System

Abstract

An object of a system according to an embodiment is to complement a bias of thinking of a user and support creation of a deep idea.SOLUTION: A system according to an embodiment includes a character generation unit, a discussion development unit, and a thought complementing unit. The character generation unit generates a character based on the idea of the user. The discussion development unit develops the discussion using the character generated by the character generation unit. The thought complementing unit complements the bias of the thought of the user based on the discussion developed by the discussion developing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to compensate for biases in one person's thinking when generating ideas or making decisions.

[0005] The system according to the embodiment aims to compensate for biases in the user's thinking and support the creation of profound ideas. [Means for solving the problem]

[0006] The system according to the embodiment includes a character generation unit, a discussion development unit, and a thought completion unit. The character generation unit generates a character based on a user's idea. The discussion development unit develops a discussion using the character generated by the character generation unit. The thought completion unit complements biases in the user's thinking based on the discussion developed by the discussion development unit. [Effects of the Invention]

[0007] The system according to the embodiment can compensate for biases in the user's thinking and support the creation of profound ideas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The idea generation and decision-making support system according to an embodiment of the present invention is a system in which an automatically generated character develops a discussion on a user's idea and draws a conclusion. As a result, the idea generation and decision-making support system compensates for biases in the user's thinking, enabling the creation of deep ideas and optimal decision-making.

[0029] An idea generation and decision-making support system according to an embodiment includes a character generation unit, a discussion development unit, and a thought completion unit. The character generation unit generates characters based on a user's ideas. For example, the generation AI creates characters with different perspectives and expertise based on ideas or themes input by the user. The generation AI generates characters using text generation AI (e.g., LLM). The generation AI can also generate character appearances and voices using multimodal generation AI. The generation AI can also analyze user input data and generate appropriate characters. For example, if a user inputs a "new product idea," the generation AI generates characters such as a marketing expert, engineer, and consumer representative, and has them express their opinions from their respective perspectives. The discussion development unit develops a discussion using the characters generated by the character generation unit. For example, the generation AI allows the characters to express their opinions from their respective perspectives and advance the discussion. The generation AI can also refer to past discussion history to develop a consistent discussion. The generation AI can also analyze user responses in real time and dynamically adjust the direction of the discussion. For example, the character asks a more in-depth question about a topic that the user shows interest in. The thought complementation unit complements biases in the user's thinking based on the discussion developed by the discussion development unit. For example, the generation AI generates characters with different perspectives and opinions to complement biases in the user's thinking. The generation AI can also use an emotion estimation function to generate characters based on the user's emotional state and develop discussions that are sensitive to the user's emotions. As a result, the ideation and decision-making support system according to the embodiment can generate characters based on the user's ideas, develop discussions, and complement biases in the user's thinking. For example, if the user is biased toward a technical perspective, the generation AI can generate a character with a marketing or consumer perspective to engage in a balanced discussion.

[0030] The character generation unit can generate a character based on user input data. For example, the character generation unit analyzes text data input by the user and generates an appropriate character. For example, the generation AI creates characters with different perspectives or expertise based on ideas or themes input by the user. The character generation unit can also analyze voice data input by the user and generate a character. For example, the generation AI analyzes the user's voice data and generates an appropriate character. The character generation unit can also analyze image data input by the user and generate a character. For example, the generation AI analyzes the user's image data and generates an appropriate character. In this way, characters can be generated based on user input data.

[0031] The discussion development unit can develop a discussion using characters. In the discussion development unit, for example, a generation AI generates the content of the characters' statements and advances the discussion. For example, the generation AI advances the discussion by having the characters express their opinions from their respective positions. The discussion development unit can also refer to past discussion history to develop a consistent discussion. For example, the generation AI analyzes past discussion history to ensure consistency in the content of the characters' statements. The discussion development unit can also analyze user responses in real time and dynamically adjust the direction of the discussion. For example, the generation AI analyzes the user's facial expressions and voice tone in real time to adjust the direction of the discussion according to the user's interests and concerns. This allows a discussion to be developed using the generated characters.

[0032] The thought completion unit can generate characters with different perspectives and opinions to complement the user's biases in thinking. For example, the generation AI analyzes the user's biases in thinking and generates characters with different perspectives and opinions to complement them. For example, if the user is biased toward a technical perspective, the generation AI can generate a character with a marketing or consumer perspective to engage in balanced discussions. The thought completion unit can also use the emotion estimation function to analyze the user's emotions toward the character's comments and provide feedback according to the emotions. For example, the emotion estimation function can be used to analyze the user's emotions toward the character's comments in real time and provide feedback that elicits positive emotions. This makes it possible to generate characters with different perspectives and opinions to complement the user's biases in thinking.

[0033] In the discussion development section, the generation AI refers to past discussion history in response to a character's comments, allowing the discussion to be developed in a consistent manner. In the discussion development section, for example, the generation AI analyzes past discussion history to make the content of a character's comments consistent. For example, it refers to conclusions and opinions reached in previous discussions and makes new comments based on them. The discussion development section can also adjust the progress of a discussion based on past discussion history. For example, it may revisit issues that came up in past discussions and discuss solutions to those issues. This allows the generation AI to refer to past discussion history in response to a character's comments, allowing the discussion to be developed in a consistent manner.

[0034] The discussion development unit can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, the discussion development unit can add audio effects to the characters' remarks to enhance the realism of the discussion. For example, the tone of the audio can be changed to emphasize important points. The discussion development unit can also add visual effects to the characters' remarks to enhance the visual appeal of the discussion. For example, animations can be displayed in sync with the characters' remarks. In this way, the audio and visual effects can be added to the characters' discussions to enhance the user's sense of immersion.

[0035] The discussion development unit can generate characters with different cultural backgrounds and develop discussions from an international perspective. For example, the generation AI can generate characters with different cultural backgrounds and have them express their opinions from their respective perspectives. For example, characters with Asian, European, and American cultural backgrounds can express their opinions from their respective perspectives. The discussion development unit can also use characters with different cultural backgrounds to develop discussions from an international perspective. For example, discussions can be held based on the cultures and historical backgrounds of different countries. This makes it possible to generate characters with different cultural backgrounds and develop discussions from an international perspective.

