system

Abstract

The system according to the embodiment aims to interact with an AI avatar of a specific person. [Solution] A system according to an embodiment includes a selection unit, a dialogue unit, a reception unit, a generation unit, and an output unit. The selection unit receives a selection from a user of a specific person with whom the user wishes to converse. The dialogue unit initiates a dialogue with an AI avatar selected by the selection unit based on the user's input. The reception unit receives the user's input. The generation unit generates a response based on the input received by the reception unit. The output unit outputs the response generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have limited the means by which dialogue with specific people can be realized, and have faced challenges in terms of time and cost.

[0005] The system according to the embodiment aims to interact with an AI avatar of a specific person. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a dialogue unit, a reception unit, a generation unit, and an output unit. The selection unit receives a selection from a user of a specific person with whom the user wishes to converse. The dialogue unit initiates a dialogue between the AI ​​avatar selected by the selection unit and the AI ​​avatar based on the user's input. The reception unit receives the user's input. The generation unit generates a response based on the input received by the reception unit. The output unit outputs the response generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can interact with an AI avatar of a particular person. [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 dialogue system according to an embodiment of the present invention allows users to enjoy real-time conversations with AI avatars of celebrities. Unlike social networking sites and online salons, this system offers advantages in terms of time and cost. Specifically, it comprises the following steps: First, the user selects an AI avatar of the celebrity with whom they wish to converse. Next, the selected AI avatar initiates a conversation based on the user's input. The AI ​​avatar generates appropriate responses to the user's questions and comments, engaging in real-time conversations. Furthermore, this system can also be used to reunite with loved ones lost in an earthquake or accident. The user inputs information about the loved one, and an AI avatar is generated based on that information. The generated AI avatar then converses with the user, providing a moving experience of reunion. This system allows users to enjoy real-time conversations with celebrities or loved ones, providing a moving experience without time or cost constraints. This dialogue system allows users to enjoy real-time conversations with AI avatars of celebrities. It can also be used to reunite with loved ones lost in an earthquake or accident, providing a moving experience.

[0029] A dialogue system according to an embodiment includes a selection unit, a dialogue unit, a reception unit, a generation unit, and an output unit. The selection unit receives a user's selection of a specific person with whom the user wishes to converse. The specific person may include a celebrity, a fictional character, or a personal acquaintance. The dialogue unit initiates a dialogue with an AI avatar selected by the selection unit based on the user's input. The dialogue may be text-based, voice-based, video-based, or other formats. The reception unit receives user input. The input may include keyboard input, voice input, touch input, or other formats. The generation unit generates a response based on the input received by the reception unit. The response may be generated in the form of a text response, voice response, video response, or other format. The output unit outputs the response generated by the generation unit in real time. The definition of real time includes the acceptable delay time and response speed. This allows the dialogue system to allow the user to enjoy a real-time dialogue with the celebrity's AI avatar.

[0030] The dialogue system includes a generation unit that inputs information about a specific person and generates an AI avatar based on that information. The generation unit generates the AI ​​avatar based on the information about the specific person input by the user. The information about the specific person includes the name, photo, voice data, personality traits, etc. For example, if a user inputs the name and photo of a loved one they have lost, the generation unit generates an AI avatar based on that information. The generated AI avatar can converse with the user, providing a moving sense of reunion. In this way, the dialogue system can generate an AI avatar based on information about the loved one they have lost, providing the user with a moving experience.

[0031] The generation unit can generate an AI avatar based on user input. The generation unit generates the AI ​​avatar based on information input by the user. User input includes text, voice, images, etc. For example, if a user inputs the name of a celebrity, the generation unit generates an AI avatar based on that name. Also, if a user inputs voice data, the generation unit can generate an AI avatar based on that voice data. Furthermore, if a user inputs an image, the generation unit can generate an AI avatar based on that image. In this way, the generation unit can generate an AI avatar based on the user's input and provide a personalized interaction experience.

[0032] The dialogue unit allows the generated AI avatar to converse with the user. The dialogue unit allows the generated AI avatar to converse with the user. The dialogue can be in the form of text, voice, video, or other formats. For example, the generated AI avatar can respond to the user's questions in text. The generated AI avatar can also respond to the user's comments in voice. Furthermore, the generated AI avatar can make video calls with the user. This allows the dialogue unit to converse with the user in real time, providing a dialogue experience.

[0033] The output unit can output the generated response in real time. The output unit outputs the response generated by the generation unit in real time. The definition of real time includes the allowable range of delay time and the response speed. For example, the output unit can instantly display the generated text response. The output unit can also instantly play back the generated voice response. Furthermore, the output unit can also instantly play back the generated video response. In this way, the output unit can output the generated response in real time, thereby realizing a smooth dialogue.

[0034] The selection unit can select an AI avatar of a celebrity with whom the user wants to interact. The selection unit selects an AI avatar of a celebrity with whom the user wants to interact. Celebrities include actors, singers, politicians, etc. For example, when a user inputs the name of an actor, the selection unit selects the AI ​​avatar of that actor. Also, when a user inputs the name of a singer, the selection unit can select the AI ​​avatar of that singer. Furthermore, when a user inputs the name of a politician, the selection unit can select the AI ​​avatar of that politician. In this way, the selection unit can select the AI ​​avatar of the celebrity with whom the user wants to interact, providing a personalized interaction experience.

[0035] The dialogue system includes a selection unit that analyzes the user's past selection history and proposes appropriate celebrity candidates. The selection unit analyzes the user's past selection history and proposes optimal celebrity candidates. The past selection history includes a list of selected celebrities and the date and time of selection. For example, the selection unit preferentially proposes AI avatars of celebrities that the user has frequently selected in the past. It is also possible to preferentially propose celebrities in a specific genre based on the user's past selection history. Furthermore, it is possible to analyze the user's past selection history and propose new AI avatars of celebrities that match the user's preferences. As a result, the selection unit can provide a more appropriate dialogue experience by analyzing the user's past selection history and proposing optimal celebrity candidates.

