system

Abstract

The system according to this embodiment aims to enable generating AIs to exchange information with each other. [Solution] The system according to the embodiment comprises a protocol provider unit, a connection unit, and an information exchange unit. The protocol provider unit provides a communication protocol for generating AIs to exchange information with each other. The connection unit uses the communication protocol provided by the protocol provider unit to connect the generating AIs to each other at a higher level. The information exchange unit allows the generating AIs connected by the connection unit to exchange information.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the generative AIs of each company are independent and it is difficult to make mutual use of them.

[0005] The system according to the embodiment aims to enable the generative AIs to exchange information with each other.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a protocol provider unit, a connection unit, and an information exchange unit. The protocol provider unit provides a communication protocol for generating AIs to exchange information with each other. The connection unit uses the communication protocol provided by the protocol provider unit to connect the generating AIs to a higher level. The information exchange unit allows the generating AIs connected by the connection unit to exchange information. [Effects of the Invention]

[0007] The system according to this embodiment can enable the generated AIs to exchange information 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied 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). [[ID=第十九]]

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving 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 interoperability system according to an embodiment of the present invention is a system comprising: a protocol provider unit that provides a communication protocol for generative AIs to exchange information with each other; a connection unit that uses the communication protocol provided by the protocol provider unit to connect generative AIs to each other at a higher level; and an information exchange unit that allows generative AIs connected by the connection unit to exchange information with each other. The generative AI interoperability system concerns the establishment of a standard protocol that enables the interoperability of generative AIs, and aims to solve current problems and expand the use of generative AIs. Specifically, it aims to enable generative AIs to connect to each other at a higher level using a communication protocol called MetaLink. One of the current problems is that because each company's generative AI is independent, it is difficult to use different generative AIs together. For example, if one generative AI is good at image generation and another generative AI cannot use that generative AI's strengths, it cannot fully utilize the strengths of that generative AI. In contrast, by establishing a standard protocol, it becomes possible to use different generative AIs together at a higher level, and the strengths of each generative AI can be maximized. The specific contents of the standard protocol will be explained below. This protocol is a communication protocol for generative AIs to connect to each other at a higher level, and is called "MetaLink". The following functions are realized by using MetaLink. Automated communication, faster response times, parallel processing, greater flexibility in responses, layered disclosure of information, interoperability between manufacturers, and enhanced security. This enables high-level collaboration that cannot be achieved through API integration, while keeping information closed between generative AIs. For example, one generative AI can utilize the image generation capabilities of another generative AI, and yet another generative AI can analyze the results. The social value and uniqueness of the standard protocol will also be discussed. MLAIP (Meta Link Artificial Intelligence Protocol) makes the technology widely available to the public, making it free for use by people all over the world. This will expand the use of generative AI and increase the possibility of new ideas and businesses emerging. For example, new network services and business consulting services using generative AI are conceivable. Hypotheses and verification of new businesses using MLAIP will be explained.For example, MLAIP can be packaged and monetized directly by providing business consulting services and maintenance. Furthermore, by offering new network services using MLAIP, it's possible to build and monetize multifaceted network services that go beyond social networking services (SNS). In this way, establishing a standard protocol expands the world of generative AI and creates limitless possibilities. This enables generative AI interoperability systems to connect with each other at a higher level and exchange information.

[0029] The AI-generating interoperability system according to this embodiment comprises a protocol provider unit, a connection unit, and an information exchange unit. The protocol provider unit provides a communication protocol for AI-generatings to exchange information with each other. The protocol provider unit provides, for example, a communication protocol named MetaLink. MetaLink is a communication protocol for AI-generatings to connect to each other at a higher level, and provides functions such as automated communication, faster response, parallel processing, flexible response, layered disclosure of information, interoperability between manufacturers, and security assurance. For example, the protocol provider unit enables AI-generatings to exchange information while keeping it closed using MetaLink. The connection unit enables AI-generatings to connect to each other at a higher level using the communication protocol provided by the protocol provider unit. The connection unit includes, for example, a procedure for AI-generatings to connect to each other at a higher level. The procedure includes an authentication process, a method for establishing a connection, and error handling. For example, the connection unit executes an authentication process to establish a connection between AI-generatings and ensures the stability of the connection. The information exchange unit enables AI-generatings connected by the connection unit to exchange information. The information exchange unit enables high-level collaboration between generating AIs that cannot be achieved through API integration alone. This high-level collaboration includes aspects such as data integration, interoperability, and real-time capabilities. For example, the information exchange unit ensures security by allowing generating AIs to exchange information in a closed manner. As a result, the generating AI interoperability system according to this embodiment enables generating AIs to connect at a higher level and exchange information.

[0030] The protocol provider provides a communication protocol for generative AIs to exchange information. Specifically, it provides a communication protocol called MetaLink. MetaLink is a communication protocol for higher-level connectivity between generative AIs, providing features such as automated communication, faster response times, parallel processing, flexible responses, layered disclosure of information, interoperability between manufacturers, and security. MetaLink provides a standardized means for generative AIs to communicate with each other, and is designed to enable seamless collaboration between generative AIs from different manufacturers and platforms. For example, MetaLink includes a protocol for generative AIs to efficiently exchange various data formats such as text, images, and audio. This allows generative AIs to process different data formats uniformly and respond quickly. MetaLink also incorporates encryption technology and authentication processes to ensure communication security. This protects information exchanged between generative AIs from unauthorized access and tampering by third parties. Furthermore, MetaLink enables layered information exchange between generative AIs, allowing control over the scope of information disclosure as needed. For example, specific information can be set to be shared only among specific generative AIs. This makes it possible to efficiently share only the necessary information while maintaining its confidentiality. The protocol provider will provide MetaLink, which integrates these functions, thereby enabling advanced cooperation between generating AIs and improving the overall efficiency and security of the system.

[0031] The connection unit enables the generation AIs to connect at a higher level using the communication protocol provided by the protocol provider unit. Specifically, this includes procedures for the generation AIs to connect at a higher level. These procedures include an authentication process, connection establishment methods, and error handling. First, the connection unit performs an authentication process between the generation AIs to confirm that each generation AI is legitimate. The authentication process includes authentication methods using public-key cryptography and digital certificates. This allows the connection unit to establish a highly reliable connection. Next, the connection unit performs procedures to establish a connection between the generation AIs. Connection establishment methods include connection establishment procedures using the TCP / IP protocol and real-time communication methods using WebSocket. This allows the generation AIs to exchange information in a stable communication environment. Furthermore, the connection unit performs error handling procedures to ensure connection stability. Error handling includes detecting and retrying communication errors, re-establishing the connection, and retransmitting data. This allows the connection unit to improve the reliability of communication and facilitate smooth cooperation between the generation AIs. By integrating and executing these procedures, the connection unit realizes a higher-level connection between the generation AIs and strengthens the overall system cooperation.

