system

Abstract

The system according to this embodiment aims to provide advice on contemporary problems by utilizing the wisdom of great figures from history. [Solution] The system according to this embodiment comprises a reception unit, a selection unit, and an advice unit. The reception unit receives inquiries from users. The selection unit analyzes the inquiries received by the reception unit and selects the most suitable historical figure. The advice unit has the historical figure AI selected by the selection unit provide advice.

Description

Technical Field

[0004]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

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 utilize the wisdom of great people in history to solve modern problems.

[0005] The system according to the embodiment aims to provide advice on modern problems by utilizing the wisdom of great people in history.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a selection unit, and an advice unit. The reception unit receives the consultation content from the user. The selection unit analyzes the consultation content received by the reception unit and selects the most suitable great person. The advice unit provides advice by the great person AI selected by the selection unit.

Effects of the Invention

[0007] The system according to this embodiment can provide advice on contemporary problems by utilizing the wisdom of great figures from history. [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 Great Figure AI Dialogue System according to an embodiment of the present invention is a new AI service that utilizes the wisdom of historical figures in the modern age. This Great Figure AI Dialogue System allows users to converse with a Great Figure AI via video chat, and is characterized by its ability to provide advice from the perspective of a great person on a wide range of issues, from business problems to everyday problems. For example, when stuck on a business strategy, one can consult with Oda Nobunaga, or when having relationship problems, one can ask Sakamoto Ryoma for advice, allowing users to select the most suitable Great Figure for the situation and engage in dialogue. Initially, the service will be launched for business people, and in the future, its scope of use will be expanded to students, teachers, and the general public. By utilizing the wisdom of history in the modern age, it will be a groundbreaking tool that provides new perspectives and concrete solutions. By borrowing the wisdom of great figures, users can take a step towards the future. Specifically, it consists of the following steps: First, the user inputs the consultation content via video chat. Next, the AI ​​analyzes the input consultation content and selects the most suitable Great Figure. The selected Great Figure AI provides advice based on the user's consultation content. For example, if the user seeks advice on business strategy, the Oda Nobunaga AI will provide strategic guidance, and if they have relationship problems, the Sakamoto Ryoma AI will offer appropriate advice. In this way, users can leverage the wisdom of historical figures to solve modern problems. This service is specifically designed for business professionals and is particularly beneficial for managers and leaders. For example, it can be used in various business scenarios such as formulating business strategies, developing new businesses, and talent development. Furthermore, in the future, by expanding its use to students, teachers, and the general public, it can also be used to help solve problems in education and daily life. The following technologies are used to realize this service. First, a behavior generation model for the historical figure AI is necessary, which generates the language model and behavior of the historical figure. Next, an interface is needed for the user and the historical figure AI to interact via video chat. In addition, a backend server and database are used to manage the user's consultation content and the historical figure AI's responses. By combining these technologies, a new AI service that applies the wisdom of historical figures to the modern age is realized. As a result, the historical figure AI dialogue system can solve modern problems by providing advice from the most suitable historical figure AI based on the user's consultation content.

[0029] The AI ​​dialogue system for great historical figures according to this embodiment comprises a reception unit, a selection unit, and an advice unit. The reception unit receives inquiries from users. For example, the user can input their inquiry via video chat into the reception unit. The reception unit can also convert the user's input into a format that is easy to analyze. The selection unit analyzes the inquiry received by the reception unit and selects the most suitable great historical figure. For example, the selection unit applies criteria for selecting great historical figures based on the user's inquiry and selects the most suitable great historical figure. The selection unit can use AI to match the user's inquiry with the knowledge and experience of great historical figures. The advice unit has the great historical figure AI selected by the selection unit provide advice. For example, the advice unit has the selected great historical figure AI provide specific advice based on the user's inquiry. The advice unit can use AI to generate advice based on the knowledge and experience of great historical figures. As a result, the AI ​​dialogue system for great historical figures can solve modern problems by having the most suitable great historical figure AI provide advice based on the user's inquiry.

[0030] The reception desk receives inquiries from users. For example, users can input their inquiries via video chat. Specifically, users can start a video chat using a dedicated application or website via a PC or smartphone. The video chat interface is intuitive and easy to use, designed to allow users to easily input their inquiries. Furthermore, the reception desk can convert user input into a format that is easy to analyze. For example, speech recognition technology is used to convert the user's voice into text, and natural language processing technology is used to format the text in an easily analyzable format. This ensures that the user's inquiries are accurately analyzed and can smoothly proceed to the next processing step. The reception desk also implements security measures such as data encryption and access control to protect user privacy. This allows users to input their inquiries with confidence. Additionally, the reception desk can refer to a user's past inquiry history to provide ongoing support. For example, it can provide more appropriate responses based on previous inquiries and advice. This allows the reception desk to provide flexible and effective support tailored to the user's needs.

[0031] The selection department analyzes the consultation content received by the reception department and selects the most suitable expert. For example, the selection department applies the criteria for selecting an expert based on the user's consultation content to select the most suitable expert. Specifically, the selection department uses AI to analyze the user's consultation content in detail and extract categories and keywords related to the consultation content. For example, if the consultation is about business, an expert with knowledge of management and marketing will be selected. The selection department has a database of the experts' knowledge and experience, and can match the user's consultation content with the experts' knowledge and experience. The AI ​​uses natural language processing technology to understand the user's consultation content and select the appropriate expert. For example, if a user consults, "I have a new business idea, but I don't know how to proceed," the selection department will select an expert with expertise in business strategy. The selection department can also select a more appropriate expert by considering the user's past consultation history and individual needs. This allows the selection department to quickly and accurately select the expert best suited to the user's consultation content and move on to the next advice step. Furthermore, the selection unit includes a function to explain the selection criteria and process to users in order to ensure transparency in the selection process. This allows users to have confidence in the selection process.

