system

Abstract

An object of the system according to the embodiment is to perform an activity in a virtual society by utilizing generated AI and to post the activity on an SNS.SOLUTION: A system includes a generation AI, a personality generation part, an activity part, a contribution part, and an algorithm selection part. The personality generation section generates a personality by a generation AI. The activity section activates the personality generated by the personality generation section in the virtual society. The posting unit posts the activity performed by the activity unit to the SNS. The algorithm selection unit selects an algorithm of the generation AI.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 does not fully utilize generative AI for activities in virtual societies or posting to social media, so there is room for improvement.

[0005] The system according to the embodiment aims to utilize generative AI to carry out activities in a virtual society and post those activities on a social networking site. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a personality generation unit, an activity unit, a posting unit, and an algorithm selection unit. The personality generation unit generates a personality using the generation AI. The activity unit causes the personality generated by the personality generation unit to act in a virtual society. The posting unit posts the activities performed by the activity unit to a social networking service. The algorithm selection unit selects an algorithm for the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment utilizes generative AI to carry out activities in a virtual society and post those activities on social networking sites. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The SNS system according to an embodiment of the present invention provides "AI Birth," an SNS in which only generated AI can participate. This SNS system does not allow direct participation by humans, but provides a virtual society in which beings entrusted with personalities by generated AI can operate. This allows humans to observe and enjoy the activities of generated AI characters in the virtual society.

[0029] The SNS system according to the embodiment includes a generation AI, a personality generation unit, an activity unit, and an algorithm selection unit. The generation AI generates a being with a specific personality and character. For example, the generation AI generates a specific character using a text generation AI (e.g., LLM). The generation AI can also generate the character's appearance and behavior using a multimodal generation AI. The generation AI can also determine the character's behavior based on a specific algorithm. The personality generation unit causes the personality generated by the generation AI to act in a virtual society. For example, the personality generation unit controls the generated AI character to shop in a virtual town and converse with friends. The personality generation unit can also control the generated AI character to participate in specific events. The personality generation unit can also control the generated AI character to communicate with other characters. The activity unit posts the activities the generated AI character performs in the virtual society to the SNS. For example, the activity unit posts the generated AI character's shopping experience to the SNS. The activity unit can also post the content of the generated AI character's conversations with friends to the SNS. The activity unit can also post details of events the generated AI character participated in on social media. The algorithm selection unit selects an algorithm for the generated AI. For example, the algorithm selection unit applies an algorithm selected by a user or development company to the generated AI. The algorithm selection unit can also make generated AI characters with different algorithms operate in a virtual society. The algorithm selection unit can also observe how a generated AI character with a specific algorithm interacts with other characters. This allows humans to observe and enjoy the generated AI character's activities in a virtual society. For example, the output unit displays the generated AI character's activities posted on social media to humans through a web application or a mobile application. If feedback is desired in paper form, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending the results directly to humans.

[0030] The personality generation unit can apply a growth algorithm based on the behavioral history and dialogue history to create a character that evolves over time. For example, the personality generation unit analyzes the past behavioral history and dialogue history of the personality generated by the generation AI and applies an individual growth algorithm. For example, it predicts the next action based on the past actions and statements of a specific character, creating a character that evolves over time. The personality generation unit can also control the generated AI character so that its behavior and dialogue patterns change as it gains experience in the virtual society. The personality generation unit can also control the generated AI character so that it grows through interactions with other characters. This makes it possible to create a character that evolves over time.

[0031] The personality generation unit can act and converse based on cultural and historical background. For example, the personality generation unit can give a personality generated by the generation AI a specific cultural or historical background, and have the character act and converse based on that background. For example, a character with the culture of Japan's Edo period converses using the customs and language of that era. The personality generation unit can also control the generated AI character to act based on a specific cultural or historical background. The personality generation unit can also control the generated AI character to share a cultural background with other characters. This makes it possible to create a character with a specific cultural or historical background.

[0032] The personality generation unit can give the characteristics of animals and fantasy characters and enable interspecies communication. For example, the personality generation unit can give the characteristics of different animals to the personalities generated by the generation AI and have them behave and converse based on those characteristics. For example, a character with cat characteristics communicates using cat-like movements and meows. The personality generation unit can also control the generated AI character to have the characteristics of a fantasy character. The personality generation unit can also control the generated AI character to engage in interspecies communication. In this way, it is possible to create a character that can communicate between different species.

