system

Abstract

The system according to this embodiment aims to propose and seamlessly provide the optimal generation AI service based on the user's requests and needs. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives user requests and needs. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes the optimal generation AI service based on the information analyzed by the analysis unit. The provision unit makes the service proposed by the proposal unit available for seamless use within the platform.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a user individually uses a plurality of generative AI services, there is a problem that management and selection are complicated.

[0005] The system according to the embodiment aims to propose an optimal generative AI service based on user desires and needs and provide it seamlessly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a service provision unit. The reception unit receives user requests and needs. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes the optimal generation AI service based on the information analyzed by the analysis unit. The service provision unit makes the service proposed by the proposal unit available for seamless use within the platform. [Effects of the Invention]

[0007] The system according to this embodiment can propose and seamlessly provide the optimal generation AI service based on the user's requests and needs. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 所0 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^ include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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 service aggregation platform according to an embodiment of the present invention is a system that centralizes and manages all generative AI services through a single interface, proposing and providing the optimal generative AI service according to the user's requests and needs. The generative AI service aggregation platform works as follows: the user accesses the platform and inputs their requests and needs. Next, the generative AI analyzes the user's input and proposes the optimal generative AI service. The proposed service is provided for seamless use within the platform. For example, a user inputs "I want to use a translation service." This information is input to the generative AI. Next, the generative AI analyzes the input information and proposes the optimal generative AI service. The generative AI understands the user's requests and needs and selects the most suitable service. For example, if a user requests a translation service, the generative AI proposes the most suitable one from among several translation services. The proposed service is provided for seamless use within the platform. The user can use the proposed service without having to move to another platform. For example, if a user uses the proposed translation service, they can complete the translation work within the platform. This mechanism allows users to easily use a variety of generative AI services on a single platform. Because the service is tailored to the user's requests and needs, it is easy to use even for people who find it difficult to use AI-generated services, such as those who are tech-savvy or the elderly. Furthermore, because services can be used seamlessly within the platform, users can avoid the hassle of switching to other platforms. For example, people who experience language barriers in foreign language learning, translation, or communication can easily use AI-generated translation services. Also, people who find it difficult to find necessary information from a vast amount of data can efficiently acquire information using AI-generated translation. Additionally, students and young people who find it difficult to find suitable information during job hunting can use AI-generated translation to obtain optimal information for creating application documents and preparing for interviews.People who have difficulty accessing or understanding information in various aspects of their daily lives, such as dementia patients and the elderly, can easily obtain the necessary information using generative AI. In this way, the generative AI service aggregation platform uses generative AI to consolidate and manage diverse services through a single point of contact, proposing and providing the most suitable service according to the user's requests and needs, thereby creating an environment that is easy to use for people who feel that generative AI services are difficult to use, such as the information-disadvantaged and the elderly. As a result, the generative AI service aggregation platform can propose and seamlessly provide the most suitable generative AI service based on the user's requests and needs.

[0029] The generative AI service aggregation platform according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs user requests and needs. User requests and needs include, but are not limited to, technical requests and service-related needs. The reception unit inputs user requests and needs, for example, using a web interface. The reception unit can also input user requests and needs using a mobile application. Furthermore, the reception unit can also input user requests and needs using voice input. For example, the reception unit uses a web interface for users to input requests and needs in text format. When using a mobile application, users can input requests and needs using a smartphone or tablet. When using voice input, users can input requests and needs by voice using a microphone. The analysis unit analyzes the information received by the reception unit using generative AI. The analysis is performed, for example, using natural language processing technology, but is not limited to this example. For example, the analysis unit analyzes user requests and needs using generative AI. The analysis unit can also analyze user requests and needs using data mining technology. Furthermore, the analysis unit can also analyze user requests and needs using machine learning algorithms. For example, the analysis unit can use natural language processing technology to analyze user input text and extract requests and needs. When using data mining technology, the analysis unit extracts useful information from large amounts of data to identify user requests and needs. When using machine learning algorithms, the analysis unit learns from past data and predicts user requests and needs. The proposal unit proposes the optimal generative AI service based on the information analyzed by the analysis unit. Proposals are made, for example, based on the user's past behavior history, but are not limited to such examples. For example, the proposal unit can use generative AI to propose the optimal generative AI service for the user. The proposal unit can also use machine learning algorithms to propose the optimal generative AI service for the user. The proposal unit can also propose the optimal generative AI service based on the user's past behavior history.For example, the proposal department uses generative AI to select the optimal generative AI service based on the user's requests and needs. When using machine learning algorithms, the proposal department learns from past data and proposes the optimal generative AI service to the user. When based on the user's past behavior history, the proposal department analyzes the services and behavior patterns the user has used in the past and proposes the optimal generative AI service. The service provider ensures that the services proposed by the proposal department can be used seamlessly within the platform. Seamless use means, for example, providing an integrated user interface that does not require the user to perform any additional actions, but is not limited to such examples. For example, the service provider ensures that the user can use the proposed service without moving to another platform. The service provider can also enable the user to use the proposed service without requiring any additional actions. Furthermore, the service provider can provide an integrated user interface to enable the user to use the service seamlessly. For example, the service provider ensures that the user does not need to move to another platform when using the proposed service. The service provider automates the service usage procedure so that the user does not need to perform any additional actions. When providing an integrated user interface, the service provider ensures that the user can use multiple services on a single screen. As a result, the generative AI service aggregation platform according to this embodiment can propose and seamlessly provide the optimal generative AI service based on the user's requests and needs.

[0030] The reception desk receives user requests and needs. These requests and needs include, but are not limited to, technical requests and service-related needs. The reception desk can receive user requests and needs, for example, via a web interface. It can also receive requests and needs via a mobile application. Furthermore, it can receive requests and needs via voice input. For example, the reception desk uses a web interface where users can enter requests and needs in text format. When using a mobile application, users can enter requests and needs using a smartphone or tablet. When using voice input, users can enter requests and needs by voice using a microphone. The reception desk is designed to allow users to enter requests and needs intuitively and easily, regardless of the device they use. For example, the web interface employs a simple and user-friendly design for easy access. The mobile application provides an interface optimized for touch operation, allowing users to enter requests smoothly. In the case of voice input, speech recognition technology is used to accurately convert user speech into text, enabling rapid input of requests and needs. Furthermore, the reception section has a function that automatically categorizes user input and sends it to the analysis section. This ensures that user requests and needs are quickly and accurately communicated to the analysis section, allowing it to proceed to the next step. The reception section is designed with maximum user convenience in mind, allowing for smooth input of requests and needs in any situation.

