system

Abstract

The system according to this embodiment aims to identify and coordinate an appropriate AI agent based on what the user wants to do. [Solution] The system according to the embodiment comprises a reception unit, a identification unit, and a collaboration unit. The reception unit receives input from the user about what they want to do. The identification unit analyzes the information entered by the reception unit and identifies the corresponding generation AI agent. The collaboration unit communicates and collaborates with the generation AI agents identified by the identification unit.

Description

Technical Field

[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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to select and cooperate appropriate generation AI agents from a large number of generation AI agents.

[0005] The system according to the embodiment aims to identify and cooperate appropriate generation AI agents according to what the user wants to do.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an identification unit, and a cooperation unit. The reception unit inputs what the user wants to do. The identification unit analyzes the information input by the reception unit and identifies the corresponding generation AI agent. The cooperation unit enables the generation AI agents identified by the identification unit to communicate and cooperate with each other. [Effects of the Invention]

[0007] The system according to this embodiment can identify and integrate an appropriate generating AI agent according to what the user wants to do. [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 generation AI agent management system according to an embodiment of the present invention manages what kinds of agents are available and, when a user tells it what they want to do, it responds by recommending a generation AI agent in the relevant category. This generation AI agent management system introduces the necessary generation AI agents from a large number of agents to achieve the user's request, and the generation AI agents can converse with each other, significantly increasing the range of what they can do. For example, the user inputs what they want to do. For example, they input, "I want to arrange a trip." This information is input to the generation AI agent. Next, the generation AI agent analyzes the input information and identifies a generation AI agent in the relevant category. For example, agents related to travel arrangements may be identified, such as an agent for booking accommodations, an agent for arranging transportation, and an agent for making restaurant reservations. The generation AI agents converse with each other and make specific arrangements to achieve the user's request. For example, the accommodation booking agent checks the availability of accommodations, the transportation arrangement agent arranges transportation tickets, and the restaurant reservation agent makes restaurant reservations. With this mechanism, the user only needs to tell one agent what they want to do, and multiple agents can work together to make the arrangements. This significantly reduces the user's effort and allows them to efficiently achieve their goals. For example, when traveling, even if there are separate agents for booking accommodations, arranging transportation, and making restaurant reservations, you can simply consult with one agent about what you want to do on your trip, and they will handle all the arrangements. In this way, the range of what can be done can be greatly expanded by having the generated AI agents communicate with each other. As a result, the generated AI agent management system can work together with the generated AI agents to efficiently achieve what the user wants.

[0029] The generation AI agent management system according to this embodiment comprises a reception unit, a identification unit, and a collaboration unit. The reception unit receives input from the user about what they want to do. What the user wants to do includes, but is not limited to, arranging travel, managing work tasks, and engaging in hobby activities. For example, the reception unit receives input from the user such as "I want to arrange travel." The identification unit analyzes the information entered by the reception unit and identifies the corresponding generation AI agent. For example, the identification unit uses generation AI to analyze the user's input and identify an agent related to travel arrangements. For example, the identification unit identifies an agent who makes accommodation reservations, an agent who arranges transportation, an agent who makes restaurant reservations, etc. The collaboration unit communicates and collaborates with the generation AI agents identified by the identification unit. For example, the collaboration unit allows the accommodation reservation agent to check the availability of accommodation, the transportation arrangement agent to arrange transportation tickets, and the restaurant reservation agent to make restaurant reservations. The collaboration unit allows the generation AI agents to communicate with each other and make specific arrangements to achieve what the user wants to do. As a result, the generation AI agent management system according to this embodiment can arrange for generation AI agents to cooperate with each other in order to efficiently achieve what the user wants to do.

[0030] The reception desk receives input from the user about what they want to do. This includes, but is not limited to, things like arranging travel, managing work tasks, and engaging in hobbies. For example, the reception desk might receive input from a user such as "I want to arrange travel." Specifically, the reception desk receives the user's input in natural language through the user interface and sends it to the system as text data. The user interface is provided on various platforms, such as web browsers and mobile applications. The user's input is sent to the reception desk in real time, and the system processes it immediately. To accurately receive user input, the reception desk uses natural language processing (NLP) techniques to analyze the meaning of the input. For example, if a user inputs "I want to travel to Tokyo next Friday," the reception desk analyzes this and recognizes it as a request regarding travel arrangements. Furthermore, the reception desk saves the user's input for use in subsequent processing. This allows the reception desk to accurately receive what the user wants and provides a foundation for smooth overall system processing.

[0031] The identification unit analyzes the information entered by the reception unit and identifies the relevant generative AI agent. For example, the identification unit uses generative AI to analyze the user's input and identify an agent for travel arrangements. Specifically, the identification unit uses natural language processing (NLP) technology to analyze the user's input in detail. For example, if a user enters "I want to travel to Tokyo next Friday," the identification unit analyzes this content and identifies an agent for travel arrangements. The identification unit uses a generative AI model to analyze the user's input. The generative AI model has learned from a large amount of text data and can understand the user's input and identify the appropriate agent. For example, the generative AI model extracts keywords related to travel arrangements from the user's input and identifies agents for booking accommodations, arranging transportation, and booking restaurants based on these keywords. To identify these agents, the identification unit uses the generative AI model to analyze the user's input and identify the appropriate agent. This allows the identification unit to identify the appropriate generative AI agent to efficiently achieve what the user wants to do.

