system

Abstract

The system according to this embodiment aims to enable users to select and utilize the optimal AI even without specialized knowledge. [Solution] The system according to the embodiment comprises a reception unit, a determination unit, an instruction unit, and a provision unit. The reception unit receives questions and prompts from the user. The determination unit analyzes the information received by the reception unit and determines the optimal AI. The instruction unit issues work instructions to the AI ​​determined by the determination unit. The provision unit provides the results generated based on the instructions issued by the instruction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a user to determine which AI to use to obtain an optimal result, and there is a problem that it is difficult to select an appropriate AI without expertise.

[0005] The system according to the embodiment aims to enable a user to select and use an optimal AI even without expertise.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a determination unit, an instruction unit, and a provision unit. The reception unit receives questions and prompts from the user. The determination unit analyzes the information received by the reception unit and determines the optimal AI. The instruction unit issues work instructions to the AI ​​determined by the determination unit. The provision unit provides the results generated based on the instructions issued by the instruction unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to select and utilize the optimal AI even without specialized knowledge. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a 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 AI ​​integrated service according to an embodiment of the present invention is a service for users who are unsure which AI to use to obtain the best results, given the many types of AI available. It automatically determines the optimal AI based on the content of the question or prompt and issues work instructions to the appropriate AI. The AI ​​integrated service eliminates the need for users to contract for individual AIs, as the usage fee includes the usage fees for all AIs. This AI service is chosen by everyone who is considering introducing and utilizing AI but lacks the knowledge to know where to start, or who finds it troublesome to study and deepen their knowledge. For example, the user inputs a question or prompt. For example, the user inputs "I want to generate text." This information is sent to the AI ​​integrated service. Next, the AI ​​integrated service analyzes the input information and automatically determines the optimal AI. For example, if a prompt for text generation is entered, an AI specializing in natural language processing is selected as the optimal AI. Subsequently, the AI ​​integrated service issues work instructions to the selected AI. For example, it issues instructions to the AI ​​specializing in natural language processing to generate text. The AI ​​generates text based on the instructions and returns the result to the AI ​​integrated service. Finally, the AI ​​integrated service provides the generated result to the user. Users can utilize the optimal AI within a single service without having to contract for multiple AIs individually. This system allows users to easily use the best AI even without AI knowledge. Furthermore, since the usage fees for all AIs are included, there is no need to worry about costs. For example, if a company's IT manager wants to introduce AI but doesn't know which AI to choose, this service allows them to easily select the optimal AI. In addition, because the AI ​​integrated service generates data via API, it prevents learning and reduces the risk of information leakage. For example, when a company inputs confidential information into an AI, it prevents the information from being leaked to the outside. Thus, the AI ​​integrated service is the AI ​​service of choice for everyone who is considering introducing and utilizing AI but lacks the knowledge, doesn't know where to start, or finds it troublesome to study and deepen their knowledge. As a result, the AI ​​integrated service can automatically determine the optimal AI based on the user's questions and prompts and issue work instructions to the appropriate AI.

[0029] The AI ​​integrated service according to this embodiment comprises a reception unit, a determination unit, an instruction unit, and a provision unit. The reception unit receives questions and prompts from the user. For example, if the user inputs "I want to generate text," the reception unit receives this information. The reception unit can receive questions and prompts in various formats, such as text, voice, and image formats. The determination unit analyzes the information received by the reception unit and determines the optimal AI. For example, if a prompt for text generation is entered, the determination unit determines that an AI specialized in natural language processing is the optimal AI. The determination unit can determine the optimal AI from among multiple AIs, such as an AI specialized in natural language processing, an AI specialized in image processing, or an AI specialized in programming. The instruction unit issues work instructions to the AI ​​determined by the determination unit. For example, the instruction unit issues a text generation instruction to an AI specialized in natural language processing. The instruction unit can issue work instructions to the selected AI via an API. The provision unit provides the results generated based on the instructions issued by the instruction unit. For example, the provision unit provides the generated text to the user. The provision unit can prevent information leakage by providing the generated results in encryption. As a result, the AI ​​management service according to the embodiment can automatically determine the optimal AI based on the user's questions and prompts, and issue work instructions to the appropriate AI.

[0030] The reception unit receives questions and prompts from users. For example, if a user inputs "I want to generate text," the reception unit receives this information. The reception unit can accept questions and prompts in various formats, including text, audio, and image. Specifically, in the case of text, it receives the text entered by the user using a keyboard; in the case of audio, it uses speech recognition technology to convert the audio data into text and accepts it; and in the case of image, it uses image recognition technology to analyze the text and objects within the image, extracting and accepting the necessary information. Furthermore, the reception unit also has the function of temporarily saving the user's input and holding the information necessary for subsequent processing. For example, if a user inputs multiple questions in succession, the reception unit accepts them sequentially and forms a queue for processing in the appropriate order. The reception unit also has the function of performing initial filtering on the user's input to exclude inappropriate content and spam. As a result, the reception unit can efficiently receive questions and prompts in various formats from users and appropriately manage the information necessary for subsequent processing.

[0031] The judgment unit analyzes the information received by the reception unit and determines the optimal AI. For example, if a text generation prompt is entered, the judgment unit will determine that an AI specializing in natural language processing is the optimal AI. The judgment unit can determine the optimal AI from among multiple AIs, such as an AI specializing in natural language processing, an AI specializing in image processing, or an AI specializing in programming. Specifically, the judgment unit uses natural language processing technology and image analysis technology to analyze the received information. For example, if a text generation prompt is entered, the judgment unit analyzes the prompt, extracts keywords and context, and selects the optimal AI. If an image processing prompt is entered, the judgment unit analyzes objects and text in the image and selects an appropriate image processing AI. The judgment unit can also select a more personalized AI by considering the user's past usage history and preferences. For example, for users who have frequently used a particular AI in the past, that AI will be selected preferentially. The judgment unit also considers the AI's performance, processing speed, and current load status when selecting the optimal AI. This allows the judgment unit to quickly and accurately determine the AI ​​best suited to the user's needs, enabling efficient subsequent processing.

