System

Abstract

An object of the system according to the embodiment is to read a user's emotion and express it as a poem, a story, or a visual art.SOLUTION: The system includes an emotion reader, a poem generator, a story generator, and a visual art generator. The emotion reading unit reads an emotion from an input of a user. The poem generation unit generates a poem based on the emotion read by the emotion reading unit. The story generation unit generates a story based on the emotion read by the emotion reading unit. The visual art generation unit generates a visual art based on the emotion read by the emotion reading unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the challenge of making it difficult to properly interpret a user's emotions and express them in poetry, stories, or visual art.

[0005] The system according to the embodiment aims to read the user's emotions and express them as poetry, stories, or visual art. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion reading unit, a poetry generating unit, a story generating unit, and a visual art generating unit. The emotion reading unit reads emotions from a user's input. The poetry generating unit generates poetry based on the emotions read by the emotion reading unit. The story generating unit generates a story based on the emotions read by the emotion reading unit. The visual art generating unit generates visual art based on the emotions read by the emotion reading unit. [Effects of the Invention]

[0007] The system according to the embodiment can read the user's emotions and express them as poetry, stories, or visual art. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1)

[0029] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0030] The Emotion Expressor AI system can also be equipped with a learning material generation unit that generates learning materials based on user input. For example, if a user inputs a "math problem," the generation AI will generate explanations and practice questions related to that problem. If a user inputs a "historical event," the generation AI will generate detailed explanations and quizzes related to that event. This allows the system to provide a variety of learning materials to support users' learning.

[0031] The Emotion Expressor AI system can also be equipped with a health advice module that provides health advice based on user input. For example, if the user feels "fatigue," the AI ​​generator can provide advice on relaxation methods and nutritional supplementation. If the user feels "stressed," the AI ​​generator can provide stress relief and relaxation techniques. This can support the user's health management.

[0032] The Emotion Expressor AI system can also be equipped with an interior design suggestion unit that suggests interior designs based on user input. For example, if a user is looking for a "relaxing space," the generation AI will suggest an interior design with a relaxing effect. If a user is looking for a "modern space," the generation AI will suggest an interior design with a modern design. This allows the system to suggest interior designs that suit the user's preferences and provide a comfortable living space.

[0033] The Emotion Expressor AI system can also include a fitness suggestion unit that proposes a personalized fitness plan based on the user's input. For example, if the user requests "strength training," the AI ​​will propose a training plan tailored to that goal. If the user requests "aerobic exercise," the AI ​​will propose an exercise plan tailored to that goal. This allows the system to provide a personalized plan tailored to the user's fitness goals.

[0034] The Emotion Expressor AI system can also include a financial advice module that provides personal financial advice based on user input. For example, if a user wants to create a "savings plan," the AI ​​generator will suggest a savings plan tailored to that goal. If the user is considering "investment," the AI ​​generator will provide investment advice tailored to that goal. This can support the user's financial management.

[0035] The processing flow of the first embodiment will be briefly explained below.

[0036] Step 1: The emotion reader reads emotions from the user's input. For example, if the user inputs "sad," the generation AI analyzes the user's emotions from those words. Also, if the user inputs a recorded voice, the generation AI reads emotions from the tone and rhythm of the voice. Furthermore, if the user inputs an image of their choice, the generation AI analyzes emotions from that image. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI reads emotions based on that prompt. Step 2: The poetry generator generates a poem based on the emotion detected by the emotion reader. For example, if a user enters "sad," the generator AI generates a poem that reflects that emotion. The generator AI selects beautiful words that capture the nuances of the emotion and puts them into a poem. Step 3: The story generation unit generates a story based on the emotions read by the emotion reading unit. For example, if the user inputs "sad," the generation AI generates a story that reflects that emotion. The generation AI selects beautiful words that capture the nuances of the emotion and shapes them into a story. Step 4: The visual art generator generates visual art based on the emotion detected by the emotion reader. For example, if a user enters "sad," the generator AI generates visual art that reflects that emotion. The generator AI generates beautiful artwork that captures the color and shape of the emotion.

