System

Abstract

An object of a system according to an embodiment is to support communication by generating an imaginary person in accordance with a user's need.SOLUTION: A system includes a reception unit, a generation unit, a communication unit, and an adjustment unit. The reception unit receives information from a user. The generation unit generates an imaginary person on the basis of the information received by the reception unit. The communication unit supports communication between the imaginary person generated by the generation unit and the user. The adjustment unit adjusts the personality or the way of speaking of the imaginary person on the basis of the feedback obtained by the communication unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to adjust to individual needs when users communicate with fictional people.

[0005] The system according to the embodiment aims to generate a fictional person according to the needs of the user and to support communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a communication unit, and an adjustment unit. The reception unit receives information from a user. The generation unit generates a fictional human based on the information received by the reception unit. The communication unit supports communication between the user and the fictional human generated by the generation unit. The adjustment unit adjusts the personality or speaking style of the fictional human based on feedback obtained by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a fictional person according to the user's needs and support communication. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A fictional character creation system according to an embodiment of the present invention generates fictional characters based on user information, supports communication, and adjusts the fictional character based on feedback. The fictional character creation system allows users to create fictional characters with different appearances, personalities (speech patterns), knowledge, and conversation histories. This allows users to communicate with the fictional characters through various media, such as email, telephone, and VR. For example, in the fictional character creation system, users input information to create a fictional character. For example, they specify appearance characteristics, personality traits, and knowledge levels. This information is input into an LLM, which analyzes it to generate a fictional character. The generated fictional character has a unique appearance, personality, knowledge, and conversation history based on the specified information. The fictional character creation system then allows users to communicate with the generated fictional character through various media. For example, users can send and receive messages via email, talk on the phone, or interact in a VR environment. This allows users to enjoy realistic communication with the fictional character. Furthermore, the fictional character becomes close to the user and grows together with them. For example, as a user frequently communicates with a fictional character, the fictional character's knowledge and conversation skills improve. The fictional character's personality and speaking style may also be adjusted based on user feedback. This allows the fictional character creation system to provide individuals and corporations with a variety of communication methods, allowing it to grow alongside and support users. This allows the fictional character creation system to allow users to enjoy realistic communication with fictional characters. For example, as a user frequently communicates with a fictional character, the fictional character's knowledge and conversation skills improve. The fictional character's personality and speaking style may also be adjusted based on user feedback. This allows the fictional character creation system to provide individuals and corporations with a variety of communication methods, allowing it to grow alongside and support users.

[0029] A fictional character creation system according to an embodiment includes a reception unit, a generation unit, a communication unit, and an adjustment unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, appearance characteristics, personality traits, and knowledge range. The reception unit can receive, for example, text information, audio information, and image information. The generation unit uses an LLM to generate a fictional character based on the information received by the reception unit. The generation unit generates a fictional character with, for example, appearance, personality, knowledge, and conversation history. The generation unit generates a fictional character with detailed appearance based on, for example, appearance characteristics specified by a user. The generation unit can also generate a fictional character with detailed personality traits based on personality traits specified by a user. The generation unit can also generate a fictional character with detailed knowledge based on the knowledge range specified by a user. For example, the generation unit inputs information specified by the user into the LLM, which analyzes the information to generate a fictional character. The communication unit supports communication between the user and the fictional character generated by the generation unit. The communication unit supports communication between the fictional human and the user using media such as email, telephone, and VR. The communication unit can, for example, send and receive messages via email, have telephone conversations, and interact in a VR environment. The adjustment unit adjusts the fictional human's personality and speaking style based on feedback obtained by the communication unit. The adjustment unit adjusts the fictional human's personality and speaking style based on user feedback, for example. For example, if the user is relaxed, the adjustment unit adjusts the fictional human to have a calm personality and speaking style. Furthermore, if the user is excited, the adjustment unit can adjust the fictional human to have a lively personality and speaking style. Furthermore, if the user is stressed, the adjustment unit can adjust the fictional human to have a soothing personality and speaking style. As a result, the fictional human creation system according to the embodiment can generate a fictional human based on information from the user, support communication, and adjust the fictional human based on feedback.

[0030] The generation unit can generate a fictional human with appearance, personality, knowledge, and conversation history. The generation unit, for example, generates a fictional human with appearance characteristics. For example, the generation unit generates a fictional human by specifying facial features, body type, clothing, etc. The generation unit can also generate a fictional human with personality tendencies. For example, the generation unit generates a fictional human by specifying personality traits such as introversion, extroversion, and emotionality. The generation unit can also generate a fictional human with a range of knowledge. For example, the generation unit generates a fictional human by specifying specialized knowledge, general knowledge, knowledge in a specific field, etc. The generation unit can also generate a fictional human with a conversation history. For example, the generation unit generates a fictional human by specifying the content of past conversations, the date and time of the conversations, the conversation partners, etc. In this way, a fictional human with appearance, personality, knowledge, and conversation history can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input user-specified information into the LLM, which then analyzes it to generate a fictional human.

[0031] The communication unit can support communication between the fictional person and the user using media such as email, telephone, and VR. The communication unit, for example, sends and receives messages between the fictional person and the user via email. For example, the communication unit sends and receives messages using text email or HTML email. The communication unit can also support conversations between the fictional person and the user via telephone. For example, the communication unit supports conversations using voice calls or video calls. The communication unit can also conduct dialogue between the fictional person and the user in a VR environment. For example, the communication unit conducts dialogue using a VR headset or a VR application. This makes it possible to support communication between the fictional person and the user using various media. Some or all of the above-described processing in the communication unit may be performed using, for example, an LLM, or may be performed without an LLM. For example, the communication unit can use an LLM to support communication between the fictional person and the user using media such as email, telephone, and VR.

[0032] The adjustment unit can adjust the personality and speaking style of the fictional human based on user feedback. The adjustment unit adjusts the personality and speaking style of the fictional human based on user feedback, for example. For example, if the user is relaxed, the adjustment unit adjusts the fictional human to have a calm personality and speaking style. Furthermore, if the user is excited, the adjustment unit can adjust the fictional human to have a lively personality and speaking style. Furthermore, if the user is stressed, the adjustment unit can adjust the fictional human to have a soothing personality and speaking style. In this way, the personality and speaking style of the fictional human can be adjusted based on user feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the adjustment unit can input user feedback into an LLM, which analyzes the feedback and adjusts the personality and speaking style of the fictional human.

[0033] The generation unit can generate a personality that will become a company representative. The generation unit generates, for example, a personality that will become a company representative. For example, the generation unit generates a personality that reflects the company's philosophy, vision, values, etc. The generation unit can also generate a personality that has the appearance and personality of a company representative. For example, the generation unit generates a personality by specifying the appearance characteristics and personality tendencies of a company representative. This makes it possible to generate a personality that will become a company representative for a corporation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input the company's philosophy and vision into an LLM, which then analyzes the data and generates a personality that will become a company representative.