[0032] The instruction unit issues work instructions to the AI ​​determined by the judgment unit. For example, the instruction unit issues a text generation instruction to an AI specialized in natural language processing. The instruction unit can issue work instructions to selected AIs via APIs. Specifically, based on the information received from the judgment unit, the instruction unit generates an appropriate API call and sends specific work instructions to the selected AI. For example, if a text generation prompt is entered, the instruction unit generates an API request including that prompt and sends it to the natural language processing AI. If an image processing prompt is entered, the instruction unit generates an API request including image data and sends it to the image processing AI. The instruction unit also generates a request including a callback URL and token to receive the AI's processing results, ensuring security and preparing to receive the results. Furthermore, the instruction unit has the functionality to monitor the AI's processing status and issue re-instructions or handle errors as needed. For example, if the AI's processing times out or an error occurs, the instruction unit will either issue instructions again or switch to a different AI. This allows the instruction unit to quickly and accurately issue appropriate work instructions to the determined AI and efficiently generate results that meet the user's requirements.

[0033] The delivery unit provides results generated based on instructions issued by the instruction unit. For example, the delivery unit provides the generated text to the user. The delivery unit can prevent information leakage by encrypting the generated results before providing them. Specifically, the delivery unit temporarily stores the generated results received from the AI ​​and prepares them for provision to the user. For example, it converts the generated text into a format that meets the user's requirements and provides it in the appropriate format. If it is the result of image processing, it converts the generated image to the appropriate resolution and format and provides it to the user. The delivery unit also has security features to encrypt the generated results, encrypting the data before providing it to the user to prevent information leakage. Furthermore, the delivery unit has a function to collect user feedback and use it to improve the quality and method of providing the generated results. For example, it provides an interface where users can provide evaluations and comments on the generated results, and uses that feedback to improve the entire system. The delivery unit also has a function to manage the history of the generated result provision, allowing users to review past results. In this way, the delivery unit can provide generated results to users safely and efficiently, improving user satisfaction.

[0034] The determination unit can determine the optimal AI from among multiple AIs, such as AIs specialized in natural language processing, AIs specialized in image processing, and AIs specialized in programming. For example, when using an AI specialized in natural language processing to generate text, the determination unit can select an AI with functions such as text analysis and dialogue generation. Similarly, when using an AI specialized in image processing to generate images, the determination unit can select an AI with functions such as image recognition and image generation. Furthermore, when using an AI specialized in programming to generate code, the determination unit can select an AI with functions such as code generation and bug detection. This allows the system to automatically determine the optimal AI from among multiple AIs. Some or all of the above-described processes in the determination unit may be performed using an AI, or they may not. For example, the determination unit can determine the optimal AI using an AI model that takes user questions or prompts as input and outputs the optimal AI.

[0035] The instruction unit can issue work instructions to selected AIs via APIs. For example, when the instruction unit issues a text generation instruction to an AI specialized in natural language processing, it can issue the instruction using a REST API. Similarly, when the instruction unit issues an image generation instruction to an AI specialized in image processing, it can issue the instruction using a GraphQL API. Furthermore, when the instruction unit issues a code generation instruction to an AI specialized in programming, it can issue the instruction using a specific API. This allows the instruction unit to issue work instructions to selected AIs via APIs. Some or all of the above-described processes in the instruction unit may be performed using AI, or without AI. For example, the instruction unit can issue work instructions using an AI model that takes work instructions for a selected AI as input and outputs work instructions.

[0036] The service provider can provide the generated results to the user. For example, if the service provider provides generated text to the user, it can display the results through a web application or a mobile application. If the service provider provides generated images to the user, it can provide them in a format that can be downloaded as image files. Furthermore, if the service provider provides generated code to the user, it can provide it as a text file. In this way, the service provider can provide the generated results to the user. The service provider can prevent information leakage by providing the generated results in encryption. For example, the service provider can encrypt the generated results using methods such as AES encryption or RSA encryption and provide them to the user. In this way, information leakage can be prevented by providing the generated results in encryption. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the results using an AI model that takes the generated results as input and outputs encrypted results.

[0037] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions questions and prompts that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and prompts to be used during specific time periods based on the user's past question history. This allows the reception desk to select the optimal reception method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select a reception method using an AI model that takes the user's past question history as input and outputs the optimal reception method.

[0038] The reception desk can filter questions and prompts based on the user's current areas of interest. For example, it can prioritize displaying relevant questions and prompts based on keywords the user has recently searched for. It can also suggest relevant questions and prompts based on topics the user has shown interest in in the past. Furthermore, the reception desk can analyze the user's social media activity and filter questions and prompts related to their current areas of interest. This allows for filtering questions and prompts based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can perform filtering using an AI model that takes user area of ​​interest data as input and outputs filtered questions and prompts.

[0039] The reception desk can prioritize receiving questions and prompts that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions and prompts related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions and prompts related to their travel destination. Furthermore, if the user is participating in a specific event, the reception desk can prioritize receiving questions and prompts related to that event. This allows for the prioritization of highly relevant questions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can receive questions using an AI model that takes the user's geographical location as input and outputs highly relevant questions.

[0040] The reception desk can analyze a user's social media activity and receive relevant questions when receiving questions or prompts. For example, the reception desk can prioritize receiving relevant questions and prompts based on what the user has recently posted. It can also suggest relevant questions and prompts based on the activity of accounts the user follows. Furthermore, the reception desk can receive relevant questions and prompts based on the topics of groups and communities the user participates in. This allows the reception desk to receive relevant questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can receive questions using an AI model that takes the user's social media activity data as input and outputs relevant questions.

[0041] The judgment unit can optimize its judgment algorithm by referring to past judgment data during the judgment process. For example, the judgment unit can adjust an algorithm to select the optimal AI based on past judgment results. The judgment unit can also extract specific patterns from past judgment data and optimize the judgment algorithm. Furthermore, the judgment unit can analyze past judgment data and develop algorithms to improve judgment accuracy. This allows the judgment algorithm to be optimized based on past judgment data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take past judgment data as input and optimize the judgment algorithm using an AI model that optimizes the judgment algorithm.

[0042] The determination unit can determine the optimal AI by considering the user's attribute information during the determination process. For example, the determination unit can select the optimal AI based on the user's age and gender. It can also select the optimal AI based on the user's occupation and interests. Furthermore, the determination unit can select the optimal AI based on the user's past usage history. This allows the determination of the optimal AI based on the user's attribute information. Some or all of the above-described processes in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can determine the optimal AI using an AI model that takes the user's attribute information as input and outputs the optimal AI.

