system

Abstract

The system according to this embodiment aims to effectively represent the user's image. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image from the user. The analysis unit analyzes the image received by the reception unit. The generation unit generates an image based on the information analyzed by the analysis unit. The provision unit provides the image generated by the generation 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 character of the chatbot; 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, insufficient assistance is provided for effectively expressing the user's image, and there is room for improvement.

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

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs the user's image. The analysis unit analyzes the image input by the reception unit. The generation unit generates an image based on the information analyzed by the analysis unit. The provision unit provides the image generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can effectively represent the user's image. [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) The system according to an embodiment of the present invention is a system that assists in expressing floating images through language, pictures, etc. In this system, the user inputs an image floating in their mind into the AI, the AI ​​analyzes the image, and expresses it in the form of language, pictures, etc. For example, the user may input an image in language such as "white clouds floating in a blue sky," or they may input a simple sketch into the AI. Next, the AI ​​analyzes the input image and extracts information to express that image in a concrete form based on the language or picture input by the user. For example, it analyzes the image "white clouds floating in a blue sky" input in language and identifies the specific shape and arrangement of the blue sky and white clouds. Based on the analyzed information, the AI ​​expresses the image in the form of language, pictures, etc. For example, the AI ​​generates a concrete picture based on the image "white clouds floating in a blue sky." At this time, the AI ​​filters to avoid creating existing characters. This allows the user to express their image in a concrete form while maintaining their originality. For example, when an artist expresses their image in a painting, they can use the AI ​​to express that image in a concrete form. Furthermore, AI can be used to express one's image in concrete words in linguistic expressions such as speeches and songs. This allows the system to efficiently input, analyze, generate, and provide the user's image.

[0029] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image from the user. The user's image includes, but is not limited to, visual images, conceptual images, and emotional images. The reception unit can receive images in the form of, for example, language, pictures, audio, and video. For example, the user may receive an image in language, such as "white clouds floating in a blue sky," or they may draw a simple sketch and input it to the AI. The analysis unit analyzes the image received by the reception unit. The analysis unit can analyze the image using methods such as image analysis, text analysis, and audio analysis. For example, it analyzes the image "white clouds floating in a blue sky" received in language and identifies the specific shape and arrangement of the blue sky and white clouds. The generation unit generates an image based on the information analyzed by the analysis unit. The generation unit can generate an image using methods such as an image generation algorithm and a text generation algorithm. For example, the AI ​​generates a specific picture based on the image "white clouds floating in a blue sky." The provision unit provides the image generated by the generation unit. The providing unit can provide images in various ways, such as display methods and output formats. For example, this could include a display for showing the generated image to the user, or a printer for printing it. This allows the system to efficiently input, analyze, generate, and provide the user's image.

[0030] The reception unit receives images from the user. These images may include, but are not limited to, visual, conceptual, or emotional images. The reception unit can accept images in various formats, such as language, pictures, audio, and video. For example, a user might input an image using language, such as "white clouds floating in a blue sky," or they might draw a simple sketch for the AI. The reception unit has multiple interfaces to accept user-input images in diverse formats. For example, the text input interface allows users to input linguistic images using a keyboard. The drawing interface, using a touchscreen or pen tablet, allows users to directly draw sketches. The voice input interface allows users to input images via voice using a microphone, which is then converted to text using speech recognition technology. Furthermore, the video input interface allows users to record videos using a camera and input specific scenes or movements from those videos as images. This allows the reception unit to flexibly accept images to meet diverse user needs. The reception unit temporarily stores the input images and performs necessary preprocessing before sending them to the analysis unit. For example, in the case of text input, grammar and spell checks are performed, and in the case of voice input, noise reduction and speech normalization are performed. This allows the analysis unit to analyze the image efficiently and accurately.

[0031] The analysis unit analyzes the images input by the reception unit. The analysis unit can analyze images using methods such as image analysis, text analysis, and audio analysis. For example, it can analyze an image input in language, such as "white clouds floating in a blue sky," and identify the specific shapes and arrangements of the blue sky and white clouds. The analysis unit utilizes AI technology to analyze the input images in detail. In image analysis, it uses computer vision technology to recognize specific objects and scenes from input sketches and videos and extract their features. For example, it can analyze the shape of the clouds and the color of the sky drawn in a sketch and identify their positional relationships. In text analysis, it uses natural language processing technology to analyze the input linguistic image and understand its meaning. For example, it can analyze the text "white clouds floating in a blue sky," identify the two elements, blue sky and white clouds, and understand their relationship. In audio analysis, it uses speech recognition technology to convert the input audio into text and analyze its content. For example, if a user inputs "white clouds floating in a blue sky" via voice input, the voice is converted to text and analyzed in the same way as text analysis. The analysis unit integrates these analysis results to extract the user's image as concrete information. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and trained models. As a result, the analysis unit can accurately understand the user's image and provide the necessary information to the generation unit.