[0032] The advisory department provides advice from AI-generated historical figures selected by the selection department. For example, the advisory department provides specific advice based on the user's consultation content. Specifically, the advisory department generates advice based on the knowledge and experience of historical figures using AI. For example, when a selected historical figure AI provides advice on business strategy, it will propose specific action plans and risk management methods based on past success and failure cases. The advisory department can customize the format and content of the advice according to the user's consultation content. For example, if the user requests a detailed explanation, the advisory department will provide visually easy-to-understand advice using charts and graphs. Also, if the user requests a specific action plan in a short time, the advisory department will provide concise and practical advice. Furthermore, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by reporting the results of implementing the advice, the advisory department can analyze the results and reflect them in future advice. In this way, the advisory department can always provide users with the latest and most optimal advice and provide powerful support for solving modern problems. Furthermore, the advisory section includes features to securely manage advice and feedback data in order to protect user privacy. This allows users to receive advice with peace of mind.

[0033] The system includes a generation unit that generates an AI model for generating the actions of great historical figures. The generation unit generates an AI model for generating the actions of great historical figures. The generation unit uses algorithms, for example, to generate language models and actions of great historical figures. The generation unit can use AI to generate language models and actions of great historical figures. For example, the generation unit can train a large amount of text data to generate a language model of a great historical figure. The generation unit can also train action data to generate actions of great historical figures. By generating an AI model for generating the actions of great historical figures, the system can reproduce the language models and actions of those figures.

[0034] The system includes an interface unit that provides a video chat interface. The interface unit provides a video chat interface. For example, the interface unit provides a video chat interface for a user to interact with a Great Figure AI. The interface unit allows the user to interact with the Great Figure AI through the video chat interface. For example, the interface unit allows the user to input their questions to the Great Figure AI through the video chat interface, and the Great Figure AI provides advice. This enables the user and the Great Figure AI to interact through video chat.

[0035] The system includes a management unit that manages backend servers and databases. The management unit manages the backend servers and databases. For example, the management unit manages backend servers and databases for managing user inquiries and responses from the Great Figure AI. The management unit stores user inquiries and Great Figure AI responses in the database and makes them accessible as needed. For example, the management unit stores user inquiries in the database and manages Great Figure AI responses. This allows for the management of user inquiries and Great Figure AI responses.

[0036] The reception department can analyze the user's past consultation history and select the most suitable reception method. For example, the reception department may prioritize receiving inquiries about topics the user has frequently consulted about in the past. The reception department can also select a method for receiving inquiries during specific time slots based on the user's past consultation history. The reception department can also suggest the most suitable reception method based on the user's past consultation history. This allows the reception department to select the most suitable reception method based on the user's past consultation history. Some or all of the above processing in the reception department may be performed using AI, or not.

[0037] The reception desk can filter inquiries based on the user's current situation and areas of interest when receiving them. For example, the reception desk can prioritize inquiries related to the problems the user is currently facing. The reception desk can also filter appropriate inquiries based on the user's areas of interest. The reception desk can also accept the most suitable inquiries considering the user's current situation. This allows the reception desk to accept appropriate inquiries based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not.

[0038] The reception desk can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location when receiving inquiries. For example, if a user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. The reception desk can also filter the most relevant inquiries based on the user's current location. The reception desk can also prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not.

[0039] The reception desk can analyze the user's social media activity when receiving a consultation request and accept relevant consultations. For example, the reception desk can analyze the user's current interests from their social media activity and prioritize accepting relevant consultations. The reception desk can also filter the most relevant consultations based on the user's social media posts. The reception desk can also prioritize accepting highly relevant consultations, taking into account the user's social media activity. This allows the reception desk to accept relevant consultations based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0040] The selection unit can select a great figure based on the importance of the consultation topic. For example, in the case of a consultation regarding an important business strategy, the selection unit may select a great figure skilled in strategy. In the case of a consultation regarding relationship problems, the selection unit may select a great figure with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection unit may select a great figure with broad knowledge. This allows for the selection of the most suitable great figure based on the importance of the consultation topic. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0041] The selection unit can apply different selection algorithms depending on the category of the consultation content during the selection process. For example, in the case of a consultation regarding business strategy, the selection unit can apply an algorithm that selects a great person with strategic thinking. In the case of a consultation regarding relationship problems, the selection unit can also apply an algorithm that selects a great person with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection unit can also apply an algorithm that selects a great person with broad knowledge. This allows for the selection of the most suitable great person according to the category of the consultation content. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0042] The selection department can select a great figure based on the timing of the submission of the consultation request. For example, in the case of a consultation regarding an important business strategy, the selection department may select a great figure skilled in strategy. In the case of a consultation regarding relationship problems, the selection department may also select a great figure with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection department may also select a great figure with broad knowledge. This allows the selection of the most suitable great figure based on the timing of the submission of the consultation request. Some or all of the above-described processes in the selection department may be performed using AI or not.

[0043] The selection unit can adjust the selection of a great figure based on the relevance of the consultation content during the selection process. For example, in the case of a consultation regarding business strategy, the selection unit may select a great figure with strategic thinking. In the case of a consultation regarding relationship problems, the selection unit may also select a great figure with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection unit may also select a great figure with broad knowledge. This allows for the selection of the most suitable great figure based on the relevance of the consultation content. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0044] The advisory department can adjust the level of detail of the advice provided based on the importance of the consultation. For example, in the case of a consultation regarding an important business strategy, the advisory department will provide detailed advice. In the case of a consultation regarding relationship problems, the advisory department may also provide specific advice. In the case of a consultation regarding everyday problems, the advisory department may also provide concise advice. This allows the advisory department to provide advice with an appropriate level of detail based on the importance of the consultation. Some or all of the above processes in the advisory department may be performed using AI or not.

