System

Abstract

An object of a system according to an embodiment is to enable strangers to smoothly communicate with each other in a Metaverse.SOLUTION: A system according to an embodiment includes a profile acquiring unit, a conversation analyzing unit, a theme extracting unit, and a 3D space generating unit. The profile acquisition unit acquires profile information of an avatar. The conversation analysis unit converts the profile information acquired by the profile acquisition unit and the conversation content between the avatars into text. The theme extraction unit extracts a common theme from the data converted into the text by the conversation analysis unit. The 3D space generation unit generates a 3D space on the basis of the common theme extracted by the theme extraction 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 problem of making it difficult for strangers to communicate smoothly with each other within the metaverse.

[0005] The system according to the embodiment aims to enable strangers to communicate smoothly with each other in the metaverse. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile acquisition unit, a conversation analysis unit, a theme extraction unit, and a 3D space generation unit. The profile acquisition unit acquires profile information of avatars. The conversation analysis unit converts the profile information acquired by the profile acquisition unit and the content of conversations between avatars into text. The theme extraction unit extracts a common theme from the data converted into text by the conversation analysis unit. The 3D space generation unit generates a 3D space based on the common theme extracted by the theme extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment allows strangers to smoothly communicate with each other in the metaverse. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A communication promotion system according to an embodiment of the present invention is a system that allows strangers to communicate smoothly within the metaverse. This system uses a generation AI to interpret the profiles and conversations of people gathered in the same place and generate a common 3D space based on that. This allows strangers to communicate smoothly within the metaverse.

[0029] A communication promotion system according to an embodiment includes a profile acquisition unit, a conversation analysis unit, a theme extraction unit, and a 3D space generation unit. The profile acquisition unit acquires profile information of avatars. For example, the profile information includes the avatar's name, hobbies, interests, and occupation. The profile acquisition unit can also update the avatar's profile information in real time. The conversation analysis unit converts the profile information acquired by the profile acquisition unit and the content of conversations between avatars into text. For example, the conversation analysis unit converts conversations between avatars into text in real time using voice recognition technology. The conversation analysis unit can also input the converted text data to a generation AI. The theme extraction unit extracts a common theme from the data converted by the conversation analysis unit. For example, the theme extraction unit analyzes the text data using a generation AI to extract the common theme. The theme extraction unit can also input a prompt to the generation AI based on the common theme. The 3D space generation unit generates a 3D space based on the common theme extracted by the theme extraction unit. For example, the 3D space generation unit generates a 3D space based on the common theme using a text-to-image conversion generation AI. The 3D space generation unit can also provide the generated 3D space within the metaverse. This allows the communication promotion system according to the embodiment to enable strangers to communicate smoothly within the metaverse. For example, when people with common hobbies or interests gather together, natural conversations emerge and interactions deepen. Furthermore, the generation AI can analyze conversations in real time and make appropriate suggestions, further facilitating communication.

[0030] The profile acquisition unit can analyze the avatar's past behavioral history and convert the behavioral patterns into text. For example, the profile acquisition unit can analyze the avatar's past movement history and convert the places visited and the duration of stay into text. For example, the profile acquisition unit can record the behavioral patterns of avatars that frequently visit a specific area. The profile acquisition unit can also analyze the avatar's past activities and convert the behavioral patterns into text. For example, the profile acquisition unit can analyze the history of events and activities in which the avatar participated and record the behavioral patterns as text data. The profile acquisition unit can also analyze the behavioral patterns and extract the avatar's characteristics. For example, the profile acquisition unit can analyze the avatar's behavioral patterns using frequency analysis or time series analysis and record the characteristics as text data. In this way, more detailed profile information can be generated by analyzing the avatar's past behavioral history.

[0031] The profile acquisition unit can analyze images and videos uploaded by a user and convert visual information into text. For example, the profile acquisition unit analyzes images uploaded by a user and converts objects and scenes in the images into text. For example, it records places visited and activities from travel photos. The profile acquisition unit can also analyze videos uploaded by a user and convert visual information into text. For example, it analyzes movements and scenes in the videos and records the content as text data. The profile acquisition unit can also analyze visual information and extract user characteristics. For example, it analyzes visual information using image recognition technology or video analysis algorithms and records the characteristics as text data. In this way, visual information can be converted into text by analyzing images and videos uploaded by a user.