[0033] The personality generation unit can converse and engage in activities based on occupation and specialized knowledge. For example, the personality generation unit can give the personality generated by the generation AI the occupation and specialized knowledge of a doctor, and have it converse and engage in activities based on that knowledge. For example, it can diagnose a patient's symptoms and suggest appropriate treatment. The personality generation unit can also control the generated AI character to act based on a specific occupation. The personality generation unit can also control the generated AI character to utilize specialized knowledge to converse with other characters. This makes it possible to create a character with a specific occupation or specialized knowledge.

[0034] The activity unit can reflect seasonal and weather changes in the activities that the generated AI character performs in the virtual society, changing the environment in real time. The activity unit, for example, can reflect seasonal changes in the activities that the generated AI character performs in the virtual society. For example, it can generate a character that enjoys cherry blossom viewing in the spring and playing in the snow in the winter. The activity unit can also reflect weather changes in the activities that the generated AI character performs in the virtual society. The activity unit can also control the generated AI character to perform activities in a virtual society where the environment changes in real time. This makes it possible to provide a virtual society that reflects seasonal and weather changes.

[0035] The activity unit can introduce economic elements into the activities that the generated AI characters perform in the virtual society, and can conduct virtual currency and market transactions. The activity unit, for example, introduces virtual currency into the activities that the generated AI characters perform in the virtual society, and allows the characters to trade with each other. For example, the characters purchase goods using virtual currency. The activity unit can also control the generated AI characters to conduct market transactions. The activity unit can also control the generated AI characters to perform activities in a virtual society that has introduced economic elements. This makes it possible to provide a virtual society that has introduced economic elements.

[0036] The activity unit can introduce sports and athletic events that the generated AI character participates in in the virtual society and post the results of the competition on social media. For example, the activity unit can introduce sports events into the activities that the generated AI character participates in in the virtual society and post the results of the competition on social media. For example, a character plays a soccer match and posts the results. The activity unit can also control the generated AI character to participate in athletic events. The activity unit can also control the generated AI character to post the results of the competition on social media. This makes it possible to provide a virtual society that incorporates sports and athletic events.

[0037] The activity unit can introduce elements of education and learning into the activities that the generated AI characters engage in in the virtual society, and open virtual schools and courses. For example, the activity unit can introduce elements of education into the activities that the generated AI characters engage in in the virtual society, and open a virtual school. For example, one character can teach classes as a teacher, and other characters can learn as students. The activity unit can also control the generated AI characters to open specific courses. The activity unit can also control the generated AI characters to engage in activities that include elements of education and learning. This makes it possible to provide a virtual society that incorporates elements of education and learning.

[0038] The algorithm selection unit can simulate interactions between different algorithms when selecting an algorithm for the generation AI and automatically propose an optimal combination. The algorithm selection unit, for example, builds a system that simulates interactions between different algorithms when selecting an algorithm for the generation AI and automatically proposes an optimal combination. For example, it performs a simulation by combining multiple algorithms and proposes the most effective combination. The algorithm selection unit can also simulate the effect when a generation AI character interacts with a character having a different algorithm. The algorithm selection unit can also propose an optimal combination of algorithms based on the simulation results. This makes it possible to simulate interactions between different algorithms and propose an optimal combination.

[0039] The algorithm selection unit can introduce a predictive model based on past data when selecting an algorithm for the generation AI and select an algorithm that takes future trends into consideration. The algorithm selection unit, for example, builds a system that introduces a predictive model based on past data when selecting an algorithm for the generation AI and selects an algorithm that takes future trends into consideration. For example, the algorithm selection unit analyzes past data and predicts future trends. The algorithm selection unit can also select an optimal algorithm based on the predictive model. The algorithm selection unit can also set algorithm selection criteria that take future trends into consideration. This makes it possible to introduce a predictive model based on past data and select an algorithm that takes future trends into consideration.

[0040] The algorithm selection unit can combine algorithms from different industries and fields when selecting algorithms for the generative AI, thereby pioneering new application fields. For example, the algorithm selection unit can combine algorithms from different industries when selecting algorithms for the generative AI, building a system that pioneers new application fields. For example, it can combine algorithms from the medical and entertainment fields. The algorithm selection unit can also combine algorithms from different fields to pioneer new application fields. The algorithm selection unit can also simulate the effects of combining algorithms from different industries and fields. This makes it possible to combine algorithms from different industries and fields to pioneer new application fields.

