System

Abstract

An object of the system according to the embodiment is to efficiently generate, manage, and sell virtual personalities having various backbones.SOLUTION: A system according to an embodiment includes a generation part, a management part and a sales part. The generating section generates a virtual personality having various backbones. The managing section manages the virtual personality generated by the generating section. The sales section sells the virtual personality managed by the management section.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 technologies have had the problem of making it difficult to efficiently generate, manage, and sell virtual personalities with diverse backgrounds.

[0005] The system according to the embodiment aims to efficiently generate, manage, and sell virtual personalities with diverse backgrounds. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a management unit, and a sales unit. The generation unit generates virtual personalities with diverse backbones. The management unit manages the virtual personalities generated by the generation unit. The sales unit sells the virtual personalities managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate, manage, and sell virtual personalities with diverse backgrounds. [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 virtual personality generation system according to an embodiment of the present invention generates a large number of virtual personalities with diverse backgrounds and manages and sells them. By applying virtual personalities to AI capable of advanced dialogue, this system enables rapid and low-cost large-scale surveys, monitoring, and prototyping. Furthermore, by placing virtual personalities in the metaverse, realism and liveliness can be brought to the space. As a result, the virtual personality generation system is expected to be utilized in a variety of fields, potentially creating new demand.

[0029] A virtual personality generation system according to an embodiment includes a generation unit, a management unit, and a sales unit. The generation unit generates virtual personalities with diverse backbones. For example, the generation AI receives prompts containing instructions from a user as input and generates a virtual personality based on the prompts. The generation AI can generate a virtual personality with a specific occupation, hobby, or personality. The generation AI can generate a virtual personality using, for example, a text generation AI (e.g., LLM). The generation AI can also generate a virtual personality's backbone using a multimodal generation AI. The generation AI can also generate a virtual personality with specific attributes based on a user's instructions. The management unit manages the generated virtual personalities. For example, the management unit records the virtual personality's attributes and interaction history, allowing users to easily access them. The management unit can also update and delete the virtual personalities. The management unit manages the virtual personalities using, for example, a cloud-based database. The management unit can also manage the virtual personalities through a user interface. The sales unit sells the virtual personalities managed by the management unit. For example, the sales department sells virtual personalities through an online platform. The sales department allows users to select and purchase virtual personalities that meet their needs. The sales department can also set prices and promote virtual personalities. The sales department sells virtual personalities using, for example, an e-commerce platform. The sales department can also suggest related virtual personalities based on a user's purchase history. As a result, the virtual personality generation system according to the embodiment can generate, manage, and sell virtual personalities with diverse backgrounds.

[0030] The generation unit can generate a personality based on an actual historical figure or a fictional character as the backbone of the virtual personality. For example, the generation AI generates a virtual personality based on an actual historical figure. For example, the generation AI generates a virtual personality with the characteristics of a historical figure such as Napoleon or Cleopatra, and reflects that person's knowledge and personality in interactions with the user. The generation AI can also generate a virtual personality based on a fictional character. For example, the generation AI generates a virtual personality based on a character from a novel or movie, and generates a virtual personality with the characteristics of that character. The generation AI can also generate a virtual personality based on a specific historical figure or fictional character based on user instructions. This makes it possible to generate virtual personalities based on actual historical figures or fictional characters.

[0031] The generation unit can analyze the user's past dialogue history and customize and generate a virtual personality that is optimal for the user. In the generation unit, for example, the generation AI analyzes the user's past dialogue history and customizes and generates an optimal virtual personality based on the user's preferences and interests. For example, the generation AI generates a virtual personality that reflects the topics and expressions that the user often talks about. The generation AI can also generate a virtual personality that matches the user's personality and hobbies based on the user's dialogue history. The generation AI can also analyze the user's dialogue history in real time and dynamically customize the virtual personality according to the user's needs. This allows the user's past dialogue history to be analyzed and an optimal virtual personality to be customized and generated.

[0032] The generation unit can promote international use by reflecting the characteristics of a specific culture or region in the virtual personality it generates. For example, the generation unit causes the generation AI to reflect the characteristics of a specific culture or region in the virtual personality. For example, the generation AI generates a virtual personality that is knowledgeable about Japanese culture and that responds with an appropriate cultural background in conversations with Japanese users. The generation AI can also generate a virtual personality that has the language and customs of a specific region. The generation AI can also generate a virtual personality that has the characteristics of a specific culture or region based on user instructions. This allows the characteristics of a specific culture or region to be reflected, thereby promoting international use.

[0033] The generation unit can provide a virtual assistant capable of specialized conversations by giving the generated virtual personality a specific occupation or specialized knowledge. In the generation unit, for example, the generation AI generates a virtual personality specialized in a specific occupation. For example, the generation AI generates a virtual personality with specialized knowledge such as a doctor or lawyer, and provides specialized advice in conversations with the user. The generation AI can also generate a virtual personality that is knowledgeable in a specific technical field. The generation AI can also generate a virtual personality with a specific occupation or specialized knowledge based on the user's instructions. This makes it possible to provide a virtual assistant with a specific occupation or specialized knowledge.