[0036] The character generation unit can analyze the user's past input data and generate a character that matches the user's preferences and tendencies. For example, the character generation unit uses a generation AI to analyze the user's past input data and generate a character that matches the user's preferences and tendencies. For example, it generates a character related to a theme that the user has shown interest in in the past. The character generation unit can also customize the character's appearance and voice based on the user's past input data. For example, it generates a character based on the appearance and voice of a character that the user prefers. This makes it possible to analyze the user's past input data and generate a character that matches the user's preferences and tendencies.

[0037] The character generation unit can refer to the latest industry trends and news and incorporate a current perspective. For example, the generation AI of the character generation unit can refer to the latest industry trends and news and incorporate a current perspective into the content of the character's statements. For example, it can reflect the latest technological trends and market needs. The character generation unit can also update the content of the character's statements based on the latest news. For example, the generation AI can analyze the latest news articles and reflect them in the content of the character's statements. This makes it possible to refer to the latest industry trends and news and incorporate a current perspective.

[0038] The character generation unit can incorporate images and audio selected by the user to provide a more personalized character. For example, the character generation unit uses a generation AI to customize the character's appearance and voice based on the images and audio selected by the user. For example, the character is generated based on the character's appearance and voice preferred by the user. The character generation unit can also analyze images and audio uploaded by the user to set the character's attributes. For example, the character's appearance is set based on the images uploaded by the user, and the character's voice is set based on the audio. This allows the image and audio selected by the user to be incorporated to provide a more personalized character.

[0039] The character generation unit can simultaneously generate characters with different fields of expertise and develop discussions from multiple perspectives. For example, the character generation unit uses a generation AI to simultaneously generate characters with different fields of expertise and have them express their opinions from their respective perspectives. For example, characters such as an engineer, a marketing expert, and a consumer representative express their opinions from their respective perspectives. The character generation unit can also use characters with different fields of expertise to develop discussions from multiple perspectives. For example, characters with different fields of expertise can cooperate to discuss problem-solving. This allows characters with different fields of expertise to be simultaneously generated and discussions to be developed from multiple perspectives.

[0040] The thought completion unit can analyze a user's past discussion history, identify biases in the user's thinking, and generate a character that complements them. For example, the generation AI analyzes a user's past discussion history and identifies biases in the user's thinking. For example, if the user's perspective is biased toward a technical one, it can generate a character with a marketing or consumer perspective to complement that bias. The thought completion unit can also adjust the content of a character's remarks based on the user's past discussion history. For example, it can revisit problems that came up in past discussions and discuss solutions to those problems. This makes it possible to analyze a user's past discussion history, identify biases in the user's thinking, and generate a character that complements them.

[0041] The thought completion unit can generate characters that incorporate perspectives from different cultures and social backgrounds. For example, the generation AI generates characters that incorporate perspectives from different cultures and social backgrounds. For example, characters with Asian, European, and American cultural backgrounds express their opinions from their respective perspectives. The thought completion unit can also use characters with different cultures and social backgrounds to develop discussions from different perspectives. For example, discussions can be held based on the cultures and historical backgrounds of different countries. This makes it possible to generate characters that incorporate perspectives from different cultures and social backgrounds.

[0042] The thought completion unit can generate characters modeled after experts in different industries or fields to complement biases in the user's thinking. For example, the generation AI generates characters modeled after experts in different industries or fields, and has them express opinions from their respective perspectives. For example, characters such as engineers, marketing experts, and consumer representatives express their opinions from their respective perspectives. The thought completion unit can also complement biases in the user's thinking by using experts in different industries or fields. For example, if the user's perspective is biased toward a technical perspective, a character with a marketing or consumer perspective can be generated to complement that bias. This allows the generation of characters modeled after experts in different industries or fields to complement biases in the user's thinking.

[0043] The thought completion unit can incorporate images and audio selected by the user to provide a more personalized character. For example, the thought completion unit uses a generation AI to customize the character's appearance and voice based on the images and audio selected by the user. For example, the thought completion unit generates a character based on the character's appearance and voice that the user prefers. The thought completion unit can also analyze images and audio uploaded by the user to set the character's attributes. For example, it can set the character's appearance based on the images uploaded by the user, and set the character's voice based on the audio. This makes it possible to incorporate images and audio selected by the user and provide a more personalized character.

[0044] The discussion development unit can analyze the progress of the discussion in real time and provide feedback to lead to optimal decision-making. For example, the generation AI can analyze the progress of the discussion in real time and provide feedback to lead to optimal decision-making. For example, it can provide a new perspective if the discussion reaches an impasse. The discussion development unit can also adjust the content of the feedback depending on the progress of the discussion. For example, if the discussion is progressing actively, a character can make a statement to maintain the momentum. This makes it possible to analyze the progress of the discussion in real time and provide feedback to lead to optimal decision-making.

[0045] The discussion development section allows the generation AI to refer to past data in response to what the character says, and supports optimal decision-making. For example, the discussion development section allows the generation AI to refer to past data in response to what the character says, and supports optimal decision-making. For example, it provides opinions based on past successes and failures. The discussion development section can also adjust the progress of the discussion based on past data. For example, it can refer to past data to change the direction of the discussion. This allows the generation AI to refer to past data in response to what the character says, and supports optimal decision-making.

[0046] The discussion development unit can generate characters modeled after experts in different industries or fields to complement biases in the user's thinking. For example, the generation AI generates characters modeled after experts in different industries or fields, and has them express opinions from their respective perspectives. For example, characters such as engineers, marketing experts, and consumer representatives express their opinions from their respective perspectives. The discussion development unit can also complement biases in the user's thinking by using experts from different industries or fields. For example, if the user's perspective is biased toward a technical perspective, it can generate characters with a marketing or consumer perspective to complement that bias. This allows the generation of characters modeled after experts in different industries or fields to complement biases in the user's thinking.