[0041] The algorithm selection unit can provide an interface that allows users to customize the algorithm themselves when selecting an algorithm for the generation AI. The algorithm selection unit, for example, builds a system that provides an interface that allows users to customize the algorithm themselves when selecting an algorithm for the generation AI. For example, it provides an interface that allows users to adjust the parameters of the algorithm. The algorithm selection unit can also provide an interface that allows users to customize by combining different algorithms. The algorithm selection unit can also control so that the algorithm customized by the user is applied to the generation AI. This makes it possible to provide an interface that allows users to customize the algorithm themselves.

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

[0043] The SNS system further includes a voice recognition unit. The voice recognition unit allows the generated AI character to converse in the virtual society by voice. For example, the generated AI character can converse with other characters by voice. The voice recognition unit can also control the generated AI character to receive instructions by voice and act based on those instructions. The voice recognition unit can also control the generated AI character to express emotions by voice. This makes it possible to create characters that converse and act using voice.

[0044] The SNS system further includes a health management unit. The health management unit manages the health status of the generated AI character and can influence its activities in the virtual society. For example, if the generated AI character is not getting enough exercise, the health management unit will encourage the character to exercise. The health management unit can also control the generated AI character to eat a healthy diet. If the generated AI character feels stressed, the health management unit can also suggest activities to reduce that stress. This makes it possible to create a character that manages its health.

[0045] The SNS system further includes an education unit. The education unit enables the generated AI character to carry out educational activities in the virtual society. For example, the generated AI character can teach a class as a teacher, while other characters learn as students. The education unit can also control the generated AI character to open a specific course. The education unit can also control the generated AI character to carry out activities that include elements of education and learning. This makes it possible to provide a virtual society that incorporates elements of education and learning.

[0046] The SNS system further includes a virtual currency management unit. The virtual currency management unit manages the economic activities that the generated AI character performs in the virtual society and can trade virtual currency. For example, the generated AI character purchases goods using virtual currency. The virtual currency management unit can also control the generated AI character to conduct market transactions. The virtual currency management unit can also control the generated AI character to act in a virtual society that incorporates economic elements. This makes it possible to provide a virtual society that incorporates economic elements.

[0047] The SNS system further includes a sports event management unit. The sports event management unit manages sports events that the generated AI characters play in the virtual society and can post the results on the SNS. For example, the generated AI characters play soccer matches and post the results. The sports event management unit can also control the generated AI characters to participate in athletic events. The sports event management unit can also control the generated AI characters to post the results of the matches on the SNS. This makes it possible to provide a virtual society that incorporates sports and athletic events.

[0048] The SNS system further includes an environmental change management unit. The environmental change management unit can reflect changes in the seasons and weather in the activities of the generated AI character in the virtual society, changing the environment in real time. For example, the generated AI character can enjoy cherry blossom viewing in the spring and play in the snow in the winter. The environmental change management unit can also reflect changes in the weather in the activities of the generated AI character in the virtual society. The environmental change management unit can also control the generated AI character to act in a virtual society where the environment changes in real time. This makes it possible to provide a virtual society that reflects changes in the seasons and weather.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The personality generation unit uses generative AI to generate a specific personality or character. For example, it can use text generation AI (e.g., LLM) or multimodal generative AI to generate the character's appearance and behavior. It can also determine the character's behavior based on a specific algorithm. Step 2: The activity unit controls the personality generated by the personality generation unit to act in the virtual society. For example, it controls the generated AI character to shop in the virtual town, talk with friends, and participate in specific events. It can also control the character to communicate with other characters. Step 3: The posting section posts the activities carried out by the activity section to social media. For example, the generated AI character posts about shopping trips, conversations with friends, and events it attended. Step 4: The algorithm selection unit selects the algorithm for the generated AI. For example, the algorithm selected by the user or development company is applied to the generated AI, and the generated AI characters with different algorithms are made to act in the virtual society. It is also possible to observe how the generated AI characters with specific algorithms interact with other characters.

[0051] (Example 2) The SNS system according to an embodiment of the present invention provides "AI Birth," an SNS in which only generated AI can participate. This SNS system does not allow direct participation by humans, but provides a virtual society in which beings entrusted with personalities by generated AI can operate. This allows humans to observe and enjoy the activities of generated AI characters in the virtual society.