[0034] The communications department can support communication between fictional people and customers in a corporation's customer service and public relations activities. The communications department, for example, supports communication between fictional people and customers in a corporation's customer service. For example, the communications department handles inquiries and complaints. The communications department can also support communication between fictional people and customers in public relations activities. For example, the communications department issues press releases and posts on social media. This can support communication between fictional people and customers in a corporation's customer service and public relations activities. Some or all of the above-mentioned processing in the communications department can be performed, for example, using an LLM, or can be performed without an LLM. For example, the communications department can use an LLM to support communication between fictional people and customers in a corporation's customer service and public relations activities.

[0035] The reception unit can analyze the user's past input history and select the optimal information reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal information reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. The reception unit can also customize the optimal reception method based on information previously input by the user. This makes it possible to select the optimal information reception method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI select the optimal information reception method.

[0036] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving information. For example, the reception unit can perform filtering based on the user's current situation and areas of interest when receiving information. For example, the reception unit can receive only information that is highly relevant to the user's current situation. The reception unit can also filter unnecessary information based on the user's areas of interest. The reception unit can also preferentially receive information related to the user's current task. This makes it possible to filter information based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0037] The reception unit can select an appropriate reception means depending on the user's input method when receiving information. For example, the reception unit selects an appropriate reception means depending on the user's input method (voice, text, image, etc.) when receiving information. For example, if the user uses voice input, the reception unit can receive information using voice recognition technology. Also, if the user uses text input, the reception unit can receive information using text analysis technology. Also, if the user uses image input, the reception unit can receive information using image recognition technology. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and cause the generation AI to select an appropriate reception means.

[0038] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving highly relevant information based on the user's geographical location information when receiving information. For example, when the user is in a specific area, the reception unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving information around the user's home. This makes it possible to prioritize receiving highly relevant information taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant information.

[0039] The reception unit can analyze the user's social media activity and receive related information when receiving information. For example, the reception unit analyzes the user's social media activity and receives related information when receiving information. For example, the reception unit receives related information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related information. The reception unit can also receive related information by referring to the activity of the user's friends on social media. In this way, related information can be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to select related information.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information. The reception unit, for example, customizes the reception method by reflecting the user's past feedback when receiving information. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method from the user's past feedback. The reception unit can also reflect the user's feedback and continuously improve the reception method. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the reception method.

[0041] The generation unit can generate a detailed profile of a fictional human based on information specified by a user at the time of generation. For example, the generation unit generates a detailed profile of a fictional human based on information specified by a user at the time of generation. For example, the generation unit generates a fictional human with a detailed appearance based on appearance characteristics specified by a user. The generation unit can also generate a fictional human with a detailed personality based on personality tendencies specified by a user. The generation unit can also generate a fictional human with detailed knowledge based on a range of knowledge specified by a user. This allows a detailed profile to be generated based on information specified by a user. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input information specified by a user into an LLM, which analyzes the information and generates a detailed profile.

[0042] The generation unit can customize the knowledge range of the fictional person according to the user's interests and concerns at the time of generation. For example, the generation unit customizes the knowledge range of the fictional person according to the user's interests and concerns at the time of generation. For example, the generation unit generates a fictional person with knowledge in a field that the user is interested in. The generation unit can also generate a fictional person with specific knowledge based on the user's field of interest. The generation unit can also generate a fictional person with knowledge that the user is likely to be interested in based on the user's past search history. This makes it possible to customize the knowledge range of the fictional person according to the user's interests and concerns. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input data on the user's interests and concerns into the LLM and cause the LLM to customize the knowledge range.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past generation history during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation history during generation. For example, the generation unit can improve the accuracy of generation based on information about fictional people the user has previously generated. The generation unit can also improve the accuracy of generation by extracting specific patterns from the user's past generation history. The generation unit can also continuously improve the accuracy of generation by reflecting user feedback. This allows the accuracy of generation to be improved based on the user's past generation history. Some or all of the above-described processing in the generation unit can be performed using, or without, an LLM. For example, the generation unit can input the user's past generation history into the LLM and cause the LLM to improve the accuracy of generation.

[0044] The generation unit can customize the appearance of the fictional human based on the user's geographical background during generation. For example, the generation unit customizes the appearance of the fictional human based on the user's geographical background during generation. For example, if the user lives in a specific region, the generation unit generates a fictional human with an appearance that reflects the characteristics of that region. The generation unit can also generate a fictional human with an appearance related to a region to which the user has traveled. The generation unit can also generate a fictional human with an appearance that reflects a specific culture or customs based on the user's geographical background. This makes it possible to customize the appearance of the fictional human based on the user's geographical background. Some or all of the above-described processing in the generation unit may be performed using, or without, an LLM. For example, the generation unit can input data about the user's geographical background into the LLM and cause the LLM to customize the appearance.

[0045] The generation unit can adjust the personality of the fictional human based on the user's social media activity during generation. For example, the generation unit adjusts the personality of the fictional human based on the user's social media activity during generation. For example, the generation unit analyzes the content of the user's social media posts and generates a fictional human with a related personality. The generation unit can also generate a fictional human with a related personality based on the activity of the user's friends on social media. The generation unit can also generate a fictional human with a specific personality based on the user's social media activity pattern. This makes it possible to adjust the personality of the fictional human based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, or without, an LLM. For example, the generation unit can input data on the user's social media activity into the LLM and cause the LLM to adjust the personality.

[0046] The generation unit can customize the knowledge of the fictional person according to the user's occupation or expertise at the time of generation. For example, the generation unit customizes the knowledge of the fictional person according to the user's occupation or expertise at the time of generation. For example, the generation unit generates a fictional person with knowledge related to the user's occupation. The generation unit can also generate a fictional person with specific knowledge based on the user's expertise. The generation unit can also generate a fictional person who is knowledgeable in a specific field by reflecting the user's occupation or expertise. This makes it possible to customize the knowledge of the fictional person according to the user's occupation or expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input data on the user's occupation and expertise into the LLM and cause the LLM to customize the knowledge.

[0047] The communication unit can provide an appropriate response by referring to the user's past conversation history during communication. For example, the communication unit can provide an appropriate response by referring to the user's past conversation history during communication. For example, the communication unit can provide a relevant response based on the content of the user's past conversations. The communication unit can also extract specific patterns from the user's past conversation history to provide an optimal response. The communication unit can also reflect user feedback to continuously improve the accuracy of the response. This makes it possible to provide an optimal response based on the user's past conversation history. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or can be performed without using AI. For example, the communication unit can input the user's past conversation history into a generation AI and cause the generation AI to provide an appropriate response.