[0032] The profile acquisition unit can analyze the music and playlists selected by the user and convert the user's music preferences into text. For example, the profile acquisition unit analyzes the music genres and artists selected by the user and converts the user's music preferences into text. For example, genres such as rock and classical music are recorded. The profile acquisition unit can also analyze playlists created by the user and convert the user's music preferences into text. For example, the profile acquisition unit can analyze the characteristics of songs in the playlist and record the user's music preferences as text data. The profile acquisition unit can also analyze the user's music preferences and extract the user's characteristics. For example, the profile acquisition unit can analyze the user's music preferences using a music recommendation system or music feature extraction and record the characteristics as text data. In this way, the user's music preferences can be converted into text by analyzing the user's music and playlists selected.

[0033] The theme extraction unit can analyze long-term changes in themes by referring to the conversation history. For example, when interpreting text data, the theme extraction unit refers to past conversation history and analyzes long-term changes in themes. For example, it analyzes conversation data from the past few months to identify changes in themes. The theme extraction unit can also analyze changes in themes based on an analysis of time-series data. For example, it can identify changes in themes using trend analysis. The theme extraction unit can also analyze long-term changes in themes based on common themes. For example, it can analyze past conversation data based on common themes to identify changes in themes. In this way, it is possible to analyze long-term changes in themes by referring to past conversation history.

[0034] The theme extraction unit can automatically translate text in different languages ​​and extract common themes across multiple languages. The theme extraction unit, for example, builds a system that automatically translates text in different languages ​​and extracts common themes across multiple languages. For example, it automatically translates text in English, Japanese, and Chinese and identifies common themes. The theme extraction unit can also automatically translate text in different languages ​​using a machine translation algorithm. For example, it automatically translates text using an algorithm with high translation accuracy. The theme extraction unit can also automatically translate text based on an evaluation standard for translation accuracy. For example, it extracts common themes based on text with high translation accuracy. In this way, it is possible to extract common themes across multiple languages ​​by automatically translating text in different languages.

[0035] The theme extraction unit can also analyze image and audio data to extract common themes from multimodal information. For example, in interpreting text data, the theme extraction unit analyzes image data to extract common themes from multimodal information. For example, the theme extraction unit analyzes objects and scenes in an image to identify common themes. The theme extraction unit can also analyze audio data to extract common themes from multimodal information. For example, the theme extraction unit can analyze audio data using an audio analysis algorithm to identify common themes. The theme extraction unit can also integrate multimodal information such as text, images, and audio to extract common themes. For example, the theme extraction unit can analyze a combination of text data and image data to identify common themes. This allows the theme extraction unit to extract common themes from multimodal information by analyzing image and audio data as well.

[0036] The theme extraction unit can extract interdisciplinary themes by referring to data from different industries and fields. The theme extraction unit, for example, refers to data from different industries and fields and extracts interdisciplinary themes by interpreting text data. For example, the theme can be identified by integrating data from the medical field and the technology field. The theme extraction unit can also combine and analyze data from different industries to extract interdisciplinary themes. For example, the theme can be identified by analyzing data from different industries based on common challenges or goals. The theme extraction unit can also analyze data based on interdisciplinary themes. For example, a common theme can be identified based on data from different industries. In this way, interdisciplinary themes can be extracted by referring to data from different industries and fields.

[0037] The 3D space generation unit can provide an individually customized space by reflecting the user's past behavior history. The 3D space generation unit, for example, analyzes the user's past behavior history and reflects it in the generated 3D space. For example, it can provide a space customized based on places visited or events attended in the past. The 3D space generation unit can also customize the space based on the user's preferences. For example, it can adjust the design of the space based on the user's behavior history and preferences. The 3D space generation unit can also customize the space based on the user's characteristics. For example, it can customize the space based on the user's behavior patterns and preferences. In this way, it is possible to provide an individually customized space by reflecting the user's past behavior history.

[0038] The 3D space generation unit can provide a dynamically changing space by reflecting real-time user feedback. For example, the 3D space generation unit collects real-time user feedback and reflects it in the generated 3D space. For example, the design of the space is changed based on the user's opinions and requests. The 3D space generation unit can also dynamically change the space based on real-time feedback. For example, the layout and design of the space can be adjusted according to the user's feedback. The 3D space generation unit can also optimize the space based on the user's feedback. For example, the function and design of the space can be improved based on the user's feedback. In this way, a dynamically changing space can be provided by reflecting the user's real-time feedback.