[0034] The management unit can analyze the dialogue history of the virtual personality and automatically update the virtual personality according to the user's preferences. For example, the management system analyzes the dialogue history of the virtual personality and automatically updates the virtual personality according to the user's preferences. For example, the management system expands the virtual personality's knowledge based on topics that the user frequently talks about. The management system can also adjust the virtual personality's personality and hobbies based on the user's dialogue history. The management system can also improve the virtual personality's behavior based on user feedback. In this way, the dialogue history of the virtual personality can be analyzed and automatically updated according to the user's preferences.

[0035] The management department can analyze the usage of the virtual personality and propose an optimal sales strategy. For example, the management system analyzes the usage of the virtual personality and proposes an optimal sales strategy. For example, the management system develops and sells new virtual personalities based on the characteristics of popular virtual personalities. The management system can also analyze the frequency and patterns of use of users and identify target markets. The management system can also adjust pricing and promotion methods based on sales data. In this way, the usage of the virtual personality can be analyzed and an optimal sales strategy can be proposed.

[0036] The management unit can display advertisements customized for the user based on the attributes of the virtual personality. For example, the management system displays advertisements customized for the user based on the attributes of the virtual personality. For example, if the virtual personality is interested in sports, the management system displays advertisements for sports-related products. The management system can also customize advertisements based on the age and gender of the virtual personality. The management system can also display relevant advertisements based on the user's past behavioral data. This allows advertisements customized for the user to be displayed based on the attributes of the virtual personality.

[0037] The management unit can analyze the user's interests and concerns based on the virtual personality's dialogue history and suggest related virtual personalities. For example, the management system can analyze the user's interests and concerns based on the virtual personality's dialogue history and suggest related virtual personalities. For example, the management system can suggest virtual personalities related to topics the user often talks about. The management system can also identify the user's interests and concerns based on the user's dialogue history. The management system can also suggest related virtual personalities based on the user's past behavioral data. In this way, the user's interests and concerns can be analyzed based on the virtual personality's dialogue history and related virtual personalities can be suggested.

[0038] The generation unit can dynamically change the content of the survey questions to collect more specific information. For example, the generation unit builds a system in which the generation AI dynamically changes the content of the survey questions to collect more specific information. For example, the generation AI can generate additional questions based on the respondent's initial answer to collect more detailed information. The generation AI can also adjust the content of the questions in real time depending on the respondent's response. The generation AI can also dynamically change the content of the questions based on a specific trigger. This makes it possible to dynamically change the content of the survey questions to collect more specific information.

[0039] The generation unit can analyze the background information of survey respondents and evaluate the reliability of the answers. For example, the generation unit constructs a system in which the generation AI analyzes the background information of survey respondents and evaluates the reliability of the answers. For example, the generation AI scores the reliability of the answers based on the respondent's occupation and educational background. The generation AI can also evaluate reliability based on the respondent's age and gender. The generation AI can also evaluate reliability based on the respondent's past behavioral data. This makes it possible to analyze the background information of survey respondents and evaluate the reliability of the answers.

[0040] The generation unit can automatically generate questionnaires in different languages, enabling international data collection. The generation unit, for example, builds a system in which the generation AI automatically generates questionnaires in different languages, enabling international data collection. For example, the generation AI provides questionnaires in multiple languages, such as English, French, and Chinese. The generation AI can also automatically generate questionnaires based on a user's language settings. The generation AI can also translate languages ​​in real time and provide questionnaires in different languages. This makes it possible to automatically generate questionnaires in different languages, enabling international data collection.

[0041] The generation unit can analyze the results of prototyping and automatically suggest improvements. For example, the generation unit builds a system in which a generation AI analyzes the results of prototyping and automatically suggests improvements. For example, the generation AI can suggest improvements to an interface based on user feedback. The generation AI can also suggest improvements to a product based on the results of prototyping tests. The generation AI can also dynamically suggest improvements to prototyping based on user behavior data. This makes it possible to analyze the results of prototyping and automatically suggest improvements.

[0042] The generation unit can dynamically change the prototyping scenario and conduct tests under different conditions. The generation unit, for example, builds a system in which the generation AI dynamically changes the prototyping scenario and conducts tests under different conditions. For example, the generation AI provides different scenarios to different user groups and compares the results. The generation AI can also change the prototyping scenario based on a specific trigger. The generation AI can also adjust the scenario in real time and conduct tests under different conditions. This makes it possible to dynamically change the prototyping scenario and conduct tests under different conditions.

[0043] The generation unit visualizes the results of prototyping, allowing the user to intuitively understand. The generation unit, for example, builds a system in which a generation AI visualizes the results of prototyping, allowing the user to intuitively understand. For example, the generation AI displays the usage status of the prototype in graphs and charts. The generation AI can also provide the test results as infographics. The generation AI can also visualize user behavior data, allowing the user to intuitively understand. In this way, the prototyping results can be visualized, allowing the user to intuitively understand.