[0032] The collaboration unit communicates and collaborates with the generative AI agents identified by the specific unit. For example, the collaboration unit has a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. Specifically, the collaboration unit communicates with the generative AI agents identified by the specific unit and makes concrete arrangements to achieve what the user wants to do. The collaboration unit uses a generative AI model to enable the generative AI agents to communicate with each other. The generative AI model has learned from a large amount of text data and can engage in natural conversations with the generative AI agents. For example, the generative AI agents communicate with each other to check the availability of accommodations, arrange transportation tickets, and make restaurant reservations. The collaboration unit uses a generative AI model to enable the generative AI agents to communicate with each other and make concrete arrangements to efficiently achieve what the user wants to do. As a result, the collaboration unit can communicate with the generative AI agents and make concrete arrangements to efficiently achieve what the user wants to do. Furthermore, the collaboration unit uses the generative AI model to enable the generative AI agents to converse with each other and makes specific arrangements to efficiently achieve what the user wants to do. This allows the collaboration unit to communicate with the generative AI agents and make specific arrangements to efficiently achieve what the user wants to do.

[0033] The collaborative unit allows the generating AI agents to converse with each other and make specific arrangements to achieve what the user wants. For example, the collaborative unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. The collaborative unit allows the generating AI agents to converse with each other and make specific arrangements to achieve what the user wants. For example, the collaborative unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. In this way, the generating AI agents can converse with each other and make specific arrangements to achieve what the user wants.

[0034] The collaboration unit includes a recording unit that records the content of conversations between generating AI agents. The collaboration unit can, for example, record the content of conversations between generating AI agents and refer to it later. This allows for the recording and later reference of the conversation content between generating AI agents.

[0035] The identification unit can identify the corresponding category of generation AI agent using generation AI. For example, the identification unit analyzes the user's input using generation AI to identify the corresponding category of generation AI agent. This allows for accurate identification of the corresponding category of generation AI agent by using generation AI.

[0036] The reception desk allows users to input what they want to do. For example, the reception desk can accept input from a user such as, "I want to arrange a trip." This allows the system to process information based on the user's input.

[0037] The collaboration unit allows the generating AI agents to work together to arrange what the user wants to do. For example, the collaboration unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. The collaboration unit allows the generating AI agents to work together to arrange what the user wants to do. For example, the collaboration unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. In this way, the collaboration between the generating AI agents allows for efficient arrangement of what the user wants to do.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display suggestions for things the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest things the user will want to do at a specific time of day based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested.

[0039] The input field can be enhanced with a feature that automatically completes input based on the user's current situation and areas of interest. For example, if the user indicates an interest in travel, the input field will automatically complete travel-related activities. For example, if the user enters work-related activities, the input field will automatically complete related tasks. For example, if the user enters hobbies, the input field will automatically complete related activities. This reduces the effort required for input by automatically completing input based on the user's current situation and areas of interest.

[0040] The reception desk can present highly relevant input suggestions by considering the user's geographical location. For example, based on the user's current location, the reception desk can suggest things to do nearby. For example, if the user is traveling, the reception desk can suggest things to do at their travel destination. For example, if the user is at home, the reception desk can suggest things to do at home. In this way, by considering the user's geographical location, it can present highly relevant input suggestions.

[0041] The reception desk can analyze a user's social media activity and suggest relevant inputs. For example, the reception desk can suggest relevant activities based on what the user has shared on social media. For example, the reception desk can suggest relevant activities based on the activity of accounts the user follows. For example, the reception desk can suggest relevant activities based on the activity of groups the user participates in. In this way, by analyzing the user's social media activity, it is possible to suggest relevant inputs.

[0042] The identification unit can optimize a specific algorithm by referring to past identification history. For example, the identification unit optimizes a specific algorithm based on the generation AI agent that the user has used in the past. For example, the identification unit suggests the optimal generation AI agent from the user's past identification history. For example, the identification unit analyzes the user's past identification history and improves the specific algorithm. In this way, the specific algorithm can be optimized by referring to past identification history.

[0043] The identification unit can apply different identification algorithms depending on the category of the user's input. For example, if the user inputs things they want to do related to travel, the identification unit will apply a travel-specific identification algorithm. For example, if the user inputs things they want to do related to work, the identification unit will apply a work-specific identification algorithm. For example, if the user inputs things they want to do related to hobbies, the identification unit will apply a hobby-specific identification algorithm. By applying different identification algorithms depending on the category of the user's input, the accuracy of identification is improved.