[0039] The 3D space generation unit can generate a composite space that combines different themes and scenarios, thereby providing a new experience to the user. The 3D space generation unit can generate a composite space that combines different themes and scenarios, for example, to provide a new experience to the user. For example, a space that combines a music studio and a cafe can be provided. The 3D space generation unit can also generate a space that combines different themes. For example, a space that combines sports and entertainment can be provided. The 3D space generation unit can also generate a space that combines different scenarios. For example, a space that combines education and games can be provided. In this way, a composite space that combines different themes and scenarios can be generated, thereby providing a new experience to the user.

[0040] The 3D space generation unit can provide an individually customized space by reflecting images and videos uploaded by the user. For example, the 3D space generation unit analyzes images and videos uploaded by the user and reflects them in the generated 3D space. For example, it can provide a space customized based on the user's photos. The 3D space generation unit can also customize the space based on images and videos uploaded by the user. For example, it can adjust the design of the space based on the user's videos. The 3D space generation unit can also customize the space based on the user's visual information. For example, it can analyze the user's images and videos using image recognition technology and customize the space based on that information. This makes it possible to provide an individually customized space by reflecting the images and videos uploaded by the user.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The communication promotion system may further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and blood pressure in real time and analyzes the data. For example, if the heart rate is high, it may make suggestions for relaxation. The health management unit may also record the user's amount of exercise and evaluate the user's health condition. For example, it may record the number of steps taken and calories burned and provide advice for maintaining health. The health management unit may also record the user's diet and evaluate nutritional balance. For example, it may analyze photos of meals and evaluate nutrient intake. This allows the user's health condition to be monitored and appropriate advice may be provided to support health maintenance.

[0043] The communication promotion system can further include an activity suggestion unit that suggests new activities that the user may be interested in based on the user's past behavioral history. The activity suggestion unit can, for example, analyze the history of events and activities that the user has participated in in the past and suggest similar events. The activity suggestion unit can also analyze the user's behavioral patterns and suggest activities that will stimulate new hobbies or interests. Furthermore, the activity suggestion unit can suggest activities that match the season or trends based on the user's behavioral history. This makes it possible to broaden the user's interests by suggesting new activities based on the user's past behavioral history.

[0044] The communication promotion system may further include a marketing proposal unit that proposes related products and services based on the user's hobbies and interests. For example, if the user is interested in music, the marketing proposal unit may propose the latest music albums and concert tickets. If the user is interested in sports, the marketing proposal unit may also propose sports equipment and event information. Furthermore, the marketing proposal unit may propose related online courses and workshops based on the user's hobbies and interests. This makes it possible to increase user satisfaction by proposing related products and services based on the user's hobbies and interests.

[0045] The communication promotion system may further include a location information providing unit that acquires real-time location information of the user and provides related information based on the location information. For example, when the user is in a specific location, the location information providing unit may suggest events or tourist spots related to that location. The location information providing unit may also analyze the user's movement history and suggest new places based on places previously visited. Furthermore, the location information providing unit may suggest nearby restaurants or cafes based on the user's location information. This improves user convenience by providing related information based on the user's real-time location information.

[0046] The communication promotion system can further include a purchase suggestion unit that analyzes the user's past purchase history and suggests new products that the user may be interested in. The purchase suggestion unit can, for example, analyze the user's history of past purchases of products and services and suggest similar products. The purchase suggestion unit can also analyze the user's purchasing patterns and suggest products based on new trends. Furthermore, the purchase suggestion unit can suggest products that match the season or event based on the user's purchase history. This can improve the user's purchasing experience by suggesting new products based on the user's past purchase history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The profile acquisition unit acquires the avatar's profile information, such as the avatar's name, hobbies, interests, occupation, etc. The profile acquisition unit can also update the avatar's profile information in real time. Step 2: The conversation analysis unit converts the profile information acquired by the profile acquisition unit and the content of the conversation between the avatars into text. For example, the conversation analysis unit converts the conversation between the avatars into text in real time using voice recognition technology. The conversation analysis unit can also input the converted text data into the generation AI. Step 3: The theme extraction unit extracts common themes from the data converted to text by the conversation analysis unit. For example, the theme extraction unit uses the generation AI to analyze the text data and extract common themes. The theme extraction unit can also input prompts to the generation AI based on the common themes. Step 4: The 3D space generator generates a 3D space based on the common theme extracted by the theme extractor. For example, the 3D space generator generates a 3D space based on the common theme using text-to-image conversion generation AI. The 3D space generator can also provide the generated 3D space within the metaverse.

