display unit can also improve the display method by reflecting feedback provided by the child in real time. The display unit can also optimize the display method by predicting feedback provided by the child in the future. This allows the display method to be continuously improved by reflecting feedback.

[0050] When displaying the 3D model, the display unit can select the optimal display method by taking into account the child's geographical location information. For example, if the child is outdoors, the display unit can display the child on a bright screen. If the child is indoors, the display unit can also display the child on a dark screen. If the child is in a specific location, the display unit can also display the child using a background related to that location. This makes it possible to provide the optimal display method by taking into account the geographical location information.

[0051] When displaying a 3D model, the display unit can analyze the child's social media activity and display related 3D models. For example, the display unit can prioritize displaying 3D models that the child has shared on social media. The display unit can also prioritize displaying 3D models that the child has "liked" on social media. The display unit can also display 3D models based on the style of artists the child follows on social media. This allows related 3D models to be displayed efficiently by analyzing social media activity.

[0052] When displaying the 3D model, the display unit can customize the display content by reflecting the child's past feedback. For example, the display unit can prioritize display content that the child has liked in the past. The display unit can also avoid display content that the child has dissatisfied with in the past. The display unit can also adjust the display content based on feedback provided by the child in the past. This allows the optimal display content to be provided by reflecting past feedback.

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

[0054] When generating a 3D model, the generator can learn the child's past drawing style and customize the 3D model based on that style. For example, it can prioritize colors and shapes that are particularly frequently used in the child's past drawings. The generator can also learn the characteristics of characters the child has drawn in the past and reflect them in the new 3D model. Furthermore, the generator can set the background and environment of the 3D model based on the theme of the child's past drawings. This makes it possible to generate a 3D model that suits the child's personality and preferences.

[0055] When generating a 3D model, the generator can adjust the generation algorithm based on the child's current interests. For example, it can prioritize incorporating characters and themes that the child has recently become interested in. The generator can also generate a 3D model based on the style of anime or movies the child has recently watched. Furthermore, it can also reflect elements related to topics the child has recently been talking about in the 3D model. This makes it possible to generate a 3D model that is in line with the child's current interests.

[0056] When displaying a 3D model, the display unit can customize the display method by reflecting the child's past feedback. For example, it can prioritize the use of display methods that the child has previously preferred. It can also avoid display methods that the child has previously disliked. Furthermore, it can adjust the display method based on the child's past feedback. This makes it possible to provide the optimal display method by reflecting past feedback.

[0057] When generating a 3D model, the generation unit can improve the accuracy of the generation by referring to the child's past generation results. For example, the generation algorithm can be optimized based on data on 3D models generated by the child in the past. The generation unit can also generate a 3D model by referring to the style of 3D models that the child has preferred in the past. Furthermore, the generation accuracy can be improved based on feedback provided by the child in the past. In this way, the accuracy of the generation can be improved by referring to past generation results.

[0058] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the drawing. For example, a generation algorithm dedicated to characters can be applied to a character drawing. A generation algorithm dedicated to landscapes can also be applied to a landscape drawing. Furthermore, a generation algorithm dedicated to abstract paintings can also be applied to an abstract painting. In this way, by applying a generation algorithm depending on the category of the drawing, a more appropriate 3D model can be generated.

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

[0060] Step 1: The reader reads the child's drawing. For example, the reader can take a photo of the drawing using the device's camera. Alternatively, the reader can directly read a drawing submitted in digital format. Step 2: The generator uses the generation AI to analyze the drawing read by the reader and generate a 3D model. For example, the generation AI analyzes the image data and extracts the characteristics of the drawing. The generation AI generates a 3D model based on the analyzed information. For example, it recreates the character in the drawing in three dimensions and applies colors and textures. Step 3: The display unit displays the 3D model generated by the generation unit in real space. For example, the 3D model is displayed in real space through a camera of the device.