[0037] (Example 2) The Emotion Expressor AI system according to an embodiment of the present invention is a system that reads emotions from words, voices, or selected images input by a user and converts them into poetry, stories, or visual art, thereby enabling the user to express their emotions in a variety of ways and broaden the scope of self-expression.

[0038] The Emotion Expressor AI system according to the embodiment includes an emotion reader, a poetry generator, a story generator, and a visual art generator. The emotion reader reads emotions from user input. For example, if the user inputs "sad," the generation AI analyzes the user's emotions from the words. If the user inputs recorded audio, the generation AI reads emotions from the tone and rhythm of the audio. If the user inputs an image of their choice, the generation AI analyzes emotions from the image. The generation AI receives input prompts containing instructions on what the user wants the generation AI to do, and the generation AI reads emotions based on the prompts. The poetry generator generates a poem based on the read emotions. For example, if the user inputs "sad," the generation AI generates a poem that reflects the emotion. The generation AI selects beautiful words that capture the nuances of the emotion and turns them into a poem or story. The generation AI receives input prompts for generating a poem or story that reflects the emotion, and the generation AI generates a poem or story based on the prompts. The story generator generates a story based on the read emotions. For example, if a user inputs "sad," the generation AI generates a story that reflects that emotion. The generation AI selects beautiful words that capture the nuances of the emotion and shapes them into a story. The generation AI receives input from a prompt for generating a story that reflects the emotion, and the generation AI generates the story based on that prompt. The visual art generation unit generates visual art based on the emotion it reads. For example, if a user inputs "sad," the generation AI generates visual art that reflects that emotion. The generation AI generates beautiful artwork that captures the color and shape of the emotion. The generation AI receives input from a prompt for generating visual art that reflects the emotion, and the generation AI generates visual art based on that prompt. This allows the emotion expressor AI system according to the embodiment to express the user's emotions in a variety of ways and expand the scope of self-expression. For example, if a user inputs "sad," the generation AI expresses that emotion as a poem, story, or visual art, allowing the user to communicate their emotions to others through the artwork.

[0039] The emotion reading unit tracks changes in emotions in real time in response to user input, making it possible to capture even the most subtle of emotional fluctuations. For example, the emotion reading unit uses a generation AI to track changes in emotions in real time in response to text or voice input by the user. For example, when a user inputs a long piece of text, the system analyzes the emotional fluctuations in each phrase and captures even the most subtle of emotional fluctuations. This makes it possible to capture even the most subtle of emotional fluctuations in real time.

[0040] The emotion reading unit can read emotions with higher accuracy by referencing the user's past input history and analyzing long-term emotional trends. The emotion reading unit, for example, analyzes the user's past text input history to identify long-term emotional trends. For example, it analyzes past input content in chronological order to extract patterns of emotional fluctuations. This improves the accuracy of emotion reading by analyzing long-term emotional trends.

[0041] The emotion reading unit uses the emotion estimation function to analyze the user's facial expressions and body movements when inputting, allowing for a multifaceted reading of emotions. For example, the emotion reading unit uses a camera to analyze the user's facial expressions when inputting and captures emotional fluctuations. For example, it analyzes the movement of facial muscles to identify subtle changes in emotions. This allows for a multifaceted reading of emotions.

[0042] The emotion reading unit can analyze environmental sounds and background sounds in addition to the text and voice input by the user to identify background factors of the emotion. For example, the emotion reading unit can analyze the surrounding environmental sounds in addition to the text and voice input by the user to identify background factors of the emotion. For example, it can analyze the influence of ambient noise and music to capture fluctuations in emotion. This makes it possible to identify background factors of the emotion.

