system

Abstract

The system according to this embodiment aims to select and execute the most suitable plugin in response to a user request. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an execution unit, a provision unit, and an analysis unit. The reception unit receives requests from users. The analysis unit analyzes the requests received by the reception unit. The execution unit selects and executes the optimal plugin based on the requests analyzed by the analysis unit. The provision unit provides the user with the results executed by the execution unit. The analysis unit asks the user about their satisfaction level based on the results provided by the provision unit, and the generating AI analyzes the results.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there is a problem that it is difficult to automatically provide an optimal solution for various requests from users.

[0005] The system according to the embodiment aims to select and execute an optimal plugin for a request from a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an execution unit, a provision unit, and an analysis unit. The reception unit receives requests from users. The analysis unit analyzes the requests received by the reception unit. The execution unit selects and executes the optimal plugin based on the requests analyzed by the analysis unit. The provision unit provides the user with the results executed by the execution unit. The analysis unit asks the user about their satisfaction level based on the results provided by the provision unit, and the generating AI analyzes the results. [Effects of the Invention]

[0007] The system according to this embodiment can select and execute the most suitable plugin in response to a user request. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[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 total solution-compatible concierge system according to an embodiment of the present invention is a system that automatically receives requests from users, analyzes them using a generating AI, and selects and executes the optimal plugin. This system receives requests from users, analyzes them using a generating AI, selects and executes the optimal plugin. It provides the user with the results of the request execution, asks about their satisfaction level, and the generating AI analyzes the results to provide personalized and optimized answers for future requests. This platform allows the robot to spontaneously arrive at the optimal solution for a wide variety of requests from clients, expanding its range of activities. For example, the total solution-compatible concierge system receives requests from users in natural language. For example, if a user requests, "Please tell me about nearby restaurants," the system analyzes the request, selects and executes the optimal plugin. Next, the system uses the generating AI to analyze the content of the request and decide which plugin to use. For example, the generating AI selects the "restaurant search plugin" and executes that plugin. Next, the system provides the user with the results of the execution. For example, the system displays a list of nearby restaurants to the user. Next, the system asks the user about their satisfaction level, and the generating AI analyzes the answer. For example, the system asks, "Was this information helpful?" and collects the user's response. Then, based on the collected satisfaction data, the system provides personalized responses to future requests. For instance, the system prioritizes displaying restaurants that the user has previously given high ratings to. This allows the total solution-oriented concierge system to respond quickly and accurately to user requests, thereby improving user satisfaction.

[0029] The total solution-compatible concierge system according to this embodiment comprises a reception unit, an analysis unit, an execution unit, a provision unit, and an analysis unit. The reception unit receives requests from users. User requests include, but are not limited to, text format, audio format, and image format. The reception unit analyzes, for example, text format requests using natural language processing technology. The reception unit can also convert audio format requests into text data using speech recognition technology. Furthermore, the reception unit can analyze image format requests using image recognition technology. For example, the reception unit analyzes text format requests using natural language processing technology to understand the content of the request. Audio format requests are converted into text data using speech recognition technology and then analyzed. Image format requests are analyzed using image recognition technology to understand the content of the request. The analysis unit analyzes the requests received by the reception unit using a generation AI. The analysis includes, for example, understanding the content of the request and determining which plugin to use, but is not limited to this example. For example, the generation AI analyzes the content of the request using a text generation AI (e.g., LLM). Furthermore, the analysis unit can analyze the content of the request using a multimodal generation AI. The analysis unit can also extract and analyze important parts of the request using a generation AI. For example, the text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the request and performs analysis based on that information. The execution unit selects and executes the optimal plugin based on the request analyzed by the analysis unit. Execution includes, but is not limited to, the selection and execution of a plugin. For example, the execution unit uses the generation AI to select the optimal plugin for the request. The execution unit can also execute the selected plugin. Furthermore, the execution unit can collect the execution results of the plugin and pass them on to the next process.For example, the execution unit uses a generative AI to select the most suitable plugin for a request and executes that plugin. Depending on the content of the request, the selected plugin may include, for example, a restaurant search plugin, a weather forecast plugin, or a traffic information plugin. The delivery unit provides the user with the results executed by the execution unit. Delivery includes, but is not limited to, displaying or notifying the user of the execution results. For example, the delivery unit displays the execution results on the user's device. The delivery unit may also notify the user of the execution results via email or messaging apps. The delivery unit may also notify the user of the execution results by voice. For example, the delivery unit displays the execution results on the user's smartphone or tablet. The execution results are displayed in text format, image format, graph format, etc. The execution results may also be notified to the user via email or messaging apps. The execution results may also be notified by voice, for example, through a smart speaker. The analysis unit asks the user about their satisfaction level based on the results provided by the delivery unit, and the generative AI analyzes the answer. Analysis includes, but is not limited to, evaluating satisfaction levels and optimizing for future requests. For example, the analysis department can ask users about their satisfaction level in a questionnaire format and collect the responses. The analysis department can also analyze the collected satisfaction data using a generative AI to optimize for future requests. Furthermore, the analysis department can provide feedback based on the satisfaction data to improve the accuracy of requests. For example, the analysis department can ask users about their satisfaction level in a questionnaire format and collect the responses. The collected satisfaction data is analyzed using a generative AI to optimize for future requests. The satisfaction data is provided as feedback to improve the accuracy of requests. As a result, the total solution-compatible concierge system according to this embodiment can respond quickly and accurately to user requests and improve user satisfaction.

