system

Abstract

An object of the system according to the embodiment is to record the content of dreams viewed during sleep, convert the content into a specific form, and utilize the content.SOLUTION: A system according to an embodiment includes a dream recording unit, a generation unit, a posting unit, and a trading unit. The dream recording unit records the details of the user's dreams. The generation part analyzes the content of the dream recorded by the dream recording part and converts it into a concrete form. The posting unit posts the content generated by the generation unit to the DreamHUB. The buying and selling unit enables the content posted by the posting unit to be bought and sold.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 problem of not being able to adequately record the content of dreams seen during sleep, convert them into a concrete form, and utilize them.

[0005] The system according to the embodiment aims to record the content of dreams seen during sleep, convert it into a concrete form, and utilize it. [Means for solving the problem]

[0006] The system according to the embodiment includes a dream recording unit, a generation unit, a posting unit, and a buying and selling unit. The dream recording unit records the content of the user's dreams. The generation unit analyzes the content of the dreams recorded by the dream recording unit and converts it into a concrete form. The posting unit posts the content generated by the generation unit to the Dream HUB. The buying and selling unit makes the content posted by the posting unit available for buying and selling. [Effects of the Invention]

[0007] The system according to the embodiment can record the content of dreams seen during sleep, convert it into a concrete form, and utilize it. [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) The Dream HUB system according to an embodiment of the present invention is a system that uses AI to create a form from the content of dreams that people have while sleeping and then broadcasts it to the world. As a result, the Dream HUB system records the content of dreams, converts them into a concrete form, and broadcasts and sells them, allowing the content of dreams to be applied in a variety of fields.

[0029] The Dream HUB system according to the embodiment includes a dream recording unit, a generation unit, a posting unit, and a buying and selling unit. The dream recording unit records the content of a user's dreams. For example, after a user has a dream, it records the content in text format. The dream recording unit can also use voice input to dictate and record the content of a dream immediately after the user wakes up. The generation unit analyzes the recorded dream content and converts it into a concrete form. For example, the generation AI converts the dream content into text or illustrations using a text generation AI (e.g., LLM). The generation AI can also convert the dream content into video or audio using a multimodal generation AI. The posting unit posts the content generated by the generation unit to Dream HUB. For example, the generated text or illustrations are posted on the Dream HUB platform so that other users can view them. The buying and selling unit enables the content posted by the posting unit to be bought and sold. For example, it sets a price for the posted dream content so that other users can purchase it. In this way, the Dream HUB system records dream content, converts it into a concrete form, and enables posting and buying and selling.

[0030] The dream recording unit uses voice input to dictate the content of a dream immediately after the user wakes up, and the generation unit can analyze and shape the content of the dream. For example, the dream recording unit dictates the content of a dream immediately after the user wakes up and records the voice data. For example, the user speaks the content of their dream into a smartphone and records the voice data. In the generation unit, the generation AI analyzes the recorded voice data and shapes it. For example, the generation AI converts the voice data into text and generates it in a specific form. The generation unit can also use voice input to have the user dictate the content of a dream and then convert it into a specific form based on that. This allows the content of dreams to be recorded quickly and accurately using voice input.

[0031] The generation unit can refer to a database of the user's past dreams and convert them into a form that has continuity and consistency. For example, when the generation AI analyzes the content of a dream, the generation unit can refer to a database of the user's past dreams and convert them into a form that has continuity and consistency. For example, the generation unit can complement the content of the current dream based on the data of dreams the user has had in the past and generate a consistent story. The generation unit can also analyze the content of the current dream based on the data of past dreams and convert it into a form that has continuity. In this way, by referring to the data of past dreams, it is possible to give continuity and consistency to the content of the dream.

