system

Abstract

The system according to this embodiment aims to alleviate loneliness and understand the perspectives of others by allowing users to engage in dialogue while impersonating a specific person. [Solution] The system according to the embodiment comprises a selection unit, a learning unit, and a dialogue unit. The selection unit allows the user to select a person with whom they wish to interact. The learning unit learns information about the person selected by the selection unit. The dialogue unit engages in dialogue based on the information learned by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to fully immerse oneself in a conversation as a specific person and it is insufficient as a means for alleviating loneliness and understanding the thinking of others.

[0005] The system according to the embodiment aims to alleviate loneliness and understand the thinking of others by fully immersing oneself in a conversation as a specific person.

Means for Solving the Problems

[0006] The system according to the embodiment includes a selection unit, a learning unit, and a dialogue unit. The selection unit selects a person with whom the user wants to have a conversation. The learning unit learns the person information selected by the selection unit. The dialogue unit conducts a conversation based on the information learned by the learning unit.

Effects of the Invention

[0007] The system according to this embodiment allows users to alleviate loneliness and understand the perspectives of others by engaging in dialogue while impersonating a specific person. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dialogue system according to an embodiment of the present invention is a system that uses AI to thoroughly learn information about historical figures, ancestors, and other people involved in a person's life, allowing the user to converse with that person. This dialogue system provides the following functions: First, the user selects a person they wish to converse with, and the AI ​​learns information about that person. Next, the user begins a conversation with that person, and the AI ​​reproduces that person's way of thinking and aspects of their personality. For example, if it is a historical figure, the user can learn in detail about the historical context and the reasons for their actions. Also, if it is a boss or someone the user doesn't get along with, the user can understand their desires and thoughts, which can help improve communication. This system is a groundbreaking solution that allows users to understand the thoughts and desires of others and gain hints for inventions and history without feeling lonely. For example, by enjoying a conversation with the AI, the user can alleviate feelings of loneliness. By using the AI ​​as a confidant, the user can reduce daily worries and stress. Furthermore, the user can learn what other people are thinking and what they want. For example, by knowing what a boss wants or the thoughts of someone the user doesn't get along with, the user can improve communication. The user can learn the thoughts of inventors and historical figures and understand the reasons behind their actions. This can be used to find hints for inventions and unravel historical mysteries. The dialogue system allows users to understand the thoughts and desires of others without feeling isolated, and to gain insights into inventions and history.

[0029] The dialogue system according to this embodiment comprises a selection unit, a learning unit, and a dialogue unit. The selection unit allows the user to select a person they wish to converse with. For example, the selection unit can allow the user to select a person they wish to converse with from a list. The selection unit can also allow the user to input the name of the person they wish to converse with. Furthermore, the selection unit can also allow the user to select a category for the person they wish to converse with. For example, the selection unit can provide categories such as historical figures, inventors, or ancestors. The learning unit learns information about the person selected by the selection unit. For example, the learning unit learns information about the person using AI. The learning unit analyzes in detail the background information of the person's actions and statements. For example, the learning unit analyzes in detail the historical events that underlie the person's actions. Furthermore, the learning unit analyzes in detail the cultural factors that underlie the person's statements. Furthermore, the learning unit analyzes in detail the personal experiences that underlie the person's actions and statements. The dialogue unit conducts a dialogue based on the information learned by the learning unit. For example, the dialogue unit conducts a dialogue based on information learned using AI. The dialogue unit estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will engage in dialogue in a calm tone. If the user is excited, the dialogue unit will engage in dialogue in a lively tone. Furthermore, if the user is sad, the dialogue unit will engage in dialogue in a comforting tone. As a result, the dialogue system according to this embodiment can allow the user to select a person they want to talk to, learn information about that person, and then engage in dialogue.

[0030] The dialogue system includes a consultation unit where the user inputs the content they wish to discuss, and an AI conducts a dialogue based on that input. The consultation unit allows the user to input the content they wish to discuss. For example, the user can input the content they wish to discuss in text. The consultation unit also allows the user to input the content they wish to discuss in voice. Furthermore, the consultation unit allows the user to select the content they wish to discuss from a set of options. For example, the consultation unit provides options such as technical consultation, psychological consultation, and business consultation. The AI ​​conducts the dialogue based on the content input by the consultation unit. For example, the AI ​​analyzes the consultation content entered by the user and generates an appropriate response. The AI ​​estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated emotions. For example, if the user is relaxed, the AI ​​will conduct the dialogue in a calm tone. If the user is excited, the AI ​​will conduct the dialogue in an active tone. Furthermore, if the user is sad, the AI ​​will conduct the dialogue in a comforting tone. This allows the dialogue system to conduct a dialogue based on the content the user wishes to discuss.

[0031] The dialogue system includes an understanding unit to understand the thoughts and desires of others. This understanding unit provides information to help the user understand the thoughts and desires of others. For example, it can display others' thoughts and desires as text. It can also explain others' thoughts and desires verbally. Furthermore, it can visually display others' thoughts and desires using graphs and charts. For example, it provides information such as others' opinions, hopes, and goals. The understanding unit estimates the user's emotions and adjusts how information is presented based on the estimated emotions. For example, if the user is relaxed, it explains others' thoughts and desires in a calm tone. If the user is excited, it explains others' thoughts and desires in an active tone. Furthermore, if the user is sad, it explains others' thoughts and desires in a comforting tone. This allows the dialogue system to enable the user to understand the thoughts and desires of others.

[0032] The learning unit can learn about individuals using AI. The learning unit learns about individuals using AI. The AI ​​learns using techniques such as neural networks and deep learning. The learning unit analyzes in detail the background information of individuals' actions and statements. For example, the learning unit analyzes in detail the historical events that lie behind individuals' actions. The learning unit also analyzes in detail the cultural factors that lie behind individuals' statements. Furthermore, the learning unit analyzes in detail the personal experiences that lie behind individuals' actions and statements. This improves the accuracy of learning about individuals by using AI. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or not using generative AI. For example, the learning unit can input background information about individuals' actions and statements into generative AI, which can then perform a detailed analysis.

