system

Abstract

The system according to this embodiment aims to effectively represent the images that the user has in mind. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The provision unit provides an image generated by the analysis unit. The learning unit performs learning based on the image provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sufficient assistance for effectively expressing the image in the user's mind has not been provided, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively express the image in the user's mind.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit receives the user's input. The analysis unit analyzes the information received by the reception unit. The provision unit provides the image generated by the analysis unit. The learning unit performs learning based on the image provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively represent the image that the user has in mind. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An image generation system according to an embodiment of the present invention is a system that assists users in expressing images they have in mind through language, pictures, etc. This image generation system allows users to describe their imagined image using words or pictures, and input this description into the AI. The AI ​​then analyzes the input description and transforms the image into a concrete form. The generated picture or text is provided to the user, who can modify it as needed. Furthermore, the AI ​​learns from the user's past input data to generate images with higher accuracy. For example, if a user describes "white clouds floating in a blue sky," the AI ​​analyzes the description and generates a concrete picture. The user can then review the generated picture and make modifications as needed, such as "I'd like the clouds to be a little bigger." The AI ​​learns these modifications and incorporates them into subsequent image generation. This allows users to transform their imagined images into concrete forms. As a result, the image generation system can concretely represent the user's images.

[0029] The image generation system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives, for example, an image described by the user in words or pictures. For example, if the user describes "white clouds floating in a blue sky," the reception unit can accept that description. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes text input using, for example, natural language processing technology and analyzes picture input using image analysis technology. For example, the analysis unit analyzes the text "white clouds floating in a blue sky" entered by the user and generates a concrete image based on its content. The provision unit provides the image generated by the analysis unit to the user. The provision unit displays the generated image on the user's device, for example. For example, the provision unit can display the generated image on the user's smartphone or tablet. The learning unit performs learning based on the image provided by the provision unit. The learning unit, for example, learns to improve the accuracy of the AI ​​based on user feedback. For instance, the learning unit receives feedback from the providing unit, such as "I'd like the clouds to be a little bigger," and uses that feedback to learn the AI. As a result, the image generation system according to this embodiment can concretely represent the user's image.

[0030] The reception unit can accept images described by the user in words or pictures. For example, the reception unit can accept an image described by the user in words. For example, if the user describes an image as "white clouds floating in a blue sky," the reception unit can accept that description. The reception unit can also accept images described by the user in pictures. For example, a picture drawn by the user on paper can be scanned and input into the reception unit as digital data. In this way, by accepting images described by the user in words or pictures, information for generating a concrete image can be obtained. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input text data entered by the user into a generating AI and have the generating AI perform analysis of the text data.

[0031] The analysis unit can analyze user input and generate concrete images. For example, the analysis unit can analyze text input using natural language processing technology. For instance, it can analyze the text "white clouds floating in a blue sky" entered by the user and generate a concrete image based on its content. The analysis unit can also analyze picture input using image analysis technology. For example, it can analyze a picture scanned and entered by the user and generate a concrete image based on its content. In this way, concrete images can be generated by analyzing user input. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user-entered text data into a generation AI and have the generation AI perform the analysis of the text data.

[0032] The providing unit can provide the generated image to the user and receive user feedback. The providing unit can, for example, display the generated image on the user's device. For example, the providing unit can display the generated image on the user's smartphone or tablet. The providing unit can also receive user feedback. For example, the providing unit can receive feedback from the user such as "I would like the clouds to be a little bigger" and send that feedback to the analysis unit. In this way, by providing the generated image to the user and receiving feedback, the accuracy of the image can be improved. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated image into a generation AI and have the generation AI display the image.

[0033] The learning unit can learn to improve the accuracy of the AI ​​based on user feedback. For example, the learning unit can learn to improve the accuracy of the AI ​​based on user feedback. For example, the learning unit can receive feedback from the providing unit, such as "I want the clouds to be a little bigger," and use that feedback to learn the AI. In this way, the accuracy of the AI ​​can be improved by learning based on user feedback. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user feedback data into the generating AI and have the generating AI perform analysis of the feedback data and learn from it.