[0032] The dream recording unit collects the user's brainwave data in real time, and the generation unit can analyze the brainwave data to give shape to the content of the dream. The dream recording unit, for example, collects the user's brainwave data in real time. For example, it records the content of dreams in real time using an brainwave sensor worn by the user while sleeping. In the generation unit, the generation AI analyzes the collected brainwave data to give shape to the content of the dream. For example, the generation AI analyzes the brainwave data and converts it into a concrete form. The generation unit can also analyze the content of the dream based on the brainwave data and convert it into a concrete form. In this way, the content of the dream can be recorded and analyzed more accurately by using the brainwave data.

[0033] The dream recording unit can input sketches and illustrations drawn by the user into the generation unit and convert them into a concrete form based on them. The dream recording unit, for example, records sketches and illustrations drawn by the user. For example, the user sketches the content of their dream and records the image data. In the generation unit, the generation AI analyzes the recorded sketches and illustrations and converts them into a concrete form. For example, the generation AI analyzes the sketches and illustrations and generates a concrete scene. The generation unit can also convert the content of a dream into a concrete form based on the sketches and illustrations. This makes it possible to visually record and analyze the content of a dream using sketches and illustrations.

[0034] The posting unit allows the generation AI to automatically tag and recommend related dream content. For example, the posting unit allows the generation AI to automatically tag the posted dream content and recommend related dream content. For example, related keywords are extracted based on the dream content and other dream content is recommended based on those keywords. The posting unit also allows the generation AI to automatically tag and recommend related dream content. For example, the generation AI analyzes the dream content and recommends related dream content. This improves user convenience by automatically tagging and recommending related dream content.

[0035] The posting unit can have the generation AI automatically translate the content so that it can be viewed by users of different languages. The posting unit can, for example, have the generation AI automatically translate the content of a posted dream so that it can be viewed by users of different languages. For example, the content of a dream posted in English can be automatically translated into Japanese or French so that it can be viewed by users of different languages. The posting unit can also have the generation AI automatically translate the content so that it can be viewed by users of different languages. For example, the generation AI can translate the content of a dream into multiple languages ​​so that it can be viewed by users of different languages. In this way, the dream content can be viewed by automatically translating it.

[0036] The posting unit can have the generation AI automatically visualize the content and enable it to be viewed as a video or animation. The posting unit can, for example, have the generation AI automatically visualize the content of a posted dream and enable it to be viewed as a video or animation. For example, the generation AI can generate an animation based on the content of the dream and enable the user to view the video. The posting unit can also have the generation AI automatically visualize the content and enable it to be viewed as a video or animation. For example, the generation AI can visualize the content of the dream and enable the user to view the video. In this way, the content of the dream can be visually enjoyed by automatically visualizing it.

[0037] The posting unit can add an interactive function that allows other users to comment and rate in real time. The posting unit can add an interactive function that allows other users to comment and rate in real time on the content of a posted dream, for example. For example, a user posts a comment on the content of a dream, and the comment is displayed immediately. The posting unit can also add an interactive function that allows other users to comment and rate in real time. For example, a user can rate the content of a dream, and the rating is reflected in real time. This allows comments and ratings to be made in real time, which promotes interaction between users.

[0038] The buying and selling unit allows the generation AI to automatically evaluate the price and present a fair price. For example, when buying and selling dream content, the buying and selling unit allows the generation AI to automatically evaluate the price and present a fair price. For example, the price is calculated based on the uniqueness and market value of the dream content and presented to the user. The buying and selling unit also allows the generation AI to automatically evaluate the price and present a fair price. For example, the generation AI evaluates the price based on market data and presents a fair price. In this way, by presenting a fair price, the buying and selling of dream content is conducted fairly.

[0039] The buying and selling section allows the generation AI to automatically generate a contract, ensuring the transparency and reliability of the transaction. For example, when buying and selling the content of a dream, the generation AI automatically generates a contract, ensuring the transparency and reliability of the transaction. For example, a contract is automatically generated based on the terms of sale of the content of the dream and provided to the user. The buying and selling section can also allow the generation AI to automatically generate a contract, ensuring the transparency and reliability of the transaction. For example, the generation AI automatically fills in the necessary items based on the contract format and generates the contract. In this way, the automatic generation of a contract ensures the transparency and reliability of the transaction.