[0044] The identification unit can identify highly relevant generative AI agents by considering the user's geographical location information. For example, the identification unit identifies generative AI agents available nearby based on the user's current location. For example, if the user is traveling, the identification unit identifies generative AI agents available at the travel destination. For example, if the user is at home, the identification unit identifies generative AI agents available at home. In this way, highly relevant generative AI agents can be identified by considering the user's geographical location information.

[0045] The identification unit can analyze a user's social media activity and identify relevant generative AI agents. For example, the identification unit can identify relevant generative AI agents based on content shared by the user on social media. For example, the identification unit can identify relevant generative AI agents based on the activity of accounts followed by the user. For example, the identification unit can identify relevant generative AI agents based on the activity of groups the user participates in. In this way, relevant generative AI agents can be identified by analyzing a user's social media activity.

[0046] The collaboration unit can optimize the collaboration algorithm by referring to the past collaboration history of the generating AI agents. For example, the collaboration unit proposes the optimal collaboration algorithm based on the past collaboration history of the generating AI agents. For example, the collaboration unit analyzes the past collaboration history of the generating AI agents and improves the collaboration algorithm. For example, the collaboration unit proposes the optimal collaboration method from the past collaboration history of the generating AI agents. In this way, the collaboration algorithm can be optimized by referring to the past collaboration history of the generating AI agents.

[0047] The collaboration unit can select the optimal collaboration method by considering the attribute information of the generating AI agent. For example, the collaboration unit can select the optimal collaboration method based on the generating AI agent's area of ​​expertise. For example, the collaboration unit can select the optimal collaboration method based on the generating AI agent's capabilities. For example, the collaboration unit can select the optimal collaboration method based on the generating AI agent's past performance. In this way, the optimal collaboration method can be selected by considering the attribute information of the generating AI agent.

[0048] The collaboration unit can optimize the collaboration method by considering the geographical distribution of the generating AI agents. For example, if the generating AI agents are in different regions, the collaboration unit will propose the optimal collaboration method. For example, if the generating AI agents are in the same region, the collaboration unit will propose a rapid collaboration method. For example, if the generating AI agents are in different time zones, the collaboration unit will propose the optimal collaboration method. In this way, by considering the geographical distribution of the generating AI agents, the optimal collaboration method can be proposed.

[0049] The collaboration unit can improve the accuracy of collaboration by referring to relevant literature for the generating AI agent. For example, the collaboration unit proposes the optimal collaboration method based on the relevant literature for the generating AI agent. For example, the collaboration unit analyzes the relevant literature for the generating AI agent and improves the collaboration algorithm. For example, the collaboration unit proposes the optimal collaboration method from the relevant literature for the generating AI agent. This improves the accuracy of collaboration by referring to the relevant literature for the generating AI agent.

[0050] The recording unit can optimize the recording algorithm by referring to past recording data. For example, the recording unit proposes an optimal recording algorithm based on past recording data. For example, the recording unit analyzes past recording data and improves the recording algorithm. For example, the recording unit proposes an optimal recording method from past recording data. In this way, the recording algorithm can be optimized by referring to past recording data.

[0051] The recording unit can customize the recorded content during recording by considering the attribute information of the generating AI agent. For example, the recording unit can customize the recorded content based on the generating AI agent's area of ​​expertise. For example, the recording unit can customize the recorded content based on the generating AI agent's capabilities. For example, the recording unit can customize the recorded content based on the generating AI agent's past performance. In this way, the recorded content can be customized by considering the attribute information of the generating AI agent.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The AI-generated agent management system can further analyze a user's past behavior history and suggest the most suitable AI-generated agent based on the tasks the user has previously performed and the options they have selected. For example, it can record the agents a user has used when making travel arrangements in the past and prioritize suggesting the same agents for future travel arrangements. It can also prioritize suggesting agents if a user has previously given a particular agent a high rating. Furthermore, by learning the characteristics of agents that users prefer based on their past behavior history, it can provide more personalized suggestions. This allows users to leverage their past experiences and achieve their goals more efficiently.

[0054] The AI-generated agent management system can suggest the most suitable agent by considering the user's geographical location. For example, it can suggest nearby agents based on the user's current location. If the user is traveling, it can suggest agents available at their travel destination. It can also suggest agents available at home if the user is at home. This allows for the suggestion of highly relevant agents by considering the user's geographical location.

[0055] The AI-generated agent management system can analyze a user's social media activity and suggest relevant agents. For example, it can suggest relevant agents based on what the user shares on social media, the activity of accounts the user follows, and the activity of groups the user participates in. In this way, by analyzing a user's social media activity, it can suggest relevant agents.

[0056] The AI-generated agent management system can analyze a user's past input history and suggest the optimal input method. For example, it can automatically display suggestions for things the user has frequently entered in the past. It prioritizes suggesting input methods the user has used in the past (voice, text, etc.). It can also predict and suggest things the user will want to do at specific times of the day based on their past input history. In this way, by analyzing the user's past input history, it can suggest the optimal input method.