[0043] The determination unit can determine the optimal AI by considering the user's geographical distribution during the determination process. For example, if the user is in a specific region, the determination unit will prioritize selecting an AI related to that region. Furthermore, if the user is traveling, the determination unit can prioritize selecting an AI related to the travel destination. Additionally, if the user is participating in a specific event, the determination unit can prioritize selecting an AI related to that event. This allows the determination of the optimal AI based on the user's geographical distribution. Some or all of the above processing in the determination unit may be performed using AI, or without AI. For example, the determination unit can determine the optimal AI using an AI model that takes the user's geographical distribution data as input and outputs the optimal AI.

[0044] The determination unit can improve the accuracy of its determination by referring to relevant literature during the determination process. For example, the determination unit can optimize its determination algorithm by referring to the latest research papers. It can also improve the accuracy of its determination by referring to relevant patent documents. Furthermore, the determination unit can improve its determination algorithm by referring to specialized books. In this way, the accuracy of the determination can be improved by referring to relevant literature. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can optimize its algorithm using an AI model that takes relevant literature data as input and optimizes the determination algorithm.

[0045] The instruction unit can optimize the instruction algorithm by referring to past instruction data when issuing work instructions. For example, the instruction unit can adjust the optimal instruction algorithm based on past work instruction results. The instruction unit can also extract specific patterns from past instruction data and optimize the instruction algorithm. Furthermore, the instruction unit can analyze past instruction data and develop algorithms to improve instruction accuracy. This allows the instruction algorithm to be optimized based on past instruction data. Some or all of the above processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can take past instruction data as input and optimize the instruction algorithm using an AI model that optimizes the instruction algorithm.

[0046] The instruction unit can provide optimal instructions when issuing work instructions, taking into account the user's attribute information. For example, the instruction unit can provide optimal work instructions based on the user's age and gender. It can also provide optimal work instructions based on the user's occupation and interests. Furthermore, the instruction unit can provide optimal work instructions based on the user's past usage history. This allows for the provision of optimal instructions based on the user's attribute information. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can provide instructions using an AI model that takes the user's attribute information as input and outputs optimal instructions.

[0047] The instruction unit can provide optimal instructions when issuing work instructions, taking into account the user's geographical location information. For example, if the user is in a specific region, the instruction unit can provide work instructions related to that region. Furthermore, if the user is traveling, the instruction unit can provide work instructions related to the travel destination. In addition, if the user is participating in a specific event, the instruction unit can provide work instructions related to that event. This allows for optimal instructions based on the user's geographical location information. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can provide instructions using an AI model that takes the user's geographical location information as input and outputs optimal instructions.

[0048] The instruction unit can improve the accuracy of instructions by referring to relevant literature when issuing work instructions. For example, the instruction unit can optimize the instruction algorithm by referring to the latest research papers. It can also improve the accuracy of instructions by referring to relevant patent documents. Furthermore, the instruction unit can improve the instruction algorithm by referring to specialized books. In this way, the accuracy of instructions can be improved by referring to relevant literature. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can optimize the algorithm using an AI model that optimizes the instruction algorithm with relevant literature data as input.

[0049] The service provider can optimize its service algorithm by referring to past service data when providing results. For example, the service provider can adjust the optimal service algorithm based on past service results. It can also extract specific patterns from past service data and optimize the service algorithm. Furthermore, the service provider can analyze past service data and develop algorithms to improve service accuracy. This allows for the optimization of the service algorithm based on past service data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize its algorithm using an AI model that takes past service data as input and optimizes the service algorithm.

[0050] The service provider can select the optimal service delivery method when providing results, taking into account the user's attribute information. For example, the service provider can select the optimal service delivery method based on the user's age and gender. It can also select the optimal service delivery method based on the user's occupation and interests. Furthermore, it can select the optimal service delivery method based on the user's past usage history. This allows for the selection of the optimal service delivery method based on the user's attribute information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a service delivery method using an AI model that takes user attribute information as input and outputs the optimal service delivery method.

[0051] The service provider can select the optimal delivery method when providing results, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing results related to that region. Furthermore, if the user is traveling, the service provider can prioritize providing results related to their travel destination. Additionally, if the user is participating in a specific event, the service provider can prioritize providing results related to that event. This allows the service provider to select the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a delivery method using an AI model that takes the user's geographical location information as input and outputs the optimal delivery method.

[0052] The data provider can improve the accuracy of its results by referring to relevant literature. For example, it can optimize its data provider algorithm by referring to the latest research papers. It can also improve the accuracy of its data provider by referring to relevant patent documents. Furthermore, it can improve its data provider algorithm by referring to specialized books. In this way, the accuracy of the data provider can be improved by referring to relevant literature. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can optimize its algorithm using an AI model that optimizes the data provider algorithm with relevant literature data as input.

[0053] The service provider can optimize its service algorithm by referring to past service data when providing results. For example, the service provider can adjust the optimal service algorithm based on past service results. It can also extract specific patterns from past service data and optimize the service algorithm. Furthermore, the service provider can analyze past service data and develop algorithms to improve service accuracy. This allows for the optimization of the service algorithm based on past service data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize its algorithm using an AI model that takes past service data as input and optimizes the service algorithm.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze the user's past question history and select the optimal reception method. For example, it can automatically display questions and prompts that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions and prompts that the user will use at specific times based on their past question history. This allows the reception desk to select the optimal reception method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can select a reception method using an AI model that takes the user's past question history as input and outputs the optimal reception method.

[0056] The reception unit can filter questions and prompts based on the user's current areas of interest. For example, it can prioritize displaying relevant questions and prompts based on keywords the user has recently searched for. It can also suggest relevant questions and prompts based on topics the user has shown interest in in the past. Furthermore, it can analyze the user's social media activity and filter questions and prompts related to their current areas of interest. This allows for filtering questions and prompts based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can perform filtering using an AI model that takes user area of ​​interest data as input and outputs filtered questions and prompts.

[0057] The judgment unit can optimize its judgment algorithm by referring to past judgment data during the judgment process. For example, it can adjust the algorithm for selecting the optimal AI based on past judgment results. It can also extract specific patterns from past judgment data and optimize the judgment algorithm. Furthermore, it can analyze past judgment data and develop algorithms to improve judgment accuracy. This allows the judgment algorithm to be optimized based on past judgment data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take past judgment data as input and optimize the algorithm using an AI model that optimizes the judgment algorithm.