[0031] The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed using, for example, natural language processing technology, but is not limited to this example. For example, the analysis unit uses generative AI to analyze user requests and needs. The analysis unit can also use data mining technology to analyze user requests and needs. Furthermore, the analysis unit can use machine learning algorithms to analyze user requests and needs. For example, the analysis unit uses natural language processing technology to analyze user input text and extract requests and needs. When using data mining technology, the analysis unit extracts useful information from large amounts of data to identify user requests and needs. When using machine learning algorithms, the analysis unit learns from past data to predict user requests and needs. The analysis unit uses generative AI to analyze user requests and needs in detail. Specifically, it uses natural language processing technology to analyze user input text and extract requests and needs. For example, if a user enters "I would like to request a new website design," the analytics unit extracts the request "website design" from the text and, from the keyword "new," understands that the user is seeking a completely new design, not a redesign of an existing one. When using data mining techniques, the analytics unit extracts useful information from large amounts of data to identify user requests and needs. For example, by analyzing past user request data and finding common patterns and trends, it can predict new requests. When using machine learning algorithms, the analytics unit learns from past data to predict user requests and needs. For example, by learning what services users with similar requests have used in the past, it can propose the most suitable service to a new user. The analytics unit combines these technologies to analyze user requests and needs from multiple perspectives and provide the foundational information for proposing the most suitable service.

[0032] The proposal department proposes the optimal generative AI service based on the information analyzed by the analysis department. Proposals are made, for example, based on the user's past behavior history, but are not limited to such examples. For example, the proposal department uses generative AI to propose the optimal generative AI service to the user. The proposal department can also use machine learning algorithms to propose the optimal generative AI service to the user. Furthermore, the proposal department can propose the optimal generative AI service based on the user's past behavior history. For example, the proposal department uses generative AI to select the optimal generative AI service based on the user's requests and needs. When using machine learning algorithms, the proposal department learns from past data and proposes the optimal generative AI service to the user. When based on the user's past behavior history, the proposal department analyzes the services and behavior patterns the user has used in the past and proposes the optimal generative AI service. The proposal department proposes the optimal generative AI service to the user based on the information provided by the analysis department. Specifically, it uses generative AI to select the service that best suits the user's requests and needs. For example, if a user enters "I would like to request a new website design," the proposal department will refer to services used by users with similar requests in the past and propose the optimal design service. When using machine learning algorithms, the proposal unit learns from past data and proposes the most suitable generative AI service to the user. For example, it identifies services that have received high ratings for specific requests from past data and proposes those services to new users. When based on the user's past behavior history, the proposal unit analyzes the services and behavior patterns the user has used in the past and proposes the most suitable generative AI service. For example, it analyzes the history of services the user has used in the past and proposes services used by other users with similar requests. By utilizing these technologies, the proposal unit can propose the most suitable generative AI service to the user and meet their requests and needs.

[0033] The service provider ensures that the services proposed by the proposal team are seamlessly available within the platform. Seamless availability means, for example, providing an integrated user interface that requires no additional user action. For example, the service provider can enable users to use the proposed services without having to move to another platform. The service provider can also enable users to use the proposed services without requiring any additional action. The service provider can also provide an integrated user interface that allows users to use the services seamlessly. For example, the service provider ensures that users do not need to move to another platform when using the proposed services. The service provider automates the service usage process so that users do not need to take any additional action. When providing an integrated user interface, the service provider allows users to use multiple services on a single screen. The service provider ensures that the services proposed by the proposal team are seamlessly available within the platform. Specifically, this means providing an integrated user interface that requires no additional user action. For example, the service provider ensures that users can use the proposed services without having to move to another platform. This significantly improves convenience because users can use all services within a single platform. Furthermore, the service provider can enable users to access the proposed services without requiring any additional actions. For example, users may not need to re-enter login or payment information when using the proposed services. In addition, the service provider can provide an integrated user interface to enable users to use the services seamlessly. For example, by allowing users to access multiple services on a single screen, the service provider can reduce the effort required and improve the user experience. Through these functions, the service provider can provide an environment in which users can smoothly utilize the proposed services, thereby increasing user satisfaction.

[0034] The reception unit can provide an interface for users to input their requests and needs. For example, the reception unit can provide an interface for users to input their requests and needs using a web interface. The reception unit can also provide an interface for users to input their requests and needs using a mobile application. The reception unit can also provide an interface for users to input their requests and needs using voice input. This makes input easier by providing an interface for users to input their requests and needs. The interface includes, but is not limited to, a web interface, a mobile application, and a voice input interface. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input an interface for users to input their requests and needs into a generating AI and have the generating AI provide the interface.

[0035] The analysis unit can analyze user requests and needs and select the optimal generative AI service. The analysis unit can analyze user requests and needs using, for example, natural language processing technology. The analysis unit can also analyze user requests and needs using, for example, data mining technology. The analysis unit can also analyze user requests and needs using, for example, machine learning algorithms. By analyzing user requests and needs and selecting the optimal generative AI service, the analysis unit can provide the user with the best possible service. The analysis includes, but is not limited to, natural language processing, data mining, and machine learning algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input user requests and needs into the generative AI and have the generative AI perform the analysis.

[0036] The proposal unit can propose selected generative AI services to the user. For example, the proposal unit can use a generative AI to propose the optimal generative AI service to the user. The proposal unit can also use a machine learning algorithm to propose the optimal generative AI service to the user. The proposal unit can also propose the optimal generative AI service based on the user's past behavior history. In this way, by proposing selected generative AI services to the user, the user can choose the optimal service. Proposals include, but are not limited to, generative AI, machine learning algorithms, and the user's past behavior history. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not using a generative AI. For example, the proposal unit can input selected generative AI services into a generative AI and have the generative AI execute the proposal.

[0037] The provider can make the proposed generative AI service seamlessly available within the platform. For example, the provider can enable users to use the proposed service without having to move to another platform. The provider can also enable users to use the proposed service without requiring any additional operations. The provider can also provide an integrated user interface to enable users to use the service seamlessly. This improves user convenience by enabling the proposed generative AI service to be used seamlessly within the platform. Seamless use refers to, for example, providing an integrated user interface that does not require users to perform any additional operations, but is not limited to such examples. Some or all of the processing described above in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the proposed generative AI service into a generative AI and have the generative AI perform seamless use.

[0038] The service provider can enable users to access the proposed service without having to move to another platform. For example, the service provider can enable users to access the proposed service without having to move to another platform. The service provider can also enable users to access the proposed service without requiring any additional actions. The service provider can also provide an integrated user interface, enabling users to access the service seamlessly. This further enhances user convenience by enabling users to access the proposed service without having to move to another platform. Accessing the service without moving to another platform means, for example, that users do not need to move between websites or applications, but is not limited to such examples. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the proposed service into a generating AI and have the generating AI execute it so that it can be used without moving to another platform.