[0034] The analysis unit can learn from the user's past input data to generate images with higher accuracy. For example, the analysis unit learns the text data "white clouds floating in a blue sky" that the user previously entered, and uses that content to generate images in the future. This makes it possible to generate images with higher accuracy by learning from the user's past input data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past input data into a generation AI and have the generation AI perform data learning and analysis.

[0035] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest similar input methods by referring to content the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform data analysis and select the reception method.

[0036] The reception unit can filter input based on the user's current projects and areas of interest. For example, the reception unit can prioritize accepting only input related to the user's current projects. The reception unit can also filter and accept highly relevant input based on the user's areas of interest. Furthermore, the reception unit can filter input based on areas the user has shown interest in in the past. This allows the reception unit to accept highly relevant input by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform data filtering.

[0037] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize accepting inputs related to that region. Similarly, if the user is traveling, the reception unit can prioritize accepting inputs related to their travel destination. Furthermore, if the user is at home, the reception unit can prioritize accepting inputs related to their home. This allows the reception unit to prioritize highly relevant inputs by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI, which can then perform data analysis and determine input priorities.

[0038] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on what the user has shared on social media. It can also prioritize accepting relevant inputs based on the accounts the user follows on social media. Furthermore, it can prioritize accepting relevant inputs based on what the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant inputs. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform data analysis and determine the priority of inputs.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during the analysis. For example, the analysis unit performs a detailed analysis for high-importance inputs. It can also perform a concise analysis for low-importance inputs. Furthermore, it can perform an analysis with an appropriate level of detail for moderately important inputs. By adjusting the level of detail of the analysis based on the importance of the inputs, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the input data into a generating AI and have the generating AI perform the data analysis and adjust the level of detail.

[0040] The analysis unit can apply different analysis algorithms depending on the input category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text input. It can also apply an image analysis algorithm to picture input. Furthermore, it can apply a speech recognition algorithm to voice input. By applying different analysis algorithms depending on the input category, it can provide appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the input data category to a generating AI and have the generating AI perform data analysis and algorithm application.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the inputs during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted inputs. It can also postpone the analysis of older inputs. Furthermore, it can appropriately analyze inputs submitted at a moderate stage. By determining the priority of analysis based on the submission date of the inputs, the analysis can be performed in an appropriate order. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the input data into a generating AI and have the generating AI perform the data analysis and priority determination.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the inputs during analysis. For example, the analysis unit may prioritize the analysis of inputs with high relevance. It can also postpone the analysis of inputs with low relevance. Furthermore, it can appropriately analyze inputs with moderate relevance. In this way, by adjusting the order of analysis based on the relevance of the inputs, the analysis can be performed in an appropriate order. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the input data into a generating AI and have the generating AI perform the data analysis and order adjustment.

[0043] The providing unit can adjust the level of detail provided based on the importance of the generated images at the time of provision. For example, the providing unit can provide detailed information for high-importance images, concise information for low-importance images, and information with an appropriate level of detail for medium-importance images. By adjusting the level of detail based on the importance of the generated images, appropriate information can be provided. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input image importance data into a generating AI and have the generating AI perform data analysis and level of detail adjustment.

[0044] The providing unit can apply different providing algorithms depending on the image category at the time of provision. For example, the providing unit can apply a natural language generation algorithm to text images. It can also apply an image generation algorithm to picture images. Furthermore, it can apply a speech generation algorithm to audio images. In this way, by applying different providing algorithms depending on the image category, the appropriate image can be provided. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input image category data into a generating AI and have the generating AI perform data analysis and algorithm application.

[0045] The delivery unit can determine the priority of image delivery based on the submission date of the images. For example, the delivery unit may prioritize the delivery of recently generated images. It may also postpone the delivery of older images. Furthermore, it may appropriately deliver images with a moderate submission date. By determining the priority of delivery based on the submission date of the images, images can be delivered in an appropriate order. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input image submission date data into a generating AI and have the generating AI perform data analysis and priority determination.

[0046] The distribution unit can adjust the order of distribution based on the relevance of the images at the time of distribution. For example, the distribution unit may prioritize providing images with high relevance. It may also postpone providing images with low relevance. Furthermore, it may provide images with moderate relevance in appropriate proportions. In this way, by adjusting the order of distribution based on the relevance of the images, images can be provided in an appropriate order. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit may input image relevance data into a generating AI and have the generating AI perform data analysis and order adjustment.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. In this way, by referring to past learning data, the learning algorithm can be optimized and its accuracy improved. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform data analysis and algorithm optimization.