[0044] The generation unit can automatically generate prototyping scenarios according to different industries and applications. The generation unit, for example, builds a system in which the generation AI automatically generates prototyping scenarios according to different industries and applications. For example, the generation AI generates scenarios specialized for the medical field or the education field. The generation AI can also generate prototyping scenarios according to the manufacturing industry or the service industry. The generation AI can also customize prototyping scenarios based on the user's needs. This makes it possible to automatically generate prototyping scenarios according to different industries and applications.

[0045] The generation unit can analyze the behavioral history of the virtual personality in the metaverse and learn optimal behavioral patterns. For example, the generation unit constructs a system in which a generation AI analyzes the behavioral history of the virtual personality in the metaverse and learns optimal behavioral patterns. For example, the generation AI learns to make the virtual personality show appropriate reactions in response to the user's behavior. The generation AI can also optimize behavioral patterns based on the virtual personality's past behavioral data. The generation AI can also analyze behavioral history in real time and dynamically adjust the virtual personality's behavior. This makes it possible to analyze the behavioral history of the virtual personality in the metaverse and learn optimal behavioral patterns.

[0046] The generation unit can dynamically change the scenario in the metaverse and generate a program so that the virtual personality adapts. The generation unit, for example, builds a system in which a generation AI dynamically changes the scenario in the metaverse and generates a program so that the virtual personality adapts. For example, the generation AI changes the scenario according to the user's actions, so that the virtual personality responds appropriately. The generation AI can also change the scenario based on a specific trigger and generate a program so that the virtual personality adapts. The generation AI can also adjust the scenario in real time so that the virtual personality dynamically adapts. This makes it possible to dynamically change the scenario in the metaverse and generate a program so that the virtual personality adapts.

[0047] The generation unit can have the virtual personality in the metaverse implement specific events or campaigns to promote user participation. The generation unit, for example, builds a system in which a generation AI has the virtual personality in the metaverse implement specific events or campaigns to promote user participation. For example, the generation AI has the virtual personality provide event information or explain campaigns. The generation AI can also have the virtual personality suggest events or campaigns based on user behavior. The generation AI can also adjust the content of events or campaigns in real time to promote user participation. This allows the virtual personality in the metaverse to implement specific events or campaigns to promote user participation.

[0048] The generation unit can reflect the characteristics of different cultures and regions in virtual personalities within the metaverse, promoting international use. For example, the generation unit builds a system in which a generation AI reflects the characteristics of different cultures and regions in virtual personalities within the metaverse, promoting international use. For example, the generation AI allows virtual personalities to understand the customs of specific cultures and regions and utilize that knowledge. The generation AI can also program virtual personalities to speak different languages. The generation AI can also generate virtual personalities with the characteristics of specific cultures and regions based on user instructions. This allows virtual personalities within the metaverse to reflect the characteristics of different cultures and regions, promoting international use.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The generation unit can give the virtual personality a specific health condition or medical history, which can be used for medical simulations and training. For example, the generation AI can generate a virtual patient with a medical history such as diabetes or high blood pressure, allowing medical professionals to simulate diagnosis and treatment. The generation AI can also generate virtual patients with specific symptoms, which can be used for medical training. The generation AI can also generate virtual personalities with specific health conditions and medical histories based on user instructions. This makes it possible to provide virtual personalities that can be used for medical simulations and training.

[0051] The generation unit can give the virtual personality a specific educational background or academic history, allowing it to be used for educational simulations and training. For example, the generation AI can generate a virtual teacher with a specific degree or qualification, and simulate an educational setting. The generation AI can also generate a virtual student with a specific educational background, allowing it to be used for educational training. The generation AI can also generate a virtual personality with a specific educational background or academic history based on the user's instructions. This makes it possible to provide a virtual personality that can be used for educational simulations and training.

[0052] The generation unit can give the virtual personality specific hobbies and interests, providing common topics of conversation with the user. For example, the generation AI can generate a virtual personality with hobbies such as sports or music, providing common topics of conversation with the user. The generation AI can also generate a virtual personality with specific interests, enriching the conversation with the user. The generation AI can also generate a virtual personality with specific hobbies and interests based on the user's instructions. This makes it possible to generate a virtual personality that can provide common topics of conversation with the user.

[0053] The generation unit can provide the virtual personality with specific language skills to support language learning. For example, the generation AI can generate a virtual personality that speaks a language such as English or French to support the user's language learning. The generation AI can also generate a virtual personality with specific language skills to promote language learning through dialogue with the user. The generation AI can also generate a virtual personality with specific language skills based on the user's instructions. This makes it possible to provide a virtual personality that supports language learning.