[0040] In the buying and selling department, the generation AI can automatically propose related business ideas and visualizations. For example, when buying and selling the content of a dream, the generation AI can automatically propose related business ideas and visualizations. For example, it can propose new business ideas based on the content of the dream. In addition, the buying and selling department can automatically propose related business ideas and visualizations. For example, the generation AI can analyze the content of a dream and propose visualizations. This will expand the scope of use of the content of dreams by proposing related business ideas and visualizations.

[0041] The buying and selling unit can add a crowdfunding function that allows other users to jointly purchase and use dream content. For example, when buying and selling dream content, the buying and selling unit adds a crowdfunding function that allows other users to jointly purchase and use dream content. For example, multiple users pool their funds to purchase dream content. The buying and selling unit can also add a crowdfunding function that allows other users to jointly purchase and use dream content. For example, dream content is jointly purchased through a crowdfunding platform. In this way, adding the crowdfunding function makes it easier to purchase dream content.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Dream HUB system can also be equipped with an educational content generator based on the content of a user's dreams. For example, the AI ​​generates specific educational content based on the knowledge and skills the user learned in their dreams. The educational content generator can also reference data from the user's past dreams to generate content with continuity and consistency. This allows users to study in a way that is tailored to the content of their dreams.

[0044] The Dream HUB system can also be equipped with a fashion coordination generation unit that generates fashion coordination based on the content of the user's dreams. For example, the generation AI generates a specific fashion coordination based on the clothes and styles seen in the user's dreams. The fashion coordination generation unit can also reference data from the user's past dreams to generate coordination that provides continuity and consistency. This allows users to enjoy fashion that matches the content of their dreams.

[0045] The Dream HUB system can also be equipped with an art generation unit that generates artwork based on the content of a user's dreams. For example, the generation AI generates specific artwork based on the scenery or characters the user sees in their dreams. The art generation unit can also reference data from the user's past dreams to generate artwork with continuity and consistency. This allows users to enjoy artwork that matches the content of their dreams.

[0046] The Dream HUB system can also be equipped with a story generation unit based on the content of the user's dream. For example, the generation AI can generate a specific story based on the events and characters the user experienced in their dream. The story generation unit can also reference data from the user's past dreams to generate a story with continuity and consistency. This allows the user to enjoy a story that matches the content of their dream.

[0047] The Dream HUB system can also include a game scenario generator that generates game scenarios based on the content of a user's dreams. For example, the generation AI generates specific game scenarios based on the adventures and challenges experienced by the user in their dreams. The game scenario generator can also reference data from the user's past dreams to generate scenarios with continuity and consistency. This allows users to enjoy game scenarios that match the content of their dreams.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The dream recorder records the content of the user's dream. For example, after the user has a dream, the content is recorded in text format. The dream recorder can also use voice input to dictate the content of the dream immediately after the user wakes up and record it. Step 2: The generator analyzes the recorded dream content and converts it into a concrete form. For example, the generator AI can use text generation AI (e.g., LLM) to convert the dream content into text or illustrations. The generator AI can also use multimodal generation AI to convert the dream content into video or audio. Step 3: The posting unit posts the content generated by the generation unit to Dream HUB. For example, the generated text or illustrations are posted on the Dream HUB platform so that they can be viewed by other users. Step 4: The buying and selling section makes the content posted by the posting section available for buying and selling. For example, the posted dream content may be priced and made available for purchase by other users.

[0050] (Example 2) The Dream HUB system according to an embodiment of the present invention is a system that uses AI to create a form from the content of dreams that people have while sleeping and then broadcasts it to the world. As a result, the Dream HUB system records the content of dreams, converts them into a concrete form, and broadcasts and sells them, allowing the content of dreams to be applied in a variety of fields.