[0030] The reception desk receives requests from users. These requests may include, but are not limited to, text, audio, and image formats. For example, the reception desk analyzes text requests using natural language processing technology. Specifically, it uses natural language processing technology to perform grammatical and semantic analysis of the text and accurately understand the intent of the request. For example, if a user requests "Tell me about nearby restaurants," the natural language processing technology extracts the keyword "nearby restaurants" and recognizes the intent of the request as "restaurant search." The reception desk can also convert audio requests into text data using speech recognition technology. Speech recognition technology analyzes the audio signal, recognizes phonemes and words, and converts them into text. For example, if a user requests "What's the weather like tomorrow?" using voice, the speech recognition technology analyzes the audio signal and converts it into the text data "Tomorrow's weather." Furthermore, the reception desk can also analyze image requests using image recognition technology. Image recognition technology analyzes image data and recognizes objects and text within the image. For example, if a user uploads an image of a restaurant menu, image recognition technology analyzes the text and images of the dishes in the menu to understand the request. This allows the reception desk to receive and appropriately analyze a variety of requests from users.

[0031] The analysis unit uses generative AI to analyze requests received by the reception unit. Analysis can, for example, understand the content of a request and determine which plugin to use, but is not limited to such examples. Specifically, the generative AI uses text generation AI (e.g., LLM) to analyze the content of requests. Text generation AI has learned from large amounts of text data and possesses advanced natural language processing capabilities. For example, if a user requests "Tell me about nearby restaurants," the text generation AI understands the intent of the request and decides to use a restaurant search plugin. The analysis unit can also analyze the content of requests using multimodal generative AI. Multimodal generative AI can handle multiple modals, including not only text but also images and audio. For example, if a user uploads a restaurant menu image, the multimodal generative AI analyzes the text and images of the dishes within the image to understand the intent of the request. Furthermore, the analysis unit can use generative AI to extract and analyze important parts of a request. The generative AI uses keyword extraction technology to pick out particularly important information from the request and performs analysis based on that information. For example, if a user requests "What's the weather like tomorrow?", the generating AI extracts the keywords "tomorrow" and "weather" and decides to use a weather forecast plugin. This allows the analysis unit to quickly and accurately analyze the request received by the reception unit and select the most suitable plugin.

[0032] The execution unit selects and executes the most suitable plugin based on the request analyzed by the analysis unit. Execution includes, but is not limited to, the selection and execution of a plugin. Specifically, the execution unit uses a generation AI to select the most suitable plugin for a request. The generation AI has an algorithm for analyzing the content of the request and selecting the most suitable plugin. For example, if a user requests "Tell me about nearby restaurants," the generation AI selects a restaurant search plugin. The execution unit can also execute the selected plugin. Depending on the content of the request, the selected plugin may include, for example, a restaurant search plugin, a weather forecast plugin, or a traffic information plugin. For example, the restaurant search plugin searches for nearby restaurants based on the user's current location and provides the results. The weather forecast plugin obtains the weather forecast for a specified area and provides the results. The traffic information plugin obtains traffic information for a specified route and provides the results. The execution unit can also collect the execution results of the plugins and pass them on to the next process. For example, as an execution result of the restaurant search plugin, a list of nearby restaurants is generated. This list is passed on to the next providing unit and provided to the user. This allows the execution unit to select and execute the optimal plugin based on the request analyzed by the analysis unit, enabling it to respond quickly and accurately to user requests.