[0057] The AI ​​agent management system can add a feature that automatically completes input based on the user's current situation and areas of interest. For example, if a user expresses interest in travel, it can automatically complete travel-related activities. If a user enters work-related activities, it can automatically complete related tasks. It can also automatically complete related activities if a user enters hobbies. This reduces the effort required for input by automatically completing input based on the user's current situation and areas of interest.

[0058] The AI ​​agent management system can suggest highly relevant input options by considering the user's geographical location. For example, it can suggest things the user can do nearby based on their current location. If the user is traveling, it can suggest things they can do at their travel destination. It can also suggest things the user can do at home if they are at home. This allows the system to present highly relevant input options by considering the user's geographical location.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk inputs what the user wants to do. What the user wants to do includes, but is not limited to, things like arranging travel, managing work tasks, or engaging in hobby activities. For example, the reception desk will accept if the user inputs "I want to arrange travel." Step 2: The identification unit analyzes the information entered by the reception unit and identifies the corresponding generating AI agent. For example, the identification unit uses the generating AI to analyze the user's input and identify an agent related to travel arrangements. For example, the identification unit identifies agents who make accommodation reservations, agents who arrange transportation, agents who make restaurant reservations, etc. Step 3: The collaboration unit communicates and collaborates with the generated AI agents identified by the identification unit. For example, the collaboration unit has a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. The collaboration unit can communicate with the generated AI agents and make specific arrangements to achieve what the user wants to do.

[0061] (Example of form 2) The generation AI agent management system according to an embodiment of the present invention manages what kinds of agents are available and, when a user tells it what they want to do, it responds by recommending a generation AI agent in the relevant category. This generation AI agent management system introduces the necessary generation AI agents from a large number of agents to achieve the user's request, and the generation AI agents can converse with each other, significantly increasing the range of what they can do. For example, the user inputs what they want to do. For example, they input, "I want to arrange a trip." This information is input to the generation AI agent. Next, the generation AI agent analyzes the input information and identifies a generation AI agent in the relevant category. For example, agents related to travel arrangements may be identified, such as an agent for booking accommodations, an agent for arranging transportation, and an agent for making restaurant reservations. The generation AI agents converse with each other and make specific arrangements to achieve the user's request. For example, the accommodation booking agent checks the availability of accommodations, the transportation arrangement agent arranges transportation tickets, and the restaurant reservation agent makes restaurant reservations. With this mechanism, the user only needs to tell one agent what they want to do, and multiple agents can work together to make the arrangements. This significantly reduces the user's effort and allows them to efficiently achieve their goals. For example, when traveling, even if there are separate agents for booking accommodations, arranging transportation, and making restaurant reservations, you can simply consult with one agent about what you want to do on your trip, and they will handle all the arrangements. In this way, the range of what can be done can be greatly expanded by having the generated AI agents communicate with each other. As a result, the generated AI agent management system can work together with the generated AI agents to efficiently achieve what the user wants.

[0062] The generation AI agent management system according to this embodiment comprises a reception unit, a identification unit, and a collaboration unit. The reception unit receives input from the user about what they want to do. What the user wants to do includes, but is not limited to, arranging travel, managing work tasks, and engaging in hobby activities. For example, the reception unit receives input from the user such as "I want to arrange travel." The identification unit analyzes the information entered by the reception unit and identifies the corresponding generation AI agent. For example, the identification unit uses generation AI to analyze the user's input and identify an agent related to travel arrangements. For example, the identification unit identifies an agent who makes accommodation reservations, an agent who arranges transportation, an agent who makes restaurant reservations, etc. The collaboration unit communicates and collaborates with the generation AI agents identified by the identification unit. For example, the collaboration unit allows the accommodation reservation agent to check the availability of accommodation, the transportation arrangement agent to arrange transportation tickets, and the restaurant reservation agent to make restaurant reservations. The collaboration unit allows the generation AI agents to communicate with each other and make specific arrangements to achieve what the user wants to do. As a result, the generation AI agent management system according to this embodiment can arrange for generation AI agents to cooperate with each other in order to efficiently achieve what the user wants to do.

[0063] The reception desk receives input from the user about what they want to do. This includes, but is not limited to, things like arranging travel, managing work tasks, and engaging in hobbies. For example, the reception desk might receive input from a user such as "I want to arrange travel." Specifically, the reception desk receives the user's input in natural language through the user interface and sends it to the system as text data. The user interface is provided on various platforms, such as web browsers and mobile applications. The user's input is sent to the reception desk in real time, and the system processes it immediately. To accurately receive user input, the reception desk uses natural language processing (NLP) techniques to analyze the meaning of the input. For example, if a user inputs "I want to travel to Tokyo next Friday," the reception desk analyzes this and recognizes it as a request regarding travel arrangements. Furthermore, the reception desk saves the user's input for use in subsequent processing. This allows the reception desk to accurately receive what the user wants and provides a foundation for smooth overall system processing.