[0039] The reception desk can analyze the user's past requests and needs history and provide the optimal input interface. For example, the reception desk can automatically display requests and needs that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requests and needs that the user will use at a specific time of day based on the user's past input history. By analyzing the user's past history, the reception desk can provide the optimal input interface and streamline the input process. Past requests and needs history includes, but is not limited to, past search history and purchase history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's past requests and needs history into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0040] The reception desk can customize input fields based on the user's current situation and areas of interest when they input requests or needs. For example, when the user inputs their current situation, the reception desk can prioritize displaying items related to their areas of interest. The reception desk can also automatically suggest input fields related to a specific area of ​​interest if the user has such an area of ​​interest. The reception desk can also customize and display highly relevant input fields based on the user's current situation. This allows for more appropriate input by customizing input fields based on the user's current situation and areas of interest. Current situation and areas of interest include, but are not limited to, the user's current geographical location and recent search topics. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current situation and areas of interest into a generating AI and have the generating AI perform the customization of input fields.

[0041] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location when they input requests or needs. For example, if the user is in a specific region, the reception desk will prioritize displaying input fields related to that region. The reception desk can also suggest highly relevant services and information based on the user's current location. For example, if the user is traveling, the reception desk can prioritize displaying input fields related to their travel destination. This ensures that highly relevant input fields are prioritized by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0042] The reception desk can analyze the user's social media activity and suggest relevant input fields when requests or needs are entered. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant input fields. The reception desk can also suggest input fields related to the user's areas of interest based on the user's social media activity history. The reception desk can also suggest relevant input fields considering the user's social media friendships. In this way, relevant input fields are suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI suggest relevant input fields.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the user's past requests and needs history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the user's past requests and needs history. The analysis unit can also improve the accuracy of its analysis by referring to the user's past service history. The analysis unit can also improve the accuracy of its analysis by analyzing the user's past input history. This improves the accuracy of the analysis by referring to the user's past history. Past requests and needs history includes, but is not limited to, past search history and purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past requests and needs history into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0044] The analysis unit can apply different analysis methods depending on the category of requests and needs during analysis. For example, the analysis unit can apply a language analysis method to a request for a translation service. For example, the analysis unit can also apply an image analysis method to a request for an image generation service. For example, the analysis unit can also apply a speech analysis method to a request for a speech recognition service. By applying an analysis method according to the category of requests and needs, the accuracy of the analysis results is improved. The categories of requests and needs include, but are not limited to, technical requests and service-related needs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of requests and needs into a generative AI and have the generative AI perform the application of the analysis method.

[0045] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis process. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. The analysis unit can also analyze highly relevant information based on the user's current location. For example, if the user is traveling, the analysis unit can prioritize analyzing information related to their travel destination. This improves the accuracy of the analysis by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can analyze the content of the user's social media posts and analyze related information. The analysis unit can also analyze information related to the user's areas of interest based on the user's social media activity history. The analysis unit can also analyze related information by considering the user's social media friendships. This improves the accuracy of the analysis by referring to the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0047] The proposal unit can make optimal suggestions by referring to the user's past requests and needs history when making suggestions. For example, the proposal unit can make optimal suggestions based on the user's past requests and needs history. The proposal unit can also make optimal suggestions by referring to the user's past service usage history. The proposal unit can also make optimal suggestions by analyzing the user's past input history. In this way, optimal suggestions are made by referring to the user's past history. Past requests and needs history includes, but is not limited to, past search history and purchase history. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the user's past requests and needs history into a generative AI and have the generative AI execute the optimal suggestion.

[0048] The proposal unit can apply different proposal algorithms depending on the category of requests and needs during the proposal process. For example, the proposal unit can apply a language analysis algorithm to requests for translation services. For example, the proposal unit can apply an image analysis algorithm to requests for image generation services. For example, the proposal unit can apply a speech analysis algorithm to requests for speech recognition services. By applying proposal algorithms according to the category of requests and needs, the accuracy of the proposal is improved. The categories of requests and needs include, but are not limited to, technical requests and service-related needs. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the categories of requests and needs into a generative AI and have the generative AI apply the proposal algorithm.

[0049] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will prioritize suggesting services related to that region. The suggestion unit can also suggest highly relevant services based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggesting services related to their travel destination. This ensures that highly relevant suggestions are made by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI execute the optimal suggestion.

[0050] The suggestion unit can improve the accuracy of its suggestions by referring to the user's social media activity when making suggestions. For example, the suggestion unit can analyze the user's social media posts and suggest relevant services. The suggestion unit can also suggest services related to the user's areas of interest based on the user's social media activity history. The suggestion unit can also suggest relevant services by considering the user's social media friendships. This improves the accuracy of suggestions by referring to the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI perform the task of improving the accuracy of suggestions.

[0051] The service provider can select the optimal service delivery method by referring to the user's past service usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's past service usage history. The service provider can also predict and suggest preferred service delivery methods based on the user's past usage history. For example, the service provider can analyze the user's past usage history and select the most efficient service delivery method. In this way, the optimal service delivery method is selected by referring to the user's past usage history. Past service usage history includes, but is not limited to, past usage history and feedback. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past service usage history into a generating AI and have the generating AI select the optimal service delivery method.

[0052] The service provider can customize the means of service delivery based on the user's current situation at the time of delivery. For example, the service provider can suggest the optimal means of delivery when the user inputs their current situation. The service provider can also customize the means of delivery according to the situation if the user is in a specific situation. The service provider can also customize and provide a highly relevant means of delivery based on the user's current situation. This ensures that the optimal means of delivery is selected by customizing the means of delivery based on the user's current situation. Current situation includes, but is not limited to, the current geographical location and current activity status. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's current situation into a generating AI and have the generating AI perform the customization of the means of delivery.

[0053] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing services related to that region. The service provider can also provide highly relevant services based on the user's current location. For example, if the user is traveling, the service provider can prioritize providing services related to their travel destination. This ensures that a highly relevant service delivery method is selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0054] The service provider can, at the time of service provision, propose a means of service delivery by referring to the user's social media activity. For example, the service provider can analyze the content of the user's social media posts and provide relevant services. For example, the service provider can also provide services related to the user's areas of interest based on the user's social media activity history. For example, the service provider can also provide relevant services by considering the user's social media friendships. In this way, the optimal means of service delivery is proposed by referring to the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI propose a means of service delivery.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can provide input assistance by referring to the user's past behavioral history when the user inputs requests and needs. For example, it can automatically display as suggestions services and requests that the user has frequently used in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest requests and needs that the user will use at specific times of day based on the user's past behavioral history. This makes input more efficient by referring to the user's past history. Past behavioral history includes, but is not limited to, past search history and usage history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past behavioral history into a generating AI and have the generating AI perform input assistance.