[0032] The Information Exchange Unit facilitates information exchange between generating AIs connected by the Connection Unit. Specifically, it enables high-level collaboration between generating AIs that cannot be achieved through API integration. This high-level collaboration includes data integration, interoperability, and real-time capabilities. First, the Information Exchange Unit unifies the format and content of information exchanged between generating AIs to enhance data integration. This allows data generated by different generating AIs to be processed in a consistent format and shared efficiently. Next, the Information Exchange Unit uses common data formats and communication protocols to ensure interoperability between generating AIs. This enables generating AIs from different manufacturers and platforms to seamlessly collaborate and exchange information. Furthermore, to ensure real-time capabilities, the Information Exchange Unit uses low-latency communication methods, allowing generating AIs to exchange information quickly. This enables generating AIs to share information in real time and respond quickly. In addition, the Information Exchange Unit ensures security by keeping information exchange between generating AIs closed. Specifically, it implements encryption and access control to prevent unauthorized access and information leakage by third parties. This enables the information exchange unit to achieve advanced collaboration between generating AIs, improving the overall efficiency and security of the system.

[0033] The protocol provider will provide a communication protocol called MetaLink. MetaLink is a communication protocol for higher-level connections between generating AIs, and it provides functions such as automated communication, faster response times, parallel processing, flexible responses, layered disclosure of information, interoperability between manufacturers, and security assurance. For example, MetaLink allows generating AIs to exchange information while keeping it closed. The specific specifications and features of MetaLink include the technologies used, compatible devices, and security functions. For example, MetaLink ensures data security using encryption technology and performs access control through an authentication process. By providing a communication protocol called MetaLink, higher-level connections between generating AIs become possible.

[0034] The connection section includes procedures for higher-level connectivity between generating AIs. These procedures include authentication processes, connection establishment methods, and error handling. For example, the connection section performs an authentication process to establish a connection between generating AIs and ensures connection stability. The connection section can also automate the procedures for higher-level connectivity between generating AIs. For example, the connection section can automatically establish a connection between generating AIs and handle errors. This enables efficient connectivity by including procedures for higher-level connectivity between generating AIs.

[0035] The information exchange unit allows generating AIs to exchange information while keeping it confidential. The information exchange unit enables higher-level collaboration between generating AIs that cannot be achieved through API integration. This higher-level collaboration includes aspects such as data integration, interoperability, and real-time capabilities. For example, the information exchange unit ensures security by allowing generating AIs to exchange information while keeping it confidential. The information exchange unit uses encryption technology and access control to enable this confidential exchange. For example, the information exchange unit encrypts data and protects privacy through access control. This ensures security by allowing generating AIs to exchange information while keeping it confidential.

[0036] The Information Exchange Unit enables higher-level collaboration between generating AIs that cannot be achieved through API integration alone. This higher-level collaboration includes data integration, interoperability, and real-time capabilities. For example, the Information Exchange Unit ensures security by allowing generating AIs to exchange information in a closed manner. To enable higher-level collaboration between generating AIs that cannot be achieved through API integration alone, the Information Exchange Unit employs technologies to enhance data integration. For example, the Information Exchange Unit performs data normalization and database integration to improve data integration. This allows generating AIs to perform higher-level collaboration that cannot be achieved through API integration alone, enabling more advanced information exchange.

[0037] The protocol provider offers features such as automated communication, faster response times, parallel processing, flexible responses, layered disclosure information, interoperability between manufacturers, and security. Automated communication includes trigger conditions, scheduling, and error handling. For example, the protocol provider automatically initiates data exchange based on trigger conditions and periodically exchanges data through scheduling. Faster response times include caching, data compression, and network optimization. For example, the protocol provider uses caching to speed up data responses and improves communication efficiency through data compression. Parallel processing includes thread usage, distributed processing, and load balancing. For example, the protocol provider uses threads for parallel processing and distributes the load through distributed processing. Flexible responses include customizable settings, dynamic protocol changes, and flexible error handling. For example, the protocol provider allows for flexible responses to user requirements by customizing settings. Layered disclosure information includes hierarchical information structures, access permissions, and data abstraction levels. For example, the protocol provider ensures data security by organizing information into a hierarchical structure and setting access permissions. Interoperability between manufacturers includes compatible protocols, standardized interfaces, and interoperability testing. For example, the protocol provider provides compatible protocols and ensures interoperability between manufacturers through standardized interfaces. Security measures include encryption technology, authentication processes, and access control. For example, the protocol provider uses encryption technology to ensure data security and implements access control through authentication processes. This enables efficient information exchange between generating AIs by providing features such as automated communication, faster response times, parallel processing, greater flexibility in responses, layered disclosure of information, interoperability between manufacturers, and enhanced security.

[0038] The protocol provider selects the optimal protocol version based on the characteristics of the generating AI when providing a protocol. For example, the protocol provider provides a protocol version optimized for image data exchange to a generating AI specialized in image generation. For a generating AI specialized in text generation, the protocol provider provides a protocol version optimized for text data exchange. For a generating AI specialized in speech generation, the protocol provider provides a protocol version optimized for speech data exchange. This enables efficient information exchange by selecting the optimal protocol version based on the characteristics of the generating AI. The characteristics of the generating AI include processing power, data format, and the algorithm used. For example, the protocol provider selects the optimal protocol version according to the processing power of the generating AI and adjusts the protocol according to the data format. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input the characteristics data of the generating AI into the generating AI and have the generating AI select the optimal protocol version.

[0039] The protocol provider optimizes the protocol by referring to the generation AI's past communication history when providing the protocol. For example, the protocol provider prioritizes providing protocol versions that have shown a high communication success rate in the past. The protocol provider selects the optimal protocol version for a specific time period from the past communication history. The protocol provider analyzes the past communication history and provides the most efficient protocol version. As a result, the communication success rate is improved by optimizing the protocol by referring to the generation AI's past communication history. Past communication history includes log data, history retention period, and analysis method. For example, the protocol provider evaluates the performance of the protocol based on the past communication history and selects the optimal protocol version. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input past communication history data into the generation AI and have the generation AI perform protocol optimization.

[0040] The protocol provider unit provides the optimal protocol when providing a protocol, taking into account the geographical location information of the generating AI. For example, if the generating AI is located in a specific region, the protocol provider unit provides a protocol optimized for the communication environment of that region. If the generating AI is moving, the protocol provider unit provides a protocol adapted to the communication environment of the destination. If the generating AI is located in a specific country, the protocol provider unit provides a protocol adapted to the communication regulations of that country. In this way, by providing the optimal protocol considering the geographical location information of the generating AI, it is possible to provide a protocol adapted to the communication environment. Geographical location information includes GPS data, IP addresses, location information services, etc. For example, the protocol provider unit evaluates the communication environment based on the geographical location information of the generating AI and selects the optimal protocol. Some or all of the above processing in the protocol provider unit may be performed using AI or not using AI. For example, the protocol provider unit can input geographical location information data into the generating AI and have the generating AI select the optimal protocol.

[0041] The protocol provider analyzes the social media activity of the generative AI and provides relevant protocols when providing protocols. For example, if the generative AI is frequently used on social media, the protocol provider provides a protocol optimized for that activity. If the generative AI is actively using a particular social media platform, the protocol provider provides a protocol adapted to that platform. The protocol provider selects the optimal protocol version based on the generative AI's social media activity. This enables efficient information exchange by analyzing the generative AI's social media activity and providing relevant protocols. Social media activity includes post content, follower count, engagement rate, etc. For example, the protocol provider evaluates the performance of a protocol based on the generative AI's social media activity and selects the optimal protocol version. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input social media activity data into the generative AI and have the generative AI select relevant protocols.