[0064] The identification unit analyzes the information entered by the reception unit and identifies the relevant generative AI agent. For example, the identification unit uses generative AI to analyze the user's input and identify an agent for travel arrangements. Specifically, the identification unit uses natural language processing (NLP) technology to analyze the user's input in detail. For example, if a user enters "I want to travel to Tokyo next Friday," the identification unit analyzes this content and identifies an agent for travel arrangements. The identification unit uses a generative AI model to analyze the user's input. The generative AI model has learned from a large amount of text data and can understand the user's input and identify the appropriate agent. For example, the generative AI model extracts keywords related to travel arrangements from the user's input and identifies agents for booking accommodations, arranging transportation, and booking restaurants based on these keywords. To identify these agents, the identification unit uses the generative AI model to analyze the user's input and identify the appropriate agent. This allows the identification unit to identify the appropriate generative AI agent to efficiently achieve what the user wants to do.

[0065] The collaboration unit communicates and collaborates with the generative AI agents identified by the specific unit. For example, the collaboration unit has a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. Specifically, the collaboration unit communicates with the generative AI agents identified by the specific unit and makes concrete arrangements to achieve what the user wants to do. The collaboration unit uses a generative AI model to enable the generative AI agents to communicate with each other. The generative AI model has learned from a large amount of text data and can engage in natural conversations with the generative AI agents. For example, the generative AI agents communicate with each other to check the availability of accommodations, arrange transportation tickets, and make restaurant reservations. The collaboration unit uses a generative AI model to enable the generative AI agents to communicate with each other and make concrete arrangements to efficiently achieve what the user wants to do. As a result, the collaboration unit can communicate with the generative AI agents and make concrete arrangements to efficiently achieve what the user wants to do. Furthermore, the collaboration unit uses the generative AI model to enable the generative AI agents to converse with each other and makes specific arrangements to efficiently achieve what the user wants to do. This allows the collaboration unit to communicate with the generative AI agents and make specific arrangements to efficiently achieve what the user wants to do.

[0066] The collaborative unit allows the generating AI agents to converse with each other and make specific arrangements to achieve what the user wants. For example, the collaborative unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. The collaborative unit allows the generating AI agents to converse with each other and make specific arrangements to achieve what the user wants. For example, the collaborative unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. In this way, the generating AI agents can converse with each other and make specific arrangements to achieve what the user wants.

[0067] The collaboration unit includes a recording unit that records the content of conversations between generating AI agents. The collaboration unit can, for example, record the content of conversations between generating AI agents and refer to it later. This allows for the recording and later reference of the conversation content between generating AI agents.

[0068] The identification unit can identify the corresponding category of generation AI agent using generation AI. For example, the identification unit analyzes the user's input using generation AI to identify the corresponding category of generation AI agent. This allows for accurate identification of the corresponding category of generation AI agent by using generation AI.

[0069] The reception desk allows users to input what they want to do. For example, the reception desk can accept input from a user such as, "I want to arrange a trip." This allows the system to process information based on the user's input.

[0070] The collaboration unit allows the generating AI agents to work together to arrange what the user wants to do. For example, the collaboration unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. The collaboration unit allows the generating AI agents to work together to arrange what the user wants to do. For example, the collaboration unit can have a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. In this way, the collaboration between the generating AI agents allows for efficient arrangement of what the user wants to do.

[0071] The reception desk can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk will provide a simple interface and minimize the input steps. If the user is relaxed, for example, the reception desk will provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk will prioritize voice input to allow the user to quickly input what they want to do. This improves user convenience by adjusting the display of the input interface 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.

[0072] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display suggestions for things the user has frequently entered in the past. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest things the user will want to do at a specific time of day based on their past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested.

[0073] The input field can be enhanced with a feature that automatically completes input based on the user's current situation and areas of interest. For example, if the user indicates an interest in travel, the input field will automatically complete travel-related activities. For example, if the user enters work-related activities, the input field will automatically complete related tasks. For example, if the user enters hobbies, the input field will automatically complete related activities. This reduces the effort required for input by automatically completing input based on the user's current situation and areas of interest.

[0074] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize displaying relaxing activities. For example, if the user is relaxed, the reception desk will prioritize displaying challenging activities. For example, if the user is in a hurry, the reception desk will prioritize displaying activities that can be completed quickly. This allows for a response tailored to the user's needs by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The reception desk can present highly relevant input suggestions by considering the user's geographical location. For example, based on the user's current location, the reception desk can suggest things to do nearby. For example, if the user is traveling, the reception desk can suggest things to do at their travel destination. For example, if the user is at home, the reception desk can suggest things to do at home. In this way, by considering the user's geographical location, it can present highly relevant input suggestions.

[0076] The reception desk can analyze a user's social media activity and suggest relevant inputs. For example, the reception desk can suggest relevant activities based on what the user has shared on social media. For example, the reception desk can suggest relevant activities based on the activity of accounts the user follows. For example, the reception desk can suggest relevant activities based on the activity of groups the user participates in. In this way, by analyzing the user's social media activity, it is possible to suggest relevant inputs.