[0045] The advisory department can apply different advisory algorithms depending on the category of the consultation when providing advice. For example, in the case of a consultation regarding business strategy, the advisory department can apply an advisory algorithm of a great person with strategic thinking. In the case of a consultation regarding relationship problems, the advisory department can also apply an advisory algorithm of a great person with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the advisory department can also apply an advisory algorithm of a great person with broad knowledge. This allows the advisory department to apply the most suitable advisory algorithm depending on the category of the consultation. Some or all of the above processing in the advisory department may be performed using AI or not.

[0046] The advisory department can prioritize advice based on when the consultation content is submitted. For example, the advisory department will prioritize advice on important business strategies. The advisory department can also provide advice quickly on relationship problems. The advisory department can also provide advice with normal priority on everyday problems. This allows for the provision of advice with appropriate priority based on when the consultation content is submitted. Some or all of the above processes in the advisory department may be performed using AI or not.

[0047] The advisory department can adjust the order of advice based on the relevance of the consultation content when providing advice. For example, in the case of a consultation regarding business strategy, the advisory department may prioritize providing advice from great figures with strategic thinking. In the case of a consultation regarding relationship problems, the advisory department may also prioritize providing advice from great figures with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the advisory department may also prioritize providing advice from great figures with broad knowledge. This allows the advisory department to provide advice in an appropriate order based on the relevance of the consultation content. Some or all of the above processing in the advisory department may be performed using AI or not.

[0048] The generation unit can select the optimal generation method by referring to past data when generating language models and behaviors. For example, the generation unit can generate an optimal language model based on past user consultations and responses. The generation unit can also generate natural behaviors by referring to past behavior data of great AI figures. The generation unit can also analyze past data and generate language models and behaviors tailored to the user's preferences. This makes it possible to generate optimal language models and behaviors based on past data. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0049] The generation unit can consider the attribute information of great figures when generating language models and actions. For example, the generation unit can generate strategic language models and actions based on the attribute information of Oda Nobunaga. The generation unit can also generate language models and actions with excellent interpersonal skills based on the attribute information of Sakamoto Ryoma. The generation unit can also generate appropriate language models and actions by considering the attribute information of great figures. This makes it possible to generate appropriate language models and actions based on the attribute information of great figures. Some or all of the above processing in the generation unit may be performed using AI or not.

[0050] The generation unit can consider the geographical background of historical figures when generating language models and actions. For example, the generation unit can generate language models and actions related to Japan during the Sengoku period based on the geographical background of Oda Nobunaga. The generation unit can also generate language models and actions related to Japan during the Bakumatsu period based on the geographical background of Sakamoto Ryoma. The generation unit can also generate appropriate language models and actions by considering the geographical background of historical figures. This allows for the generation of appropriate language models and actions based on the geographical background of historical figures. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0051] The generation unit can improve the accuracy of generation by referring to relevant literature when generating language models and actions. For example, the generation unit can refer to literature on Oda Nobunaga to generate strategic language models and actions. The generation unit can also refer to literature on Sakamoto Ryoma to generate language models and actions with excellent interpersonal skills. The generation unit can also refer to relevant literature on great figures to generate appropriate language models and actions. This allows for improved accuracy in generating language models and actions based on relevant literature. Some or all of the above processing in the generation unit may be performed using AI or not.

[0052] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit provides the optimal display method based on the interface settings the user has used in the past. The interface unit can also provide an interface tailored to the user's preferences based on the user's past operation history. The interface unit can also analyze the user's past operation history and select the optimal display method. This allows the interface unit to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, or it may be performed without using AI.

[0053] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. If the user is using a tablet, the interface unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the interface unit can also provide a concise and highly visible display method. This allows the interface unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the interface unit may be performed using AI, or it may be performed without using AI.

[0054] The management department can optimize its management algorithms by referring to historical data during management. For example, the management department can select the optimal data management algorithm based on historical data. The management department can also analyze historical data and propose efficient data management methods. The management department can also optimize its management algorithms by referring to historical data. This allows it to apply the optimal management algorithm based on historical data. Some or all of the above processes in the management department may be performed using AI or not.

[0055] The management department can weight management data based on when the consultation content was submitted. For example, the management department might assign a higher weight to consultations concerning important business strategies. It can also assign an appropriate weight to consultations concerning interpersonal relationship problems. It can also assign a normal weight to consultations concerning everyday problems. This allows the data to be managed with appropriate weighting based on when the consultation content was submitted. Some or all of the above processing in the management department may be performed using AI or not.

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

[0057] The AI ​​dialogue system for historical figures may further include a selection unit that analyzes the user's past consultation history and selects the most suitable historical figure. The selection unit may, for example, select a relevant historical figure based on the topics the user has frequently consulted about in the past. It may also prioritize the selection of a specific historical figure based on the user's past consultation history. It may also suggest the most suitable historical figure based on the user's past consultation history. In this way, the most suitable historical figure can be selected based on the user's past consultation history. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0058] The great figure AI dialogue system may further include a reception unit that filters based on the user's current situation and areas of interest. The reception unit, for example, prioritizes receiving consultations related to the problems the user is currently facing. It can also filter appropriate consultations based on the user's areas of interest. It can also receive the most suitable consultations considering the user's current situation. This ensures that appropriate consultations are received based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0059] The Great Figure AI Dialogue System may further include a reception unit that prioritizes receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving inquiries related to that region. It can also filter the most relevant inquiries based on the user's current location. It can also prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not.