[0049] (Example 2) A communication promotion system according to an embodiment of the present invention is a system that allows strangers to communicate smoothly within the metaverse. This system uses a generation AI to interpret the profiles and conversations of people gathered in the same place and generate a common 3D space based on that. This allows strangers to communicate smoothly within the metaverse.

[0050] A communication promotion system according to an embodiment includes a profile acquisition unit, a conversation analysis unit, a theme extraction unit, and a 3D space generation unit. The profile acquisition unit acquires profile information of avatars. For example, the profile information includes the avatar's name, hobbies, interests, and occupation. The profile acquisition unit can also update the avatar's profile information in real time. The conversation analysis unit converts the profile information acquired by the profile acquisition unit and the content of conversations between avatars into text. For example, the conversation analysis unit converts conversations between avatars into text in real time using voice recognition technology. The conversation analysis unit can also input the converted text data to a generation AI. The theme extraction unit extracts a common theme from the data converted by the conversation analysis unit. For example, the theme extraction unit analyzes the text data using a generation AI to extract the common theme. The theme extraction unit can also input a prompt to the generation AI based on the common theme. The 3D space generation unit generates a 3D space based on the common theme extracted by the theme extraction unit. For example, the 3D space generation unit generates a 3D space based on the common theme using a text-to-image conversion generation AI. The 3D space generation unit can also provide the generated 3D space within the metaverse. This allows the communication promotion system according to the embodiment to enable strangers to communicate smoothly within the metaverse. For example, when people with common hobbies or interests gather together, natural conversations emerge and interactions deepen. Furthermore, the generation AI can analyze conversations in real time and make appropriate suggestions, further facilitating communication.

[0051] The profile acquisition unit can analyze the facial expressions and movements of the avatar in real time and convert the emotions into text using an emotion estimation function. The profile acquisition unit, for example, captures the avatar's facial expressions with a camera and estimates the emotions using facial expression recognition technology. For example, it analyzes smiling and angry expressions in real time and records the emotions as text data. The profile acquisition unit can also analyze the avatar's movements and estimate the emotions. For example, it can analyze the avatar's movements in real time using a movement analysis algorithm and record the emotions as text data. The profile acquisition unit can also convert the avatar's emotions into text using an emotion estimation function. For example, it can analyze the avatar's emotions in real time using an emotion recognition algorithm and record the emotions as text data. In this way, by converting the avatar's emotions into text in real time, more detailed profile information can be generated.

[0052] The profile acquisition unit can analyze the avatar's past behavioral history and convert the behavioral patterns into text. For example, the profile acquisition unit can analyze the avatar's past movement history and convert the places visited and the duration of stay into text. For example, the profile acquisition unit can record the behavioral patterns of avatars that frequently visit a specific area. The profile acquisition unit can also analyze the avatar's past activities and convert the behavioral patterns into text. For example, the profile acquisition unit can analyze the history of events and activities in which the avatar participated and record the behavioral patterns as text data. The profile acquisition unit can also analyze the behavioral patterns and extract the avatar's characteristics. For example, the profile acquisition unit can analyze the avatar's behavioral patterns using frequency analysis or time series analysis and record the characteristics as text data. In this way, more detailed profile information can be generated by analyzing the avatar's past behavioral history.

[0053] The profile acquisition unit can analyze the tone and speed of the avatar's voice and convert the emotion into text using an emotion estimation function. The profile acquisition unit, for example, analyzes the tone of the avatar's voice to estimate the emotion. For example, a high-pitched voice indicates excitement or joy, while a low-pitched voice indicates calmness or sadness. The profile acquisition unit can also analyze the speed of the avatar's voice to estimate the emotion. For example, a fast-paced voice indicates tension or impatience, while a slow-paced voice indicates relaxation or calmness. The profile acquisition unit can also convert the avatar's emotion into text using the emotion estimation function. For example, a voice analysis algorithm can analyze the tone and speed of the avatar's voice in real time and record the emotion as text data. In this way, emotions can be converted into text by analyzing the tone and speed of the avatar's voice.