[0061] (Example 2) A system according to an embodiment of the present invention reads a child's drawing, converts it into 3D using a generation AI, and displays it in real space using AR technology. This system reads the child's drawing into a device, analyzes the drawing using a generation AI, generates a 3D model, and displays it in real space using AR technology. For example, a user photographs the child's drawing with a device's camera and imports the image into an app. The generation AI then analyzes the image data and extracts characteristics of the drawing. For example, it analyzes the shape, color, and placement of characters and objects in the drawing. The generation AI then generates a 3D model based on the analyzed information. For example, it reproduces the characters in the drawing in three dimensions and applies colors and textures. The generated 3D model is then displayed in real space using AR technology. For example, the 3D model is displayed in real space using a device's camera. This allows a child's drawing to be displayed in three dimensions in the real world, allowing the child to experience their creation in real space. This allows the system to explore children's creativity in the real world. For example, parents and children can enjoy watching their child's drawing move around in real space. This will deepen family communication and stimulate children's creativity.

[0062] The system according to the embodiment includes a reading unit, a generating unit, and a display unit. The reading unit reads a child's drawing. For example, the drawing can be photographed using a device's camera. The reading unit can also directly read a drawing submitted in digital format. The generating unit uses a generation AI to analyze the drawing read by the reading unit and generate a 3D model. For example, the generation AI analyzes image data and extracts characteristics of the drawing. The generation AI generates a 3D model based on the analyzed information. For example, the generation AI may reproduce a character in the drawing in three dimensions and apply colors and textures. The display unit displays the 3D model generated by the generating unit in real space. For example, the 3D model is displayed in real space via the device's camera. This allows the system according to the embodiment to create a 3D version of the child's drawing and display it in real space. This allows children to experience their creativity in the real world.

[0063] The reading unit can capture a picture of the drawing using the device's camera. Examples of the device's camera include, but are not limited to, a smartphone camera, a webcam, etc. The reading unit can, for example, capture a picture of the drawing using the device's camera and import the image into the app. The reading unit can also capture a picture of the drawing in real time using the device's camera and analyze it immediately. This allows the drawing to be easily read by using the device's camera.

[0064] The generation unit can analyze the image data and extract features of the picture. The image data includes, but is not limited to, file formats such as JPEG and PNG. The generation unit can analyze the image data using, for example, image processing technology to extract features of the picture. For example, the generation unit can analyze the shape, color, and placement of characters and objects in the picture. The generation unit can also extract features of the picture using a feature extraction algorithm. For example, the generation unit can analyze color distribution and shape patterns to identify the features of the picture. This allows the image data to be analyzed to accurately extract the features of the picture.

[0065] The generation unit can generate a 3D model based on the analyzed information. The analyzed information includes, but is not limited to, feature vectors and segment information. The generation unit generates the 3D model based on the analyzed information. For example, the generation unit reproduces a character in a picture in three dimensions and applies color and texture. The generation unit can also generate the 3D model using a polygon model or a voxel model. For example, the generation unit reproduces the shape of a character using a polygon model and expresses the details of an object using a voxel model. This allows for the creation of an accurate 3D model by generating the 3D model based on the analyzed information.

[0066] The display unit can display a 3D model in real space through the device's camera. Methods for displaying through the device's camera include, but are not limited to, AR applications, camera feed overlays, etc. The display unit can display the 3D model in real space through the device's camera. For example, the display unit can display the 3D model in real space through the device's camera using AR technology. The display unit can also display the 3D model in real space using projection mapping technology. For example, the display unit can project the 3D model into real space using a projector. This allows the 3D model to be displayed three-dimensionally in real space by displaying it through the device's camera.

[0067] The display unit can display the character drawn by the child moving around in real space. Methods of expressing the movement include, but are not limited to, animation, physical simulation, and the like. For example, the display unit can display the character drawn by the child moving around in real space using animation. The display unit can also display the character moving naturally in real space using physical simulation. For example, the display unit can simulate the character's movement in real time and display it in real space. This further stimulates the child's creativity by displaying the character drawn by the child moving around in real space.