[0033] The service provider provides the user with the results executed by the execution unit. This includes, but is not limited to, displaying or notifying the user of the results. Specifically, the service provider displays the results on the user's device. The results can be displayed in text, image, or graph format. For example, a restaurant search plugin might generate a list of nearby restaurants. This list is displayed on the user's smartphone or tablet. The service provider can also notify the user of the results via email or messaging apps. For example, a weather forecast plugin might generate a weather forecast for a specified area. This forecast is notified via email or messaging apps. The service provider can also notify the user of the results via voice. For example, the weather forecast can be delivered to the user via a smart speaker. Furthermore, the service provider can customize the results according to the user's preferences. For example, if a user prefers a particular type of restaurant, the service provider can customize the restaurant search results based on that information. This allows the service provider to quickly and appropriately deliver the results executed by the execution unit to the user, thereby improving user satisfaction.

[0034] The analysis department asks users about their satisfaction based on the results provided by the service department, and the generative AI analyzes the responses. The analysis includes, but is not limited to, evaluating satisfaction and optimizing for future requests. Specifically, the analysis department asks users about their satisfaction in a questionnaire format and collects the responses. The questionnaire includes, for example, questions about satisfaction with the results and areas for improvement. For example, users rate restaurant search results as "satisfied" or "dissatisfied." The analysis department can also use the generative AI to analyze the collected satisfaction data and optimize for future requests. The generative AI analyzes the satisfaction data and learns the user's preferences and tendencies. For example, if a user prefers a particular type of restaurant, the AI ​​customizes the next restaurant search results based on that information. The analysis department can also provide feedback based on the satisfaction data to improve the accuracy of requests. For example, if a user rates a weather forecast result as "dissatisfied," the AI ​​analyzes the reason and identifies areas for improvement to enhance the accuracy of the weather forecast plugin. This allows the analysis department to improve the overall system performance by evaluating user satisfaction based on the results provided by the service department and optimizing for future requests.

[0035] The execution unit can search for necessary plugins if they are not installed and confirm with the user whether to install them. For example, if a necessary plugin is not installed, the execution unit can search for the plugin on the internet. The execution unit can also present the search results to the user and confirm whether to install them. For example, if a necessary plugin is not installed, the execution unit can search for the plugin on the internet and present the results to the user. The user can then select a plugin to install from the presented plugins. The execution unit can also automatically install the selected plugin. This allows for installation after confirming with the user, even if a necessary plugin is not installed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, if a necessary plugin is not installed, the execution unit can use an AI model to search for the plugin and confirm its installation.

[0036] The reception unit can receive user requests in natural language. For example, the reception unit can receive requests entered by users in natural language. The reception unit can also receive natural language requests using voice input. For example, the reception unit can receive requests entered by users in natural language as text data. Requests using voice input are converted into text data using speech recognition technology and then accepted. This improves usability by allowing user requests to be received in natural language. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can analyze natural language requests using an AI model and then accept them.

[0037] The analysis unit can understand the content of the request and determine which plugin to use. For example, the analysis unit analyzes the content of the request and selects the optimal plugin. The analysis unit can also use a generative AI to understand the content of the request. For example, the analysis unit analyzes the content of the request and selects the optimal plugin. The generative AI understands the content of the request and determines which plugin to use. This allows the analysis unit to understand the content of the request and select the appropriate plugin. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit can input the content of the request into the generative AI and have the generative AI select the optimal plugin.

[0038] The service provider can provide the user with the results of the request execution. For example, the service provider can display the execution results on the user's device. The service provider can also notify the user of the execution results via email or messaging apps. For example, the service provider can display the execution results on the user's smartphone or tablet. The execution results can be displayed in text format, image format, graph format, etc. The execution results may also be notified to the user via email or messaging apps. The execution results may also be notified by voice, for example, by a smart speaker. By providing the user with the results of the request execution, user satisfaction is improved. 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 analyze the execution results using an AI model and provide them to the user.