[0033] The dialogue unit can engage in conversation based on information learned using AI. The AI ​​uses technologies such as neural networks and deep learning to conduct the conversation. The dialogue unit estimates the user's emotions and adjusts the tone and content of the conversation based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will engage in conversation in a calm tone. If the user is excited, the dialogue unit will engage in conversation in an active tone. Furthermore, if the user is sad, the dialogue unit will engage in conversation in a comforting tone. In this way, the accuracy of the conversation is improved by using AI. Some or all of the above processing in the dialogue unit may be performed using, for example, generative AI, or not using generative AI. For example, the dialogue unit can input learned information into generative AI, and the generative AI can adjust the tone and content of the conversation.

[0034] The dialogue system includes a selection unit that analyzes the user's past selection history and recommends the most suitable person. The selection unit analyzes the user's past selection history and recommends the most suitable person. For example, the selection unit prioritizes displaying people that the user has frequently selected in the past. Also, if the user is interested in a particular theme, the selection unit recommends people related to that theme. Furthermore, the selection unit predicts and recommends people that were selected during a specific time period based on the user's past selection history. This allows the system to recommend the most suitable person based on the user's past selection history. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the selection unit can input the user's past selection history data into a generative AI, and the generative AI can recommend the most suitable person.

[0035] The dialogue system includes a selection unit that filters results based on the user's current interests and circumstances when making a selection. For example, if the user is experiencing work-related problems, the selection unit will recommend someone with business expertise. If the user is interested in history, the selection unit will prioritize displaying historical figures. Furthermore, if the user is experiencing family problems, the selection unit will recommend someone related to family. This allows the user to select someone who matches their current interests and circumstances. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without one. For example, the selection unit can input data on the user's current interests and circumstances into a generative AI, which can then perform the filtering.

[0036] The dialogue system includes a selection section that, when a selection is made, prioritizes displaying highly relevant individuals while considering the user's geographical location. For example, if the user is in a specific region, the selection section displays historical figures associated with that region. If the user is traveling, the selection section displays individuals associated with the travel destination. Furthermore, if the user is at home, the selection section prioritizes displaying family members or ancestors. This allows the system to display highly relevant individuals based on the user's geographical location. Some or all of the above processing in the selection section may be performed using, for example, a generative AI, or without one. For example, the selection section can input the user's geographical location data into a generative AI, which can then display highly relevant individuals.

[0037] The dialogue system includes a selection unit that analyzes the user's social media activity and recommends relevant individuals when a selection is made. The selection unit, for example, displays individuals the user frequently mentions on social media. Furthermore, if the user posts about a specific topic, the selection unit recommends individuals related to that topic. Additionally, the selection unit analyzes the user's social media friendships and displays relevant individuals. This allows for the recommendation of relevant individuals based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using, for example, generative AI, or without generative AI. For example, the selection unit can input the user's social media activity data into a generative AI, which can then recommend relevant individuals.

[0038] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects an efficient learning algorithm based on previously learned data. Furthermore, the learning unit extracts specific patterns from past learning data and optimizes the learning algorithm. In addition, the learning unit analyzes past learning data and selects algorithms to improve learning accuracy. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI and use the generative AI to optimize the learning algorithm.

[0039] The learning unit can analyze in detail the background information of a person's actions and statements during the learning process. The learning unit analyzes in detail the background information of a person's actions and statements. For example, the learning unit can analyze in detail the historical events behind a person's actions. Furthermore, the learning unit can analyze in detail the cultural factors behind a person's statements. In addition, the learning unit can analyze in detail the personal experiences behind a person's actions and statements. This detailed analysis of the background information of a person's actions and statements improves the accuracy of learning. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input background information of a person's actions and statements into a generative AI, which can then perform a detailed analysis.

[0040] The learning unit can improve the accuracy of its learning by referring to relevant literature and materials about a person during the learning process. For example, the learning unit can learn by referring to historical documents about a person. It can also learn by referring to academic papers about a person. Furthermore, it can learn by referring to interviews and testimonies about a person. This improves the accuracy of learning by referring to relevant literature and materials. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input relevant literature and materials about a person into a generative AI, and the generative AI can improve the accuracy of its learning.

[0041] The learning unit can perform learning while considering the geographical background information of individuals. For example, the learning unit can learn while considering the geographical background of the region where the individual was born and raised. Furthermore, the learning unit can learn while considering the geographical background of the region where the individual has been active. In addition, the learning unit can learn while considering the geographical background of the region that has influenced the individual. This improves the accuracy of learning by considering geographical background information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the geographical background information of individuals into a generative AI and perform learning using the generative AI.

[0042] The dialogue unit can generate optimal dialogue content by referring to the user's past dialogue history during a conversation. For example, the dialogue unit generates relevant dialogue content based on what the user has said in the past. The dialogue unit also selects topics that the user might be interested in from their past dialogue history. Furthermore, the dialogue unit analyzes the user's past dialogue history to generate optimal dialogue content. This allows the dialogue unit to generate optimal dialogue content based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using, for example, a generation AI, or without a generation AI. For example, the dialogue unit can input the user's past dialogue history data into a generation AI, which can then generate optimal dialogue content.

[0043] The dialogue unit can conduct dialogue based on background information of the characters' actions and statements. The dialogue unit can conduct dialogue based on historical events that are the background of the characters' actions. The dialogue unit can also conduct dialogue based on cultural factors that are the background of the characters' statements. Furthermore, the dialogue unit can conduct dialogue based on personal experiences that are the background of the characters' actions and statements. By conducting dialogue based on background information of the characters' actions and statements, the accuracy of the dialogue is improved. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input background information of the characters' actions and statements into a generative AI and conduct dialogue using the generative AI.

[0044] The dialogue system includes a dialogue unit that customizes the dialogue content based on the user's current situation and interests during a conversation. For example, if the user is experiencing work-related problems, the dialogue unit provides work-related dialogue content. Similarly, if the user is interested in history, it provides history-related dialogue content. Furthermore, if the user is experiencing family problems, it provides family-related dialogue content. This allows the system to provide dialogue content tailored to the user's current situation and interests. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without one. For instance, the dialogue unit can input the user's current situation and interest data into a generative AI, which can then customize the dialogue content.

