system

Abstract

The system according to the embodiment aims to enable users to interact with selected characters in real time. According to an embodiment, the system includes a reception unit, a learning unit, and a dialogue unit. The reception unit receives a character selection from a user. The learning unit learns the personality of the character selected by the reception unit. The dialogue unit conducts dialogue based on the information learned by the learning 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 had the problem that it is difficult for users to interact with the characters they select in real time, limiting the entertainment value of the technology.

[0005] The system according to the embodiment aims to enable users to interact with selected characters in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a learning unit, and a dialogue unit. The reception unit receives a character selection from a user. The learning unit learns the personality of the character selected by the reception unit. The dialogue unit conducts a dialogue based on the information learned by the learning unit. [Effects of the Invention]

[0007] Systems according to embodiments may enable users to interact with selected characters in real time. [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) An entertainment platform according to an embodiment of the present invention is a system that allows users to enjoy real-time conversations with characters selected by the user. In this system, a user selects a character through an app, and an AI learns the character's personality and background, and then the system engages in real-time conversations. For example, a user selects a character through an app, and an AI learns the character's personality and background. The AI ​​then engages in real-time conversations based on the information learned. This allows users to enjoy free conversations with the characters they have selected. Furthermore, this system is expected to be useful as content for supporting characters and fan clubs. This allows the entertainment platform to allow users to enjoy real-time conversations with characters selected by the user. For example, a user can enjoy everyday conversations with their favorite characters. This system is also expected to be useful as content for supporting characters and fan clubs.

[0029] An entertainment platform according to an embodiment includes a reception unit, a learning unit, and a dialogue unit. The reception unit receives a character selection from a user. For example, the user can select a character through an app. The learning unit learns the personality and background of the selected character. For example, the learning unit analyzes and learns the character's past episodes and personality traits using AI. The dialogue unit engages in real-time dialogue based on the information learned by the learning unit. For example, the dialogue unit generates a response based on the character's personality and background in response to content input by the user. This allows the user to enjoy free conversation with the character selected by the user. As a result, the entertainment platform according to an embodiment allows the user to enjoy real-time dialogue with the character selected by the user.

[0030] The reception unit allows the user to select a character through the app. The reception unit, for example, provides an interface for the user to select a character through the app. For example, a list of characters may be displayed on the app screen, and the user may select a character by tapping on it. The reception unit may also provide a function for the user to select a character while checking detailed information about the character. This allows the user to select a character through the app.

[0031] The learning unit can analyze and learn a character's episodes and personality traits using AI. For example, the learning unit can analyze a character's past episodes and personality traits using AI and learn them. For example, the learning unit can analyze the scenes and lines in which the character appears and extract personality traits. The learning unit can also learn by referring to the character's background information and opinions from the fan community. Furthermore, the learning unit can improve the accuracy of learning by referring to works related to the character. This allows for detailed learning of the character's personality and background.

[0032] The dialogue unit can generate a response based on the character's personality in response to content input by the user. For example, the dialogue unit generates a response based on the character's personality and background in response to content input by the user. For example, when a user asks a character a question, the dialogue unit uses the AI ​​to return an appropriate response based on the character's personality and background. The dialogue unit can also estimate the user's emotions and generate a response based on the emotions. Furthermore, the dialogue unit can improve the accuracy of the response by referring to the user's past dialogue history. This makes it possible to generate a response based on the character's personality and background.

[0033] The dialogue unit may provide a chat screen for users to interact with characters. For example, the dialogue unit may display text entered by the user and display the character's response in real time. The dialogue unit may also provide an interface that allows users to check the character's facial expressions and movements. Furthermore, the dialogue unit may also provide a function for recording and analyzing the content of the dialogue. This allows users to use the chat screen for interacting with characters.

[0034] The dialogue unit can provide a function for recording and analyzing the content of the dialogue. For example, the dialogue unit provides a function for recording the content of the dialogue and analyzing it later. For example, the dialogue unit can save a dialogue log between the user and the character and analyze dialogue patterns and trends. The dialogue unit can also save the content of the dialogue as text data and analyze it using natural language processing technology. Furthermore, the dialogue unit has a function for visualizing the content of the dialogue and providing feedback to the user. This allows the content of the dialogue to be recorded and analyzed.