[0051] The Dream HUB system according to the embodiment includes a dream recording unit, a generation unit, a posting unit, and a buying and selling unit. The dream recording unit records the content of a user's dreams. For example, after a user has a dream, it records the content in text format. The dream recording unit can also use voice input to dictate and record the content of a dream immediately after the user wakes up. The generation unit analyzes the recorded dream content and converts it into a concrete form. For example, the generation AI converts the dream content into text or illustrations using a text generation AI (e.g., LLM). The generation AI can also convert the dream content into video or audio using a multimodal generation AI. The posting unit posts the content generated by the generation unit to Dream HUB. For example, the generated text or illustrations are posted on the Dream HUB platform so that other users can view them. The buying and selling unit enables the content posted by the posting unit to be bought and sold. For example, it sets a price for the posted dream content so that other users can purchase it. In this way, the Dream HUB system records dream content, converts it into a concrete form, and enables posting and buying and selling.

[0052] The generation unit can reconstruct dream scenes based on the user's emotional state and prioritize the generation of scenes with a strong emotional impact. For example, when the generation AI analyzes the content of a dream, the generation unit monitors the user's emotional state in real time and prioritizes the reconstruction of scenes with a strong emotional impact. For example, it analyzes the intensity of fear or joy felt by the user in the dream and generates scenes based on that emotion. The generation unit can also use an emotion estimation function to convert the dream into a form that emphasizes emotionally positive elements. For example, it generates scenes that emphasize the joy and happiness felt by the user in the dream. This prioritizes the generation of scenes with a strong emotional impact, making the content of the dream more appealing.

[0053] The dream recording unit uses voice input to dictate the content of a dream immediately after the user wakes up, and the generation unit can analyze and shape the content of the dream. For example, the dream recording unit dictates the content of a dream immediately after the user wakes up and records the voice data. For example, the user speaks the content of their dream into a smartphone and records the voice data. In the generation unit, the generation AI analyzes the recorded voice data and shapes it. For example, the generation AI converts the voice data into text and generates it in a specific form. The generation unit can also use voice input to have the user dictate the content of a dream and then convert it into a specific form based on that. This allows the content of dreams to be recorded quickly and accurately using voice input.

[0054] The generation unit can refer to a database of the user's past dreams and convert them into a form that has continuity and consistency. For example, when the generation AI analyzes the content of a dream, the generation unit can refer to a database of the user's past dreams and convert them into a form that has continuity and consistency. For example, the generation unit can complement the content of the current dream based on the data of dreams the user has had in the past and generate a consistent story. The generation unit can also analyze the content of the current dream based on the data of past dreams and convert it into a form that has continuity. In this way, by referring to the data of past dreams, it is possible to give continuity and consistency to the content of the dream.

[0055] The dream recording unit collects the user's brainwave data in real time, and the generation unit can analyze the brainwave data to give shape to the content of the dream. The dream recording unit, for example, collects the user's brainwave data in real time. For example, it records the content of dreams in real time using an brainwave sensor worn by the user while sleeping. In the generation unit, the generation AI analyzes the collected brainwave data to give shape to the content of the dream. For example, the generation AI analyzes the brainwave data and converts it into a concrete form. The generation unit can also analyze the content of the dream based on the brainwave data and convert it into a concrete form. In this way, the content of the dream can be recorded and analyzed more accurately by using the brainwave data.

[0056] The generation unit can use the emotion estimation function to convert the dream content into one that emphasizes emotionally positive elements. For example, when the generation AI analyzes the content of a dream, the generation unit uses the user's emotion estimation function to convert the dream content into one that emphasizes emotionally positive elements. For example, it generates a scene that emphasizes the joy and happiness the user felt in the dream. The generation unit can also use the emotion estimation function to convert the dream content into one that emphasizes emotionally positive elements. For example, the generation AI converts the dream content into one that emphasizes positive elements based on the emotion score. In this way, emphasizing positive elements makes the dream content more appealing.