[0058] The instruction unit can optimize the instruction algorithm by referring to past instruction data when issuing work instructions. For example, it can adjust the optimal instruction algorithm based on past work instruction results. It can also extract specific patterns from past instruction data and optimize the instruction algorithm. Furthermore, it can analyze past instruction data and develop algorithms to improve instruction accuracy. This allows the instruction algorithm to be optimized based on past instruction data. Some or all of the above processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can optimize the algorithm using an AI model that takes past instruction data as input and optimizes the instruction algorithm.

[0059] The service delivery unit can optimize its delivery algorithm by referring to past delivery data when providing results. For example, it can adjust the optimal delivery algorithm based on past delivery results. It can also extract specific patterns from past delivery data and optimize the delivery algorithm. Furthermore, it can analyze past delivery data and develop algorithms to improve delivery accuracy. This allows for the optimization of the delivery algorithm based on past delivery data. Some or all of the above processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can optimize its algorithm using an AI model that takes past delivery data as input and optimizes the delivery algorithm.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives questions and prompts from the user. For example, if the user types "I want to generate text," the reception desk receives this information. The reception desk can accept questions and prompts in various formats, including text, audio, and image formats. Step 2: The judgment unit analyzes the information received by the reception unit and determines the optimal AI. For example, if a text generation prompt is entered, the judgment unit determines that an AI specializing in natural language processing is the optimal AI. The judgment unit can determine the optimal AI from among multiple AIs, such as an AI specializing in natural language processing, an AI specializing in image processing, or an AI specializing in programming. Step 3: The instruction unit issues work instructions to the AI ​​determined by the judgment unit. For example, the instruction unit issues a text generation instruction to an AI specialized in natural language processing. The instruction unit can issue work instructions to selected AIs via an API. Step 4: The provider unit provides the results generated based on the instructions issued by the instruction unit. For example, the provider unit provides the generated text to the user. The provider unit can prevent information leakage by providing the generated results in an encrypted state.

[0062] (Example of form 2) The AI ​​integrated service according to an embodiment of the present invention is a service for users who are unsure which AI to use to obtain the best results, given the many types of AI available. It automatically determines the optimal AI based on the content of the question or prompt and issues work instructions to the appropriate AI. The AI ​​integrated service eliminates the need for users to contract for individual AIs, as the usage fee includes the usage fees for all AIs. This AI service is chosen by everyone who is considering introducing and utilizing AI but lacks the knowledge to know where to start, or who finds it troublesome to study and deepen their knowledge. For example, the user inputs a question or prompt. For example, the user inputs "I want to generate text." This information is sent to the AI ​​integrated service. Next, the AI ​​integrated service analyzes the input information and automatically determines the optimal AI. For example, if a prompt for text generation is entered, an AI specializing in natural language processing is selected as the optimal AI. Subsequently, the AI ​​integrated service issues work instructions to the selected AI. For example, it issues instructions to the AI ​​specializing in natural language processing to generate text. The AI ​​generates text based on the instructions and returns the result to the AI ​​integrated service. Finally, the AI ​​integrated service provides the generated result to the user. Users can utilize the optimal AI within a single service without having to contract for multiple AIs individually. This system allows users to easily use the best AI even without AI knowledge. Furthermore, since the usage fees for all AIs are included, there is no need to worry about costs. For example, if a company's IT manager wants to introduce AI but doesn't know which AI to choose, this service allows them to easily select the optimal AI. In addition, because the AI ​​integrated service generates data via API, it prevents learning and reduces the risk of information leakage. For example, when a company inputs confidential information into an AI, it prevents the information from being leaked to the outside. Thus, the AI ​​integrated service is the AI ​​service of choice for everyone who is considering introducing and utilizing AI but lacks the knowledge, doesn't know where to start, or finds it troublesome to study and deepen their knowledge. As a result, the AI ​​integrated service can automatically determine the optimal AI based on the user's questions and prompts and issue work instructions to the appropriate AI.

[0063] The AI ​​integrated service according to this embodiment comprises a reception unit, a determination unit, an instruction unit, and a provision unit. The reception unit receives questions and prompts from the user. For example, if the user inputs "I want to generate text," the reception unit receives this information. The reception unit can receive questions and prompts in various formats, such as text, voice, and image formats. The determination unit analyzes the information received by the reception unit and determines the optimal AI. For example, if a prompt for text generation is entered, the determination unit determines that an AI specialized in natural language processing is the optimal AI. The determination unit can determine the optimal AI from among multiple AIs, such as an AI specialized in natural language processing, an AI specialized in image processing, or an AI specialized in programming. The instruction unit issues work instructions to the AI ​​determined by the determination unit. For example, the instruction unit issues a text generation instruction to an AI specialized in natural language processing. The instruction unit can issue work instructions to the selected AI via an API. The provision unit provides the results generated based on the instructions issued by the instruction unit. For example, the provision unit provides the generated text to the user. The provision unit can prevent information leakage by providing the generated results in encryption. As a result, the AI ​​management service according to the embodiment can automatically determine the optimal AI based on the user's questions and prompts, and issue work instructions to the appropriate AI.

[0064] The reception unit receives questions and prompts from users. For example, if a user inputs "I want to generate text," the reception unit receives this information. The reception unit can accept questions and prompts in various formats, including text, audio, and image. Specifically, in the case of text, it receives the text entered by the user using a keyboard; in the case of audio, it uses speech recognition technology to convert the audio data into text and accepts it; and in the case of image, it uses image recognition technology to analyze the text and objects within the image, extracting and accepting the necessary information. Furthermore, the reception unit also has the function of temporarily saving the user's input and holding the information necessary for subsequent processing. For example, if a user inputs multiple questions in succession, the reception unit accepts them sequentially and forms a queue for processing in the appropriate order. The reception unit also has the function of performing initial filtering on the user's input to exclude inappropriate content and spam. As a result, the reception unit can efficiently receive questions and prompts in various formats from users and appropriately manage the information necessary for subsequent processing.