[0045] The dialogue system includes a dialogue unit that analyzes the user's social media activity during a conversation and provides relevant dialogue content. The dialogue unit analyzes the user's social media activity during a conversation and provides relevant dialogue content. For example, the dialogue unit provides dialogue content based on topics that the user frequently mentions on social media. Furthermore, if the user posts about a specific topic, the dialogue unit provides dialogue content related to that topic. In addition, the dialogue unit analyzes the user's social media friendships and provides relevant dialogue content. This allows the system to provide relevant dialogue content based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's social media activity data into a generative AI, which can then provide relevant dialogue content.

[0046] The consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. The consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. For example, the consultation unit can provide relevant advice based on the content of past consultations the user has had. The consultation unit can also select advice that the user might be interested in from the user's past consultation history. Furthermore, the consultation unit can analyze the user's past consultation history and provide optimal advice. This allows the consultation unit to provide optimal advice based on past consultation history. Some or all of the above processes in the consultation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the consultation unit can input the user's past consultation history data into a generative AI, and the generative AI can provide optimal advice.

[0047] The consultation unit can provide optimal advice by taking into account the user's geographical location information during a consultation. For example, if the user is in a specific region, the consultation unit will provide advice relevant to that region. If the user is traveling, the consultation unit will provide advice relevant to the travel destination. Furthermore, if the user is at home, the consultation unit will provide advice that can be taken at home. This allows the consultation unit to provide optimal advice based on the user's geographical location information. Some or all of the above processing in the consultation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the consultation unit can input the user's geographical location data into a generative AI, and the generative AI can provide optimal advice.

[0048] The understanding unit can provide the optimal understanding method by referring to the user's past dialogue history when understanding. The understanding unit can provide the optimal understanding method by referring to the user's past dialogue history when understanding. For example, the understanding unit can provide relevant understanding methods based on what the user has said in the past. The understanding unit can also select understanding methods that are likely to be of interest to the user from the user's past dialogue history. Furthermore, the understanding unit can analyze the user's past dialogue history and provide the optimal understanding method. This allows the understanding unit to provide the optimal understanding method based on past dialogue history. Some or all of the above processing in the understanding unit may be performed using, for example, a generative AI, or without a generative AI. For example, the understanding unit can input the user's past dialogue history data into a generative AI, and the generative AI can provide the optimal understanding method.

[0049] The understanding unit can provide the optimal understanding method when understanding, taking into account the user's geographical location information. For example, if the user is in a specific region, the understanding unit provides an understanding method relevant to that region. Furthermore, if the user is traveling, the understanding unit provides an understanding method relevant to the travel destination. Additionally, if the user is at home, the understanding unit provides an understanding method that can be performed at home. This allows the understanding unit to provide the optimal understanding method based on the user's geographical location information. Some or all of the above processing in the understanding unit may be performed using, for example, a generative AI, or without a generative AI. For example, the understanding unit can input the user's geographical location data into a generative AI, which can then provide the optimal understanding method.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The dialogue system may also include a trend analysis unit that analyzes the user's selection history and learns the tendencies of the people the user has chosen in the past. For example, the trend analysis unit analyzes the categories and characteristics of people the user has chosen in the past and reflects those tendencies in the next selection. The trend analysis unit also learns the patterns of people the user has chosen at specific times and recommends people suitable for those times in the next selection. Furthermore, the trend analysis unit learns from the user's selection history who was chosen in specific emotional states and recommends people suitable for those emotional states in the next selection. This enables more personalized person selection based on the user's selection history.

[0052] The dialogue system can also include a resource provision section that automatically searches for and provides relevant literature and materials based on the user's inquiry. For example, if a user seeks technical advice, the resource provision section will provide the latest research papers and patent information related to that technology. If a user seeks psychological advice, the resource provision section will recommend reliable articles and books on psychology. Furthermore, if a user seeks business advice, the resource provision section will provide case studies on business strategies and market analysis reports. This allows the user to obtain useful information related to their inquiry.

[0053] A dialogue system can also include a behavioral analysis unit that analyzes the behavioral patterns and past statements of others in order for the user to understand the thoughts and desires of others. For example, the behavioral analysis unit analyzes the past behavioral history of others and estimates the intentions and purposes behind those actions. It also analyzes the past statements of others and estimates the emotions and values ​​behind those statements. Furthermore, the behavioral analysis unit visually displays the patterns of others' behavior and statements, providing them in a way that is easy for the user to understand. This allows the user to gain a deeper understanding of the background of others' behavior and statements.

[0054] The dialogue system can also include a multimedia learning unit within its learning section that incorporates relevant video and audio data in addition to learning about individuals. For example, the multimedia learning unit could incorporate documentary videos and interview audio about historical figures as learning data. It could also incorporate lecture videos and technical explanation videos about inventors. Furthermore, it could incorporate family video messages and audio recordings about ancestors as learning data. This allows the learning unit to learn from diverse data, including not only text information but also video and audio, enabling more realistic dialogue.

[0055] A dialogue system can also include a style adaptation unit within its dialogue section that customizes the way the dialogue progresses based on the user's dialogue style and preferences. For example, if the user prefers short dialogues, the style adaptation unit will provide concise dialogue that gets straight to the point. If the user prefers detailed explanations, the style adaptation unit will provide more in-depth information. Furthermore, if the user enjoys humor, the style adaptation unit will incorporate humorous expressions into the dialogue. This enables flexible dialogue that is tailored to the user's dialogue style and preferences.

[0056] A dialogue system can also include a real-time update unit that updates the dialogue content in real time based on the user's current interests and circumstances. For example, if the user develops a new interest, the real-time update unit incorporates the latest information related to that interest into the dialogue. Furthermore, if there is a change in the user's current situation, the real-time update unit provides dialogue content tailored to that situation. Additionally, if the user is participating in a specific event, the real-time update unit incorporates information related to that event into the dialogue. This allows the system to provide up-to-date dialogue content that is relevant to the user's interests and circumstances.