[0057] The suggestion section can customize suggestions based on the user's current situation and areas of interest. For example, when a user enters their current situation, suggestions related to their areas of interest are prioritized. If a user has a specific area of ​​interest, suggestions related to that area can be automatically generated. Furthermore, highly relevant suggestions can be customized and displayed based on the user's current situation. This allows for more appropriate suggestions by customizing them based on the user's current situation and areas of interest. Current situation and areas of interest include, but are not limited to, the user's current geographical location and recent search topics. Some or all of the processing described above in the suggestion section may be performed using AI, for example, or not. For example, the suggestion section can input the user's current situation and areas of interest into a generating AI and have the generating AI customize the suggestions.

[0058] The reception unit can analyze the user's social media activity and suggest relevant input fields. For example, it can analyze the content of the user's social media posts and suggest relevant input fields. It can also suggest input fields related to the user's areas of interest based on the user's social media activity history. Furthermore, it can suggest relevant input fields considering the user's social media friendships. In this way, relevant input fields are suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity into a generating AI and have the generating AI suggest relevant input fields.

[0059] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information. For example, if the user is in a specific region, it can prioritize the analysis of information related to that region. It can also analyze highly relevant information based on the user's current location. Furthermore, if the user is traveling, it can prioritize the analysis of information related to their travel destination. This improves the accuracy of the analysis by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0060] The service delivery unit can select the optimal delivery method by referring to the user's past service usage history at the time of delivery. For example, it can select the optimal delivery method based on the user's past service usage history. It can also predict and suggest preferred delivery methods based on the user's past usage history. Furthermore, it can analyze the user's past usage history and select the most efficient delivery method. In this way, the optimal delivery method is selected by referring to the user's past usage history. Past service usage history includes, but is not limited to, past usage history and feedback. Some or all of the above processing in the service delivery unit may be performed using, for example, AI, or not using AI. For example, the service delivery unit can input the user's past service usage history into a generating AI and have the generating AI select the optimal delivery method.

[0061] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, if the user is in a specific region, it can prioritize suggesting services related to that region. It can also suggest highly relevant services based on the user's current location. Furthermore, if the user is traveling, it can prioritize suggesting services related to their travel destination. In this way, highly relevant suggestions are made by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI execute the optimal suggestion.

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

[0063] Step 1: The reception desk inputs the user's requests and needs. These requests and needs may include, for example, technical requests and service-related needs. The reception desk can input the user's requests and needs using a web interface, mobile application, voice input, etc. For example, this may involve inputting in text format using a web interface, inputting using a smartphone or tablet, or inputting by voice using a microphone. Step 2: The analysis unit uses generative AI to analyze the information received by the reception unit. The analysis is performed using natural language processing technology, data mining technology, machine learning algorithms, etc. For example, natural language processing technology is used to analyze the user's input text and extract requests and needs. When using data mining technology, useful information is extracted from a large amount of data to identify the user's requests and needs. When using machine learning algorithms, past data is learned to predict the user's requests and needs. Step 3: The proposal unit proposes the optimal generative AI service based on the information analyzed by the analysis unit. The proposal is made using generative AI or machine learning algorithms. For example, the optimal generative AI service can be proposed based on the user's past behavior history. When using generative AI, the optimal generative AI service is selected based on the user's requests and needs. When using machine learning algorithms, past data is learned and the optimal generative AI service is proposed to the user. When based on the user's past behavior history, the services and behavior patterns the user has used in the past are analyzed and the optimal generative AI service is proposed. Step 4: The service provider ensures that the services proposed by the proposal provider are seamlessly available within the platform. Seamless availability means providing an integrated user interface that requires no additional user action. For example, users should be able to use the proposed services without having to move to another platform. The service provider automates the service usage process and allows users to access multiple services on a single screen.

[0064] (Example of form 2) The generative AI service aggregation platform according to an embodiment of the present invention is a system that centralizes and manages all generative AI services through a single interface, proposing and providing the optimal generative AI service according to the user's requests and needs. The generative AI service aggregation platform works as follows: the user accesses the platform and inputs their requests and needs. Next, the generative AI analyzes the user's input and proposes the optimal generative AI service. The proposed service is provided for seamless use within the platform. For example, a user inputs "I want to use a translation service." This information is input to the generative AI. Next, the generative AI analyzes the input information and proposes the optimal generative AI service. The generative AI understands the user's requests and needs and selects the most suitable service. For example, if a user requests a translation service, the generative AI proposes the most suitable one from among several translation services. The proposed service is provided for seamless use within the platform. The user can use the proposed service without having to move to another platform. For example, if a user uses the proposed translation service, they can complete the translation work within the platform. This mechanism allows users to easily use a variety of generative AI services on a single platform. Because the service is tailored to the user's requests and needs, it is easy to use even for people who find it difficult to use AI-generated services, such as those who are tech-savvy or the elderly. Furthermore, because services can be used seamlessly within the platform, users can avoid the hassle of switching to other platforms. For example, people who experience language barriers in foreign language learning, translation, or communication can easily use AI-generated translation services. Also, people who find it difficult to find necessary information from a vast amount of data can efficiently acquire information using AI-generated translation. Additionally, students and young people who find it difficult to find suitable information during job hunting can use AI-generated translation to obtain optimal information for creating application documents and preparing for interviews.People who have difficulty accessing or understanding information in various aspects of their daily lives, such as dementia patients and the elderly, can easily obtain the necessary information using generative AI. In this way, the generative AI service aggregation platform uses generative AI to consolidate and manage diverse services through a single point of contact, proposing and providing the most suitable service according to the user's requests and needs, thereby creating an environment that is easy to use for people who feel that generative AI services are difficult to use, such as the information-disadvantaged and the elderly. As a result, the generative AI service aggregation platform can propose and seamlessly provide the most suitable generative AI service based on the user's requests and needs.

