system

Abstract

The system according to this embodiment aims to enable generated AIs to safely and efficiently engage in dialogue and negotiation with each other. [Solution] The system according to the embodiment comprises a protocol creation unit, a dialogue unit, and a reporting unit. The protocol creation unit creates a unique protocol for dialogue and negotiation between generated AIs. The dialogue unit has the generated AIs engage in dialogue and negotiation based on the protocol created by the protocol creation unit. The reporting unit reports the results of the dialogue and negotiation conducted by the dialogue unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to exchange information across the boundaries of development companies in the dialogue and negotiation between generative AIs, and there is a risk of leakage of proprietary information.

[0005] The system according to the embodiment aims to enable generative AIs to safely and efficiently conduct dialogue and negotiation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a protocol creation unit, a dialogue unit, and a reporting unit. The protocol creation unit creates a unique protocol for dialogue and negotiation between generated AIs. The dialogue unit has the generated AIs engage in dialogue and negotiation based on the protocol created by the protocol creation unit. The reporting unit reports the results of the dialogue and negotiation conducted by the dialogue unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows generated AIs to safely and efficiently engage in dialogue and negotiation with each other. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also 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 generative AI agent system according to an embodiment of the present invention is a system that utilizes a generative AI as an agent to engage in dialogue and negotiation with generative AIs used by others. In the current situation where generative AIs are centrally managed by each development company, the generative AI agent system enables the exchange of information across development company boundaries to facilitate coordination with others. By creating a unique protocol for use between generative AIs, the generative AI agent system can conceal proprietary information while transcending development company boundaries. This allows for faster overall decision-making by enabling generative AIs to coordinate in advance. Furthermore, even if the generative AI agent system is limited to coordination and negotiation within the same system, it can reduce the risk of user-specific information being leaked to others. For example, instructions are given to the generative AI from the user via chat or conversation. The generative AI agent system communicates using a defined dialogue protocol (metadata, etc.) to engage in dialogue and negotiation with other generative AIs. Communication between generative AIs is conducted using metadata, etc., conforming to a certain standard, and includes virus checks and exclusion of deceptive information. Ultimately, agreement is reached through conversation between humans. This mechanism allows the generative AI agent system to create its own unique protocol for use among the generative AIs themselves, thereby concealing proprietary information across development company boundaries and accelerating the overall decision-making process. It also reduces the risk of user-specific information being leaked to others. As a result, the generative AI agent system can efficiently conduct dialogue and negotiations among the generative AIs and report the results to the user.

[0029] The generative AI agent system according to this embodiment comprises a protocol creation unit, a dialogue unit, and a reporting unit. The protocol creation unit creates a unique protocol for dialogue and negotiation between generative AIs. The protocol creation unit creates, for example, an information container for communication between generative AIs. The protocol creation unit can create an information container for efficient communication between generative AIs. When creating an information container for communication between generative AIs, the protocol creation unit considers the data format and the type of information to be stored. The protocol creation unit performs communication between generative AIs using metadata etc. that conforms to a certain standard. The protocol creation unit improves the accuracy of communication by performing communication between generative AIs using metadata etc. that conforms to a certain standard. When performing communication between generative AIs using metadata etc. that conforms to a certain standard, the protocol creation unit considers the format of the communication protocol and metadata. When performing communication between generative AIs, the protocol creation unit performs virus checks and excludes deceptive information. The protocol creation unit improves the security of communication by performing virus checks and excludes deceptive information when performing communication between generative AIs. The protocol creation unit considers the antivirus software to be used and the frequency of checks when generating AIs communicate with each other. The dialogue unit allows the generating AIs to communicate and negotiate based on the protocol created by the protocol creation unit. When the generating AIs communicate and negotiate with each other, the dialogue unit uses metadata etc. that conforms to a certain standard for communication. The dialogue unit improves the accuracy of communication when generating AIs communicate and negotiate with each other by using metadata etc. that conforms to a certain standard. When the generating AIs communicate and negotiate with each other, the dialogue unit considers the format of the communication protocol and metadata. When the generating AIs communicate and negotiate with each other, the dialogue unit performs virus checks and excludes deceptive information. When the generating AIs communicate and negotiate with each other, the dialogue unit improves the security of communication by performing virus checks and excludes deceptive information. When the generating AIs communicate and negotiate with each other, the dialogue unit considers the antivirus software to be used and the frequency of checks. The reporting unit reports the results of the dialogue and negotiation conducted by the dialogue unit to the user. The reporting department will report the results of the dialogue and negotiations between the generated AIs to the user via chat, conversation, etc.The reporting unit reports the results of the dialogue and negotiation between the generating AIs to the user via chat or conversation, allowing the user to confirm the results. The reporting unit considers formats such as text chat and voice conversation when reporting the results of the dialogue and negotiation between the generating AIs to the user. As a result, the generating AI agent system according to this embodiment can efficiently conduct dialogue and negotiation between the generating AIs and report the results to the user.

[0030] The protocol creation department will create a proprietary protocol for dialogue and negotiation between generating AIs. Specifically, it will create an information container for communication between generating AIs, thereby improving communication efficiency. The information container will be designed considering the data format and the type of information to be stored, and communication between generating AIs will be conducted using metadata conforming to a certain standard. This will improve the accuracy of communication and enable smooth dialogue between generating AIs. The protocol creation department will meticulously design the communication protocol and metadata format to ensure reliable communication between generating AIs. In addition, to ensure the security of communication, virus checks and the exclusion of deceptive information will be implemented. Specifically, the security of communication will be improved by considering the antivirus software used and the frequency of checks. When generating AIs communicate, the protocol creation department will introduce the latest antivirus technology and perform regular updates to always respond to the latest threats. Furthermore, the protocol creation department will introduce technologies to optimize communication bandwidth and latency to ensure efficient communication between generating AIs. This will enable real-time dialogue and negotiation between generating AIs, allowing for rapid decision-making. The protocol creation unit meticulously analyzes the environment and conditions under which the generated AIs communicate with each other and designs the optimal communication protocol. For example, it enhances the protocol's redundancy and error handling functions to ensure uninterrupted communication even when the generated AIs operate in different network environments. This ensures stable dialogue and negotiation between the generated AIs, improving the overall reliability of the system.

[0031] The dialogue unit facilitates dialogue and negotiation between generated AIs based on protocols created by the protocol creation unit. Specifically, it improves the accuracy of communication by using metadata and other data that conform to certain standards for communication between generated AIs. When generating AIs engage in dialogue and negotiation, the dialogue unit considers the communication protocol and metadata format to provide an optimal dialogue environment. Through the dialogue unit, the generated AIs exchange information and proceed with negotiations. The dialogue unit introduces technologies to minimize communication delays and data loss to ensure smooth dialogue between generated AIs. Furthermore, the dialogue unit performs virus checks and filters out deceptive information when generating AIs engage in dialogue and negotiation. This improves the security of communication and makes dialogue between generated AIs more reliable. The dialogue unit takes optimal security measures, considering the antivirus software used and the frequency of checks. For example, before communication between generated AIs takes place, the dialogue unit scans all data to confirm that it does not contain viruses or malicious information. The dialogue unit also monitors the dialogue and negotiation process between generated AIs in real time, and takes immediate action if abnormal behavior or malicious communication is detected. This allows the dialogue unit to support safe and efficient dialogue and negotiation between the generative AIs. Furthermore, the dialogue unit records the history of the dialogue and negotiation between the generative AIs, making it available for later reference. This allows for detailed analysis of the process and results of the dialogue and negotiation between the generative AIs, which can be used to improve and optimize the system. The dialogue unit analyzes in detail the environment and conditions under which the dialogue and negotiation between the generative AIs takes place and provides the optimal dialogue protocol. This ensures smooth dialogue and negotiation between the generative AIs and improves the overall performance of the system.