[0054] The generation unit can give the virtual personality specific business skills and use them for business simulations and training. For example, the generation AI generates a virtual business partner with skills such as marketing and financial management, and performs a business simulation. The generation AI can also generate a virtual personality with specific business skills and use them for business training. The generation AI can also generate a virtual personality with specific business skills based on a user's instructions. This makes it possible to provide a virtual personality that can be used for business simulations and training.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The generation unit generates a virtual personality with a diverse backbone. For example, the generation AI receives as input a prompt containing instructions from a user and generates a virtual personality based on the prompt. The generation AI can generate a virtual personality with a specific occupation, hobbies, and personality. The generation AI can generate a virtual personality using, for example, a text generation AI (e.g., LLM). The generation AI can also generate the backbone of the virtual personality using a multimodal generation AI. The generation AI can also generate a virtual personality with specific attributes based on user instructions. Step 2: The management unit manages the generated virtual personalities. For example, the management unit records the attributes and interaction history of the virtual personalities and makes them easily accessible to users. The management unit can also update and delete the virtual personalities. The management unit manages the virtual personalities using, for example, a cloud-based database. The management unit can also manage the virtual personalities through a user interface. Step 3: The sales department sells the virtual personalities managed by the management department. For example, the sales department sells the virtual personalities through an online platform. The sales department allows users to select and purchase a virtual personality that meets their needs. The sales department can also set prices and promote the virtual personalities. For example, the sales department sells the virtual personalities using an e-commerce platform. The sales department can also suggest related virtual personalities based on the user's purchase history.

[0057] (Example 2) The virtual personality generation system according to an embodiment of the present invention generates a large number of virtual personalities with diverse backgrounds and manages and sells them. By applying virtual personalities to AI capable of advanced dialogue, this system enables rapid and low-cost large-scale surveys, monitoring, and prototyping. Furthermore, by placing virtual personalities in the metaverse, realism and liveliness can be brought to the space. As a result, the virtual personality generation system is expected to be utilized in a variety of fields, potentially creating new demand.

[0058] A virtual personality generation system according to an embodiment includes a generation unit, a management unit, and a sales unit. The generation unit generates virtual personalities with diverse backbones. For example, the generation AI receives prompts containing instructions from a user as input and generates a virtual personality based on the prompts. The generation AI can generate a virtual personality with a specific occupation, hobby, or personality. The generation AI can generate a virtual personality using, for example, a text generation AI (e.g., LLM). The generation AI can also generate a virtual personality's backbone using a multimodal generation AI. The generation AI can also generate a virtual personality with specific attributes based on a user's instructions. The management unit manages the generated virtual personalities. For example, the management unit records the virtual personality's attributes and interaction history, allowing users to easily access them. The management unit can also update and delete the virtual personalities. The management unit manages the virtual personalities using, for example, a cloud-based database. The management unit can also manage the virtual personalities through a user interface. The sales unit sells the virtual personalities managed by the management unit. For example, the sales department sells virtual personalities through an online platform. The sales department allows users to select and purchase virtual personalities that meet their needs. The sales department can also set prices and promote virtual personalities. The sales department sells virtual personalities using, for example, an e-commerce platform. The sales department can also suggest related virtual personalities based on a user's purchase history. As a result, the virtual personality generation system according to the embodiment can generate, manage, and sell virtual personalities with diverse backgrounds.

[0059] The generation unit can incorporate an emotion estimation function and adjust the generated virtual personality to have a specific emotion. The generation unit, for example, incorporates an emotion estimation function into the generation AI and adjusts the generated virtual personality to have a specific emotion. For example, the generation AI generates a virtual personality with emotions such as joy or sadness, so that the virtual personality shows emotional reactions in interactions with the user. The generation AI can also use an emotion estimation algorithm to adjust the emotions of the virtual personality in real time. The generation AI can also dynamically change the emotions of the virtual personality according to the user's emotions. This allows the generated virtual personality to be adjusted to have a specific emotion.

[0060] The generation unit can generate a personality based on an actual historical figure or a fictional character as the backbone of the virtual personality. For example, the generation AI generates a virtual personality based on an actual historical figure. For example, the generation AI generates a virtual personality with the characteristics of a historical figure such as Napoleon or Cleopatra, and reflects that person's knowledge and personality in interactions with the user. The generation AI can also generate a virtual personality based on a fictional character. For example, the generation AI generates a virtual personality based on a character from a novel or movie, and generates a virtual personality with the characteristics of that character. The generation AI can also generate a virtual personality based on a specific historical figure or fictional character based on user instructions. This makes it possible to generate virtual personalities based on actual historical figures or fictional characters.

[0061] The generation unit can analyze the user's past dialogue history and customize and generate a virtual personality that is optimal for the user. In the generation unit, for example, the generation AI analyzes the user's past dialogue history and customizes and generates an optimal virtual personality based on the user's preferences and interests. For example, the generation AI generates a virtual personality that reflects the topics and expressions that the user often talks about. The generation AI can also generate a virtual personality that matches the user's personality and hobbies based on the user's dialogue history. The generation AI can also analyze the user's dialogue history in real time and dynamically customize the virtual personality according to the user's needs. This allows the user's past dialogue history to be analyzed and an optimal virtual personality to be customized and generated.