[0060] The Great Figure AI Dialogue System may further include a reception desk that analyzes the user's social media activity and receives relevant inquiries. The reception desk, for example, analyzes the user's current interests from their social media activity and prioritizes receiving relevant inquiries. It can also filter the most appropriate inquiries based on the user's social media posts. It can also prioritize receiving inquiries with high relevance, taking into account the user's social media activity. This allows the system to receive relevant inquiries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0061] The great person AI dialogue system may further include an interface unit that selects the optimal display method by referring to the user's past operation history. The interface unit may, for example, provide the optimal display method based on the interface settings the user has used in the past. It may also provide an interface tailored to the user's preferences based on the user's past operation history. It may also analyze the user's past operation history and select the optimal display method. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI or not using AI.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives inquiries from users. For example, users can input their inquiries via video chat. The reception desk can also convert the user's input into a format that is easy to analyze. Step 2: The selection unit analyzes the consultation content received by the reception unit and selects the most suitable historical figure. For example, it applies the criteria for selecting a historical figure based on the user's consultation content to select the most suitable historical figure. The selection unit can use AI to match the user's consultation content with the knowledge and experience of a historical figure. Step 3: The advice unit provides advice from the great historical figure AI selected by the selection unit. For example, the selected great historical figure AI provides specific advice based on the user's consultation content. The advice unit can use AI to generate advice based on the knowledge and experience of great historical figures.

[0064] (Example of form 2) The Great Figure AI Dialogue System according to an embodiment of the present invention is a new AI service that utilizes the wisdom of historical figures in the modern age. This Great Figure AI Dialogue System allows users to converse with a Great Figure AI via video chat, and is characterized by its ability to provide advice from the perspective of a great person on a wide range of issues, from business problems to everyday problems. For example, when stuck on a business strategy, one can consult with Oda Nobunaga, or when having relationship problems, one can ask Sakamoto Ryoma for advice, allowing users to select the most suitable Great Figure for the situation and engage in dialogue. Initially, the service will be launched for business people, and in the future, its scope of use will be expanded to students, teachers, and the general public. By utilizing the wisdom of history in the modern age, it will be a groundbreaking tool that provides new perspectives and concrete solutions. By borrowing the wisdom of great figures, users can take a step towards the future. Specifically, it consists of the following steps: First, the user inputs the consultation content via video chat. Next, the AI ​​analyzes the input consultation content and selects the most suitable Great Figure. The selected Great Figure AI provides advice based on the user's consultation content. For example, if the user seeks advice on business strategy, the Oda Nobunaga AI will provide strategic guidance, and if they have relationship problems, the Sakamoto Ryoma AI will offer appropriate advice. In this way, users can leverage the wisdom of historical figures to solve modern problems. This service is specifically designed for business professionals and is particularly beneficial for managers and leaders. For example, it can be used in various business scenarios such as formulating business strategies, developing new businesses, and talent development. Furthermore, in the future, by expanding its use to students, teachers, and the general public, it can also be used to help solve problems in education and daily life. The following technologies are used to realize this service. First, a behavior generation model for the historical figure AI is necessary, which generates the language model and behavior of the historical figure. Next, an interface is needed for the user and the historical figure AI to interact via video chat. In addition, a backend server and database are used to manage the user's consultation content and the historical figure AI's responses. By combining these technologies, a new AI service that applies the wisdom of historical figures to the modern age is realized. As a result, the historical figure AI dialogue system can solve modern problems by providing advice from the most suitable historical figure AI based on the user's consultation content.

[0065] The AI ​​dialogue system for great historical figures according to this embodiment comprises a reception unit, a selection unit, and an advice unit. The reception unit receives inquiries from users. For example, the user can input their inquiry via video chat into the reception unit. The reception unit can also convert the user's input into a format that is easy to analyze. The selection unit analyzes the inquiry received by the reception unit and selects the most suitable great historical figure. For example, the selection unit applies criteria for selecting great historical figures based on the user's inquiry and selects the most suitable great historical figure. The selection unit can use AI to match the user's inquiry with the knowledge and experience of great historical figures. The advice unit has the great historical figure AI selected by the selection unit provide advice. For example, the advice unit has the selected great historical figure AI provide specific advice based on the user's inquiry. The advice unit can use AI to generate advice based on the knowledge and experience of great historical figures. As a result, the AI ​​dialogue system for great historical figures can solve modern problems by having the most suitable great historical figure AI provide advice based on the user's inquiry.

[0066] The reception desk receives inquiries from users. For example, users can input their inquiries via video chat. Specifically, users can start a video chat using a dedicated application or website via a PC or smartphone. The video chat interface is intuitive and easy to use, designed to allow users to easily input their inquiries. Furthermore, the reception desk can convert user input into a format that is easy to analyze. For example, speech recognition technology is used to convert the user's voice into text, and natural language processing technology is used to format the text in an easily analyzable format. This ensures that the user's inquiries are accurately analyzed and can smoothly proceed to the next processing step. The reception desk also implements security measures such as data encryption and access control to protect user privacy. This allows users to input their inquiries with confidence. Additionally, the reception desk can refer to a user's past inquiry history to provide ongoing support. For example, it can provide more appropriate responses based on previous inquiries and advice. This allows the reception desk to provide flexible and effective support tailored to the user's needs.

[0067] The selection department analyzes the consultation content received by the reception department and selects the most suitable expert. For example, the selection department applies the criteria for selecting an expert based on the user's consultation content to select the most suitable expert. Specifically, the selection department uses AI to analyze the user's consultation content in detail and extract categories and keywords related to the consultation content. For example, if the consultation is about business, an expert with knowledge of management and marketing will be selected. The selection department has a database of the experts' knowledge and experience, and can match the user's consultation content with the experts' knowledge and experience. The AI ​​uses natural language processing technology to understand the user's consultation content and select the appropriate expert. For example, if a user consults, "I have a new business idea, but I don't know how to proceed," the selection department will select an expert with expertise in business strategy. The selection department can also select a more appropriate expert by considering the user's past consultation history and individual needs. This allows the selection department to quickly and accurately select the expert best suited to the user's consultation content and move on to the next advice step. Furthermore, the selection unit includes a function to explain the selection criteria and process to users in order to ensure transparency in the selection process. This allows users to have confidence in the selection process.