[0032] The reporting unit reports the results of the dialogues and negotiations conducted by the dialogue unit to the user. Specifically, it reports the results of the dialogues and negotiations between the generative AIs via chat or conversation, allowing the user to check the results. The reporting unit reports the results of the dialogues and negotiations between the generative AIs in the form of text chat or voice conversation, providing information in a way that is easy for the user to understand. For example, the reporting unit summarizes the results of the dialogues and negotiations between the generative AIs and highlights the important points. This allows the user to quickly grasp the results of the dialogues and negotiations between the generative AIs and make appropriate decisions. Furthermore, the reporting unit provides dashboards and graphs to visually display the results of the dialogues and negotiations between the generative AIs. This makes it easier for the user to intuitively understand the results of the dialogues and negotiations between the generative AIs. The reporting unit updates the results of the dialogues and negotiations between the generative AIs in real time, providing the latest information. For example, if dialogues and negotiations between generative AIs are in progress, the reporting unit reports the progress and interim results sequentially, allowing the user to understand the situation. The reporting unit also saves the results of the dialogues and negotiations between the generative AIs so that they can be referenced later. This allows users to review the results of past conversations and negotiations, and use this information to inform future strategies and improvements. When reporting the results of conversations and negotiations between generating AIs, the reporting unit considers the user's needs and requirements and selects the most appropriate reporting format. For example, if the user prefers visual information, the report will use graphs and charts; if they prefer text-based information, a detailed text report will be provided. This allows the reporting unit to provide information to the user in the most optimal way, maximizing the effectiveness of the generating AI agent system.

[0033] The protocol creation unit creates an information container for communication between generated AIs. For example, the protocol creation unit can create an information container for communication between generated AIs. The protocol creation unit can create an information container for efficient communication between generated AIs. When creating an information container for communication between generated AIs, the protocol creation unit considers the data format and the type of information to be stored. When creating an information container for communication between generated AIs, the protocol creation unit can include the processing of the generated AIs. By including the processing of the generated AIs when creating an information container for communication between generated AIs, the protocol creation unit can efficiently create the information container. By including the processing of the generated AIs when creating an information container for communication between generated AIs, the protocol creation unit can quickly create the information container. As a result, the protocol creation unit can create an information container for efficient communication between generated AIs.

[0034] The dialogue unit handles communication between generating AIs using metadata conforming to a certain standard. For example, the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard. The dialogue unit improves the accuracy of communication by handling communication between generating AIs using metadata conforming to a certain standard. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it considers the communication protocol and the format of the metadata. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it can include processing by the generating AI. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it can improve the accuracy of communication by including processing by the generating AI. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it can improve the efficiency of communication by including processing by the generating AI. As a result, the dialogue unit can improve the accuracy of communication by handling communication between generating AIs using metadata conforming to a certain standard.

[0035] The dialogue unit performs virus checks and deceptive information filtering. The dialogue unit, for example, performs virus checks and deceptive information filtering. The dialogue unit improves the security of communications by performing virus checks and deceptive information filtering. When performing virus checks and deceptive information filtering, the dialogue unit considers the antivirus software used and the frequency of checks. When performing virus checks and deceptive information filtering, the dialogue unit may include processing by generating AI. By including processing by generating AI when performing virus checks and deceptive information filtering, the dialogue unit can improve the security of communications. By including processing by generating AI when performing virus checks and deceptive information filtering, the dialogue unit can improve the accuracy of communications. As a result, the dialogue unit can improve the security of communications by performing virus checks and deceptive information filtering.

[0036] The reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc. For example, the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc. By reporting the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., the user can confirm the results. When the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., it considers formats such as text chat and voice conversation. When the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., it may include the processing of the generating AIs. By including the processing of the generating AIs when reporting the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., the reporting unit can improve the accuracy of the report. By including the processing of the generating AIs when reporting the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., the reporting unit can improve the efficiency of the report. As a result, the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., allowing the user to confirm the results.

[0037] The protocol creation unit creates protocols to conceal user-specific information. For example, the protocol creation unit creates protocols to conceal user-specific information. By creating protocols to conceal user-specific information, the protocol creation unit reduces the risk of information leakage. When creating protocols to conceal user-specific information, the protocol creation unit considers the specific content and type of information, such as personal information and confidential information. The protocol creation unit can include generation AI processing when creating protocols to conceal user-specific information. By including generation AI processing when creating protocols to conceal user-specific information, the protocol creation unit can efficiently create protocols. By including generation AI processing when creating protocols to conceal user-specific information, the protocol creation unit can quickly create protocols. As a result, the protocol creation unit can reduce the risk of information leakage by creating protocols to conceal user-specific information.

[0038] The protocol creation unit generates the optimal protocol by referring to past dialogue and negotiation history when creating a protocol. For example, the protocol creation unit generates the optimal protocol by referring to past dialogue and negotiation history when creating a protocol. The protocol creation unit can generate the optimal protocol by referring to past dialogue and negotiation history when creating a protocol. When the protocol creation unit refers to past dialogue and negotiation history when creating a protocol, it considers the format of the history data and the reference algorithm. When the protocol creation unit refers to past dialogue and negotiation history when creating a protocol, it can include the processing of the generation AI. By including the processing of the generation AI when referring to past dialogue and negotiation history when creating a protocol, the protocol creation unit can efficiently generate the optimal protocol. By including the processing of the generation AI when referring to past dialogue and negotiation history when creating a protocol, the protocol creation unit can quickly generate the optimal protocol. Thus, the protocol creation unit can generate the optimal protocol by referring to past dialogue and negotiation history.

[0039] The protocol creation unit applies different protocols depending on the performance and characteristics of the generated AI during protocol creation. For example, the protocol creation unit applies different protocols depending on the performance and characteristics of the generated AI during protocol creation. By applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can perform optimal dialogue and negotiation. When applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit considers evaluation criteria such as processing speed and memory capacity. When applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can include the processing of the generated AI. By including the processing of the generated AI when applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can efficiently apply the optimal protocol. By including the processing of the generated AI when applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can quickly apply the optimal protocol. As a result, the protocol creation unit can perform optimal dialogue and negotiation by applying different protocols depending on the performance and characteristics of the generated AI.

[0040] The protocol creation unit generates the optimal protocol by considering the user's geographical location information during protocol creation. For example, the protocol creation unit generates the optimal protocol by considering the user's geographical location information during protocol creation. The protocol creation unit can generate the optimal protocol by considering the user's geographical location information during protocol creation. When considering the user's geographical location information during protocol creation, the protocol creation unit considers methods for acquiring data such as GPS data and location information services. When considering the user's geographical location information during protocol creation, the protocol creation unit can include processing by the generation AI. By including processing by the generation AI when considering the user's geographical location information during protocol creation, the protocol creation unit can efficiently generate the optimal protocol. By including processing by the generation AI when considering the user's geographical location information during protocol creation, the protocol creation unit can quickly generate the optimal protocol. Thus, the protocol creation unit can generate the optimal protocol by considering the user's geographical location information.

[0041] The protocol creation unit analyzes the user's social media activity and generates relevant protocols when creating protocols. For example, the protocol creation unit analyzes the user's social media activity and generates relevant protocols when creating protocols. The protocol creation unit can generate relevant protocols by analyzing the user's social media activity when creating protocols. When analyzing the user's social media activity during protocol creation, the protocol creation unit considers criteria such as analysis of post content and analysis of follower count. When analyzing the user's social media activity during protocol creation, the protocol creation unit can include generation AI processing. By including generation AI processing when analyzing the user's social media activity during protocol creation, the protocol creation unit can efficiently generate relevant protocols. By including generation AI processing when analyzing the user's social media activity during protocol creation, the protocol creation unit can quickly generate relevant protocols. Thus, the protocol creation unit can generate relevant protocols by analyzing the user's social media activity.