[0036] The dialogue system includes a selection unit that filters celebrity options based on the user's current interests and trends. The selection unit filters celebrity options based on the user's current interests and trends. Current interests and trends include social media trends and news articles. For example, the selection unit preferentially presents AI avatars of relevant celebrities based on current news and trends. The selection unit can also filter AI avatars of celebrities based on keywords recently searched by the user. Furthermore, the selection unit can suggest AI avatars of celebrities of interest to the user based on the user's social media activities. In this way, the selection unit can filter celebrity options based on the user's current interests and trends, providing a more interesting dialogue experience.

[0037] The dialogue system includes a selection unit that prioritizes displaying celebrities associated with a region, taking into account the user's geographical location information. The selection unit prioritizes displaying celebrities associated with a region, taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, and the like. For example, if the user is in a specific region, the selection unit prioritizes displaying AI avatars of celebrities associated with that region. Also, if the user is traveling, the selection unit can suggest AI avatars of celebrities associated with the user's travel destination. Furthermore, based on the user's geographical location information, the selection unit can display AI avatars of celebrities associated with local events or news. In this way, the selection unit prioritizes displaying celebrities associated with a region, taking into account the user's geographical location information, thereby providing a more appropriate dialogue experience.

[0038] The dialogue system includes a selection unit that analyzes the user's social media activity and suggests related celebrities. The selection unit analyzes the user's social media activity and suggests related celebrities. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the selection unit preferentially suggests AI avatars of celebrities the user follows. It is also possible to suggest AI avatars of celebrities related to posts that the user has liked or commented on. Furthermore, it is also possible to analyze the user's social media activity and suggest AI avatars of celebrities in genres that the user is interested in. In this way, the selection unit can analyze the user's social media activity and suggest related celebrities, providing a more appropriate dialogue experience.

[0039] The dialogue system includes a dialogue unit that analyzes the user's responses in real time during the dialogue and adjusts the progress of the dialogue. The dialogue unit analyzes the user's responses in real time during the dialogue and optimizes the progress of the dialogue. The definition of real time includes the acceptable range of delay time and response speed. For example, the dialogue may proceed by digging deeper into a topic in which the user has shown interest. If the user is bored, the dialogue unit may also provide a new topic to stimulate the dialogue. Furthermore, if the user has an unpleasant reaction, the dialogue unit may change the topic and continue the dialogue. In this way, the dialogue unit can analyze the user's responses in real time, optimize the progress of the dialogue, and provide a more appropriate dialogue experience.

[0040] The dialogue system includes a dialogue unit that applies different dialogue algorithms depending on the content of the dialogue. The dialogue unit applies different dialogue algorithms depending on the content of the dialogue. Dialogue algorithms include rule-based and machine learning-based algorithms. For example, when a user asks a question, the dialogue unit applies a dialogue algorithm specialized for providing information. Also, when a user brings up an emotional topic, the dialogue unit can apply a dialogue algorithm that shows empathy. Furthermore, when a user is looking for entertainment, the dialogue unit can apply a dialogue algorithm that incorporates humor. In this way, the dialogue unit can apply a dialogue algorithm according to the content of the dialogue and provide a more appropriate dialogue experience.

[0041] The dialogue system includes a dialogue unit that refers to the user's past dialogue history during a dialogue and provides related topics. The dialogue unit refers to the user's past dialogue history during a dialogue and provides related topics. The past dialogue history includes the dialogue content, dialogue date and time, etc. For example, the dialogue can proceed by revisiting topics that the user talked about in the past. It can also provide new topics that the user may be interested in based on the user's past dialogue history. It can also provide related information based on the content of questions the user has asked in the past. In this way, the dialogue unit can refer to the user's past dialogue history and provide related topics, thereby providing a more appropriate dialogue experience.

[0042] The dialogue system includes a dialogue unit that provides additional information and suggestions based on the user's interests during the dialogue. The dialogue unit provides additional information and suggestions based on the user's interests during the dialogue. Interests and concerns include survey results and past behavioral history. For example, the dialogue unit can suggest articles and videos related to topics that the user has shown interest in. It can also introduce events and news that the user may be interested in. It can also suggest new AI avatars of celebrities based on the user's interests. This allows the dialogue unit to provide additional information and suggestions based on the user's interests, improving the quality of the dialogue.

[0043] The dialogue system includes a reception unit that analyzes the user's input history and suggests an appropriate input method. The reception unit analyzes the user's input history and suggests the optimal input method. The input history includes the input content, the input date and time, etc. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The system can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's input history and suggest the most efficient input method. This allows the reception unit to analyze the user's input history and suggest the optimal input method, providing a more appropriate input experience.

[0044] The dialogue system includes a reception unit that applies different reception algorithms depending on the content of a user's input. The reception unit applies different reception algorithms depending on the content of the user's input. Reception algorithms include rule-based and machine learning-based algorithms. For example, when a user inputs a question, the reception unit applies a reception algorithm specialized for providing information. Also, when a user inputs an emotional message, the reception unit can apply a reception algorithm that shows empathy. Furthermore, when a user is looking for entertainment, the reception unit can apply a reception algorithm that incorporates humor. In this way, the reception unit can apply a reception algorithm that suits the content of the user's input and provide a more appropriate input experience.

[0045] The dialogue system includes a reception unit that prioritizes receiving related input content in consideration of the user's geographical location information. The reception unit prioritizes receiving related input content in consideration of the user's geographical location information. Geographical location information includes GPS data, IP address, and the like. For example, if the user is in a specific area, the reception unit prioritizes receiving input content related to that area. Also, if the user is traveling, the reception unit can prioritize receiving input content related to the travel destination. Furthermore, based on the user's geographical location information, the reception unit can prioritize receiving input content related to local events and news. In this way, the reception unit prioritizes receiving related input content in consideration of the user's geographical location information, thereby providing a more appropriate input experience.