[0042] The connection unit selects the optimal connection method based on the characteristics of the generating AI during connection. For example, the connection unit provides the optimal connection method for exchanging image data for a generating AI specialized in image generation. For a generating AI specialized in text generation, the connection unit provides the optimal connection method for exchanging text data. For a generating AI specialized in speech generation, the connection unit provides the optimal connection method for exchanging speech data. This enables efficient connection by selecting the optimal connection method based on the characteristics of the generating AI. The characteristics of the generating AI include processing power, data format, and the algorithm used. For example, the connection unit selects the optimal connection method according to the processing power of the generating AI and adjusts the connection according to the data format. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input the characteristics data of the generating AI into the generating AI and cause the generating AI to select the optimal connection method.

[0043] The connection unit optimizes the connection by referring to the generation AI's past connection history during connection. For example, the connection unit prioritizes providing connection methods that have shown a high connection success rate in the past. The connection unit selects the optimal connection method for a specific time period from the past connection history. The connection unit analyzes the past connection history and provides the most efficient connection method. As a result, the connection success rate is improved by optimizing the connection by referring to the generation AI's past connection history. Past connection history includes log data, history retention period, and analysis method. For example, the connection unit evaluates connection performance based on past connection history and selects the optimal connection method. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input past connection history data into the generation AI and have the generation AI perform connection optimization.

[0044] The connection unit provides the optimal connection method when connecting, taking into account the geographical location information of the generating AI. For example, if the generating AI is located in a specific region, the connection unit provides a connection method optimized for the communication environment of that region. If the generating AI is moving, the connection unit provides a connection method adapted to the communication environment of the destination. If the generating AI is located in a specific country, the connection unit provides a connection method adapted to the communication regulations of that country. In this way, by providing the optimal connection method considering the geographical location information of the generating AI, a connection adapted to the communication environment can be provided. Geographical location information includes GPS data, IP addresses, location information services, etc. For example, the connection unit evaluates the communication environment based on the geographical location information of the generating AI and selects the optimal connection method. Some or all of the above processing in the connection unit may be performed using AI or not using AI. For example, the connection unit can input geographical location data to the generating AI and have the generating AI select the optimal connection method.

[0045] The connection unit analyzes the social media activity of the generative AI during connection and provides a relevant connection method. For example, if the generative AI is frequently used on social media, the connection unit provides a connection method optimized for that activity. If the generative AI is actively used on a specific social media platform, the connection unit provides a connection method adapted to that platform. The connection unit selects the optimal connection method from the generative AI's social media activity. This enables efficient connection by analyzing the generative AI's social media activity and providing a relevant connection method. Social media activity includes post content, follower count, engagement rate, etc. For example, the connection unit evaluates the performance of the connection based on the generative AI's social media activity and selects the optimal connection method. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input social media activity data into the generative AI and have the generative AI select a relevant connection method.

[0046] The information exchange unit selects the optimal information exchange method based on the characteristics of the generating AI during information exchange. For example, the information exchange unit provides the optimal information exchange method for exchanging image data to a generating AI specialized in image generation. For a generating AI specialized in text generation, the information exchange unit provides the optimal information exchange method for exchanging text data. For a generating AI specialized in speech generation, the information exchange unit provides the optimal information exchange method for exchanging speech data. This enables efficient information exchange by selecting the optimal information exchange method based on the characteristics of the generating AI. The characteristics of the generating AI include processing power, data format, and the algorithm used. For example, the information exchange unit selects the optimal information exchange method according to the processing power of the generating AI and adjusts the information exchange according to the data format. Some or all of the above processing in the information exchange unit may be performed using AI or without AI. For example, the information exchange unit can input the characteristics data of the generating AI into the generating AI and have the generating AI select the optimal information exchange method.

[0047] The information exchange unit optimizes information exchange by referring to the generation AI's past information exchange history during information exchange. For example, the information exchange unit prioritizes providing methods that have shown a high success rate in the past. The information exchange unit selects the optimal information exchange method for a specific time period from the past information exchange history. The information exchange unit analyzes the past information exchange history and provides the most efficient information exchange method. As a result, the success rate of information exchange is improved by optimizing information exchange by referring to the generation AI's past information exchange history. Past information exchange history includes log data, history retention period, and analysis method. For example, the information exchange unit evaluates the performance of information exchange based on past information exchange history and selects the optimal information exchange method. Some or all of the above processing in the information exchange unit may be performed using AI or not. For example, the information exchange unit can input past information exchange history data into the generation AI and have the generation AI perform information exchange optimization.

[0048] The information exchange unit provides the optimal information exchange method when exchanging information, taking into account the geographical location information of the generating AI. For example, if the generating AI is located in a specific region, the information exchange unit provides an information exchange method optimized for the communication environment of that region. If the generating AI is moving, the information exchange unit provides an information exchange method adapted to the communication environment of the destination. If the generating AI is located in a specific country, the information exchange unit provides an information exchange method adapted to the communication regulations of that country. In this way, by providing the optimal information exchange method considering the geographical location information of the generating AI, it is possible to provide information exchange adapted to the communication environment. Geographical location information includes GPS data, IP addresses, location information services, etc. For example, the information exchange unit evaluates the communication environment based on the geographical location information of the generating AI and selects the optimal information exchange method. Some or all of the above processing in the information exchange unit may be performed using AI or not using AI. For example, the information exchange unit can input geographical location data into the generating AI and have the generating AI select the optimal information exchange method.

[0049] The information exchange unit analyzes the social media activity of the generating AI during information exchange and provides relevant information exchange methods. For example, if the generating AI is frequently used on social media, the information exchange unit provides an information exchange method optimized for that activity. If the generating AI is actively using a specific social media platform, the information exchange unit provides an information exchange method adapted to that platform. The information exchange unit selects the optimal information exchange method from the generating AI's social media activity. This enables efficient information exchange by analyzing the generating AI's social media activity and providing relevant information exchange methods. Social media activity includes post content, follower count, engagement rate, etc. For example, the information exchange unit evaluates the performance of information exchange based on the generating AI's social media activity and selects the optimal information exchange method. Some or all of the above processing in the information exchange unit may be performed using AI or not. For example, the information exchange unit can input social media activity data into the generating AI and have the generating AI select relevant information exchange methods.

[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 AI ​​generation interoperability system can also include a data filtering unit. This unit pre-filters information exchanged between AI generation systems, removing unnecessary and duplicate data. For example, the data filtering unit can evaluate image quality and remove low-quality images before sending image data generated by an image generation AI to another AI. It can also correct grammatical errors and spelling mistakes in text data generated by a text generation AI before sending it to another AI. Furthermore, it can remove noise and improve sound quality in audio data generated by a speech generation AI before sending it to another AI. This improves the quality of information exchanged between AI generation systems, enabling more efficient information exchange.