[0077] The identification unit can estimate the user's emotions and improve the accuracy of specific tasks based on the estimated emotions. For example, if the user is stressed, the identification unit will prioritize identifying a relaxing generative AI agent. For example, if the user is relaxed, the identification unit will prioritize identifying a challenging generative AI agent. For example, if the user is in a hurry, the identification unit will prioritize identifying a generative AI agent that can respond quickly. This allows for the identification of a more appropriate generative AI agent by improving the accuracy of specific tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) and multimodal generation AIs.

[0078] The identification unit can optimize a specific algorithm by referring to past identification history. For example, the identification unit optimizes a specific algorithm based on the generation AI agent that the user has used in the past. For example, the identification unit suggests the optimal generation AI agent from the user's past identification history. For example, the identification unit analyzes the user's past identification history and improves the specific algorithm. In this way, the specific algorithm can be optimized by referring to past identification history.

[0079] The identification unit can apply different identification algorithms depending on the category of the user's input. For example, if the user inputs things they want to do related to travel, the identification unit will apply a travel-specific identification algorithm. For example, if the user inputs things they want to do related to work, the identification unit will apply a work-specific identification algorithm. For example, if the user inputs things they want to do related to hobbies, the identification unit will apply a hobby-specific identification algorithm. By applying different identification algorithms depending on the category of the user's input, the accuracy of identification is improved.

[0080] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is stressed, the identification unit provides a simple and highly visible display method. For example, if the user is relaxed, the identification unit provides a display method that includes detailed information. For example, if the user is in a hurry, the identification unit provides a display method that gets straight to the point. This improves user convenience by adjusting the display method of the identification results 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.

[0081] The identification unit can identify highly relevant generative AI agents by considering the user's geographical location information. For example, the identification unit identifies generative AI agents available nearby based on the user's current location. For example, if the user is traveling, the identification unit identifies generative AI agents available at the travel destination. For example, if the user is at home, the identification unit identifies generative AI agents available at home. In this way, highly relevant generative AI agents can be identified by considering the user's geographical location information.

[0082] The identification unit can analyze a user's social media activity and identify relevant generative AI agents. For example, the identification unit can identify relevant generative AI agents based on content shared by the user on social media. For example, the identification unit can identify relevant generative AI agents based on the activity of accounts followed by the user. For example, the identification unit can identify relevant generative AI agents based on the activity of groups the user participates in. In this way, relevant generative AI agents can be identified by analyzing a user's social media activity.

[0083] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is stressed, the interaction unit provides a simple and quick interaction method. For example, if the user is relaxed, the interaction unit provides a detailed interaction method. For example, if the user is in a hurry, the interaction unit provides a quick response method. This allows for a response that meets the user's needs by adjusting the interaction method 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.

[0084] The collaboration unit can optimize the collaboration algorithm by referring to the past collaboration history of the generating AI agents. For example, the collaboration unit proposes the optimal collaboration algorithm based on the past collaboration history of the generating AI agents. For example, the collaboration unit analyzes the past collaboration history of the generating AI agents and improves the collaboration algorithm. For example, the collaboration unit proposes the optimal collaboration method from the past collaboration history of the generating AI agents. In this way, the collaboration algorithm can be optimized by referring to the past collaboration history of the generating AI agents.

[0085] The collaboration unit can select the optimal collaboration method by considering the attribute information of the generating AI agent. For example, the collaboration unit can select the optimal collaboration method based on the generating AI agent's area of ​​expertise. For example, the collaboration unit can select the optimal collaboration method based on the generating AI agent's capabilities. For example, the collaboration unit can select the optimal collaboration method based on the generating AI agent's past performance. In this way, the optimal collaboration method can be selected by considering the attribute information of the generating AI agent.

[0086] The integration unit can estimate the user's emotions and adjust the display method of the integration results based on the estimated user emotions. For example, if the user is stressed, the integration unit provides a simple and highly visible display method. For example, if the user is relaxed, the integration unit provides a display method that includes detailed information. For example, if the user is in a hurry, the integration unit provides a display method that gets straight to the point. This improves user convenience by adjusting the display method of the integration results 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.

[0087] The collaboration unit can optimize the collaboration method by considering the geographical distribution of the generating AI agents. For example, if the generating AI agents are in different regions, the collaboration unit will propose the optimal collaboration method. For example, if the generating AI agents are in the same region, the collaboration unit will propose a rapid collaboration method. For example, if the generating AI agents are in different time zones, the collaboration unit will propose the optimal collaboration method. In this way, by considering the geographical distribution of the generating AI agents, the optimal collaboration method can be proposed.

[0088] The collaboration unit can improve the accuracy of collaboration by referring to relevant literature for the generating AI agent. For example, the collaboration unit proposes the optimal collaboration method based on the relevant literature for the generating AI agent. For example, the collaboration unit analyzes the relevant literature for the generating AI agent and improves the collaboration algorithm. For example, the collaboration unit proposes the optimal collaboration method from the relevant literature for the generating AI agent. This improves the accuracy of collaboration by referring to the relevant literature for the generating AI agent.