[0048] The communication unit can customize the response content according to the user's current situation and task during communication. For example, the communication unit customizes the response content according to the user's current situation and task during communication. For example, the communication unit provides a highly relevant response according to the user's current situation. The communication unit can also provide an optimal response based on the user's current task. The communication unit can also analyze the user's current situation and customize specific response content. This makes it possible to customize the response content according to the user's current situation and task. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input data on the user's current situation and task to the generation AI and cause the generation AI to customize the response content.

[0049] The communication unit can improve the response method by reflecting user feedback during communication. For example, the communication unit improves the response method by reflecting user feedback during communication. For example, the communication unit improves the response method based on feedback provided by the user. The communication unit can also preferentially select a specific response method from the user's past feedback. The communication unit can also reflect user feedback to continuously improve the response method. In this way, the response method can be improved based on user feedback. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input user feedback into a generation AI and cause the generation AI to improve the response method.

[0050] The communication unit can provide an optimal response during communication by taking into account the user's geographical location information. For example, the communication unit can provide an optimal response during communication by taking into account the user's geographical location information. For example, if the user is in a specific area, the communication unit can provide information related to that area. Furthermore, if the user is traveling, the communication unit can provide information related to the user's travel destination. Furthermore, if the user is at home, the communication unit can provide information about the area around the user's home. This makes it possible to provide an optimal response by taking into account the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the user's geographical location information into a generation AI and cause the generation AI to provide an optimal response.

[0051] The communication unit can provide a relevant response by analyzing the user's social media activity during communication. For example, the communication unit can provide a relevant response by analyzing the user's social media activity during communication. For example, the communication unit can provide a relevant response based on information shared by the user on social media. The communication unit can also analyze the user's social media activity and provide a relevant response. The communication unit can also provide a relevant response by referring to the activity of the user's friends on social media. In this way, a relevant response can be provided based on the user's social media activity. Some or all of the above-described processing in the communication unit can be performed using, for example, AI, or can be performed without using AI. For example, the communication unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide a relevant response.

[0052] The communication unit can customize the response method by reflecting the user's past feedback during communication. For example, the communication unit customizes the response method by reflecting the user's past feedback during communication. For example, the communication unit suggests an optimal response method based on feedback provided by the user in the past. The communication unit can also preferentially select a specific response method from the user's past feedback. The communication unit can also reflect the user's feedback to continuously improve the response method. This makes it possible to customize the response method based on the user's past feedback. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the user's past feedback into a generation AI and cause the generation AI to customize the response method.

[0053] The adjustment unit can select the optimal adjustment method by referring to the user's past feedback during adjustment. For example, the adjustment unit selects the optimal adjustment method by referring to the user's past feedback during adjustment. For example, the adjustment unit selects the optimal adjustment method based on feedback provided by the user in the past. The adjustment unit can also preferentially select a specific adjustment method from the user's past feedback. The adjustment unit can also reflect the user's feedback and continuously improve the adjustment method. This makes it possible to select the optimal adjustment method based on the user's past feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback into the generation AI and cause the generation AI to select the optimal adjustment method.

[0054] The adjustment unit can improve the accuracy of the adjustment by analyzing the knowledge and conversation history of the fictional person during adjustment. For example, the adjustment unit can improve the accuracy of the adjustment by analyzing the knowledge and conversation history of the fictional person during adjustment. For example, the adjustment unit analyzes the knowledge and conversation history of the fictional person and selects an optimal adjustment method. The adjustment unit can also analyze the conversation history of the fictional person and preferentially select a specific adjustment method. The adjustment unit can also continuously improve the accuracy of the adjustment based on the knowledge and conversation history of the fictional person. This makes it possible to improve the accuracy of the adjustment based on the knowledge and conversation history of the fictional person. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the knowledge and conversation history of the fictional person into the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0055] The adjustment unit can customize the adjustment content according to the user's current situation and task during adjustment. The adjustment unit, for example, customizes the adjustment content according to the user's current situation and task during adjustment. For example, the adjustment unit provides highly relevant adjustment content according to the user's current situation. The adjustment unit can also provide optimal adjustment content based on the user's current task. The adjustment unit can also analyze the user's current situation and customize specific adjustment content. This makes it possible to customize the adjustment content according to the user's current situation and task. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data on the user's current situation and task into the generation AI and cause the generation AI to customize the adjustment content.

[0056] The adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information during adjustment. For example, the adjustment unit selects the optimal adjustment method by taking into account the user's geographical location information during adjustment. For example, if the user is in a specific area, the adjustment unit selects an adjustment method related to that area. Furthermore, if the user is traveling, the adjustment unit can also select an adjustment method related to the travel destination. Furthermore, if the user is at home, the adjustment unit can also select an adjustment method based on information about the area around the user's home. In this way, the optimal adjustment method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal adjustment method.

[0057] The adjustment unit can customize the adjustment content by analyzing the user's social media activity during the adjustment. For example, the adjustment unit customizes the adjustment content by analyzing the user's social media activity during the adjustment. For example, the adjustment unit analyzes the user's social media posts and provides related adjustment content. The adjustment unit can also provide related adjustment content by referring to the activities of the user's friends on social media. The adjustment unit can also customize specific adjustment content based on the user's social media activity patterns. This makes it possible to customize the adjustment content based on the user's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the user's social media activity into a generation AI and cause the generation AI to customize the adjustment content.

[0058] The adjustment unit can improve the adjustment method by reflecting the user's past feedback during adjustment. The adjustment unit, for example, improves the adjustment method by reflecting the user's past feedback during adjustment. For example, the adjustment unit suggests an optimal adjustment method based on feedback provided by the user in the past. The adjustment unit can also preferentially select a specific adjustment method from the user's past feedback. The adjustment unit can also reflect the user's feedback and continuously improve the adjustment method. This makes it possible to improve the adjustment method based on the user's past feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback into the generation AI and cause the generation AI to improve the adjustment method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When generating a fictional human, the generation unit can analyze the user's past behavioral patterns and predict the behavior of the fictional human to be generated. For example, the generation unit can analyze what actions the user has taken in the past and predict how the fictional human will react to those actions. The generation unit can also suggest what actions the fictional human should take based on the user's past behavioral patterns. Furthermore, the generation unit can predict how the fictional human will grow based on the user's behavioral patterns. This makes it possible to generate a more realistic fictional human based on the user's past behavioral patterns.