[0052] The AI ​​generation interoperability system can also be equipped with a data compression unit. The data compression unit compresses the information exchanged between AI generation systems, improving communication efficiency. For example, the data compression unit can compress image data generated by an image generation AI before sending it to another AI generation system, reducing the amount of data transmitted. It can also compress text data generated by a text generation AI before sending it to another AI generation system, improving communication speed. Furthermore, it can compress audio data generated by a speech generation AI before sending it to another AI generation system, improving communication efficiency. As a result, the communication efficiency of information exchanged between AI generation systems is improved, enabling rapid information exchange.

[0053] The AI ​​generation interoperability system can also be equipped with a data encryption unit. The data encryption unit encrypts information exchanged between AI generation systems to ensure security. For example, the data encryption unit can encrypt image data generated by an image generation AI before sending it to another AI generation system, preventing unauthorized access. Similarly, it can encrypt text data generated by a text generation AI before sending it to another AI generation system, preventing data leakage. Furthermore, it can encrypt voice data generated by a voice generation AI before sending it to another AI generation system, improving communication security. This enhances the security of information exchanged between AI generation systems, enabling safe information exchange.

[0054] The AI ​​generation interoperability system can also be equipped with a data cache unit. The data cache unit temporarily stores and reuses information exchanged between the AI ​​generation units, thereby improving communication efficiency. For example, the data cache unit can temporarily store image data generated by an image generation AI and provide it from the cache when the same data is sent again. Similarly, it can temporarily store text data generated by a text generation AI and provide it from the cache when the same data is sent again. Furthermore, it can temporarily store audio data generated by a speech generation AI and provide it from the cache when the same data is sent again. This improves the communication efficiency of information exchanged between the AI ​​generation units, enabling rapid information exchange.

[0055] The AI ​​generation interoperability system can also include a data conversion unit. This unit converts information exchanged between AI generation systems into different formats to ensure compatibility. For example, the data conversion unit can convert image data generated by an image generation AI into a format usable by other AI generation systems. It can also convert text data generated by a text generation AI into a format usable by other AI generation systems. Furthermore, it can convert audio data generated by a speech generation AI into a format usable by other AI generation systems. This improves the compatibility of information exchanged between AI generation systems, enabling efficient information exchange.

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

[0057] Step 1: The protocol provider unit provides a communication protocol for the generated AIs to exchange information. For example, it provides a communication protocol called MetaLink, which offers features such as automated communication, faster response times, parallel processing, greater flexibility in handling, layered disclosure of information, interoperability between manufacturers, and security assurance. Step 2: The connection unit uses the communication protocol provided by the protocol provider unit to connect the generated AIs to a higher level. The connection unit includes procedures for the generated AIs to connect to a higher level, such as authentication processes, connection establishment methods, and error handling. Step 3: The information exchange unit facilitates information exchange between the generating AIs connected by the connection unit. The information exchange unit enables high-level collaboration between the generating AIs that cannot be achieved through API integration, ensuring data integration, interoperability, and real-time capabilities.

[0058] (Example of form 2) The generative AI interoperability system according to an embodiment of the present invention is a system comprising: a protocol provider unit that provides a communication protocol for generative AIs to exchange information with each other; a connection unit that uses the communication protocol provided by the protocol provider unit to connect generative AIs to each other at a higher level; and an information exchange unit that allows generative AIs connected by the connection unit to exchange information with each other. The generative AI interoperability system concerns the establishment of a standard protocol that enables the interoperability of generative AIs, and aims to solve current problems and expand the use of generative AIs. Specifically, it aims to enable generative AIs to connect to each other at a higher level using a communication protocol called MetaLink. One of the current problems is that because each company's generative AI is independent, it is difficult to use different generative AIs together. For example, if one generative AI is good at image generation and another generative AI cannot use that generative AI's strengths, it cannot fully utilize the strengths of that generative AI. In contrast, by establishing a standard protocol, it becomes possible to use different generative AIs together at a higher level, and the strengths of each generative AI can be maximized. The specific contents of the standard protocol will be explained below. This protocol is a communication protocol for generative AIs to connect to each other at a higher level, and is called "MetaLink". The following functions are realized by using MetaLink. Automated communication, faster response times, parallel processing, greater flexibility in responses, layered disclosure of information, interoperability between manufacturers, and enhanced security. This enables high-level collaboration that cannot be achieved through API integration, while keeping information closed between generative AIs. For example, one generative AI can utilize the image generation capabilities of another generative AI, and yet another generative AI can analyze the results. The social value and uniqueness of the standard protocol will also be discussed. MLAIP (Meta Link Artificial Intelligence Protocol) makes the technology widely available to the public, making it free for use by people all over the world. This will expand the use of generative AI and increase the possibility of new ideas and businesses emerging. For example, new network services and business consulting services using generative AI are conceivable. Hypotheses and verification of new businesses using MLAIP will be explained.For example, MLAIP can be packaged and monetized directly by providing business consulting services and maintenance. Furthermore, by offering new network services using MLAIP, it's possible to build and monetize multifaceted network services that go beyond social networking services (SNS). In this way, establishing a standard protocol expands the world of generative AI and creates limitless possibilities. This enables generative AI interoperability systems to connect with each other at a higher level and exchange information.

[0059] The AI-generating interoperability system according to this embodiment comprises a protocol provider unit, a connection unit, and an information exchange unit. The protocol provider unit provides a communication protocol for AI-generatings to exchange information with each other. The protocol provider unit provides, for example, a communication protocol named MetaLink. MetaLink is a communication protocol for AI-generatings to connect to each other at a higher level, and provides functions such as automated communication, faster response, parallel processing, flexible response, layered disclosure of information, interoperability between manufacturers, and security assurance. For example, the protocol provider unit enables AI-generatings to exchange information while keeping it closed using MetaLink. The connection unit enables AI-generatings to connect to each other at a higher level using the communication protocol provided by the protocol provider unit. The connection unit includes, for example, a procedure for AI-generatings to connect to each other at a higher level. The procedure includes an authentication process, a method for establishing a connection, and error handling. For example, the connection unit executes an authentication process to establish a connection between AI-generatings and ensures the stability of the connection. The information exchange unit enables AI-generatings connected by the connection unit to exchange information. The information exchange unit enables high-level collaboration between generating AIs that cannot be achieved through API integration alone. This high-level collaboration includes aspects such as data integration, interoperability, and real-time capabilities. For example, the information exchange unit ensures security by allowing generating AIs to exchange information in a closed manner. As a result, the generating AI interoperability system according to this embodiment enables generating AIs to connect at a higher level and exchange information.

[0060] The protocol provider provides a communication protocol for generative AIs to exchange information. Specifically, it provides a communication protocol called MetaLink. MetaLink is a communication protocol for higher-level connectivity between generative AIs, providing features such as automated communication, faster response times, parallel processing, flexible responses, layered disclosure of information, interoperability between manufacturers, and security. MetaLink provides a standardized means for generative AIs to communicate with each other, and is designed to enable seamless collaboration between generative AIs from different manufacturers and platforms. For example, MetaLink includes a protocol for generative AIs to efficiently exchange various data formats such as text, images, and audio. This allows generative AIs to process different data formats uniformly and respond quickly. MetaLink also incorporates encryption technology and authentication processes to ensure communication security. This protects information exchanged between generative AIs from unauthorized access and tampering by third parties. Furthermore, MetaLink enables layered information exchange between generative AIs, allowing control over the scope of information disclosure as needed. For example, specific information can be set to be shared only among specific generative AIs. This makes it possible to efficiently share only the necessary information while maintaining its confidentiality. The protocol provider will provide MetaLink, which integrates these functions, thereby enabling advanced cooperation between generating AIs and improving the overall efficiency and security of the system.