[0054] The profile acquisition unit can analyze images and videos uploaded by a user and convert visual information into text. For example, the profile acquisition unit analyzes images uploaded by a user and converts objects and scenes in the images into text. For example, it records places visited and activities from travel photos. The profile acquisition unit can also analyze videos uploaded by a user and convert visual information into text. For example, it analyzes movements and scenes in the videos and records the content as text data. The profile acquisition unit can also analyze visual information and extract user characteristics. For example, it analyzes visual information using image recognition technology or video analysis algorithms and records the characteristics as text data. In this way, visual information can be converted into text by analyzing images and videos uploaded by a user.

[0055] The profile acquisition unit can analyze the music and playlists selected by the user and convert the user's music preferences into text. For example, the profile acquisition unit analyzes the music genres and artists selected by the user and converts the user's music preferences into text. For example, genres such as rock and classical music are recorded. The profile acquisition unit can also analyze playlists created by the user and convert the user's music preferences into text. For example, the profile acquisition unit can analyze the characteristics of songs in the playlist and record the user's music preferences as text data. The profile acquisition unit can also analyze the user's music preferences and extract the user's characteristics. For example, the profile acquisition unit can analyze the user's music preferences using a music recommendation system or music feature extraction and record the characteristics as text data. In this way, the user's music preferences can be converted into text by analyzing the user's music and playlists selected.

[0056] The profile acquisition unit can use the emotion estimation function to suggest topics that the user is likely to be interested in in real time based on the avatar's profile information. The profile acquisition unit, for example, analyzes the avatar's profile information and suggests topics that the user is likely to be interested in in real time. For example, it suggests related events and news based on hobbies and interests. The profile acquisition unit can also use the emotion estimation function to suggest topics based on the user's emotions. For example, it can use an emotion recognition algorithm to analyze the user's emotions in real time and suggest topics based on those emotions. The profile acquisition unit can also analyze the user's past behavioral history and suggest topics that the user is likely to be interested in. For example, it can suggest topics based on an analysis of the user's past behavioral history and profile information. This makes it possible to suggest topics that the user is likely to be interested in in real time based on the avatar's profile information.

[0057] The theme extraction unit can use the emotion estimation function to preferentially extract themes that are likely to evoke emotional empathy. The theme extraction unit, for example, analyzes text data and uses the emotion estimation function to extract themes that are likely to evoke emotional empathy. For example, it preferentially extracts themes that contain a large number of positive emotions. The theme extraction unit can also extract themes based on emotion analysis results. For example, it preferentially extracts themes with high emotion scores. The theme extraction unit can also extract themes based on empathy evaluation criteria. For example, it preferentially extracts themes with high empathy. In this way, communication can proceed smoothly by preferentially extracting themes that are likely to evoke emotional empathy.

[0058] The theme extraction unit can analyze long-term changes in themes by referring to the conversation history. For example, when interpreting text data, the theme extraction unit refers to past conversation history and analyzes long-term changes in themes. For example, it analyzes conversation data from the past few months to identify changes in themes. The theme extraction unit can also analyze changes in themes based on an analysis of time-series data. For example, it can identify changes in themes using trend analysis. The theme extraction unit can also analyze long-term changes in themes based on common themes. For example, it can analyze past conversation data based on common themes to identify changes in themes. In this way, it is possible to analyze long-term changes in themes by referring to past conversation history.

[0059] The theme extraction unit can automatically translate text in different languages ​​and extract common themes across multiple languages. The theme extraction unit, for example, builds a system that automatically translates text in different languages ​​and extracts common themes across multiple languages. For example, it automatically translates text in English, Japanese, and Chinese and identifies common themes. The theme extraction unit can also automatically translate text in different languages ​​using a machine translation algorithm. For example, it automatically translates text using an algorithm with high translation accuracy. The theme extraction unit can also automatically translate text based on an evaluation standard for translation accuracy. For example, it extracts common themes based on text with high translation accuracy. In this way, it is possible to extract common themes across multiple languages ​​by automatically translating text in different languages.