[0057] The dream recording unit can input sketches and illustrations drawn by the user into the generation unit and convert them into a concrete form based on them. The dream recording unit, for example, records sketches and illustrations drawn by the user. For example, the user sketches the content of their dream and records the image data. In the generation unit, the generation AI analyzes the recorded sketches and illustrations and converts them into a concrete form. For example, the generation AI analyzes the sketches and illustrations and generates a concrete scene. The generation unit can also convert the content of a dream into a concrete form based on the sketches and illustrations. This makes it possible to visually record and analyze the content of a dream using sketches and illustrations.

[0058] The posting unit allows the generation AI to automatically tag and recommend related dream content. For example, the posting unit allows the generation AI to automatically tag the posted dream content and recommend related dream content. For example, related keywords are extracted based on the dream content and other dream content is recommended based on those keywords. The posting unit also allows the generation AI to automatically tag and recommend related dream content. For example, the generation AI analyzes the dream content and recommends related dream content. This improves user convenience by automatically tagging and recommending related dream content.

[0059] The posting unit can have the generation AI automatically translate the content so that it can be viewed by users of different languages. The posting unit can, for example, have the generation AI automatically translate the content of a posted dream so that it can be viewed by users of different languages. For example, the content of a dream posted in English can be automatically translated into Japanese or French so that it can be viewed by users of different languages. The posting unit can also have the generation AI automatically translate the content so that it can be viewed by users of different languages. For example, the generation AI can translate the content of a dream into multiple languages ​​so that it can be viewed by users of different languages. In this way, the dream content can be viewed by automatically translating it.

[0060] The posting unit can use the emotion estimation function to collect the user's emotional reactions and prioritize displaying dream content that is likely to be emotionally relatable. For example, the posting unit can use the emotion estimation function to collect the user's emotional reactions to the posted dream content and prioritize displaying content that is likely to be emotionally relatable. For example, based on the user's emotion score, it can prioritize displaying dream content that is highly relatable. The posting unit can also use the emotion estimation function to collect the user's emotional reactions and prioritize displaying content that is likely to be emotionally relatable. For example, based on the emotion score, it can prioritize displaying dream content that is highly relatable. In this way, by preferentially displaying content that is likely to be emotionally relatable, it is easier to attract the user's attention.

[0061] The posting unit can have the generation AI automatically visualize the content and enable it to be viewed as a video or animation. The posting unit can, for example, have the generation AI automatically visualize the content of a posted dream and enable it to be viewed as a video or animation. For example, the generation AI can generate an animation based on the content of the dream and enable the user to view the video. The posting unit can also have the generation AI automatically visualize the content and enable it to be viewed as a video or animation. For example, the generation AI can visualize the content of the dream and enable the user to view the video. In this way, the content of the dream can be visually enjoyed by automatically visualizing it.

[0062] The posting unit can add an interactive function that allows other users to comment and rate in real time. The posting unit can add an interactive function that allows other users to comment and rate in real time on the content of a posted dream, for example. For example, a user posts a comment on the content of a dream, and the comment is displayed immediately. The posting unit can also add an interactive function that allows other users to comment and rate in real time. For example, a user can rate the content of a dream, and the rating is reflected in real time. This allows comments and ratings to be made in real time, which promotes interaction between users.

[0063] The posting unit can use the emotion estimation function to filter based on the user's emotions and prioritize displaying dream content that elicits positive emotions. The posting unit, for example, can use the emotion estimation function to filter the posted dream content and prioritize displaying content that elicits positive emotions. For example, based on the user's emotion score, dream content that elicits positive emotions is prioritized. The posting unit can also use the emotion estimation function to filter based on the user's emotions and prioritize displaying content that elicits positive emotions. For example, the generation AI can prioritize displaying dream content that elicits positive emotions based on the emotion score. This prioritizes displaying content that elicits positive emotions, thereby improving user satisfaction.

[0064] The buying and selling unit allows the generation AI to automatically evaluate the price and present a fair price. For example, when buying and selling dream content, the buying and selling unit allows the generation AI to automatically evaluate the price and present a fair price. For example, the price is calculated based on the uniqueness and market value of the dream content and presented to the user. The buying and selling unit also allows the generation AI to automatically evaluate the price and present a fair price. For example, the generation AI evaluates the price based on market data and presents a fair price. In this way, by presenting a fair price, the buying and selling of dream content is conducted fairly.