[0061] The connection unit enables the generation AIs to connect at a higher level using the communication protocol provided by the protocol provider unit. Specifically, this includes procedures for the generation AIs to connect at a higher level. These procedures include an authentication process, connection establishment methods, and error handling. First, the connection unit performs an authentication process between the generation AIs to confirm that each generation AI is legitimate. The authentication process includes authentication methods using public-key cryptography and digital certificates. This allows the connection unit to establish a highly reliable connection. Next, the connection unit performs procedures to establish a connection between the generation AIs. Connection establishment methods include connection establishment procedures using the TCP / IP protocol and real-time communication methods using WebSocket. This allows the generation AIs to exchange information in a stable communication environment. Furthermore, the connection unit performs error handling procedures to ensure connection stability. Error handling includes detecting and retrying communication errors, re-establishing the connection, and retransmitting data. This allows the connection unit to improve the reliability of communication and facilitate smooth cooperation between the generation AIs. By integrating and executing these procedures, the connection unit realizes a higher-level connection between the generation AIs and strengthens the overall system cooperation.

[0062] The Information Exchange Unit facilitates information exchange between generating AIs connected by the Connection Unit. Specifically, it enables high-level collaboration between generating AIs that cannot be achieved through API integration. This high-level collaboration includes data integration, interoperability, and real-time capabilities. First, the Information Exchange Unit unifies the format and content of information exchanged between generating AIs to enhance data integration. This allows data generated by different generating AIs to be processed in a consistent format and shared efficiently. Next, the Information Exchange Unit uses common data formats and communication protocols to ensure interoperability between generating AIs. This enables generating AIs from different manufacturers and platforms to seamlessly collaborate and exchange information. Furthermore, to ensure real-time capabilities, the Information Exchange Unit uses low-latency communication methods, allowing generating AIs to exchange information quickly. This enables generating AIs to share information in real time and respond quickly. In addition, the Information Exchange Unit ensures security by keeping information exchange between generating AIs closed. Specifically, it implements encryption and access control to prevent unauthorized access and information leakage by third parties. This enables the information exchange unit to achieve advanced collaboration between generating AIs, improving the overall efficiency and security of the system.

[0063] The protocol provider will provide a communication protocol called MetaLink. MetaLink is a communication protocol for higher-level connections between generating AIs, and it provides functions such as automated communication, faster response times, parallel processing, flexible responses, layered disclosure of information, interoperability between manufacturers, and security assurance. For example, MetaLink allows generating AIs to exchange information while keeping it closed. The specific specifications and features of MetaLink include the technologies used, compatible devices, and security functions. For example, MetaLink ensures data security using encryption technology and performs access control through an authentication process. By providing a communication protocol called MetaLink, higher-level connections between generating AIs become possible.

[0064] The connection section includes procedures for higher-level connectivity between generating AIs. These procedures include authentication processes, connection establishment methods, and error handling. For example, the connection section performs an authentication process to establish a connection between generating AIs and ensures connection stability. The connection section can also automate the procedures for higher-level connectivity between generating AIs. For example, the connection section can automatically establish a connection between generating AIs and handle errors. This enables efficient connectivity by including procedures for higher-level connectivity between generating AIs.

[0065] The information exchange unit allows generating AIs to exchange information while keeping it confidential. The information exchange unit enables higher-level collaboration between generating AIs that cannot be achieved through API integration. This higher-level collaboration includes aspects such as data integration, interoperability, and real-time capabilities. For example, the information exchange unit ensures security by allowing generating AIs to exchange information while keeping it confidential. The information exchange unit uses encryption technology and access control to enable this confidential exchange. For example, the information exchange unit encrypts data and protects privacy through access control. This ensures security by allowing generating AIs to exchange information while keeping it confidential.

[0066] The Information Exchange Unit enables higher-level collaboration between generating AIs that cannot be achieved through API integration alone. This higher-level collaboration includes data integration, interoperability, and real-time capabilities. For example, the Information Exchange Unit ensures security by allowing generating AIs to exchange information in a closed manner. To enable higher-level collaboration between generating AIs that cannot be achieved through API integration alone, the Information Exchange Unit employs technologies to enhance data integration. For example, the Information Exchange Unit performs data normalization and database integration to improve data integration. This allows generating AIs to perform higher-level collaboration that cannot be achieved through API integration alone, enabling more advanced information exchange.

[0067] The protocol provider offers features such as automated communication, faster response times, parallel processing, flexible responses, layered disclosure information, interoperability between manufacturers, and security. Automated communication includes trigger conditions, scheduling, and error handling. For example, the protocol provider automatically initiates data exchange based on trigger conditions and periodically exchanges data through scheduling. Faster response times include caching, data compression, and network optimization. For example, the protocol provider uses caching to speed up data responses and improves communication efficiency through data compression. Parallel processing includes thread usage, distributed processing, and load balancing. For example, the protocol provider uses threads for parallel processing and distributes the load through distributed processing. Flexible responses include customizable settings, dynamic protocol changes, and flexible error handling. For example, the protocol provider allows for flexible responses to user requirements by customizing settings. Layered disclosure information includes hierarchical information structures, access permissions, and data abstraction levels. For example, the protocol provider ensures data security by organizing information into a hierarchical structure and setting access permissions. Interoperability between manufacturers includes compatible protocols, standardized interfaces, and interoperability testing. For example, the protocol provider provides compatible protocols and ensures interoperability between manufacturers through standardized interfaces. Security measures include encryption technology, authentication processes, and access control. For example, the protocol provider uses encryption technology to ensure data security and implements access control through authentication processes. This enables efficient information exchange between generating AIs by providing features such as automated communication, faster response times, parallel processing, greater flexibility in responses, layered disclosure of information, interoperability between manufacturers, and enhanced security.

[0068] The protocol provider estimates the user's emotions and adjusts the timing of protocol delivery based on the estimated emotions. For example, if the user is stressed, the protocol provider delays protocol delivery and waits until the user is relaxed. If the user is relaxed, the protocol provider immediately delivers the protocol to facilitate a smooth connection. If the user is in a hurry, the protocol provider speeds up protocol delivery and initiates a connection quickly. This allows the protocol to be delivered at the optimal time for the user by adjusting the timing of protocol delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0069] The protocol provider selects the optimal protocol version based on the characteristics of the generating AI when providing a protocol. For example, the protocol provider provides a protocol version optimized for image data exchange to a generating AI specialized in image generation. For a generating AI specialized in text generation, the protocol provider provides a protocol version optimized for text data exchange. For a generating AI specialized in speech generation, the protocol provider provides a protocol version optimized for speech data exchange. This enables efficient information exchange by selecting the optimal protocol version based on the characteristics of the generating AI. The characteristics of the generating AI include processing power, data format, and the algorithm used. For example, the protocol provider selects the optimal protocol version according to the processing power of the generating AI and adjusts the protocol according to the data format. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input the characteristics data of the generating AI into the generating AI and have the generating AI select the optimal protocol version.