[0057] A dialogue system can also include a geographically adaptive unit that customizes the dialogue content based on the user's geographical location. For example, if the user is in a specific region, the geographically adaptive unit will provide dialogue content related to the history and culture of that region. If the user is traveling, the geographically adaptive unit will provide dialogue content related to tourist information and local customs of the travel destination. Furthermore, if the user is at home, the geographically adaptive unit will provide dialogue content related to events around their home and local news. This allows for the provision of more relevant dialogue content based on the user's geographical location.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The selection section allows the user to choose the person they want to interact with. The selection section allows the user to select the person they want to interact with from a list. The user can also enter the name of the person they want to interact with. Furthermore, the user can select the category of the person they want to interact with. For example, categories such as historical figures, inventors, and ancestors are provided. Step 2: The learning unit learns the person information selected by the selection unit. The learning unit uses AI to learn the person information and analyzes in detail the background information of the person's actions and statements. For example, it analyzes in detail historical events, cultural factors, personal experiences, etc. Step 3: The dialogue unit engages in conversation based on the information learned by the learning unit. The dialogue unit uses AI to conduct conversations based on the learned information, estimates the user's emotions, and adjusts the tone and content of the conversation based on the estimated emotions. For example, if the user is relaxed, the dialogue will be conducted in a calm tone; if excited, in an energetic tone; and if sad, in a comforting tone.

[0060] (Example of form 2) The dialogue system according to an embodiment of the present invention is a system that uses AI to thoroughly learn information about historical figures, ancestors, and other people involved in a person's life, allowing the user to converse with that person. This dialogue system provides the following functions: First, the user selects a person they wish to converse with, and the AI ​​learns information about that person. Next, the user begins a conversation with that person, and the AI ​​reproduces that person's way of thinking and aspects of their personality. For example, if it is a historical figure, the user can learn in detail about the historical context and the reasons for their actions. Also, if it is a boss or someone the user doesn't get along with, the user can understand their desires and thoughts, which can help improve communication. This system is a groundbreaking solution that allows users to understand the thoughts and desires of others and gain hints for inventions and history without feeling lonely. For example, by enjoying a conversation with the AI, the user can alleviate feelings of loneliness. By using the AI ​​as a confidant, the user can reduce daily worries and stress. Furthermore, the user can learn what other people are thinking and what they want. For example, by knowing what a boss wants or the thoughts of someone the user doesn't get along with, the user can improve communication. The user can learn the thoughts of inventors and historical figures and understand the reasons behind their actions. This can be used to find hints for inventions and unravel historical mysteries. The dialogue system allows users to understand the thoughts and desires of others without feeling isolated, and to gain insights into inventions and history.

[0061] The dialogue system according to this embodiment comprises a selection unit, a learning unit, and a dialogue unit. The selection unit allows the user to select a person they wish to converse with. For example, the selection unit can allow the user to select a person they wish to converse with from a list. The selection unit can also allow the user to input the name of the person they wish to converse with. Furthermore, the selection unit can also allow the user to select a category for the person they wish to converse with. For example, the selection unit can provide categories such as historical figures, inventors, or ancestors. The learning unit learns information about the person selected by the selection unit. For example, the learning unit learns information about the person using AI. The learning unit analyzes in detail the background information of the person's actions and statements. For example, the learning unit analyzes in detail the historical events that underlie the person's actions. Furthermore, the learning unit analyzes in detail the cultural factors that underlie the person's statements. Furthermore, the learning unit analyzes in detail the personal experiences that underlie the person's actions and statements. The dialogue unit conducts a dialogue based on the information learned by the learning unit. For example, the dialogue unit conducts a dialogue based on information learned using AI. The dialogue unit estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will engage in dialogue in a calm tone. If the user is excited, the dialogue unit will engage in dialogue in a lively tone. Furthermore, if the user is sad, the dialogue unit will engage in dialogue in a comforting tone. As a result, the dialogue system according to this embodiment can allow the user to select a person they want to talk to, learn information about that person, and then engage in dialogue.

[0062] The dialogue system includes a consultation unit where the user inputs the content they wish to discuss, and an AI conducts a dialogue based on that input. The consultation unit allows the user to input the content they wish to discuss. For example, the user can input the content they wish to discuss in text. The consultation unit also allows the user to input the content they wish to discuss in voice. Furthermore, the consultation unit allows the user to select the content they wish to discuss from a set of options. For example, the consultation unit provides options such as technical consultation, psychological consultation, and business consultation. The AI ​​conducts the dialogue based on the content input by the consultation unit. For example, the AI ​​analyzes the consultation content entered by the user and generates an appropriate response. The AI ​​estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated emotions. For example, if the user is relaxed, the AI ​​will conduct the dialogue in a calm tone. If the user is excited, the AI ​​will conduct the dialogue in an active tone. Furthermore, if the user is sad, the AI ​​will conduct the dialogue in a comforting tone. This allows the dialogue system to conduct a dialogue based on the content the user wishes to discuss.

[0063] The dialogue system includes an understanding unit to understand the thoughts and desires of others. This understanding unit provides information to help the user understand the thoughts and desires of others. For example, it can display others' thoughts and desires as text. It can also explain others' thoughts and desires verbally. Furthermore, it can visually display others' thoughts and desires using graphs and charts. For example, it provides information such as others' opinions, hopes, and goals. The understanding unit estimates the user's emotions and adjusts how information is presented based on the estimated emotions. For example, if the user is relaxed, it explains others' thoughts and desires in a calm tone. If the user is excited, it explains others' thoughts and desires in an active tone. Furthermore, if the user is sad, it explains others' thoughts and desires in a comforting tone. This allows the dialogue system to enable the user to understand the thoughts and desires of others.

[0064] The learning unit can learn about individuals using AI. The learning unit learns about individuals using AI. The AI ​​learns using techniques such as neural networks and deep learning. The learning unit analyzes in detail the background information of individuals' actions and statements. For example, the learning unit analyzes in detail the historical events that lie behind individuals' actions. The learning unit also analyzes in detail the cultural factors that lie behind individuals' statements. Furthermore, the learning unit analyzes in detail the personal experiences that lie behind individuals' actions and statements. This improves the accuracy of learning about individuals by using AI. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or not using generative AI. For example, the learning unit can input background information about individuals' actions and statements into generative AI, which can then perform a detailed analysis.

[0065] The dialogue unit can engage in conversation based on information learned using AI. The AI ​​uses technologies such as neural networks and deep learning to conduct the conversation. The dialogue unit estimates the user's emotions and adjusts the tone and content of the conversation based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will engage in conversation in a calm tone. If the user is excited, the dialogue unit will engage in conversation in an active tone. Furthermore, if the user is sad, the dialogue unit will engage in conversation in a comforting tone. In this way, the accuracy of the conversation is improved by using AI. Some or all of the above processing in the dialogue unit may be performed using, for example, generative AI, or not using generative AI. For example, the dialogue unit can input learned information into generative AI, and the generative AI can adjust the tone and content of the conversation.