[0039] The analysis department can ask users about their satisfaction levels and perform analysis to improve the accuracy of requests. For example, the analysis department can ask users about their satisfaction levels in a questionnaire format and collect the responses. The analysis department can also analyze the collected satisfaction data using a generative AI to optimize future requests. For example, the analysis department can ask users about their satisfaction levels in a questionnaire format and collect the responses. The collected satisfaction data is analyzed using a generative AI to optimize future requests. The satisfaction data is provided as feedback to improve the accuracy of requests. In this way, the accuracy of requests can be improved by analyzing user satisfaction. Some or all of the above processing in the analysis department is performed using a generative AI. For example, the analysis department can input user satisfaction data into a generative AI and have the generative AI perform analysis to improve the accuracy of requests.

[0040] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. 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 requests to be used during specific time periods based on the user's past request history. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. It can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request 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 analyze the user's past request history using an AI model and select the optimal reception method.

[0041] The reception unit can filter requests based on the user's current situation and areas of interest when receiving them. For example, when a user enters their current situation, the reception unit will prioritize displaying requests related to that situation. The reception unit can also filter and display relevant requests based on the user's areas of interest. Furthermore, the reception unit can narrow down the list of potential requests based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception unit will prioritize displaying requests related to that situation. It will filter and display relevant requests based on the user's areas of interest. It will narrow down the list of potential requests based on the user's current situation and areas of interest. This allows the reception unit to receive more relevant requests by filtering requests based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's current situation and areas of interest into an AI model and have the AI ​​model perform the request filtering.

[0042] The reception unit can prioritize receiving requests based on the user's geographical location when receiving requests. For example, if the user is in a specific region, the reception unit will prioritize requests related to that region. The reception unit can also prioritize requests related to locations close to the user's current location. Furthermore, the reception unit can filter and receive requests based on the user's geographical location. For example, if the reception unit is in a specific region, it will prioritize requests related to that region. It will prioritize requests related to locations close to the user's current location. It will filter and receive requests based on the user's geographical location. This allows the reception unit to receive more relevant requests by prioritizing requests based on the user's geographical location. 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 input the user's geographical location into an AI model and have the AI ​​model perform the request filtering.

[0043] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and accept relevant requests preferentially. The reception unit can also filter and accept relevant requests based on the user's social media activity history. Furthermore, the reception unit can analyze the user's interests on social media and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and accept relevant requests preferentially. It can filter and accept relevant requests based on the user's social media activity history. It can analyze the user's interests on social media and accept relevant requests. In this way, relevant requests can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into an AI model and have the AI ​​model perform the request filtering.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the request during the analysis. For example, the analysis unit performs a detailed analysis for high-importance requests. It can also perform a concise analysis for low-importance requests. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the request. For example, the analysis unit performs a detailed analysis for high-importance requests, a concise analysis for low-importance requests, and adjusts the depth of the analysis according to the importance of the request. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the request. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input request importance data into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the request during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical requests. It can also apply a business-oriented analysis algorithm to business-related requests. Furthermore, it can apply an entertainment-oriented analysis algorithm to entertainment-related requests. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the request. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input request category data into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analyses based on when the requests were submitted. For example, the analysis unit can determine the priority of analyses based on the time period in which the requests were submitted. The analysis unit can also adjust the order of analyses according to when the requests were submitted. Furthermore, the analysis unit can adjust the analysis schedule based on when the requests were submitted. For example, the analysis unit can determine the priority of analyses based on the time period in which the requests were submitted. It can adjust the order of analyses according to when the requests were submitted. It can adjust the analysis schedule according to when the requests were submitted. This allows for the provision of more appropriate analysis results by determining the priority of analyses based on when the requests were submitted. Some or all of the above processes in the analysis unit are performed using a generating AI. For example, the analysis unit can input request submission time data into the generating AI and have the generating AI perform the determination of analysis priorities.