[0048] The learning unit can weight the training data based on the submission date of the input data during training. For example, the learning unit can assign a high weight to recently submitted input data. It can also assign a low weight to older input data. Furthermore, it can assign a moderate weight to input data submitted at a moderate time. In this way, appropriate weighting can be achieved by weighting the training data based on the submission date of the input data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the input data submission date data into a generating AI and have the generating AI perform data analysis and weighting.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can retrieve additional information from relevant external databases based on user input data. For example, if a user inputs "white clouds floating in a blue sky," the analysis unit can retrieve current weather information from a weather database and generate a more realistic image. Similarly, if a user inputs "ancient Roman buildings," the analysis unit can retrieve relevant architectural information from a history database and generate a detailed image. Furthermore, if a user inputs "a city of the future," the analysis unit can retrieve relevant prediction information from a future prediction database and generate a futuristic image. In this way, by retrieving additional information from relevant external databases based on user input data, it is possible to generate more detailed and realistic images.

[0051] When providing generated images to users, the service provider can adjust the display method based on the user's past feedback. For example, if a user previously provided feedback requesting "more detailed explanations," the service provider can add detailed explanations to the generated image. Similarly, if a user previously requested a "simpler display," the service provider can display the generated image simply. Furthermore, if a user previously requested a "visually stimulating display," the service provider can display the generated image in a visually stimulating manner. By adjusting the display method based on the user's past feedback, the service provider can provide more appropriate images.

[0052] The learning unit can evaluate the quality of generated images based on user feedback. For example, if a user provides feedback such as "This image is very good," the learning unit can evaluate that image as high quality and incorporate this into future learning. If a user provides feedback such as "This image is not very good," the learning unit can evaluate that image as low quality and learn areas for improvement. Furthermore, if a user provides feedback such as "This part is good, but other parts need improvement," the learning unit can use that partial evaluation to focus on learning specific parts. In this way, by evaluating the quality of generated images based on user feedback, it is possible to generate higher quality images.

[0053] The input system analyzes user input data in real time and provides appropriate suggestions based on the input. For example, if a user starts typing "blue sky," the input system can display suggestions such as "white clouds floating in a blue sky" or "blue sky and sea." If a user starts typing "ancient Rome," the input system can display suggestions such as "ancient Roman buildings" or "ancient Roman culture." Furthermore, if a user starts typing "future city," the input system can display suggestions such as "future city scenery" or "future city buildings." In this way, the system can support user input by analyzing user input data in real time and providing appropriate suggestions.

[0054] The provider can adjust the display method according to the characteristics of the user's device when providing the generated image to the user. For example, if the user is using a smartphone, the provider can optimize the generated image to the smartphone screen size for display. Similarly, if the user is using a tablet, the provider can optimize the generated image to the tablet screen size for display. Furthermore, if the user is using a desktop computer, the provider can optimize the generated image to the desktop computer screen size for display. This allows for the provision of more easily viewable images by adjusting the display method according to the characteristics of the user's device.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, if the user describes "white clouds floating in a blue sky," the reception desk can accept that description. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes text input using natural language processing technology and analyzes picture input using image analysis technology. For example, it analyzes the text "white clouds floating in a blue sky" entered by the user and generates a concrete image based on its content. Step 3: The distribution unit provides the user with the image generated by the analysis unit. The distribution unit displays the generated image on the user's device. For example, the generated image can be displayed on the user's smartphone or tablet. Step 4: The learning unit learns based on the images provided by the provider unit. The learning unit learns to improve the accuracy of the AI ​​based on user feedback. For example, the user may provide feedback from the provider unit such as "I would like the clouds to be a little bigger," and the AI ​​will learn based on that feedback.