[0068] The advisory department provides advice from AI-generated historical figures selected by the selection department. For example, the advisory department provides specific advice based on the user's consultation content. Specifically, the advisory department generates advice based on the knowledge and experience of historical figures using AI. For example, when a selected historical figure AI provides advice on business strategy, it will propose specific action plans and risk management methods based on past success and failure cases. The advisory department can customize the format and content of the advice according to the user's consultation content. For example, if the user requests a detailed explanation, the advisory department will provide visually easy-to-understand advice using charts and graphs. Also, if the user requests a specific action plan in a short time, the advisory department will provide concise and practical advice. Furthermore, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, by reporting the results of implementing the advice, the advisory department can analyze the results and reflect them in future advice. In this way, the advisory department can always provide users with the latest and most optimal advice and provide powerful support for solving modern problems. Furthermore, the advisory section includes features to securely manage advice and feedback data in order to protect user privacy. This allows users to receive advice with peace of mind.

[0069] The system includes a generation unit that generates an AI model for generating the actions of great historical figures. The generation unit generates an AI model for generating the actions of great historical figures. The generation unit uses algorithms, for example, to generate language models and actions of great historical figures. The generation unit can use AI to generate language models and actions of great historical figures. For example, the generation unit can train a large amount of text data to generate a language model of a great historical figure. The generation unit can also train action data to generate actions of great historical figures. By generating an AI model for generating the actions of great historical figures, the system can reproduce the language models and actions of those figures.

[0070] The system includes an interface unit that provides a video chat interface. The interface unit provides a video chat interface. For example, the interface unit provides a video chat interface for a user to interact with a Great Figure AI. The interface unit allows the user to interact with the Great Figure AI through the video chat interface. For example, the interface unit allows the user to input their questions to the Great Figure AI through the video chat interface, and the Great Figure AI provides advice. This enables the user and the Great Figure AI to interact through video chat.

[0071] The system includes a management unit that manages backend servers and databases. The management unit manages the backend servers and databases. For example, the management unit manages backend servers and databases for managing user inquiries and responses from the Great Figure AI. The management unit stores user inquiries and Great Figure AI responses in the database and makes them accessible as needed. For example, the management unit stores user inquiries in the database and manages Great Figure AI responses. This allows for the management of user inquiries and Great Figure AI responses.

[0072] The reception desk can estimate the user's emotions and adjust the timing of receiving the consultation based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can immediately accept the consultation. If the user is relaxed, the reception desk can also accept the consultation at an appropriate time. If the user is in a hurry, the reception desk can also prioritize accepting the consultation. This allows for consultations to be received at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The reception department can analyze the user's past consultation history and select the most suitable reception method. For example, the reception department may prioritize receiving inquiries about topics the user has frequently consulted about in the past. The reception department can also select a method for receiving inquiries during specific time slots based on the user's past consultation history. The reception department can also suggest the most suitable reception method based on the user's past consultation history. This allows the reception department to select the most suitable reception method based on the user's past consultation history. Some or all of the above processing in the reception department may be performed using AI, or not.

[0074] The reception desk can filter inquiries based on the user's current situation and areas of interest when receiving them. For example, the reception desk can prioritize inquiries related to the problems the user is currently facing. The reception desk can also filter appropriate inquiries based on the user's areas of interest. The reception desk can also accept the most suitable inquiries considering the user's current situation. This allows the reception desk to accept appropriate inquiries based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not.

[0075] The reception desk can estimate the user's emotions and determine the priority of the consultation content to be received based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prioritize urgent consultations. If the user is relaxed, the reception desk can also accept consultations with normal priority. If the user is in a hurry, the reception desk can also prioritize important consultations. This allows the reception desk to determine the priority of consultation content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception desk can prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location when receiving inquiries. For example, if a user is in a specific region, the reception desk will prioritize receiving inquiries related to that region. The reception desk can also filter the most relevant inquiries based on the user's current location. The reception desk can also prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not.

[0077] The reception desk can analyze the user's social media activity when receiving a consultation request and accept relevant consultations. For example, the reception desk can analyze the user's current interests from their social media activity and prioritize accepting relevant consultations. The reception desk can also filter the most relevant consultations based on the user's social media posts. The reception desk can also prioritize accepting highly relevant consultations, taking into account the user's social media activity. This allows the reception desk to accept relevant consultations based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0078] The selection unit can estimate the user's emotions and adjust the selection criteria for the most suitable great figure based on the estimated emotions. For example, if the user is stressed, the selection unit will select a great figure that promotes relaxation. If the user is relaxed, the selection unit may also select a great figure that offers deep insights. If the user is in a hurry, the selection unit may also select a great figure that offers quick advice. This allows for the selection of the most suitable great figure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The selection unit can select a great figure based on the importance of the consultation topic. For example, in the case of a consultation regarding an important business strategy, the selection unit may select a great figure skilled in strategy. In the case of a consultation regarding relationship problems, the selection unit may select a great figure with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection unit may select a great figure with broad knowledge. This allows for the selection of the most suitable great figure based on the importance of the consultation topic. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0080] The selection unit can apply different selection algorithms depending on the category of the consultation content during the selection process. For example, in the case of a consultation regarding business strategy, the selection unit can apply an algorithm that selects a great person with strategic thinking. In the case of a consultation regarding relationship problems, the selection unit can also apply an algorithm that selects a great person with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection unit can also apply an algorithm that selects a great person with broad knowledge. This allows for the selection of the most suitable great person according to the category of the consultation content. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0081] The selection unit can estimate the user's emotions and determine the priority of the great figures to select based on the estimated emotions. For example, if the user is stressed, the selection unit may prioritize selecting a great figure that promotes relaxation. If the user is relaxed, the selection unit may also prioritize selecting a great figure that offers deep insights. If the user is in a hurry, the selection unit may also prioritize selecting a great figure that offers quick advice. This allows the priority of great figures to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The selection department can select a great figure based on the timing of the submission of the consultation request. For example, in the case of a consultation regarding an important business strategy, the selection department may select a great figure skilled in strategy. In the case of a consultation regarding relationship problems, the selection department may also select a great figure with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection department may also select a great figure with broad knowledge. This allows the selection of the most suitable great figure based on the timing of the submission of the consultation request. Some or all of the above-described processes in the selection department may be performed using AI or not.