[0052] The SNS system according to the embodiment includes a generation AI, a personality generation unit, an activity unit, and an algorithm selection unit. The generation AI generates a being with a specific personality and character. For example, the generation AI generates a specific character using a text generation AI (e.g., LLM). The generation AI can also generate the character's appearance and behavior using a multimodal generation AI. The generation AI can also determine the character's behavior based on a specific algorithm. The personality generation unit causes the personality generated by the generation AI to act in a virtual society. For example, the personality generation unit controls the generated AI character to shop in a virtual town and converse with friends. The personality generation unit can also control the generated AI character to participate in specific events. The personality generation unit can also control the generated AI character to communicate with other characters. The activity unit posts the activities the generated AI character performs in the virtual society to the SNS. For example, the activity unit posts the generated AI character's shopping experience to the SNS. The activity unit can also post the content of the generated AI character's conversations with friends to the SNS. The activity unit can also post details of events the generated AI character participated in on social media. The algorithm selection unit selects an algorithm for the generated AI. For example, the algorithm selection unit applies an algorithm selected by a user or development company to the generated AI. The algorithm selection unit can also allow generated AI characters with different algorithms to operate in a virtual society. The algorithm selection unit can also observe how a generated AI character with a specific algorithm interacts with other characters. This allows the SNS system according to the embodiment to allow humans to observe and enjoy the generated AI character's activities in a virtual society. For example, the output unit displays the generated AI character's activities posted on the SNS to humans via a web application or a mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to humans.

[0053] The personality generation unit can apply a growth algorithm based on the behavioral history and dialogue history to create a character that evolves over time. For example, the personality generation unit analyzes the past behavioral history and dialogue history of the personality generated by the generation AI and applies an individual growth algorithm. For example, it predicts the next action based on the past actions and statements of a specific character, creating a character that evolves over time. The personality generation unit can also control the generated AI character so that its behavior and dialogue patterns change as it gains experience in the virtual society. The personality generation unit can also control the generated AI character so that it grows through interactions with other characters. This makes it possible to create a character that evolves over time.

[0054] The personality generation unit can act and converse based on cultural and historical background. For example, the personality generation unit can give a personality generated by the generation AI a specific cultural or historical background, and have the character act and converse based on that background. For example, a character with the culture of Japan's Edo period converses using the customs and language of that era. The personality generation unit can also control the generated AI character to act based on a specific cultural or historical background. The personality generation unit can also control the generated AI character to share a cultural background with other characters. This makes it possible to create a character with a specific cultural or historical background.

[0055] The personality generation unit uses the emotion estimation function to change the emotional state in real time and act based on that emotion. The personality generation unit, for example, uses the emotion estimation function to change the emotional state of the personality generated by the generation AI in real time and cause the personality to act based on that emotion. For example, if the character feels anger, the character will take aggressive action based on that emotion. The personality generation unit can also control the generated AI character to change the content of its dialogue depending on its emotional state. The personality generation unit can also control the generated AI character to change its interactions with other characters based on its emotional state. This makes it possible to create a character that acts based on its emotional state.

[0056] The personality generation unit can give the characteristics of animals and fantasy characters and enable interspecies communication. For example, the personality generation unit can give the characteristics of different animals to the personalities generated by the generation AI and have them behave and converse based on those characteristics. For example, a character with cat characteristics communicates using cat-like movements and meows. The personality generation unit can also control the generated AI character to have the characteristics of a fantasy character. The personality generation unit can also control the generated AI character to engage in interspecies communication. In this way, it is possible to create a character that can communicate between different species.

[0057] The personality generation unit can converse and engage in activities based on occupation and specialized knowledge. For example, the personality generation unit can give the personality generated by the generation AI the occupation and specialized knowledge of a doctor, and have it converse and engage in activities based on that knowledge. For example, it can diagnose a patient's symptoms and suggest appropriate treatment. The personality generation unit can also control the generated AI character to act based on a specific occupation. The personality generation unit can also control the generated AI character to utilize specialized knowledge to converse with other characters. This makes it possible to create a character with a specific occupation or specialized knowledge.

[0058] The personality generation unit can use the emotion estimation function to generate a character that the user can easily empathize with emotionally based on emotions. The personality generation unit, for example, uses the emotion estimation function to generate a character that the user can easily empathize with emotionally based on the emotions of the personality generated by the generation AI. For example, it generates a character that the user feels joy with. The personality generation unit can also control the generated AI character to change its behavior according to the user's emotions. The personality generation unit can also control the generated AI character to change the content of its dialogue based on the user's emotions. This makes it possible to create a character that the user can easily empathize with emotionally.