[0066] The dialogue system includes a selection unit that estimates the user's emotions and presents options for people the user may want to interact with based on the estimated emotions. For example, if the user is sad, the selection unit prioritizes presenting people who can offer encouragement or comfort. If the user is excited, the selection unit presents interesting historical figures. Furthermore, if the user is feeling lonely, the selection unit presents friendly ancestors. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the selection of an appropriate person based on the user's emotions. Some or all of the above-described processing in the selection unit may be performed using, for example, a generative AI, or not. For example, the selection unit can input the user's emotion data into a generative AI, which can then present options for people the user may want to interact with.

[0067] The dialogue system includes a selection unit that analyzes the user's past selection history and recommends the most suitable person. The selection unit analyzes the user's past selection history and recommends the most suitable person. For example, the selection unit prioritizes displaying people that the user has frequently selected in the past. Also, if the user is interested in a particular theme, the selection unit recommends people related to that theme. Furthermore, the selection unit predicts and recommends people that were selected during a specific time period based on the user's past selection history. This allows the system to recommend the most suitable person based on the user's past selection history. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the selection unit can input the user's past selection history data into a generative AI, and the generative AI can recommend the most suitable person.

[0068] The dialogue system includes a selection unit that filters results based on the user's current interests and circumstances when making a selection. For example, if the user is experiencing work-related problems, the selection unit will recommend someone with business expertise. If the user is interested in history, the selection unit will prioritize displaying historical figures. Furthermore, if the user is experiencing family problems, the selection unit will recommend someone related to family. This allows the user to select someone who matches their current interests and circumstances. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without one. For example, the selection unit can input data on the user's current interests and circumstances into a generative AI, which can then perform the filtering.

[0069] The dialogue system includes a selection unit that estimates the user's emotions and adjusts the display order of options based on the estimated emotions. The selection unit estimates the user's emotions and adjusts the display order of options based on the estimated emotions. For example, if the user is stressed, the selection unit will first display a person who can help them relax. If the user is excited, the selection unit will first display a person who can engage in a stimulating conversation. Furthermore, if the user is tired, the selection unit will first display a person who can provide comfort. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for the display of people in an appropriate order according to the user's emotions. Some or all of the above processing in the selection unit may be performed using a generative AI, for example, or without a generative AI. For example, the selection unit can input user emotion data into a generative AI, and the generative AI can adjust the display order of options.

[0070] The dialogue system includes a selection section that, when a selection is made, prioritizes displaying highly relevant individuals while considering the user's geographical location. For example, if the user is in a specific region, the selection section displays historical figures associated with that region. If the user is traveling, the selection section displays individuals associated with the travel destination. Furthermore, if the user is at home, the selection section prioritizes displaying family members or ancestors. This allows the system to display highly relevant individuals based on the user's geographical location. Some or all of the above processing in the selection section may be performed using, for example, a generative AI, or without one. For example, the selection section can input the user's geographical location data into a generative AI, which can then display highly relevant individuals.

[0071] The dialogue system includes a selection unit that analyzes the user's social media activity and recommends relevant individuals when a selection is made. The selection unit, for example, displays individuals the user frequently mentions on social media. Furthermore, if the user posts about a specific topic, the selection unit recommends individuals related to that topic. Additionally, the selection unit analyzes the user's social media friendships and displays relevant individuals. This allows for the recommendation of relevant individuals based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using, for example, generative AI, or without generative AI. For example, the selection unit can input the user's social media activity data into a generative AI, which can then recommend relevant individuals.

[0072] The dialogue system includes a learning unit that estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit prioritizes learning data of individuals who can engage in calm conversations. If the user is excited, the learning unit prioritizes learning data of individuals who can engage in stimulating conversations. Furthermore, if the user is sad, the learning unit prioritizes learning data of individuals who can provide comfort and encouragement. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for the selection of appropriate training data according to the user's emotions. Some or all of the above-described processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user emotion data into a generative AI, which can then select the training data.

[0073] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects an efficient learning algorithm based on previously learned data. Furthermore, the learning unit extracts specific patterns from past learning data and optimizes the learning algorithm. In addition, the learning unit analyzes past learning data and selects algorithms to improve learning accuracy. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI and use the generative AI to optimize the learning algorithm.

[0074] The learning unit can analyze in detail the background information of a person's actions and statements during the learning process. The learning unit analyzes in detail the background information of a person's actions and statements. For example, the learning unit can analyze in detail the historical events behind a person's actions. Furthermore, the learning unit can analyze in detail the cultural factors behind a person's statements. In addition, the learning unit can analyze in detail the personal experiences behind a person's actions and statements. This detailed analysis of the background information of a person's actions and statements improves the accuracy of learning. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input background information of a person's actions and statements into a generative AI, which can then perform a detailed analysis.

[0075] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will set a low learning frequency. If the user is excited, the learning unit will set a high learning frequency. Furthermore, if the user is sad, the learning unit will set a medium learning frequency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows learning to be performed at an appropriate frequency according to the user's emotions. Some or all of the above processing in the learning unit may be performed using a generative AI, or not. For example, the learning unit can input user emotion data into a generative AI, and the generative AI can adjust the learning frequency.

[0076] The learning unit can improve the accuracy of its learning by referring to relevant literature and materials about a person during the learning process. For example, the learning unit can learn by referring to historical documents about a person. It can also learn by referring to academic papers about a person. Furthermore, it can learn by referring to interviews and testimonies about a person. This improves the accuracy of learning by referring to relevant literature and materials. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input relevant literature and materials about a person into a generative AI, and the generative AI can improve the accuracy of its learning.

[0077] The learning unit can perform learning while considering the geographical background information of individuals. For example, the learning unit can learn while considering the geographical background of the region where the individual was born and raised. Furthermore, the learning unit can learn while considering the geographical background of the region where the individual has been active. In addition, the learning unit can learn while considering the geographical background of the region that has influenced the individual. This improves the accuracy of learning by considering geographical background information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the geographical background information of individuals into a generative AI and perform learning using the generative AI.