[0065] The generative AI service aggregation platform according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs user requests and needs. User requests and needs include, but are not limited to, technical requests and service-related needs. The reception unit inputs user requests and needs, for example, using a web interface. The reception unit can also input user requests and needs using a mobile application. Furthermore, the reception unit can also input user requests and needs using voice input. For example, the reception unit uses a web interface for users to input requests and needs in text format. When using a mobile application, users can input requests and needs using a smartphone or tablet. When using voice input, users can input requests and needs by voice using a microphone. The analysis unit analyzes the information received by the reception unit using generative AI. The analysis is performed, for example, using natural language processing technology, but is not limited to this example. For example, the analysis unit analyzes user requests and needs using generative AI. The analysis unit can also analyze user requests and needs using data mining technology. Furthermore, the analysis unit can also analyze user requests and needs using machine learning algorithms. For example, the analysis unit can use natural language processing technology to analyze user input text and extract requests and needs. When using data mining technology, the analysis unit extracts useful information from large amounts of data to identify user requests and needs. When using machine learning algorithms, the analysis unit learns from past data and predicts user requests and needs. The proposal unit proposes the optimal generative AI service based on the information analyzed by the analysis unit. Proposals are made, for example, based on the user's past behavior history, but are not limited to such examples. For example, the proposal unit can use generative AI to propose the optimal generative AI service for the user. The proposal unit can also use machine learning algorithms to propose the optimal generative AI service for the user. The proposal unit can also propose the optimal generative AI service based on the user's past behavior history.For example, the proposal department uses generative AI to select the optimal generative AI service based on the user's requests and needs. When using machine learning algorithms, the proposal department learns from past data and proposes the optimal generative AI service to the user. When based on the user's past behavior history, the proposal department analyzes the services and behavior patterns the user has used in the past and proposes the optimal generative AI service. The service provider ensures that the services proposed by the proposal department can be used seamlessly within the platform. Seamless use means, for example, providing an integrated user interface that does not require the user to perform any additional actions, but is not limited to such examples. For example, the service provider ensures that the user can use the proposed service without moving to another platform. The service provider can also enable the user to use the proposed service without requiring any additional actions. Furthermore, the service provider can provide an integrated user interface to enable the user to use the service seamlessly. For example, the service provider ensures that the user does not need to move to another platform when using the proposed service. The service provider automates the service usage procedure so that the user does not need to perform any additional actions. When providing an integrated user interface, the service provider ensures that the user can use multiple services on a single screen. As a result, the generative AI service aggregation platform according to this embodiment can propose and seamlessly provide the optimal generative AI service based on the user's requests and needs.

[0066] The reception desk receives user requests and needs. These requests and needs include, but are not limited to, technical requests and service-related needs. The reception desk can receive user requests and needs, for example, via a web interface. It can also receive requests and needs via a mobile application. Furthermore, it can receive requests and needs via voice input. For example, the reception desk uses a web interface where users can enter requests and needs in text format. When using a mobile application, users can enter requests and needs using a smartphone or tablet. When using voice input, users can enter requests and needs by voice using a microphone. The reception desk is designed to allow users to enter requests and needs intuitively and easily, regardless of the device they use. For example, the web interface employs a simple and user-friendly design for easy access. The mobile application provides an interface optimized for touch operation, allowing users to enter requests smoothly. In the case of voice input, speech recognition technology is used to accurately convert user speech into text, enabling rapid input of requests and needs. Furthermore, the reception section has a function that automatically categorizes user input and sends it to the analysis section. This ensures that user requests and needs are quickly and accurately communicated to the analysis section, allowing it to proceed to the next step. The reception section is designed with maximum user convenience in mind, allowing for smooth input of requests and needs in any situation.

[0067] The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed using, for example, natural language processing technology, but is not limited to this example. For example, the analysis unit uses generative AI to analyze user requests and needs. The analysis unit can also use data mining technology to analyze user requests and needs. Furthermore, the analysis unit can use machine learning algorithms to analyze user requests and needs. For example, the analysis unit uses natural language processing technology to analyze user input text and extract requests and needs. When using data mining technology, the analysis unit extracts useful information from large amounts of data to identify user requests and needs. When using machine learning algorithms, the analysis unit learns from past data to predict user requests and needs. The analysis unit uses generative AI to analyze user requests and needs in detail. Specifically, it uses natural language processing technology to analyze user input text and extract requests and needs. For example, if a user enters "I would like to request a new website design," the analytics unit extracts the request "website design" from the text and, from the keyword "new," understands that the user is seeking a completely new design, not a redesign of an existing one. When using data mining techniques, the analytics unit extracts useful information from large amounts of data to identify user requests and needs. For example, by analyzing past user request data and finding common patterns and trends, it can predict new requests. When using machine learning algorithms, the analytics unit learns from past data to predict user requests and needs. For example, by learning what services users with similar requests have used in the past, it can propose the most suitable service to a new user. The analytics unit combines these technologies to analyze user requests and needs from multiple perspectives and provide the foundational information for proposing the most suitable service.

[0068] The proposal department proposes the optimal generative AI service based on the information analyzed by the analysis department. Proposals are made, for example, based on the user's past behavior history, but are not limited to such examples. For example, the proposal department uses generative AI to propose the optimal generative AI service to the user. The proposal department can also use machine learning algorithms to propose the optimal generative AI service to the user. Furthermore, the proposal department can propose the optimal generative AI service based on the user's past behavior history. For example, the proposal department uses generative AI to select the optimal generative AI service based on the user's requests and needs. When using machine learning algorithms, the proposal department learns from past data and proposes the optimal generative AI service to the user. When based on the user's past behavior history, the proposal department analyzes the services and behavior patterns the user has used in the past and proposes the optimal generative AI service. The proposal department proposes the optimal generative AI service to the user based on the information provided by the analysis department. Specifically, it uses generative AI to select the service that best suits the user's requests and needs. For example, if a user enters "I would like to request a new website design," the proposal department will refer to services used by users with similar requests in the past and propose the optimal design service. When using machine learning algorithms, the proposal unit learns from past data and proposes the most suitable generative AI service to the user. For example, it identifies services that have received high ratings for specific requests from past data and proposes those services to new users. When based on the user's past behavior history, the proposal unit analyzes the services and behavior patterns the user has used in the past and proposes the most suitable generative AI service. For example, it analyzes the history of services the user has used in the past and proposes services used by other users with similar requests. By utilizing these technologies, the proposal unit can propose the most suitable generative AI service to the user and meet their requests and needs.

[0069] The service provider ensures that the services proposed by the proposal team are seamlessly available within the platform. Seamless availability means, for example, providing an integrated user interface that requires no additional user action. For example, the service provider can enable users to use the proposed services without having to move to another platform. The service provider can also enable users to use the proposed services without requiring any additional action. The service provider can also provide an integrated user interface that allows users to use the services seamlessly. For example, the service provider ensures that users do not need to move to another platform when using the proposed services. The service provider automates the service usage process so that users do not need to take any additional action. When providing an integrated user interface, the service provider allows users to use multiple services on a single screen. The service provider ensures that the services proposed by the proposal team are seamlessly available within the platform. Specifically, this means providing an integrated user interface that requires no additional user action. For example, the service provider ensures that users can use the proposed services without having to move to another platform. This significantly improves convenience because users can use all services within a single platform. Furthermore, the service provider can enable users to access the proposed services without requiring any additional actions. For example, users may not need to re-enter login or payment information when using the proposed services. In addition, the service provider can provide an integrated user interface to enable users to use the services seamlessly. For example, by allowing users to access multiple services on a single screen, the service provider can reduce the effort required and improve the user experience. Through these functions, the service provider can provide an environment in which users can smoothly utilize the proposed services, thereby increasing user satisfaction.