[0046] The dialogue system includes a reception unit that analyzes the user's social media activity and receives related input content. The reception unit analyzes the user's social media activity and receives related input content. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the reception unit may preferentially receive input content related to celebrities the user follows on social media. The reception unit may also preferentially receive input content related to posts on which the user has "liked" or commented. Furthermore, the reception unit may analyze the user's social media activity and preferentially receive input content related to genres of interest. This allows the reception unit to analyze the user's social media activity, receive related input content, and provide a more appropriate input experience.

[0047] The dialogue system includes a generation unit that generates an appropriate response by referring to the user's past dialogue history when generating a response. The generation unit generates an optimal response by referring to the user's past dialogue history when generating a response. The past dialogue history includes the content of the dialogue and the date and time of the dialogue. For example, the response may be generated by revisiting a topic that the user previously discussed. The response may also include new topics that the user may be interested in based on the user's past dialogue history. Furthermore, the response may include related information based on the content of questions the user has previously asked. In this way, the generation unit can generate an optimal response by referring to the user's past dialogue history, thereby providing a more appropriate dialogue experience.

[0048] The dialogue system includes a generation unit that generates an appropriate response by taking into account the user's geographical location information. The generation unit generates an optimal response by taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, and the like. For example, if the user is in a specific area, the generation unit includes information related to that area in the response. Also, if the user is traveling, the generation unit can include information related to the travel destination in the response. Furthermore, based on the user's geographical location information, the generation unit can include information related to local events and news in the response. This allows the generation unit to generate an optimal response by taking into account the user's geographical location information, thereby providing a more appropriate dialogue experience.

[0049] The dialogue system includes a generation unit that analyzes the user's social media activity and generates a relevant response when generating a response. The generation unit analyzes the user's social media activity and generates a relevant response when generating a response. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, information related to celebrities the user follows on social media may be included in the response. Information related to posts that the user has liked or commented on may also be included in the response. Furthermore, the user's social media activity may be analyzed and information related to genres of interest may be included in the response. This allows the generation unit to analyze the user's social media activity, generate a relevant response, and provide a more appropriate dialogue experience.

[0050] The dialogue system includes an output unit that, when outputting a response, selects an appropriate output method by referring to the user's past dialogue history. When outputting a response, the output unit selects the optimal output method by referring to the user's past dialogue history. The past dialogue history includes the content of the dialogue and the date and time of the dialogue. For example, the output unit preferentially uses an output method (voice, text, etc.) that the user previously preferred. The dialogue system can also select an output method suitable for a specific time period from the user's past dialogue history. Furthermore, the dialogue system can analyze the user's past dialogue history and select the most effective output method. In this way, the output unit can refer to the user's past dialogue history, select the optimal output method, and provide a more appropriate dialogue experience.

[0051] The dialogue system includes an output unit that customizes the output based on the user's current interests and trends when outputting a response. The output unit customizes the output based on the user's current interests and trends when outputting a response. Current interests and trends include social media trends and news articles. For example, the output is customized based on topics that the user has recently been interested in. The output can also be customized based on keywords recently searched by the user. Furthermore, topics of interest can be included in the output based on the user's social media activities. This allows the output unit to customize the output based on the user's current interests and trends and provide a more appropriate response.

[0052] The dialogue system includes an output unit that selects an appropriate output method in consideration of the user's geographical location information when outputting a response. The output unit selects the optimal output method in consideration of the user's geographical location information when outputting a response. Geographical location information includes GPS data, IP address, etc. For example, if the user is in a specific area, the output unit may prioritize outputting information related to that area. Also, if the user is traveling, the output unit may prioritize outputting information related to the travel destination. Furthermore, based on the user's geographical location information, the output unit may prioritize outputting information related to local events and news. In this way, the output unit can select the optimal output method in consideration of the user's geographical location information and provide a more appropriate dialogue experience.

[0053] The dialogue system includes an output unit that analyzes the user's social media activity and outputs a relevant response when outputting a response. The output unit analyzes the user's social media activity and outputs a relevant response when outputting a response. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, information related to celebrities the user follows on social media can be preferentially output. Information related to posts that the user has liked or commented on can also be preferentially output. Furthermore, the user's social media activity can be analyzed and information related to genres of interest can be preferentially output. In this way, the output unit can analyze the user's social media activity and output a relevant response, providing a more appropriate dialogue experience.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The dialogue system may include a progress optimization unit that analyzes the user's past dialogue history and optimizes the dialogue progress. For example, topics that the user has shown interest in in the past may be prioritized. Also, by avoiding topics that the user has avoided in the past, the dialogue can progress smoothly. Furthermore, it is possible to provide topics that are appropriate for a specific time period based on the user's past dialogue history. In this way, the progress optimization unit can analyze the user's past dialogue history, optimize the dialogue progress, and provide a more appropriate dialogue experience.

[0056] The dialogue system may include a local information providing unit that provides topics related to a region, taking into account the user's geographical location information. For example, if the user is in a specific region, the local information providing unit may provide events and news about that region. Also, if the user is traveling, the local information providing unit may provide tourist information and recommended spots for the travel destination. Furthermore, the local information providing unit may provide topics related to the culture and history of the region, based on the user's geographical location information. In this way, the local information providing unit may provide topics related to the region, taking into account the user's geographical location information, and provide a more appropriate dialogue experience.

[0057] The dialogue system may include a social media analysis unit that analyzes the user's social media activity and provides related topics. For example, topics related to celebrities the user follows on social media may be provided. Also, topics related to posts that the user has liked or commented on may be provided. Furthermore, the dialogue system may analyze the user's social media activity and provide topics related to genres of interest. In this way, the social media analysis unit can analyze the user's social media activity, provide related topics, and provide a more appropriate dialogue experience.

[0058] The dialogue system may include a content optimization unit that analyzes the user's past selection history and optimizes the content of the dialogue. For example, it may provide topics related to AI avatars of celebrities that the user has previously selected. It may also provide new topics that the user may be interested in based on the user's past selection history. It may also analyze the user's past selection history and provide topics related to a specific genre. In this way, the content optimization unit can analyze the user's past selection history, optimize the content of the dialogue, and provide a more appropriate dialogue experience.