[0089] The recording unit can estimate the user's emotions and adjust the importance of the recorded content based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording information of high importance. For example, if the user is relaxed, the recording unit will record detailed information. For example, if the user is in a hurry, the recording unit will record concise information. In this way, by adjusting the importance of the recorded content according to the user's emotions, important information can be prioritized. 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.

[0090] The recording unit can optimize the recording algorithm by referring to past recording data. For example, the recording unit proposes an optimal recording algorithm based on past recording data. For example, the recording unit analyzes past recording data and improves the recording algorithm. For example, the recording unit proposes an optimal recording method from past recording data. In this way, the recording algorithm can be optimized by referring to past recording data.

[0091] The recording unit can estimate the user's emotions and adjust the recording frequency based on the estimated emotions. For example, if the user is stressed, the recording unit will increase the frequency of recording only important information. If the user is relaxed, the recording unit will record detailed information more frequently. If the user is in a hurry, the recording unit will record concise information at appropriate intervals. This allows for the appropriate recording of important information by adjusting the recording frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The recording unit can customize the recorded content during recording by considering the attribute information of the generating AI agent. For example, the recording unit can customize the recorded content based on the generating AI agent's area of ​​expertise. For example, the recording unit can customize the recorded content based on the generating AI agent's capabilities. For example, the recording unit can customize the recorded content based on the generating AI agent's past performance. In this way, the recorded content can be customized by considering the attribute information of the generating AI agent.

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

[0094] The AI-generated agent management system can further analyze a user's past behavior history and suggest the most suitable AI-generated agent based on the tasks the user has previously performed and the options they have selected. For example, it can record the agents a user has used when making travel arrangements in the past and prioritize suggesting the same agents for future travel arrangements. It can also prioritize suggesting agents if a user has previously given a particular agent a high rating. Furthermore, by learning the characteristics of agents that users prefer based on their past behavior history, it can provide more personalized suggestions. This allows users to leverage their past experiences and achieve their goals more efficiently.

[0095] The generative AI agent management system can estimate a user's emotions and adjust agent suggestions based on those estimates. For example, if a user is stressed, it will prioritize suggesting agents that can help them relax. If the user is relaxed, it will suggest agents capable of handling challenging tasks. Furthermore, if the user is in a hurry, it can suggest agents that can respond quickly. This allows for a response tailored to the user's needs by suggesting the most suitable agent based on their emotions. Emotion estimation is achieved using an emotion engine and generative AI.

[0096] The AI-generated agent management system can suggest the most suitable agent by considering the user's geographical location. For example, it can suggest nearby agents based on the user's current location. If the user is traveling, it can suggest agents available at their travel destination. It can also suggest agents available at home if the user is at home. This allows for the suggestion of highly relevant agents by considering the user's geographical location.

[0097] The AI-generated agent management system can analyze a user's social media activity and suggest relevant agents. For example, it can suggest relevant agents based on what the user shares on social media, the activity of accounts the user follows, and the activity of groups the user participates in. In this way, by analyzing a user's social media activity, it can suggest relevant agents.

[0098] The generative AI agent management system can estimate a user's emotions and adjust the agent's interaction method based on that estimation. For example, if the user is stressed, it can provide a simple and quick interaction method. If the user is relaxed, it can provide a more detailed interaction method. It can also provide a quick response method if the user is in a hurry. This allows for a response tailored to the user's needs by providing the optimal interaction method according to their emotions. Emotion estimation is achieved using an emotion engine and generative AI.

[0099] The AI-generated agent management system can analyze a user's past input history and suggest the optimal input method. For example, it can automatically display suggestions for things the user has frequently entered in the past. It prioritizes suggesting input methods the user has used in the past (voice, text, etc.). It can also predict and suggest things the user will want to do at specific times of the day based on their past input history. In this way, by analyzing the user's past input history, it can suggest the optimal input method.

[0100] The generative AI agent management system can estimate a user's emotions and adjust the display of the input interface based on those emotions. For example, if a user is stressed, it provides a simple interface and minimizes the input steps. If a user is relaxed, it provides detailed input options and suggests customizable input methods. Furthermore, if a user is in a hurry, it can prioritize voice input, allowing them to quickly input what they want. This improves user convenience by providing an optimal input interface tailored to the user's emotions. Emotion estimation is achieved using an emotion engine and generative AI.

[0101] The AI ​​agent management system can add a feature that automatically completes input based on the user's current situation and areas of interest. For example, if a user expresses interest in travel, it can automatically complete travel-related activities. If a user enters work-related activities, it can automatically complete related tasks. It can also automatically complete related activities if a user enters hobbies. This reduces the effort required for input by automatically completing input based on the user's current situation and areas of interest.

[0102] The generative AI agent management system can estimate a user's emotions and prioritize input based on those emotions. For example, if a user is stressed, it will prioritize displaying relaxing activities. If a user is relaxed, it will prioritize displaying challenging activities. Furthermore, if a user is in a hurry, it can prioritize displaying activities that can be completed quickly. This allows for a more personalized approach by providing optimal input prioritization based on the user's emotions. Emotion estimation is achieved using an emotion engine and generative AI.