[0057] (Example of form 2) An image generation system according to an embodiment of the present invention is a system that assists users in expressing images they have in mind through language, pictures, etc. This image generation system allows users to describe their imagined image using words or pictures, and input this description into the AI. The AI ​​then analyzes the input description and transforms the image into a concrete form. The generated picture or text is provided to the user, who can modify it as needed. Furthermore, the AI ​​learns from the user's past input data to generate images with higher accuracy. For example, if a user describes "white clouds floating in a blue sky," the AI ​​analyzes the description and generates a concrete picture. The user can then review the generated picture and make modifications as needed, such as "I'd like the clouds to be a little bigger." The AI ​​learns these modifications and incorporates them into subsequent image generation. This allows users to transform their imagined images into concrete forms. As a result, the image generation system can concretely represent the user's images.

[0058] The image generation system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a learning unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives, for example, an image described by the user in words or pictures. For example, if the user describes "white clouds floating in a blue sky," the reception unit can accept that description. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes text input using, for example, natural language processing technology and analyzes picture input using image analysis technology. For example, the analysis unit analyzes the text "white clouds floating in a blue sky" entered by the user and generates a concrete image based on its content. The provision unit provides the image generated by the analysis unit to the user. The provision unit displays the generated image on the user's device, for example. For example, the provision unit can display the generated image on the user's smartphone or tablet. The learning unit performs learning based on the image provided by the provision unit. The learning unit, for example, learns to improve the accuracy of the AI ​​based on user feedback. For instance, the learning unit receives feedback from the providing unit, such as "I'd like the clouds to be a little bigger," and uses that feedback to learn the AI. As a result, the image generation system according to this embodiment can concretely represent the user's image.

[0059] The reception unit can accept images described by the user in words or pictures. For example, the reception unit can accept an image described by the user in words. For example, if the user describes an image as "white clouds floating in a blue sky," the reception unit can accept that description. The reception unit can also accept images described by the user in pictures. For example, a picture drawn by the user on paper can be scanned and input into the reception unit as digital data. In this way, by accepting images described by the user in words or pictures, information for generating a concrete image can be obtained. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input text data entered by the user into a generating AI and have the generating AI perform analysis of the text data.

[0060] The analysis unit can analyze user input and generate concrete images. For example, the analysis unit can analyze text input using natural language processing technology. For instance, it can analyze the text "white clouds floating in a blue sky" entered by the user and generate a concrete image based on its content. The analysis unit can also analyze picture input using image analysis technology. For example, it can analyze a picture scanned and entered by the user and generate a concrete image based on its content. In this way, concrete images can be generated by analyzing user input. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user-entered text data into a generation AI and have the generation AI perform the analysis of the text data.

[0061] The providing unit can provide the generated image to the user and receive user feedback. The providing unit can, for example, display the generated image on the user's device. For example, the providing unit can display the generated image on the user's smartphone or tablet. The providing unit can also receive user feedback. For example, the providing unit can receive feedback from the user such as "I would like the clouds to be a little bigger" and send that feedback to the analysis unit. In this way, by providing the generated image to the user and receiving feedback, the accuracy of the image can be improved. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated image into a generation AI and have the generation AI display the image.

[0062] The learning unit can learn to improve the accuracy of the AI ​​based on user feedback. For example, the learning unit can learn to improve the accuracy of the AI ​​based on user feedback. For example, the learning unit can receive feedback from the providing unit, such as "I want the clouds to be a little bigger," and use that feedback to learn the AI. In this way, the accuracy of the AI ​​can be improved by learning based on user feedback. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user feedback data into the generating AI and have the generating AI perform analysis of the feedback data and learn from it.

[0063] The analysis unit can learn from the user's past input data to generate images with higher accuracy. For example, the analysis unit learns the text data "white clouds floating in a blue sky" that the user previously entered, and uses that content to generate images in the future. This makes it possible to generate images with higher accuracy by learning from the user's past input data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past input data into a generation AI and have the generation AI perform data learning and analysis.

[0064] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is relaxed, the reception unit can slow down the input acceptance timing to give the user ample time to think. If the user is anxious, the reception unit can immediately display the input screen to accept input quickly. If the user is focused, the reception unit can also adjust the input acceptance timing to help the user maintain their concentration. By adjusting the input acceptance timing according to the user's emotions, input can be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and timing adjustment.

[0065] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest similar input methods by referring to content the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform data analysis and select the reception method.