[0043] The emotion reading unit can learn emotional expressions from different cultures and languages, enabling emotion reading from a global perspective. The emotion reading unit, for example, learns emotional expressions from different cultures to improve the accuracy of emotion reading. For example, it learns the unique emotional expressions and nuances of each culture and captures emotional fluctuations. This enables emotion reading from a global perspective.

[0044] The emotion reading unit uses the emotion estimation function to provide real-time feedback on the emotions the user is feeling when inputting, thereby helping the user to express their emotions more accurately. For example, the emotion reading unit provides real-time feedback on the emotions the user is feeling when inputting, and visually displays emotional fluctuations. For example, it displays an emotion score in a graph or color, allowing the user to confirm their emotions. This helps the user to express their emotions more accurately.

[0045] The poetry generation unit generates poems and stories in different styles and genres based on the user's emotions, allowing the user to create works tailored to their preferences. For example, the generation AI generates poems in different styles based on the user's emotions. For example, if the user enters "sad," the generation AI generates a lyric poem that reflects that emotion. This allows the generation of poems and stories tailored to the user's preferences.

[0046] The poetry generation unit can dynamically adjust the length and structure of a poem or story according to the intensity and type of the user's emotion. In the poetry generation unit, for example, the generation AI dynamically adjusts the length of a poem according to the intensity of the user's emotion. For example, if the intensity of the emotion is high, a long poem is generated, and if the intensity of the emotion is low, a short poem is generated. This makes it possible to adjust the length and structure of a poem or story according to the intensity and type of emotion.

[0047] The poetry generation unit can use the emotion estimation function to analyze the emotional response of the user when reading the generated poem or story and reflect this in the next generation. For example, the poetry generation unit analyzes the emotional response of the user when reading the generated poem and reflects this data in the next generation. For example, if the user feels "joy" after reading a poem, the next generation will generate a poem that evokes a similar emotion. This allows the user's emotional response to be reflected in the next generation.

[0048] The poetry generation unit can provide more personalized works by reflecting specific themes and keywords selected by the user when generating poems or stories. For example, the poetry generation unit generates poems that reflect a specific theme selected by the user. For example, if the user selects "love" as the theme, the generation AI generates a poem based on that theme. This allows for the provision of personalized works based on the user's selection.

[0049] The poetry generation unit can express the generated poems and stories in different media, providing the user with a diverse range of experiences. For example, the poetry generation unit expresses the generated poem as audio, providing the user with an auditory experience. For example, the generation AI recites the poem and provides the audio to the user. This allows the user to have a diverse range of experiences by expressing the poem in different media.

[0050] The poetry generation unit can use the emotion estimation function to collect the emotional reactions of others when a user shares a created poem or story, and use this data to improve the work. For example, the poetry generation unit can collect the emotional reactions of others when a user shares a created poem, and use this data to improve the work. For example, it can analyze the emotions felt by others when reading a poem and reflect this in the next generation. In this way, it is possible to collect the emotional reactions of others and use this data to improve the work.

[0051] The visual art generation unit generates visual art in different styles and techniques based on the user's emotions, allowing the unit to provide works tailored to the user's preferences. For example, the generation AI generates visual art in different styles based on the user's emotions. For example, if the user inputs "sad," the generation AI generates an abstract painting that reflects that emotion. This allows the generation of visual art tailored to the user's preferences.

[0052] The visual art generation unit can dynamically adjust the color and composition of the visual art according to the intensity and type of the user's emotion. In the visual art generation unit, for example, the generation AI dynamically adjusts the color of the visual art according to the intensity of the user's emotion. For example, when the intensity of the emotion is high, vivid colors are used, and when the intensity of the emotion is low, pale colors are used. This allows the color and composition of the visual art to be adjusted according to the intensity and type of emotion.