[0060] The theme extraction unit can also analyze image and audio data to extract common themes from multimodal information. For example, in interpreting text data, the theme extraction unit analyzes image data to extract common themes from multimodal information. For example, the theme extraction unit analyzes objects and scenes in an image to identify common themes. The theme extraction unit can also analyze audio data to extract common themes from multimodal information. For example, the theme extraction unit can analyze audio data using an audio analysis algorithm to identify common themes. The theme extraction unit can also integrate multimodal information such as text, images, and audio to extract common themes. For example, the theme extraction unit can analyze a combination of text data and image data to identify common themes. This allows the theme extraction unit to extract common themes from multimodal information by analyzing image and audio data as well.

[0061] The theme extraction unit can extract interdisciplinary themes by referring to data from different industries and fields. The theme extraction unit, for example, refers to data from different industries and fields and extracts interdisciplinary themes by interpreting text data. For example, the theme can be identified by integrating data from the medical field and the technology field. The theme extraction unit can also combine and analyze data from different industries to extract interdisciplinary themes. For example, the theme can be identified by analyzing data from different industries based on common challenges or goals. The theme extraction unit can also analyze data based on interdisciplinary themes. For example, a common theme can be identified based on data from different industries. In this way, interdisciplinary themes can be extracted by referring to data from different industries and fields.

[0062] The theme extraction unit can use the emotion estimation function to extract and suggest themes that evoke the most positive emotions in the user in real time. The theme extraction unit, for example, uses the emotion estimation function to build a system that extracts themes that evoke the most positive emotions in the user in real time. For example, themes with high emotion scores are preferentially suggested. The theme extraction unit can also extract themes based on emotion analysis results. For example, themes are extracted based on a positivity evaluation standard. The theme extraction unit can also suggest themes based on the user's emotions. For example, an emotion recognition algorithm is used to analyze the user's emotions in real time, and themes are suggested based on the emotions. This allows the theme that evokes the most positive emotions in the user to be extracted and suggested in real time.

[0063] The 3D space generation unit can use the emotion estimation function to apply colors and sound effects according to the user's emotions in real time. For example, the 3D space generation unit uses the emotion estimation function to apply colors according to the user's emotions to the generated 3D space in real time. For example, bright colors are used when the user is feeling positive. The 3D space generation unit can also apply sound effects according to the user's emotions in real time. For example, calm music is played when the user is feeling relaxed. The 3D space generation unit can also use the emotion estimation function to adjust the design of the space according to the user's emotions in real time. For example, the color and sound effects of the space are adjusted based on the emotion score. This allows colors and sound effects according to the user's emotions to be applied in real time.

[0064] The 3D space generation unit can provide an individually customized space by reflecting the user's past behavior history. The 3D space generation unit, for example, analyzes the user's past behavior history and reflects it in the generated 3D space. For example, it can provide a space customized based on places visited or events attended in the past. The 3D space generation unit can also customize the space based on the user's preferences. For example, it can adjust the design of the space based on the user's behavior history and preferences. The 3D space generation unit can also customize the space based on the user's characteristics. For example, it can customize the space based on the user's behavior patterns and preferences. In this way, it is possible to provide an individually customized space by reflecting the user's past behavior history.

[0065] The 3D space generation unit can provide a dynamically changing space by reflecting real-time user feedback. For example, the 3D space generation unit collects real-time user feedback and reflects it in the generated 3D space. For example, the design of the space is changed based on the user's opinions and requests. The 3D space generation unit can also dynamically change the space based on real-time feedback. For example, the layout and design of the space can be adjusted according to the user's feedback. The 3D space generation unit can also optimize the space based on the user's feedback. For example, the function and design of the space can be improved based on the user's feedback. In this way, a dynamically changing space can be provided by reflecting the user's real-time feedback.

[0066] The 3D space generation unit can generate a composite space that combines different themes and scenarios, thereby providing a new experience to the user. The 3D space generation unit can generate a composite space that combines different themes and scenarios, for example, to provide a new experience to the user. For example, a space that combines a music studio and a cafe can be provided. The 3D space generation unit can also generate a space that combines different themes. For example, a space that combines sports and entertainment can be provided. The 3D space generation unit can also generate a space that combines different scenarios. For example, a space that combines education and games can be provided. In this way, a composite space that combines different themes and scenarios can be generated, thereby providing a new experience to the user.