[0032] The generation unit generates images based on information analyzed by the analysis unit. The generation unit can generate images using methods such as image generation algorithms and text generation algorithms. For example, an AI can generate a specific image based on the image of "white clouds floating in a blue sky." The generation unit uses the latest generative AI technology to generate high-quality content from the user's image. The image generation algorithm utilizes a deep learning-based generation model to generate realistic images based on the input information. For example, it can depict natural landscapes by considering the specific shapes and arrangements of the blue sky and white clouds. The text generation algorithm uses natural language generation technology to generate text and stories based on the input image. For example, it can generate poems or stories themed around "white clouds floating in a blue sky." The generation unit can also combine these generation algorithms to generate complex content. For example, it can generate picture books combining images and text, or animations combining audio and video. The generation unit evaluates the quality of the generated content and makes corrections and improvements as needed. For example, it adjusts the color tone and composition of the generated images, or corrects the grammar and expression of the generated text. This allows the generation unit to provide high-quality content from the user's image.

[0033] The provider unit provides images generated by the generator unit. The provider unit can provide images in various ways, such as display methods and output formats. For example, this includes displays for showing generated images to users and printers for printing. The provider unit has multiple output methods to provide generated content to users in the most optimal form. For example, it can display generated images and videos in real time using a display. It can also print generated images in high resolution using a printer and provide them to users. Furthermore, it can save them as digital data and upload them to cloud storage for later access by users. The provider unit can customize generated content according to user needs. For example, it can adjust the size and resolution of generated images, or change the font and layout of generated text. It can also combine multiple content items and provide them in the format desired by the user. For example, it can provide posters combining generated images and text, or presentations combining generated videos and audio. The provider unit collects feedback from users and uses it to improve the delivery method and output format. For example, by allowing users to rate and comment on the provided content, the provider unit can improve the quality of its service based on that feedback. This allows the provider unit to provide users with high-quality content in the most optimal form and increase satisfaction.

[0034] The reception desk can accept images in various formats, including language, pictures, audio, and video. For example, users can input images in language format using text input. The reception desk can also accept images in picture format using handwriting input. Furthermore, users can input images in audio format using speech recognition technology. For example, a user can input an image using a microphone and convert it to text using speech recognition technology. Additionally, users can input images in video format using video analysis technology. For example, a user can input an image using a camera and analyze it using video analysis technology. This allows the reception desk to accept images in a variety of formats.

[0035] The analysis unit may include a consideration unit that takes into account the user's past input data and preferences. For example, the analysis unit can improve the accuracy of the analysis based on the user's past input data. For instance, it can analyze data previously entered by the user and find similar patterns. The analysis unit can also customize the analysis results based on the user's preferences. For example, it can adjust the analysis results to take into account the user's preferred colors and shapes. Furthermore, the analysis unit can analyze the user's past input data and select the optimal analysis method. For example, it can select the most effective analysis method based on analysis methods previously used by the user. This allows the analysis unit to improve the accuracy of the analysis based on the user's past data and preferences.

[0036] The generation unit may include a filtering unit that filters out existing characters. For example, if a generated image is similar to an existing character, the generation unit can automatically correct it. For example, random elements can be added to ensure that the generated image does not match a specific character. The generation unit can also compare the generated image with a database to confirm that it is different from an existing character. For example, the generated image can be compared with characters registered in the database and a similarity score can be calculated. This allows the generation unit to generate images while maintaining originality.

[0037] The provider unit may include an editing unit that allows users to edit the generated images. For example, the provider unit may provide an interface that allows users to freely edit the generated images. For instance, it may provide editing tools that allow users to change the color and shape of the generated images. Furthermore, the provider unit may provide a function that allows users to add new elements to the generated images. For example, users may add text or images to the generated images. This allows the provider unit to freely edit the generated images.

[0038] The input system can analyze the user's past input history and suggest the most suitable input format. For example, it can automatically display input formats that the user has frequently used in the past (language, picture, voice, etc.) as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest input formats that the user will use at specific times based on their past input history. In this way, the input system can suggest the most suitable input format based on the user's past input history.

[0039] The input system can filter the input content based on the user's current activities and areas of interest when images are entered. For example, the input system can prioritize images related to the user's current project. It can also input relevant images based on the user's areas of interest. Furthermore, if the input system receives keywords related to the user's current activities, it can filter the input content based on those keywords. This allows the input system to input images relevant to the user's current activities and areas of interest.

[0040] The reception system can prioritize inputting images that are highly relevant to the user's geographical location, taking this into account when users input images. For example, it can prioritize inputting images related to the user's current location. It can also prioritize inputting images related to the user's travel destination if the user is traveling. Furthermore, if the user is interested in a particular region, it can prioritize inputting images related to that region. This allows the reception system to input highly relevant images based on the user's geographical location.