[0068] The reading unit can estimate the child's emotions and adjust the timing of drawing reading based on the emotion data. For example, if the child is excited, the reading unit can maintain the child's excitement by reading the drawing immediately. If the child is calm, the reading unit can also read the drawing at a slower pace. If the child is tired, the reading unit can also read the drawing after a break. In this way, by adjusting the reading timing according to the child's emotions, the drawing can be read at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] The reading unit can analyze the child's past drawing history and select the optimal reading method. For example, the reading unit prioritizes reading themes that the child has previously liked. The reading unit can also adjust the reading method to match the style of drawings that the child has previously drawn. The reading unit can also adjust the reading timing based on the frequency of drawings that the child has previously drawn. In this way, the optimal reading method can be selected by analyzing the child's past drawing history.

[0070] When loading drawings, the loading unit can filter them based on the child's current interests and concerns. For example, the loading unit prioritizes loading characters that the child is currently interested in. The loading unit can also filter based on the themes of anime or movies that the child has recently watched. The loading unit can also prioritize loading drawings related to topics that the child has recently been talking about. In this way, by filtering based on the child's current interests and concerns, it is possible to load drawings that are more interesting.

[0071] When reading a drawing, the reading unit can select the optimal reading means depending on the child's input method. For example, if the child gives instructions by voice, the reading unit reads the drawing using voice recognition. Also, if the child gives instructions by text, the reading unit can read the drawing using text analysis. Also, if the child provides an image, the reading unit can read the drawing using image recognition. In this way, by selecting the optimal reading means depending on the child's input method, it is possible to efficiently read drawings.

[0072] The reading unit can estimate the child's emotions and determine the priority of drawings to be read based on the emotion data. For example, if the child is excited, the reading unit can prioritize reading drawings with a fun theme. Also, if the child is calm, the reading unit can prioritize reading drawings with a quiet theme. Also, if the child is tired, the reading unit can prioritize reading drawings with a relaxing theme. In this way, by determining the priority of drawings based on the child's emotions, the drawings can be read in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When loading drawings, the loading unit can prioritize loading highly relevant drawings in consideration of the child's geographical location information. For example, the loading unit can prioritize loading drawings with a theme related to the child's current location. The loading unit can also prioritize loading drawings with a theme related to places the child has visited in the past. The loading unit can also prioritize loading drawings with a theme related to places the child plans to visit in the future. In this way, highly relevant drawings can be prioritized by taking the geographical location information into consideration.

[0074] When loading a drawing, the loading unit can analyze the child's social media activity and load related drawings. For example, the loading unit can prioritize loading drawings that the child has shared on social media. The loading unit can also prioritize loading drawings that the child has "liked" on social media. The loading unit can also load drawings based on the style of artists the child follows on social media. This makes it possible to efficiently load related drawings by analyzing social media activity.

[0075] When reading a drawing, the reading unit can customize the reading method by reflecting the child's past feedback. For example, the reading unit can preferentially use a reading method that the child has previously preferred. The reading unit can also avoid a reading method that the child has previously dissatisfied with. The reading unit can also adjust the reading method based on feedback that the child has previously provided. In this way, the optimal reading method can be provided by reflecting the past feedback.

[0076] The generation unit can estimate the child's emotions and adjust the representation of the 3D model based on the emotion data. For example, if the child is excited, the generation unit generates a 3D model with vivid colors and movement. If the child is calm, the generation unit can also generate a 3D model with soft colors and gentle movement. If the child is tired, the generation unit can also generate a simple, visually less daunting 3D model. This allows for the generation of a more appropriate 3D model by adjusting the representation of the 3D model based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When generating a 3D model, the generation unit can adjust the level of detail of the 3D model based on the importance of the drawing. For example, if a drawing is particularly popular with a child, the generation unit can generate a detailed 3D model. Alternatively, if a drawing is less interesting to a child, the generation unit can generate a simplified 3D model. Alternatively, if a child is paying attention to a particular part, the generation unit can reproduce that part in detail. In this way, by adjusting the level of detail of the 3D model based on the importance of the drawing, a 3D model with an appropriate level of detail can be generated.