[0066] The reception unit can filter input based on the user's current projects and areas of interest. For example, the reception unit can prioritize accepting only input related to the user's current projects. The reception unit can also filter and accept highly relevant input based on the user's areas of interest. Furthermore, the reception unit can filter input based on areas the user has shown interest in in the past. This allows the reception unit to accept highly relevant input by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform data filtering.

[0067] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize receiving high-priority inputs. Conversely, if the user is relaxed, the reception unit can also accept low-priority inputs. Furthermore, if the user is in a hurry, the reception unit can also prioritize receiving high-urgency inputs. In this way, by prioritizing inputs according to the user's emotions, important inputs can be received preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and priority determination.

[0068] The reception unit can prioritize accepting inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize accepting inputs related to that region. Similarly, if the user is traveling, the reception unit can prioritize accepting inputs related to their travel destination. Furthermore, if the user is at home, the reception unit can prioritize accepting inputs related to their home. This allows the reception unit to prioritize highly relevant inputs by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into a generating AI, which can then perform data analysis and determine input priorities.

[0069] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on what the user has shared on social media. It can also prioritize accepting relevant inputs based on the accounts the user follows on social media. Furthermore, it can prioritize accepting relevant inputs based on what the user has "liked" on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant inputs. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI perform data analysis and determine the priority of inputs.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the presentation method.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during the analysis. For example, the analysis unit performs a detailed analysis for high-importance inputs. It can also perform a concise analysis for low-importance inputs. Furthermore, it can perform an analysis with an appropriate level of detail for moderately important inputs. By adjusting the level of detail of the analysis based on the importance of the inputs, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the input data into a generating AI and have the generating AI perform the data analysis and adjust the level of detail.

[0072] The analysis unit can apply different analysis algorithms depending on the input category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text input. It can also apply an image analysis algorithm to picture input. Furthermore, it can apply a speech recognition algorithm to voice input. By applying different analysis algorithms depending on the input category, it can provide appropriate analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the input data category to a generating AI and have the generating AI perform data analysis and algorithm application.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of the analysis length.

[0074] The analysis unit can determine the priority of analysis based on the submission date of the inputs during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted inputs. It can also postpone the analysis of older inputs. Furthermore, it can appropriately analyze inputs submitted at a moderate stage. By determining the priority of analysis based on the submission date of the inputs, the analysis can be performed in an appropriate order. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the input data into a generating AI and have the generating AI perform the data analysis and priority determination.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the inputs during analysis. For example, the analysis unit may prioritize the analysis of inputs with high relevance. It can also postpone the analysis of inputs with low relevance. Furthermore, it can appropriately analyze inputs with moderate relevance. In this way, by adjusting the order of analysis based on the relevance of the inputs, the analysis can be performed in an appropriate order. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the input data into a generating AI and have the generating AI perform the data analysis and order adjustment.

[0076] The service provider can estimate the user's emotions and adjust how images are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed images. If the user is in a hurry, the service provider can provide concise images. If the user is excited, the service provider can provide visually stimulating images. By adjusting how images are displayed according to the user's emotions, the service provider can provide appropriate images. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0077] The providing unit can adjust the level of detail provided based on the importance of the generated images at the time of provision. For example, the providing unit can provide detailed information for high-importance images, concise information for low-importance images, and information with an appropriate level of detail for medium-importance images. By adjusting the level of detail based on the importance of the generated images, appropriate information can be provided. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input image importance data into a generating AI and have the generating AI perform data analysis and level of detail adjustment.

[0078] The providing unit can apply different providing algorithms depending on the image category at the time of provision. For example, the providing unit can apply a natural language generation algorithm to text images. It can also apply an image generation algorithm to picture images. Furthermore, it can apply a speech generation algorithm to audio images. In this way, by applying different providing algorithms depending on the image category, the appropriate image can be provided. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input image category data into a generating AI and have the generating AI perform data analysis and algorithm application.

[0079] The service provider can estimate the user's emotions and adjust the length of the images it provides based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide a short, concise image. If the user is relaxed, the service provider can provide a detailed image. If the user is excited, the service provider can provide a visually stimulating image. By adjusting the length of the images according to the user's emotions, the service provider can provide appropriate images. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and image length adjustment.