[0059] The dialogue system may include a trend customization unit that customizes the content of the dialogue based on the user's current interests and trends. For example, the content of the dialogue may be customized based on topics that the user has recently been interested in. The content of the dialogue may also be customized based on keywords recently searched by the user. Furthermore, topics of interest may be included in the content of the dialogue based on the user's social media activity. In this way, the trend customization unit can customize the content of the dialogue based on the user's current interests and trends, thereby providing a more appropriate dialogue experience.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The selection unit accepts a selection from the user of a specific person with whom the user wants to interact. The specific person may include a celebrity, a fictional character, a personal acquaintance, etc. Step 2: The dialogue unit initiates a dialogue with the AI ​​avatar selected by the selection unit based on the user's input. The dialogue can be text-based, voice-based, video-based, or other formats. Step 3: The reception unit receives user input, including keyboard input, voice input, and touch input. Step 4: The generator generates a response based on the input received by the receiver, in the form of a text response, a voice response, a video response, or the like. Step 5: The output unit outputs the response generated by the generation unit in real time. The definition of real time includes the tolerance for delay time and response speed.

[0062] (Example 2) A dialogue system according to an embodiment of the present invention allows users to enjoy real-time conversations with AI avatars of celebrities. Unlike social networking sites and online salons, this system offers advantages in terms of time and cost. Specifically, it comprises the following steps: First, the user selects an AI avatar of the celebrity with whom they wish to converse. Next, the selected AI avatar initiates a conversation based on the user's input. The AI ​​avatar generates appropriate responses to the user's questions and comments, engaging in real-time conversations. Furthermore, this system can also be used to reunite with loved ones lost in an earthquake or accident. The user inputs information about the loved one, and an AI avatar is generated based on that information. The generated AI avatar then converses with the user, providing a moving experience of reunion. This system allows users to enjoy real-time conversations with celebrities or loved ones, providing a moving experience without time or cost constraints. This dialogue system allows users to enjoy real-time conversations with AI avatars of celebrities. It can also be used to reunite with loved ones lost in an earthquake or accident, providing a moving experience.

[0063] A dialogue system according to an embodiment includes a selection unit, a dialogue unit, a reception unit, a generation unit, and an output unit. The selection unit receives a user's selection of a specific person with whom the user wishes to converse. The specific person may include a celebrity, a fictional character, or a personal acquaintance. The dialogue unit initiates a dialogue with an AI avatar selected by the selection unit based on the user's input. The dialogue may be text-based, voice-based, video-based, or other formats. The reception unit receives user input. The input may include keyboard input, voice input, touch input, or other formats. The generation unit generates a response based on the input received by the reception unit. The response may be generated in the form of a text response, voice response, video response, or other format. The output unit outputs the response generated by the generation unit in real time. The definition of real time includes the acceptable delay time and response speed. This allows the dialogue system to allow the user to enjoy a real-time dialogue with the celebrity's AI avatar.

[0064] The dialogue system includes a generation unit that inputs information about a specific person and generates an AI avatar based on that information. The generation unit generates the AI ​​avatar based on the information about the specific person input by the user. The information about the specific person includes the name, photo, voice data, personality traits, etc. For example, if a user inputs the name and photo of a loved one they have lost, the generation unit generates an AI avatar based on that information. The generated AI avatar can converse with the user, providing a moving sense of reunion. In this way, the dialogue system can generate an AI avatar based on information about the loved one they have lost, providing the user with a moving experience.

[0065] The generation unit can generate an AI avatar based on user input. The generation unit generates the AI ​​avatar based on information input by the user. User input includes text, voice, images, etc. For example, if a user inputs the name of a celebrity, the generation unit generates an AI avatar based on that name. Also, if a user inputs voice data, the generation unit can generate an AI avatar based on that voice data. Furthermore, if a user inputs an image, the generation unit can generate an AI avatar based on that image. In this way, the generation unit can generate an AI avatar based on the user's input and provide a personalized interaction experience.

[0066] The dialogue unit allows the generated AI avatar to converse with the user. The dialogue unit allows the generated AI avatar to converse with the user. The dialogue can be in the form of text, voice, video, or other formats. For example, the generated AI avatar can respond to the user's questions in text. The generated AI avatar can also respond to the user's comments in voice. Furthermore, the generated AI avatar can make video calls with the user. This allows the dialogue unit to converse with the user in real time, providing a dialogue experience.

[0067] The output unit can output the generated response in real time. The output unit outputs the response generated by the generation unit in real time. The definition of real time includes the allowable range of delay time and the response speed. For example, the output unit can instantly display the generated text response. The output unit can also instantly play back the generated voice response. Furthermore, the output unit can also instantly play back the generated video response. In this way, the output unit can output the generated response in real time, thereby realizing a smooth dialogue.

[0068] The selection unit can select an AI avatar of a celebrity with whom the user wants to interact. The selection unit selects an AI avatar of a celebrity with whom the user wants to interact. Celebrities include actors, singers, politicians, etc. For example, when a user inputs the name of an actor, the selection unit selects the AI ​​avatar of that actor. Also, when a user inputs the name of a singer, the selection unit can select the AI ​​avatar of that singer. Furthermore, when a user inputs the name of a politician, the selection unit can select the AI ​​avatar of that politician. In this way, the selection unit can select the AI ​​avatar of the celebrity with whom the user wants to interact, providing a personalized interaction experience.

[0069] The dialogue system includes a selection unit that analyzes a user's emotions and presents celebrity options based on the analyzed user emotions. The selection unit estimates the user's emotions and presents celebrity options based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is sad, the selection unit may preferentially present AI avatars of celebrities who can offer words of encouragement and comfort. Alternatively, if the user is excited, the selection unit may preferentially present AI avatars of celebrities who are highly entertaining. Furthermore, if the user is relaxed, the selection unit may preferentially present AI avatars of celebrities who can engage in calm conversations. This allows the selection unit to present celebrity options according to the user's emotions, providing a more appropriate dialogue experience.