[0041] The reception desk can analyze a user's social media activity and input relevant images when they input images. For example, it can input relevant images based on images and videos that the user has shared on social media. It can also analyze the content of posts from accounts that the user follows on social media and input relevant images. Furthermore, it can input relevant images based on hashtags that the user has used on social media. In this way, the reception desk can input relevant images based on the user's social media activity.

[0042] The analysis unit can improve the accuracy of its analysis by considering the user's past input data and preferences during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on data previously entered by the user. Furthermore, the analysis unit can customize the analysis results based on the user's preferences. In addition, the analysis unit can analyze the user's past input data and select the optimal analysis method. This allows the analysis unit to improve the accuracy of its analysis based on the user's past data and preferences.

[0043] The analysis unit can apply different analysis algorithms depending on the image category during analysis. For example, in the case of a language image, the analysis unit can apply a natural language processing algorithm. It can also apply an image analysis algorithm to a pictorial image. Furthermore, it can apply a speech analysis algorithm to an audio image. This allows the analysis unit to apply the most suitable analysis algorithm for each image category.

[0044] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis process. For example, the analysis unit can prioritize analyzing images related to the user's current location. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing images related to their travel destination. Additionally, if the user is interested in a particular region, the analysis unit can prioritize analyzing images related to that region. This allows the analysis unit to improve the accuracy of its analysis based on the user's geographical location information.

[0045] The analysis unit can analyze users' social media activity during analysis and utilize relevant data for analysis. For example, the analysis unit can analyze relevant data based on images and videos shared by users on social media. It can also analyze the content of posts from accounts that users follow on social media and utilize relevant data. Furthermore, the analysis unit can analyze relevant data based on hashtags used by users on social media. This allows the analysis unit to utilize relevant data based on users' social media activity for analysis.

[0046] The generation unit can filter the generated image to avoid creating an existing character. For example, if a generated image is similar to an existing character, the generation unit can automatically correct it. The generation unit can also add random elements to ensure that the generated image does not match a specific character. Furthermore, the generation unit can compare the generated image against a database to verify that it is different from an existing character. This allows the generation unit to generate images while maintaining originality.

[0047] The generation unit can select the optimal generation method by referring to the user's past generation data during generation. For example, the generation unit can select the optimal generation method based on images the user has previously generated. Furthermore, the generation unit can analyze the user's past generation data and select a generation method tailored to their preferences. In addition, the generation unit can refer to the user's past generation data to select the most efficient generation method. Thus, the generation unit can select the optimal generation method based on the user's past generation data.

[0048] The generation unit can generate the most suitable image by considering the user's geographical location information during the generation process. For example, it can generate an image related to the user's current location. It can also generate an image related to the user's travel destination if the user is traveling. Furthermore, if the user is interested in a particular region, it can generate an image related to that region. This allows the generation unit to generate the most suitable image based on the user's geographical location information.

[0049] The generation unit can analyze the user's social media activity during generation and utilize relevant data for generation. For example, the generation unit can generate relevant data based on images and videos shared by the user on social media. It can also analyze the content of posts from accounts the user follows on social media and generate relevant data. Furthermore, the generation unit can generate relevant data based on hashtags used by the user on social media. This allows the generation unit to utilize relevant data based on the user's social media activity for generation.

[0050] The provider unit may include an editing unit that allows users to edit the generated images at the time of delivery. For example, the provider unit may provide an interface that allows users to freely edit the generated images. Furthermore, the provider unit may provide editing tools that allow users to change the color and shape of the generated images. In addition, the provider unit may provide a function that allows users to add new elements to the generated images. This allows the provider unit to freely edit the generated images.

[0051] The delivery unit can select the optimal delivery method by referring to the user's past editing history at the time of delivery. For example, the delivery unit can propose the optimal delivery method based on the user's past edits. Furthermore, the delivery unit can analyze the user's past editing history and select a delivery method tailored to their preferences. In addition, the delivery unit can select the most efficient delivery method by referring to the user's past editing history. This allows the delivery unit to select the optimal delivery method based on the user's past editing history.

[0052] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can prioritize providing images related to the user's current location. Furthermore, if the user is traveling, the service provider can prioritize providing images related to their travel destination. Additionally, if the user is interested in a particular region, the service provider can prioritize providing images related to that region. This allows the service provider to select the optimal delivery method based on the user's geographical location information.

[0053] The service provider can analyze the user's social media activity and use relevant data in providing the service. For example, the service provider can provide relevant data based on images and videos that the user has shared on social media. Furthermore, the service provider can analyze the content of posts from accounts that the user follows on social media and provide relevant data. In addition, the service provider can provide relevant data based on hashtags used by the user on social media. This allows the service provider to use relevant data based on the user's social media activity in providing the service.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can consider the user's past input data and preferences when analyzing the user's image. For example, it can improve the accuracy of the analysis based on data the user has previously entered. It can also customize the analysis results based on the user's preferences. Furthermore, it can analyze the user's past input data and select the optimal analysis method. As a result, the analysis unit can improve the accuracy of the analysis based on the user's past data and preferences.