[0047] The discussion development unit can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, the discussion development unit can add audio effects to the characters' remarks to enhance the realism of the discussion. For example, the tone of the audio can be changed to emphasize important points. The discussion development unit can also add visual effects to the characters' remarks to enhance the visual appeal of the discussion. For example, animations can be displayed in sync with the characters' remarks. In this way, the audio and visual effects can be added to the characters' discussions to enhance the user's sense of immersion.

[0048] The discussion development unit can analyze the content of user comments in real time and dynamically adjust the direction of the discussion. For example, the discussion development unit uses a generation AI to analyze the content of user comments in real time and dynamically adjust the direction of the discussion. For example, if a user proposes a new idea, the direction of the discussion can be changed based on that idea. The discussion development unit can also adjust the progress of the discussion according to the content of user comments. For example, if a user shows interest in a particular topic, the discussion on that topic can be deepened. This makes it possible to analyze the content of user comments in real time and dynamically adjust the direction of the discussion.

[0049] The discussion development section allows the generation AI to refer to past discussion history in response to user comments and develop a consistent discussion. For example, the discussion development section allows the generation AI to analyze past discussion history and make the user's comments consistent. For example, it refers to conclusions and opinions reached in previous discussions and makes new comments based on them. The discussion development section can also adjust the progress of the discussion based on past discussion history. For example, it can re-raise issues that came up in past discussions and discuss solutions to them. This allows the generation AI to refer to past discussion history in response to user comments and develop a consistent discussion.

[0050] The discussion development section allows the generation AI to provide feedback from different perspectives in response to user comments, deepening the discussion. For example, the generation AI provides feedback from different perspectives in response to user comments. For example, it may state opinions from a marketing or consumer perspective in response to a technical perspective. The discussion development section can also adjust the content of the feedback depending on the content of the user's comments. For example, if a user shows interest in a particular topic, it will provide feedback from different perspectives on that topic. This allows the generation AI to provide feedback from different perspectives in response to user comments, deepening the discussion.

[0051] In the discussion development section, the generation AI adds audio and visual effects to what the user says, making the discussion more immersive. In the discussion development section, for example, the generation AI adds audio effects to what the user says to make the discussion more realistic. For example, it changes the tone of the audio to emphasize important points. The discussion development section can also add visual effects to what the user says to make the discussion more visually appealing. For example, it can display animations in sync with what the user says. This allows the generation AI to add audio and visual effects to what the user says to make the discussion more immersive.

[0052] The character generation unit can analyze the user's past usage history and generate the most suitable character. For example, the character generation unit uses a generation AI to analyze the user's past usage history and generate a character that matches the user's preferences and tendencies. For example, it generates a character related to a theme that the user has shown interest in in the past. The character generation unit can also customize the character's appearance and voice based on the user's past usage history. For example, it generates a character based on the appearance and voice of a character that the user prefers. This makes it possible to analyze the user's past usage history and generate the most suitable character.

[0053] The character generation unit can refer to the latest industry trends and news and incorporate a current perspective. For example, the generation AI of the character generation unit can refer to the latest industry trends and news and incorporate a current perspective into the content of the character's statements. For example, it can reflect the latest technological trends and market needs. The character generation unit can also update the content of the character's statements based on the latest news. For example, the generation AI can analyze the latest news articles and reflect them in the content of the character's statements. This makes it possible to refer to the latest industry trends and news and incorporate a current perspective.

[0054] The discussion development unit can generate characters modeled after experts in different industries or fields to complement biases in the user's thinking. For example, the generation AI generates characters modeled after experts in different industries or fields, and has them express opinions from their respective perspectives. For example, characters such as engineers, marketing experts, and consumer representatives express their opinions from their respective perspectives. The discussion development unit can also complement biases in the user's thinking by using experts from different industries or fields. For example, if the user's perspective is biased toward a technical perspective, it can generate characters with a marketing or consumer perspective to complement that bias. This allows the generation of characters modeled after experts in different industries or fields to complement biases in the user's thinking.

[0055] The discussion development unit can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, the discussion development unit can add audio effects to the characters' remarks to enhance the realism of the discussion. For example, the tone of the audio can be changed to emphasize important points. The discussion development unit can also add visual effects to the characters' remarks to enhance the visual appeal of the discussion. For example, animations can be displayed in sync with the characters' remarks. In this way, the audio and visual effects can be added to the characters' discussions to enhance the user's sense of immersion.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] Ideation and decision-making support systems can analyze a user's past discussion history, identify biases in the user's thinking, and generate characters that complement those biases. For example, if a user is biased toward a technical perspective, a character with a marketing or consumer perspective can be generated to complement that bias. It can also revisit problems that arose in past discussions and discuss solutions. This makes it possible to analyze a user's past discussion history, identify biases in the user's thinking, and generate characters that complement those biases.

[0058] The discussion development section can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, audio effects can be added to the characters' remarks to make the discussion feel more realistic. The tone of the audio can also be changed to emphasize important points. Animations can also be displayed in sync with the characters' remarks to enhance the visual appeal of the discussion. This allows the user to add audio and visual effects to the characters' discussions to enhance the user's sense of immersion.

[0059] The discussion development module can generate characters with different cultural backgrounds and develop discussions from an international perspective. For example, characters with Asian, European, and American cultural backgrounds can express their opinions from their respective perspectives. It can also hold discussions based on the cultures and historical backgrounds of different countries. This allows characters with different cultural backgrounds to be generated and discussions to be developed from an international perspective.

[0060] The character generation unit can analyze the user's past input data and generate a character that matches the user's preferences and tendencies. For example, it can generate a character related to a theme that the user has shown interest in in the past. It can also customize the character's appearance and voice based on the user's past input data. This allows the user's past input data to be analyzed and a character that matches the user's preferences and tendencies to be generated.