[0070] The dialogue system includes a selection unit that analyzes the user's past selection history and proposes appropriate celebrity candidates. The selection unit analyzes the user's past selection history and proposes optimal celebrity candidates. The past selection history includes a list of selected celebrities and the date and time of selection. For example, the selection unit preferentially proposes AI avatars of celebrities that the user has frequently selected in the past. It is also possible to preferentially propose celebrities in a specific genre based on the user's past selection history. Furthermore, it is possible to analyze the user's past selection history and propose new AI avatars of celebrities that match the user's preferences. As a result, the selection unit can provide a more appropriate dialogue experience by analyzing the user's past selection history and proposing optimal celebrity candidates.

[0071] The dialogue system includes a selection unit that filters celebrity options based on the user's current interests and trends. The selection unit filters celebrity options based on the user's current interests and trends. Current interests and trends include social media trends and news articles. For example, the selection unit preferentially presents AI avatars of relevant celebrities based on current news and trends. The selection unit can also filter AI avatars of celebrities based on keywords recently searched by the user. Furthermore, the selection unit can suggest AI avatars of celebrities of interest to the user based on the user's social media activities. In this way, the selection unit can filter celebrity options based on the user's current interests and trends, providing a more interesting dialogue experience.

[0072] The dialogue system includes a selection unit that analyzes a user's emotions and adjusts the display order of celebrity options based on the analyzed user emotions. The selection unit estimates the user's emotions and adjusts the display order of celebrity options based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is tired, the selection unit can first display an AI avatar of a celebrity that can relax them. Also, if the user is excited, the selection unit can first display an AI avatar of a celebrity that is highly entertaining. Furthermore, if the user is sad, the selection unit can first display an AI avatar of a celebrity that can offer words of comfort. This allows the selection unit to adjust the display order according to the user's emotions and provide a more appropriate dialogue experience.

[0073] The dialogue system includes a selection unit that prioritizes displaying celebrities associated with a region, taking into account the user's geographical location information. The selection unit prioritizes displaying celebrities associated with a region, taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, and the like. For example, if the user is in a specific region, the selection unit prioritizes displaying AI avatars of celebrities associated with that region. Also, if the user is traveling, the selection unit can suggest AI avatars of celebrities associated with the user's travel destination. Furthermore, based on the user's geographical location information, the selection unit can display AI avatars of celebrities associated with local events or news. In this way, the selection unit prioritizes displaying celebrities associated with a region, taking into account the user's geographical location information, thereby providing a more appropriate dialogue experience.

[0074] The dialogue system includes a selection unit that analyzes the user's social media activity and suggests related celebrities. The selection unit analyzes the user's social media activity and suggests related celebrities. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the selection unit preferentially suggests AI avatars of celebrities the user follows. It is also possible to suggest AI avatars of celebrities related to posts that the user has liked or commented on. Furthermore, it is also possible to analyze the user's social media activity and suggest AI avatars of celebrities in genres that the user is interested in. In this way, the selection unit can analyze the user's social media activity and suggest related celebrities, providing a more appropriate dialogue experience.

[0075] The dialogue system includes a dialogue unit that analyzes a user's emotions and adjusts the tone and content of the dialogue based on the analyzed user emotions. The dialogue unit estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is sad, the dialogue unit provides comforting words or an encouraging message. Also, if the user is excited, the dialogue unit can provide an entertaining topic. Furthermore, if the user is relaxed, the dialogue unit can proceed with the dialogue in a calm tone. This allows the dialogue unit to adjust the tone and content of the dialogue according to the user's emotions and provide a more appropriate dialogue experience.

[0076] The dialogue system includes a dialogue unit that analyzes the user's responses in real time during the dialogue and adjusts the progress of the dialogue. The dialogue unit analyzes the user's responses in real time during the dialogue and optimizes the progress of the dialogue. The definition of real time includes the acceptable range of delay time and response speed. For example, the dialogue may proceed by digging deeper into a topic in which the user has shown interest. If the user is bored, the dialogue unit may also provide a new topic to stimulate the dialogue. Furthermore, if the user has an unpleasant reaction, the dialogue unit may change the topic and continue the dialogue. In this way, the dialogue unit can analyze the user's responses in real time, optimize the progress of the dialogue, and provide a more appropriate dialogue experience.

[0077] The dialogue system includes a dialogue unit that applies different dialogue algorithms depending on the content of the dialogue. The dialogue unit applies different dialogue algorithms depending on the content of the dialogue. Dialogue algorithms include rule-based and machine learning-based algorithms. For example, when a user asks a question, the dialogue unit applies a dialogue algorithm specialized for providing information. Also, when a user brings up an emotional topic, the dialogue unit can apply a dialogue algorithm that shows empathy. Furthermore, when a user is looking for entertainment, the dialogue unit can apply a dialogue algorithm that incorporates humor. In this way, the dialogue unit can apply a dialogue algorithm according to the content of the dialogue and provide a more appropriate dialogue experience.

[0078] The dialogue system includes a dialogue unit that analyzes a user's emotions and adjusts the length of a dialogue based on the analyzed user emotions. The dialogue unit estimates the user's emotions and adjusts the length of a dialogue based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is in a hurry, the dialogue unit provides a short, to-the-point dialogue. If the user is relaxed, the dialogue unit can also provide a detailed dialogue. Furthermore, if the user is excited, the dialogue unit can also provide a fast-paced dialogue. In this way, the dialogue unit can adjust the length of a dialogue according to the user's emotions and provide a more appropriate dialogue experience.

[0079] The dialogue system includes a dialogue unit that refers to the user's past dialogue history during a dialogue and provides related topics. The dialogue unit refers to the user's past dialogue history during a dialogue and provides related topics. The past dialogue history includes the dialogue content, dialogue date and time, etc. For example, the dialogue can proceed by revisiting topics that the user talked about in the past. It can also provide new topics that the user may be interested in based on the user's past dialogue history. It can also provide related information based on the content of questions the user has asked in the past. In this way, the dialogue unit can refer to the user's past dialogue history and provide related topics, thereby providing a more appropriate dialogue experience.