[0056] The reception system can prioritize inputting images that are highly relevant to the user's geographical location when the user inputs images. For example, it can prioritize images related to the user's current location. If the user is traveling, it can also prioritize images related to their travel destination. Furthermore, if the user is interested in a particular region, it can prioritize images related to that region. This allows the reception system to input highly relevant images based on the user's geographical location.

[0057] The analysis unit can analyze a user's social media activity and use relevant data to analyze their image. For example, it can analyze relevant data based on images and videos that the user has shared on social media. It can also analyze the content of posts from accounts that the user follows on social media and use that to analyze relevant data. Furthermore, it can analyze relevant data based on hashtags that the user has used on social media. In this way, the analysis unit can use relevant data based on the user's social media activity to analyze their image.

[0058] The generation unit can select the optimal generation method when generating a user's image by referring to the user's past generation data. For example, it can select the optimal generation method based on images the user has generated in the past. It can also analyze the user's past generation data and select a generation method that suits their preferences. Furthermore, it can refer to the user's past generation data to select the most efficient generation method. In this way, the generation unit can select the optimal generation method based on the user's past generation data.

[0059] The reception system can filter user input based on their current activities and areas of interest. For example, it can prioritize images related to the user's current project. It can also filter images based on the user's areas of interest. Furthermore, if the user enters keywords related to their current activities, the system can filter the input based on those keywords. This allows the reception system to input images relevant to the user's current activities and areas of interest.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs the user's image. The user's image can include visual images, conceptual images, emotional images, etc. The reception desk can input images in various formats such as language, pictures, audio, and video. For example, the user may input an image in language, such as "white clouds floating in a blue sky," or they may input a simple sketch for the AI. Step 2: The analysis unit analyzes the image input by the reception unit. The analysis unit can analyze the image using methods such as image analysis, text analysis, and audio analysis. For example, it can analyze an image input in language, such as "white clouds floating in a blue sky," to identify the specific shapes and arrangements of the blue sky and white clouds. Step 3: The generation unit generates an image based on the information analyzed by the analysis unit. The generation unit can generate images using methods such as image generation algorithms and text generation algorithms. For example, the AI ​​can generate a specific picture based on the image of "white clouds floating in a blue sky." Step 4: The providing unit provides the image generated by the generating unit. The providing unit can provide the image in various ways, such as display methods and output formats. For example, this may include a display for showing the generated image to the user or a printer for printing.

[0062] (Example of form 2) The system according to an embodiment of the present invention is a system that assists in expressing floating images through language, pictures, etc. In this system, the user inputs an image floating in their mind into the AI, the AI ​​analyzes the image, and expresses it in the form of language, pictures, etc. For example, the user may input an image in language such as "white clouds floating in a blue sky," or they may input a simple sketch into the AI. Next, the AI ​​analyzes the input image and extracts information to express that image in a concrete form based on the language or picture input by the user. For example, it analyzes the image "white clouds floating in a blue sky" input in language and identifies the specific shape and arrangement of the blue sky and white clouds. Based on the analyzed information, the AI ​​expresses the image in the form of language, pictures, etc. For example, the AI ​​generates a concrete picture based on the image "white clouds floating in a blue sky." At this time, the AI ​​filters to avoid creating existing characters. This allows the user to express their image in a concrete form while maintaining their originality. For example, when an artist expresses their image in a painting, they can use the AI ​​to express that image in a concrete form. Furthermore, AI can be used to express one's image in concrete words in linguistic expressions such as speeches and songs. This allows the system to efficiently input, analyze, generate, and provide the user's image.

[0063] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an image from the user. The user's image includes, but is not limited to, visual images, conceptual images, and emotional images. The reception unit can receive images in the form of, for example, language, pictures, audio, and video. For example, the user may receive an image in language, such as "white clouds floating in a blue sky," or they may draw a simple sketch and input it to the AI. The analysis unit analyzes the image received by the reception unit. The analysis unit can analyze the image using methods such as image analysis, text analysis, and audio analysis. For example, it analyzes the image "white clouds floating in a blue sky" received in language and identifies the specific shape and arrangement of the blue sky and white clouds. The generation unit generates an image based on the information analyzed by the analysis unit. The generation unit can generate an image using methods such as an image generation algorithm and a text generation algorithm. For example, the AI ​​generates a specific picture based on the image "white clouds floating in a blue sky." The provision unit provides the image generated by the generation unit. The providing unit can provide images in various ways, such as display methods and output formats. For example, this could include a display for showing the generated image to the user, or a printer for printing it. This allows the system to efficiently input, analyze, generate, and provide the user's image.