[0061] The character generation unit can refer to the latest industry trends and news to incorporate a current perspective. For example, the generation AI can refer to the latest industry trends and news to incorporate a current perspective into the content of the character's speech. It can also reflect the latest technological trends and market needs. It can also update the content of the character's speech based on the latest news. This allows it to refer to the latest industry trends and news to incorporate a current perspective.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The character generation unit generates a character based on the user's idea. For example, the generation AI creates characters with different perspectives and expertise based on the idea or theme entered by the user. The generation AI generates characters using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to generate the character's appearance and voice. The generation AI can also analyze the user's input data and generate an appropriate character. For example, if a user enters a "new product idea," the generation AI will generate characters such as a marketing expert, engineer, and consumer representative, and have them express their opinions from their respective perspectives. Step 2: The discussion development section uses the characters generated by the character generation section to develop the discussion. For example, the generation AI allows the characters to express their opinions from their respective positions and advance the discussion. The generation AI can also refer to past discussion history to develop a consistent discussion. The generation AI can also analyze user responses in real time and dynamically adjust the direction of the discussion. For example, the characters can ask more in-depth questions about topics that the user has shown interest in. Step 3: The thought complementing unit complements biases in the user's thinking based on the discussion developed by the discussion development unit. For example, the generation AI generates characters with different perspectives and opinions to complement biases in the user's thinking. The generation AI can also use an emotion estimation function to generate characters based on the user's emotional state and develop discussions that are in line with the user's emotions. This allows the ideation and decision-making support system according to the embodiment to generate characters based on the user's ideas, develop discussions, and complement biases in the user's thinking. For example, if the user is biased toward a technical perspective, the generation AI can generate characters with a marketing or consumer perspective to engage in balanced discussions.

[0064] (Example 2) The idea generation and decision-making support system according to an embodiment of the present invention is a system in which an automatically generated character develops a discussion on a user's idea and draws a conclusion. As a result, the idea generation and decision-making support system compensates for biases in the user's thinking, enabling the creation of deep ideas and optimal decision-making.

[0065] An idea generation and decision-making support system according to an embodiment includes a character generation unit, a discussion development unit, and a thought completion unit. The character generation unit generates characters based on a user's ideas. For example, the generation AI creates characters with different perspectives and expertise based on ideas or themes input by the user. The generation AI generates characters using text generation AI (e.g., LLM). The generation AI can also generate character appearances and voices using multimodal generation AI. The generation AI can also analyze user input data and generate appropriate characters. For example, if a user inputs a "new product idea," the generation AI generates characters such as a marketing expert, engineer, and consumer representative, and has them express their opinions from their respective perspectives. The discussion development unit develops a discussion using the characters generated by the character generation unit. For example, the generation AI allows the characters to express their opinions from their respective perspectives and advance the discussion. The generation AI can also refer to past discussion history to develop a consistent discussion. The generation AI can also analyze user responses in real time and dynamically adjust the direction of the discussion. For example, the character asks a more in-depth question about a topic that the user shows interest in. The thought complementation unit complements biases in the user's thinking based on the discussion developed by the discussion development unit. For example, the generation AI generates characters with different perspectives and opinions to complement biases in the user's thinking. The generation AI can also use an emotion estimation function to generate characters based on the user's emotional state and develop discussions that are sensitive to the user's emotions. As a result, the ideation and decision-making support system according to the embodiment can generate characters based on the user's ideas, develop discussions, and complement biases in the user's thinking. For example, if the user is biased toward a technical perspective, the generation AI can generate a character with a marketing or consumer perspective to engage in a balanced discussion.

[0066] The character generation unit can generate a character based on user input data. For example, the character generation unit analyzes text data input by the user and generates an appropriate character. For example, the generation AI creates characters with different perspectives or expertise based on ideas or themes input by the user. The character generation unit can also analyze voice data input by the user and generate a character. For example, the generation AI analyzes the user's voice data and generates an appropriate character. The character generation unit can also analyze image data input by the user and generate a character. For example, the generation AI analyzes the user's image data and generates an appropriate character. In this way, characters can be generated based on user input data.

[0067] The discussion development unit can develop a discussion using characters. In the discussion development unit, for example, a generation AI generates the content of the characters' statements and advances the discussion. For example, the generation AI advances the discussion by having the characters express their opinions from their respective positions. The discussion development unit can also refer to past discussion history to develop a consistent discussion. For example, the generation AI analyzes past discussion history to ensure consistency in the content of the characters' statements. The discussion development unit can also analyze user responses in real time and dynamically adjust the direction of the discussion. For example, the generation AI analyzes the user's facial expressions and voice tone in real time to adjust the direction of the discussion according to the user's interests and concerns. This allows a discussion to be developed using the generated characters.

[0068] The thought completion unit can generate characters with different perspectives and opinions to complement the user's biases in thinking. For example, the generation AI analyzes the user's biases in thinking and generates characters with different perspectives and opinions to complement them. For example, if the user is biased toward a technical perspective, the generation AI can generate a character with a marketing or consumer perspective to engage in balanced discussions. The thought completion unit can also use the emotion estimation function to analyze the user's emotions toward the character's comments and provide feedback according to the emotions. For example, the emotion estimation function can be used to analyze the user's emotions toward the character's comments in real time and provide feedback that elicits positive emotions. This makes it possible to generate characters with different perspectives and opinions to complement the user's biases in thinking.

[0069] The discussion development section allows the generation AI to analyze the user's reaction to the character's comments in real time and dynamically adjust the direction of the discussion. For example, the generation AI analyzes the user's facial expressions and tone of voice in real time in response to the character's comments and adjusts the direction of the discussion according to the user's interests. For example, the character may ask a question that delves deeper into a topic that the user has shown interest in. The discussion development section can also adjust the progress of the discussion based on the user's reaction. For example, if a user expresses dissatisfaction, the character may make a statement to resolve that dissatisfaction. This allows the generation AI to analyze the user's reaction to the character's comments in real time and dynamically adjust the direction of the discussion.

[0070] In the discussion development section, the generation AI refers to past discussion history in response to a character's comments, allowing the discussion to be developed in a consistent manner. In the discussion development section, for example, the generation AI analyzes past discussion history to make the content of a character's comments consistent. For example, it refers to conclusions and opinions reached in previous discussions and makes new comments based on them. The discussion development section can also adjust the progress of a discussion based on past discussion history. For example, it may revisit issues that came up in past discussions and discuss solutions to those issues. This allows the generation AI to refer to past discussion history in response to a character's comments, allowing the discussion to be developed in a consistent manner.