[0062] The generation unit can promote international use by reflecting the characteristics of a specific culture or region in the virtual personality it generates. For example, the generation unit causes the generation AI to reflect the characteristics of a specific culture or region in the virtual personality. For example, the generation AI generates a virtual personality that is knowledgeable about Japanese culture and that responds with an appropriate cultural background in conversations with Japanese users. The generation AI can also generate a virtual personality that has the language and customs of a specific region. The generation AI can also generate a virtual personality that has the characteristics of a specific culture or region based on user instructions. This allows the characteristics of a specific culture or region to be reflected, thereby promoting international use.

[0063] The generation unit can provide a virtual assistant capable of specialized conversations by giving the generated virtual personality a specific occupation or specialized knowledge. In the generation unit, for example, the generation AI generates a virtual personality specialized in a specific occupation. For example, the generation AI generates a virtual personality with specialized knowledge such as a doctor or lawyer, and provides specialized advice in conversations with the user. The generation AI can also generate a virtual personality that is knowledgeable in a specific technical field. The generation AI can also generate a virtual personality with a specific occupation or specialized knowledge based on the user's instructions. This makes it possible to provide a virtual assistant with a specific occupation or specialized knowledge.

[0064] The generation unit can use the emotion estimation function to make the generated virtual personality dynamically change according to the user's emotions. The generation unit can, for example, use the emotion estimation function to make the generated virtual personality dynamically change according to the user's emotions. For example, the generation AI can make the virtual personality react in a comforting manner when the user is sad. The generation AI can also make the virtual personality show empathy when the user is happy. The generation AI can also adjust the behavior and dialogue of the virtual personality in real time according to the user's emotions. This allows the generated virtual personality to dynamically change according to the user's emotions.

[0065] The management unit can analyze the dialogue history of the virtual personality and automatically update the virtual personality according to the user's preferences. For example, the management system analyzes the dialogue history of the virtual personality and automatically updates the virtual personality according to the user's preferences. For example, the management system expands the virtual personality's knowledge based on topics that the user frequently talks about. The management system can also adjust the virtual personality's personality and hobbies based on the user's dialogue history. The management system can also improve the virtual personality's behavior based on user feedback. In this way, the dialogue history of the virtual personality can be analyzed and automatically updated according to the user's preferences.

[0066] The management department can analyze the usage of the virtual personality and propose an optimal sales strategy. For example, the management system analyzes the usage of the virtual personality and proposes an optimal sales strategy. For example, the management system develops and sells new virtual personalities based on the characteristics of popular virtual personalities. The management system can also analyze the frequency and patterns of use of users and identify target markets. The management system can also adjust pricing and promotion methods based on sales data. In this way, the usage of the virtual personality can be analyzed and an optimal sales strategy can be proposed.

[0067] The management unit can display advertisements customized for the user based on the attributes of the virtual personality. For example, the management system displays advertisements customized for the user based on the attributes of the virtual personality. For example, if the virtual personality is interested in sports, the management system displays advertisements for sports-related products. The management system can also customize advertisements based on the age and gender of the virtual personality. The management system can also display relevant advertisements based on the user's past behavioral data. This allows advertisements customized for the user to be displayed based on the attributes of the virtual personality.

[0068] The management unit can analyze the user's interests and concerns based on the virtual personality's dialogue history and suggest related virtual personalities. For example, the management system can analyze the user's interests and concerns based on the virtual personality's dialogue history and suggest related virtual personalities. For example, the management system can suggest virtual personalities related to topics the user often talks about. The management system can also identify the user's interests and concerns based on the user's dialogue history. The management system can also suggest related virtual personalities based on the user's past behavioral data. In this way, the user's interests and concerns can be analyzed based on the virtual personality's dialogue history and related virtual personalities can be suggested.

[0069] The management unit uses the emotion estimation function to enable the sales platform to present recommended virtual personalities in accordance with the user's emotions. The management unit, for example, uses the emotion estimation function to enable the sales platform to present recommended virtual personalities in accordance with the user's emotions. For example, if the user is feeling stressed, the emotion estimation function can suggest a virtual personality that will relax the user. Furthermore, if the user is excited, the emotion estimation function can also suggest a virtual personality that will calm the user. Furthermore, the emotion estimation function can analyze the user's emotion data in real time and dynamically suggest an optimal virtual personality. In this way, the emotion estimation function can be used to present recommended virtual personalities in accordance with the user's emotions.

[0070] The generation unit can perform sentiment analysis on the survey responses and provide data based on emotional responses. For example, the generation AI can perform sentiment analysis on the survey responses and provide data based on emotional responses. For example, the generation AI can identify responses in which the respondent expressed positive emotions and aggregate that data. The generation AI can also identify responses in which the respondent expressed negative emotions and analyze that data. The generation AI can also analyze the respondent's emotional responses in real time and provide emotional data. This makes it possible to perform sentiment analysis on the survey responses and provide data based on emotional responses.