[0053] The visual art generation unit can use the emotion estimation function to analyze the emotional response of the user when viewing the generated visual art and reflect that data in the next generation. For example, the visual art generation unit analyzes the emotional response of the user when viewing the generated visual art and reflects that data in the next generation. For example, if the user feels "joy" when viewing the art, the next generation will generate art that elicits a similar emotion. This allows the user's emotional response to be reflected in the next generation.

[0054] The visual art generation unit can provide more personalized works by reflecting specific themes or motifs selected by the user when generating visual art. The visual art generation unit generates visual art that reflects a specific theme selected by the user. For example, if the user selects "nature" as the theme, the generation AI generates art based on that theme. This makes it possible to provide personalized visual art based on the user's selection.

[0055] The visual art generation unit can express the generated visual art in different media, providing the user with a diverse range of experiences. For example, the visual art generation unit expresses the generated visual art as a 3D model, providing the user with a visual experience. For example, the generation AI creates a 3D model of the art and provides the model to the user. This allows the user to have a diverse range of experiences by expressing the art in different media.

[0056] The visual art generation unit can use the emotion estimation function to collect the emotional reactions of others when a user shares the generated visual art and use the collected data to improve the work. For example, the visual art generation unit can collect the emotional reactions of others when a user shares the generated visual art and use the collected data to improve the work. For example, the unit can analyze the emotions felt by others when viewing the art and reflect them in the next generation. In this way, the collected emotional reactions of others can be used to improve the work.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The Emotion Expressor AI system can also be equipped with a music generation unit that generates music based on the user's emotions. For example, if the user inputs "joy," the generation AI will generate music with a bright melody that reflects that emotion. If the user inputs "sad," the generation AI will generate music with a melancholic melody that reflects that emotion. This allows the user's emotions to be expressed in the form of music, providing a variety of means of emotional expression.

[0059] The Emotion Expressor AI system can also be equipped with a cooking suggestion unit that suggests cooking recipes based on the user's emotions. For example, if the user is feeling "stressed," the generation AI will suggest a recipe for a relaxing herbal tea. If the user is feeling "joyed," the generation AI will suggest a recipe for a luxurious dish suitable for a celebration. This allows the system to suggest dishes that correspond to the user's emotions and support emotional care.

[0060] The Emotion Expressor AI system can also be equipped with an exercise suggestion unit that suggests exercise programs based on the user's emotions. For example, if the user feels "fatigued," the AI ​​generator will suggest a relaxing yoga program. If the user feels "energetic," the AI ​​generator will suggest a high-intensity training program. This allows the system to suggest exercise programs that correspond to the user's emotions and support health management.

[0061] The Emotion Expressor AI system can also be equipped with a travel suggestion unit that suggests travel plans based on the user's emotions. For example, if the user is feeling "stressed," the generation AI might suggest a relaxing hot spring trip. If the user is feeling "adventurous," the generation AI might suggest an active adventure tour. This allows the system to suggest travel plans that match the user's emotions and help them feel refreshed.

[0062] The Emotion Expressor AI system can also be equipped with a fashion suggestion unit that suggests fashion coordination based on the user's emotions. For example, if the user is feeling "confident," the generation AI will suggest a stylish outfit. If the user is "depressed," the generation AI will suggest a bright-colored outfit that will lift their spirits. This allows the system to suggest fashion coordination that matches the user's emotions and support their everyday style.

[0063] The Emotion Expressor AI system can also be equipped with a learning material generation unit that generates learning materials based on user input. For example, if a user inputs a "math problem," the generation AI will generate explanations and practice questions related to that problem. If a user inputs a "historical event," the generation AI will generate detailed explanations and quizzes related to that event. This allows the system to provide a variety of learning materials to support users' learning.

[0064] The Emotion Expressor AI system can also be equipped with a health advice module that provides health advice based on user input. For example, if the user feels "fatigue," the AI ​​generator can provide advice on relaxation methods and nutritional supplementation. If the user feels "stressed," the AI ​​generator can provide stress relief and relaxation techniques. This can support the user's health management.