[0042] The dialogue unit improves the accuracy of the dialogue by considering the interrelationships between the generating AIs during the dialogue. The dialogue unit can improve the accuracy of the dialogue by considering the interrelationships between the generating AIs. When considering the interrelationships between the generating AIs, the dialogue unit considers the communication protocols and cooperative algorithms between the AIs. When considering the interrelationships between the generating AIs during the dialogue, the dialogue unit can include the processing of the generating AIs. When considering the interrelationships between the generating AIs during the dialogue, the dialogue unit can efficiently improve the accuracy of the dialogue by including the processing of the generating AIs. When considering the interrelationships between the generating AIs during the dialogue, the dialogue unit can rapidly improve the accuracy of the dialogue by including the processing of the generating AIs. As a result, the dialogue unit can improve the accuracy of the dialogue by considering the interrelationships between the generating AIs.

[0043] The dialogue unit applies different dialogue algorithms during dialogue, depending on the characteristics and performance of the generative AI. For example, the dialogue unit applies different dialogue algorithms during dialogue, depending on the characteristics and performance of the generative AI. By applying different dialogue algorithms depending on the characteristics and performance of the generative AI, the dialogue unit can perform optimal dialogue. When applying different dialogue algorithms depending on the characteristics and performance of the generative AI, the dialogue unit considers algorithms such as rule-based and machine learning-based algorithms. When applying different dialogue algorithms depending on the characteristics and performance of the generative AI during dialogue, the dialogue unit can include processing of the generative AI. By including processing of the generative AI when applying different dialogue algorithms depending on the characteristics and performance of the generative AI during dialogue, the dialogue unit can efficiently apply dialogue algorithms. By including processing of the generative AI when applying different dialogue algorithms depending on the characteristics and performance of the generative AI during dialogue, the dialogue unit can quickly apply dialogue algorithms. This allows the dialogue unit to perform optimal dialogue by applying different dialogue algorithms depending on the characteristics and performance of the generative AI.

[0044] The dialogue unit conducts dialogue while considering the geographical distribution of the generated AI. For example, the dialogue unit conducts dialogue while considering the geographical distribution of the generated AI. By considering the geographical distribution of the generated AI, the dialogue unit can conduct optimal dialogue. When considering the geographical distribution of the generated AI, the dialogue unit considers the AI ​​placement and geographical characteristics of each region. When considering the geographical distribution of the generated AI, the dialogue unit can include processing of the generated AI. When considering the geographical distribution of the generated AI, the dialogue unit can conduct dialogue efficiently by including processing of the generated AI. When considering the geographical distribution of the generated AI, the dialogue unit can conduct dialogue quickly by including processing of the generated AI. As a result, the dialogue unit can conduct optimal dialogue by considering the geographical distribution of the generated AI.

[0045] The dialogue unit improves the accuracy of the dialogue by referring to relevant literature for the generative AI during the dialogue. For example, the dialogue unit improves the accuracy of the dialogue by referring to relevant literature for the generative AI during the dialogue. The dialogue unit can improve the accuracy of the dialogue by referring to relevant literature for the generative AI. When the dialogue unit refers to relevant literature for the generative AI, it considers literature databases and citation algorithms. When the dialogue unit refers to relevant literature for the generative AI during the dialogue, it can include processing for the generative AI. When the dialogue unit refers to relevant literature for the generative AI during the dialogue, it can efficiently improve the accuracy of the dialogue by including processing for the generative AI. When the dialogue unit refers to relevant literature for the generative AI during the dialogue, it can rapidly improve the accuracy of the dialogue by including processing for the generative AI. As a result, the dialogue unit can improve the accuracy of the dialogue by referring to relevant literature for the generative AI.

[0046] The reporting department adjusts the level of detail in its reports based on the importance of the dialogues and negotiations. For example, the reporting department adjusts the level of detail in its reports based on the importance of the dialogues and negotiations. By adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department can provide more appropriate reports. When adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department considers criteria such as impact assessment and urgency assessment. When adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department may include processing by generative AI. By including processing by generative AI when adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department can efficiently adjust the level of detail in its reports. By including processing by generative AI when adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department can quickly adjust the level of detail in its reports. As a result, the reporting department can provide more appropriate reports by adjusting the level of detail in its reports based on the importance of the dialogues and negotiations.

[0047] The reporting unit applies different reporting algorithms depending on the category of the dialogue / negotiation when reporting. For example, the reporting unit applies different reporting algorithms depending on the category of the dialogue / negotiation when reporting. By applying different reporting algorithms depending on the category of the dialogue / negotiation, the reporting unit can provide more appropriate reports. When applying different reporting algorithms depending on the category of the dialogue / negotiation, the reporting unit considers algorithms such as rule-based and machine learning-based algorithms. When applying different reporting algorithms depending on the category of the dialogue / negotiation when reporting, the reporting unit may include generative AI processing. By including generative AI processing when applying different reporting algorithms depending on the category of the dialogue / negotiation when reporting, the reporting unit can apply the reporting algorithms efficiently. By including generative AI processing when applying different reporting algorithms depending on the category of the dialogue / negotiation when reporting, the reporting unit can apply the reporting algorithms quickly. As a result, the reporting unit can provide more appropriate reports by applying different reporting algorithms depending on the category of the dialogue / negotiation.

[0048] The reporting department determines the priority of reports based on the timing of dialogue and negotiation submissions when reporting. For example, the reporting department determines the priority of reports based on the timing of dialogue and negotiation submissions when reporting. By determining the priority of reports based on the timing of dialogue and negotiation submissions, the reporting department can provide more appropriate reports. When determining the priority of reports based on the timing of dialogue and negotiation submissions, the reporting department considers criteria such as submission deadlines and submission order. When determining the priority of reports based on the timing of dialogue and negotiation submissions when reporting, the reporting department may include processing by a generative AI. By including processing by a generative AI when determining the priority of reports based on the timing of dialogue and negotiation submissions when reporting, the reporting department can efficiently determine the priority of reports. By including processing by a generative AI when determining the priority of reports based on the timing of dialogue and negotiation submissions when reporting, the reporting department can quickly determine the priority of reports. As a result, the reporting department can provide more appropriate reports by determining the priority of reports based on the timing of dialogue and negotiation submissions.

[0049] The reporting department adjusts the order of reports based on the relevance of the dialogues and negotiations when reporting. For example, the reporting department adjusts the order of reports based on the relevance of the dialogues and negotiations when reporting. By adjusting the order of reports based on the relevance of the dialogues and negotiations, the reporting department can provide more appropriate reports. When adjusting the order of reports based on the relevance of the dialogues and negotiations, the reporting department considers criteria such as the relevance of topics and the relevance of content. When adjusting the order of reports based on the relevance of the dialogues and negotiations when reporting, the reporting department may include processing by generative AI. By including processing by generative AI when adjusting the order of reports based on the relevance of the dialogues and negotiations when reporting, the reporting department can efficiently adjust the order of reports. By including processing by generative AI when adjusting the order of reports based on the relevance of the dialogues and negotiations when reporting, the reporting department can quickly adjust the order of reports. As a result, the reporting department can provide more appropriate reports by adjusting the order of reports based on the relevance of the dialogues and negotiations.

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

[0051] The generative AI agent system can also include a history analysis unit that analyzes the user's past conversation history and customizes the conversation content based on the user's preferences and tendencies. For example, if the user has shown interest in a particular topic in the past, the conversation unit can prioritize providing information related to that topic. Also, if the user has preferred a particular format of conversation in the past, the conversation unit can conduct conversations in that format. Furthermore, it can analyze how the user has reacted to specific problems in the past and take appropriate action when similar problems arise. In this way, the generative AI agent system can provide conversations that meet the individual needs of the user.

[0052] The generating AI agent system can also include a location information analysis unit that customizes the dialogue content by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize providing information related to that region. If the user is traveling, it can provide tourist information and transportation information for their destination. Furthermore, if the user is participating in a specific event, it can provide information related to that event. This allows the generating AI agent system to provide dialogue tailored to the user's current situation.