[0035] The reception unit can analyze the user's past character selection history and recommend the most suitable character. For example, the reception unit analyzes the user's past character selection history and recommends the most suitable character. For example, the reception unit analyzes the tendency of characters selected by the user in the past and recommends characters with similar personalities. It can also recommend characters with new episodes of characters frequently selected by the user. Furthermore, if the user likes characters of a particular genre, it can also recommend new characters of that genre. In this way, it is possible to recommend the most suitable character based on the user's past character selection history.

[0036] The reception unit can perform filtering based on the user's current interests and concerns when selecting a character. For example, the reception unit can perform filtering based on the user's current interests and concerns when selecting a character. For example, the reception unit can suggest related characters based on keywords recently searched by the user. It can also suggest characters based on movies or anime recently viewed by the user. It can also suggest characters related to events recently attended by the user. This allows characters to be filtered based on the user's current interests and concerns.

[0037] The reception unit can provide an optimal selection means depending on the user's input method when selecting a character. For example, when selecting a character, the reception unit provides an optimal selection means depending on the user's input method. For example, if the user inputs a character name by voice, a character can be selected using voice recognition technology. Also, if the user inputs a character name by text, a character can be selected using text analysis technology. Furthermore, if the user uploads an image, a character can be selected using image recognition technology. In this way, an optimal character selection means can be provided depending on the user's input method.

[0038] The reception unit can prioritize displaying highly relevant characters in consideration of the user's geographical location information when selecting a character. For example, the reception unit prioritizes displaying highly relevant characters in consideration of the user's geographical location information when selecting a character. For example, if the user is in a specific area, characters related to that area can be displayed preferentially. Also, if the user is traveling, characters related to the travel destination can be displayed preferentially. Furthermore, if the user is participating in a specific event, characters related to that event can be displayed preferentially. In this way, highly relevant characters can be displayed preferentially based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and recommend related characters when selecting a character. For example, the reception unit can analyze the user's social media activity and recommend related characters when selecting a character. For example, the reception unit can preferentially display characters that the user follows on social media. It can also recommend characters related to posts that the user has "liked" on social media. It can also recommend characters that the user's friends have shared on social media. This makes it possible to recommend related characters based on the user's social media activity.

[0040] The reception unit can customize the selection method by reflecting the user's past feedback when selecting a character. The reception unit, for example, customizes the selection method by reflecting the user's past feedback when selecting a character. For example, characters that the user has previously rated highly can be preferentially displayed. Characters that the user has previously rated poorly can also be excluded. Furthermore, the selection method can be customized based on the user's past feedback. This allows the character selection method to be customized based on the user's past feedback.

[0041] The learning unit can adjust the level of detail of learning based on important episodes of the character during learning. For example, the learning unit adjusts the level of detail of learning based on important episodes of the character during learning. For example, the learning unit prioritizes learning of major episodes of the character. Also, important episodes that shape the character's personality can be learned in detail. Furthermore, episodes related to the character's background can be learned with a focus on learning. In this way, the level of detail of learning can be adjusted based on important episodes of the character.

[0042] The learning unit can apply different learning algorithms depending on the character category during learning. For example, the learning unit applies different learning algorithms depending on the character category during learning. For example, a learning algorithm that emphasizes action scenes can be applied to an action character. A learning algorithm that emphasizes humor can also be applied to a comedy character. Furthermore, a learning algorithm that emphasizes emotional expression can also be applied to a drama character. In this way, different learning algorithms can be applied depending on the character category.

[0043] The learning unit can improve the accuracy of learning by referring to the user's past dialogue history during learning. For example, the learning unit can improve the accuracy of learning by referring to the user's past dialogue history during learning. For example, the learning unit can learn the character's responses based on the content of dialogues the user has had in the past. It can also strengthen learning on a specific topic from the user's dialogue history. It can also analyze the user's dialogue history and improve the accuracy of learning. This makes it possible to improve the accuracy of learning based on the user's past dialogue history.

[0044] The learning unit can determine the learning priority based on the time of appearance of the character during learning. For example, the learning unit determines the learning priority based on the time of appearance of the character during learning. For example, the learning unit prioritizes learning of the episode in which the character first appears. It is also possible to prioritize learning of an episode that marks an important turning point for the character. It is also possible to prioritize learning of the character's latest episode. In this way, it is possible to determine the learning priority based on the time of appearance of the character.