[0047] The analysis unit can adjust the order of analysis based on the relevance of requests during analysis. For example, the analysis unit prioritizes analyzing requests with high relevance. The analysis unit can also adjust the order of analysis based on the relevance of requests. Furthermore, the analysis unit can adjust the analysis schedule considering the relevance of requests. For example, the analysis unit prioritizes analyzing requests with high relevance. It adjusts the order of analysis based on the relevance of requests. It adjusts the analysis schedule considering the relevance of requests. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of requests. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input request relevance data into the generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The execution unit can improve execution accuracy by considering the interrelationships of requests during execution. For example, if multiple requests are related, the execution unit will integrate and execute them. The execution unit can also analyze the interrelationships of requests and select the optimal execution method. Furthermore, the execution unit can adjust the execution order by considering the interrelationships of requests. For example, if multiple requests are related, the execution unit will integrate and execute them. It will analyze the interrelationships of requests and select the optimal execution method. It will adjust the execution order by considering the interrelationships of requests. In this way, the execution accuracy can be improved by considering the interrelationships of requests. Some or all of the above processing in the execution unit is performed using a generation AI. For example, the execution unit can input request interrelationship data into the generation AI and have the generation AI perform adjustments to improve execution accuracy.

[0049] The execution unit can perform actions while considering the attribute information of the request submitter. For example, the execution unit can select the optimal execution method based on the request submitter's age and gender. The execution unit can also adjust the content of the execution based on the request submitter's occupation and hobbies. Furthermore, the execution unit can adjust the order of execution while considering the attribute information of the request submitter. For example, the execution unit selects the optimal execution method based on the request submitter's age and gender. It adjusts the content of the execution based on the request submitter's occupation and hobbies. It adjusts the order of execution while considering the attribute information of the request submitter. This makes it possible to perform more appropriate execution by considering the attribute information of the request submitter. Some or all of the above processing in the execution unit is performed using a generation AI. For example, the execution unit can input the request submitter's attribute information into the generation AI and have the generation AI perform the execution adjustments.

[0050] The execution unit can perform execution while considering the geographical distribution of requests. For example, the execution unit can analyze the geographical distribution of requests and select the optimal execution method. The execution unit can also adjust the execution order based on the geographical distribution of requests. Furthermore, the execution unit can adjust the execution schedule while considering the geographical distribution of requests. For example, the execution unit analyzes the geographical distribution of requests and selects the optimal execution method. It adjusts the execution order based on the geographical distribution of requests. It adjusts the execution schedule while considering the geographical distribution of requests. This makes it possible to perform more appropriate execution by considering the geographical distribution of requests. Some or all of the above processing in the execution unit is performed using a generative AI. For example, the execution unit can input geographical distribution data of requests into the generative AI and have the generative AI perform the execution adjustments.

[0051] The execution unit can improve the accuracy of execution by referring to the relevant literature for the request during execution. For example, the execution unit can refer to the relevant literature for the request and select the optimal execution method. The execution unit can also adjust the content of the execution based on the relevant literature for the request. Furthermore, the execution unit can adjust the order of execution considering the relevant literature for the request. For example, the execution unit can refer to the relevant literature for the request and select the optimal execution method. It can adjust the content of the execution based on the relevant literature for the request. It can adjust the order of execution considering the relevant literature for the request. In this way, the accuracy of execution can be improved by referring to the relevant literature for the request. Some or all of the above processing in the execution unit is performed using a generation AI. For example, the execution unit can input the relevant literature data for the request into the generation AI and have the generation AI perform the adjustments to the execution.

[0052] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also select the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. For example, the service provider can prioritize providing display methods that the user has used in the past. It can select the optimal display method based on the user's past operation history. It can analyze the user's past operation history and provide the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past operation history. 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 input the user's past operation history data into an AI model and have the AI ​​model perform the selection of the display method.

[0053] The information provider can adjust the displayed content based on the user's current situation and areas of interest at the time of delivery. For example, when the user enters their current situation, the information provider will prioritize displaying information related to that situation. The information provider can also filter and display relevant information based on the user's areas of interest. Furthermore, the information provider can customize the displayed content based on the user's current situation and areas of interest. For example, when the information provider enters their current situation, it will prioritize displaying information related to that situation. It will filter and display relevant information based on the user's areas of interest. It will customize the displayed content based on the user's current situation and areas of interest. This makes it possible to provide more appropriate information by adjusting the displayed content based on the user's current situation and areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input data on the user's current situation and areas of interest into an AI model and have the AI ​​model perform the adjustment of the displayed content.