[0064] The reception unit receives images from the user. These images may include, but are not limited to, visual, conceptual, or emotional images. The reception unit can accept images in various formats, such as language, pictures, audio, and video. For example, a user might input an image using language, such as "white clouds floating in a blue sky," or they might draw a simple sketch for the AI. The reception unit has multiple interfaces to accept user-input images in diverse formats. For example, the text input interface allows users to input linguistic images using a keyboard. The drawing interface, using a touchscreen or pen tablet, allows users to directly draw sketches. The voice input interface allows users to input images via voice using a microphone, which is then converted to text using speech recognition technology. Furthermore, the video input interface allows users to record videos using a camera and input specific scenes or movements from those videos as images. This allows the reception unit to flexibly accept images to meet diverse user needs. The reception unit temporarily stores the input images and performs necessary preprocessing before sending them to the analysis unit. For example, in the case of text input, grammar and spell checks are performed, and in the case of voice input, noise reduction and speech normalization are performed. This allows the analysis unit to analyze the image efficiently and accurately.

[0065] The analysis unit analyzes the images input by the reception unit. The analysis unit can analyze images using methods such as image analysis, text analysis, and audio analysis. For example, it can analyze an image input in language, such as "white clouds floating in a blue sky," and identify the specific shapes and arrangements of the blue sky and white clouds. The analysis unit utilizes AI technology to analyze the input images in detail. In image analysis, it uses computer vision technology to recognize specific objects and scenes from input sketches and videos and extract their features. For example, it can analyze the shape of the clouds and the color of the sky drawn in a sketch and identify their positional relationships. In text analysis, it uses natural language processing technology to analyze the input linguistic image and understand its meaning. For example, it can analyze the text "white clouds floating in a blue sky," identify the two elements, blue sky and white clouds, and understand their relationship. In audio analysis, it uses speech recognition technology to convert the input audio into text and analyze its content. For example, if a user inputs "white clouds floating in a blue sky" via voice input, the voice is converted to text and analyzed in the same way as text analysis. The analysis unit integrates these analysis results to extract the user's image as concrete information. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and trained models. As a result, the analysis unit can accurately understand the user's image and provide the necessary information to the generation unit.

[0066] The generation unit generates images based on information analyzed by the analysis unit. The generation unit can generate images using methods such as image generation algorithms and text generation algorithms. For example, an AI can generate a specific image based on the image of "white clouds floating in a blue sky." The generation unit uses the latest generative AI technology to generate high-quality content from the user's image. The image generation algorithm utilizes a deep learning-based generation model to generate realistic images based on the input information. For example, it can depict natural landscapes by considering the specific shapes and arrangements of the blue sky and white clouds. The text generation algorithm uses natural language generation technology to generate text and stories based on the input image. For example, it can generate poems or stories themed around "white clouds floating in a blue sky." The generation unit can also combine these generation algorithms to generate complex content. For example, it can generate picture books combining images and text, or animations combining audio and video. The generation unit evaluates the quality of the generated content and makes corrections and improvements as needed. For example, it adjusts the color tone and composition of the generated images, or corrects the grammar and expression of the generated text. This allows the generation unit to provide high-quality content from the user's image.

[0067] The provider unit provides images generated by the generator unit. The provider unit can provide images in various ways, such as display methods and output formats. For example, this includes displays for showing generated images to users and printers for printing. The provider unit has multiple output methods to provide generated content to users in the most optimal form. For example, it can display generated images and videos in real time using a display. It can also print generated images in high resolution using a printer and provide them to users. Furthermore, it can save them as digital data and upload them to cloud storage for later access by users. The provider unit can customize generated content according to user needs. For example, it can adjust the size and resolution of generated images, or change the font and layout of generated text. It can also combine multiple content items and provide them in the format desired by the user. For example, it can provide posters combining generated images and text, or presentations combining generated videos and audio. The provider unit collects feedback from users and uses it to improve the delivery method and output format. For example, by allowing users to rate and comment on the provided content, the provider unit can improve the quality of its service based on that feedback. This allows the provider unit to provide users with high-quality content in the most optimal form and increase satisfaction.

[0068] The reception desk can accept images in various formats, including language, pictures, audio, and video. For example, users can input images in language format using text input. The reception desk can also accept images in picture format using handwriting input. Furthermore, users can input images in audio format using speech recognition technology. For example, a user can input an image using a microphone and convert it to text using speech recognition technology. Additionally, users can input images in video format using video analysis technology. For example, a user can input an image using a camera and analyze it using video analysis technology. This allows the reception desk to accept images in a variety of formats.

[0069] The analysis unit may include a consideration unit that takes into account the user's past input data and preferences. For example, the analysis unit can improve the accuracy of the analysis based on the user's past input data. For instance, it can analyze data previously entered by the user and find similar patterns. The analysis unit can also customize the analysis results based on the user's preferences. For example, it can adjust the analysis results to take into account the user's preferred colors and shapes. Furthermore, the analysis unit can analyze the user's past input data and select the optimal analysis method. For example, it can select the most effective analysis method based on analysis methods previously used by the user. This allows the analysis unit to improve the accuracy of the analysis based on the user's past data and preferences.