[0045] The learning unit can improve the accuracy of learning by referring to works related to the character during learning. For example, the learning unit can improve the accuracy of learning by referring to works related to the character during learning. For example, the learning unit can learn by referring to other works in which the character appears. The learning unit can also learn by referring to spin-off works of the character. Furthermore, the learning unit can learn by referring to comics and novels related to the character. In this way, the accuracy of learning can be improved by referring to works related to the character.

[0046] The learning unit can customize the learning content by reflecting the opinions of the character's fan community when learning. The learning unit, for example, customizes the learning content by reflecting the opinions of the character's fan community when learning. For example, episodes that are popular in the fan community can be prioritized for learning. Topics being discussed in the fan community can also be reflected in the learning. Furthermore, the learning content can be customized based on the opinions of the fan community. In this way, the learning content can be customized by reflecting the opinions of the character's fan community.

[0047] The dialogue unit can adjust the level of detail of a response based on an important episode of a character during a dialogue. The dialogue unit, for example, adjusts the level of detail of a response based on an important episode of a character during a dialogue. For example, a detailed response can be generated based on a major episode of a character. A response can also be generated based on an important episode that shapes the character's personality. Furthermore, a response can be generated based on an episode related to the character's background. This makes it possible to adjust the level of detail of a response based on an important episode of a character.

[0048] The dialogue unit can apply different dialogue algorithms depending on the character category during dialogue. For example, the dialogue unit can apply different dialogue algorithms depending on the character category during dialogue. For example, a dialogue algorithm that emphasizes action scenes can be applied to an action character. A dialogue algorithm that emphasizes humor can also be applied to a comedy character. Furthermore, a dialogue algorithm that emphasizes emotional expression can also be applied to a drama character. In this way, different dialogue algorithms can be applied depending on the character category.

[0049] The dialogue unit can improve the accuracy of responses during dialogue by referring to the user's past dialogue history. For example, the dialogue unit can improve the accuracy of responses during dialogue by referring to the user's past dialogue history. For example, the dialogue unit can generate a character's response based on the content of dialogues the user has had in the past. It can also enhance responses on specific topics from the user's dialogue history. It can also analyze the user's dialogue history to improve the accuracy of responses. This makes it possible to improve the accuracy of responses based on the user's past dialogue history.

[0050] The dialogue unit can determine the priority of responses based on the appearance time of a character during a dialogue. The dialogue unit, for example, determines the priority of responses based on the appearance time of a character during a dialogue. For example, a response can be generated based on an episode in which the character first appears. A response can also be generated based on an episode that is an important turning point for the character. A response can also be generated based on the most recent episode of the character. In this way, the priority of responses can be determined based on the appearance time of a character.

[0051] The dialogue unit may improve the accuracy of a response by referring to works related to the character during a dialogue. The dialogue unit may improve the accuracy of a response by, for example, referring to works related to the character during a dialogue. For example, a response may be generated by referring to other works in which the character appears. A response may also be generated by referring to spin-off works of the character. A response may also be generated by referring to comics or novels related to the character. This may improve the accuracy of a response by referring to works related to the character.

[0052] The dialogue unit can customize the response content by reflecting the opinions of the character's fan community during the dialogue. The dialogue unit, for example, customizes the response content by reflecting the opinions of the character's fan community during the dialogue. For example, the dialogue unit generates a response based on an episode that is popular in the fan community. The dialogue unit can also reflect topics being discussed in the fan community in the response. Furthermore, the dialogue unit can customize the response content based on the opinions of the fan community. In this way, the dialogue unit can customize the response content by reflecting the opinions of the character's fan community.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The entertainment platform may include a dialogue unit that analyzes a user's past dialogue history and personalizes the content of the dialogue. For example, if a user has frequently talked about a particular topic in the past, dialogue related to that topic may be preferentially provided. Dialogue may also be generated that reflects the characteristics of characters that the user has previously preferred. Furthermore, the platform may learn from the user's past dialogue history and improve the accuracy of the dialogue. This makes it possible to provide personalized dialogue based on the user's past dialogue history.

[0055] The entertainment platform may include a dialogue unit that customizes dialogue content in consideration of the user's current geographic location information. For example, if the user is in a specific area, topics related to that area may be provided. Also, if the user is traveling, information and stories related to the travel destination may be provided. Furthermore, if the user is participating in a specific event, dialogue related to the event may be provided. In this way, the dialogue content can be customized based on the user's geographic location information.