[0061] The adjustment unit can take the user's cultural background into consideration when adjusting the fictional character's personality and speaking style. For example, if the user belongs to a specific cultural sphere, the adjustment unit adjusts the fictional character to have a personality and speaking style that is appropriate for that culture. In addition, if the user has a multicultural background, the adjustment unit can also adjust the fictional character to have a personality and speaking style that is appropriate for multiple cultures. Furthermore, if the user has a specific religious background, the adjustment unit can also adjust the fictional character to have a personality and speaking style that is appropriate for that religion. This makes it possible to provide a fictional character that is appropriate for the user's cultural background.

[0062] When accepting input information from a user, the accepting unit can analyze the user's past input patterns and suggest the optimal input method. For example, the accepting unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The accepting unit can also suggest the optimal input method for a specific time period based on the user's past input patterns. Furthermore, the accepting unit can customize the input method based on the user's past input patterns. This makes it possible to suggest the optimal input method based on the user's past input patterns.

[0063] When generating a fictional character, the generation unit can customize the knowledge of the fictional character based on the user's occupation or expertise. For example, if the user is engaged in the medical field, the generation unit can generate a fictional character with medical knowledge. If the user is engaged in the education field, the generation unit can also generate a fictional character with educational knowledge. Furthermore, if the user is engaged in the technical field, the generation unit can also generate a fictional character with technical knowledge. This makes it possible to provide a fictional character appropriate for the user's occupation or expertise.

[0064] When supporting communication between the fictitious person and the user, the communication unit can adjust the content of the response by referring to the user's past communication history. For example, the communication unit can provide a relevant response based on the content of conversations the user has had in the past. The communication unit can also extract specific patterns from the user's past communication history and provide an optimal response. Furthermore, the communication unit can reflect user feedback to continuously improve the accuracy of the response. This makes it possible to provide an optimal response based on the user's past communication history.

[0065] The adjustment unit can take into account the user's current task and goal when adjusting the fictional person's personality and speaking style. For example, if the user is concentrating on work, the adjustment unit adjusts the fictional person to have a professional personality and speaking style. In addition, if the user is relaxed, the adjustment unit can adjust the fictional person to have a calm personality and speaking style. Furthermore, if the user is learning, the adjustment unit can adjust the fictional person to have a knowledgeable personality and speaking style that is good at teaching. In this way, it is possible to provide an appropriate fictional person according to the user's current task and goal.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives information from the user. The information from the user includes, for example, characteristics of appearance, personality traits, range of knowledge, etc. The reception unit can receive text information, audio information, image information, etc. Step 2: The generation unit uses the LLM to generate a fictional human based on the information received by the reception unit. The generation unit generates a fictional human with appearance, personality, knowledge, and conversation history. For example, it can generate a fictional human with detailed appearance based on the appearance characteristics specified by the user, and a fictional human with detailed personality based on personality tendencies. It can also generate a fictional human with detailed knowledge based on the range of knowledge. Step 3: The communication unit supports communication between the fictional human generated by the generation unit and the user. The communication unit supports communication between the fictional human and the user using media such as email, telephone, and VR. For example, it is possible to send and receive messages via email, have conversations by telephone, or have dialogues in a VR environment. Step 4: The adjustment unit adjusts the personality and speaking style of the fictional human based on the feedback obtained by the communication unit. For example, the adjustment unit adjusts the personality and speaking style of the fictional human based on the user's feedback. If the user is relaxed, the fictional human can be adjusted to have a calm personality and speaking style, and if the user is excited, the fictional human can be adjusted to have a lively personality and speaking style. Also, if the user is stressed, the fictional human can be adjusted to have a soothing personality and speaking style.

[0068] (Example 2) A fictional character creation system according to an embodiment of the present invention generates fictional characters based on user information, supports communication, and adjusts the fictional character based on feedback. The fictional character creation system allows users to create fictional characters with different appearances, personalities (speech patterns), knowledge, and conversation histories. This allows users to communicate with the fictional characters through various media, such as email, telephone, and VR. For example, in the fictional character creation system, users input information to create a fictional character. For example, they specify appearance characteristics, personality traits, and knowledge levels. This information is input into an LLM, which analyzes it to generate a fictional character. The generated fictional character has a unique appearance, personality, knowledge, and conversation history based on the specified information. The fictional character creation system then allows users to communicate with the generated fictional character through various media. For example, users can send and receive messages via email, talk on the phone, or interact in a VR environment. This allows users to enjoy realistic communication with the fictional character. Furthermore, the fictional character becomes close to the user and grows together with them. For example, as a user frequently communicates with a fictional character, the fictional character's knowledge and conversation skills improve. The fictional character's personality and speaking style may also be adjusted based on user feedback. This allows the fictional character creation system to provide individuals and corporations with a variety of communication methods, allowing it to grow alongside and support users. This allows the fictional character creation system to allow users to enjoy realistic communication with fictional characters. For example, as a user frequently communicates with a fictional character, the fictional character's knowledge and conversation skills improve. The fictional character's personality and speaking style may also be adjusted based on user feedback. This allows the fictional character creation system to provide individuals and corporations with a variety of communication methods, allowing it to grow alongside and support users.

[0069] A fictional character creation system according to an embodiment includes a reception unit, a generation unit, a communication unit, and an adjustment unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, appearance characteristics, personality traits, and knowledge range. The reception unit can receive, for example, text information, audio information, and image information. The generation unit uses an LLM to generate a fictional character based on the information received by the reception unit. The generation unit generates a fictional character with, for example, appearance, personality, knowledge, and conversation history. The generation unit generates a fictional character with detailed appearance based on, for example, appearance characteristics specified by a user. The generation unit can also generate a fictional character with detailed personality traits based on personality traits specified by a user. The generation unit can also generate a fictional character with detailed knowledge based on the knowledge range specified by a user. For example, the generation unit inputs information specified by the user into the LLM, which analyzes the information to generate a fictional character. The communication unit supports communication between the user and the fictional character generated by the generation unit. The communication unit supports communication between the fictional human and the user using media such as email, telephone, and VR. The communication unit can, for example, send and receive messages via email, have telephone conversations, and interact in a VR environment. The adjustment unit adjusts the fictional human's personality and speaking style based on feedback obtained by the communication unit. The adjustment unit adjusts the fictional human's personality and speaking style based on user feedback, for example. For example, if the user is relaxed, the adjustment unit adjusts the fictional human to have a calm personality and speaking style. Furthermore, if the user is excited, the adjustment unit can adjust the fictional human to have a lively personality and speaking style. Furthermore, if the user is stressed, the adjustment unit can adjust the fictional human to have a soothing personality and speaking style. As a result, the fictional human creation system according to the embodiment can generate a fictional human based on information from the user, support communication, and adjust the fictional human based on feedback.