[0053] The generative AI agent system can also include a social media analysis unit that analyzes the user's social media activity and customizes the conversation content based on the user's interests. For example, if a user frequently posts about a particular topic, the conversation unit can prioritize providing information related to that topic. Furthermore, if it is confirmed through social media that a user is participating in a particular event, the system can provide information related to that event. Additionally, if a user shows interest in a particular brand or product, the system can provide information related to that brand or product. This allows the generative AI agent system to provide conversations tailored to the user's individual interests.

[0054] The generative AI agent system can also include a history analysis unit that analyzes the user's past interaction history and customizes protocols based on the user's preferences and tendencies. For example, if a user has previously preferred a particular type of protocol, the protocol creation unit can create a protocol in that format. It can also analyze how a user has reacted to specific problems in the past and create an appropriate protocol when a similar problem arises. Furthermore, if a user has previously shown interest in a particular topic, it can prioritize creating protocols related to that topic. In this way, the generative AI agent system can provide protocols that meet the individual needs of the user.

[0055] The generative AI agent system can further include a location information analysis unit that customizes protocols by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize creating protocols related to that region. If the user is traveling, it can create protocols that include tourist information and transportation information for their destination. Furthermore, if the user is participating in a specific event, it can create protocols related to that event. This allows the generative AI agent system to provide protocols tailored to the user's current situation.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The protocol creation unit creates a proprietary protocol for dialogue and negotiation between the generated AIs. Specifically, it creates an information container for communication between the generated AIs, considering the data format and the types of information to be stored. Furthermore, it improves the accuracy and security of communication by performing communication using metadata that conforms to a certain standard, and by performing virus checks and excluding deceptive information. Step 2: The dialogue unit conducts dialogue and negotiations between the generated AIs based on the protocols created by the protocol creation unit. During dialogue and negotiation, communication is conducted using metadata that conforms to a certain standard, taking into consideration the format of the communication protocol and metadata. Furthermore, the accuracy and security of communication are improved by performing virus checks and excluding deceptive information. Step 3: The reporting department reports the results of the dialogue and negotiations conducted by the dialogue department to the user. When reporting, the report will be made via chat or conversation, and the format should be such as text chat or voice conversation so that the user can confirm the results.

[0058] (Example of form 2) The generative AI agent system according to an embodiment of the present invention is a system that utilizes a generative AI as an agent to engage in dialogue and negotiation with generative AIs used by others. In the current situation where generative AIs are centrally managed by each development company, the generative AI agent system enables the exchange of information across development company boundaries to facilitate coordination with others. By creating a unique protocol for use between generative AIs, the generative AI agent system can conceal proprietary information while transcending development company boundaries. This allows for faster overall decision-making by enabling generative AIs to coordinate in advance. Furthermore, even if the generative AI agent system is limited to coordination and negotiation within the same system, it can reduce the risk of user-specific information being leaked to others. For example, instructions are given to the generative AI from the user via chat or conversation. The generative AI agent system communicates using a defined dialogue protocol (metadata, etc.) to engage in dialogue and negotiation with other generative AIs. Communication between generative AIs is conducted using metadata, etc., conforming to a certain standard, and includes virus checks and exclusion of deceptive information. Ultimately, agreement is reached through conversation between humans. This mechanism allows the generative AI agent system to create its own unique protocol for use among the generative AIs themselves, thereby concealing proprietary information across development company boundaries and accelerating the overall decision-making process. It also reduces the risk of user-specific information being leaked to others. As a result, the generative AI agent system can efficiently conduct dialogue and negotiations among the generative AIs and report the results to the user.

[0059] The generative AI agent system according to this embodiment comprises a protocol creation unit, a dialogue unit, and a reporting unit. The protocol creation unit creates a unique protocol for dialogue and negotiation between generative AIs. The protocol creation unit creates, for example, an information container for communication between generative AIs. The protocol creation unit can create an information container for efficient communication between generative AIs. When creating an information container for communication between generative AIs, the protocol creation unit considers the data format and the type of information to be stored. The protocol creation unit performs communication between generative AIs using metadata etc. that conforms to a certain standard. The protocol creation unit improves the accuracy of communication by performing communication between generative AIs using metadata etc. that conforms to a certain standard. When performing communication between generative AIs using metadata etc. that conforms to a certain standard, the protocol creation unit considers the format of the communication protocol and metadata. When performing communication between generative AIs, the protocol creation unit performs virus checks and excludes deceptive information. The protocol creation unit improves the security of communication by performing virus checks and excludes deceptive information when performing communication between generative AIs. The protocol creation unit considers the antivirus software to be used and the frequency of checks when generating AIs communicate with each other. The dialogue unit allows the generating AIs to communicate and negotiate based on the protocol created by the protocol creation unit. When the generating AIs communicate and negotiate with each other, the dialogue unit uses metadata etc. that conforms to a certain standard for communication. The dialogue unit improves the accuracy of communication when generating AIs communicate and negotiate with each other by using metadata etc. that conforms to a certain standard. When the generating AIs communicate and negotiate with each other, the dialogue unit considers the format of the communication protocol and metadata. When the generating AIs communicate and negotiate with each other, the dialogue unit performs virus checks and excludes deceptive information. When the generating AIs communicate and negotiate with each other, the dialogue unit improves the security of communication by performing virus checks and excludes deceptive information. When the generating AIs communicate and negotiate with each other, the dialogue unit considers the antivirus software to be used and the frequency of checks. The reporting unit reports the results of the dialogue and negotiation conducted by the dialogue unit to the user. The reporting department will report the results of the dialogue and negotiations between the generated AIs to the user via chat, conversation, etc.The reporting unit reports the results of the dialogue and negotiation between the generating AIs to the user via chat or conversation, allowing the user to confirm the results. The reporting unit considers formats such as text chat and voice conversation when reporting the results of the dialogue and negotiation between the generating AIs to the user. As a result, the generating AI agent system according to this embodiment can efficiently conduct dialogue and negotiation between the generating AIs and report the results to the user.

[0060] The protocol creation department will create a proprietary protocol for dialogue and negotiation between generating AIs. Specifically, it will create an information container for communication between generating AIs, thereby improving communication efficiency. The information container will be designed considering the data format and the type of information to be stored, and communication between generating AIs will be conducted using metadata conforming to a certain standard. This will improve the accuracy of communication and enable smooth dialogue between generating AIs. The protocol creation department will meticulously design the communication protocol and metadata format to ensure reliable communication between generating AIs. In addition, to ensure the security of communication, virus checks and the exclusion of deceptive information will be implemented. Specifically, the security of communication will be improved by considering the antivirus software used and the frequency of checks. When generating AIs communicate, the protocol creation department will introduce the latest antivirus technology and perform regular updates to always respond to the latest threats. Furthermore, the protocol creation department will introduce technologies to optimize communication bandwidth and latency to ensure efficient communication between generating AIs. This will enable real-time dialogue and negotiation between generating AIs, allowing for rapid decision-making. The protocol creation unit meticulously analyzes the environment and conditions under which the generated AIs communicate with each other and designs the optimal communication protocol. For example, it enhances the protocol's redundancy and error handling functions to ensure uninterrupted communication even when the generated AIs operate in different network environments. This ensures stable dialogue and negotiation between the generated AIs, improving the overall reliability of the system.