[0056] The entertainment platform may include a dialogue unit that analyzes a user's social media activity and customizes the content of the dialogue. For example, the entertainment platform may provide topics related to characters the user follows on social media. The entertainment platform may also generate dialogue related to posts the user has "liked." Furthermore, the entertainment platform may provide dialogue related to characters shared by the user's friends. This allows the entertainment platform to customize the content of the dialogue based on the user's social media activity.

[0057] The entertainment platform may include a dialogue unit that customizes the content of the dialogue by reflecting the user's past feedback. For example, a dialogue style that the user has previously rated highly may be preferentially provided. A dialogue style that the user has previously rated poorly may also be avoided. Furthermore, the content of the dialogue may be customized based on the user's past feedback. This allows the content of the dialogue to be customized based on the user's past feedback.

[0058] The entertainment platform may include a dialogue unit that filters dialogue content based on the user's current interests. For example, the platform may provide related topics based on keywords recently searched by the user. The platform may also provide dialogue content based on movies or anime recently watched by the user. Furthermore, the platform may provide dialogue related to events recently attended by the user. This allows the platform to filter dialogue content based on the user's current interests.

[0059] The entertainment platform may be equipped with a dialogue unit that evolves the content of the dialogue by referring to the user's past dialogue history. For example, if the user has previously spoken in depth about a particular topic, new information related to that topic may be provided. It may also be possible to introduce new episodes about characters that the user has previously shown interest in. Furthermore, it may learn from the user's dialogue history and enrich the content of the dialogue. This allows the content of the dialogue to evolve based on the user's past dialogue history.

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

[0061] Step 1: The reception unit receives a character selection from the user. For example, the user can select a character through the app. Step 2: The learning unit learns the selected character's personality and background. For example, the learning unit uses AI to analyze and learn the character's past episodes and personality traits. Step 3: The dialogue unit engages in real-time dialogue based on the information learned by the learning unit. For example, the dialogue unit generates a response to the user's input based on the character's personality and background.

[0062] (Example 2) An entertainment platform according to an embodiment of the present invention is a system that allows users to enjoy real-time conversations with characters selected by the user. In this system, a user selects a character through an app, and an AI learns the character's personality and background, and then the system engages in real-time conversations. For example, a user selects a character through an app, and an AI learns the character's personality and background. The AI ​​then engages in real-time conversations based on the information learned. This allows users to enjoy free conversations with the characters they have selected. Furthermore, this system is expected to be useful as content for supporting characters and fan clubs. This allows the entertainment platform to allow users to enjoy real-time conversations with characters selected by the user. For example, a user can enjoy everyday conversations with their favorite characters. This system is also expected to be useful as content for supporting characters and fan clubs.

[0063] An entertainment platform according to an embodiment includes a reception unit, a learning unit, and a dialogue unit. The reception unit receives a character selection from a user. For example, the user can select a character through an app. The learning unit learns the personality and background of the selected character. For example, the learning unit analyzes and learns the character's past episodes and personality traits using AI. The dialogue unit engages in real-time dialogue based on the information learned by the learning unit. For example, the dialogue unit generates a response based on the character's personality and background in response to content input by the user. This allows the user to enjoy free conversation with the character selected by the user. As a result, the entertainment platform according to an embodiment allows the user to enjoy real-time dialogue with the character selected by the user.

[0064] The reception unit allows the user to select a character through the app. The reception unit, for example, provides an interface for the user to select a character through the app. For example, a list of characters may be displayed on the app screen, and the user may select a character by tapping on it. The reception unit may also provide a function for the user to select a character while checking detailed information about the character. This allows the user to select a character through the app.

[0065] The learning unit can analyze and learn a character's episodes and personality traits using AI. For example, the learning unit can analyze a character's past episodes and personality traits using AI and learn them. For example, the learning unit can analyze the scenes and lines in which the character appears and extract personality traits. The learning unit can also learn by referring to the character's background information and opinions from the fan community. Furthermore, the learning unit can improve the accuracy of learning by referring to works related to the character. This allows for detailed learning of the character's personality and background.

[0066] The dialogue unit can generate a response based on the character's personality in response to content input by the user. For example, the dialogue unit generates a response based on the character's personality and background in response to content input by the user. For example, when a user asks a character a question, the dialogue unit uses the AI ​​to return an appropriate response based on the character's personality and background. The dialogue unit can also estimate the user's emotions and generate a response based on the emotions. Furthermore, the dialogue unit can improve the accuracy of the response by referring to the user's past dialogue history. This makes it possible to generate a response based on the character's personality and background.