[0080] The delivery unit can determine the priority of image delivery based on the submission date of the images. For example, the delivery unit may prioritize the delivery of recently generated images. It may also postpone the delivery of older images. Furthermore, it may appropriately deliver images with a moderate submission date. By determining the priority of delivery based on the submission date of the images, images can be delivered in an appropriate order. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input image submission date data into a generating AI and have the generating AI perform data analysis and priority determination.

[0081] The distribution unit can adjust the order of distribution based on the relevance of the images at the time of distribution. For example, the distribution unit may prioritize providing images with high relevance. It may also postpone providing images with low relevance. Furthermore, it may provide images with moderate relevance in appropriate proportions. In this way, by adjusting the order of distribution based on the relevance of the images, images can be provided in an appropriate order. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit may input image relevance data into a generating AI and have the generating AI perform data analysis and order adjustment.

[0082] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. If the user is excited, the learning unit can also select visually stimulating training data. This allows for appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and training data selection.

[0083] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. The learning unit can also adjust the parameters of the learning algorithm based on past learning data. Furthermore, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. In this way, by referring to past learning data, the learning algorithm can be optimized and its accuracy improved. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform data analysis and algorithm optimization.

[0084] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will learn more frequently. Conversely, if the user is in a hurry, the learning unit can reduce the learning frequency. Furthermore, if the user is excited, the learning unit can learn at a moderate frequency. In this way, by adjusting the learning frequency according to the user's emotions, learning can be performed at an appropriate frequency. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the learning frequency.

[0085] The learning unit can weight the training data based on the submission date of the input data during training. For example, the learning unit can assign a high weight to recently submitted input data. It can also assign a low weight to older input data. Furthermore, it can assign a moderate weight to input data submitted at a moderate time. In this way, appropriate weighting can be achieved by weighting the training data based on the submission date of the input data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the input data submission date data into a generating AI and have the generating AI perform data analysis and weighting. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text or voice input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input using natural language processing technology and image analysis technology. The provision unit is implemented by the output device 40 of the smart device 14 and displays the generated image on the user's device. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns to improve the accuracy of the AI ​​based on user feedback. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input using natural language processing technology and image analysis technology. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated image to the user. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns to improve the accuracy of the AI ​​based on user feedback. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and learning unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input using natural language processing technology and image analysis technology. The provision unit is implemented, for example, by the display 343 of the headset terminal 314 and displays the generated image on the user's device. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns to improve the accuracy of the AI ​​based on user feedback. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and learning unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input using natural language processing technology and image analysis technology. The provision unit is implemented, for example, by the speaker 240 of the robot 414 and provides the generated image to the user. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns to improve the accuracy of the AI ​​based on user feedback.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can retrieve additional information from relevant external databases based on user input data. For example, if a user inputs "white clouds floating in a blue sky," the analysis unit can retrieve current weather information from a weather database and generate a more realistic image. Similarly, if a user inputs "ancient Roman buildings," the analysis unit can retrieve relevant architectural information from a history database and generate a detailed image. Furthermore, if a user inputs "a city of the future," the analysis unit can retrieve relevant prediction information from a future prediction database and generate a futuristic image. In this way, by retrieving additional information from relevant external databases based on user input data, it is possible to generate more detailed and realistic images.

[0088] When providing generated images to users, the service provider can adjust the display method based on the user's past feedback. For example, if a user previously provided feedback requesting "more detailed explanations," the service provider can add detailed explanations to the generated image. Similarly, if a user previously requested a "simpler display," the service provider can display the generated image simply. Furthermore, if a user previously requested a "visually stimulating display," the service provider can display the generated image in a visually stimulating manner. By adjusting the display method based on the user's past feedback, the service provider can provide more appropriate images.

[0089] The learning unit can evaluate the quality of generated images based on user feedback. For example, if a user provides feedback such as "This image is very good," the learning unit can evaluate that image as high quality and incorporate this into future learning. If a user provides feedback such as "This image is not very good," the learning unit can evaluate that image as low quality and learn areas for improvement. Furthermore, if a user provides feedback such as "This part is good, but other parts need improvement," the learning unit can use that partial evaluation to focus on learning specific parts. In this way, by evaluating the quality of generated images based on user feedback, it is possible to generate higher quality images.