[0061] The dialogue unit facilitates dialogue and negotiation between generated AIs based on protocols created by the protocol creation unit. Specifically, it improves the accuracy of communication by using metadata and other data that conform to certain standards for communication between generated AIs. When generating AIs engage in dialogue and negotiation, the dialogue unit considers the communication protocol and metadata format to provide an optimal dialogue environment. Through the dialogue unit, the generated AIs exchange information and proceed with negotiations. The dialogue unit introduces technologies to minimize communication delays and data loss to ensure smooth dialogue between generated AIs. Furthermore, the dialogue unit performs virus checks and filters out deceptive information when generating AIs engage in dialogue and negotiation. This improves the security of communication and makes dialogue between generated AIs more reliable. The dialogue unit takes optimal security measures, considering the antivirus software used and the frequency of checks. For example, before communication between generated AIs takes place, the dialogue unit scans all data to confirm that it does not contain viruses or malicious information. The dialogue unit also monitors the dialogue and negotiation process between generated AIs in real time, and takes immediate action if abnormal behavior or malicious communication is detected. This allows the dialogue unit to support safe and efficient dialogue and negotiation between the generative AIs. Furthermore, the dialogue unit records the history of the dialogue and negotiation between the generative AIs, making it available for later reference. This allows for detailed analysis of the process and results of the dialogue and negotiation between the generative AIs, which can be used to improve and optimize the system. The dialogue unit analyzes in detail the environment and conditions under which the dialogue and negotiation between the generative AIs takes place and provides the optimal dialogue protocol. This ensures smooth dialogue and negotiation between the generative AIs and improves the overall performance of the system.

[0062] The reporting unit reports the results of the dialogues and negotiations conducted by the dialogue unit to the user. Specifically, it reports the results of the dialogues and negotiations between the generative AIs via chat or conversation, allowing the user to check the results. The reporting unit reports the results of the dialogues and negotiations between the generative AIs in the form of text chat or voice conversation, providing information in a way that is easy for the user to understand. For example, the reporting unit summarizes the results of the dialogues and negotiations between the generative AIs and highlights the important points. This allows the user to quickly grasp the results of the dialogues and negotiations between the generative AIs and make appropriate decisions. Furthermore, the reporting unit provides dashboards and graphs to visually display the results of the dialogues and negotiations between the generative AIs. This makes it easier for the user to intuitively understand the results of the dialogues and negotiations between the generative AIs. The reporting unit updates the results of the dialogues and negotiations between the generative AIs in real time, providing the latest information. For example, if dialogues and negotiations between generative AIs are in progress, the reporting unit reports the progress and interim results sequentially, allowing the user to understand the situation. The reporting unit also saves the results of the dialogues and negotiations between the generative AIs so that they can be referenced later. This allows users to review the results of past conversations and negotiations, and use this information to inform future strategies and improvements. When reporting the results of conversations and negotiations between generating AIs, the reporting unit considers the user's needs and requirements and selects the most appropriate reporting format. For example, if the user prefers visual information, the report will use graphs and charts; if they prefer text-based information, a detailed text report will be provided. This allows the reporting unit to provide information to the user in the most optimal way, maximizing the effectiveness of the generating AI agent system.

[0063] The protocol creation unit creates an information container for communication between generated AIs. For example, the protocol creation unit can create an information container for communication between generated AIs. The protocol creation unit can create an information container for efficient communication between generated AIs. When creating an information container for communication between generated AIs, the protocol creation unit considers the data format and the type of information to be stored. When creating an information container for communication between generated AIs, the protocol creation unit can include the processing of the generated AIs. By including the processing of the generated AIs when creating an information container for communication between generated AIs, the protocol creation unit can efficiently create the information container. By including the processing of the generated AIs when creating an information container for communication between generated AIs, the protocol creation unit can quickly create the information container. As a result, the protocol creation unit can create an information container for efficient communication between generated AIs.

[0064] The dialogue unit handles communication between generating AIs using metadata conforming to a certain standard. For example, the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard. The dialogue unit improves the accuracy of communication by handling communication between generating AIs using metadata conforming to a certain standard. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it considers the communication protocol and the format of the metadata. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it can include processing by the generating AI. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it can improve the accuracy of communication by including processing by the generating AI. When the dialogue unit handles communication between generating AIs using metadata conforming to a certain standard, it can improve the efficiency of communication by including processing by the generating AI. As a result, the dialogue unit can improve the accuracy of communication by handling communication between generating AIs using metadata conforming to a certain standard.

[0065] The dialogue unit performs virus checks and deceptive information filtering. The dialogue unit, for example, performs virus checks and deceptive information filtering. The dialogue unit improves the security of communications by performing virus checks and deceptive information filtering. When performing virus checks and deceptive information filtering, the dialogue unit considers the antivirus software used and the frequency of checks. When performing virus checks and deceptive information filtering, the dialogue unit may include processing by generating AI. By including processing by generating AI when performing virus checks and deceptive information filtering, the dialogue unit can improve the security of communications. By including processing by generating AI when performing virus checks and deceptive information filtering, the dialogue unit can improve the accuracy of communications. As a result, the dialogue unit can improve the security of communications by performing virus checks and deceptive information filtering.

[0066] The reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc. For example, the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc. By reporting the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., the user can confirm the results. When the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., it considers formats such as text chat and voice conversation. When the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., it may include the processing of the generating AIs. By including the processing of the generating AIs when reporting the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., the reporting unit can improve the accuracy of the report. By including the processing of the generating AIs when reporting the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., the reporting unit can improve the efficiency of the report. As a result, the reporting unit reports the results of the dialogue and negotiations between the generating AIs to the user via chat, conversation, etc., allowing the user to confirm the results.

[0067] The protocol creation unit creates protocols to conceal user-specific information. For example, the protocol creation unit creates protocols to conceal user-specific information. By creating protocols to conceal user-specific information, the protocol creation unit reduces the risk of information leakage. When creating protocols to conceal user-specific information, the protocol creation unit considers the specific content and type of information, such as personal information and confidential information. The protocol creation unit can include generation AI processing when creating protocols to conceal user-specific information. By including generation AI processing when creating protocols to conceal user-specific information, the protocol creation unit can efficiently create protocols. By including generation AI processing when creating protocols to conceal user-specific information, the protocol creation unit can quickly create protocols. As a result, the protocol creation unit can reduce the risk of information leakage by creating protocols to conceal user-specific information.

[0068] The protocol creation unit estimates the user's emotions and adjusts the protocol creation method based on the estimated user emotions. For example, the protocol creation unit estimates the user's emotions and adjusts the protocol creation method based on the estimated user emotions. By estimating the user's emotions and adjusting the protocol creation method based on the estimated user emotions, the protocol creation unit can create more appropriate protocols. When estimating the user's emotions and adjusting the protocol creation method based on the estimated user emotions, the protocol creation unit considers emotion analysis algorithms and emotion types. When estimating the user's emotions and adjusting the protocol creation method based on the estimated user emotions, the protocol creation unit may include generative AI processing. By including generative AI processing when estimating the user's emotions and adjusting the protocol creation method based on the estimated user emotions, the protocol creation unit can create protocols quickly. This allows the protocol creation unit to create more appropriate protocols by adjusting the protocol creation method based on user emotions.

[0069] The protocol creation unit generates the optimal protocol by referring to past dialogue and negotiation history when creating a protocol. For example, the protocol creation unit generates the optimal protocol by referring to past dialogue and negotiation history when creating a protocol. The protocol creation unit can generate the optimal protocol by referring to past dialogue and negotiation history when creating a protocol. When the protocol creation unit refers to past dialogue and negotiation history when creating a protocol, it considers the format of the history data and the reference algorithm. When the protocol creation unit refers to past dialogue and negotiation history when creating a protocol, it can include the processing of the generation AI. By including the processing of the generation AI when referring to past dialogue and negotiation history when creating a protocol, the protocol creation unit can efficiently generate the optimal protocol. By including the processing of the generation AI when referring to past dialogue and negotiation history when creating a protocol, the protocol creation unit can quickly generate the optimal protocol. Thus, the protocol creation unit can generate the optimal protocol by referring to past dialogue and negotiation history.