[0067] The dialogue unit may provide a chat screen for users to interact with characters. For example, the dialogue unit may display text entered by the user and display the character's response in real time. The dialogue unit may also provide an interface that allows users to check the character's facial expressions and movements. Furthermore, the dialogue unit may also provide a function for recording and analyzing the content of the dialogue. This allows users to use the chat screen for interacting with characters.

[0068] The dialogue unit can provide a function for recording and analyzing the content of the dialogue. For example, the dialogue unit provides a function for recording the content of the dialogue and analyzing it later. For example, the dialogue unit can save a dialogue log between the user and the character and analyze dialogue patterns and trends. The dialogue unit can also save the content of the dialogue as text data and analyze it using natural language processing technology. Furthermore, the dialogue unit has a function for visualizing the content of the dialogue and providing feedback to the user. This allows the content of the dialogue to be recorded and analyzed.

[0069] The entertainment platform includes a reception unit that estimates a user's emotion and suggests a character selection based on the estimated emotion. The reception unit, for example, estimates the user's emotion and suggests a character selection based on the estimated emotion. For example, if the user is feeling stressed, a relaxing character can be suggested. Also, if the user is excited, an active character can be suggested. Furthermore, if the user is sad, a comforting character can be suggested. In this way, character selection can be suggested based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] The reception unit can analyze the user's past character selection history and recommend the most suitable character. For example, the reception unit analyzes the user's past character selection history and recommends the most suitable character. For example, the reception unit analyzes the tendency of characters selected by the user in the past and recommends characters with similar personalities. It can also recommend characters with new episodes of characters frequently selected by the user. Furthermore, if the user likes characters of a particular genre, it can also recommend new characters of that genre. In this way, it is possible to recommend the most suitable character based on the user's past character selection history.

[0071] The reception unit can perform filtering based on the user's current interests and concerns when selecting a character. For example, the reception unit can perform filtering based on the user's current interests and concerns when selecting a character. For example, the reception unit can suggest related characters based on keywords recently searched by the user. It can also suggest characters based on movies or anime recently viewed by the user. It can also suggest characters related to events recently attended by the user. This allows characters to be filtered based on the user's current interests and concerns.

[0072] The reception unit can provide an optimal selection means depending on the user's input method when selecting a character. For example, when selecting a character, the reception unit provides an optimal selection means depending on the user's input method. For example, if the user inputs a character name by voice, a character can be selected using voice recognition technology. Also, if the user inputs a character name by text, a character can be selected using text analysis technology. Furthermore, if the user uploads an image, a character can be selected using image recognition technology. In this way, an optimal character selection means can be provided depending on the user's input method.

[0073] The reception unit can estimate the user's emotions and determine the priority of character selection based on the estimated emotions. The reception unit, for example, estimates the user's emotions and determines the priority of character selection based on the estimated emotions. For example, if the user is relaxed, a relaxing character can be preferentially displayed. Also, if the user is excited, an active character can be preferentially displayed. Furthermore, if the user is sad, a comforting character can be preferentially displayed. In this way, the priority of character selection can be determined based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The reception unit can prioritize displaying highly relevant characters in consideration of the user's geographical location information when selecting a character. For example, the reception unit prioritizes displaying highly relevant characters in consideration of the user's geographical location information when selecting a character. For example, if the user is in a specific area, characters related to that area can be displayed preferentially. Also, if the user is traveling, characters related to the travel destination can be displayed preferentially. Furthermore, if the user is participating in a specific event, characters related to that event can be displayed preferentially. In this way, highly relevant characters can be displayed preferentially based on the user's geographical location information.

[0075] The reception unit can analyze the user's social media activity and recommend related characters when selecting a character. For example, the reception unit can analyze the user's social media activity and recommend related characters when selecting a character. For example, the reception unit can preferentially display characters that the user follows on social media. It can also recommend characters related to posts that the user has "liked" on social media. It can also recommend characters that the user's friends have shared on social media. This makes it possible to recommend related characters based on the user's social media activity.

[0076] The reception unit can customize the selection method by reflecting the user's past feedback when selecting a character. The reception unit, for example, customizes the selection method by reflecting the user's past feedback when selecting a character. For example, characters that the user has previously rated highly can be preferentially displayed. Characters that the user has previously rated poorly can also be excluded. Furthermore, the selection method can be customized based on the user's past feedback. This allows the character selection method to be customized based on the user's past feedback.