[0080] The dialogue system includes a dialogue unit that provides additional information and suggestions based on the user's interests during the dialogue. The dialogue unit provides additional information and suggestions based on the user's interests during the dialogue. Interests and concerns include survey results and past behavioral history. For example, the dialogue unit can suggest articles and videos related to topics that the user has shown interest in. It can also introduce events and news that the user may be interested in. It can also suggest new AI avatars of celebrities based on the user's interests. This allows the dialogue unit to provide additional information and suggestions based on the user's interests, improving the quality of the dialogue.

[0081] The dialogue system includes a reception unit that analyzes a user's emotions and adjusts an input reception method based on the analyzed user emotions. The reception unit estimates the user's emotions and adjusts the input reception method based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input. In this way, the reception unit can adjust the input reception method according to the user's emotions and provide a more appropriate input experience.

[0082] The dialogue system includes a reception unit that analyzes the user's input history and suggests an appropriate input method. The reception unit analyzes the user's input history and suggests the optimal input method. The input history includes the input content, the input date and time, etc. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The system can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's input history and suggest the most efficient input method. This allows the reception unit to analyze the user's input history and suggest the optimal input method, providing a more appropriate input experience.

[0083] The dialogue system includes a reception unit that applies different reception algorithms depending on the content of a user's input. The reception unit applies different reception algorithms depending on the content of the user's input. Reception algorithms include rule-based and machine learning-based algorithms. For example, when a user inputs a question, the reception unit applies a reception algorithm specialized for providing information. Also, when a user inputs an emotional message, the reception unit can apply a reception algorithm that shows empathy. Furthermore, when a user is looking for entertainment, the reception unit can apply a reception algorithm that incorporates humor. In this way, the reception unit can apply a reception algorithm that suits the content of the user's input and provide a more appropriate input experience.

[0084] The dialogue system includes a reception unit that analyzes a user's emotions and prioritizes inputs based on the analyzed user emotions. The reception unit estimates the user's emotions and prioritizes inputs based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is in a hurry, the reception unit can prioritize inputs to process them quickly. Also, if the user is relaxed, the reception unit can prioritize detailed inputs. Furthermore, if the user is stressed, the reception unit can prioritize simple inputs. This allows the reception unit to prioritize inputs according to the user's emotions and provide a more appropriate input experience.

[0085] The dialogue system includes a reception unit that prioritizes receiving related input content in consideration of the user's geographical location information. The reception unit prioritizes receiving related input content in consideration of the user's geographical location information. Geographical location information includes GPS data, IP address, and the like. For example, if the user is in a specific area, the reception unit prioritizes receiving input content related to that area. Also, if the user is traveling, the reception unit can prioritize receiving input content related to the travel destination. Furthermore, based on the user's geographical location information, the reception unit can prioritize receiving input content related to local events and news. In this way, the reception unit prioritizes receiving related input content in consideration of the user's geographical location information, thereby providing a more appropriate input experience.

[0086] The dialogue system includes a reception unit that analyzes the user's social media activity and receives related input content. The reception unit analyzes the user's social media activity and receives related input content. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the reception unit may preferentially receive input content related to celebrities the user follows on social media. The reception unit may also preferentially receive input content related to posts on which the user has "liked" or commented. Furthermore, the reception unit may analyze the user's social media activity and preferentially receive input content related to genres of interest. This allows the reception unit to analyze the user's social media activity, receive related input content, and provide a more appropriate input experience.

[0087] The dialogue system includes a generation unit that analyzes a user's emotions and adjusts a response generation method based on the analyzed user emotions. The generation unit estimates the user's emotions and adjusts a response generation method based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is sad, the generation unit generates comforting words or an encouraging message. Also, if the user is excited, the generation unit can generate a highly entertaining response. Furthermore, if the user is relaxed, the generation unit can generate a response with a calm tone. This allows the generation unit to adjust the response generation method according to the user's emotions and provide a more appropriate response.

[0088] The dialogue system includes a generation unit that generates an appropriate response by referring to the user's past dialogue history when generating a response. The generation unit generates an optimal response by referring to the user's past dialogue history when generating a response. The past dialogue history includes the content of the dialogue and the date and time of the dialogue. For example, the response may be generated by revisiting a topic that the user previously discussed. The response may also include new topics that the user may be interested in based on the user's past dialogue history. Furthermore, the response may include related information based on the content of questions the user has previously asked. In this way, the generation unit can generate an optimal response by referring to the user's past dialogue history, thereby providing a more appropriate dialogue experience.

[0089] The dialogue system includes a generation unit that analyzes a user's emotions and prioritizes responses based on the analyzed user emotions. The generation unit estimates the user's emotions and prioritizes responses based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is in a hurry, the generation unit can generate a quick response. If the user is relaxed, the generation unit can also generate a detailed response. Furthermore, if the user is stressed, the generation unit can generate a response that includes simple, comforting words. This allows the generation unit to prioritize responses according to the user's emotions and provide more appropriate responses.

[0090] The dialogue system includes a generation unit that generates an appropriate response by taking into account the user's geographical location information. The generation unit generates an optimal response by taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, and the like. For example, if the user is in a specific area, the generation unit includes information related to that area in the response. Also, if the user is traveling, the generation unit can include information related to the travel destination in the response. Furthermore, based on the user's geographical location information, the generation unit can include information related to local events and news in the response. This allows the generation unit to generate an optimal response by taking into account the user's geographical location information, thereby providing a more appropriate dialogue experience.

[0091] The dialogue system includes a generation unit that analyzes the user's social media activity and generates a relevant response when generating a response. The generation unit analyzes the user's social media activity and generates a relevant response when generating a response. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, information related to celebrities the user follows on social media may be included in the response. Information related to posts that the user has liked or commented on may also be included in the response. Furthermore, the user's social media activity may be analyzed and information related to genres of interest may be included in the response. This allows the generation unit to analyze the user's social media activity, generate a relevant response, and provide a more appropriate dialogue experience.