[0070] The generation unit can generate a fictional human with appearance, personality, knowledge, and conversation history. The generation unit, for example, generates a fictional human with appearance characteristics. For example, the generation unit generates a fictional human by specifying facial features, body type, clothing, etc. The generation unit can also generate a fictional human with personality tendencies. For example, the generation unit generates a fictional human by specifying personality traits such as introversion, extroversion, and emotionality. The generation unit can also generate a fictional human with a range of knowledge. For example, the generation unit generates a fictional human by specifying specialized knowledge, general knowledge, knowledge in a specific field, etc. The generation unit can also generate a fictional human with a conversation history. For example, the generation unit generates a fictional human by specifying the content of past conversations, the date and time of the conversations, the conversation partners, etc. In this way, a fictional human with appearance, personality, knowledge, and conversation history can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input user-specified information into the LLM, which then analyzes it to generate a fictional human.

[0071] The communication unit can support communication between the fictional person and the user using media such as email, telephone, and VR. The communication unit, for example, sends and receives messages between the fictional person and the user via email. For example, the communication unit sends and receives messages using text email or HTML email. The communication unit can also support conversations between the fictional person and the user via telephone. For example, the communication unit supports conversations using voice calls or video calls. The communication unit can also conduct dialogue between the fictional person and the user in a VR environment. For example, the communication unit conducts dialogue using a VR headset or a VR application. This makes it possible to support communication between the fictional person and the user using various media. Some or all of the above-described processing in the communication unit may be performed using, for example, an LLM, or may be performed without an LLM. For example, the communication unit can use an LLM to support communication between the fictional person and the user using media such as email, telephone, and VR.

[0072] The adjustment unit can adjust the personality and speaking style of the fictional human based on user feedback. The adjustment unit adjusts the personality and speaking style of the fictional human based on user feedback, for example. For example, if the user is relaxed, the adjustment unit adjusts the fictional human to have a calm personality and speaking style. Furthermore, if the user is excited, the adjustment unit can adjust the fictional human to have a lively personality and speaking style. Furthermore, if the user is stressed, the adjustment unit can adjust the fictional human to have a soothing personality and speaking style. In this way, the personality and speaking style of the fictional human can be adjusted based on user feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the adjustment unit can input user feedback into an LLM, which analyzes the feedback and adjusts the personality and speaking style of the fictional human.

[0073] The generation unit can generate a personality that will become a company representative. The generation unit generates, for example, a personality that will become a company representative. For example, the generation unit generates a personality that reflects the company's philosophy, vision, values, etc. The generation unit can also generate a personality that has the appearance and personality of a company representative. For example, the generation unit generates a personality by specifying the appearance characteristics and personality tendencies of a company representative. This makes it possible to generate a personality that will become a company representative for a corporation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input the company's philosophy and vision into an LLM, which then analyzes the data and generates a personality that will become a company representative.

[0074] The communications department can support communication between fictional people and customers in a corporation's customer service and public relations activities. The communications department, for example, supports communication between fictional people and customers in a corporation's customer service. For example, the communications department handles inquiries and complaints. The communications department can also support communication between fictional people and customers in public relations activities. For example, the communications department issues press releases and posts on social media. This can support communication between fictional people and customers in a corporation's customer service and public relations activities. Some or all of the above-mentioned processing in the communications department can be performed, for example, using an LLM, or can be performed without an LLM. For example, the communications department can use an LLM to support communication between fictional people and customers in a corporation's customer service and public relations activities.

[0075] The reception unit can estimate the user's emotion and adjust the timing of receiving information based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of receiving information based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit receives information at a timing when the user can relax. Furthermore, if the user is excited, the reception unit can immediately receive information and provide a prompt response. Furthermore, if the user is tired, the reception unit can receive information after the user has rested. This makes it possible to adjust the timing of receiving information based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0076] The reception unit can analyze the user's past input history and select the optimal information reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal information reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. The reception unit can also customize the optimal reception method based on information previously input by the user. This makes it possible to select the optimal information reception method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI select the optimal information reception method.

[0077] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving information. For example, the reception unit can perform filtering based on the user's current situation and areas of interest when receiving information. For example, the reception unit can receive only information that is highly relevant to the user's current situation. The reception unit can also filter unnecessary information based on the user's areas of interest. The reception unit can also preferentially receive information related to the user's current task. This makes it possible to filter information based on the user's current situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or can be performed without using AI. For example, the reception unit can input data on the user's current situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0078] The reception unit can select an appropriate reception means depending on the user's input method when receiving information. For example, the reception unit selects an appropriate reception means depending on the user's input method (voice, text, image, etc.) when receiving information. For example, if the user uses voice input, the reception unit can receive information using voice recognition technology. Also, if the user uses text input, the reception unit can receive information using text analysis technology. Also, if the user uses image input, the reception unit can receive information using image recognition technology. This makes it possible to select the optimal reception means depending on the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and cause the generation AI to select an appropriate reception means.

[0079] The reception unit can estimate the user's emotion and determine the priority of information to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of information to be received based on the estimated user's emotion. For example, when the user is expressing an urgent emotion, the reception unit can prioritize receiving important information. Furthermore, when the user is relaxed, the reception unit can prioritize receiving normal information. Furthermore, when the user is excited, the reception unit can prioritize receiving highly relevant information. This makes it possible to determine the priority of information based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0080] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving highly relevant information based on the user's geographical location information when receiving information. For example, when the user is in a specific area, the reception unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the reception unit can prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving information around the user's home. This makes it possible to prioritize receiving highly relevant information taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant information.

[0081] The reception unit can analyze the user's social media activity and receive related information when receiving information. For example, the reception unit analyzes the user's social media activity and receives related information when receiving information. For example, the reception unit receives related information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and receive related information. The reception unit can also receive related information by referring to the activity of the user's friends on social media. In this way, related information can be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into a generation AI and cause the generation AI to select related information.

[0082] The reception unit can customize the reception method by reflecting the user's past feedback when receiving information. The reception unit, for example, customizes the reception method by reflecting the user's past feedback when receiving information. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially select a specific reception method from the user's past feedback. The reception unit can also reflect the user's feedback and continuously improve the reception method. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback into a generation AI and have the generation AI customize the reception method.

[0083] The generation unit can estimate the user's emotions and adjust the appearance and personality of the fictional human based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the appearance and personality of the fictional human based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a fictional human with a calm appearance and personality. If the user is excited, the generation unit can also generate a fictional human with a lively appearance and personality. If the user is stressed, the generation unit can also generate a fictional human with a soothing appearance and personality. This allows the appearance and personality of the fictional human to be adjusted based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the appearance and personality.

[0084] The generation unit can generate a detailed profile of a fictional human based on information specified by a user at the time of generation. For example, the generation unit generates a detailed profile of a fictional human based on information specified by a user at the time of generation. For example, the generation unit generates a fictional human with a detailed appearance based on appearance characteristics specified by a user. The generation unit can also generate a fictional human with a detailed personality based on personality tendencies specified by a user. The generation unit can also generate a fictional human with detailed knowledge based on a range of knowledge specified by a user. This allows a detailed profile to be generated based on information specified by a user. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input information specified by a user into an LLM, which analyzes the information and generates a detailed profile.