[0065] The buying and selling section allows the generation AI to automatically generate a contract, ensuring the transparency and reliability of the transaction. For example, when buying and selling the content of a dream, the generation AI automatically generates a contract, ensuring the transparency and reliability of the transaction. For example, a contract is automatically generated based on the terms of sale of the content of the dream and provided to the user. The buying and selling section can also allow the generation AI to automatically generate a contract, ensuring the transparency and reliability of the transaction. For example, the generation AI automatically fills in the necessary items based on the contract format and generates the contract. In this way, the automatic generation of a contract ensures the transparency and reliability of the transaction.

[0066] The buying and selling unit can use the emotion estimation function to adjust the price based on the user's emotional response. For example, when buying and selling dream content, the buying and selling unit uses the emotion estimation function to adjust the price based on the user's emotional response. For example, the price is increased or decreased based on the user's emotional score. The buying and selling unit can also use the emotion estimation function to adjust the price based on the user's emotional response. For example, the generation AI adjusts the price based on the emotional score. In this way, adjusting the price based on the emotional response improves user satisfaction.

[0067] In the buying and selling department, the generation AI can automatically propose related business ideas and visualizations. For example, when buying and selling the content of a dream, the generation AI can automatically propose related business ideas and visualizations. For example, it can propose new business ideas based on the content of the dream. In addition, the buying and selling department can automatically propose related business ideas and visualizations. For example, the generation AI can analyze the content of a dream and propose visualizations. This will expand the scope of use of the content of dreams by proposing related business ideas and visualizations.

[0068] The buying and selling unit can add a crowdfunding function that allows other users to jointly purchase and use dream content. For example, when buying and selling dream content, the buying and selling unit adds a crowdfunding function that allows other users to jointly purchase and use dream content. For example, multiple users pool their funds to purchase dream content. The buying and selling unit can also add a crowdfunding function that allows other users to jointly purchase and use dream content. For example, dream content is jointly purchased through a crowdfunding platform. In this way, adding the crowdfunding function makes it easier to purchase dream content.

[0069] The buying and selling unit can use the emotion estimation function to propose a marketing strategy based on the user's emotions. For example, when buying and selling dream content, the buying and selling unit uses the emotion estimation function to propose a marketing strategy based on the user's emotions. For example, the marketing strategy is formulated based on the user's emotion score. The buying and selling unit can also use the emotion estimation function to propose a marketing strategy based on the user's emotions. For example, the generation AI proposes a marketing strategy based on the emotion score. This makes it possible to propose a marketing strategy based on emotions, thereby enabling effective marketing.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The Dream HUB system can also be equipped with a personalized music generation unit based on the content of the user's dream. For example, the generation AI can generate specific music based on the emotions and scenes the user experienced in their dream. The music generation unit can also reference data from the user's past dreams to generate music with continuity and consistency. This allows users to enjoy music that matches the content of their dreams.

[0072] The Dream HUB system can also be equipped with a fitness program generator that generates a fitness program based on the content of the user's dreams. For example, the AI ​​generates a specific fitness program based on the activities and emotions experienced in the user's dreams. The fitness program generator can also reference the user's past dream data to generate a program with continuity and consistency. This allows the user to practice a fitness program that matches the content of their dreams.

[0073] The Dream HUB system can also be equipped with a cooking recipe generator based on the content of a user's dreams. For example, the generation AI generates a specific cooking recipe based on the ingredients and dishes the user saw in their dream. The cooking recipe generator can also reference data from the user's past dreams to generate recipes with continuity and consistency. This allows users to enjoy cooking that matches the content of their dreams.