[0092] The dialogue system includes an output unit that analyzes a user's emotions and adjusts the response output method based on the analyzed user's emotions. The output unit estimates the user's emotions and adjusts the response output method based on the estimated user's emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is sad, the output unit outputs a response in a calm tone. Also, if the user is excited, the output unit can output a response in an energetic tone. Furthermore, if the user is relaxed, the output unit can output a response in a calm tone. This allows the output unit to adjust the response output method according to the user's emotions and provide a more appropriate response.

[0093] The dialogue system includes an output unit that, when outputting a response, selects an appropriate output method by referring to the user's past dialogue history. When outputting a response, the output unit selects the optimal output method by referring to the user's past dialogue history. The past dialogue history includes the content of the dialogue and the date and time of the dialogue. For example, the output unit preferentially uses an output method (voice, text, etc.) that the user previously preferred. The dialogue system can also select an output method suitable for a specific time period from the user's past dialogue history. Furthermore, the dialogue system can analyze the user's past dialogue history and select the most effective output method. In this way, the output unit can refer to the user's past dialogue history, select the optimal output method, and provide a more appropriate dialogue experience.

[0094] The dialogue system includes an output unit that customizes the output based on the user's current interests and trends when outputting a response. The output unit customizes the output based on the user's current interests and trends when outputting a response. Current interests and trends include social media trends and news articles. For example, the output is customized based on topics that the user has recently been interested in. The output can also be customized based on keywords recently searched by the user. Furthermore, topics of interest can be included in the output based on the user's social media activities. This allows the output unit to customize the output based on the user's current interests and trends and provide a more appropriate response.

[0095] The dialogue system includes an output unit that analyzes a user's emotions and adjusts the output order of responses based on the analyzed user emotions. The output unit estimates the user's emotions and adjusts the output order of responses based on the estimated user emotions. Specific types of emotions and analysis methods include emotion classifications such as joy, sadness, and anger, and emotion analysis algorithms. For example, if the user is in a hurry, the output unit outputs important information first. Alternatively, if the user is relaxed, the output unit can output detailed information in an orderly manner. Furthermore, if the user is stressed, the output unit can output simple, important information first. This allows the output unit to adjust the output order of responses according to the user's emotions and provide more appropriate responses.

[0096] The dialogue system includes an output unit that selects an appropriate output method in consideration of the user's geographical location information when outputting a response. The output unit selects the optimal output method in consideration of the user's geographical location information when outputting a response. Geographical location information includes GPS data, IP address, etc. For example, if the user is in a specific area, the output unit may prioritize outputting information related to that area. Also, if the user is traveling, the output unit may prioritize outputting information related to the travel destination. Furthermore, based on the user's geographical location information, the output unit may prioritize outputting information related to local events and news. In this way, the output unit can select the optimal output method in consideration of the user's geographical location information and provide a more appropriate dialogue experience.

[0097] The dialogue system includes an output unit that analyzes the user's social media activity and outputs a relevant response when outputting a response. The output unit analyzes the user's social media activity and outputs a relevant response when outputting a response. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, information related to celebrities the user follows on social media can be preferentially output. Information related to posts that the user has liked or commented on can also be preferentially output. Furthermore, the user's social media activity can be analyzed and information related to genres of interest can be preferentially output. In this way, the output unit can analyze the user's social media activity and output a relevant response, providing a more appropriate dialogue experience. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, dialogue unit, reception unit, generation unit, and output unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 and selects an AI avatar of a celebrity with which the user wants to interact. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and the selected AI avatar starts a dialogue based on the user's input. The reception unit is realized, for example, by the reception device 38 of the smart device 14 and accepts the user's input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the user's input. The output unit is realized, for example, by the output device 40 of the smart device 14 and outputs the generated response in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, dialogue unit, reception unit, generation unit, and output unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and selects an AI avatar of a celebrity with whom the user wishes to converse. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and the selected AI avatar begins a dialogue based on the user's input. The reception unit is realized, for example, by the microphone 238 of the smart glasses 214 and accepts the user's input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the user's input. The output unit is realized, for example, by the speaker 240 of the smart glasses 214 and outputs the generated response in real time. === Hard Collateral 1-3 === Each of the multiple elements including the selection unit, dialogue unit, reception unit, generation unit, and output unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset-type terminal 314 and selects an AI avatar of a celebrity with whom the user wants to converse. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and the selected AI avatar starts a dialogue based on the user's input. The reception unit is realized, for example, by the microphone 238 of the headset-type terminal 314 and receives the user's input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the user's input. The output unit is realized, for example, by the speaker 240 of the headset-type terminal 314 and outputs the generated response in real time. === Hard Collateral 1-4 === Each of the multiple elements including the selection unit, dialogue unit, reception unit, generation unit, and output unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and selects an AI avatar of a celebrity with which the user wants to converse. The dialogue unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and the selected AI avatar starts a dialogue based on the user's input. The reception unit is realized, for example, by the microphone 238 of the robot 414 and receives the user's input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a response based on the user's input. The output unit is realized, for example, by the speaker 240 of the robot 414 and outputs the generated response in real time.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The dialogue system may include a timing adjustment unit that estimates the user's emotions and adjusts the timing of starting a dialogue based on the estimated emotions. For example, if the user is feeling stressed, the timing adjustment unit delays the start of the dialogue to provide the user with time to relax. If the user is excited, the timing adjustment unit immediately starts the dialogue to maintain the user's excitement. If the user is sad, the timing adjustment unit gently starts the dialogue to be considerate of the user's emotions. This allows the timing adjustment unit to adjust the start timing of the dialogue according to the user's emotions and provide a more appropriate dialogue experience.