[0071] The discussion development unit can use the emotion estimation function to analyze the user's emotion regarding the character's remarks and provide feedback according to the emotion. The discussion development unit, for example, can use the emotion estimation function to analyze the user's emotion regarding the character's remarks in real time and provide feedback that elicits positive emotions. For example, if the user shows a happy expression, the character makes a remark that reinforces that emotion. The discussion development unit can also adjust the progress of the discussion according to the user's emotions. For example, if the user is feeling anxious, the character makes a remark to ease the anxiety. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the character's remarks and provide feedback according to the emotion.

[0072] The discussion development unit can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, the discussion development unit can add audio effects to the characters' remarks to enhance the realism of the discussion. For example, the tone of the audio can be changed to emphasize important points. The discussion development unit can also add visual effects to the characters' remarks to enhance the visual appeal of the discussion. For example, animations can be displayed in sync with the characters' remarks. In this way, the audio and visual effects can be added to the characters' discussions to enhance the user's sense of immersion.

[0073] The discussion development unit can generate characters with different cultural backgrounds and develop discussions from an international perspective. For example, the generation AI can generate characters with different cultural backgrounds and have them express their opinions from their respective perspectives. For example, characters with Asian, European, and American cultural backgrounds can express their opinions from their respective perspectives. The discussion development unit can also use characters with different cultural backgrounds to develop discussions from an international perspective. For example, discussions can be held based on the cultures and historical backgrounds of different countries. This makes it possible to generate characters with different cultural backgrounds and develop discussions from an international perspective.

[0074] The discussion development unit can use the emotion estimation function to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions. The discussion development unit, for example, can use the emotion estimation function to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions. For example, if the user smiles, the character makes a remark that reinforces that emotion. The discussion development unit can also adjust the progress of the discussion according to the user's emotions. For example, if the user is feeling stressed, the character makes a remark to relieve that stress. In this way, the emotion estimation function can be used to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions.

[0075] The character generation unit can analyze the user's past input data and generate a character that matches the user's preferences and tendencies. For example, the character generation unit uses a generation AI to analyze the user's past input data and generate a character that matches the user's preferences and tendencies. For example, it generates a character related to a theme that the user has shown interest in in the past. The character generation unit can also customize the character's appearance and voice based on the user's past input data. For example, it generates a character based on the appearance and voice of a character that the user prefers. This makes it possible to analyze the user's past input data and generate a character that matches the user's preferences and tendencies.

[0076] The character generation unit can refer to the latest industry trends and news and incorporate a current perspective. For example, the generation AI of the character generation unit can refer to the latest industry trends and news and incorporate a current perspective into the content of the character's statements. For example, it can reflect the latest technological trends and market needs. The character generation unit can also update the content of the character's statements based on the latest news. For example, the generation AI can analyze the latest news articles and reflect them in the content of the character's statements. This makes it possible to refer to the latest industry trends and news and incorporate a current perspective.

[0077] The character generation unit uses the emotion estimation function to generate a character according to the user's emotional state, allowing for discussions that are sensitive to the user's emotions. The character generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate a character that is sensitive to the emotion. For example, if the user is feeling stressed, a character that helps the user relax is generated. The character generation unit can also adjust the content of the character's statements according to the user's emotional state. For example, if the user shows a happy expression, the character makes statements that reinforce that emotion. In this way, the emotion estimation function can be used to generate a character according to the user's emotional state, allowing for discussions that are sensitive to the user's emotions.

[0078] The character generation unit can incorporate images and audio selected by the user to provide a more personalized character. For example, the character generation unit uses a generation AI to customize the character's appearance and voice based on the images and audio selected by the user. For example, the character is generated based on the character's appearance and voice preferred by the user. The character generation unit can also analyze images and audio uploaded by the user to set the character's attributes. For example, the character's appearance is set based on the images uploaded by the user, and the character's voice is set based on the audio. This allows the image and audio selected by the user to be incorporated to provide a more personalized character.

[0079] The character generation unit can simultaneously generate characters with different fields of expertise and develop discussions from multiple perspectives. For example, the character generation unit uses a generation AI to simultaneously generate characters with different fields of expertise and have them express their opinions from their respective perspectives. For example, characters such as an engineer, a marketing expert, and a consumer representative express their opinions from their respective perspectives. The character generation unit can also use characters with different fields of expertise to develop discussions from multiple perspectives. For example, characters with different fields of expertise can cooperate to discuss problem-solving. This allows characters with different fields of expertise to be simultaneously generated and discussions to be developed from multiple perspectives.

[0080] The character generation unit uses the emotion estimation function to generate a character according to the user's emotion, allowing for discussions that elicit positive emotions. The character generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate a character according to that emotion. For example, if the user is feeling stressed, a character designed to relax the user is generated. The character generation unit can also adjust the content of the character's statements according to the user's emotional state. For example, if the user shows a happy expression, the character makes statements that reinforce that emotion. This allows for the emotion estimation function to generate a character according to the user's emotion and for discussions that elicit positive emotions.

[0081] The thought completion unit can analyze a user's past discussion history, identify biases in the user's thinking, and generate a character that complements them. For example, the generation AI analyzes a user's past discussion history and identifies biases in the user's thinking. For example, if the user's perspective is biased toward a technical one, it can generate a character with a marketing or consumer perspective to complement that bias. The thought completion unit can also adjust the content of a character's remarks based on the user's past discussion history. For example, it can revisit problems that came up in past discussions and discuss solutions to those problems. This makes it possible to analyze a user's past discussion history, identify biases in the user's thinking, and generate a character that complements them.

[0082] The thought completion unit can generate characters that incorporate perspectives from different cultures and social backgrounds. For example, the generation AI generates characters that incorporate perspectives from different cultures and social backgrounds. For example, characters with Asian, European, and American cultural backgrounds express their opinions from their respective perspectives. The thought completion unit can also use characters with different cultures and social backgrounds to develop discussions from different perspectives. For example, discussions can be held based on the cultures and historical backgrounds of different countries. This makes it possible to generate characters that incorporate perspectives from different cultures and social backgrounds.