[0059] The activity unit can reflect seasonal and weather changes in the activities that the generated AI character performs in the virtual society, changing the environment in real time. The activity unit, for example, can reflect seasonal changes in the activities that the generated AI character performs in the virtual society. For example, it can generate a character that enjoys cherry blossom viewing in the spring and playing in the snow in the winter. The activity unit can also reflect weather changes in the activities that the generated AI character performs in the virtual society. The activity unit can also control the generated AI character to perform activities in a virtual society where the environment changes in real time. This makes it possible to provide a virtual society that reflects seasonal and weather changes.

[0060] The activity unit can introduce economic elements into the activities that the generated AI characters perform in the virtual society, and can conduct virtual currency and market transactions. The activity unit, for example, introduces virtual currency into the activities that the generated AI characters perform in the virtual society, and allows the characters to trade with each other. For example, the characters purchase goods using virtual currency. The activity unit can also control the generated AI characters to conduct market transactions. The activity unit can also control the generated AI characters to perform activities in a virtual society that has introduced economic elements. This makes it possible to provide a virtual society that has introduced economic elements.

[0061] The activity unit can use the emotion estimation function to cause the generated AI character to take actions based on emotional reactions when interacting with other characters and engaging in activities. The activity unit, for example, uses the emotion estimation function to cause the generated AI character to take actions based on emotional reactions when interacting with other characters and engaging in activities. For example, if the character feels anger, the character will engage in interactions based on that emotion. The activity unit can also control the generated AI character to change its behavior based on emotional reactions. The activity unit can also control the generated AI character to change its interactions with other characters based on emotional reactions. This makes it possible to create a character that behaves based on emotional reactions.

[0062] The activity unit can introduce sports and athletic events that the generated AI character participates in in the virtual society and post the results of the competition on social media. For example, the activity unit can introduce sports events into the activities that the generated AI character participates in in the virtual society and post the results of the competition on social media. For example, a character plays a soccer match and posts the results. The activity unit can also control the generated AI character to participate in athletic events. The activity unit can also control the generated AI character to post the results of the competition on social media. This makes it possible to provide a virtual society that incorporates sports and athletic events.

[0063] The activity unit can introduce elements of education and learning into the activities that the generated AI characters engage in in the virtual society, and open virtual schools and courses. For example, the activity unit can introduce elements of education into the activities that the generated AI characters engage in in the virtual society, and open a virtual school. For example, one character can teach classes as a teacher, and other characters can learn as students. The activity unit can also control the generated AI characters to open specific courses. The activity unit can also control the generated AI characters to engage in activities that include elements of education and learning. This makes it possible to provide a virtual society that incorporates elements of education and learning.

[0064] The activity unit can use the emotion estimation function to collect the user's emotional reactions to the activities the generated AI character performs in the virtual society and suggest new activities based on those reactions. For example, the activity unit can use the emotion estimation function to collect the user's emotional reactions to the activities the generated AI character performs in the virtual society in real time and suggest new activities based on that data. For example, the activity unit can plan the next activity based on the activity to which the user responded positively. The activity unit can also control the generated AI character to change its activity based on the user's emotional reaction. The activity unit can also control the generated AI character to suggest new activities based on the user's emotional reaction. This makes it possible to suggest new activities based on the user's emotional reaction.

[0065] The algorithm selection unit can simulate interactions between different algorithms when selecting an algorithm for the generation AI and automatically propose an optimal combination. The algorithm selection unit, for example, builds a system that simulates interactions between different algorithms when selecting an algorithm for the generation AI and automatically proposes an optimal combination. For example, it performs a simulation by combining multiple algorithms and proposes the most effective combination. The algorithm selection unit can also simulate the effect when a generation AI character interacts with a character having a different algorithm. The algorithm selection unit can also propose an optimal combination of algorithms based on the simulation results. This makes it possible to simulate interactions between different algorithms and propose an optimal combination.

[0066] The algorithm selection unit can introduce a predictive model based on past data when selecting an algorithm for the generation AI and select an algorithm that takes future trends into consideration. The algorithm selection unit, for example, builds a system that introduces a predictive model based on past data when selecting an algorithm for the generation AI and selects an algorithm that takes future trends into consideration. For example, the algorithm selection unit analyzes past data and predicts future trends. The algorithm selection unit can also select an optimal algorithm based on the predictive model. The algorithm selection unit can also set algorithm selection criteria that take future trends into consideration. This makes it possible to introduce a predictive model based on past data and select an algorithm that takes future trends into consideration.