[0070] The protocol creation unit applies different protocols depending on the performance and characteristics of the generated AI during protocol creation. For example, the protocol creation unit applies different protocols depending on the performance and characteristics of the generated AI during protocol creation. By applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can perform optimal dialogue and negotiation. When applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit considers evaluation criteria such as processing speed and memory capacity. When applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can include the processing of the generated AI. By including the processing of the generated AI when applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can efficiently apply the optimal protocol. By including the processing of the generated AI when applying different protocols depending on the performance and characteristics of the generated AI during protocol creation, the protocol creation unit can quickly apply the optimal protocol. As a result, the protocol creation unit can perform optimal dialogue and negotiation by applying different protocols depending on the performance and characteristics of the generated AI.

[0071] The protocol creation unit estimates the user's emotions and determines the priority of protocols based on the estimated user emotions. For example, the protocol creation unit estimates the user's emotions and determines the priority of protocols based on the estimated user emotions. By estimating the user's emotions and determining the priority of protocols based on the estimated user emotions, the protocol creation unit can create more appropriate protocols. When estimating the user's emotions and determining the priority of protocols based on the estimated user emotions, the protocol creation unit considers emotion analysis algorithms and emotion types. The protocol creation unit may include generative AI processing when estimating the user's emotions and determining the priority of protocols based on the estimated user emotions. By including generative AI processing when estimating the user's emotions and determining the priority of protocols based on the estimated user emotions, the protocol creation unit can quickly determine the priority of protocols. This allows the protocol creation unit to create more appropriate protocols by determining the priority of protocols based on user emotions.

[0072] The protocol creation unit generates the optimal protocol by considering the user's geographical location information during protocol creation. For example, the protocol creation unit generates the optimal protocol by considering the user's geographical location information during protocol creation. The protocol creation unit can generate the optimal protocol by considering the user's geographical location information during protocol creation. When considering the user's geographical location information during protocol creation, the protocol creation unit considers methods for acquiring data such as GPS data and location information services. When considering the user's geographical location information during protocol creation, the protocol creation unit can include processing by the generation AI. By including processing by the generation AI when considering the user's geographical location information during protocol creation, the protocol creation unit can efficiently generate the optimal protocol. By including processing by the generation AI when considering the user's geographical location information during protocol creation, the protocol creation unit can quickly generate the optimal protocol. Thus, the protocol creation unit can generate the optimal protocol by considering the user's geographical location information.

[0073] The protocol creation unit analyzes the user's social media activity and generates relevant protocols when creating protocols. For example, the protocol creation unit analyzes the user's social media activity and generates relevant protocols when creating protocols. The protocol creation unit can generate relevant protocols by analyzing the user's social media activity when creating protocols. When analyzing the user's social media activity during protocol creation, the protocol creation unit considers criteria such as analysis of post content and analysis of follower count. When analyzing the user's social media activity during protocol creation, the protocol creation unit can include generation AI processing. By including generation AI processing when analyzing the user's social media activity during protocol creation, the protocol creation unit can efficiently generate relevant protocols. By including generation AI processing when analyzing the user's social media activity during protocol creation, the protocol creation unit can quickly generate relevant protocols. Thus, the protocol creation unit can generate relevant protocols by analyzing the user's social media activity.

[0074] The dialogue unit estimates the user's emotions and adjusts the dialogue process based on the estimated emotions. For example, the dialogue unit estimates the user's emotions and adjusts the dialogue process based on the estimated emotions. By estimating the user's emotions and adjusting the dialogue process based on the estimated emotions, the dialogue unit can conduct more appropriate dialogues. When estimating the user's emotions and adjusting the dialogue process based on the estimated emotions, the dialogue unit considers emotion analysis algorithms and emotion types. The dialogue unit may include generative AI processing when estimating the user's emotions and adjusting the dialogue process based on the estimated emotions. By including generative AI processing when estimating the user's emotions and adjusting the dialogue process based on the estimated emotions, the dialogue unit can efficiently adjust the dialogue process. By including generative AI processing when estimating the user's emotions and adjusting the dialogue process based on the estimated emotions, the dialogue unit can quickly adjust the dialogue process. This allows the dialogue unit to conduct more appropriate dialogues by adjusting the dialogue process based on the user's emotions.

[0075] The dialogue unit improves the accuracy of the dialogue by considering the interrelationships between the generating AIs during the dialogue. The dialogue unit can improve the accuracy of the dialogue by considering the interrelationships between the generating AIs. When considering the interrelationships between the generating AIs, the dialogue unit considers the communication protocols and cooperative algorithms between the AIs. When considering the interrelationships between the generating AIs during the dialogue, the dialogue unit can include the processing of the generating AIs. When considering the interrelationships between the generating AIs during the dialogue, the dialogue unit can efficiently improve the accuracy of the dialogue by including the processing of the generating AIs. When considering the interrelationships between the generating AIs during the dialogue, the dialogue unit can rapidly improve the accuracy of the dialogue by including the processing of the generating AIs. As a result, the dialogue unit can improve the accuracy of the dialogue by considering the interrelationships between the generating AIs.

[0076] The dialogue unit applies different dialogue algorithms during dialogue, depending on the characteristics and performance of the generative AI. For example, the dialogue unit applies different dialogue algorithms during dialogue, depending on the characteristics and performance of the generative AI. By applying different dialogue algorithms depending on the characteristics and performance of the generative AI, the dialogue unit can perform optimal dialogue. When applying different dialogue algorithms depending on the characteristics and performance of the generative AI, the dialogue unit considers algorithms such as rule-based and machine learning-based algorithms. When applying different dialogue algorithms depending on the characteristics and performance of the generative AI during dialogue, the dialogue unit can include processing of the generative AI. By including processing of the generative AI when applying different dialogue algorithms depending on the characteristics and performance of the generative AI during dialogue, the dialogue unit can efficiently apply dialogue algorithms. By including processing of the generative AI when applying different dialogue algorithms depending on the characteristics and performance of the generative AI during dialogue, the dialogue unit can quickly apply dialogue algorithms. This allows the dialogue unit to perform optimal dialogue by applying different dialogue algorithms depending on the characteristics and performance of the generative AI.

[0077] The dialogue unit estimates the user's emotions and adjusts the way the dialogue is displayed based on the estimated emotions. For example, the dialogue unit estimates the user's emotions and adjusts the way the dialogue is displayed based on the estimated emotions. By estimating the user's emotions and adjusting the way the dialogue is displayed based on the estimated emotions, the dialogue unit can provide a more appropriate display method. When estimating the user's emotions and adjusting the way the dialogue is displayed based on the estimated emotions, the dialogue unit considers emotion analysis algorithms and types of emotions. When estimating the user's emotions and adjusting the way the dialogue is displayed based on the estimated emotions, the dialogue unit may include generative AI processing. By including generative AI processing when estimating the user's emotions and adjusting the way the dialogue is displayed based on the estimated emotions, the dialogue unit can efficiently adjust the way the dialogue is displayed. By including generative AI processing when estimating the user's emotions and adjusting the way the dialogue is displayed based on the estimated emotions, the dialogue unit can quickly adjust the way the dialogue is displayed. This allows the dialogue unit to provide a more appropriate display method by adjusting the way the dialogue is displayed based on the user's emotions.

[0078] The dialogue unit conducts dialogue while considering the geographical distribution of the generated AI. For example, the dialogue unit conducts dialogue while considering the geographical distribution of the generated AI. By considering the geographical distribution of the generated AI, the dialogue unit can conduct optimal dialogue. When considering the geographical distribution of the generated AI, the dialogue unit considers the AI ​​placement and geographical characteristics of each region. When considering the geographical distribution of the generated AI, the dialogue unit can include processing of the generated AI. When considering the geographical distribution of the generated AI, the dialogue unit can conduct dialogue efficiently by including processing of the generated AI. When considering the geographical distribution of the generated AI, the dialogue unit can conduct dialogue quickly by including processing of the generated AI. As a result, the dialogue unit can conduct optimal dialogue by considering the geographical distribution of the generated AI.