[0085] The generation unit can customize the knowledge range of the fictional person according to the user's interests and concerns at the time of generation. For example, the generation unit customizes the knowledge range of the fictional person according to the user's interests and concerns at the time of generation. For example, the generation unit generates a fictional person with knowledge in a field that the user is interested in. The generation unit can also generate a fictional person with specific knowledge based on the user's field of interest. The generation unit can also generate a fictional person with knowledge that the user is likely to be interested in based on the user's past search history. This makes it possible to customize the knowledge range of the fictional person according to the user's interests and concerns. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input data on the user's interests and concerns into the LLM and cause the LLM to customize the knowledge range.

[0086] The generation unit can improve the accuracy of generation by referring to the user's past generation history during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation history during generation. For example, the generation unit can improve the accuracy of generation based on information about fictional people the user has previously generated. The generation unit can also improve the accuracy of generation by extracting specific patterns from the user's past generation history. The generation unit can also continuously improve the accuracy of generation by reflecting user feedback. This allows the accuracy of generation to be improved based on the user's past generation history. Some or all of the above-described processing in the generation unit can be performed using, or without, an LLM. For example, the generation unit can input the user's past generation history into the LLM and cause the LLM to improve the accuracy of generation.

[0087] The generation unit can estimate the user's emotions and adjust the fictional human's speaking style based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the fictional human's speaking style based on the estimated user's emotions. For example, the generation unit can generate a fictional human with a calm speaking style when the user is relaxed. The generation unit can also generate a fictional human with a lively speaking style when the user is excited. The generation unit can also generate a fictional human with a soothing speaking style when the user is stressed. This allows the fictional human's speaking style to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the speaking style.

[0088] The generation unit can customize the appearance of the fictional human based on the user's geographical background during generation. For example, the generation unit customizes the appearance of the fictional human based on the user's geographical background during generation. For example, if the user lives in a specific region, the generation unit generates a fictional human with an appearance that reflects the characteristics of that region. The generation unit can also generate a fictional human with an appearance related to a region to which the user has traveled. The generation unit can also generate a fictional human with an appearance that reflects a specific culture or customs based on the user's geographical background. This makes it possible to customize the appearance of the fictional human based on the user's geographical background. Some or all of the above-described processing in the generation unit may be performed using, or without, an LLM. For example, the generation unit can input data about the user's geographical background into the LLM and cause the LLM to customize the appearance.

[0089] The generation unit can adjust the personality of the fictional human based on the user's social media activity during generation. For example, the generation unit adjusts the personality of the fictional human based on the user's social media activity during generation. For example, the generation unit analyzes the content of the user's social media posts and generates a fictional human with a related personality. The generation unit can also generate a fictional human with a related personality based on the activity of the user's friends on social media. The generation unit can also generate a fictional human with a specific personality based on the user's social media activity pattern. This makes it possible to adjust the personality of the fictional human based on the user's social media activity. Some or all of the above-described processing in the generation unit may be performed using, or without, an LLM. For example, the generation unit can input data on the user's social media activity into the LLM and cause the LLM to adjust the personality.

[0090] The generation unit can customize the knowledge of the fictional person according to the user's occupation or expertise at the time of generation. For example, the generation unit customizes the knowledge of the fictional person according to the user's occupation or expertise at the time of generation. For example, the generation unit generates a fictional person with knowledge related to the user's occupation. The generation unit can also generate a fictional person with specific knowledge based on the user's expertise. The generation unit can also generate a fictional person who is knowledgeable in a specific field by reflecting the user's occupation or expertise. This makes it possible to customize the knowledge of the fictional person according to the user's occupation or expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the generation unit can input data on the user's occupation and expertise into the LLM and cause the LLM to customize the knowledge.

[0091] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, the communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user is relaxed, the communication unit can communicate in a calm tone. If the user is excited, the communication unit can communicate in a lively tone. If the user is stressed, the communication unit can communicate in a soothing tone. This allows the communication method to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit can be performed using an AI, for example, or without an AI. For example, the communication unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the communication method.

[0092] The communication unit can provide an appropriate response by referring to the user's past conversation history during communication. For example, the communication unit can provide an appropriate response by referring to the user's past conversation history during communication. For example, the communication unit can provide a relevant response based on the content of the user's past conversations. The communication unit can also extract specific patterns from the user's past conversation history to provide an optimal response. The communication unit can also reflect user feedback to continuously improve the accuracy of the response. This makes it possible to provide an optimal response based on the user's past conversation history. Some or all of the above-mentioned processing in the communication unit can be performed using AI, for example, or can be performed without using AI. For example, the communication unit can input the user's past conversation history into a generation AI and cause the generation AI to provide an appropriate response.

[0093] The communication unit can customize the response content according to the user's current situation and task during communication. For example, the communication unit customizes the response content according to the user's current situation and task during communication. For example, the communication unit provides a highly relevant response according to the user's current situation. The communication unit can also provide an optimal response based on the user's current task. The communication unit can also analyze the user's current situation and customize specific response content. This makes it possible to customize the response content according to the user's current situation and task. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input data on the user's current situation and task to the generation AI and cause the generation AI to customize the response content.

[0094] The communication unit can improve the response method by reflecting user feedback during communication. For example, the communication unit improves the response method by reflecting user feedback during communication. For example, the communication unit improves the response method based on feedback provided by the user. The communication unit can also preferentially select a specific response method from the user's past feedback. The communication unit can also reflect user feedback to continuously improve the response method. In this way, the response method can be improved based on user feedback. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input user feedback into a generation AI and cause the generation AI to improve the response method.

[0095] The communication unit can estimate the user's emotions and determine the priority of communication based on the estimated user emotions. The communication unit, for example, estimates the user's emotions and determines the priority of communication based on the estimated user emotions. For example, when the user is expressing an urgent emotion, the communication unit can prioritize important communication. Furthermore, when the user is relaxed, the communication unit can prioritize normal communication. Furthermore, when the user is excited, the communication unit can prioritize highly relevant communication. This makes it possible to determine the priority of communication based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit may be performed using an AI, for example, or without an AI. For example, the communication unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of communication.

[0096] The communication unit can provide an optimal response during communication by taking into account the user's geographical location information. For example, the communication unit can provide an optimal response during communication by taking into account the user's geographical location information. For example, if the user is in a specific area, the communication unit can provide information related to that area. Furthermore, if the user is traveling, the communication unit can provide information related to the user's travel destination. Furthermore, if the user is at home, the communication unit can provide information about the area around the user's home. This makes it possible to provide an optimal response by taking into account the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the user's geographical location information into a generation AI and cause the generation AI to provide an optimal response.