[0083] The selection unit can adjust the selection of a great figure based on the relevance of the consultation content during the selection process. For example, in the case of a consultation regarding business strategy, the selection unit may select a great figure with strategic thinking. In the case of a consultation regarding relationship problems, the selection unit may also select a great figure with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the selection unit may also select a great figure with broad knowledge. This allows for the selection of the most suitable great figure based on the relevance of the consultation content. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0084] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit will provide advice in gentle language. If the user is relaxed, the advice unit can also provide detailed advice. If the user is in a hurry, the advice unit can provide concise advice. This allows the advice unit to provide advice in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The advisory department can adjust the level of detail of the advice provided based on the importance of the consultation. For example, in the case of a consultation regarding an important business strategy, the advisory department will provide detailed advice. In the case of a consultation regarding relationship problems, the advisory department may also provide specific advice. In the case of a consultation regarding everyday problems, the advisory department may also provide concise advice. This allows the advisory department to provide advice with an appropriate level of detail based on the importance of the consultation. Some or all of the above processes in the advisory department may be performed using AI or not.

[0086] The advisory department can apply different advisory algorithms depending on the category of the consultation when providing advice. For example, in the case of a consultation regarding business strategy, the advisory department can apply an advisory algorithm of a great person with strategic thinking. In the case of a consultation regarding relationship problems, the advisory department can also apply an advisory algorithm of a great person with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the advisory department can also apply an advisory algorithm of a great person with broad knowledge. This allows the advisory department to apply the most suitable advisory algorithm depending on the category of the consultation. Some or all of the above processing in the advisory department may be performed using AI or not.

[0087] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit will provide short, concise advice. If the user is relaxed, the advice unit can also provide detailed advice. If the user is in a hurry, the advice unit can also provide brief advice. This allows the advice to be of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The advisory department can prioritize advice based on when the consultation content is submitted. For example, the advisory department will prioritize advice on important business strategies. The advisory department can also provide advice quickly on relationship problems. The advisory department can also provide advice with normal priority on everyday problems. This allows for the provision of advice with appropriate priority based on when the consultation content is submitted. Some or all of the above processes in the advisory department may be performed using AI or not.

[0089] The advisory department can adjust the order of advice based on the relevance of the consultation content when providing advice. For example, in the case of a consultation regarding business strategy, the advisory department may prioritize providing advice from great figures with strategic thinking. In the case of a consultation regarding relationship problems, the advisory department may also prioritize providing advice from great figures with excellent interpersonal skills. In the case of a consultation regarding everyday problems, the advisory department may also prioritize providing advice from great figures with broad knowledge. This allows the advisory department to provide advice in an appropriate order based on the relevance of the consultation content. Some or all of the above processing in the advisory department may be performed using AI or not.

[0090] The generation unit can estimate the user's emotions and adjust the generation method of the great person's language model and actions based on the estimated user emotions. For example, if the user is stressed, the generation unit will generate gentle language and calm actions. If the user is relaxed, the generation unit can also generate detailed explanations and natural actions. If the user is in a hurry, the generation unit can also generate concise language and quick actions. This allows for the generation of appropriate language models and actions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0091] The generation unit can select the optimal generation method by referring to past data when generating language models and behaviors. For example, the generation unit can generate an optimal language model based on past user consultations and responses. The generation unit can also generate natural behaviors by referring to past behavior data of great AI figures. The generation unit can also analyze past data and generate language models and behaviors tailored to the user's preferences. This makes it possible to generate optimal language models and behaviors based on past data. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0092] The generation unit can consider the attribute information of great figures when generating language models and actions. For example, the generation unit can generate strategic language models and actions based on the attribute information of Oda Nobunaga. The generation unit can also generate language models and actions with excellent interpersonal skills based on the attribute information of Sakamoto Ryoma. The generation unit can also generate appropriate language models and actions by considering the attribute information of great figures. This makes it possible to generate appropriate language models and actions based on the attribute information of great figures. Some or all of the above processing in the generation unit may be performed using AI or not.

[0093] The generation unit can estimate the user's emotions and determine the priority of language models and actions to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating gentle language and calm actions. If the user is relaxed, the generation unit may also prioritize generating detailed explanations and natural actions. If the user is in a hurry, the generation unit may also prioritize generating concise language and quick actions. This allows for the generation of language models and actions with appropriate priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0094] The generation unit can consider the geographical background of historical figures when generating language models and actions. For example, the generation unit can generate language models and actions related to Japan during the Sengoku period based on the geographical background of Oda Nobunaga. The generation unit can also generate language models and actions related to Japan during the Bakumatsu period based on the geographical background of Sakamoto Ryoma. The generation unit can also generate appropriate language models and actions by considering the geographical background of historical figures. This allows for the generation of appropriate language models and actions based on the geographical background of historical figures. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0095] The generation unit can improve the accuracy of generation by referring to relevant literature when generating language models and actions. For example, the generation unit can refer to literature on Oda Nobunaga to generate strategic language models and actions. The generation unit can also refer to literature on Sakamoto Ryoma to generate language models and actions with excellent interpersonal skills. The generation unit can also refer to relevant literature on great figures to generate appropriate language models and actions. This allows for improved accuracy in generating language models and actions based on relevant literature. Some or all of the above processing in the generation unit may be performed using AI or not.