[0070] The protocol provider optimizes the protocol by referring to the generation AI's past communication history when providing the protocol. For example, the protocol provider prioritizes providing protocol versions that have shown a high communication success rate in the past. The protocol provider selects the optimal protocol version for a specific time period from the past communication history. The protocol provider analyzes the past communication history and provides the most efficient protocol version. As a result, the communication success rate is improved by optimizing the protocol by referring to the generation AI's past communication history. Past communication history includes log data, history retention period, and analysis method. For example, the protocol provider evaluates the performance of the protocol based on the past communication history and selects the optimal protocol version. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input past communication history data into the generation AI and have the generation AI perform protocol optimization.

[0071] The protocol provider estimates the user's emotions and adjusts the protocol delivery method based on the estimated emotions. For example, if the user is nervous, the protocol provider selects a simple and intuitive protocol delivery method. If the user is relaxed, the protocol provider selects a protocol delivery method that includes detailed explanations. If the user is in a hurry, the protocol provider selects a method that delivers the protocol quickly. By adjusting the protocol delivery method according to the user's emotions, the protocol can be delivered in the most optimal way for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The protocol provider unit provides the optimal protocol when providing a protocol, taking into account the geographical location information of the generating AI. For example, if the generating AI is located in a specific region, the protocol provider unit provides a protocol optimized for the communication environment of that region. If the generating AI is moving, the protocol provider unit provides a protocol adapted to the communication environment of the destination. If the generating AI is located in a specific country, the protocol provider unit provides a protocol adapted to the communication regulations of that country. In this way, by providing the optimal protocol considering the geographical location information of the generating AI, it is possible to provide a protocol adapted to the communication environment. Geographical location information includes GPS data, IP addresses, location information services, etc. For example, the protocol provider unit evaluates the communication environment based on the geographical location information of the generating AI and selects the optimal protocol. Some or all of the above processing in the protocol provider unit may be performed using AI or not using AI. For example, the protocol provider unit can input geographical location information data into the generating AI and have the generating AI select the optimal protocol.

[0073] The protocol provider analyzes the social media activity of the generative AI and provides relevant protocols when providing protocols. For example, if the generative AI is frequently used on social media, the protocol provider provides a protocol optimized for that activity. If the generative AI is actively using a particular social media platform, the protocol provider provides a protocol adapted to that platform. The protocol provider selects the optimal protocol version based on the generative AI's social media activity. This enables efficient information exchange by analyzing the generative AI's social media activity and providing relevant protocols. Social media activity includes post content, follower count, engagement rate, etc. For example, the protocol provider evaluates the performance of a protocol based on the generative AI's social media activity and selects the optimal protocol version. Some or all of the above processing in the protocol provider may be performed using AI or not. For example, the protocol provider can input social media activity data into the generative AI and have the generative AI select relevant protocols.

[0074] The connection unit estimates the user's emotions and adjusts the connection procedure based on the estimated emotions. For example, if the user is stressed, the connection unit simplifies the connection procedure and completes the connection quickly. If the user is relaxed, the connection unit provides a detailed connection procedure to provide reassurance. If the user is in a hurry, the connection unit provides the shortest possible connection procedure and starts the connection quickly. In this way, by adjusting the connection procedure according to the user's emotions, the optimal connection procedure can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The connection unit selects the optimal connection method based on the characteristics of the generating AI during connection. For example, the connection unit provides the optimal connection method for exchanging image data for a generating AI specialized in image generation. For a generating AI specialized in text generation, the connection unit provides the optimal connection method for exchanging text data. For a generating AI specialized in speech generation, the connection unit provides the optimal connection method for exchanging speech data. This enables efficient connection by selecting the optimal connection method based on the characteristics of the generating AI. The characteristics of the generating AI include processing power, data format, and the algorithm used. For example, the connection unit selects the optimal connection method according to the processing power of the generating AI and adjusts the connection according to the data format. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input the characteristics data of the generating AI into the generating AI and cause the generating AI to select the optimal connection method.

[0076] The connection unit optimizes the connection by referring to the generation AI's past connection history during connection. For example, the connection unit prioritizes providing connection methods that have shown a high connection success rate in the past. The connection unit selects the optimal connection method for a specific time period from the past connection history. The connection unit analyzes the past connection history and provides the most efficient connection method. As a result, the connection success rate is improved by optimizing the connection by referring to the generation AI's past connection history. Past connection history includes log data, history retention period, and analysis method. For example, the connection unit evaluates connection performance based on past connection history and selects the optimal connection method. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input past connection history data into the generation AI and have the generation AI perform connection optimization.

[0077] The connection unit estimates the user's emotions and determines connection priorities based on the estimated emotions. For example, if the user is nervous, the connection unit sets a high priority for the connection and initiates the connection quickly. If the user is relaxed, the connection unit sets a low priority for the connection and prioritizes other tasks. If the user is in a hurry, the connection unit sets the highest priority for the connection and initiates the connection immediately. This allows the connection to be provided with the optimal priority for the user by determining connection priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The connection unit provides the optimal connection method when connecting, taking into account the geographical location information of the generating AI. For example, if the generating AI is located in a specific region, the connection unit provides a connection method optimized for the communication environment of that region. If the generating AI is moving, the connection unit provides a connection method adapted to the communication environment of the destination. If the generating AI is located in a specific country, the connection unit provides a connection method adapted to the communication regulations of that country. In this way, by providing the optimal connection method considering the geographical location information of the generating AI, a connection adapted to the communication environment can be provided. Geographical location information includes GPS data, IP addresses, location information services, etc. For example, the connection unit evaluates the communication environment based on the geographical location information of the generating AI and selects the optimal connection method. Some or all of the above processing in the connection unit may be performed using AI or not using AI. For example, the connection unit can input geographical location data to the generating AI and have the generating AI select the optimal connection method.

[0079] The connection unit analyzes the social media activity of the generative AI during connection and provides a relevant connection method. For example, if the generative AI is frequently used on social media, the connection unit provides a connection method optimized for that activity. If the generative AI is actively used on a specific social media platform, the connection unit provides a connection method adapted to that platform. The connection unit selects the optimal connection method from the generative AI's social media activity. This enables efficient connection by analyzing the generative AI's social media activity and providing a relevant connection method. Social media activity includes post content, follower count, engagement rate, etc. For example, the connection unit evaluates the performance of the connection based on the generative AI's social media activity and selects the optimal connection method. Some or all of the above processing in the connection unit may be performed using AI or not. For example, the connection unit can input social media activity data into the generative AI and have the generative AI select a relevant connection method.