[0065] The judgment unit analyzes the information received by the reception unit and determines the optimal AI. For example, if a text generation prompt is entered, the judgment unit will determine that an AI specializing in natural language processing is the optimal AI. The judgment unit can determine the optimal AI from among multiple AIs, such as an AI specializing in natural language processing, an AI specializing in image processing, or an AI specializing in programming. Specifically, the judgment unit uses natural language processing technology and image analysis technology to analyze the received information. For example, if a text generation prompt is entered, the judgment unit analyzes the prompt, extracts keywords and context, and selects the optimal AI. If an image processing prompt is entered, the judgment unit analyzes objects and text in the image and selects an appropriate image processing AI. The judgment unit can also select a more personalized AI by considering the user's past usage history and preferences. For example, for users who have frequently used a particular AI in the past, that AI will be selected preferentially. The judgment unit also considers the AI's performance, processing speed, and current load status when selecting the optimal AI. This allows the judgment unit to quickly and accurately determine the AI ​​best suited to the user's needs, enabling efficient subsequent processing.

[0066] The instruction unit issues work instructions to the AI ​​determined by the judgment unit. For example, the instruction unit issues a text generation instruction to an AI specialized in natural language processing. The instruction unit can issue work instructions to selected AIs via APIs. Specifically, based on the information received from the judgment unit, the instruction unit generates an appropriate API call and sends specific work instructions to the selected AI. For example, if a text generation prompt is entered, the instruction unit generates an API request including that prompt and sends it to the natural language processing AI. If an image processing prompt is entered, the instruction unit generates an API request including image data and sends it to the image processing AI. The instruction unit also generates a request including a callback URL and token to receive the AI's processing results, ensuring security and preparing to receive the results. Furthermore, the instruction unit has the functionality to monitor the AI's processing status and issue re-instructions or handle errors as needed. For example, if the AI's processing times out or an error occurs, the instruction unit will either issue instructions again or switch to a different AI. This allows the instruction unit to quickly and accurately issue appropriate work instructions to the determined AI and efficiently generate results that meet the user's requirements.

[0067] The delivery unit provides results generated based on instructions issued by the instruction unit. For example, the delivery unit provides the generated text to the user. The delivery unit can prevent information leakage by encrypting the generated results before providing them. Specifically, the delivery unit temporarily stores the generated results received from the AI ​​and prepares them for provision to the user. For example, it converts the generated text into a format that meets the user's requirements and provides it in the appropriate format. If it is the result of image processing, it converts the generated image to the appropriate resolution and format and provides it to the user. The delivery unit also has security features to encrypt the generated results, encrypting the data before providing it to the user to prevent information leakage. Furthermore, the delivery unit has a function to collect user feedback and use it to improve the quality and method of providing the generated results. For example, it provides an interface where users can provide evaluations and comments on the generated results, and uses that feedback to improve the entire system. The delivery unit also has a function to manage the history of the generated result provision, allowing users to review past results. In this way, the delivery unit can provide generated results to users safely and efficiently, improving user satisfaction.

[0068] The determination unit can determine the optimal AI from among multiple AIs, such as AIs specialized in natural language processing, AIs specialized in image processing, and AIs specialized in programming. For example, when using an AI specialized in natural language processing to generate text, the determination unit can select an AI with functions such as text analysis and dialogue generation. Similarly, when using an AI specialized in image processing to generate images, the determination unit can select an AI with functions such as image recognition and image generation. Furthermore, when using an AI specialized in programming to generate code, the determination unit can select an AI with functions such as code generation and bug detection. This allows the system to automatically determine the optimal AI from among multiple AIs. Some or all of the above-described processes in the determination unit may be performed using an AI, or they may not. For example, the determination unit can determine the optimal AI using an AI model that takes user questions or prompts as input and outputs the optimal AI.

[0069] The instruction unit can issue work instructions to selected AIs via APIs. For example, when the instruction unit issues a text generation instruction to an AI specialized in natural language processing, it can issue the instruction using a REST API. Similarly, when the instruction unit issues an image generation instruction to an AI specialized in image processing, it can issue the instruction using a GraphQL API. Furthermore, when the instruction unit issues a code generation instruction to an AI specialized in programming, it can issue the instruction using a specific API. This allows the instruction unit to issue work instructions to selected AIs via APIs. Some or all of the above-described processes in the instruction unit may be performed using AI, or without AI. For example, the instruction unit can issue work instructions using an AI model that takes work instructions for a selected AI as input and outputs work instructions.

[0070] The service provider can provide the generated results to the user. For example, if the service provider provides generated text to the user, it can display the results through a web application or a mobile application. If the service provider provides generated images to the user, it can provide them in a format that can be downloaded as image files. Furthermore, if the service provider provides generated code to the user, it can provide it as a text file. In this way, the service provider can provide the generated results to the user. The service provider can prevent information leakage by providing the generated results in encryption. For example, the service provider can encrypt the generated results using methods such as AES encryption or RSA encryption and provide them to the user. In this way, information leakage can be prevented by providing the generated results in encryption. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the results using an AI model that takes the generated results as input and outputs encrypted results.

[0071] The reception desk can estimate the user's emotions and adjust how questions and prompts are presented based on those estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of questions and prompts. This allows the reception desk to adjust how questions and prompts are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can adjust its reception method using an AI model that takes user emotion data as input and adjusts the reception method based on the emotion.

[0072] The reception desk can analyze the user's past question history and select the optimal reception method. For example, the reception desk can automatically display as suggestions questions and prompts that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions and prompts to be used during specific time periods based on the user's past question history. This allows the reception desk to select the optimal reception method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can select a reception method using an AI model that takes the user's past question history as input and outputs the optimal reception method.

[0073] The reception desk can filter questions and prompts based on the user's current areas of interest. For example, it can prioritize displaying relevant questions and prompts based on keywords the user has recently searched for. It can also suggest relevant questions and prompts based on topics the user has shown interest in in the past. Furthermore, the reception desk can analyze the user's social media activity and filter questions and prompts related to their current areas of interest. This allows for filtering questions and prompts based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can perform filtering using an AI model that takes user area of ​​interest data as input and outputs filtered questions and prompts.

[0074] The reception desk can estimate the user's emotions and determine the priority of questions and prompts to receive based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize important questions and prompts. If the user is relaxed, the reception desk may also prioritize detailed questions and prompts. Furthermore, if the user is in a hurry, the reception desk may prioritize questions and prompts that require a quick response. This allows the reception desk to determine the priority of questions and prompts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 may take user emotion data as input and determine priorities using an AI model that determines priorities.