[0077] The learning unit can estimate the user's emotions and adjust the learning content based on the estimated emotions. The learning unit, for example, estimates the user's emotions and adjusts the learning content based on the estimated emotions. For example, if the user is relaxed, it can prioritize learning relaxing episodes. Also, if the user is excited, it can prioritize learning active episodes. Furthermore, if the user is sad, it can prioritize learning comforting episodes. In this way, the learning content can be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The learning unit can adjust the level of detail of learning based on important episodes of the character during learning. For example, the learning unit adjusts the level of detail of learning based on important episodes of the character during learning. For example, the learning unit prioritizes learning of major episodes of the character. Also, important episodes that shape the character's personality can be learned in detail. Furthermore, episodes related to the character's background can be learned with a focus on learning. In this way, the level of detail of learning can be adjusted based on important episodes of the character.

[0079] The learning unit can apply different learning algorithms depending on the character category during learning. For example, the learning unit applies different learning algorithms depending on the character category during learning. For example, a learning algorithm that emphasizes action scenes can be applied to an action character. A learning algorithm that emphasizes humor can also be applied to a comedy character. Furthermore, a learning algorithm that emphasizes emotional expression can also be applied to a drama character. In this way, different learning algorithms can be applied depending on the character category.

[0080] The learning unit can improve the accuracy of learning by referring to the user's past dialogue history during learning. For example, the learning unit can improve the accuracy of learning by referring to the user's past dialogue history during learning. For example, the learning unit can learn the character's responses based on the content of dialogues the user has had in the past. It can also strengthen learning on a specific topic from the user's dialogue history. It can also analyze the user's dialogue history and improve the accuracy of learning. This makes it possible to improve the accuracy of learning based on the user's past dialogue history.

[0081] The learning unit can estimate the user's emotions and determine learning priorities based on the estimated emotions. The learning unit, for example, estimates the user's emotions and determines learning priorities based on the estimated emotions. For example, if the user is relaxed, it can prioritize learning relaxing episodes. Also, if the user is excited, it can prioritize learning active episodes. Furthermore, if the user is sad, it can prioritize learning comforting episodes. In this way, it is possible to determine learning priorities based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The learning unit can determine the learning priority based on the time of appearance of the character during learning. For example, the learning unit determines the learning priority based on the time of appearance of the character during learning. For example, the learning unit prioritizes learning of the episode in which the character first appears. It is also possible to prioritize learning of an episode that marks an important turning point for the character. It is also possible to prioritize learning of the character's latest episode. In this way, it is possible to determine the learning priority based on the time of appearance of the character.

[0083] The learning unit can improve the accuracy of learning by referring to works related to the character during learning. For example, the learning unit can improve the accuracy of learning by referring to works related to the character during learning. For example, the learning unit can learn by referring to other works in which the character appears. The learning unit can also learn by referring to spin-off works of the character. Furthermore, the learning unit can learn by referring to comics and novels related to the character. In this way, the accuracy of learning can be improved by referring to works related to the character.

[0084] The learning unit can customize the learning content by reflecting the opinions of the character's fan community when learning. The learning unit, for example, customizes the learning content by reflecting the opinions of the character's fan community when learning. For example, episodes that are popular in the fan community can be prioritized for learning. Topics being discussed in the fan community can also be reflected in the learning. Furthermore, the learning content can be customized based on the opinions of the fan community. In this way, the learning content can be customized by reflecting the opinions of the character's fan community.

[0085] The dialogue unit can estimate the user's emotions and adjust the dialogue expression method based on the estimated emotions. The dialogue unit, for example, estimates the user's emotions and adjusts the dialogue expression method based on the estimated emotions. For example, if the user is relaxed, a calm expression method can be used. If the user is excited, a lively expression method can be used. Furthermore, if the user is sad, a comforting expression method can be used. In this way, the dialogue expression method can be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The dialogue unit can adjust the level of detail of a response based on an important episode of a character during a dialogue. The dialogue unit, for example, adjusts the level of detail of a response based on an important episode of a character during a dialogue. For example, a detailed response can be generated based on a major episode of a character. A response can also be generated based on an important episode that shapes the character's personality. Furthermore, a response can be generated based on an episode related to the character's background. This makes it possible to adjust the level of detail of a response based on an important episode of a character.