[0078] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the drawing. For example, the generation unit applies a generation algorithm dedicated to characters to a character drawing. The generation unit can also apply a generation algorithm dedicated to landscapes to a landscape drawing. The generation unit can also apply a generation algorithm dedicated to abstract paintings to an abstract painting. In this way, by applying a generation algorithm depending on the category of the drawing, a more appropriate 3D model can be generated.

[0079] When generating a 3D model, the generation unit can improve the accuracy of the generation by referring to the child's past generation results. For example, the generation unit optimizes the generation algorithm based on data of 3D models generated by the child in the past. The generation unit can also generate a 3D model by referring to the style of 3D models that the child has preferred in the past. The generation unit can also improve the accuracy of the generation based on feedback provided by the child in the past. In this way, the accuracy of the generation can be improved by referring to the past generation results.

[0080] The generation unit can estimate the child's emotions and adjust the length of the 3D model based on the emotion data. For example, if the child is excited, the generation unit can generate a longer 3D model. If the child is calm, the generation unit can also generate a standard-length 3D model. If the child is tired, the generation unit can also generate a shorter 3D model. This allows for the generation of a more appropriate 3D model by adjusting the length of the 3D model based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When generating a 3D model, the generation unit can determine the generation priority based on the time of submission of the drawing. For example, the generation unit can prioritize the creation of a 3D model from a drawing that was submitted most recently. The generation unit can also postpone drawings that were submitted earlier. The generation unit can also dynamically adjust the generation priority based on the time of submission. In this way, by determining the generation priority based on the time of submission, the most recent drawing can be prioritized as a 3D model.

[0082] When generating 3D models, the generation unit can adjust the order of generation based on the relevance of the drawings. For example, the generation unit can prioritize creating 3D models of drawings related to a theme that the child is currently interested in. The generation unit can also prioritize creating 3D models of drawings that are highly relevant to drawings that the child has drawn in the past. The generation unit can also prioritize creating 3D models of drawings that are related to drawings that the child plans to draw in the future. In this way, by adjusting the order of generation based on the relevance, it is possible to prioritize creating 3D models that are more relevant.

[0083] When generating a 3D model, the generation unit can adjust the use of technical terminology in accordance with the child's level of expertise. For example, if the child is a beginner, the generation unit can generate a 3D model using simple technical terminology. If the child is an intermediate learner, the generation unit can also generate a 3D model using standard technical terminology. If the child is an advanced learner, the generation unit can also generate a 3D model using detailed technical terminology. In this way, by adjusting the use of technical terminology according to the child's level of expertise, it is possible to generate a 3D model that is easy for children to understand.

[0084] The display unit can estimate the child's emotions and adjust the display method of the 3D model based on the emotion data. For example, if the child is excited, the display unit can provide a dynamic display method. If the child is calm, the display unit can also provide a quiet display method. If the child is tired, the display unit can also provide a simple, visually less stressful display method. This allows for a more appropriate display method to be provided by adjusting the display method based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When displaying the 3D model, the display unit can select the optimal display method by referring to the child's past display history. For example, the display unit can preferentially use a display method that the child has previously preferred. The display unit can also avoid a display method that the child has previously dissatisfied with. The display unit can also adjust the display method based on feedback provided by the child in the past. In this way, the optimal display method can be selected by referring to the past display history.

[0086] When displaying the 3D model, the display unit can customize the display content according to the child's current task. For example, if the child is studying, the display unit can provide display content including educational elements. If the child is playing, the display unit can also provide display content including fun elements. If the child is taking a break, the display unit can also provide display content that helps the child relax. In this way, by customizing the display content according to the current task, more appropriate display content can be provided.

[0087] The display unit can improve the display method by reflecting the child's feedback when displaying the 3D model. For example, the display unit adjusts the display method based on feedback provided by the child in the past. The display unit can also improve the display method by reflecting feedback provided by the child in real time. The display unit can also optimize the display method by predicting feedback provided by the child in the future. This allows the display method to be continuously improved by reflecting feedback.