[0071] The generation unit can dynamically change the content of the survey questions to collect more specific information. For example, the generation unit builds a system in which the generation AI dynamically changes the content of the survey questions to collect more specific information. For example, the generation AI can generate additional questions based on the respondent's initial answer to collect more detailed information. The generation AI can also adjust the content of the questions in real time depending on the respondent's response. The generation AI can also dynamically change the content of the questions based on a specific trigger. This makes it possible to dynamically change the content of the survey questions to collect more specific information.

[0072] The generation unit can analyze the background information of survey respondents and evaluate the reliability of the answers. For example, the generation unit constructs a system in which the generation AI analyzes the background information of survey respondents and evaluates the reliability of the answers. For example, the generation AI scores the reliability of the answers based on the respondent's occupation and educational background. The generation AI can also evaluate reliability based on the respondent's age and gender. The generation AI can also evaluate reliability based on the respondent's past behavioral data. This makes it possible to analyze the background information of survey respondents and evaluate the reliability of the answers.

[0073] The generation unit can automatically generate questionnaires in different languages, enabling international data collection. The generation unit, for example, builds a system in which the generation AI automatically generates questionnaires in different languages, enabling international data collection. For example, the generation AI provides questionnaires in multiple languages, such as English, French, and Chinese. The generation AI can also automatically generate questionnaires based on a user's language settings. The generation AI can also translate languages ​​in real time and provide questionnaires in different languages. This makes it possible to automatically generate questionnaires in different languages, enabling international data collection.

[0074] The generation unit can use the emotion estimation function to automatically generate follow-up questions according to the emotions of the survey respondent. The generation unit, for example, uses the emotion estimation function to build a system that automatically generates follow-up questions according to the emotions of the survey respondent. For example, if the respondent expresses dissatisfaction, the emotion estimation function generates questions that inquire into the reason in detail. Furthermore, if the respondent is satisfied, the emotion estimation function can also generate questions that seek more detailed opinions. Furthermore, the emotion estimation function can analyze the respondent's emotion data in real time and dynamically generate optimal follow-up questions. In this way, the emotion estimation function can be used to automatically generate follow-up questions according to the emotions of the survey respondent.

[0075] The generation unit can incorporate an emotion estimation function into a prototyping scenario and simulate a user's emotional reactions. For example, the generation unit builds a system in which a generation AI incorporates an emotion estimation function into a prototyping scenario and simulates a user's emotional reactions. For example, the generation AI simulates emotional reactions to a user interface of a new product. The generation AI can also adjust the prototyping scenario based on user emotion data. The generation AI can also analyze a user's emotional reactions in real time and provide simulation results. This makes it possible to incorporate an emotion estimation function into a prototyping scenario and simulate a user's emotional reactions.

[0076] The generation unit can analyze the results of prototyping and automatically suggest improvements. For example, the generation unit builds a system in which a generation AI analyzes the results of prototyping and automatically suggests improvements. For example, the generation AI can suggest improvements to an interface based on user feedback. The generation AI can also suggest improvements to a product based on the results of prototyping tests. The generation AI can also dynamically suggest improvements to prototyping based on user behavior data. This makes it possible to analyze the results of prototyping and automatically suggest improvements.

[0077] The generation unit can dynamically change the prototyping scenario and conduct tests under different conditions. The generation unit, for example, builds a system in which the generation AI dynamically changes the prototyping scenario and conducts tests under different conditions. For example, the generation AI provides different scenarios to different user groups and compares the results. The generation AI can also change the prototyping scenario based on a specific trigger. The generation AI can also adjust the scenario in real time and conduct tests under different conditions. This makes it possible to dynamically change the prototyping scenario and conduct tests under different conditions.

[0078] The generation unit visualizes the results of prototyping, allowing the user to intuitively understand. The generation unit, for example, builds a system in which a generation AI visualizes the results of prototyping, allowing the user to intuitively understand. For example, the generation AI displays the usage status of the prototype in graphs and charts. The generation AI can also provide the test results as infographics. The generation AI can also visualize user behavior data, allowing the user to intuitively understand. In this way, the prototyping results can be visualized, allowing the user to intuitively understand.

[0079] The generation unit can automatically generate prototyping scenarios according to different industries and applications. The generation unit, for example, builds a system in which the generation AI automatically generates prototyping scenarios according to different industries and applications. For example, the generation AI generates scenarios specialized for the medical field or the education field. The generation AI can also generate prototyping scenarios according to the manufacturing industry or the service industry. The generation AI can also customize prototyping scenarios based on the user's needs. This makes it possible to automatically generate prototyping scenarios according to different industries and applications.