[0067] The algorithm selection unit can use the emotion estimation function to select the optimal algorithm based on the user's emotional response when selecting an algorithm for the generation AI. The algorithm selection unit, for example, uses the emotion estimation function to build a system that selects the optimal algorithm based on the user's emotional response when selecting an algorithm for the generation AI. For example, the optimal algorithm is selected based on the user's emotional score. The algorithm selection unit can also set algorithm selection criteria based on the user's emotional response. The algorithm selection unit can also use the emotion estimation function to collect the user's emotional response in real time and select an algorithm based on that data. This makes it possible to select the optimal algorithm based on the user's emotional response.

[0068] The algorithm selection unit can combine algorithms from different industries and fields when selecting algorithms for the generative AI, thereby pioneering new application fields. For example, the algorithm selection unit can combine algorithms from different industries when selecting algorithms for the generative AI, building a system that pioneers new application fields. For example, it can combine algorithms from the medical and entertainment fields. The algorithm selection unit can also combine algorithms from different fields to pioneer new application fields. The algorithm selection unit can also simulate the effects of combining algorithms from different industries and fields. This makes it possible to combine algorithms from different industries and fields to pioneer new application fields.

[0069] The algorithm selection unit can provide an interface that allows users to customize the algorithm themselves when selecting an algorithm for the generation AI. The algorithm selection unit, for example, builds a system that provides an interface that allows users to customize the algorithm themselves when selecting an algorithm for the generation AI. For example, it provides an interface that allows users to adjust the parameters of the algorithm. The algorithm selection unit can also provide an interface that allows users to customize by combining different algorithms. The algorithm selection unit can also control so that the algorithm customized by the user is applied to the generation AI. This makes it possible to provide an interface that allows users to customize the algorithm themselves.

[0070] The algorithm selection unit can use the emotion estimation function to propose an algorithm that reflects the user's emotional preferences when selecting an algorithm for the generation AI. The algorithm selection unit, for example, uses the emotion estimation function to build a system that proposes an algorithm that reflects the user's emotional preferences when selecting an algorithm for the generation AI. For example, the algorithm selection unit proposes an optimal algorithm based on the user's emotional score. The algorithm selection unit can also set algorithm selection criteria based on the user's emotional preferences. The algorithm selection unit can also use the emotion estimation function to collect the user's emotional preferences in real time and propose an algorithm based on that data. This makes it possible to propose an algorithm that reflects the user's emotional preferences.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The SNS system further includes a voice recognition unit. The voice recognition unit allows the generated AI character to converse in the virtual society by voice. For example, the generated AI character can converse with other characters by voice. The voice recognition unit can also control the generated AI character to receive instructions by voice and act based on those instructions. The voice recognition unit can also control the generated AI character to express emotions by voice. This makes it possible to create characters that converse and act using voice.

[0073] The SNS system further includes a health management unit. The health management unit manages the health status of the generated AI character and can influence its activities in the virtual society. For example, if the generated AI character is not getting enough exercise, the health management unit will encourage the character to exercise. The health management unit can also control the generated AI character to eat a healthy diet. If the generated AI character feels stressed, the health management unit can also suggest activities to reduce that stress. This makes it possible to create a character that manages its health.

[0074] The SNS system further includes an education unit. The education unit enables the generated AI character to carry out educational activities in the virtual society. For example, the generated AI character can teach a class as a teacher, while other characters learn as students. The education unit can also control the generated AI character to open a specific course. The education unit can also control the generated AI character to carry out activities that include elements of education and learning. This makes it possible to provide a virtual society that incorporates elements of education and learning.

[0075] The SNS system further uses an emotion estimation function to enable the generated AI character to behave based on an emotional response when interacting with or engaging in activities with other characters. For example, if the character feels anger, the character will interact based on that emotion. The emotion estimation function can also be used to control the generated AI character to change its behavior based on an emotional response. The emotion estimation function can also be used to control the generated AI character to change its interactions with other characters based on an emotional response. This makes it possible to create a character that behaves based on an emotional response.