[0097] The communication unit can provide a relevant response by analyzing the user's social media activity during communication. For example, the communication unit can provide a relevant response by analyzing the user's social media activity during communication. For example, the communication unit can provide a relevant response based on information shared by the user on social media. The communication unit can also analyze the user's social media activity and provide a relevant response. The communication unit can also provide a relevant response by referring to the activity of the user's friends on social media. In this way, a relevant response can be provided based on the user's social media activity. Some or all of the above-described processing in the communication unit can be performed using, for example, AI, or can be performed without using AI. For example, the communication unit can input data on the user's social media activity into a generation AI and cause the generation AI to provide a relevant response.

[0098] The communication unit can customize the response method by reflecting the user's past feedback during communication. For example, the communication unit customizes the response method by reflecting the user's past feedback during communication. For example, the communication unit suggests an optimal response method based on feedback provided by the user in the past. The communication unit can also preferentially select a specific response method from the user's past feedback. The communication unit can also reflect the user's feedback to continuously improve the response method. This makes it possible to customize the response method based on the user's past feedback. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input the user's past feedback into a generation AI and cause the generation AI to customize the response method.

[0099] The adjustment unit can estimate the user's emotions and adjust the personality and speaking style of the fictional human based on the estimated user's emotions. The adjustment unit, for example, estimates the user's emotions and adjusts the personality and speaking style of the fictional human based on the estimated user's emotions. For example, if the user is relaxed, the adjustment unit adjusts the fictional human to have a calm personality and speaking style. Furthermore, if the user is excited, the adjustment unit can adjust the fictional human to have a lively personality and speaking style. Furthermore, if the user is stressed, the adjustment unit can adjust the fictional human to have a soothing personality and speaking style. In this way, the personality and speaking style of the fictional human can be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's emotional data into the generation AI and have the generation AI adjust the personality and speaking style.

[0100] The adjustment unit can select the optimal adjustment method by referring to the user's past feedback during adjustment. For example, the adjustment unit selects the optimal adjustment method by referring to the user's past feedback during adjustment. For example, the adjustment unit selects the optimal adjustment method based on feedback provided by the user in the past. The adjustment unit can also preferentially select a specific adjustment method from the user's past feedback. The adjustment unit can also reflect the user's feedback and continuously improve the adjustment method. This makes it possible to select the optimal adjustment method based on the user's past feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback into the generation AI and cause the generation AI to select the optimal adjustment method.

[0101] The adjustment unit can improve the accuracy of the adjustment by analyzing the knowledge and conversation history of the fictional person during adjustment. For example, the adjustment unit can improve the accuracy of the adjustment by analyzing the knowledge and conversation history of the fictional person during adjustment. For example, the adjustment unit analyzes the knowledge and conversation history of the fictional person and selects an optimal adjustment method. The adjustment unit can also analyze the conversation history of the fictional person and preferentially select a specific adjustment method. The adjustment unit can also continuously improve the accuracy of the adjustment based on the knowledge and conversation history of the fictional person. This makes it possible to improve the accuracy of the adjustment based on the knowledge and conversation history of the fictional person. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the knowledge and conversation history of the fictional person into the generation AI and cause the generation AI to improve the accuracy of the adjustment.

[0102] The adjustment unit can customize the adjustment content according to the user's current situation and task during adjustment. The adjustment unit, for example, customizes the adjustment content according to the user's current situation and task during adjustment. For example, the adjustment unit provides highly relevant adjustment content according to the user's current situation. The adjustment unit can also provide optimal adjustment content based on the user's current task. The adjustment unit can also analyze the user's current situation and customize specific adjustment content. This makes it possible to customize the adjustment content according to the user's current situation and task. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input data on the user's current situation and task into the generation AI and cause the generation AI to customize the adjustment content.

[0103] The adjustment unit can estimate the user's emotions and determine the priority of adjustments based on the estimated user emotions. The adjustment unit, for example, estimates the user's emotions and determines the priority of adjustments based on the estimated user emotions. For example, when the user is expressing an urgent emotion, the adjustment unit can prioritize important adjustments. Furthermore, when the user is relaxed, the adjustment unit can prioritize normal adjustments. Furthermore, when the user is excited, the adjustment unit can prioritize highly relevant adjustments. This makes it possible to determine the priority of adjustments based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of adjustments.

[0104] The adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information during adjustment. For example, the adjustment unit selects the optimal adjustment method by taking into account the user's geographical location information during adjustment. For example, if the user is in a specific area, the adjustment unit selects an adjustment method related to that area. Furthermore, if the user is traveling, the adjustment unit can also select an adjustment method related to the travel destination. Furthermore, if the user is at home, the adjustment unit can also select an adjustment method based on information about the area around the user's home. In this way, the optimal adjustment method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal adjustment method.

[0105] The adjustment unit can customize the adjustment content by analyzing the user's social media activity during the adjustment. For example, the adjustment unit customizes the adjustment content by analyzing the user's social media activity during the adjustment. For example, the adjustment unit analyzes the user's social media posts and provides related adjustment content. The adjustment unit can also provide related adjustment content by referring to the activities of the user's friends on social media. The adjustment unit can also customize specific adjustment content based on the user's social media activity patterns. This makes it possible to customize the adjustment content based on the user's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the user's social media activity into a generation AI and cause the generation AI to customize the adjustment content.

[0106] The adjustment unit can improve the adjustment method by reflecting the user's past feedback during adjustment. The adjustment unit, for example, improves the adjustment method by reflecting the user's past feedback during adjustment. For example, the adjustment unit suggests an optimal adjustment method based on feedback provided by the user in the past. The adjustment unit can also preferentially select a specific adjustment method from the user's past feedback. The adjustment unit can also reflect the user's feedback and continuously improve the adjustment method. This makes it possible to improve the adjustment method based on the user's past feedback. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback into the generation AI and cause the generation AI to improve the adjustment method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, communication unit, and adjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives information from a user using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fictional human using an LLM. The communication unit supports communication between the fictional human and the user using the output device 40 of the smart device 14. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the personality and speaking style of the fictional human based on feedback. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, communication unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives information from the user using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fictional human using the LLM. The communication unit supports communication between the fictional human and the user using the speaker 240 of the smart glasses 214. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the personality and speaking style of the fictional human based on the feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, communication unit, and adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives information from the user using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fictional human using an LLM. The communication unit supports communication between the fictional human and the user using the speaker 240 of the headset type terminal 314. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the personality and speaking style of the fictional human based on the feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, communication unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives information from the user using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a fictional human using an LLM. The communication unit supports communication between the fictional human and the user using the speaker 240 of the robot 414. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the personality and speaking style of the fictional human based on the feedback.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When generating a fictional human, the generation unit can analyze the user's past behavioral patterns and predict the behavior of the fictional human to be generated. For example, the generation unit can analyze what actions the user has taken in the past and predict how the fictional human will react to those actions. The generation unit can also suggest what actions the fictional human should take based on the user's past behavioral patterns. Furthermore, the generation unit can predict how the fictional human will grow based on the user's behavioral patterns. This makes it possible to generate a more realistic fictional human based on the user's past behavioral patterns.