[0083] The thought completion unit uses the emotion estimation function to generate a character that corresponds to the user's emotional state, allowing for discussions that are sensitive to the user's emotions. The thought completion unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate a character that corresponds to that emotion. For example, if the user is feeling stressed, a character that helps the user relax is generated. The thought completion unit can also adjust the content of the character's statements according to the user's emotional state. For example, if the user shows a happy expression, the character makes statements that reinforce that emotion. In this way, the emotion estimation function can be used to generate a character that corresponds to the user's emotional state, allowing for discussions that are sensitive to the user's emotions.

[0084] The thought completion unit can generate characters modeled after experts in different industries or fields to complement biases in the user's thinking. For example, the generation AI generates characters modeled after experts in different industries or fields, and has them express opinions from their respective perspectives. For example, characters such as engineers, marketing experts, and consumer representatives express their opinions from their respective perspectives. The thought completion unit can also complement biases in the user's thinking by using experts in different industries or fields. For example, if the user's perspective is biased toward a technical perspective, a character with a marketing or consumer perspective can be generated to complement that bias. This allows the generation of characters modeled after experts in different industries or fields to complement biases in the user's thinking.

[0085] The thought completion unit can incorporate images and audio selected by the user to provide a more personalized character. For example, the thought completion unit uses a generation AI to customize the character's appearance and voice based on the images and audio selected by the user. For example, the thought completion unit generates a character based on the character's appearance and voice that the user prefers. The thought completion unit can also analyze images and audio uploaded by the user to set the character's attributes. For example, it can set the character's appearance based on the images uploaded by the user, and set the character's voice based on the audio. This makes it possible to incorporate images and audio selected by the user and provide a more personalized character.

[0086] The thought completion unit uses the emotion estimation function to generate a character that corresponds to the user's emotion, and can develop a discussion that elicits positive emotions. The thought completion unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate a character that corresponds to that emotion. For example, if the user is feeling stressed, a character that helps the user relax is generated. The thought completion unit can also adjust the content of the character's statements according to the user's emotional state. For example, if the user shows a happy expression, the character makes statements that reinforce that emotion. In this way, the emotion estimation function can be used to generate a character that corresponds to the user's emotion, and a discussion that elicits positive emotions can be developed.

[0087] The discussion development unit can analyze the progress of the discussion in real time and provide feedback to lead to optimal decision-making. For example, the generation AI can analyze the progress of the discussion in real time and provide feedback to lead to optimal decision-making. For example, it can provide a new perspective if the discussion reaches an impasse. The discussion development unit can also adjust the content of the feedback depending on the progress of the discussion. For example, if the discussion is progressing actively, a character can make a statement to maintain the momentum. This makes it possible to analyze the progress of the discussion in real time and provide feedback to lead to optimal decision-making.

[0088] The discussion development section allows the generation AI to refer to past data in response to what the character says, and supports optimal decision-making. For example, the discussion development section allows the generation AI to refer to past data in response to what the character says, and supports optimal decision-making. For example, it provides opinions based on past successes and failures. The discussion development section can also adjust the progress of the discussion based on past data. For example, it can refer to past data to change the direction of the discussion. This allows the generation AI to refer to past data in response to what the character says, and supports optimal decision-making.

[0089] The discussion development unit can use the emotion estimation function to analyze the user's emotional state and support optimal decision-making according to the emotion. The discussion development unit, for example, can use the emotion estimation function to analyze the user's emotional state and support optimal decision-making according to the emotion. For example, if the user is feeling stressed, it can provide advice to help the user relax. The discussion development unit can also adjust the decision-making process according to the user's emotional state. For example, if the user is feeling anxious, it can provide feedback to alleviate the anxiety. In this way, it is possible to use the emotion estimation function to analyze the user's emotional state and support optimal decision-making according to the emotion.

[0090] The discussion development unit can generate characters modeled after experts in different industries or fields to complement biases in the user's thinking. For example, the generation AI generates characters modeled after experts in different industries or fields, and has them express opinions from their respective perspectives. For example, characters such as engineers, marketing experts, and consumer representatives express their opinions from their respective perspectives. The discussion development unit can also complement biases in the user's thinking by using experts from different industries or fields. For example, if the user's perspective is biased toward a technical perspective, it can generate characters with a marketing or consumer perspective to complement that bias. This allows the generation of characters modeled after experts in different industries or fields to complement biases in the user's thinking.

[0091] The discussion development unit can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, the discussion development unit can add audio effects to the characters' remarks to enhance the realism of the discussion. For example, the tone of the audio can be changed to emphasize important points. The discussion development unit can also add visual effects to the characters' remarks to enhance the visual appeal of the discussion. For example, animations can be displayed in sync with the characters' remarks. In this way, the audio and visual effects can be added to the characters' discussions to enhance the user's sense of immersion.

[0092] The discussion development unit can use the emotion estimation function to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions. The discussion development unit, for example, can use the emotion estimation function to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions. For example, if the user smiles, the character makes a remark that reinforces that emotion. The discussion development unit can also adjust the progress of the discussion according to the user's emotions. For example, if the user is feeling stressed, the character makes a remark to relieve that stress. In this way, the emotion estimation function can be used to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions.

[0093] The discussion development unit can analyze the content of user comments in real time and dynamically adjust the direction of the discussion. For example, the discussion development unit uses a generation AI to analyze the content of user comments in real time and dynamically adjust the direction of the discussion. For example, if a user proposes a new idea, the direction of the discussion can be changed based on that idea. The discussion development unit can also adjust the progress of the discussion according to the content of user comments. For example, if a user shows interest in a particular topic, the discussion on that topic can be deepened. This makes it possible to analyze the content of user comments in real time and dynamically adjust the direction of the discussion.