[0080] The information exchange unit estimates the user's emotions and adjusts the method of information exchange based on the estimated emotions. For example, if the user is nervous, the information exchange unit selects a simple and intuitive method of information exchange. If the user is relaxed, the information exchange unit selects a method of information exchange that includes detailed information. If the user is in a hurry, the information exchange unit selects a method of information exchange that allows for quick exchange. By adjusting the method of information exchange according to the user's emotions, the system can provide information exchange in the most optimal way for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the information exchange unit may be performed using AI or not. For example, the information exchange unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The information exchange unit selects the optimal information exchange method based on the characteristics of the generating AI during information exchange. For example, the information exchange unit provides the optimal information exchange method for exchanging image data to a generating AI specialized in image generation. For a generating AI specialized in text generation, the information exchange unit provides the optimal information exchange method for exchanging text data. For a generating AI specialized in speech generation, the information exchange unit provides the optimal information exchange method for exchanging speech data. This enables efficient information exchange by selecting the optimal information exchange method based on the characteristics of the generating AI. The characteristics of the generating AI include processing power, data format, and the algorithm used. For example, the information exchange unit selects the optimal information exchange method according to the processing power of the generating AI and adjusts the information exchange according to the data format. Some or all of the above processing in the information exchange unit may be performed using AI or without AI. For example, the information exchange unit can input the characteristics data of the generating AI into the generating AI and have the generating AI select the optimal information exchange method.

[0082] The information exchange unit optimizes information exchange by referring to the generation AI's past information exchange history during information exchange. For example, the information exchange unit prioritizes providing methods that have shown a high success rate in the past. The information exchange unit selects the optimal information exchange method for a specific time period from the past information exchange history. The information exchange unit analyzes the past information exchange history and provides the most efficient information exchange method. As a result, the success rate of information exchange is improved by optimizing information exchange by referring to the generation AI's past information exchange history. Past information exchange history includes log data, history retention period, and analysis method. For example, the information exchange unit evaluates the performance of information exchange based on past information exchange history and selects the optimal information exchange method. Some or all of the above processing in the information exchange unit may be performed using AI or not. For example, the information exchange unit can input past information exchange history data into the generation AI and have the generation AI perform information exchange optimization.

[0083] The information exchange unit estimates the user's emotions and determines the priority of information exchange based on the estimated emotions. For example, if the user is nervous, the information exchange unit sets a high priority for information exchange and exchanges information quickly. If the user is relaxed, the information exchange unit sets a low priority for information exchange and prioritizes other tasks. If the user is in a hurry, the information exchange unit sets the highest priority for information exchange and exchanges information immediately. In this way, by determining the priority of information exchange according to the user's emotions, information exchange can be provided with the optimal priority for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information exchange unit may be performed using AI or not. For example, the information exchange unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The information exchange unit provides the optimal information exchange method when exchanging information, taking into account the geographical location information of the generating AI. For example, if the generating AI is located in a specific region, the information exchange unit provides an information exchange method optimized for the communication environment of that region. If the generating AI is moving, the information exchange unit provides an information exchange method adapted to the communication environment of the destination. If the generating AI is located in a specific country, the information exchange unit provides an information exchange method adapted to the communication regulations of that country. In this way, by providing the optimal information exchange method considering the geographical location information of the generating AI, it is possible to provide information exchange adapted to the communication environment. Geographical location information includes GPS data, IP addresses, location information services, etc. For example, the information exchange unit evaluates the communication environment based on the geographical location information of the generating AI and selects the optimal information exchange method. Some or all of the above processing in the information exchange unit may be performed using AI or not using AI. For example, the information exchange unit can input geographical location data into the generating AI and have the generating AI select the optimal information exchange method.

[0085] The information exchange unit analyzes the social media activity of the generating AI during information exchange and provides relevant information exchange methods. For example, if the generating AI is frequently used on social media, the information exchange unit provides an information exchange method optimized for that activity. If the generating AI is actively using a specific social media platform, the information exchange unit provides an information exchange method adapted to that platform. The information exchange unit selects the optimal information exchange method from the generating AI's social media activity. This enables efficient information exchange by analyzing the generating AI's social media activity and providing relevant information exchange methods. Social media activity includes post content, follower count, engagement rate, etc. For example, the information exchange unit evaluates the performance of information exchange based on the generating AI's social media activity and selects the optimal information exchange method. Some or all of the above processing in the information exchange unit may be performed using AI or not. For example, the information exchange unit can input social media activity data into the generating AI and have the generating AI select relevant information exchange methods.

[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 AI ​​generation interoperability system can also include a data filtering unit. This unit pre-filters information exchanged between AI generation systems, removing unnecessary and duplicate data. For example, the data filtering unit can evaluate image quality and remove low-quality images before sending image data generated by an image generation AI to another AI. It can also correct grammatical errors and spelling mistakes in text data generated by a text generation AI before sending it to another AI. Furthermore, it can remove noise and improve sound quality in audio data generated by a speech generation AI before sending it to another AI. This improves the quality of information exchanged between AI generation systems, enabling more efficient information exchange.

[0088] The AI ​​generation interoperability system can also be equipped with a data compression unit. The data compression unit compresses the information exchanged between AI generation systems, improving communication efficiency. For example, the data compression unit can compress image data generated by an image generation AI before sending it to another AI generation system, reducing the amount of data transmitted. It can also compress text data generated by a text generation AI before sending it to another AI generation system, improving communication speed. Furthermore, it can compress audio data generated by a speech generation AI before sending it to another AI generation system, improving communication efficiency. As a result, the communication efficiency of information exchanged between AI generation systems is improved, enabling rapid information exchange.

[0089] The AI ​​generation interoperability system can also be equipped with a data encryption unit. The data encryption unit encrypts information exchanged between AI generation systems to ensure security. For example, the data encryption unit can encrypt image data generated by an image generation AI before sending it to another AI generation system, preventing unauthorized access. Similarly, it can encrypt text data generated by a text generation AI before sending it to another AI generation system, preventing data leakage. Furthermore, it can encrypt voice data generated by a voice generation AI before sending it to another AI generation system, improving communication security. This enhances the security of information exchanged between AI generation systems, enabling safe information exchange.

[0090] The AI ​​generation interoperability system can also be equipped with a data cache unit. The data cache unit temporarily stores and reuses information exchanged between the AI ​​generation units, thereby improving communication efficiency. For example, the data cache unit can temporarily store image data generated by an image generation AI and provide it from the cache when the same data is sent again. Similarly, it can temporarily store text data generated by a text generation AI and provide it from the cache when the same data is sent again. Furthermore, it can temporarily store audio data generated by a speech generation AI and provide it from the cache when the same data is sent again. This improves the communication efficiency of information exchanged between the AI ​​generation units, enabling rapid information exchange.

[0091] The AI ​​generation interoperability system can also include a data conversion unit. This unit converts information exchanged between AI generation systems into different formats to ensure compatibility. For example, the data conversion unit can convert image data generated by an image generation AI into a format usable by other AI generation systems. It can also convert text data generated by a text generation AI into a format usable by other AI generation systems. Furthermore, it can convert audio data generated by a speech generation AI into a format usable by other AI generation systems. This improves the compatibility of information exchanged between AI generation systems, enabling efficient information exchange.