[0075] The reception desk can prioritize receiving questions and prompts that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving questions and prompts related to that region. Similarly, if the user is traveling, the reception desk can prioritize receiving questions and prompts related to their travel destination. Furthermore, if the user is participating in a specific event, the reception desk can prioritize receiving questions and prompts related to that event. This allows for the prioritization of highly relevant questions based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can receive questions using an AI model that takes the user's geographical location as input and outputs highly relevant questions.

[0076] The reception desk can analyze a user's social media activity and receive relevant questions when receiving questions or prompts. For example, the reception desk can prioritize receiving relevant questions and prompts based on what the user has recently posted. It can also suggest relevant questions and prompts based on the activity of accounts the user follows. Furthermore, the reception desk can receive relevant questions and prompts based on the topics of groups and communities the user participates in. This allows the reception desk to receive relevant questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can receive questions using an AI model that takes the user's social media activity data as input and outputs relevant questions.

[0077] The judgment unit can estimate the user's emotions and adjust the optimal AI judgment criteria based on the estimated user emotions. For example, if the user is relaxed, the judgment unit can perform a detailed analysis and select the optimal AI. If the user is in a hurry, the judgment unit can also prioritize selecting an AI that can produce results quickly. Furthermore, if the user is stressed, the judgment unit can select a simple and intuitive AI. In this way, the optimal AI judgment criteria can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take user emotion data as input and adjust the judgment criteria using an AI model that adjusts the judgment criteria.

[0078] The judgment unit can optimize its judgment algorithm by referring to past judgment data during the judgment process. For example, the judgment unit can adjust an algorithm to select the optimal AI based on past judgment results. The judgment unit can also extract specific patterns from past judgment data and optimize the judgment algorithm. Furthermore, the judgment unit can analyze past judgment data and develop algorithms to improve judgment accuracy. This allows the judgment algorithm to be optimized based on past judgment data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take past judgment data as input and optimize the judgment algorithm using an AI model that optimizes the judgment algorithm.

[0079] The determination unit can determine the optimal AI by considering the user's attribute information during the determination process. For example, the determination unit can select the optimal AI based on the user's age and gender. It can also select the optimal AI based on the user's occupation and interests. Furthermore, the determination unit can select the optimal AI based on the user's past usage history. This allows the determination of the optimal AI based on the user's attribute information. Some or all of the above-described processes in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can determine the optimal AI using an AI model that takes the user's attribute information as input and outputs the optimal AI.

[0080] The judgment unit can estimate the user's emotions and adjust the display order of the judgment results based on the estimated user emotions. For example, if the user is nervous, the judgment unit can prioritize displaying important judgment results. It can also prioritize displaying detailed judgment results if the user is relaxed. Furthermore, if the user is in a hurry, the judgment unit can prioritize displaying judgment results that require immediate attention. This allows the display order of judgment results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 judgment unit may be performed using AI, or not. For example, the judgment unit can take user emotion data as input and adjust the display order using an AI model that adjusts the display order.

[0081] The determination unit can determine the optimal AI by considering the user's geographical distribution during the determination process. For example, if the user is in a specific region, the determination unit will prioritize selecting an AI related to that region. Furthermore, if the user is traveling, the determination unit can prioritize selecting an AI related to the travel destination. Additionally, if the user is participating in a specific event, the determination unit can prioritize selecting an AI related to that event. This allows the determination of the optimal AI based on the user's geographical distribution. Some or all of the above processing in the determination unit may be performed using AI, or without AI. For example, the determination unit can determine the optimal AI using an AI model that takes the user's geographical distribution data as input and outputs the optimal AI.

[0082] The determination unit can improve the accuracy of its determination by referring to relevant literature during the determination process. For example, the determination unit can optimize its determination algorithm by referring to the latest research papers. It can also improve the accuracy of its determination by referring to relevant patent documents. Furthermore, the determination unit can improve its determination algorithm by referring to specialized books. In this way, the accuracy of the determination can be improved by referring to relevant literature. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can optimize its algorithm using an AI model that takes relevant literature data as input and optimizes the determination algorithm.

[0083] The instruction unit can estimate the user's emotions and adjust the method of giving work instructions based on the estimated emotions. For example, if the user is tense, the instruction unit can provide simple and intuitive work instructions. If the user is relaxed, the instruction unit can also provide detailed work instructions. Furthermore, if the user is in a hurry, the instruction unit can provide work instructions that require a quick response. This allows the method of giving work instructions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can take user emotion data as input and adjust the method of giving work instructions using an AI model that adjusts the method of giving work instructions.

[0084] The instruction unit can optimize the instruction algorithm by referring to past instruction data when issuing work instructions. For example, the instruction unit can adjust the optimal instruction algorithm based on past work instruction results. The instruction unit can also extract specific patterns from past instruction data and optimize the instruction algorithm. Furthermore, the instruction unit can analyze past instruction data and develop algorithms to improve instruction accuracy. This allows the instruction algorithm to be optimized based on past instruction data. Some or all of the above processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can take past instruction data as input and optimize the instruction algorithm using an AI model that optimizes the instruction algorithm.

[0085] The instruction unit can provide optimal instructions when issuing work instructions, taking into account the user's attribute information. For example, the instruction unit can provide optimal work instructions based on the user's age and gender. It can also provide optimal work instructions based on the user's occupation and interests. Furthermore, the instruction unit can provide optimal work instructions based on the user's past usage history. This allows for the provision of optimal instructions based on the user's attribute information. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can provide instructions using an AI model that takes the user's attribute information as input and outputs optimal instructions.

[0086] The instruction unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, if the user is stressed, the instruction unit may prioritize important work instructions. It may also prioritize detailed work instructions if the user is relaxed. Furthermore, if the user is in a hurry, it may prioritize work instructions that require immediate attention. This allows the instruction unit to prioritize instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, or not. For example, the instruction unit may take user emotion data as input and determine the priority of instructions using an AI model that determines the priority of instructions.

[0087] The instruction unit can provide optimal instructions when issuing work instructions, taking into account the user's geographical location information. For example, if the user is in a specific region, the instruction unit can provide work instructions related to that region. Furthermore, if the user is traveling, the instruction unit can provide work instructions related to the travel destination. In addition, if the user is participating in a specific event, the instruction unit can provide work instructions related to that event. This allows for optimal instructions based on the user's geographical location information. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can provide instructions using an AI model that takes the user's geographical location information as input and outputs optimal instructions.