[0080] The generation unit can adjust the behavior of the virtual personality in the metaverse using an emotion estimation function to achieve more natural dialogue. The generation unit, for example, builds a system in which a generation AI adjusts the behavior of the virtual personality in the metaverse using an emotion estimation function to achieve more natural dialogue. For example, the generation AI makes the virtual personality show an appropriate reaction according to the user's emotions. The generation AI can also adjust the behavior of the virtual personality in real time to achieve more natural dialogue. The generation AI can also dynamically change the behavior of the virtual personality based on the user's emotion data. This allows the behavior of the virtual personality in the metaverse to be adjusted using the emotion estimation function to achieve more natural dialogue.

[0081] The generation unit can analyze the behavioral history of the virtual personality in the metaverse and learn optimal behavioral patterns. For example, the generation unit constructs a system in which a generation AI analyzes the behavioral history of the virtual personality in the metaverse and learns optimal behavioral patterns. For example, the generation AI learns to make the virtual personality show appropriate reactions in response to the user's behavior. The generation AI can also optimize behavioral patterns based on the virtual personality's past behavioral data. The generation AI can also analyze behavioral history in real time and dynamically adjust the virtual personality's behavior. This makes it possible to analyze the behavioral history of the virtual personality in the metaverse and learn optimal behavioral patterns.

[0082] The generation unit can dynamically change the scenario in the metaverse and generate a program so that the virtual personality adapts. The generation unit, for example, builds a system in which a generation AI dynamically changes the scenario in the metaverse and generates a program so that the virtual personality adapts. For example, the generation AI changes the scenario according to the user's actions, so that the virtual personality responds appropriately. The generation AI can also change the scenario based on a specific trigger and generate a program so that the virtual personality adapts. The generation AI can also adjust the scenario in real time so that the virtual personality dynamically adapts. This makes it possible to dynamically change the scenario in the metaverse and generate a program so that the virtual personality adapts.

[0083] The generation unit can have the virtual personality in the metaverse implement specific events or campaigns to promote user participation. The generation unit, for example, builds a system in which a generation AI has the virtual personality in the metaverse implement specific events or campaigns to promote user participation. For example, the generation AI has the virtual personality provide event information or explain campaigns. The generation AI can also have the virtual personality suggest events or campaigns based on user behavior. The generation AI can also adjust the content of events or campaigns in real time to promote user participation. This allows the virtual personality in the metaverse to implement specific events or campaigns to promote user participation.

[0084] The generation unit can reflect the characteristics of different cultures and regions in virtual personalities within the metaverse, promoting international use. For example, the generation unit builds a system in which a generation AI reflects the characteristics of different cultures and regions in virtual personalities within the metaverse, promoting international use. For example, the generation AI allows virtual personalities to understand the customs of specific cultures and regions and utilize that knowledge. The generation AI can also program virtual personalities to speak different languages. The generation AI can also generate virtual personalities with the characteristics of specific cultures and regions based on user instructions. This allows virtual personalities within the metaverse to reflect the characteristics of different cultures and regions, promoting international use.

[0085] The generation unit can use the emotion estimation function to cause the virtual personality in the metaverse to dynamically change its behavior in accordance with the user's emotions. The generation unit, for example, uses the emotion estimation function to build a system in which the virtual personality in the metaverse dynamically changes its behavior in accordance with the user's emotions. For example, the emotion estimation function can cause the virtual personality to empathize when the user is happy. Furthermore, the emotion estimation function can cause the virtual personality to react in a comforting manner when the user is sad. Furthermore, the emotion estimation function can analyze the user's emotion data in real time and dynamically adjust the behavior of the virtual personality. In this way, the emotion estimation function can cause the virtual personality in the metaverse to dynamically change its behavior in accordance with the user's emotions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The generation unit can give the virtual personality a specific health condition or medical history, which can be used for medical simulations and training. For example, the generation AI can generate a virtual patient with a medical history such as diabetes or high blood pressure, allowing medical professionals to simulate diagnosis and treatment. The generation AI can also generate virtual patients with specific symptoms, which can be used for medical training. The generation AI can also generate virtual personalities with specific health conditions and medical histories based on user instructions. This makes it possible to provide virtual personalities that can be used for medical simulations and training.

[0088] The generation unit uses the emotion estimation function to enable the generated virtual personality to recommend appropriate music according to the user's emotions. For example, the generation AI can recommend calming music when the user wants to relax. The generation AI can also recommend upbeat music when the user wants to cheer up. The generation AI can also analyze the user's emotional data in real time and dynamically recommend the most appropriate music. This allows the generated virtual personality to recommend appropriate music according to the user's emotions.

[0089] The generation unit can give the virtual personality a specific educational background or academic history, allowing it to be used for educational simulations and training. For example, the generation AI can generate a virtual teacher with a specific degree or qualification, and simulate an educational setting. The generation AI can also generate a virtual student with a specific educational background, allowing it to be used for educational training. The generation AI can also generate a virtual personality with a specific educational background or academic history based on the user's instructions. This makes it possible to provide a virtual personality that can be used for educational simulations and training.

[0090] The generation unit uses the emotion estimation function to enable the generated virtual personality to provide appropriate feedback according to the user's emotions. For example, the generation AI can provide words of encouragement when the user is feeling down. The generation AI can also provide words of praise when the user achieves success. The generation AI can also analyze the user's emotion data in real time and dynamically provide optimal feedback. This allows the generated virtual personality to provide appropriate feedback according to the user's emotions.