[0070] The reception unit can provide an interface for users to input their requests and needs. For example, the reception unit can provide an interface for users to input their requests and needs using a web interface. The reception unit can also provide an interface for users to input their requests and needs using a mobile application. The reception unit can also provide an interface for users to input their requests and needs using voice input. This makes input easier by providing an interface for users to input their requests and needs. The interface includes, but is not limited to, a web interface, a mobile application, and a voice input interface. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input an interface for users to input their requests and needs into a generating AI and have the generating AI provide the interface.

[0071] The analysis unit can analyze user requests and needs and select the optimal generative AI service. The analysis unit can analyze user requests and needs using, for example, natural language processing technology. The analysis unit can also analyze user requests and needs using, for example, data mining technology. The analysis unit can also analyze user requests and needs using, for example, machine learning algorithms. By analyzing user requests and needs and selecting the optimal generative AI service, the analysis unit can provide the user with the best possible service. The analysis includes, but is not limited to, natural language processing, data mining, and machine learning algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input user requests and needs into the generative AI and have the generative AI perform the analysis.

[0072] The proposal unit can propose selected generative AI services to the user. For example, the proposal unit can use a generative AI to propose the optimal generative AI service to the user. The proposal unit can also use a machine learning algorithm to propose the optimal generative AI service to the user. The proposal unit can also propose the optimal generative AI service based on the user's past behavior history. In this way, by proposing selected generative AI services to the user, the user can choose the optimal service. Proposals include, but are not limited to, generative AI, machine learning algorithms, and the user's past behavior history. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not using a generative AI. For example, the proposal unit can input selected generative AI services into a generative AI and have the generative AI execute the proposal.

[0073] The provider can make the proposed generative AI service seamlessly available within the platform. For example, the provider can enable users to use the proposed service without having to move to another platform. The provider can also enable users to use the proposed service without requiring any additional operations. The provider can also provide an integrated user interface to enable users to use the service seamlessly. This improves user convenience by enabling the proposed generative AI service to be used seamlessly within the platform. Seamless use refers to, for example, providing an integrated user interface that does not require users to perform any additional operations, but is not limited to such examples. Some or all of the processing described above in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the proposed generative AI service into a generative AI and have the generative AI perform seamless use.

[0074] The service provider can enable users to access the proposed service without having to move to another platform. For example, the service provider can enable users to access the proposed service without having to move to another platform. The service provider can also enable users to access the proposed service without requiring any additional actions. The service provider can also provide an integrated user interface, enabling users to access the service seamlessly. This further enhances user convenience by enabling users to access the proposed service without having to move to another platform. Accessing the service without moving to another platform means, for example, that users do not need to move between websites or applications, but is not limited to such examples. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the proposed service into a generating AI and have the generating AI execute it so that it can be used without moving to another platform.

[0075] The reception desk can estimate the user's emotions and adjust the input method for requests and needs based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of requests and needs. This reduces user stress and makes input easier by adjusting the input method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can analyze the user's past requests and needs history and provide the optimal input interface. For example, the reception desk can automatically display requests and needs that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requests and needs that the user will use at a specific time of day based on the user's past input history. By analyzing the user's past history, the reception desk can provide the optimal input interface and streamline the input process. Past requests and needs history includes, but is not limited to, past search history and purchase history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's past requests and needs history into a generating AI and have the generating AI perform the task of providing the optimal input interface.

[0077] The reception desk can customize input fields based on the user's current situation and areas of interest when they input requests or needs. For example, when the user inputs their current situation, the reception desk can prioritize displaying items related to their areas of interest. The reception desk can also automatically suggest input fields related to a specific area of ​​interest if the user has such an area of ​​interest. The reception desk can also customize and display highly relevant input fields based on the user's current situation. This allows for more appropriate input by customizing input fields based on the user's current situation and areas of interest. Current situation and areas of interest include, but are not limited to, the user's current geographical location and recent search topics. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current situation and areas of interest into a generating AI and have the generating AI perform the customization of input fields.

[0078] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize displaying important input items and postpone other items. For example, if the user is relaxed, the reception desk may prioritize displaying detailed input items and may also provide a customizable input method. For example, if the user is in a hurry, the reception desk may prioritize displaying the most important input items to allow for quick input. This ensures that important information is entered preferentially by prioritizing input content based on 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 reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI and have the generative AI determine the priority of input content.

[0079] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location when they input requests or needs. For example, if the user is in a specific region, the reception desk will prioritize displaying input fields related to that region. The reception desk can also suggest highly relevant services and information based on the user's current location. For example, if the user is traveling, the reception desk can prioritize displaying input fields related to their travel destination. This ensures that highly relevant input fields are prioritized by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0080] The reception desk can analyze the user's social media activity and suggest relevant input fields when requests or needs are entered. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant input fields. The reception desk can also suggest input fields related to the user's areas of interest based on the user's social media activity history. The reception desk can also suggest relevant input fields considering the user's social media friendships. In this way, relevant input fields are suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI suggest relevant input fields.

[0081] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. For example, if the user is in a hurry, the analysis unit can also perform a rapid analysis and provide results quickly. For example, if the user is excited, the analysis unit can also provide visually stimulating analysis results. This improves the accuracy of the analysis results by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0082] The analysis unit can improve the accuracy of its analysis by referring to the user's past requests and needs history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the user's past requests and needs history. The analysis unit can also improve the accuracy of its analysis by referring to the user's past service history. The analysis unit can also improve the accuracy of its analysis by analyzing the user's past input history. This improves the accuracy of the analysis by referring to the user's past history. Past requests and needs history includes, but is not limited to, past search history and purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's past requests and needs history into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0083] The analysis unit can apply different analysis methods depending on the category of requests and needs during analysis. For example, the analysis unit can apply a language analysis method to a request for a translation service. For example, the analysis unit can also apply an image analysis method to a request for an image generation service. For example, the analysis unit can also apply a speech analysis method to a request for a speech recognition service. By applying an analysis method according to the category of requests and needs, the accuracy of the analysis results is improved. The categories of requests and needs include, but are not limited to, technical requests and service-related needs. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the categories of requests and needs into a generative AI and have the generative AI perform the application of the analysis method.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display that is easy for the user to understand is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0085] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis process. For example, if the user is in a specific region, the analysis unit will prioritize analyzing information related to that region. The analysis unit can also analyze highly relevant information based on the user's current location. For example, if the user is traveling, the analysis unit can prioritize analyzing information related to their travel destination. This improves the accuracy of the analysis by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0086] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can analyze the content of the user's social media posts and analyze related information. The analysis unit can also analyze information related to the user's areas of interest based on the user's social media activity history. The analysis unit can also analyze related information by considering the user's social media friendships. This improves the accuracy of the analysis by referring to the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0087] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and offer many options. If the user is in a hurry, the suggestion unit can also provide quick suggestions and narrow down the options. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the way suggestions are presented based on the user's emotions, the optimal suggestions for the user are provided. 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 processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0088] The proposal unit can make optimal suggestions by referring to the user's past requests and needs history when making suggestions. For example, the proposal unit can make optimal suggestions based on the user's past requests and needs history. The proposal unit can also make optimal suggestions by referring to the user's past service usage history. The proposal unit can also make optimal suggestions by analyzing the user's past input history. In this way, optimal suggestions are made by referring to the user's past history. Past requests and needs history includes, but is not limited to, past search history and purchase history. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the user's past requests and needs history into a generative AI and have the generative AI execute the optimal suggestion.