[0088] The display unit can estimate the child's emotions and adjust the display order of the 3D models based on the emotion data. For example, if the child is excited, the display unit can first display the most interesting 3D model. Also, if the child is calm, the display unit can display the 3D models in an orderly manner. Also, if the child is tired, the display unit can prioritize displaying 3D models that can be enjoyed in a short amount of time. In this way, by adjusting the display order based on the child's emotions, the 3D models can be displayed in a more interesting order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When displaying the 3D model, the display unit can select the optimal display method by taking into account the child's geographical location information. For example, if the child is outdoors, the display unit can display the child on a bright screen. If the child is indoors, the display unit can also display the child on a dark screen. If the child is in a specific location, the display unit can also display the child using a background related to that location. This makes it possible to provide the optimal display method by taking into account the geographical location information.

[0090] When displaying a 3D model, the display unit can analyze the child's social media activity and display related 3D models. For example, the display unit can prioritize displaying 3D models that the child has shared on social media. The display unit can also prioritize displaying 3D models that the child has "liked" on social media. The display unit can also display 3D models based on the style of artists the child follows on social media. This allows related 3D models to be displayed efficiently by analyzing social media activity.

[0091] When displaying the 3D model, the display unit can customize the display content by reflecting the child's past feedback. For example, the display unit can prioritize display content that the child has liked in the past. The display unit can also avoid display content that the child has dissatisfied with in the past. The display unit can also adjust the display content based on feedback provided by the child in the past. This allows the optimal display content to be provided by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the reading unit, generating unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit photographs a child's drawing using the camera 42 of the smart device 14, and the control unit 46A acquires the image data. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI analyzes the image data and generates a 3D model. The display unit displays the 3D model in real space using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the reading unit, generating unit, and display unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit captures a picture of a child's drawing using the camera 42 of the smart glasses 214, and the control unit 46A acquires the image data. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI analyzes the image data and generates a 3D model. The display unit displays the 3D model in real space using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reading unit, generating unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reading unit photographs a child's drawing using the camera 42 of the headset type terminal 314, and the control unit 46A acquires the image data. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI analyzes the image data and generates a 3D model. The display unit displays the 3D model in real space using, for example, the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reading unit, generating unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit photographs the child's drawing using the camera 42 of the robot 414, and the control unit 46A acquires the image data. The generating unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and a generation AI analyzes the image data and generates a 3D model. The display unit displays the 3D model in real space using, for example, the display of the robot 414 or LEDs in the eyes.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When reading a child's drawing, the reader uses voice recognition technology to analyze the child's voice and infer emotions from the tone and tempo of the voice. For example, if a child is excited, their voice is likely to be high-pitched and fast-paced. Conversely, if a child is calm, their voice is likely to be low-pitched and slow-paced. By analyzing this voice data and inferring the child's emotions, the reader can read the drawing at the optimal timing. The reader can also prioritize reading specific drawings in response to voice commands from the child. This allows for flexible responses based on the child's emotions.

[0094] When generating a 3D model, the generator can learn the child's past drawing style and customize the 3D model based on that style. For example, it can prioritize colors and shapes that are particularly frequently used in the child's past drawings. The generator can also learn the characteristics of characters the child has drawn in the past and reflect them in the new 3D model. Furthermore, the generator can set the background and environment of the 3D model based on the theme of the child's past drawings. This makes it possible to generate a 3D model that suits the child's personality and preferences.

[0095] When displaying the 3D model, the display unit can estimate the child's emotions and adjust the display method based on the emotional data. For example, if the child is excited, the 3D model's movements can be made faster and its colors can be made more vivid. Alternatively, if the child is calm, the 3D model's movements can be made slower and its colors can be made softer. Furthermore, if the child is tired, the 3D model's display can be simplified to reduce visual strain. This makes it possible to provide the optimal display method according to the child's emotions.