[0078] The dialogue system includes a dialogue unit that estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated emotions. The dialogue unit estimates the user's emotions and adjusts the tone and content of the dialogue based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will engage in dialogue in a calm tone. If the user is excited, the dialogue unit will engage in dialogue in an active tone. Furthermore, if the user is sad, the dialogue unit will engage in dialogue in a comforting tone. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for dialogue to be conducted in an appropriate tone and content according to the user's emotions. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the dialogue unit can input user emotion data into a generative AI, and the generative AI can adjust the tone and content of the dialogue.

[0079] The dialogue unit can generate optimal dialogue content by referring to the user's past dialogue history during a conversation. For example, the dialogue unit generates relevant dialogue content based on what the user has said in the past. The dialogue unit also selects topics that the user might be interested in from their past dialogue history. Furthermore, the dialogue unit analyzes the user's past dialogue history to generate optimal dialogue content. This allows the dialogue unit to generate optimal dialogue content based on past dialogue history. Some or all of the above processing in the dialogue unit may be performed using, for example, a generation AI, or without a generation AI. For example, the dialogue unit can input the user's past dialogue history data into a generation AI, which can then generate optimal dialogue content.

[0080] The dialogue unit can conduct dialogue based on background information of the characters' actions and statements. The dialogue unit can conduct dialogue based on historical events that are the background of the characters' actions. The dialogue unit can also conduct dialogue based on cultural factors that are the background of the characters' statements. Furthermore, the dialogue unit can conduct dialogue based on personal experiences that are the background of the characters' actions and statements. By conducting dialogue based on background information of the characters' actions and statements, the accuracy of the dialogue is improved. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the dialogue unit can input background information of the characters' actions and statements into a generative AI and conduct dialogue using the generative AI.

[0081] The dialogue system includes a dialogue unit that estimates the user's emotions and adjusts the length of the dialogue based on the estimated emotions. The dialogue unit estimates the user's emotions and adjusts the length of the dialogue based on the estimated emotions. For example, if the user is relaxed, the dialogue unit will have a longer dialogue. If the user is excited, the dialogue unit will have a shorter dialogue. Furthermore, if the user is sad, the dialogue unit will have a dialogue of a medium length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for dialogue of an appropriate length according to the user's emotions. Some or all of the above processing in the dialogue unit may be performed using a generative AI, for example, or without a generative AI. For example, the dialogue unit can input user emotion data into a generative AI, and the generative AI can adjust the length of the dialogue.

[0082] The dialogue system includes a dialogue unit that customizes the dialogue content based on the user's current situation and interests during a conversation. For example, if the user is experiencing work-related problems, the dialogue unit provides work-related dialogue content. Similarly, if the user is interested in history, it provides history-related dialogue content. Furthermore, if the user is experiencing family problems, it provides family-related dialogue content. This allows the system to provide dialogue content tailored to the user's current situation and interests. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without one. For instance, the dialogue unit can input the user's current situation and interest data into a generative AI, which can then customize the dialogue content.

[0083] The dialogue system includes a dialogue unit that analyzes the user's social media activity during a conversation and provides relevant dialogue content. The dialogue unit analyzes the user's social media activity during a conversation and provides relevant dialogue content. For example, the dialogue unit provides dialogue content based on topics that the user frequently mentions on social media. Furthermore, if the user posts about a specific topic, the dialogue unit provides dialogue content related to that topic. In addition, the dialogue unit analyzes the user's social media friendships and provides relevant dialogue content. This allows the system to provide relevant dialogue content based on the user's social media activity. Some or all of the above processing in the dialogue unit may be performed using, for example, a generative AI, or without a generative AI. For example, the dialogue unit can input the user's social media activity data into a generative AI, which can then provide relevant dialogue content.

[0084] The dialogue system includes a consultation unit that estimates the user's emotions and prioritizes the content of the consultation based on the estimated emotions. The consultation unit estimates the user's emotions and prioritizes the content of the consultation based on the estimated emotions. For example, if the user is feeling stressed, the consultation unit prioritizes consultations related to stress reduction. Also, if the user is excited, the consultation unit prioritizes consultations related to interest. Furthermore, if the user is sad, the consultation unit prioritizes consultations related to comfort and encouragement. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for the determination of consultation content with appropriate priorities according to the user's emotions. Some or all of the above processing in the consultation unit may be performed using a generative AI, for example, or without a generative AI. For example, the consultation unit can input the user's emotion data into a generative AI, and the generative AI can determine the priority of the consultation content.

[0085] The consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. The consultation unit can provide optimal advice by referring to the user's past consultation history during a consultation. For example, the consultation unit can provide relevant advice based on the content of past consultations the user has had. The consultation unit can also select advice that the user might be interested in from the user's past consultation history. Furthermore, the consultation unit can analyze the user's past consultation history and provide optimal advice. This allows the consultation unit to provide optimal advice based on past consultation history. Some or all of the above processes in the consultation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the consultation unit can input the user's past consultation history data into a generative AI, and the generative AI can provide optimal advice.

[0086] The dialogue system includes a consultation unit that estimates the user's emotions and adjusts the display method of the consultation content based on the estimated emotions. The consultation unit estimates the user's emotions and adjusts the display method of the consultation content based on the estimated emotions. For example, if the user is nervous, the consultation unit provides a simple and highly visible display method. If the user is relaxed, the consultation unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the consultation unit provides a display method that gets straight to the point. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the consultation content to be provided in an appropriate display method according to the user's emotions. Some or all of the above processing in the consultation unit may be performed using a generative AI, for example, or without a generative AI. For example, the consultation unit can input the user's emotion data into a generative AI, and the generative AI can adjust the display method of the consultation content.

[0087] The consultation unit can provide optimal advice by taking into account the user's geographical location information during a consultation. For example, if the user is in a specific region, the consultation unit will provide advice relevant to that region. If the user is traveling, the consultation unit will provide advice relevant to the travel destination. Furthermore, if the user is at home, the consultation unit will provide advice that can be taken at home. This allows the consultation unit to provide optimal advice based on the user's geographical location information. Some or all of the above processing in the consultation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the consultation unit can input the user's geographical location data into a generative AI, and the generative AI can provide optimal advice.