[0089] The proposal unit can apply different proposal algorithms depending on the category of requests and needs during the proposal process. For example, the proposal unit can apply a language analysis algorithm to requests for translation services. For example, the proposal unit can apply an image analysis algorithm to requests for image generation services. For example, the proposal unit can apply a speech analysis algorithm to requests for speech recognition services. By applying proposal algorithms according to the category of requests and needs, the accuracy of the proposal is improved. The categories of requests and needs include, but are not limited to, technical requests and service-related needs. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input the categories of requests and needs into a generative AI and have the generative AI apply the proposal algorithm.

[0090] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit may prioritize important suggestions and postpone others. If the user is relaxed, the suggestion unit may prioritize detailed suggestions and provide more options. If the user is in a hurry, the suggestion unit may prioritize the most important suggestions and provide suggestions quickly. This ensures that important suggestions are prioritized by prioritizing suggestions based on 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 processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0091] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will prioritize suggesting services related to that region. The suggestion unit can also suggest highly relevant services based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggesting services related to their travel destination. This ensures that highly relevant suggestions are made by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI execute the optimal suggestion.

[0092] The suggestion unit can improve the accuracy of its suggestions by referring to the user's social media activity when making suggestions. For example, the suggestion unit can analyze the user's social media posts and suggest relevant services. The suggestion unit can also suggest services related to the user's areas of interest based on the user's social media activity history. The suggestion unit can also suggest relevant services by considering the user's social media friendships. This improves the accuracy of suggestions by referring to the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the suggestion unit can input the user's social media activity into a generative AI and have the generative AI perform the task of improving the accuracy of suggestions.

[0093] The service provider can estimate the user's emotions and adjust the service delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider may select a delivery method that includes detailed explanations. For example, if the user is in a hurry, the service provider may select a quick and concise delivery method. For example, if the user is excited, the service provider may select a visually stimulating delivery method. By adjusting the service delivery method based on the user's emotions, the optimal delivery method for the user is selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the service delivery method.

[0094] The service provider can select the optimal service delivery method by referring to the user's past service usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's past service usage history. The service provider can also predict and suggest preferred service delivery methods based on the user's past usage history. For example, the service provider can analyze the user's past usage history and select the most efficient service delivery method. In this way, the optimal service delivery method is selected by referring to the user's past usage history. Past service usage history includes, but is not limited to, past usage history and feedback. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's past service usage history into a generating AI and have the generating AI select the optimal service delivery method.

[0095] The service provider can customize the means of service delivery based on the user's current situation at the time of delivery. For example, the service provider can suggest the optimal means of delivery when the user inputs their current situation. The service provider can also customize the means of delivery according to the situation if the user is in a specific situation. The service provider can also customize and provide a highly relevant means of delivery based on the user's current situation. This ensures that the optimal means of delivery is selected by customizing the means of delivery based on the user's current situation. Current situation includes, but is not limited to, the current geographical location and current activity status. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's current situation into a generating AI and have the generating AI perform the customization of the means of delivery.

[0096] The service provider can estimate the user's emotions and determine the order in which to provide services based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize important services and postpone other services. For example, if the user is relaxed, the service provider may prioritize detailed services and offer more choices. For example, if the user is in a hurry, the service provider may prioritize the most important services and provide them quickly. In this way, important services are prioritized by determining the order in which to provide services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the order in which to provide services.

[0097] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can prioritize providing services related to that region. The service provider can also provide highly relevant services based on the user's current location. For example, if the user is traveling, the service provider can prioritize providing services related to their travel destination. This ensures that a highly relevant service delivery method is selected by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0098] The service provider can, at the time of service provision, propose a means of service delivery by referring to the user's social media activity. For example, the service provider can analyze the content of the user's social media posts and provide relevant services. For example, the service provider can also provide services related to the user's areas of interest based on the user's social media activity history. For example, the service provider can also provide relevant services by considering the user's social media friendships. In this way, the optimal means of service delivery is proposed by referring to the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's social media activity into a generating AI and have the generating AI propose a means of service delivery.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can provide input assistance by referring to the user's past behavioral history when the user inputs requests and needs. For example, it can automatically display as suggestions services and requests that the user has frequently used in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest requests and needs that the user will use at specific times of day based on the user's past behavioral history. This makes input more efficient by referring to the user's past history. Past behavioral history includes, but is not limited to, past search history and usage history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past behavioral history into a generating AI and have the generating AI perform input assistance.

[0101] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, it can perform a rapid analysis and provide results quickly. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, the accuracy of the analysis results is improved by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis accuracy.

[0102] The suggestion section can customize suggestions based on the user's current situation and areas of interest. For example, when a user enters their current situation, suggestions related to their areas of interest are prioritized. If a user has a specific area of ​​interest, suggestions related to that area can be automatically generated. Furthermore, highly relevant suggestions can be customized and displayed based on the user's current situation. This allows for more appropriate suggestions by customizing them based on the user's current situation and areas of interest. Current situation and areas of interest include, but are not limited to, the user's current geographical location and recent search topics. Some or all of the processing described above in the suggestion section may be performed using AI, for example, or not. For example, the suggestion section can input the user's current situation and areas of interest into a generating AI and have the generating AI customize the suggestions.

[0103] The service provider can estimate the user's emotions and adjust the service delivery method based on the estimated emotions. For example, if the user is relaxed, it may select a delivery method that includes detailed explanations. If the user is in a hurry, it may select a quick and concise delivery method. Furthermore, if the user is excited, it may select a visually stimulating delivery method. In this way, by adjusting the service delivery method based on the user's emotions, the optimal delivery method for the user is selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the service delivery method.