[0088] The instruction unit can improve the accuracy of instructions by referring to relevant literature when issuing work instructions. For example, the instruction unit can optimize the instruction algorithm by referring to the latest research papers. It can also improve the accuracy of instructions by referring to relevant patent documents. Furthermore, the instruction unit can improve the instruction algorithm by referring to specialized books. In this way, the accuracy of instructions can be improved by referring to relevant literature. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can optimize the algorithm using an AI model that optimizes the instruction algorithm with relevant literature data as input.

[0089] The service provider can estimate the user's emotions and adjust the way results are presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible results display. If the user is relaxed, the service provider can also provide a detailed results display. Furthermore, if the user is in a hurry, the service provider can provide a concise results display. This allows the service provider to adjust the way results are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 adjust the presentation method using an AI model that takes user emotion data as input and adjusts the way results are presented.

[0090] The service provider can optimize its service algorithm by referring to past service data when providing results. For example, the service provider can adjust the optimal service algorithm based on past service results. It can also extract specific patterns from past service data and optimize the service algorithm. Furthermore, the service provider can analyze past service data and develop algorithms to improve service accuracy. This allows for the optimization of the service algorithm based on past service data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize its algorithm using an AI model that takes past service data as input and optimizes the service algorithm.

[0091] The service provider can select the optimal service delivery method when providing results, taking into account the user's attribute information. For example, the service provider can select the optimal service delivery method based on the user's age and gender. It can also select the optimal service delivery method based on the user's occupation and interests. Furthermore, it can select the optimal service delivery method based on the user's past usage history. This allows for the selection of the optimal service delivery method based on the user's attribute information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a service delivery method using an AI model that takes user attribute information as input and outputs the optimal service delivery method.

[0092] The service provider can estimate the user's emotions and determine the priority of results based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize important results. It may also prioritize detailed results if the user is relaxed. Furthermore, if the user is in a hurry, the service provider may prioritize results requiring immediate attention. This allows the service provider to determine the priority of results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider may use user emotion data as input and determine priorities using an AI model that determines the priority of results.

[0093] The service provider can select the optimal delivery method when providing results, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing results related to that region. Furthermore, if the user is traveling, the service provider can prioritize providing results related to their travel destination. Additionally, if the user is participating in a specific event, the service provider can prioritize providing results related to that event. This allows the service provider to select the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a delivery method using an AI model that takes the user's geographical location information as input and outputs the optimal delivery method.

[0094] The data provider can improve the accuracy of its results by referring to relevant literature. For example, it can optimize its data provider algorithm by referring to the latest research papers. It can also improve the accuracy of its data provider by referring to relevant patent documents. Furthermore, it can improve its data provider algorithm by referring to specialized books. In this way, the accuracy of the data provider can be improved by referring to relevant literature. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can optimize its algorithm using an AI model that optimizes the data provider algorithm with relevant literature data as input.

[0095] The service provider can estimate the user's emotions and adjust the way results are presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible results display. If the user is relaxed, the service provider can also provide a detailed results display. Furthermore, if the user is in a hurry, the service provider can provide a concise results display. This allows the service provider to adjust the way results are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 adjust the presentation method using an AI model that takes user emotion data as input and adjusts the way results are presented.

[0096] The service provider can optimize its service algorithm by referring to past service data when providing results. For example, the service provider can adjust the optimal service algorithm based on past service results. It can also extract specific patterns from past service data and optimize the service algorithm. Furthermore, the service provider can analyze past service data and develop algorithms to improve service accuracy. This allows for the optimization of the service algorithm based on past service data. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can optimize its algorithm using an AI model that takes past service data as input and optimizes the service algorithm.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the user's emotions and adjust the way questions and prompts are presented based on those emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of questions and prompts. This allows the way questions and prompts are presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can take user emotion data as input and adjust the reception method using an AI model that adjusts the reception method based on the emotion.

[0099] The judgment unit can estimate the user's emotions and adjust the optimal AI judgment criteria based on the estimated user emotions. For example, if the user is relaxed, it can perform a detailed analysis and select the optimal AI. If the user is in a hurry, it can prioritize selecting an AI that can produce results quickly. Furthermore, if the user is stressed, it can select a simple and intuitive AI. In this way, the optimal AI judgment criteria can be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take user emotion data as input and adjust the judgment criteria using an AI model that adjusts the judgment criteria.

[0100] The instruction unit can estimate the user's emotions and adjust the method of giving work instructions based on the estimated emotions. For example, if the user is tense, it can provide simple and intuitive work instructions. If the user is relaxed, it can provide detailed work instructions. Furthermore, if the user is in a hurry, it can provide work instructions that require a quick response. This allows the method of giving work instructions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can take user emotion data as input and adjust the method of giving work instructions using an AI model that adjusts the method of giving work instructions.

[0101] The service provider can estimate the user's emotions and adjust the way results are presented based on the estimated emotions. For example, if the user is nervous, a simple and highly visible results display can be provided. If the user is relaxed, a detailed results display can be provided. Furthermore, if the user is in a hurry, a concise results display can be provided. This allows the service provider to adjust the way results are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the presentation method using an AI model that takes user emotion data as input and adjusts the way results are presented.

[0102] The service provider can estimate the user's emotions and prioritize results based on those emotions. For example, if the user is stressed, important results may be prioritized. If the user is relaxed, detailed results may be prioritized. Furthermore, if the user is in a hurry, results requiring immediate attention may be prioritized. This allows for the prioritization of results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can take user emotion data as input and determine priorities using an AI model that determines the priority of results.

[0103] The reception desk can analyze the user's past question history and select the optimal reception method. For example, it can automatically display questions and prompts that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions and prompts that the user will use at specific times based on their past question history. This allows the reception desk to select the optimal reception method based on the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can select a reception method using an AI model that takes the user's past question history as input and outputs the optimal reception method.

[0104] The reception unit can filter questions and prompts based on the user's current areas of interest. For example, it can prioritize displaying relevant questions and prompts based on keywords the user has recently searched for. It can also suggest relevant questions and prompts based on topics the user has shown interest in in the past. Furthermore, it can analyze the user's social media activity and filter questions and prompts related to their current areas of interest. This allows for filtering questions and prompts based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can perform filtering using an AI model that takes user area of ​​interest data as input and outputs filtered questions and prompts.