[0070] The generation unit may include a filtering unit that filters out existing characters. For example, if a generated image is similar to an existing character, the generation unit can automatically correct it. For example, random elements can be added to ensure that the generated image does not match a specific character. The generation unit can also compare the generated image with a database to confirm that it is different from an existing character. For example, the generated image can be compared with characters registered in the database and a similarity score can be calculated. This allows the generation unit to generate images while maintaining originality.

[0071] The provider unit may include an editing unit that allows users to edit the generated images. For example, the provider unit may provide an interface that allows users to freely edit the generated images. For instance, it may provide editing tools that allow users to change the color and shape of the generated images. Furthermore, the provider unit may provide a function that allows users to add new elements to the generated images. For example, users may add text or images to the generated images. This allows the provider unit to freely edit the generated images.

[0072] The reception system can estimate the user's emotions and adjust the image input method based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick image input. In this way, the reception system can provide the optimal input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The input system can analyze the user's past input history and suggest the most suitable input format. For example, it can automatically display input formats that the user has frequently used in the past (language, picture, voice, etc.) as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest input formats that the user will use at specific times based on their past input history. In this way, the input system can suggest the most suitable input format based on the user's past input history.

[0074] The input system can filter the input content based on the user's current activities and areas of interest when images are entered. For example, the input system can prioritize images related to the user's current project. It can also input relevant images based on the user's areas of interest. Furthermore, if the input system receives keywords related to the user's current activities, it can filter the input content based on those keywords. This allows the input system to input images relevant to the user's current activities and areas of interest.

[0075] The reception unit can estimate the user's emotions and determine the priority of input images based on the estimated emotions. For example, if the user is excited, the reception unit can prioritize visually stimulating images. If the user is relaxed, the reception unit can prioritize calming images. Furthermore, if the user is stressed, the reception unit can prioritize relaxing images. In this way, the reception unit can determine the priority of input images 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.

[0076] The reception system can prioritize inputting images that are highly relevant to the user's geographical location, taking this into account when users input images. For example, it can prioritize inputting images related to the user's current location. It can also prioritize inputting images related to the user's travel destination if the user is traveling. Furthermore, if the user is interested in a particular region, it can prioritize inputting images related to that region. This allows the reception system to input highly relevant images based on the user's geographical location.

[0077] The reception desk can analyze a user's social media activity and input relevant images when they input images. For example, it can input relevant images based on images and videos that the user has shared on social media. It can also analyze the content of posts from accounts that the user follows on social media and input relevant images. Furthermore, it can input relevant images based on hashtags that the user has used on social media. In this way, the reception desk can input relevant images based on the user's social media activity.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate information. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise information. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, the analysis unit can provide the optimal analysis method 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.

[0079] The analysis unit can improve the accuracy of its analysis by considering the user's past input data and preferences during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on data previously entered by the user. Furthermore, the analysis unit can customize the analysis results based on the user's preferences. In addition, the analysis unit can analyze the user's past input data and select the optimal analysis method. This allows the analysis unit to improve the accuracy of its analysis based on the user's past data and preferences.

[0080] The analysis unit can apply different analysis algorithms depending on the image category during analysis. For example, in the case of a language image, the analysis unit can apply a natural language processing algorithm. It can also apply an image analysis algorithm to a pictorial image. Furthermore, it can apply a speech analysis algorithm to an audio image. This allows the analysis unit to apply the most suitable analysis algorithm for each image category.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize analyzing visually stimulating images. Similarly, if the user is relaxed, the analysis unit can prioritize analyzing calming images. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing images with a relaxing effect. This allows the analysis unit to determine the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis process. For example, the analysis unit can prioritize analyzing images related to the user's current location. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing images related to their travel destination. Additionally, if the user is interested in a particular region, the analysis unit can prioritize analyzing images related to that region. This allows the analysis unit to improve the accuracy of its analysis based on the user's geographical location information.

[0083] The analysis unit can analyze users' social media activity during analysis and utilize relevant data for analysis. For example, the analysis unit can analyze relevant data based on images and videos shared by users on social media. It can also analyze the content of posts from accounts that users follow on social media and utilize relevant data. Furthermore, the analysis unit can analyze relevant data based on hashtags used by users on social media. This allows the analysis unit to utilize relevant data based on users' social media activity for analysis.

[0084] The generation unit can estimate the user's emotions and adjust the way it represents the generated images based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate images with calm colors. If the user is excited, the generation unit can also generate images with vivid colors. Furthermore, if the user is stressed, the generation unit can generate images with a relaxing effect. In this way, the generation unit can provide the optimal way to represent images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The generation unit can filter the generated image to avoid creating an existing character. For example, if a generated image is similar to an existing character, the generation unit can automatically correct it. The generation unit can also add random elements to ensure that the generated image does not match a specific character. Furthermore, the generation unit can compare the generated image against a database to verify that it is different from an existing character. This allows the generation unit to generate images while maintaining originality.