[0094] The discussion development section allows the generation AI to refer to past discussion history in response to user comments and develop a consistent discussion. For example, the discussion development section allows the generation AI to analyze past discussion history and make the user's comments consistent. For example, it refers to conclusions and opinions reached in previous discussions and makes new comments based on them. The discussion development section can also adjust the progress of the discussion based on past discussion history. For example, it can re-raise issues that came up in past discussions and discuss solutions to them. This allows the generation AI to refer to past discussion history in response to user comments and develop a consistent discussion.

[0095] The discussion development unit can use the emotion estimation function to analyze the user's emotional state and provide feedback according to the emotion. The discussion development unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide feedback that elicits positive emotions. For example, if the user shows a happy expression, the character makes a statement that reinforces that emotion. The discussion development unit can also adjust the content of the feedback according to the user's emotions. For example, if the user is feeling anxious, feedback to alleviate the anxiety is provided. In this way, the emotion estimation function can be used to analyze the user's emotional state and provide feedback according to the emotion.

[0096] The discussion development section allows the generation AI to provide feedback from different perspectives in response to user comments, deepening the discussion. For example, the generation AI provides feedback from different perspectives in response to user comments. For example, it may state opinions from a marketing or consumer perspective in response to a technical perspective. The discussion development section can also adjust the content of the feedback depending on the content of the user's comments. For example, if a user shows interest in a particular topic, it will provide feedback from different perspectives on that topic. This allows the generation AI to provide feedback from different perspectives in response to user comments, deepening the discussion.

[0097] In the discussion development section, the generation AI adds audio and visual effects to what the user says, making the discussion more immersive. In the discussion development section, for example, the generation AI adds audio effects to what the user says to make the discussion more realistic. For example, it changes the tone of the audio to emphasize important points. The discussion development section can also add visual effects to what the user says to make the discussion more visually appealing. For example, it can display animations in sync with what the user says. This allows the generation AI to add audio and visual effects to what the user says to make the discussion more immersive.

[0098] The discussion development unit uses the emotion estimation function to provide feedback according to the user's emotions, and can develop a discussion that elicits positive emotions. The discussion development unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide feedback that elicits positive emotions. For example, if the user shows a happy expression, a character makes a statement that reinforces that emotion. The discussion development unit can also adjust the content of the feedback according to the user's emotions. For example, if the user is feeling anxious, it provides feedback to alleviate that anxiety. In this way, the emotion estimation function can be used to provide feedback according to the user's emotions and develop a discussion that elicits positive emotions.

[0099] The character generation unit can analyze the user's past usage history and generate the most suitable character. For example, the character generation unit uses a generation AI to analyze the user's past usage history and generate a character that matches the user's preferences and tendencies. For example, it generates a character related to a theme that the user has shown interest in in the past. The character generation unit can also customize the character's appearance and voice based on the user's past usage history. For example, it generates a character based on the appearance and voice of a character that the user prefers. This makes it possible to analyze the user's past usage history and generate the most suitable character.

[0100] The character generation unit can refer to the latest industry trends and news and incorporate a current perspective. For example, the generation AI of the character generation unit can refer to the latest industry trends and news and incorporate a current perspective into the content of the character's statements. For example, it can reflect the latest technological trends and market needs. The character generation unit can also update the content of the character's statements based on the latest news. For example, the generation AI can analyze the latest news articles and reflect them in the content of the character's statements. This makes it possible to refer to the latest industry trends and news and incorporate a current perspective.

[0101] The character generation unit uses the emotion estimation function to generate a character according to the user's emotional state, allowing for discussions that are sensitive to the user's emotions. The character generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate a character that is sensitive to the emotion. For example, if the user is feeling stressed, a character that helps the user relax is generated. The character generation unit can also adjust the content of the character's statements according to the user's emotional state. For example, if the user shows a happy expression, the character makes statements that reinforce that emotion. In this way, the emotion estimation function can be used to generate a character according to the user's emotional state, allowing for discussions that are sensitive to the user's emotions.

[0102] The discussion development unit can generate characters modeled after experts in different industries or fields to complement biases in the user's thinking. For example, the generation AI generates characters modeled after experts in different industries or fields, and has them express opinions from their respective perspectives. For example, characters such as engineers, marketing experts, and consumer representatives express their opinions from their respective perspectives. The discussion development unit can also complement biases in the user's thinking by using experts from different industries or fields. For example, if the user's perspective is biased toward a technical perspective, it can generate characters with a marketing or consumer perspective to complement that bias. This allows the generation of characters modeled after experts in different industries or fields to complement biases in the user's thinking.

[0103] The discussion development unit can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, the discussion development unit can add audio effects to the characters' remarks to enhance the realism of the discussion. For example, the tone of the audio can be changed to emphasize important points. The discussion development unit can also add visual effects to the characters' remarks to enhance the visual appeal of the discussion. For example, animations can be displayed in sync with the characters' remarks. In this way, the audio and visual effects can be added to the characters' discussions to enhance the user's sense of immersion.

[0104] The discussion development unit can use the emotion estimation function to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions. The discussion development unit, for example, can use the emotion estimation function to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions. For example, if the user smiles, the character makes a remark that reinforces that emotion. The discussion development unit can also adjust the progress of the discussion according to the user's emotions. For example, if the user is feeling stressed, the character makes a remark to relieve that stress. In this way, the emotion estimation function can be used to analyze the user's emotion toward the character's remarks in real time and develop a discussion that elicits positive emotions.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] Ideation and decision-making support systems can analyze a user's past discussion history, identify biases in the user's thinking, and generate characters that complement those biases. For example, if a user is biased toward a technical perspective, a character with a marketing or consumer perspective can be generated to complement that bias. It can also revisit problems that arose in past discussions and discuss solutions. This makes it possible to analyze a user's past discussion history, identify biases in the user's thinking, and generate characters that complement those biases.

[0107] The discussion development section can add audio and visual effects to the characters' discussions to enhance the user's sense of immersion. For example, audio effects can be added to the characters' remarks to make the discussion feel more realistic. The tone of the audio can also be changed to emphasize important points. Animations can also be displayed in sync with the characters' remarks to enhance the visual appeal of the discussion. This allows the user to add audio and visual effects to the characters' discussions to enhance the user's sense of immersion.