[0067] The 3D space generation unit can provide an individually customized space by reflecting images and videos uploaded by the user. For example, the 3D space generation unit analyzes images and videos uploaded by the user and reflects them in the generated 3D space. For example, it can provide a space customized based on the user's photos. The 3D space generation unit can also customize the space based on images and videos uploaded by the user. For example, it can adjust the design of the space based on the user's videos. The 3D space generation unit can also customize the space based on the user's visual information. For example, it can analyze the user's images and videos using image recognition technology and customize the space based on that information. This makes it possible to provide an individually customized space by reflecting the images and videos uploaded by the user.

[0068] The 3D space generation unit can use the emotion estimation function to generate and provide a space in which the user can be most relaxed in real time. The 3D space generation unit, for example, uses the emotion estimation function to build a system that generates a space in which the user can be most relaxed in real time. For example, a relaxing space with a high emotion score is provided. The 3D space generation unit can also generate a space based on the user's emotion. For example, an emotion recognition algorithm can be used to analyze the user's emotion in real time and generate a space based on that emotion. The 3D space generation unit can also generate a space based on a relaxing space. For example, a space can be generated using a relaxing design or sound effects. This allows a space in which the user can be most relaxed to be generated and provided in real time.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The communication promotion system may further include a health management unit that monitors the user's health condition. The health management unit, for example, measures the user's heart rate and blood pressure in real time and analyzes the data. For example, if the heart rate is high, it may make suggestions for relaxation. The health management unit may also record the user's amount of exercise and evaluate the user's health condition. For example, it may record the number of steps taken and calories burned and provide advice for maintaining health. The health management unit may also record the user's diet and evaluate nutritional balance. For example, it may analyze photos of meals and evaluate nutrient intake. This allows the user's health condition to be monitored and appropriate advice may be provided to support health maintenance.

[0071] The communication promotion system can further include a relaxation suggestion unit that estimates the user's emotions and suggests a relaxation method suitable for the user based on the estimated emotions. For example, if the user is feeling stressed, the relaxation suggestion unit can suggest deep breathing or meditation. If the user is feeling tired, the relaxation suggestion unit can also suggest light stretching or a break. Furthermore, if the user is relaxed, the relaxation suggestion unit can also suggest music or aromas to help the user maintain that state. In this way, by suggesting a relaxation method according to the user's emotions, it is possible to support mental and physical refreshment.

[0072] The communication promotion system can further include an activity suggestion unit that suggests new activities that the user may be interested in based on the user's past behavioral history. The activity suggestion unit can, for example, analyze the history of events and activities that the user has participated in in the past and suggest similar events. The activity suggestion unit can also analyze the user's behavioral patterns and suggest activities that will stimulate new hobbies or interests. Furthermore, the activity suggestion unit can suggest activities that match the season or trends based on the user's behavioral history. This makes it possible to broaden the user's interests by suggesting new activities based on the user's past behavioral history.

[0073] The communication promotion system can further include a learning suggestion unit that estimates the user's emotions and suggests learning content suitable for the user based on the estimated emotions. For example, if the user is concentrating, the learning suggestion unit can suggest learning content with a high level of difficulty. If the user is relaxed, the learning suggestion unit can also suggest content that allows the user to learn while relaxing. Furthermore, if the user is tired, the learning suggestion unit can also suggest content that can be learned in a short amount of time. In this way, effective learning can be supported by suggesting learning content that matches the user's emotions.

[0074] The communication promotion system may further include a marketing proposal unit that proposes related products and services based on the user's hobbies and interests. For example, if the user is interested in music, the marketing proposal unit may propose the latest music albums and concert tickets. If the user is interested in sports, the marketing proposal unit may also propose sports equipment and event information. Furthermore, the marketing proposal unit may propose related online courses and workshops based on the user's hobbies and interests. This makes it possible to increase user satisfaction by proposing related products and services based on the user's hobbies and interests.

[0075] The communication promotion system may further include an entertainment suggestion unit that estimates the user's emotions and suggests entertainment content suitable for the user based on the estimated emotions. For example, if the user is having fun, the entertainment suggestion unit may suggest comedy movies or fun games. If the user is relaxed, the entertainment suggestion unit may also suggest relaxing music or movies. If the user is sad, the entertainment suggestion unit may also suggest positive content to lift the user's spirits. In this way, the user's mood can be improved by suggesting entertainment content that matches the user's emotions.