[0054] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to select the optimal display method by considering the user's device 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 input the user's device information into an AI model and have the AI ​​model select the display method.

[0055] The service provider can provide multilingual displays according to the user's language settings at the time of delivery. For example, the service provider can automatically set the display language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide displays in a specific language if the user selects one. For example, the service provider can automatically set the display language based on the language settings of the user's device. If the user uses multiple languages, it can provide a language switching function. If the user selects a specific language, it can provide displays in that language. This enables the provision of more appropriate information by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's language setting data into an AI model and have the AI ​​model perform the display language setting.

[0056] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to past analysis data. In addition, the analysis unit can adjust the analysis schedule based on past analysis data. For example, the analysis unit selects the optimal analysis algorithm based on past analysis data. It improves the accuracy of the analysis by referring to past analysis data. It adjusts the analysis schedule based on past analysis data. This allows the analysis algorithm to be optimized by referring to past analysis data. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input past analysis data into the generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0057] The analysis unit can improve the accuracy of its analysis based on user satisfaction data. For example, the analysis unit can adjust its analysis algorithm based on user satisfaction data. It can also improve the content of the analysis by referring to user satisfaction data. Furthermore, the analysis unit can adjust the analysis schedule based on user satisfaction data. For example, the analysis unit adjusts its analysis algorithm based on user satisfaction data. It improves the content of the analysis by referring to user satisfaction data. It adjusts the analysis schedule based on user satisfaction data. This allows the analysis unit to improve its accuracy based on user satisfaction data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input user satisfaction data into the generative AI and have the generative AI perform adjustments to improve the accuracy of the analysis.

[0058] The analysis unit can weight the analysis data based on when the requests were submitted. For example, the analysis unit can weight the analysis data based on the time period in which the requests were submitted. The analysis unit can also determine the priority of the analysis data based on when the requests were submitted. Furthermore, the analysis unit can adjust the schedule of the analysis data based on when the requests were submitted. For example, the analysis unit weights the analysis data based on the time period in which the requests were submitted. It determines the priority of the analysis data based on when the requests were submitted. It adjusts the schedule of the analysis data based on when the requests were submitted. This allows for more appropriate analysis by weighting the analysis data based on when the requests were submitted. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the request submission time data into the generative AI and have the generative AI perform the weighting of the analysis data.

[0059] The analysis unit can optimize its analysis algorithm based on user feedback during analysis. For example, the analysis unit can adjust the analysis algorithm based on user feedback. The analysis unit can also improve the content of the analysis by referring to user feedback. Furthermore, the analysis unit can adjust the analysis schedule based on user feedback. For example, the analysis unit adjusts the analysis algorithm based on user feedback. It improves the content of the analysis by referring to user feedback. It adjusts the analysis schedule based on user feedback. This allows for more appropriate analysis by optimizing the analysis algorithm based on user feedback. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user feedback data into the generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analysis unit can understand the content of the request and determine which plugin to use. For example, it can analyze the content of the request and select the optimal plugin. Alternatively, a generative AI can be used to understand the content of the request. For example, it can analyze the content of the request and select the optimal plugin. The generative AI understands the content of the request and determines which plugin to use. This allows for understanding the content of the request and selecting the appropriate plugin. Some or all of the above-described processes in the analysis unit are performed using the generative AI. For example, the analysis unit can input the content of the request into the generative AI and have the generative AI select the optimal plugin.

[0062] The service provider can provide the user with the results of the request execution. For example, the results can be displayed on the user's device. The results can also be notified via email or messaging apps. For example, the results can be displayed on the user's smartphone or tablet. The results can be displayed in text format, image format, graph format, etc. The results may also be notified to the user via email or messaging apps. The results may also be notified by voice, for example, through a smart speaker. By providing the user with the results of the request execution, user satisfaction is improved. 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 analyze the results using an AI model and provide them to the user.