[0086] The generation unit can select the optimal generation method by referring to the user's past generation data during generation. For example, the generation unit can select the optimal generation method based on images the user has previously generated. Furthermore, the generation unit can analyze the user's past generation data and select a generation method tailored to their preferences. In addition, the generation unit can refer to the user's past generation data to select the most efficient generation method. Thus, the generation unit can select the optimal generation method based on the user's past generation data.

[0087] The generation unit can estimate the user's emotions and determine the priority of images to generate based on the estimated emotions. For example, if the user is excited, the generation unit can prioritize generating visually stimulating images. If the user is relaxed, the generation unit can prioritize generating calming images. Furthermore, if the user is stressed, the generation unit can prioritize generating images with a relaxing effect. In this way, the generation unit can determine the priority of images to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The generation unit can generate the most suitable image by considering the user's geographical location information during the generation process. For example, it can generate an image related to the user's current location. It can also generate an image related to the user's travel destination if the user is traveling. Furthermore, if the user is interested in a particular region, it can generate an image related to that region. This allows the generation unit to generate the most suitable image based on the user's geographical location information.

[0089] The generation unit can analyze the user's social media activity during generation and utilize relevant data for generation. For example, the generation unit can generate relevant data based on images and videos shared by the user on social media. It can also analyze the content of posts from accounts the user follows on social media and generate relevant data. Furthermore, the generation unit can generate relevant data based on hashtags used by the user on social media. This allows the generation unit to utilize relevant data based on the user's social media activity for generation.

[0090] The service provider can estimate the user's emotions and adjust the display method of the images based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, the service provider can provide the optimal display method 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.

[0091] The provider unit may include an editing unit that allows users to edit the generated images at the time of delivery. For example, the provider unit may provide an interface that allows users to freely edit the generated images. Furthermore, the provider unit may provide editing tools that allow users to change the color and shape of the generated images. In addition, the provider unit may provide a function that allows users to add new elements to the generated images. This allows the provider unit to freely edit the generated images.

[0092] The delivery unit can select the optimal delivery method by referring to the user's past editing history at the time of delivery. For example, the delivery unit can propose the optimal delivery method based on the user's past edits. Furthermore, the delivery unit can analyze the user's past editing history and select a delivery method tailored to their preferences. In addition, the delivery unit can select the most efficient delivery method by referring to the user's past editing history. This allows the delivery unit to select the optimal delivery method based on the user's past editing history.

[0093] The service provider can estimate the user's emotions and determine the priority of images to provide based on the estimated emotions. For example, if the user is excited, the service provider can prioritize providing visually stimulating images. If the user is relaxed, the service provider can prioritize providing calming images. Furthermore, if the user is stressed, the service provider can prioritize providing relaxing images. In this way, the service provider can determine the priority of images to provide 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.

[0094] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider can prioritize providing images related to the user's current location. Furthermore, if the user is traveling, the service provider can prioritize providing images related to their travel destination. Additionally, if the user is interested in a particular region, the service provider can prioritize providing images related to that region. This allows the service provider to select the optimal delivery method based on the user's geographical location information.

[0095] The service provider can analyze the user's social media activity and use relevant data in providing the service. For example, the service provider can provide relevant data based on images and videos that the user has shared on social media. Furthermore, the service provider can analyze the content of posts from accounts that the user follows on social media and provide relevant data. In addition, the service provider can provide relevant data based on hashtags used by the user on social media. This allows the service provider to use relevant data based on the user's social media activity in providing the service.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can adjust the input method when a user inputs an image, taking into account the user's current mood and physical condition. For example, if the user is tired, it can simplify the input process by presenting simple options. If the user is focused, it can provide detailed input options to allow for the input of a more specific image. Furthermore, if the user is relaxed, it can provide music or a relaxing background to create a comfortable input environment. In this way, the reception desk can provide the optimal input method according to the user's mood and physical condition.

[0098] The analysis unit can consider the user's past input data and preferences when analyzing the user's image. For example, it can improve the accuracy of the analysis based on data the user has previously entered. It can also customize the analysis results based on the user's preferences. Furthermore, it can analyze the user's past input data and select the optimal analysis method. As a result, the analysis unit can improve the accuracy of the analysis based on the user's past data and preferences.

[0099] The generation unit can estimate the user's emotions when generating an image of the user and adjust the style of the generated image based on the estimated emotions. For example, if the user is in a happy mood, it can generate an image with bright and vivid colors. If the user is in a sad mood, it can generate an image with calm colors. Furthermore, if the user is excited, it can generate a dynamic image with movement. In this way, the generation unit can provide the optimal image style according to the user's emotions.

[0100] The delivery unit can estimate the user's emotions when providing the generated image to the user and adjust the delivery method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the delivery unit can provide the optimal display method according to the user's emotions.

[0101] The reception system can prioritize inputting images that are highly relevant to the user's geographical location when the user inputs images. For example, it can prioritize images related to the user's current location. If the user is traveling, it can also prioritize images related to their travel destination. Furthermore, if the user is interested in a particular region, it can prioritize images related to that region. This allows the reception system to input highly relevant images based on the user's geographical location.