[0074] The Dream HUB system can also be equipped with a travel plan generator based on the content of a user's dreams. For example, the generation AI generates a specific travel plan based on the places and experiences the user visited in their dreams. The travel plan generator can also reference data from the user's past dreams to generate a plan with continuity and consistency. This allows users to plan a trip that matches the content of their dreams.

[0075] The Dream HUB system can also be equipped with an interior design generator based on the content of a user's dreams. For example, the AI ​​generates a specific interior design based on the rooms and furniture the user saw in their dreams. The interior design generator can also reference data from the user's past dreams to generate designs with continuity and consistency. This allows users to enjoy interior designs that match the content of their dreams.

[0076] The Dream HUB system can also be equipped with an educational content generator based on the content of a user's dreams. For example, the AI ​​generates specific educational content based on the knowledge and skills the user learned in their dreams. The educational content generator can also reference data from the user's past dreams to generate content with continuity and consistency. This allows users to study in a way that is tailored to the content of their dreams.

[0077] The Dream HUB system can also be equipped with a fashion coordination generation unit that generates fashion coordination based on the content of the user's dreams. For example, the generation AI generates a specific fashion coordination based on the clothes and styles seen in the user's dreams. The fashion coordination generation unit can also reference data from the user's past dreams to generate coordination that provides continuity and consistency. This allows users to enjoy fashion that matches the content of their dreams.

[0078] The Dream HUB system can also be equipped with an art generation unit that generates artwork based on the content of a user's dreams. For example, the generation AI generates specific artwork based on the scenery or characters the user sees in their dreams. The art generation unit can also reference data from the user's past dreams to generate artwork with continuity and consistency. This allows users to enjoy artwork that matches the content of their dreams.

[0079] The Dream HUB system can also be equipped with a story generation unit based on the content of the user's dream. For example, the generation AI can generate a specific story based on the events and characters the user experienced in their dream. The story generation unit can also reference data from the user's past dreams to generate a story with continuity and consistency. This allows the user to enjoy a story that matches the content of their dream.

[0080] The Dream HUB system can also include a game scenario generator that generates game scenarios based on the content of a user's dreams. For example, the generation AI generates specific game scenarios based on the adventures and challenges experienced by the user in their dreams. The game scenario generator can also reference data from the user's past dreams to generate scenarios with continuity and consistency. This allows users to enjoy game scenarios that match the content of their dreams.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The dream recorder records the content of the user's dream. For example, after the user has a dream, the content is recorded in text format. The dream recorder can also use voice input to dictate the content of the dream immediately after the user wakes up and record it. Step 2: The generator analyzes the recorded dream content and converts it into a concrete form. For example, the generator AI can use text generation AI (e.g., LLM) to convert the dream content into text or illustrations. The generator AI can also use multimodal generation AI to convert the dream content into video or audio. Step 3: The posting unit posts the content generated by the generation unit to Dream HUB. For example, the generated text or illustrations are posted on the Dream HUB platform so that they can be viewed by other users. Step 4: The buying and selling section makes the content posted by the posting section available for buying and selling. For example, the posted dream content may be priced and made available for purchase by other users.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dream recording unit that records the content of a user's dreams; a generation unit that analyzes the content of the dream recorded by the dream recording unit and converts it into a concrete form; a posting unit that posts the content generated by the generation unit to Dream HUB; a buying and selling unit that makes the content posted by the posting unit available for buying and selling. A system characterized by:

2. The dream recording unit dictating the dream content immediately upon the user waking up using voice input; The generation unit Analyze the content of the dream and give it shape 2. The system of claim 1.

3. The dream recording unit Collects user brainwave data in real time, The generation unit Analyze the brainwave data to give shape to the content of the dream 2. The system of claim 1.

4. The posting unit: The AI ​​automatically tags and recommends related dream content.

2. The system of claim 1.

5. The buying and selling department The generation AI automatically evaluates the price and presents the appropriate price.

2. The system of claim 1.

6. The generation unit The dream scenes are reconstructed based on the emotional state, and scenes with strong emotional impact are preferentially generated.

2. The system of claim 1.

Images