[0105] The judgment unit can optimize its judgment algorithm by referring to past judgment data during the judgment process. For example, it can adjust the algorithm for selecting the optimal AI based on past judgment results. It can also extract specific patterns from past judgment data and optimize the judgment algorithm. Furthermore, it can analyze past judgment data and develop algorithms to improve judgment accuracy. This allows the judgment algorithm to be optimized based on past judgment data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can take past judgment data as input and optimize the algorithm using an AI model that optimizes the judgment algorithm.

[0106] The instruction unit can optimize the instruction algorithm by referring to past instruction data when issuing work instructions. For example, it can adjust the optimal instruction algorithm based on past work instruction results. It can also extract specific patterns from past instruction data and optimize the instruction algorithm. Furthermore, it can analyze past instruction data and develop algorithms to improve instruction accuracy. This allows the instruction algorithm to be optimized based on past instruction data. Some or all of the above processes in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can optimize the algorithm using an AI model that takes past instruction data as input and optimizes the instruction algorithm.

[0107] The service delivery unit can optimize its delivery algorithm by referring to past delivery data when providing results. For example, it can adjust the optimal delivery algorithm based on past delivery results. It can also extract specific patterns from past delivery data and optimize the delivery algorithm. Furthermore, it can analyze past delivery data and develop algorithms to improve delivery accuracy. This allows for the optimization of the delivery algorithm based on past delivery data. Some or all of the above processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can optimize its algorithm using an AI model that takes past delivery data as input and optimizes the delivery algorithm.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk receives questions and prompts from the user. For example, if the user types "I want to generate text," the reception desk receives this information. The reception desk can accept questions and prompts in various formats, including text, audio, and image formats. Step 2: The judgment unit analyzes the information received by the reception unit and determines the optimal AI. For example, if a text generation prompt is entered, the judgment unit determines that an AI specializing in natural language processing is the optimal AI. The judgment unit can determine the optimal AI from among multiple AIs, such as an AI specializing in natural language processing, an AI specializing in image processing, or an AI specializing in programming. Step 3: The instruction unit issues work instructions to the AI ​​determined by the judgment unit. For example, the instruction unit issues a text generation instruction to an AI specialized in natural language processing. The instruction unit can issue work instructions to selected AIs via an API. Step 4: The provider unit provides the results generated based on the instructions issued by the instruction unit. For example, the provider unit provides the generated text to the user. The provider unit can prevent information leakage by providing the generated results in an encrypted state.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, determination unit, instruction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives questions and prompts from the user. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received information to determine the optimal AI. The instruction unit is implemented by the identification processing unit 290 of the data processing unit 12 and issues work instructions to the determined AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, determination unit, instruction unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives questions and prompts from the user. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the received information to determine the optimal AI. The instruction unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and issues work instructions to the determined AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, determination unit, instruction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives questions and prompts from the user. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received information to determine the optimal AI. The instruction unit is implemented by the identification processing unit 290 of the data processing unit 12 and issues work instructions to the determined AI. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the generated results to the user. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, determination unit, instruction unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives questions and prompts from the user. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received information to determine the optimal AI. The instruction unit is implemented by the identification processing unit 290 of the data processing unit 12 and issues work instructions to the determined AI. The provision unit is implemented by the speaker 240 of the robot 414 and provides the generated results to the user. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A reception desk that handles questions and prompts from users, A determination unit analyzes the information received by the aforementioned reception unit and determines the optimal AI, An instruction unit that issues work instructions to the AI ​​determined by the aforementioned determination unit, The system includes a providing unit that provides results generated based on instructions issued by the instruction unit. A system characterized by the following features. (Note 2) The determination unit, It can determine the optimal AI from among multiple AIs, such as AI specialized in natural language processing, AI specialized in image processing, and AI specialized in programming. The system described in Appendix 1, characterized by the features described herein. (Note 3) The indicator unit is, Issue work instructions to the selected AI via API. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The determination unit, It features an algorithm that analyzes the input information and automatically determines the optimal AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, By encrypting and providing the generated results, information leakage can be prevented. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how questions and prompts are received based on those estimated 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 question history and select the most suitable method of handling inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions or prompts, filtering is performed based on the user's current 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 determines the priority of questions and prompts to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions or prompts, the system prioritizes receiving questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving questions or prompts, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, It estimates the user's emotions and adjusts the optimal AI judgment criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, During the decision-making process, the decision algorithm is optimized by referring to past decision data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a decision, the optimal AI is determined by considering the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the user's emotions and adjusts the display order of the judgment results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, When making a decision, the system will consider the geographical distribution of users to determine the most suitable AI. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, When making a judgment, we refer to relevant literature to improve the accuracy of the judgment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The indicator unit is, It estimates the user's emotions and adjusts the method of giving work instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The indicator unit is, When issuing work instructions, the instruction algorithm is optimized by referring to past instruction data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The indicator unit is, When issuing work instructions, the system should consider the user's attribute information to provide the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The indicator unit is, It estimates the user's emotions and determines the priority of instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The indicator unit is, When issuing work instructions, the system takes the user's geographical location into consideration to provide the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The indicator unit is, When giving work instructions, refer to relevant literature to improve the accuracy of the instructions. 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 how results are delivered 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 results, the algorithm is optimized by referring to past data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing results, the optimal delivery method will be selected considering the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing results, the optimal delivery method will be selected considering 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 results, we will refer to relevant literature to improve the accuracy of the results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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 desk that handles questions and prompts from users, A determination unit analyzes the information received by the reception unit and determines the optimal AI, An instruction unit that issues work instructions to the AI ​​determined by the determination unit, The system includes a providing unit that provides results generated based on instructions issued by the instruction unit. A system characterized by the following features.

2. The determination unit, The system determines the optimal AI from among multiple AIs, such as an AI specialized in natural language processing, an AI specialized in image processing, and an AI specialized in programming. The system according to feature 1.

3. The indicator unit is, The selected AI is given work instructions via API. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated results to the user. The system according to feature 1.

5. The determination unit, It features an algorithm that analyzes the input information and automatically determines the optimal AI. The system according to feature 1.

6. The aforementioned supply unit is, By encrypting and providing the generated results, information leakage can be prevented. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how questions and prompts are received based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method of handling inquiries. The system according to feature 1.

9. The aforementioned reception unit is When receiving questions or prompts, filtering is performed based on the user's current areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions and prompts to accept based on the estimated user emotions. The system according to feature 1.

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