[0096] When generating a 3D model, the generator can adjust the generation algorithm based on the child's current interests. For example, it can prioritize incorporating characters and themes that the child has recently become interested in. The generator can also generate a 3D model based on the style of anime or movies the child has recently watched. Furthermore, it can also reflect elements related to topics the child has recently been talking about in the 3D model. This makes it possible to generate a 3D model that is in line with the child's current interests.

[0097] When displaying a 3D model, the display unit can customize the display method by reflecting the child's past feedback. For example, it can prioritize the use of display methods that the child has previously preferred. It can also avoid display methods that the child has previously disliked. Furthermore, it can adjust the display method based on the child's past feedback. This makes it possible to provide the optimal display method by reflecting past feedback.

[0098] The reader can estimate the child's emotions and adjust the timing of drawing import based on the emotion data. For example, if the child is excited, it can read the drawing immediately to maintain the child's excitement. If the child is calm, it can read the drawing at a slower pace. Furthermore, if the child is tired, it can read the drawing after a break. In this way, by adjusting the reading timing according to the child's emotions, it is possible to read the drawing at a more appropriate time.

[0099] When generating a 3D model, the generation unit can improve the accuracy of the generation by referring to the child's past generation results. For example, the generation algorithm can be optimized based on data on 3D models generated by the child in the past. The generation unit can also generate a 3D model by referring to the style of 3D models that the child has preferred in the past. Furthermore, the generation accuracy can be improved based on feedback provided by the child in the past. In this way, the accuracy of the generation can be improved by referring to past generation results.

[0100] When displaying 3D models, the display unit can estimate the child's emotions and adjust the display order based on the emotion data. For example, if the child is excited, the most interesting 3D model can be displayed first. If the child is calm, the 3D models can be displayed in an orderly manner. Furthermore, if the child is tired, 3D models that can be enjoyed in a short time can be displayed first. By adjusting the display order based on the child's emotions, the 3D models can be displayed in a more interesting order.

[0101] When generating a 3D model, the generation unit can apply different generation algorithms depending on the category of the drawing. For example, a generation algorithm dedicated to characters can be applied to a character drawing. A generation algorithm dedicated to landscapes can also be applied to a landscape drawing. Furthermore, a generation algorithm dedicated to abstract paintings can also be applied to an abstract painting. In this way, by applying a generation algorithm depending on the category of the drawing, a more appropriate 3D model can be generated.

[0102] When displaying the 3D model, the display unit can estimate the child's emotions and adjust the display method based on the emotion data. For example, if the child is excited, a dynamic display method can be provided. If the child is calm, a quiet display method can be provided. Furthermore, if the child is tired, a simple, less visually demanding display method can be provided. This makes it possible to provide a more appropriate display method by adjusting the display method based on the child's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reader reads the child's drawing. For example, the reader can take a photo of the drawing using the device's camera. Alternatively, the reader can directly read a drawing submitted in digital format. Step 2: The generator uses the generation AI to analyze the drawing read by the reader and generate a 3D model. For example, the generation AI analyzes the image data and extracts the characteristics of the drawing. The generation AI generates a 3D model based on the analyzed information. For example, it recreates the character in the drawing in three dimensions and applies colors and textures. Step 3: The display unit displays the 3D model generated by the generation unit in real space. For example, the 3D model is displayed in real space through a camera of the device.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reading section that reads drawings, a generation unit that analyzes the drawing read by the reading unit and generates a 3D model; a display unit that displays the 3D model generated by the generation unit in real space; Equipped with A system characterized by:

2. The reading unit Use your device's camera to take a photo of your drawing 2. The system of claim 1.

3. The generation unit Analyzing image data and extracting image features 2. The system of claim 1.

4. The generation unit Generate a 3D model based on the analyzed information 2. The system of claim 1.

5. The display unit Displaying 3D models in real space through the device's camera 2. The system of claim 1.

6. The display unit Displaying a child's drawn character moving around in real space 2. The system of claim 1.

7. The reading unit Estimate the child's emotions and adjust the timing of drawing load based on the estimated emotions.

2. The system of claim 1.

8. The reading unit Analyze your child's drawing history and select the best reading method 2. The system of claim 1.

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