[0065] The Emotion Expressor AI system can also be equipped with an interior design suggestion unit that suggests interior designs based on user input. For example, if a user is looking for a "relaxing space," the generation AI will suggest an interior design with a relaxing effect. If a user is looking for a "modern space," the generation AI will suggest an interior design with a modern design. This allows the system to suggest interior designs that suit the user's preferences and provide a comfortable living space.

[0066] The Emotion Expressor AI system can also include a fitness suggestion unit that proposes a personalized fitness plan based on the user's input. For example, if the user requests "strength training," the AI ​​will propose a training plan tailored to that goal. If the user requests "aerobic exercise," the AI ​​will propose an exercise plan tailored to that goal. This allows the system to provide a personalized plan tailored to the user's fitness goals.

[0067] The Emotion Expressor AI system can also include a financial advice module that provides personal financial advice based on user input. For example, if a user wants to create a "savings plan," the AI ​​generator will suggest a savings plan tailored to that goal. If the user is considering "investment," the AI ​​generator will provide investment advice tailored to that goal. This can support the user's financial management.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The emotion reader reads emotions from the user's input. For example, if the user inputs "sad," the generation AI analyzes the user's emotions from those words. Also, if the user inputs a recorded voice, the generation AI reads emotions from the tone and rhythm of the voice. Furthermore, if the user inputs an image of their choice, the generation AI analyzes emotions from that image. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI reads emotions based on that prompt. Step 2: The poetry generator generates a poem based on the emotion detected by the emotion reader. For example, if a user enters "sad," the generator AI generates a poem that reflects that emotion. The generator AI selects beautiful words that capture the nuances of the emotion and puts them into a poem. Step 3: The story generation unit generates a story based on the emotions read by the emotion reading unit. For example, if the user inputs "sad," the generation AI generates a story that reflects that emotion. The generation AI selects beautiful words that capture the nuances of the emotion and shapes them into a story. Step 4: The visual art generator generates visual art based on the emotion detected by the emotion reader. For example, if a user enters "sad," the generator AI generates visual art that reflects that emotion. The generator AI generates beautiful artwork that captures the color and shape of the emotion.

[0070] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0071] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0072] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0076] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0077] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0078] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0079] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0080] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0081] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0082] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0083] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0084] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0106] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0111] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0120] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0121] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0122] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0123] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0124] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0125] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0126] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0127] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0128] 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.

[0129] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0130] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0131] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0132] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0133] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0134] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0135] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0136] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0137] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion reading unit that reads emotions from user input; a poetry generating unit that generates poetry based on the emotion read by the emotion reading unit; a story generation unit that generates a story based on the emotion read by the emotion reading unit; a visual art generation unit that generates visual art based on the emotion read by the emotion reading unit. A system characterized by:

2. The emotion reading unit Tracks changes in emotions in real time in response to the user's input, capturing subtle fluctuations in emotions. The system of claim 1 .

3. The emotion reading unit In addition to the text and voice input from the user, environmental and background sounds are also analyzed to identify the underlying factors behind the emotion. The system of claim 1 .

4. The poetry generation unit: Based on the user's emotions, poems and stories in different styles and genres are generated to provide works that match the user's preferences. The system of claim 1 .

5. The poetry generation unit: The creation of poems and stories can be tailored to reflect specific themes and keywords selected by the user, resulting in more personalized works. The system of claim 1 .

6. The visual art generation unit: Based on the user's emotions, visual arts are generated in different styles and techniques to provide works that match the user's preferences. The system of claim 1 .

7. The visual art generation unit: In generating visual art, the creation of a more personalized work can be achieved by reflecting a specific theme or motif selected by the user. The system of claim 1 .

8. The emotion reading unit Using the emotion estimation function, the facial expressions and body movements of the user are analyzed when inputting information, allowing for a multifaceted interpretation of emotions. The system of claim 1 .

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