[0109] When supporting communication between the fictional human and the user, the communication unit can adjust the content of the response taking into account the user's current health condition. For example, if the user is tired, the communication unit can provide a response that helps the user relax. Also, if the user is feeling stressed, the communication unit can provide a response that helps the user reduce stress. Furthermore, if the user is in good health, the communication unit can provide a positive response. This makes it possible to provide appropriate communication according to the user's health condition.

[0110] The adjustment unit can take the user's cultural background into consideration when adjusting the fictional character's personality and speaking style. For example, if the user belongs to a specific cultural sphere, the adjustment unit adjusts the fictional character to have a personality and speaking style that is appropriate for that culture. In addition, if the user has a multicultural background, the adjustment unit can also adjust the fictional character to have a personality and speaking style that is appropriate for multiple cultures. Furthermore, if the user has a specific religious background, the adjustment unit can also adjust the fictional character to have a personality and speaking style that is appropriate for that religion. This makes it possible to provide a fictional character that is appropriate for the user's cultural background.

[0111] When generating a fictional character, the generation unit can adjust the appearance and personality of the fictional character based on the user's current mood. For example, if the user is in a happy mood, the generation unit can generate a fictional character with a bright and cheerful appearance and personality. If the user is depressed, the generation unit can also generate a fictional character with a comforting appearance and personality. Furthermore, if the user is concentrating, the generation unit can also generate a fictional character with an intelligent and calm appearance and personality. This makes it possible to provide an appropriate fictional character according to the user's mood.

[0112] When accepting input information from a user, the accepting unit can analyze the user's past input patterns and suggest the optimal input method. For example, the accepting unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The accepting unit can also suggest the optimal input method for a specific time period based on the user's past input patterns. Furthermore, the accepting unit can customize the input method based on the user's past input patterns. This makes it possible to suggest the optimal input method based on the user's past input patterns.

[0113] When generating a fictional character, the generation unit can customize the knowledge of the fictional character based on the user's occupation or expertise. For example, if the user is engaged in the medical field, the generation unit can generate a fictional character with medical knowledge. If the user is engaged in the education field, the generation unit can also generate a fictional character with educational knowledge. Furthermore, if the user is engaged in the technical field, the generation unit can also generate a fictional character with technical knowledge. This makes it possible to provide a fictional character appropriate for the user's occupation or expertise.

[0114] When supporting communication between the fictitious person and the user, the communication unit can adjust the content of the response by referring to the user's past communication history. For example, the communication unit can provide a relevant response based on the content of conversations the user has had in the past. The communication unit can also extract specific patterns from the user's past communication history and provide an optimal response. Furthermore, the communication unit can reflect user feedback to continuously improve the accuracy of the response. This makes it possible to provide an optimal response based on the user's past communication history.

[0115] The adjustment unit can take into account the user's current task and goal when adjusting the fictional person's personality and speaking style. For example, if the user is concentrating on work, the adjustment unit adjusts the fictional person to have a professional personality and speaking style. In addition, if the user is relaxed, the adjustment unit can adjust the fictional person to have a calm personality and speaking style. Furthermore, if the user is learning, the adjustment unit can adjust the fictional person to have a knowledgeable personality and speaking style that is good at teaching. In this way, it is possible to provide an appropriate fictional person according to the user's current task and goal.

[0116] When generating a fictional human, the generation unit can estimate the user's emotions and adjust the appearance and personality of the fictional human based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a fictional human with a calm appearance and personality. If the user is excited, the generation unit can also generate a fictional human with a lively appearance and personality. Furthermore, if the user is stressed, the generation unit can also generate a fictional human with a soothing appearance and personality. This makes it possible to adjust the appearance and personality of the fictional human based on the user's emotions.

[0117] When supporting communication between a fictional human and a user, the communication unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, if the user is relaxed, the communication unit can communicate in a calm tone. If the user is excited, the communication unit can also communicate in a lively tone. Furthermore, if the user is feeling stressed, the communication unit can also communicate in a soothing tone. In this way, the communication method can be adjusted based on the user's emotions.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives information from the user. The information from the user includes, for example, characteristics of appearance, personality traits, range of knowledge, etc. The reception unit can receive text information, audio information, image information, etc. Step 2: The generation unit uses the LLM to generate a fictional human based on the information received by the reception unit. The generation unit generates a fictional human with appearance, personality, knowledge, and conversation history. For example, it can generate a fictional human with detailed appearance based on the appearance characteristics specified by the user, and a fictional human with detailed personality based on personality tendencies. It can also generate a fictional human with detailed knowledge based on the range of knowledge. Step 3: The communication unit supports communication between the fictional human generated by the generation unit and the user. The communication unit supports communication between the fictional human and the user using media such as email, telephone, and VR. For example, it is possible to send and receive messages via email, have conversations by telephone, or have dialogues in a VR environment. Step 4: The adjustment unit adjusts the personality and speaking style of the fictional human based on the feedback obtained by the communication unit. For example, the adjustment unit adjusts the personality and speaking style of the fictional human based on the user's feedback. If the user is relaxed, the fictional human can be adjusted to have a calm personality and speaking style, and if the user is excited, the fictional human can be adjusted to have a lively personality and speaking style. Also, if the user is stressed, the fictional human can be adjusted to have a soothing personality and speaking style.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 reception unit that receives information from a user; a generation unit that generates a fictional person based on the information received by the reception unit; a communication unit that supports communication between the fictional person generated by the generation unit and a user; an adjustment unit that adjusts the personality or speaking style of the fictional person based on the feedback obtained by the communication unit; Equipped with A system characterized by:

2. The generation unit Generate fictional people with appearance, personality, knowledge, and conversation history 2. The system of claim 1.

3. The communication unit Supports communication between users and fictional people through email, telephone, and VR.

2. The system of claim 1.

4. The adjustment unit Adjusting the fictional character's personality and speaking style based on user feedback 2. The system of claim 1.

5. The generation unit Generate a personality to represent your company 2. The system of claim 1.

6. The communication unit Supports communication between fictional people and customers in corporate customer relations and public relations activities 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts the timing of information reception based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal method for receiving information 2. The system of claim 1.

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