[0088] The dialogue system includes an understanding unit that estimates the user's emotions and adjusts how it understands the thoughts and desires of others based on the estimated emotions. The understanding unit estimates the user's emotions and adjusts how it understands the thoughts and desires of others based on the estimated emotions. For example, if the user is relaxed, the understanding unit will explain the thoughts and desires of others in a calm tone. If the user is excited, the understanding unit will explain the thoughts and desires of others in an active tone. Furthermore, if the user is sad, the understanding unit will explain the thoughts and desires of others in a comforting tone. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the system to understand the thoughts and desires of others in an appropriate way according to the user's emotions. Some or all of the above processing in the understanding unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the understanding unit can input user emotion data into a generating AI, which can then adjust how it understands other people's thoughts and desires.

[0089] The understanding unit can provide the optimal understanding method by referring to the user's past dialogue history when understanding. The understanding unit can provide the optimal understanding method by referring to the user's past dialogue history when understanding. For example, the understanding unit can provide relevant understanding methods based on what the user has said in the past. The understanding unit can also select understanding methods that are likely to be of interest to the user from the user's past dialogue history. Furthermore, the understanding unit can analyze the user's past dialogue history and provide the optimal understanding method. This allows the understanding unit to provide the optimal understanding method based on past dialogue history. Some or all of the above processing in the understanding unit may be performed using, for example, a generative AI, or without a generative AI. For example, the understanding unit can input the user's past dialogue history data into a generative AI, and the generative AI can provide the optimal understanding method.

[0090] The dialogue system includes an understanding unit that estimates the user's emotions and adjusts how others' thoughts and desires are displayed based on the estimated emotions. The understanding unit estimates the user's emotions and adjusts how others' thoughts and desires are displayed based on the estimated emotions. For example, if the user is nervous, the understanding unit provides a simple and highly visible display method. If the user is relaxed, the understanding unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the understanding unit provides a concise display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows others' thoughts and desires to be presented in an appropriate display method according to the user's emotions. Some or all of the above processing in the understanding unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the understanding unit can input the user's emotion data into a generative AI, which can then adjust how others' thoughts and desires are displayed.

[0091] The understanding unit can provide the optimal understanding method when understanding, taking into account the user's geographical location information. For example, if the user is in a specific region, the understanding unit provides an understanding method relevant to that region. Furthermore, if the user is traveling, the understanding unit provides an understanding method relevant to the travel destination. Additionally, if the user is at home, the understanding unit provides an understanding method that can be performed at home. This allows the understanding unit to provide the optimal understanding method based on the user's geographical location information. Some or all of the above processing in the understanding unit may be performed using, for example, a generative AI, or without a generative AI. For example, the understanding unit can input the user's geographical location data into a generative AI, which can then provide the optimal understanding method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the selection unit, learning unit, dialogue unit, consultation unit, understanding unit, and emotion estimation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, allowing the user to select a person they wish to interact with. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the AI ​​to learn information about the selected person. The dialogue unit is implemented by the control unit 46A of the smart device 14, allowing the AI ​​to engage in dialogue based on the learned information. The consultation unit is implemented by the control unit 46A of the smart device 14, allowing the user to input the content they wish to discuss, and the AI ​​to engage in dialogue based on that content. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12, providing information to understand the thoughts and desires of others. The emotion estimation unit is implemented by the specific processing unit 290 of the data processing unit 12, estimating the user's emotions and adjusting the tone and content of the dialogue based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements described above, including the selection unit, learning unit, dialogue unit, consultation unit, understanding unit, and emotion estimation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to select a person they wish to interact with. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, allowing the AI ​​to learn information about the selected person. The dialogue unit is implemented by the control unit 46A of the smart glasses 214, allowing the AI ​​to engage in dialogue based on the learned information. The consultation unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input what they wish to discuss, and the AI ​​to engage in dialogue based on that input. The understanding unit is implemented by the identification processing unit 290 of the data processing unit 12, providing information to understand the thoughts and desires of others. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing unit 12, estimating the user's emotions and adjusting the tone and content of the dialogue based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements described above, including the selection unit, learning unit, dialogue unit, consultation unit, understanding unit, and emotion estimation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to select a person they wish to interact with. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, allowing the AI ​​to learn information about the selected person. The dialogue unit is implemented by the control unit 46A of the headset terminal 314, allowing the AI ​​to engage in dialogue based on the learned information. The consultation unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input what they wish to discuss, and the AI ​​to engage in dialogue based on that input. The understanding unit is implemented by the identification processing unit 290 of the data processing unit 12, providing information to understand the thoughts and desires of others. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing unit 12, estimating the user's emotions and adjusting the tone and content of the dialogue based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements described above, including the selection unit, learning unit, dialogue unit, consultation unit, understanding unit, and emotion estimation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, allowing the user to select a person they wish to interact with. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the AI ​​to learn information about the selected person. The dialogue unit is implemented by the control unit 46A of the robot 414, allowing the AI ​​to engage in dialogue based on the learned information. The consultation unit is implemented by the control unit 46A of the robot 414, allowing the user to input what they wish to discuss, and the AI ​​to engage in dialogue based on that input. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12, providing information to understand the thoughts and desires of others. The emotion estimation unit is implemented by the specific processing unit 290 of the data processing unit 12, estimating the user's emotions and adjusting the tone and content of the dialogue based on the estimated emotions.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The dialogue system may also include a trend analysis unit that analyzes the user's selection history and learns the tendencies of the people the user has chosen in the past. For example, the trend analysis unit analyzes the categories and characteristics of people the user has chosen in the past and reflects those tendencies in the next selection. The trend analysis unit also learns the patterns of people the user has chosen at specific times and recommends people suitable for those times in the next selection. Furthermore, the trend analysis unit learns from the user's selection history who was chosen in specific emotional states and recommends people suitable for those emotional states in the next selection. This enables more personalized person selection based on the user's selection history.

[0094] The dialogue system can also include a resource provision section that automatically searches for and provides relevant literature and materials based on the user's inquiry. For example, if a user seeks technical advice, the resource provision section will provide the latest research papers and patent information related to that technology. If a user seeks psychological advice, the resource provision section will recommend reliable articles and books on psychology. Furthermore, if a user seeks business advice, the resource provision section will provide case studies on business strategies and market analysis reports. This allows the user to obtain useful information related to their inquiry.