[0076] The communication promotion system may further include a location information providing unit that acquires real-time location information of the user and provides related information based on the location information. For example, when the user is in a specific location, the location information providing unit may suggest events or tourist spots related to that location. The location information providing unit may also analyze the user's movement history and suggest new places based on places previously visited. Furthermore, the location information providing unit may suggest nearby restaurants or cafes based on the user's location information. This improves user convenience by providing related information based on the user's real-time location information.

[0077] The communication promotion system may further include a fitness suggestion unit that estimates the user's emotions and suggests a fitness program suitable for the user based on the estimated emotions. For example, the fitness suggestion unit may suggest a high-intensity fitness program if the user is energetic. The fitness suggestion unit may also suggest a yoga or stretching program if the user is relaxed. Furthermore, the fitness suggestion unit may also suggest exercises for relieving stress if the user is feeling stressed. In this way, by suggesting a fitness program according to the user's emotions, effective fitness can be supported.

[0078] The communication promotion system can further include a purchase suggestion unit that analyzes the user's past purchase history and suggests new products that the user may be interested in. The purchase suggestion unit can, for example, analyze the user's history of past purchases of products and services and suggest similar products. The purchase suggestion unit can also analyze the user's purchasing patterns and suggest products based on new trends. Furthermore, the purchase suggestion unit can suggest products that match the season or event based on the user's purchase history. This can improve the user's purchasing experience by suggesting new products based on the user's past purchase history.

[0079] The communication promotion system can further include a travel suggestion unit that estimates the user's emotions and suggests a travel plan suitable for the user based on the estimated emotions. For example, if the user is feeling adventurous, the travel suggestion unit can suggest an active travel plan. If the user wants to relax, the travel suggestion unit can also suggest a travel plan to a resort or hot spring area. Furthermore, if the user is looking for a cultural experience, the travel suggestion unit can also suggest a travel plan that includes historical tourist spots and art museums. In this way, by suggesting a travel plan that suits the user's emotions, it is possible to provide a highly satisfying travel experience.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The profile acquisition unit acquires the avatar's profile information, such as the avatar's name, hobbies, interests, occupation, etc. The profile acquisition unit can also update the avatar's profile information in real time. Step 2: The conversation analysis unit converts the profile information acquired by the profile acquisition unit and the content of the conversation between the avatars into text. For example, the conversation analysis unit converts the conversation between the avatars into text in real time using voice recognition technology. The conversation analysis unit can also input the converted text data into the generation AI. Step 3: The theme extraction unit extracts common themes from the data converted to text by the conversation analysis unit. For example, the theme extraction unit uses the generation AI to analyze the text data and extract common themes. The theme extraction unit can also input prompts to the generation AI based on the common themes. Step 4: The 3D space generator generates a 3D space based on the common theme extracted by the theme extractor. For example, the 3D space generator generates a 3D space based on the common theme using text-to-image conversion generation AI. The 3D space generator can also provide the generated 3D space within the metaverse.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 profile acquisition unit that acquires profile information of an avatar; a conversation analysis unit that converts the profile information acquired by the profile acquisition unit and the content of conversations between avatars into text; a theme extraction unit that extracts common themes from the data converted into text by the conversation analysis unit; a 3D space generation unit that generates a 3D space based on the common theme extracted by the theme extraction unit; A system characterized by:

2. The profile acquisition unit The avatar's facial expressions and movements are analyzed in real time, and emotions are converted into text using an emotion estimation function.

2. The system of claim 1.

3. The profile acquisition unit Analyzes images and videos uploaded by users and converts visual information into text 2. The system of claim 1.

4. The theme extraction unit Using the emotion estimation function, the themes that are likely to evoke emotional empathy are preferentially extracted.

2. The system of claim 1.

5. The 3D space generation unit Using emotion estimation, colors and sound effects are applied in real time according to the user's emotions.

2. The system of claim 1.

6. The profile acquisition unit Analyze the avatar's past behavior history and convert the behavioral patterns into text.

2. The system of claim 1.

7. The theme extraction unit Automatically translate texts in different languages ​​and extract common themes across multiple languages 2. The system of claim 1.

8. The 3D space generation unit Provide a dynamically changing space that reflects real-time user feedback 2. The system of claim 1.

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