[0104] The reception unit can analyze the user's social media activity and suggest relevant input fields. For example, it can analyze the content of the user's social media posts and suggest relevant input fields. It can also suggest input fields related to the user's areas of interest based on the user's social media activity history. Furthermore, it can suggest relevant input fields considering the user's social media friendships. In this way, relevant input fields are suggested by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and like history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity into a generating AI and have the generating AI suggest relevant input fields.

[0105] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information. For example, if the user is in a specific region, it can prioritize the analysis of information related to that region. It can also analyze highly relevant information based on the user's current location. Furthermore, if the user is traveling, it can prioritize the analysis of information related to their travel destination. This improves the accuracy of the analysis by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the analysis accuracy improvement.

[0106] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions and offer many options. If the user is in a hurry, it can provide quick suggestions and narrow down the options. Furthermore, if the user is excited, it can provide visually stimulating suggestions. By adjusting the way suggestions are presented based on the user's emotions, the optimal suggestions are 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 suggestion unit may be performed using a generative AI, or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0107] The service delivery unit can select the optimal delivery method by referring to the user's past service usage history at the time of delivery. For example, it can select the optimal delivery method based on the user's past service usage history. It can also predict and suggest preferred delivery methods based on the user's past usage history. Furthermore, it can analyze the user's past usage history and select the most efficient delivery method. In this way, the optimal delivery method is selected by referring to the user's past usage history. Past service usage history includes, but is not limited to, past usage history and feedback. Some or all of the above processing in the service delivery unit may be performed using, for example, AI, or not using AI. For example, the service delivery unit can input the user's past service usage history into a generating AI and have the generating AI select the optimal delivery method.

[0108] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is nervous, important input fields can be displayed first, while other fields are delayed. If the user is relaxed, detailed input fields can be displayed first, and a customizable input method can be provided. Furthermore, if the user is in a hurry, the most important input fields can be displayed first, allowing for quick input. This ensures that important information is entered first by prioritizing input based on 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of input.

[0109] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, if the user is in a specific region, it can prioritize suggesting services related to that region. It can also suggest highly relevant services based on the user's current location. Furthermore, if the user is traveling, it can prioritize suggesting services related to their travel destination. In this way, highly relevant suggestions are made by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's geographical location information into a generative AI and have the generative AI execute the optimal suggestion.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk inputs the user's requests and needs. These requests and needs may include, for example, technical requests and service-related needs. The reception desk can input the user's requests and needs using a web interface, mobile application, voice input, etc. For example, this may involve inputting in text format using a web interface, inputting using a smartphone or tablet, or inputting by voice using a microphone. Step 2: The analysis unit uses generative AI to analyze the information received by the reception unit. The analysis is performed using natural language processing technology, data mining technology, machine learning algorithms, etc. For example, natural language processing technology is used to analyze the user's input text and extract requests and needs. When using data mining technology, useful information is extracted from a large amount of data to identify the user's requests and needs. When using machine learning algorithms, past data is learned to predict the user's requests and needs. Step 3: The proposal unit proposes the optimal generative AI service based on the information analyzed by the analysis unit. The proposal is made using generative AI or machine learning algorithms. For example, the optimal generative AI service can be proposed based on the user's past behavior history. When using generative AI, the optimal generative AI service is selected based on the user's requests and needs. When using machine learning algorithms, past data is learned and the optimal generative AI service is proposed to the user. When based on the user's past behavior history, the services and behavior patterns the user has used in the past are analyzed and the optimal generative AI service is proposed. Step 4: The service provider ensures that the services proposed by the proposal provider are seamlessly available within the platform. Seamless availability means providing an integrated user interface that requires no additional user action. For example, users should be able to use the proposed services without having to move to another platform. The service provider automates the service usage process and allows users to access multiple services on a single screen.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and service unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit allows users to input their requests and needs using the web interface or mobile application of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's requests and needs using generating AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal generating AI service based on the analyzed information. The service unit is implemented by the control unit 46A of the smart device 14 and enables the user to seamlessly use the service. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and service unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit allows users to input their requests and needs using the web interface or mobile application of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's requests and needs using generating AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal generating AI service based on the analyzed information. The service unit is implemented by the control unit 46A of the smart glasses 214 and enables the user to seamlessly use the service. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and service unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit allows users to input requests and needs using the web interface or mobile application of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's requests and needs using generating AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal generating AI service based on the analyzed information. The service unit is implemented by the control unit 46A of the headset terminal 314 and enables the user to seamlessly use the service. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and service unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit allows users to input their requests and needs using the robot 414's web interface or mobile application. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's requests and needs using generated AI. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal generated AI service based on the analyzed information. The service unit is implemented by the control unit 46A of the robot 414 and enables the user to seamlessly access the service. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception area where users input their requests and needs, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes the optimal generation AI service, The platform includes a provisioning unit that enables seamless use of the services proposed by the proposal unit within the platform. A system characterized by the following features. (Note 2) The aforementioned reception unit is Provides an interface for users to input their requests and needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze user requests and needs to select the most suitable AI-generated service. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The selected generative AI service is proposed to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, To enable seamless use of the proposed generative AI service within the platform. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Allows users to continue using the proposed service without having to move to another platform. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for requests and needs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past requests and needs history to provide the optimal input interface. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input requests and needs, the input fields are customized based on their current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter requests or needs, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input requests and needs, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past requests and needs history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the category of requests and needs. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we refer to the user's past requests and needs history to provide the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of requests and needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, we refer to users' social media activity to improve the accuracy of those suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we deliver the service based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past service usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the method of delivery will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which services are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we will refer to the user's social media activity to suggest a method for delivering the service. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their requests and needs, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes the optimal generation AI service, The platform includes a provisioning unit that enables seamless use of the services proposed by the proposal unit within the platform. A system characterized by the following features.

2. The aforementioned reception unit is Provides an interface for users to input their requests and needs. The system according to feature 1.

3. The aforementioned analysis unit, We analyze user requests and needs and select the most suitable AI-generated service. The system according to feature 1.

4. The aforementioned proposal section is, The selected generative AI service is proposed to the user. The system according to feature 1.

5. The aforementioned supply unit is, The proposed generative AI service will be made available seamlessly within the platform. The system according to feature 1.

6. The aforementioned supply unit is, Allows users to continue using the proposed service without having to move to another platform. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for requests and needs based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past requests and needs history to provide the optimal input interface. The system according to feature 1.

9. The aforementioned reception unit is When users input requests and needs, the input fields are customized based on their current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

Images