[0095] A dialogue system can also include a behavioral analysis unit that analyzes the behavioral patterns and past statements of others in order for the user to understand the thoughts and desires of others. For example, the behavioral analysis unit analyzes the past behavioral history of others and estimates the intentions and purposes behind those actions. It also analyzes the past statements of others and estimates the emotions and values ​​behind those statements. Furthermore, the behavioral analysis unit visually displays the patterns of others' behavior and statements, providing them in a way that is easy for the user to understand. This allows the user to gain a deeper understanding of the background of others' behavior and statements.

[0096] The dialogue system can also include a multimedia learning unit within its learning section that incorporates relevant video and audio data in addition to learning about individuals. For example, the multimedia learning unit could incorporate documentary videos and interview audio about historical figures as learning data. It could also incorporate lecture videos and technical explanation videos about inventors. Furthermore, it could incorporate family video messages and audio recordings about ancestors as learning data. This allows the learning unit to learn from diverse data, including not only text information but also video and audio, enabling more realistic dialogue.

[0097] A dialogue system can also include a style adaptation unit within its dialogue section that customizes the way the dialogue progresses based on the user's dialogue style and preferences. For example, if the user prefers short dialogues, the style adaptation unit will provide concise dialogue that gets straight to the point. If the user prefers detailed explanations, the style adaptation unit will provide more in-depth information. Furthermore, if the user enjoys humor, the style adaptation unit will incorporate humorous expressions into the dialogue. This enables flexible dialogue that is tailored to the user's dialogue style and preferences.

[0098] The dialogue system may also include a timing adjustment unit that estimates the user's emotions and adjusts the timing of the dialogue based on those emotions. For example, if the user is relaxed, the timing adjustment unit will widen the intervals between dialogues and conduct the conversation at a slower pace. If the user is excited, the timing adjustment unit will narrow the intervals between dialogues and conduct the conversation at a faster pace. Furthermore, if the user is sad, the timing adjustment unit will conduct the dialogue at appropriate intervals, showing empathy for the user's emotions. This allows the system to conduct dialogue at the appropriate timing according to the user's emotions.

[0099] The dialogue system may also include an emotional history analysis unit that analyzes the user's past selection history and learns the tendencies of the people the user has chosen in specific emotional states. For example, the emotional history analysis unit learns the tendencies of the people the user has chosen when feeling stressed and reflects that tendency in the next selection. It also learns the tendencies of the people the user has chosen when feeling relaxed and reflects that tendency in the next selection. Furthermore, it learns the tendencies of the people the user has chosen when feeling excited and reflects that tendency in the next selection. This allows the system to select an appropriate person according to the user's emotional state.

[0100] A dialogue system can also include a real-time update unit that updates the dialogue content in real time based on the user's current interests and circumstances. For example, if the user develops a new interest, the real-time update unit incorporates the latest information related to that interest into the dialogue. Furthermore, if there is a change in the user's current situation, the real-time update unit provides dialogue content tailored to that situation. Additionally, if the user is participating in a specific event, the real-time update unit incorporates information related to that event into the dialogue. This allows the system to provide up-to-date dialogue content that is relevant to the user's interests and circumstances.

[0101] The dialogue system may also include an emotion adaptation unit that estimates the user's emotions and adjusts the content of the dialogue based on those emotions. For example, if the user is relaxed, the emotion adaptation unit will provide calm dialogue. If the user is excited, it will provide lively dialogue. Furthermore, if the user is sad, it will provide comforting dialogue. This allows for dialogue with content appropriate to the user's emotions.

[0102] A dialogue system can also include a geographically adaptive unit that customizes the dialogue content based on the user's geographical location. For example, if the user is in a specific region, the geographically adaptive unit will provide dialogue content related to the history and culture of that region. If the user is traveling, the geographically adaptive unit will provide dialogue content related to tourist information and local customs of the travel destination. Furthermore, if the user is at home, the geographically adaptive unit will provide dialogue content related to events around their home and local news. This allows for the provision of more relevant dialogue content based on the user's geographical location.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The selection section allows the user to choose the person they want to interact with. The selection section allows the user to select the person they want to interact with from a list. The user can also enter the name of the person they want to interact with. Furthermore, the user can select the category of the person they want to interact with. For example, categories such as historical figures, inventors, and ancestors are provided. Step 2: The learning unit learns the person information selected by the selection unit. The learning unit uses AI to learn the person information and analyzes in detail the background information of the person's actions and statements. For example, it analyzes in detail historical events, cultural factors, personal experiences, etc. Step 3: The dialogue unit engages in conversation based on the information learned by the learning unit. The dialogue unit uses AI to conduct conversations based on the learned information, estimates the user's emotions, and adjusts the tone and content of the conversation based on the estimated emotions. For example, if the user is relaxed, the dialogue will be conducted in a calm tone; if excited, in an energetic tone; and if sad, in a comforting tone.

[0105] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0108] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection section where the user chooses the person they want to talk to, A learning unit that learns the information of the person selected by the selection unit, A dialogue unit that engages in dialogue based on the information learned by the aforementioned learning unit, Equipped with A system characterized by the following features.

2. It features a consultation section where you input the content you want to discuss, and an AI engages in a dialogue based on that content. The system according to feature 1.

3. It is equipped with an understanding component for comprehending the thoughts and desires of others. The system according to feature 1.

4. The aforementioned learning unit, Learn about people using AI The system according to feature 1.

5. The aforementioned dialogue unit, The AI ​​will engage in dialogue based on the information it has learned. The system according to feature 1.

6. The aforementioned selection unit is It estimates the user's emotions and, based on those emotions, presents options for who the user might want to interact with. The system according to feature 1.

7. The aforementioned selection unit is Analyze the user's past selection history and recommend the most suitable person. The system according to feature 1.

8. The aforementioned selection unit is When selecting, filtering is performed based on the user's current interests and circumstances. The system according to feature 1.

9. The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of options based on the estimated user emotions. The system according to feature 1.

10. The aforementioned selection unit is When making a selection, the system prioritizes displaying highly relevant individuals, taking into account the user's geographical location. The system according to feature 1.

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