[0091] The generation unit can give the virtual personality specific hobbies and interests, providing common topics of conversation with the user. For example, the generation AI can generate a virtual personality with hobbies such as sports or music, providing common topics of conversation with the user. The generation AI can also generate a virtual personality with specific interests, enriching the conversation with the user. The generation AI can also generate a virtual personality with specific hobbies and interests based on the user's instructions. This makes it possible to generate a virtual personality that can provide common topics of conversation with the user.

[0092] The generation unit uses the emotion estimation function to enable the generated virtual personality to suggest appropriate activities according to the user's emotions. For example, the generation AI can suggest relaxation activities when the user is feeling stressed. The generation AI can also suggest entertainment activities when the user is bored. The generation AI can also analyze the user's emotion data in real time and dynamically suggest optimal activities. This allows the generated virtual personality to suggest appropriate activities according to the user's emotions.

[0093] The generation unit can provide the virtual personality with specific language skills to support language learning. For example, the generation AI can generate a virtual personality that speaks a language such as English or French to support the user's language learning. The generation AI can also generate a virtual personality with specific language skills to promote language learning through dialogue with the user. The generation AI can also generate a virtual personality with specific language skills based on the user's instructions. This makes it possible to provide a virtual personality that supports language learning.

[0094] The generation unit uses the emotion estimation function to enable the generated virtual personality to tell an appropriate story according to the user's emotions. For example, the generation AI can tell a calm story when the user wants to relax. The generation AI can also tell an adventurous story when the user is excited. The generation AI can also analyze the user's emotion data in real time and dynamically perform optimal storytelling. This allows the generated virtual personality to tell an appropriate story according to the user's emotions.

[0095] The generation unit can give the virtual personality specific business skills and use them for business simulations and training. For example, the generation AI generates a virtual business partner with skills such as marketing and financial management, and performs a business simulation. The generation AI can also generate a virtual personality with specific business skills and use them for business training. The generation AI can also generate a virtual personality with specific business skills based on a user's instructions. This makes it possible to provide a virtual personality that can be used for business simulations and training.

[0096] The generation unit uses the emotion estimation function to enable the generated virtual personality to provide appropriate news and information according to the user's emotions. For example, the generation AI can provide news about topics that interest the user. The generation AI can also provide reassuring information when the user is feeling anxious. The generation AI can also analyze the user's emotion data in real time and dynamically provide optimal news and information. This allows the generated virtual personality to provide appropriate news and information according to the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The generation unit generates a virtual personality with a diverse backbone. For example, the generation AI receives as input a prompt containing instructions from a user and generates a virtual personality based on the prompt. The generation AI can generate a virtual personality with a specific occupation, hobbies, and personality. The generation AI can generate a virtual personality using, for example, a text generation AI (e.g., LLM). The generation AI can also generate the backbone of the virtual personality using a multimodal generation AI. The generation AI can also generate a virtual personality with specific attributes based on user instructions. Step 2: The management unit manages the generated virtual personalities. For example, the management unit records the attributes and interaction history of the virtual personalities and makes them easily accessible to users. The management unit can also update and delete the virtual personalities. The management unit manages the virtual personalities using, for example, a cloud-based database. The management unit can also manage the virtual personalities through a user interface. Step 3: The sales department sells the virtual personalities managed by the management department. For example, the sales department sells the virtual personalities through an online platform. The sales department allows users to select and purchase a virtual personality that meets their needs. The sales department can also set prices and promote the virtual personalities. For example, the sales department sells the virtual personalities using an e-commerce platform. The sales department can also suggest related virtual personalities based on the user's purchase history.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[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 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.

[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 (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).

[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] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates virtual personalities with diverse backgrounds; a management unit that manages the virtual personality generated by the generation unit; a sales department that sells the virtual personalities managed by the management department. A system characterized by:

2. The generation unit Incorporating an emotion estimation function and adjusting the virtual personality to have a specific emotion.

2. The system of claim 1.

3. The generation unit By reflecting the characteristics of specific cultures and regions in the virtual personalities that are generated, international use can be promoted.

2. The system of claim 1.

4. a generation unit that generates virtual personalities with diverse backgrounds; a management unit that manages the virtual personality generated by the generation unit; a sales department that sells the virtual personalities managed by the management department. A system characterized by:

5. The generation unit Conduct sentiment analysis on survey responses to provide data based on emotional responses 2. The system of claim 1.

6. The generation unit Incorporating emotion estimation into prototyping scenarios to simulate users' emotional responses 2. The system of claim 1.

7. The generation unit Generate personalities based on real historical figures and fictional characters as the backbone of your virtual persona 2. The system of claim 1.

8. The generation unit The virtual personality in the metaverse dynamically changes its behavior according to the user's emotions.

2. The system of claim 1.

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