[0079] The dialogue unit improves the accuracy of the dialogue by referring to relevant literature for the generative AI during the dialogue. For example, the dialogue unit improves the accuracy of the dialogue by referring to relevant literature for the generative AI during the dialogue. The dialogue unit can improve the accuracy of the dialogue by referring to relevant literature for the generative AI. When the dialogue unit refers to relevant literature for the generative AI, it considers literature databases and citation algorithms. When the dialogue unit refers to relevant literature for the generative AI during the dialogue, it can include processing for the generative AI. When the dialogue unit refers to relevant literature for the generative AI during the dialogue, it can efficiently improve the accuracy of the dialogue by including processing for the generative AI. When the dialogue unit refers to relevant literature for the generative AI during the dialogue, it can rapidly improve the accuracy of the dialogue by including processing for the generative AI. As a result, the dialogue unit can improve the accuracy of the dialogue by referring to relevant literature for the generative AI.

[0080] The reporting unit estimates the user's emotions and adjusts the way the report is presented based on the estimated emotions. For example, the reporting unit estimates the user's emotions and adjusts the way the report is presented based on the estimated emotions. By estimating the user's emotions and adjusting the way the report is presented based on the estimated emotions, the reporting unit can provide more appropriate reports. When estimating the user's emotions and adjusting the way the report is presented based on the estimated emotions, the reporting unit considers emotion analysis algorithms and types of emotions. The reporting unit may include generative AI processing when estimating the user's emotions and adjusting the way the report is presented based on the estimated emotions. By including generative AI processing when estimating the user's emotions and adjusting the way the report is presented based on the estimated emotions, the reporting unit can efficiently adjust the way the report is presented. By including generative AI processing when estimating the user's emotions and adjusting the way the report is presented based on the estimated emotions, the reporting unit can quickly adjust the way the report is presented. This allows the reporting unit to provide more appropriate reports by adjusting the way the report is presented based on the user's emotions.

[0081] The reporting department adjusts the level of detail in its reports based on the importance of the dialogues and negotiations. For example, the reporting department adjusts the level of detail in its reports based on the importance of the dialogues and negotiations. By adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department can provide more appropriate reports. When adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department considers criteria such as impact assessment and urgency assessment. When adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department may include processing by generative AI. By including processing by generative AI when adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department can efficiently adjust the level of detail in its reports. By including processing by generative AI when adjusting the level of detail in its reports based on the importance of the dialogues and negotiations, the reporting department can quickly adjust the level of detail in its reports. As a result, the reporting department can provide more appropriate reports by adjusting the level of detail in its reports based on the importance of the dialogues and negotiations.

[0082] The reporting unit applies different reporting algorithms depending on the category of the dialogue / negotiation when reporting. For example, the reporting unit applies different reporting algorithms depending on the category of the dialogue / negotiation when reporting. By applying different reporting algorithms depending on the category of the dialogue / negotiation, the reporting unit can provide more appropriate reports. When applying different reporting algorithms depending on the category of the dialogue / negotiation, the reporting unit considers algorithms such as rule-based and machine learning-based algorithms. When applying different reporting algorithms depending on the category of the dialogue / negotiation when reporting, the reporting unit may include generative AI processing. By including generative AI processing when applying different reporting algorithms depending on the category of the dialogue / negotiation when reporting, the reporting unit can apply the reporting algorithms efficiently. By including generative AI processing when applying different reporting algorithms depending on the category of the dialogue / negotiation when reporting, the reporting unit can apply the reporting algorithms quickly. As a result, the reporting unit can provide more appropriate reports by applying different reporting algorithms depending on the category of the dialogue / negotiation.

[0083] The reporting unit estimates the user's emotions and adjusts the length of the report based on the estimated emotions. For example, the reporting unit estimates the user's emotions and adjusts the length of the report based on the estimated emotions. By estimating the user's emotions and adjusting the length of the report based on the estimated emotions, the reporting unit can provide more appropriate reports. When estimating the user's emotions and adjusting the length of the report based on the estimated emotions, the reporting unit considers the emotions analysis algorithm and the type of emotion. When estimating the user's emotions and adjusting the length of the report based on the estimated emotions, the reporting unit may include generative AI processing. By including generative AI processing when estimating the user's emotions and adjusting the length of the report based on the estimated emotions, the reporting unit can efficiently adjust the length of the report. By including generative AI processing when estimating the user's emotions and adjusting the length of the report based on the estimated emotions, the reporting unit can quickly adjust the length of the report. This allows the reporting unit to provide more appropriate reports by adjusting the length of the report based on the user's emotions.

[0084] The reporting department determines the priority of reports based on the timing of dialogue and negotiation submissions when reporting. For example, the reporting department determines the priority of reports based on the timing of dialogue and negotiation submissions when reporting. By determining the priority of reports based on the timing of dialogue and negotiation submissions, the reporting department can provide more appropriate reports. When determining the priority of reports based on the timing of dialogue and negotiation submissions, the reporting department considers criteria such as submission deadlines and submission order. When determining the priority of reports based on the timing of dialogue and negotiation submissions when reporting, the reporting department may include processing by a generative AI. By including processing by a generative AI when determining the priority of reports based on the timing of dialogue and negotiation submissions when reporting, the reporting department can efficiently determine the priority of reports. By including processing by a generative AI when determining the priority of reports based on the timing of dialogue and negotiation submissions when reporting, the reporting department can quickly determine the priority of reports. As a result, the reporting department can provide more appropriate reports by determining the priority of reports based on the timing of dialogue and negotiation submissions.

[0085] The reporting department adjusts the order of reports based on the relevance of the dialogues and negotiations when reporting. For example, the reporting department adjusts the order of reports based on the relevance of the dialogues and negotiations when reporting. By adjusting the order of reports based on the relevance of the dialogues and negotiations, the reporting department can provide more appropriate reports. When adjusting the order of reports based on the relevance of the dialogues and negotiations, the reporting department considers criteria such as the relevance of topics and the relevance of content. When adjusting the order of reports based on the relevance of the dialogues and negotiations when reporting, the reporting department may include processing by generative AI. By including processing by generative AI when adjusting the order of reports based on the relevance of the dialogues and negotiations when reporting, the reporting department can efficiently adjust the order of reports. By including processing by generative AI when adjusting the order of reports based on the relevance of the dialogues and negotiations when reporting, the reporting department can quickly adjust the order of reports. As a result, the reporting department can provide more appropriate reports by adjusting the order of reports based on the relevance of the dialogues and negotiations.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The generative AI agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts the flow of the conversation based on the estimated emotions. For example, if the user is stressed, the conversation unit can slow down the pace of the conversation and provide more careful explanations. If the user is excited, the conversation unit can provide information quickly and keep the user interested. Furthermore, if the user is anxious, the conversation unit can use reassuring language and expressions. In this way, the generative AI agent system can provide flexible conversations that respond to the user's emotions.

[0088] The generative AI agent system can also include a history analysis unit that analyzes the user's past conversation history and customizes the conversation content based on the user's preferences and tendencies. For example, if the user has shown interest in a particular topic in the past, the conversation unit can prioritize providing information related to that topic. Also, if the user has preferred a particular format of conversation in the past, the conversation unit can conduct conversations in that format. Furthermore, it can analyze how the user has reacted to specific problems in the past and take appropriate action when similar problems arise. In this way, the generative AI agent system can provide conversations that meet the individual needs of the user.

[0089] The generative AI agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts the report content based on the estimated emotions. For example, if the user is tired, the reporting unit can provide a concise and to-the-point report. If the user is excited, the reporting unit can provide detailed and rich information to keep the user interested. Furthermore, if the user is feeling anxious, the reporting unit can use reassuring language and expressions. In this way, the generative AI agent system can provide flexible reports that respond to the user's emotions.