[0102] The analysis unit can analyze a user's social media activity and use relevant data to analyze their image. For example, it can analyze relevant data based on images and videos that the user has shared on social media. It can also analyze the content of posts from accounts that the user follows on social media and use that to analyze relevant data. Furthermore, it can analyze relevant data based on hashtags that the user has used on social media. In this way, the analysis unit can use relevant data based on the user's social media activity to analyze their image.

[0103] The generation unit can select the optimal generation method when generating a user's image by referring to the user's past generation data. For example, it can select the optimal generation method based on images the user has generated in the past. It can also analyze the user's past generation data and select a generation method that suits their preferences. Furthermore, it can refer to the user's past generation data to select the most efficient generation method. In this way, the generation unit can select the optimal generation method based on the user's past generation data.

[0104] The service provider can estimate the user's emotions when providing images to the user and determine the priority of the images to provide based on those estimated emotions. For example, if the user is excited, visually stimulating images can be prioritized. If the user is relaxed, calming images can be prioritized. Furthermore, if the user is stressed, relaxing images can be prioritized. In this way, the service provider can determine the priority of the images to provide according to the user's emotions.

[0105] The reception system can filter user input based on their current activities and areas of interest. For example, it can prioritize images related to the user's current project. It can also filter images based on the user's areas of interest. Furthermore, if the user enters keywords related to their current activities, the system can filter the input based on those keywords. This allows the reception system to input images relevant to the user's current activities and areas of interest.

[0106] The analysis unit can estimate the user's emotions when analyzing the user's image and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide highly accurate information. If the user is in a hurry, it can perform a rapid analysis and provide concise information. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, the analysis unit can provide the optimal analysis method according to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk inputs the user's image. The user's image can include visual images, conceptual images, emotional images, etc. The reception desk can input images in various formats such as language, pictures, audio, and video. For example, the user may input an image in language, such as "white clouds floating in a blue sky," or they may input a simple sketch for the AI. Step 2: The analysis unit analyzes the image input by the reception unit. The analysis unit can analyze the image using methods such as image analysis, text analysis, and audio analysis. For example, it can analyze an image input in language, such as "white clouds floating in a blue sky," to identify the specific shapes and arrangements of the blue sky and white clouds. Step 3: The generation unit generates an image based on the information analyzed by the analysis unit. The generation unit can generate images using methods such as image generation algorithms and text generation algorithms. For example, the AI ​​can generate a specific picture based on the image of "white clouds floating in a blue sky." Step 4: The providing unit provides the image generated by the generating unit. The providing unit can provide the image in various ways, such as display methods and output formats. For example, this may include a display for showing the generated image to the user or a printer for printing.

[0109] 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.

[0110] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 inputs the user's image in the form of language, pictures, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision 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 inputs the user's image in the form of language or pictures. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented 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 inputs the user's image in the form of language or pictures. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input image. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed information. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented by, for example, 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 inputs the user's image in the form of language, pictures, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input image. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an image based on the analyzed information. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated image to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception area where the user's image is entered, An analysis unit analyzes the image input by the aforementioned reception unit, A generation unit that generates an image based on the information analyzed by the analysis unit, The system comprises a providing unit that provides the image generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter images in formats such as language, pictures, audio, and video. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes a consideration section that takes into account the user's past input data and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It includes a filtering unit that filters out existing characters to prevent them from being created as existing characters. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, It features an editing section where users can edit the images they generate. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the image input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting images, the input content is filtered based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting images, the system prioritizes inputting images that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When inputting images, the system analyzes the user's social media activity and inputs relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the user's past input data and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the image category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed, and relevant data is used for the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the way images are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, filtering is performed to ensure that the character does not already exist. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system selects the optimal generation method by referring to the user's past generation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of images to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the system takes the user's geographical location information into consideration to generate the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system analyzes the user's social media activity and uses relevant data to generate the content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The service includes an editorial department that allows users to edit the generated images. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the product, the system will refer to the user's past editing history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the images to be presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and use relevant data to provide it. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 where the user's image is entered, An analysis unit analyzes the image input by the aforementioned reception unit, A generation unit that generates an image based on the information analyzed by the analysis unit, The system comprises a providing unit that provides the image generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter images in formats such as language, pictures, audio, and video. The system according to feature 1.

3. The aforementioned analysis unit, It includes a consideration section that takes into account the user's past input data and preferences. The system according to feature 1.

4. The generating unit is It includes a filtering unit that filters out existing characters to prevent them from being created as existing characters. The system according to feature 1.

5. The aforementioned supply unit is, It features an editing section where users can edit the images they generate. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the image input method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system according to feature 1.

8. The aforementioned reception unit is When inputting images, the input content is filtered based on the user's current activities and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input images based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When inputting images, the system prioritizes inputting images that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

Images