[0100] The dialogue system may include a progress optimization unit that analyzes the user's past dialogue history and optimizes the dialogue progress. For example, topics that the user has shown interest in in the past may be prioritized. Also, by avoiding topics that the user has avoided in the past, the dialogue can progress smoothly. Furthermore, it is possible to provide topics that are appropriate for a specific time period based on the user's past dialogue history. In this way, the progress optimization unit can analyze the user's past dialogue history, optimize the dialogue progress, and provide a more appropriate dialogue experience.

[0101] The dialogue system may include a content customization unit that estimates the user's emotions and customizes the content of the dialogue based on the estimated emotions. For example, if the user is happy, the content customization unit may provide a happy topic. If the user is sad, the content customization unit may provide comforting words or encouraging messages. If the user is angry, the content customization unit may provide calm dialogue to calm the user's emotions. In this way, the content customization unit may customize the content of the dialogue according to the user's emotions and provide a more appropriate dialogue experience.

[0102] The dialogue system may include a local information providing unit that provides topics related to a region, taking into account the user's geographical location information. For example, if the user is in a specific region, the local information providing unit may provide events and news about that region. Also, if the user is traveling, the local information providing unit may provide tourist information and recommended spots for the travel destination. Furthermore, the local information providing unit may provide topics related to the culture and history of the region, based on the user's geographical location information. In this way, the local information providing unit may provide topics related to the region, taking into account the user's geographical location information, and provide a more appropriate dialogue experience.

[0103] The dialogue system may include an end adjustment unit that estimates a user's emotions and adjusts the timing of the dialogue end based on the estimated emotions. For example, if the user is tired, the end adjustment unit may end the dialogue early to provide the user with time to rest. If the user is having fun, the end adjustment unit may extend the dialogue to maintain the user's enjoyment. Furthermore, if the user is in a hurry, the end adjustment unit may quickly end the dialogue to save the user's time. In this way, the end adjustment unit may adjust the timing of the dialogue end based on the user's emotions and provide a more appropriate dialogue experience.

[0104] The dialogue system may include a social media analysis unit that analyzes the user's social media activity and provides related topics. For example, topics related to celebrities the user follows on social media may be provided. Also, topics related to posts that the user has liked or commented on may be provided. Furthermore, the dialogue system may analyze the user's social media activity and provide topics related to genres of interest. In this way, the social media analysis unit can analyze the user's social media activity, provide related topics, and provide a more appropriate dialogue experience.

[0105] The dialogue system may include a tempo adjustment unit that estimates the user's emotions and adjusts the tempo of the dialogue based on the estimated emotions. For example, if the user is relaxed, the tempo adjustment unit may proceed with the dialogue at a slow tempo. Alternatively, if the user is excited, the tempo adjustment unit may proceed with the dialogue at a fast tempo. Furthermore, if the user is stressed, the tempo adjustment unit may proceed with the dialogue at a gentle tempo to calm the user's emotions. In this way, the tempo adjustment unit may adjust the tempo of the dialogue according to the user's emotions and provide a more appropriate dialogue experience.

[0106] The dialogue system may include a content optimization unit that analyzes the user's past selection history and optimizes the content of the dialogue. For example, it may provide topics related to AI avatars of celebrities that the user has previously selected. It may also provide new topics that the user may be interested in based on the user's past selection history. It may also analyze the user's past selection history and provide topics related to a specific genre. In this way, the content optimization unit can analyze the user's past selection history, optimize the content of the dialogue, and provide a more appropriate dialogue experience.

[0107] The dialogue system may include a feedback providing unit that estimates the user's emotions and provides dialogue feedback based on the estimated emotions. For example, if the user is happy, the feedback providing unit may provide positive feedback. If the user is sad, the feedback providing unit may provide comforting words or an encouraging message. If the user is angry, the feedback providing unit may provide calm feedback to calm the user's emotions. This allows the feedback providing unit to provide feedback according to the user's emotions and provide a more appropriate dialogue experience.

[0108] The dialogue system may include a trend customization unit that customizes the content of the dialogue based on the user's current interests and trends. For example, the content of the dialogue may be customized based on topics that the user has recently been interested in. The content of the dialogue may also be customized based on keywords recently searched by the user. Furthermore, topics of interest may be included in the content of the dialogue based on the user's social media activity. In this way, the trend customization unit can customize the content of the dialogue based on the user's current interests and trends, thereby providing a more appropriate dialogue experience.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The selection unit accepts a selection from the user of a specific person with whom the user wants to interact. The specific person may include a celebrity, a fictional character, a personal acquaintance, etc. Step 2: The dialogue unit initiates a dialogue with the AI ​​avatar selected by the selection unit based on the user's input. The dialogue can be text-based, voice-based, video-based, or other formats. Step 3: The reception unit receives user input, including keyboard input, voice input, and touch input. Step 4: The generator generates a response based on the input received by the receiver, in the form of a text response, a voice response, a video response, or the like. Step 5: The output unit outputs the response generated by the generation unit in real time. The definition of real time includes the tolerance for delay time and response speed.

[0111] 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.

[0112] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0113] 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [Explanation of symbols]

[0183] 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 selection unit that accepts a selection from a user of a specific person with whom the user wishes to have a conversation; a dialogue unit that causes the AI ​​avatar selected by the selection unit to start a dialogue based on a user's input; a reception unit that receives input from a user; a generation unit that generates a response based on the input received by the reception unit; an output unit that outputs the response generated by the generation unit; Equipped with A system characterized by:

2. Enter the information of a specific person, It has a generation unit that generates an AI avatar based on that information. The system of claim 1 .

3. The generation unit Generate an AI avatar based on user input The system of claim 1 .

4. The dialogue unit The generated AI avatar converses with the user. The system of claim 1 .

5. The output unit Output the generated response in real time The system of claim 1 .

6. The selection unit Users select the AI ​​avatar of the celebrity they want to interact with. The system of claim 1 .

7. The selection unit Analyze user emotions and present celebrity options based on the analyzed user emotions The system of claim 1 .

8. The selection unit Analyzes the user's past selection history and suggests suitable celebrity candidates The system of claim 1 .

9. The selection unit Filter celebrity choices based on the user's current interests and trends The system of claim 1 .

Images