[0090] The generating AI agent system can also include a location information analysis unit that customizes the dialogue content by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize providing information related to that region. If the user is traveling, it can provide tourist information and transportation information for their destination. Furthermore, if the user is participating in a specific event, it can provide information related to that event. This allows the generating AI agent system to provide dialogue tailored to the user's current situation.

[0091] The generative AI agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts the way the dialogue is displayed based on the estimated emotions. For example, if the user is stressed, the dialogue unit can be displayed using calm colors and a simple layout. If the user is excited, the dialogue unit can be displayed using vibrant colors and dynamic effects. Furthermore, if the user is anxious, the dialogue unit can be displayed using a reassuring design and font. This allows the generative AI agent system to provide flexible display methods that respond to the user's emotions.

[0092] The generative AI agent system can also include a social media analysis unit that analyzes the user's social media activity and customizes the conversation content based on the user's interests. For example, if a user frequently posts about a particular topic, the conversation unit can prioritize providing information related to that topic. Furthermore, if it is confirmed through social media that a user is participating in a particular event, the system can provide information related to that event. Additionally, if a user shows interest in a particular brand or product, the system can provide information related to that brand or product. This allows the generative AI agent system to provide conversations tailored to the user's individual interests.

[0093] The generative AI agent system may further include an emotion estimation unit that estimates the user's emotions and determines the priority of protocols based on the estimated emotions. For example, if the user feels a sense of urgency, the protocol creation unit can prioritize creating protocols for a quick response. If the user is relaxed, the protocol creation unit can create protocols that include detailed information. Furthermore, if the user is feeling anxious, the protocol creation unit can prioritize creating protocols that provide a sense of security. In this way, the generative AI agent system can provide flexible protocol creation that responds to the user's emotions.

[0094] The generative AI agent system can also include a history analysis unit that analyzes the user's past interaction history and customizes protocols based on the user's preferences and tendencies. For example, if a user has previously preferred a particular type of protocol, the protocol creation unit can create a protocol in that format. It can also analyze how a user has reacted to specific problems in the past and create an appropriate protocol when a similar problem arises. Furthermore, if a user has previously shown interest in a particular topic, it can prioritize creating protocols related to that topic. In this way, the generative AI agent system can provide protocols that meet the individual needs of the user.

[0095] The generative AI agent system can further include an emotion estimation unit that estimates the user's emotions and adjusts the length of the report based on the estimated emotions. For example, if the user is tired, the reporting unit can provide a concise and to-the-point report. If the user is excited, the reporting unit can provide detailed and rich information to keep the user interested. Furthermore, if the user is feeling anxious, the reporting unit can use reassuring language and expressions. In this way, the generative AI agent system can provide flexible reports that respond to the user's emotions.

[0096] The generative AI agent system can further include a location information analysis unit that customizes protocols by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize creating protocols related to that region. If the user is traveling, it can create protocols that include tourist information and transportation information for their destination. Furthermore, if the user is participating in a specific event, it can create protocols related to that event. This allows the generative AI agent system to provide protocols tailored to the user's current situation.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The protocol creation unit creates a proprietary protocol for dialogue and negotiation between the generated AIs. Specifically, it creates an information container for communication between the generated AIs, considering the data format and the types of information to be stored. Furthermore, it improves the accuracy and security of communication by performing communication using metadata that conforms to a certain standard, and by performing virus checks and excluding deceptive information. Step 2: The dialogue unit conducts dialogue and negotiations between the generated AIs based on the protocols created by the protocol creation unit. During dialogue and negotiation, communication is conducted using metadata that conforms to a certain standard, taking into consideration the format of the communication protocol and metadata. Furthermore, the accuracy and security of communication are improved by performing virus checks and excluding deceptive information. Step 3: The reporting department reports the results of the dialogue and negotiations conducted by the dialogue department to the user. When reporting, the report will be made via chat or conversation, and the format should be such as text chat or voice conversation so that the user can confirm the results.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the protocol creation unit, dialogue unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the protocol creation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The reporting unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the protocol creation unit, dialogue unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the protocol creation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reporting unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the protocol creation unit, dialogue unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the protocol creation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reporting unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the protocol creation unit, dialogue unit, and reporting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the protocol creation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The dialogue unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reporting unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A protocol creation unit that creates a unique protocol for dialogue and negotiation between generated AIs, A dialogue unit where generated AIs interact and negotiate with each other based on the protocol created by the protocol creation unit, The system includes a reporting unit that reports the results of the dialogue and negotiation conducted by the aforementioned dialogue unit to the user. A system characterized by the following features. (Note 2) The protocol creation unit, Create an information container that allows generating AIs to communicate with each other. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned dialogue unit, Communication between generated AIs is performed using metadata and other data that conform to a certain standard. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned dialogue unit, Perform virus checks and filter out deceptive information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting department, The results of the dialogue and negotiations between the generated AIs are reported to the user via chat, conversation, etc. The system described in Appendix 1, characterized by the features described herein. (Note 6) The protocol creation unit, Create a protocol to keep user-specific information confidential. The system described in Appendix 1, characterized by the features described herein. (Note 7) The protocol creation unit, We estimate the user's emotions and adjust how the protocol is created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The protocol creation unit, When creating a protocol, the system generates the optimal protocol by referring to past dialogue and negotiation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The protocol creation unit, When creating protocols, different protocols are applied depending on the performance and characteristics of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 10) The protocol creation unit, It estimates the user's emotions and determines the priority of protocols based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The protocol creation unit, When creating a protocol, the system generates the optimal protocol by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The protocol creation unit, When creating protocols, the system analyzes users' social media activity and generates relevant protocols. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the conversation progresses based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue unit, During dialogue, the system improves the accuracy of the conversation by considering the interrelationships between the generated AIs. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue unit, During dialogue, different dialogue algorithms are applied depending on the characteristics and performance of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue unit, It estimates the user's emotions and adjusts how the dialogue is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue unit, During dialogue, the conversation will take into account the geographical distribution of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned dialogue unit, During dialogue, we improve the accuracy of the dialogue by referring to relevant literature on generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reporting department, The system estimates the user's emotions and adjusts the way reports are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reporting department, When reporting, adjust the level of detail in the report based on the importance of the dialogue / negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reporting department, When reporting, different reporting algorithms are applied depending on the category of dialogue / negotiation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reporting department, The system estimates the user's sentiment and adjusts the length of the report based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reporting department, When reporting, prioritize reports based on when they were submitted for dialogue and negotiations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reporting department, When reporting, adjust the order of reports based on the relevance of the dialogues and negotiations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 protocol creation unit that creates a unique protocol for dialogue and negotiation between generated AIs, A dialogue unit that conducts dialogue and negotiations between generated AIs based on the protocol created by the protocol creation unit, The system includes a reporting unit that reports the results of the dialogue and negotiations conducted by the aforementioned dialogue unit to the user. A system characterized by the following features.

2. The protocol creation unit, Create an information container for the generated AIs to communicate with each other. The system according to feature 1.

3. The aforementioned dialogue unit, Communication between generated AIs is performed using metadata and other data that conform to a certain standard. The system according to feature 1.

4. The aforementioned dialogue unit, Perform virus checks and filter out deceptive information. The system according to feature 1.

5. The aforementioned reporting department, The results of the dialogue and negotiations between the generated AIs are reported to the user via chat, conversation, etc. The system according to feature 1.

6. The protocol creation unit, Create a protocol to keep user-specific information confidential. The system according to feature 1.

7. The protocol creation unit, We estimate the user's emotions and adjust how the protocol is created based on those estimated emotions. The system according to feature 1.

8. The protocol creation unit, When creating a protocol, the system generates the optimal protocol by referring to past dialogue and negotiation history. The system according to feature 1.

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