[0087] The dialogue unit can apply different dialogue algorithms depending on the character category during dialogue. For example, the dialogue unit can apply different dialogue algorithms depending on the character category during dialogue. For example, a dialogue algorithm that emphasizes action scenes can be applied to an action character. A dialogue algorithm that emphasizes humor can also be applied to a comedy character. Furthermore, a dialogue algorithm that emphasizes emotional expression can also be applied to a drama character. In this way, different dialogue algorithms can be applied depending on the character category.

[0088] The dialogue unit can improve the accuracy of responses during dialogue by referring to the user's past dialogue history. For example, the dialogue unit can improve the accuracy of responses during dialogue by referring to the user's past dialogue history. For example, the dialogue unit can generate a character's response based on the content of dialogues the user has had in the past. It can also enhance responses on specific topics from the user's dialogue history. It can also analyze the user's dialogue history to improve the accuracy of responses. This makes it possible to improve the accuracy of responses based on the user's past dialogue history.

[0089] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated emotions. The dialogue unit, for example, estimates the user's emotions and adjusts the length of the dialogue based on the estimated emotions. For example, if the user is relaxed, a longer dialogue can be provided. If the user is in a hurry, a short and to-the-point dialogue can be provided. Furthermore, if the user is excited, a lively dialogue can be provided. This allows the length of the dialogue to be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The dialogue unit can determine the priority of responses based on the appearance time of a character during a dialogue. The dialogue unit, for example, determines the priority of responses based on the appearance time of a character during a dialogue. For example, a response can be generated based on an episode in which the character first appears. A response can also be generated based on an episode that is an important turning point for the character. A response can also be generated based on the most recent episode of the character. In this way, the priority of responses can be determined based on the appearance time of a character.

[0091] The dialogue unit may improve the accuracy of a response by referring to works related to the character during a dialogue. The dialogue unit may improve the accuracy of a response by, for example, referring to works related to the character during a dialogue. For example, a response may be generated by referring to other works in which the character appears. A response may also be generated by referring to spin-off works of the character. A response may also be generated by referring to comics or novels related to the character. This may improve the accuracy of a response by referring to works related to the character.

[0092] The dialogue unit can customize the response content by reflecting the opinions of the character's fan community during the dialogue. The dialogue unit, for example, customizes the response content by reflecting the opinions of the character's fan community during the dialogue. For example, the dialogue unit generates a response based on an episode that is popular in the fan community. The dialogue unit can also reflect topics being discussed in the fan community in the response. Furthermore, the dialogue unit can customize the response content based on the opinions of the fan community. In this way, the dialogue unit can customize the response content by reflecting the opinions of the character's fan community. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, learning unit, and dialogue unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows the user to select a character through an app. The learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the personality and background of the selected character. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12, and conducts dialogue in real time based on the learned information. Furthermore, the reception unit with an emotion estimation function is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and suggests character selection based on the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, learning unit, and dialogue unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows the user to select a character through an app. The learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the personality and background of the selected character. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12, and conducts dialogue in real time based on the learned information. Furthermore, the reception unit with an emotion estimation function is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and suggests character selection based on the user's emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, learning unit, and dialogue unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows the user to select a character through an app. The learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the personality and background of the selected character. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12, and conducts dialogue in real time based on the learned information. Furthermore, the reception unit with an emotion estimation function is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and suggests character selection based on the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, learning unit, and dialogue unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to select a character through an app. The learning unit is realized by the specific processing unit 290 of the data processing device 12, and learns the personality and background of the selected character. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12, and conducts dialogue in real time based on the learned information. Furthermore, the reception unit with an emotion estimation function is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and suggests character selection based on the user's emotions.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The entertainment platform may include a dialogue unit that estimates the user's emotions and adjusts the tone of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit may use a calm and relaxing tone. If the user is excited, the dialogue unit may use a lively and energetic tone. Furthermore, if the user is sad, the dialogue unit may use a comforting and gentle tone. In this way, the dialogue tone can be provided according to the user's emotions.

[0095] The entertainment platform may include a dialogue unit that analyzes a user's past dialogue history and personalizes the content of the dialogue. For example, if a user has frequently talked about a particular topic in the past, dialogue related to that topic may be preferentially provided. Dialogue may also be generated that reflects the characteristics of characters that the user has previously preferred. Furthermore, the platform may learn from the user's past dialogue history and improve the accuracy of the dialogue. This makes it possible to provide personalized dialogue based on the user's past dialogue history.