[0103] The AI ​​agent management system can suggest highly relevant input options by considering the user's geographical location. For example, it can suggest things the user can do nearby based on their current location. If the user is traveling, it can suggest things they can do at their travel destination. It can also suggest things the user can do at home if they are at home. This allows the system to present highly relevant input options by considering the user's geographical location.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The reception desk inputs what the user wants to do. What the user wants to do includes, but is not limited to, things like arranging travel, managing work tasks, or engaging in hobby activities. For example, the reception desk will accept if the user inputs "I want to arrange travel." Step 2: The identification unit analyzes the information entered by the reception unit and identifies the corresponding generating AI agent. For example, the identification unit uses the generating AI to analyze the user's input and identify an agent related to travel arrangements. For example, the identification unit identifies agents who make accommodation reservations, agents who arrange transportation, agents who make restaurant reservations, etc. Step 3: The collaboration unit communicates and collaborates with the generated AI agents identified by the identification unit. For example, the collaboration unit has a hotel reservation agent check the availability of accommodations, a transportation arrangement agent arrange transportation tickets, and a restaurant reservation agent make restaurant reservations. The collaboration unit can communicate with the generated AI agents and make specific arrangements to achieve what the user wants to do.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the reception unit, identification unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which inputs what the user wants to do. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the user's input and identifies the corresponding generating AI agent. The collaboration unit is implemented by the identification processing unit 290 of the data processing device 12, which communicates and collaborates with the identified generating AI agents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0123] 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.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0125] Each of the multiple elements described above, including the reception unit, identification unit, and cooperation 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, which inputs what the user wants to do. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's input and identifies the corresponding generating AI agent. The cooperation unit is implemented by the identification processing unit 290 of the data processing unit 12, which communicates and cooperates with the identified generating AI agents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0141] Each of the multiple elements, including the reception unit, identification unit, and cooperation unit described above, 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, which inputs what the user wants to do. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's input and identifies the corresponding generating AI agent. The cooperation unit is implemented by the identification processing unit 290 of the data processing unit 12, which communicates and cooperates with the identified generating AI agents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0156] 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.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0158] Each of the multiple elements, including the reception unit, identification unit, and cooperation unit described above, is implemented in 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, which inputs what the user wants to do. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's input and identifies the corresponding generating AI agent. The cooperation unit is implemented by the identification processing unit 290 of the data processing unit 12, which communicates and cooperates with the identified generating AI agents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] (Note 1) A reception area where users input what they want to do, The identification unit analyzes the information entered by the reception unit and identifies the corresponding generation AI agent, The system includes a collaboration unit that communicates and cooperates with the generated AI agents identified by the aforementioned special unit. A system characterized by the following features. (Note 2) The aforementioned linkage unit is, The generated AI agents converse with each other and make specific arrangements to achieve what the user wants to do. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned linkage unit is, It includes a recording unit that records the content of conversations between generated AI agents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, The generation AI identifies the generation AI agent for the relevant category. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Enter what the user wants to do. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, The generated AI agents collaborate with each other to arrange for the user to achieve their goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Add a feature that automatically completes input based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system considers the user's geographical location to suggest highly relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, It estimates user emotions and improves specific accuracy based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, Referencing past specific history to optimize a specific algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, Apply a different specific algorithm depending on the category of the user's input. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, Identify highly relevant generative AI agents by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The specified part is, Analyze users' social media activity and identify relevant generative AI agents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, The AI ​​agents that generate the agents optimize their collaboration algorithms by referring to their past collaboration history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, The optimal collaboration method is selected by considering the attribute information of the generated AI agent. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, It estimates the user's emotions and adjusts how the collaboration results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, We optimize the collaboration method by considering the geographical distribution of the generated AI agents. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, Refer to relevant literature on generative AI agents to improve the accuracy of collaboration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recording unit is It estimates the user's emotions and adjusts the importance of the recorded content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recording unit is Optimize the recording algorithm by referring to past recording data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recording unit is It estimates the user's emotions and adjusts the recording frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recording unit is During recording, the recording content is customized by considering the attribute information of the generated AI agent. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0178] 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 area where users input what they want to do, The identification unit analyzes the information input by the reception unit and identifies the corresponding generation AI agent, The system includes a collaboration unit that communicates and cooperates with the generated AI agents identified by the aforementioned special unit. A system characterized by the following features.

2. The aforementioned linkage unit is, The generated AI agents converse with each other and make specific arrangements to achieve what the user wants to do. The system according to feature 1.

3. The aforementioned linkage unit is, It includes a recording unit that records the content of conversations between generated AI agents. The system according to feature 1.

4. The specified part is, The generation AI identifies the generation AI agent for the relevant category. The system according to feature 1.

5. The aforementioned reception unit is Enter what the user wants to do. The system according to feature 1.

6. The aforementioned linkage unit is, The generated AI agents work together to arrange for the user to achieve their goals. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is Add a feature that automatically completes input based on the user's current situation and areas of interest. The system according to feature 1.

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