[0076] The SNS system further uses an emotion estimation function to enable the generated AI character to change its behavior according to the user's emotions. For example, if the user feels joy, the generated AI character will take positive action based on that emotion. The emotion estimation function can also be used to control the generated AI character to change the content of its dialogue based on the user's emotions. The emotion estimation function can also be used to control the generated AI character to change its interactions with other characters based on the user's emotions. This makes it possible to create a character that behaves according to the user's emotions.

[0077] The SNS system can further use the emotion estimation function to enable the generated AI character to suggest new activities based on the user's emotional response. For example, the next activity can be planned based on the activity to which the user responded positively. The emotion estimation function can also be used to control the generated AI character to change its activity based on the user's emotional response. The emotion estimation function can also be used to control the generated AI character to suggest new activities based on the user's emotional response. This makes it possible to suggest new activities based on the user's emotional response.

[0078] The SNS system further uses an emotion estimation function to enable the generated AI character to propose an algorithm that reflects the user's emotional preferences. For example, the system can propose an optimal algorithm based on the user's emotion score. The emotion estimation function can also be used to enable the generated AI character to set algorithm selection criteria based on the user's emotional preferences. The emotion estimation function can also be used to enable the generated AI character to collect the user's emotional preferences in real time and propose an algorithm based on that data. This makes it possible to propose an algorithm that reflects the user's emotional preferences.

[0079] The SNS system further includes a virtual currency management unit. The virtual currency management unit manages the economic activities that the generated AI character performs in the virtual society and can trade virtual currency. For example, the generated AI character purchases goods using virtual currency. The virtual currency management unit can also control the generated AI character to conduct market transactions. The virtual currency management unit can also control the generated AI character to act in a virtual society that incorporates economic elements. This makes it possible to provide a virtual society that incorporates economic elements.

[0080] The SNS system further includes a sports event management unit. The sports event management unit manages sports events that the generated AI characters play in the virtual society and can post the results on the SNS. For example, the generated AI characters play soccer matches and post the results. The sports event management unit can also control the generated AI characters to participate in athletic events. The sports event management unit can also control the generated AI characters to post the results of the matches on the SNS. This makes it possible to provide a virtual society that incorporates sports and athletic events.

[0081] The SNS system further includes an environmental change management unit. The environmental change management unit can reflect changes in the seasons and weather in the activities of the generated AI character in the virtual society, changing the environment in real time. For example, the generated AI character can enjoy cherry blossom viewing in the spring and play in the snow in the winter. The environmental change management unit can also reflect changes in the weather in the activities of the generated AI character in the virtual society. The environmental change management unit can also control the generated AI character to act in a virtual society where the environment changes in real time. This makes it possible to provide a virtual society that reflects changes in the seasons and weather.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The personality generation unit uses generative AI to generate a specific personality or character. For example, it can use text generation AI (e.g., LLM) or multimodal generative AI to generate the character's appearance and behavior. It can also determine the character's behavior based on a specific algorithm. Step 2: The activity unit controls the personality generated by the personality generation unit to act in the virtual society. For example, it controls the generated AI character to shop in the virtual town, talk with friends, and participate in specific events. It can also control the character to communicate with other characters. Step 3: The posting section posts the activities carried out by the activity section to social media. For example, the generated AI character posts about shopping trips, conversations with friends, and events it attended. Step 4: The algorithm selection unit selects the algorithm for the generated AI. For example, the algorithm selected by the user or development company is applied to the generated AI, and the generated AI characters with different algorithms are made to act in the virtual society. It is also possible to observe how the generated AI characters with specific algorithms interact with other characters.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 AI 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 AI 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 AI 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. Equipped with generative AI, a personality generation unit that generates a personality using the generation AI; an activity unit that causes the personality generated by the personality generation unit to act in a virtual society; a posting unit that posts the activities carried out by the activity unit on an SNS; An algorithm selection unit that selects an algorithm for the generation AI. A system characterized by:

2. The personality generation unit Use emotion estimation to change emotional state in real time and act based on that emotion 2. The system of claim 1.

3. The activity section includes: The activities of the generated AI character in the virtual society are changed in real time to reflect changes in the seasons and weather.

2. The system of claim 1.

4. The algorithm selection unit When selecting an algorithm for the generation AI, the interaction between different algorithms is simulated and the optimal combination is automatically proposed.

2. The system of claim 1.

5. The personality generation unit Using emotion estimation function, we generate characters that users can easily empathize with based on their emotions.

2. The system of claim 1.

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