[0096] The entertainment platform may include a dialogue unit that customizes dialogue content in consideration of the user's current geographic location information. For example, if the user is in a specific area, topics related to that area may be provided. Also, if the user is traveling, information and stories related to the travel destination may be provided. Furthermore, if the user is participating in a specific event, dialogue related to the event may be provided. In this way, the dialogue content can be customized based on the user's geographic location information.

[0097] The entertainment platform may include a dialogue unit that analyzes a user's social media activity and customizes the content of the dialogue. For example, the entertainment platform may provide topics related to characters the user follows on social media. The entertainment platform may also generate dialogue related to posts the user has "liked." Furthermore, the entertainment platform may provide dialogue related to characters shared by the user's friends. This allows the entertainment platform to customize the content of the dialogue based on the user's social media activity.

[0098] The entertainment platform may include a dialogue unit that estimates the user's emotions and adjusts the length of the dialogue based on the estimated emotions. For example, if the user is relaxed, a longer dialogue may be provided. If the user is in a hurry, a short and to-the-point dialogue may be provided. Furthermore, if the user is excited, a lively dialogue may be provided. In this way, the length of the dialogue can be adjusted based on the user's emotions.

[0099] The entertainment platform may include a dialogue unit that customizes the content of the dialogue by reflecting the user's past feedback. For example, a dialogue style that the user has previously rated highly may be preferentially provided. A dialogue style that the user has previously rated poorly may also be avoided. Furthermore, the content of the dialogue may be customized based on the user's past feedback. This allows the content of the dialogue to be customized based on the user's past feedback.

[0100] The entertainment platform may include a dialogue unit that estimates the user's emotions and adjusts the character's facial expressions and movements based on the estimated emotions. For example, if the user is relaxed, the character's facial expressions and movements may be made calm. If the user is excited, the character's facial expressions and movements may be made lively. Furthermore, if the user is sad, the character's facial expressions and movements may be made gentle. In this way, the character's facial expressions and movements can be adjusted based on the user's emotions.

[0101] The entertainment platform may include a dialogue unit that filters dialogue content based on the user's current interests. For example, the platform may provide related topics based on keywords recently searched by the user. The platform may also provide dialogue content based on movies or anime recently watched by the user. Furthermore, the platform may provide dialogue related to events recently attended by the user. This allows the platform to filter dialogue content based on the user's current interests.

[0102] The entertainment platform may include a dialogue 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 dialogue may proceed at a slow tempo. If the user is excited, the dialogue may proceed at a fast tempo. Furthermore, if the user is sad, the dialogue may proceed at a calm tempo. In this way, the dialogue tempo can be adjusted based on the user's emotions.

[0103] The entertainment platform may be equipped with a dialogue unit that evolves the content of the dialogue by referring to the user's past dialogue history. For example, if the user has previously spoken in depth about a particular topic, new information related to that topic may be provided. It may also be possible to introduce new episodes about characters that the user has previously shown interest in. Furthermore, it may learn from the user's dialogue history and enrich the content of the dialogue. This allows the content of the dialogue to evolve based on the user's past dialogue history.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives a character selection from the user. For example, the user can select a character through the app. Step 2: The learning unit learns the selected character's personality and background. For example, the learning unit uses AI to analyze and learn the character's past episodes and personality traits. Step 3: The dialogue unit engages in real-time dialogue based on the information learned by the learning unit. For example, the dialogue unit generates a response to the user's input based on the character's personality and background.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives character selection from a user; a learning unit that learns the personality of the character selected by the reception unit; a dialogue unit that conducts dialogue based on the information learned by the learning unit; Equipped with A system characterized by:

2. The reception unit The user selects a character through the app 2. The system of claim 1.

3. The learning unit Character episodes and personality traits are analyzed and learned using AI 2. The system of claim 1.

4. The dialogue unit Generate responses based on the character's personality based on user input 2. The system of claim 1.

5. The dialogue unit Provides a chat screen for users to interact with characters 2. The system of claim 1.

6. The dialogue unit Provides the ability to record and analyze the content of conversations 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and suggest character selection based on the estimated emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's character selection history and recommend the most suitable character 2. The system of claim 1.

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