[0096] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated emotions. For example, if the user is stressed, the interface unit can provide an interface with calming colors. If the user is relaxed, the interface unit can also provide an interface with bright colors. If the user is in a hurry, the interface unit can also provide a simple and highly visible interface. This allows the interface to be displayed in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit provides the optimal display method based on the interface settings the user has used in the past. The interface unit can also provide an interface tailored to the user's preferences based on the user's past operation history. The interface unit can also analyze the user's past operation history and select the optimal display method. This allows the interface unit to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, or it may be performed without using AI.

[0098] The interface unit can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, if the user is stressed, the interface unit may simplify the operation procedures. If the user is relaxed, the interface unit may also provide detailed operation procedures. If the user is in a hurry, the interface unit may also provide procedures that allow for quick operation. This allows the interface to be provided with appropriate operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. If the user is using a tablet, the interface unit can also provide a display method optimized for a larger screen. If the user is using a smartwatch, the interface unit can also provide a concise and highly visible display method. This allows the interface unit to provide the optimal display method based on the user's device information. Some or all of the above processing in the interface unit may be performed using AI, or it may be performed without using AI.

[0100] The management department can estimate the user's emotions and select management data based on the estimated emotions. For example, if the user is stressed, the management department can prioritize managing important data. If the user is relaxed, the management department can also perform normal data management. If the user is in a hurry, the management department can also prioritize managing data that can be accessed quickly. This allows for the selection and management of appropriate data according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The management department can optimize its management algorithms by referring to historical data during management. For example, the management department can select the optimal data management algorithm based on historical data. The management department can also analyze historical data and propose efficient data management methods. The management department can also optimize its management algorithms by referring to historical data. This allows it to apply the optimal management algorithm based on historical data. Some or all of the above processes in the management department may be performed using AI or not.

[0102] The management unit can estimate the user's emotions and adjust the frequency of management based on the estimated emotions. For example, if the user is stressed, the management unit will manage the data more frequently. If the user is relaxed, the management unit can manage the data at a normal frequency. If the user is in a hurry, the management unit can manage the data quickly. This allows for data management at an appropriate frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The management department can weight management data based on when the consultation content was submitted. For example, the management department might assign a higher weight to consultations concerning important business strategies. It can also assign an appropriate weight to consultations concerning interpersonal relationship problems. It can also assign a normal weight to consultations concerning everyday problems. This allows the data to be managed with appropriate weighting based on when the consultation content was submitted. Some or all of the above processing in the management department may be performed using AI or not.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The Great Figure AI dialogue system may further include a selection unit that estimates the user's emotions and adjusts the selection criteria for the Great Figure AI based on the estimated emotions. For example, if the user is stressed, the selection unit may select a Great Figure who will help them relax. If the user is relaxed, it may also select a Great Figure who will offer deep insights. If the user is in a hurry, it may also select a Great Figure who will offer quick advice. This allows the system to select the most suitable Great Figure according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The AI ​​dialogue system for historical figures may further include a selection unit that analyzes the user's past consultation history and selects the most suitable historical figure. The selection unit may, for example, select a relevant historical figure based on the topics the user has frequently consulted about in the past. It may also prioritize the selection of a specific historical figure based on the user's past consultation history. It may also suggest the most suitable historical figure based on the user's past consultation history. In this way, the most suitable historical figure can be selected based on the user's past consultation history. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0107] The great figure AI dialogue system may further include an advisory unit that estimates the user's emotions and adjusts the way advice is expressed based on those emotions. For example, if the user is stressed, the advisory unit may provide advice in gentle language. If the user is relaxed, it may provide detailed advice. If the user is in a hurry, it may provide concise advice. This allows the system to provide advice in an appropriate manner according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The great figure AI dialogue system may further include a reception unit that filters based on the user's current situation and areas of interest. The reception unit, for example, prioritizes receiving consultations related to the problems the user is currently facing. It can also filter appropriate consultations based on the user's areas of interest. It can also receive the most suitable consultations considering the user's current situation. This ensures that appropriate consultations are received based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0109] The AI ​​dialogue system for great figures may further include a reception unit that estimates the user's emotions and determines the priority of the consultation content to be received based on the estimated emotions. For example, if the user is feeling stressed, the reception unit may prioritize receiving urgent consultation content. If the user is relaxed, it may also accept consultation content with normal priority. If the user is in a hurry, it may also prioritize receiving important consultation content. This allows the system to determine the priority of consultation content according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The Great Figure AI Dialogue System may further include a reception unit that prioritizes receiving inquiries that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving inquiries related to that region. It can also filter the most relevant inquiries based on the user's current location. It can also prioritize receiving inquiries that are highly relevant, taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI or not.