[0108] The discussion development module can generate characters with different cultural backgrounds and develop discussions from an international perspective. For example, characters with Asian, European, and American cultural backgrounds can express their opinions from their respective perspectives. It can also hold discussions based on the cultures and historical backgrounds of different countries. This allows characters with different cultural backgrounds to be generated and discussions to be developed from an international perspective.

[0109] The character generation unit can analyze the user's past input data and generate a character that matches the user's preferences and tendencies. For example, it can generate a character related to a theme that the user has shown interest in in the past. It can also customize the character's appearance and voice based on the user's past input data. This allows the user's past input data to be analyzed and a character that matches the user's preferences and tendencies to be generated.

[0110] The character generation unit can refer to the latest industry trends and news to incorporate a current perspective. For example, the generation AI can refer to the latest industry trends and news to incorporate a current perspective into the content of the character's speech. It can also reflect the latest technological trends and market needs. It can also update the content of the character's speech based on the latest news. This allows it to refer to the latest industry trends and news to incorporate a current perspective.

[0111] The discussion development unit uses the emotion estimation function to analyze the user's emotions toward the character's comments in real time, and can develop a discussion that elicits positive emotions. For example, if the user smiles, the character makes a comment that reinforces that emotion. It can also adjust the progress of the discussion according to the user's emotions. If the user is feeling stressed, the character makes a comment to relieve that stress. In this way, the emotion estimation function can be used to analyze the user's emotions toward the character's comments in real time, and can develop a discussion that elicits positive emotions.

[0112] The character generation unit uses the emotion estimation function to generate a character according to the user's emotional state, allowing for discussions that are sensitive to the user's emotions. For example, if the user is feeling stressed, a character designed to help the user relax can be generated. The character's comments can also be adjusted according to the user's emotional state. If the user shows a happy expression, the character will make comments that reinforce that emotion. This allows for the emotion estimation function to generate a character according to the user's emotional state, allowing for discussions that are sensitive to the user's emotions.

[0113] The discussion development unit can use the emotion estimation function to analyze the user's emotional state and provide feedback according to the emotion. For example, if the user shows a happy expression, the character will make a statement that reinforces that emotion. It can also adjust the content of the feedback according to the user's emotion. If the user is feeling anxious, feedback to alleviate that anxiety is provided. In this way, the emotion estimation function can be used to analyze the user's emotional state and provide feedback according to the emotion.

[0114] The discussion development unit uses the emotion estimation function to analyze the user's emotions toward the character's comments in real time, and can develop a discussion that elicits positive emotions. For example, if the user smiles, the character makes a comment that reinforces that emotion. It can also adjust the progress of the discussion according to the user's emotions. If the user is feeling stressed, the character makes a comment to relieve that stress. In this way, the emotion estimation function can be used to analyze the user's emotions toward the character's comments in real time, and can develop a discussion that elicits positive emotions.

[0115] The thought completion unit uses the emotion estimation function to generate a character that corresponds to the user's emotional state, allowing for discussions that are sensitive to the user's emotions. For example, if the user is feeling stressed, a character that helps them relax can be generated. The unit can also adjust the content of the character's remarks according to the user's emotional state. If the user shows a happy expression, the character will make remarks that reinforce that emotion. This allows for the emotion estimation function to generate a character that corresponds to the user's emotional state, allowing for discussions that are sensitive to the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The character generation unit generates a character based on the user's idea. For example, the generation AI creates characters with different perspectives and expertise based on the idea or theme entered by the user. The generation AI generates characters using text generation AI (e.g., LLM). The generation AI can also use multimodal generation AI to generate the character's appearance and voice. The generation AI can also analyze the user's input data and generate an appropriate character. For example, if a user enters a "new product idea," the generation AI will generate characters such as a marketing expert, engineer, and consumer representative, and have them express their opinions from their respective perspectives. Step 2: The discussion development section uses the characters generated by the character generation section to develop the discussion. For example, the generation AI allows the characters to express their opinions from their respective positions and advance the discussion. The generation AI can also refer to past discussion history to develop a consistent discussion. The generation AI can also analyze user responses in real time and dynamically adjust the direction of the discussion. For example, the characters can ask more in-depth questions about topics that the user has shown interest in. Step 3: The thought complementing unit complements biases in the user's thinking based on the discussion developed by the discussion development unit. For example, the generation AI generates characters with different perspectives and opinions to complement biases in the user's thinking. The generation AI can also use an emotion estimation function to generate characters based on the user's emotional state and develop discussions that are in line with the user's emotions. This allows the ideation and decision-making support system according to the embodiment to generate characters based on the user's ideas, develop discussions, and complement biases in the user's thinking. For example, if the user is biased toward a technical perspective, the generation AI can generate characters with a marketing or consumer perspective to engage in balanced discussions.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0162] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0185] 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 character generation unit that generates a character based on a user's idea; a discussion development unit that develops a discussion using the character generated by the character generation unit; a thought complementing unit that complements bias in the user's thinking based on the discussion developed by the discussion development unit. A system characterized by:

2. The thought completion unit Generate the character with a different perspective or opinion to compensate for the bias of the user's thinking.

2. The system of claim 1.

3. The argument development section Create characters with different cultural backgrounds to develop the discussion from an international perspective 2. The system of claim 1.

4. The character generation unit The character is generated according to the emotional state of the user, and the discussion is developed in accordance with the user's emotions.

2. The system of claim 1.

5. The thought completion unit The past discussion history of the user is analyzed, the bias in the user's thinking is identified, and the character that complements it is generated.

2. The system of claim 1.

6. The argument development section Analyze the progress of the discussion in real time and provide feedback to guide optimal decision-making 2. The system of claim 1.

7. The argument development section The AI ​​provides feedback from different perspectives to the user's comments, deepening the discussion.

2. The system of claim 1.

8. The character generation unit The character is generated according to the emotional state of the user, and the discussion is developed in accordance with the user's emotions.

2. The system of claim 1.

Images