[0090] The input system analyzes user input data in real time and provides appropriate suggestions based on the input. For example, if a user starts typing "blue sky," the input system can display suggestions such as "white clouds floating in a blue sky" or "blue sky and sea." If a user starts typing "ancient Rome," the input system can display suggestions such as "ancient Roman buildings" or "ancient Roman culture." Furthermore, if a user starts typing "future city," the input system can display suggestions such as "future city scenery" or "future city buildings." In this way, the system can support user input by analyzing user input data in real time and providing appropriate suggestions.

[0091] The provider can adjust the display method according to the characteristics of the user's device when providing the generated image to the user. For example, if the user is using a smartphone, the provider can optimize the generated image to the smartphone screen size for display. Similarly, if the user is using a tablet, the provider can optimize the generated image to the tablet screen size for display. Furthermore, if the user is using a desktop computer, the provider can optimize the generated image to the desktop computer screen size for display. This allows for the provision of more easily viewable images by adjusting the display method according to the characteristics of the user's device.

[0092] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and generate a highly accurate image. If the user is in a hurry, the analysis unit can perform a concise analysis and generate an image quickly. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis and generate an image that helps maintain that excitement. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate images can be generated.

[0093] The display unit can estimate the user's emotions and adjust the color tone of the images it presents based on those emotions. For example, if the user is relaxed, the display unit can provide images with calm colors. If the user is in a hurry, the display unit can provide images with bright, highly visible colors. Furthermore, if the user is excited, the display unit can provide images with vivid, stimulating colors. By adjusting the color tone of images according to the user's emotions, it is possible to provide more appropriate images.

[0094] The learning unit can estimate the user's emotions and filter the training data based on those emotions. For example, if the user is relaxed, the learning unit can use detailed training data. If the user is in a hurry, the learning unit can use concise training data. Furthermore, if the user is excited, the learning unit can use visually stimulating training data. This allows for more effective learning by filtering the training data according to the user's emotions.

[0095] The reception system can estimate the user's emotions and adjust the input interface based on those emotions. For example, if the user is relaxed, the reception system can provide a simple and calming interface. If the user is in a hurry, the reception system can provide an interface that allows for quick input. Furthermore, if the user is excited, the reception system can provide a visually stimulating interface. By adjusting the input interface according to the user's emotions, a more comfortable input experience can be provided.

[0096] The service provider can estimate the user's emotions and adjust the animation effects of the images it provides based on those emotions. For example, if the user is relaxed, the service provider can provide calm animation effects. If the user is in a hurry, the service provider can provide fast animation effects. Furthermore, if the user is excited, the service provider can provide visually stimulating animation effects. By adjusting the animation effects of images according to the user's emotions, the service provider can provide more appropriate images.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, if the user describes "white clouds floating in a blue sky," the reception desk can accept that description. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes text input using natural language processing technology and analyzes picture input using image analysis technology. For example, it analyzes the text "white clouds floating in a blue sky" entered by the user and generates a concrete image based on its content. Step 3: The distribution unit provides the user with the image generated by the analysis unit. The distribution unit displays the generated image on the user's device. For example, the generated image can be displayed on the user's smartphone or tablet. Step 4: The learning unit learns based on the images provided by the provider unit. The learning unit learns to improve the accuracy of the AI ​​based on user feedback. For example, the user may provide feedback from the provider unit such as "I would like the clouds to be a little bigger," and the AI ​​will learn based on that feedback.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit that analyzes the information received by the reception unit, A providing unit that provides the image generated by the analysis unit, A learning unit that performs learning based on the image provided by the aforementioned provisioning unit, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is The system accepts images described by the user using words or pictures. The system according to feature 1.

3. The aforementioned analysis unit, Analyze user input and generate concrete images. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated image to the user and receive user feedback. The system according to feature 1.

5. The aforementioned learning unit, The AI ​​learns to improve its accuracy based on user feedback. The system according to feature 1.

6. The aforementioned analysis unit, It learns from the user's past input data to generate images with higher accuracy. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the appropriate reception method. The system according to feature 1.

Images