[0111] The AI ​​dialogue system for historical figures may further include a generation unit that estimates the user's emotions and adjusts the generation method of the historical figure's language model and actions based on the estimated emotions. For example, if the user is stressed, the generation unit may generate gentle language and calm actions. If the user is relaxed, it may also generate detailed explanations and natural actions. If the user is in a hurry, it may also generate concise language and quick actions. This allows for the generation of appropriate language models and actions according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The Great Figure AI Dialogue System may further include a reception desk that analyzes the user's social media activity and receives relevant inquiries. The reception desk, for example, analyzes the user's current interests from their social media activity and prioritizes receiving relevant inquiries. It can also filter the most appropriate inquiries based on the user's social media posts. It can also prioritize receiving inquiries with high relevance, taking into account the user's social media activity. This allows the system to receive relevant inquiries based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0113] The AI ​​dialogue system for great figures may further include an interface unit that estimates the user's emotions and adjusts the interface display method based on the estimated emotions. For example, if the user is stressed, the interface unit may provide an interface with calming colors. If the user is relaxed, it may provide an interface with bright colors. If the user is in a hurry, it may provide a simple and highly visible interface. This allows the interface to be displayed in an appropriate way according to the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The great person AI dialogue system may further include an interface unit that selects the optimal display method by referring to the user's past operation history. The interface unit may, for example, provide the optimal display method based on the interface settings the user has used in the past. It may also provide an interface tailored to the user's preferences based on the user's past operation history. It may also analyze the user's past operation history and select the optimal display method. This makes it possible to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI or not using AI.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives inquiries from users. For example, users can input their inquiries via video chat. The reception desk can also convert the user's input into a format that is easy to analyze. Step 2: The selection unit analyzes the consultation content received by the reception unit and selects the most suitable historical figure. For example, it applies the criteria for selecting a historical figure based on the user's consultation content to select the most suitable historical figure. The selection unit can use AI to match the user's consultation content with the knowledge and experience of a historical figure. Step 3: The advice unit provides advice from the great historical figure AI selected by the selection unit. For example, the selected great historical figure AI provides specific advice based on the user's consultation content. The advice unit can use AI to generate advice based on the knowledge and experience of great historical figures.

[0117] 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.

[0118] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, selection unit, advice unit, generation unit, interface unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input consultation details via video chat. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the user's consultation details and selecting the most suitable great person. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the selected great person AI to provide advice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating an action generation model for the great person AI. The interface unit is implemented by the control unit 46A of the smart device 14, providing a video chat interface. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, managing the backend server and database. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0134] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0136] Each of the multiple elements described above, including the reception unit, selection unit, advice unit, generation unit, interface unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input consultation details via video chat. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the user's consultation details and selecting the most suitable great person. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the selected great person AI to provide advice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating an action generation model for the great person AI. The interface unit is implemented by the control unit 46A of the smart glasses 214, providing a video chat interface. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, managing the backend server and database. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, selection unit, advice unit, generation unit, interface unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input consultation details via video chat. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12, analyzing the user's consultation details and selecting the most suitable great person. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, allowing the selected great person AI to provide advice. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating an action generation model for the great person AI. The interface unit is implemented by the control unit 46A of the headset terminal 314, providing a video chat interface. The management unit is implemented by the specific processing unit 290 of the data processing unit 12, managing the backend server and database. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, selection unit, advice unit, generation unit, interface unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input consultation details via video chat. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's consultation details and selects the most suitable great person. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the selected great person AI provides advice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an action generation model for the great person AI. The interface unit is implemented by, for example, the control unit 46A of the robot 414, which provides a video chat interface. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which manages the backend server and database. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception department that receives inquiries from users, The selection department analyzes the consultation content received by the reception department and selects the most suitable historical figure. The system comprises an advisory unit in which a great person AI selected by the aforementioned selection unit provides advice. A system characterized by the following features. (Note 2) It includes a generation unit that generates behavioral models for AI based on great historical figures. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an interface unit that provides a video chat interface. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an administration unit that manages backend servers and databases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving inquiries, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving inquiries, the system prioritizes accepting inquiries that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a consultation request, the system analyzes the user's social media activity and selects relevant consultations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned selection unit is It estimates the user's emotions and adjusts the selection criteria for the most suitable historical figures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned selection unit is During the selection process, great figures will be chosen based on the importance of the issues discussed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is During the selection process, different selection algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is It estimates the user's emotions and determines the priority of great figures to select based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is During the selection process, the selection of great figures will be based on the timing of the submission of the consultation request. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is During the selection process, the selection of historical figures will be adjusted based on the relevance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advisory unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advisory unit, When providing advice, the level of detail in the advice will be adjusted based on the importance of the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advisory unit, When providing advice, different advice algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advisory unit, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advisory unit, When providing advice, we will determine the priority of the advice based on when the consultation content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advisory unit, When providing advice, the order of advice will be adjusted based on the relevance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the language model of great figures and the way behaviors are generated based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The generating unit is When generating language models and behaviors, the system selects the optimal generation method by referring to past data. The system described in Appendix 2, characterized by the features described herein. (Note 25) The generating unit is When generating language models and behaviors, attribute information of great figures is taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 26) The generating unit is It estimates the user's emotions and determines the priority of language models and actions to generate based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The generating unit is When generating language models and behaviors, the geographical background of great figures is taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 28) The generating unit is When generating language models and behaviors, we refer to relevant literature to improve the accuracy of the generation. The system described in Appendix 2, characterized by the features described herein. (Note 29) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned management department, The system estimates user emotions and selects management data based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned management department, During management, the management algorithm is optimized by referring to past data. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned management department, It estimates the user's emotions and adjusts the frequency of interventions based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned management department, During management, the management data is weighted based on when the consultation content was submitted. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0189] 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 reception department that receives inquiries from users, The selection department analyzes the consultation content received by the reception department and selects the most suitable historical figure. The system comprises an advisory unit in which an AI of a great person selected by the aforementioned selection unit provides advice. A system characterized by the following features.

2. It includes a generation unit that generates behavioral models for AI based on great historical figures. The system according to feature 1.

3. It includes an interface unit that provides a video chat interface. The system according to feature 1.

4. It includes an administration unit that manages backend servers and databases. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving inquiries based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past consultation history and select the most suitable method of contact. The system according to feature 1.

7. The aforementioned reception unit is When receiving inquiries, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving inquiries, the system prioritizes accepting inquiries that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving a consultation request, the system analyzes the user's social media activity and selects relevant consultations. The system according to feature 1.

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