[0092] The AI-generated interactive system may further include an information prioritization unit that estimates the user's emotions and determines the priority of information based on those emotions. For example, the information prioritization unit can prioritize providing important information when the user is stressed, and provide detailed information when the user is relaxed. It can also provide information quickly when the user is in a hurry, and provide information with detailed explanations when the user has ample time. This allows for optimal information delivery by prioritizing information according to the user's emotions.

[0093] The AI-generated interactive system may further include an information display adjustment unit that estimates the user's emotions and adjusts the way information is displayed based on those emotions. For example, the information display adjustment unit can select a simple and intuitive display method if the user is tense, and a display method that includes detailed information if the user is relaxed. It can also display information quickly if the user is in a hurry, and select a display method that includes detailed explanations if the user has time. By adjusting the way information is displayed according to the user's emotions, it becomes possible to display information in a way that is optimal for the user.

[0094] The AI-generated interactive system can further include an information filtering unit that estimates the user's emotions and filters information based on those emotions. For example, the information filtering unit can display only important information when the user is stressed, and display detailed information when the user is relaxed. It can also provide information quickly when the user is in a hurry, and provide information with detailed explanations when the user has ample time. This allows for the provision of optimal information to the user by filtering information according to their emotions.

[0095] The AI-generated interactive system can further include an information notification adjustment unit that estimates the user's emotions and adjusts the method of information notification based on the estimated emotions. For example, if the user is stressed, the information notification adjustment unit can select a simple and intuitive notification method, and if the user is relaxed, it can select a notification method that includes detailed information. Also, if the user is in a hurry, it can notify the user quickly, and if the user has time, it can select a notification method that includes detailed explanations. In this way, by adjusting the method of information notification according to the user's emotions, it becomes possible to provide the most optimal information notification for the user.

[0096] The AI-generated interactive system can further include an information delivery timing adjustment unit that estimates the user's emotions and adjusts the timing of information delivery based on the estimated emotions. For example, if the user is tense, the information delivery timing adjustment unit can delay the delivery and wait until the user is relaxed. If the user is relaxed, it can provide information immediately to facilitate smooth information exchange. Furthermore, if the user is in a hurry, it can speed up the delivery of information to provide it quickly. In this way, by adjusting the timing of information delivery according to the user's emotions, information can be delivered at the optimal time for the user.

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

[0098] Step 1: The protocol provider unit provides a communication protocol for the generated AIs to exchange information. For example, it provides a communication protocol called MetaLink, which offers features such as automated communication, faster response times, parallel processing, greater flexibility in handling, layered disclosure of information, interoperability between manufacturers, and security assurance. Step 2: The connection unit uses the communication protocol provided by the protocol provider unit to connect the generated AIs to a higher level. The connection unit includes procedures for the generated AIs to connect to a higher level, such as authentication processes, connection establishment methods, and error handling. Step 3: The information exchange unit facilitates information exchange between the generating AIs connected by the connection unit. The information exchange unit enables high-level collaboration between the generating AIs that cannot be achieved through API integration, ensuring data integration, interoperability, and real-time capabilities.

[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 provider, connection unit, and information exchange unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the protocol provider 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 connection 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 information exchange 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 modified 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, including the protocol provider, connection unit, and information exchange unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the protocol provider 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 connection 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 information exchange 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 provider, connection unit, and information exchange unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the protocol provider 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 connection 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 information exchange 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, including the protocol provider, connection unit, and information exchange unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the protocol provider is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The connection 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 information exchange 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 modified 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 provider unit that provides a communication protocol for generating AIs to exchange information with each other, A connection unit that uses the communication protocol provided by the protocol provider unit to connect the generated AIs to each other in a higher order, The system includes an information exchange unit that allows the generating AIs connected by the aforementioned connection unit to exchange information with each other. A system characterized by the following features. (Note 2) The protocol provider unit, It provides a communication protocol called MetaLink. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned connection part is Includes procedures for generating AIs to connect to each other at a higher level. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information exchange unit is: Generative AIs exchange information while keeping it closed to each other. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information exchange unit is: Generating AIs perform high-level collaboration that cannot be achieved through API integration alone. The system described in Appendix 1, characterized by the features described herein. (Note 6) The protocol provider unit, It provides features such as automated communication, faster response times, parallel processing, greater flexibility in responses, layered disclosure of information, interoperability between manufacturers, and enhanced security. The system described in Appendix 1, characterized by the features described herein. (Note 7) The protocol provider unit, It estimates the user's emotions and adjusts the timing of protocol delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The protocol provider unit, When providing a protocol, the optimal protocol version is selected based on the characteristics of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 9) The protocol provider unit, When providing a protocol, the AI ​​optimizes it by referring to its past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The protocol provider unit, It estimates the user's emotions and adjusts how the protocol is delivered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The protocol provider unit, When providing a protocol, the optimal protocol is provided, taking into account the geographical location information of the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 12) The protocol provider unit, When providing protocols, the generative AI analyzes social media activity and provides relevant protocols. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned connection part is It estimates the user's emotions and adjusts the connection procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned connection part is During connection, the optimal connection method is selected based on the characteristics of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned connection part is When connecting, the generated AI optimizes the connection by referring to its past connection history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned connection part is It estimates the user's emotions and determines connection priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned connection part is During connection, the optimal connection method is provided, taking into account the geographical location information of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned connection part is Upon connection, the generated AI analyzes social media activity and provides relevant connection methods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information exchange unit is: It estimates the user's emotions and adjusts the method of information exchange based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information exchange unit is: When exchanging information, the optimal information exchange method is selected based on the characteristics of the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information exchange unit is: During information exchange, the AI ​​optimizes the exchange process by referring to its past information exchange history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information exchange unit is: It estimates the user's emotions and determines the priority of information exchange based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned information exchange unit is: When exchanging information, the system provides the optimal information exchange method, taking into account the geographical location information of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information exchange unit is: During information exchange, the AI ​​analyzes the social media activity of the generating AI and provides relevant methods for information exchange. 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 provider unit that provides a communication protocol for generating AIs to exchange information with each other, A connection unit that uses the communication protocol provided by the protocol provider unit to connect the generated AIs to each other in a higher order, The system includes an information exchange unit that allows the generated AIs connected by the aforementioned connection unit to exchange information with each other. A system characterized by the following features.

2. The protocol provider unit, It provides a communication protocol called MetaLink. The system according to feature 1.

3. The aforementioned connection part is Includes procedures for higher-level connections between generating AIs. The system according to feature 1.

4. The aforementioned information exchange unit is: Generative AIs exchange information while keeping it closed to each other. The system according to feature 1.

5. The aforementioned information exchange unit is: Generative AIs perform high-level collaboration that cannot be achieved through API integration. The system according to feature 1.

6. The protocol provider unit, It provides features such as automated communication, faster response times, parallel processing, greater flexibility in responses, layered disclosure of information, interoperability between manufacturers, and enhanced security. The system according to feature 1.

7. The protocol provider unit, It estimates the user's emotions and adjusts the timing of protocol delivery based on the estimated user emotions. The system according to feature 1.

8. The protocol provider unit, When providing a protocol, the optimal protocol version is selected based on the characteristics of the generated AI. The system according to feature 1.

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