[0063] The analysis department can ask users about their satisfaction levels and perform analyses to improve the accuracy of requests. For example, it can ask users about their satisfaction levels in a questionnaire format and collect the responses. It can also analyze the collected satisfaction data using a generative AI to optimize future requests. For example, it can ask users about their satisfaction levels in a questionnaire format and collect the responses. The collected satisfaction data is analyzed using a generative AI to optimize future requests. The satisfaction data is provided as feedback to improve the accuracy of requests. In this way, the accuracy of requests can be improved by analyzing user satisfaction levels. Some or all of the above processes in the analysis department are performed using a generative AI. For example, the analysis department can input user satisfaction data into a generative AI and have the generative AI perform analyses to improve the accuracy of requests.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, it can perform a detailed analysis for high-importance requests and a concise analysis for low-importance requests. Furthermore, it can adjust the depth of the analysis according to the importance of the request. By adjusting the level of detail of the analysis based on the importance of the request, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input request importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0065] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, it can prioritize providing display methods previously used by the user. It can also select the optimal display method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and provide the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past operation history. 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 input the user's past operation history data into an AI model and have the AI ​​model perform the selection of the display method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives requests from users. User requests can be in text format, audio format, image format, etc. The reception desk analyzes text-format requests using natural language processing technology, converts audio-format requests into text data using speech recognition technology, and analyzes image-format requests using image recognition technology. Step 2: The analysis unit uses a generation AI to analyze the request received by the reception unit. The analysis understands the content of the request and determines which plugin to use. The generation AI uses a text generation AI and a multimodal generation AI to analyze the content of the request, extracting and analyzing the important parts. Step 3: The execution unit selects and executes the most suitable plugin based on the request analyzed by the analysis unit. The execution unit uses a generation AI to select the most suitable plugin for the request and executes that plugin. Depending on the content of the request, the selected plugin may include, for example, a restaurant search plugin, a weather forecast plugin, or a traffic information plugin. Step 4: The delivery unit provides the user with the results executed by the execution unit. The delivery unit can display the execution results on the user's device, notify them via email or messaging apps, and may also notify them by voice. Step 5: The analysis department asks users about their satisfaction based on the results provided by the service department, and the generative AI analyzes the responses. The analysis department asks users about their satisfaction in a questionnaire format, collects the responses, analyzes them using the generative AI, and optimizes for future requests.

[0068] (Example of form 2) The total solution-compatible concierge system according to an embodiment of the present invention is a system that automatically receives requests from users, analyzes them using a generating AI, and selects and executes the optimal plugin. This system receives requests from users, analyzes them using a generating AI, selects and executes the optimal plugin. It provides the user with the results of the request execution, asks about their satisfaction level, and the generating AI analyzes the results to provide personalized and optimized answers for future requests. This platform allows the robot to spontaneously arrive at the optimal solution for a wide variety of requests from clients, expanding its range of activities. For example, the total solution-compatible concierge system receives requests from users in natural language. For example, if a user requests, "Please tell me about nearby restaurants," the system analyzes the request, selects and executes the optimal plugin. Next, the system uses the generating AI to analyze the content of the request and decide which plugin to use. For example, the generating AI selects the "restaurant search plugin" and executes that plugin. Next, the system provides the user with the results of the execution. For example, the system displays a list of nearby restaurants to the user. Next, the system asks the user about their satisfaction level, and the generating AI analyzes the answer. For example, the system asks, "Was this information helpful?" and collects the user's response. Then, based on the collected satisfaction data, the system provides personalized responses to future requests. For instance, the system prioritizes displaying restaurants that the user has previously given high ratings to. This allows the total solution-oriented concierge system to respond quickly and accurately to user requests, thereby improving user satisfaction.

[0069] The total solution-compatible concierge system according to this embodiment comprises a reception unit, an analysis unit, an execution unit, a provision unit, and an analysis unit. The reception unit receives requests from users. User requests include, but are not limited to, text format, audio format, and image format. The reception unit analyzes, for example, text format requests using natural language processing technology. The reception unit can also convert audio format requests into text data using speech recognition technology. Furthermore, the reception unit can analyze image format requests using image recognition technology. For example, the reception unit analyzes text format requests using natural language processing technology to understand the content of the request. Audio format requests are converted into text data using speech recognition technology and then analyzed. Image format requests are analyzed using image recognition technology to understand the content of the request. The analysis unit analyzes the requests received by the reception unit using a generation AI. The analysis includes, for example, understanding the content of the request and determining which plugin to use, but is not limited to this example. For example, the generation AI analyzes the content of the request using a text generation AI (e.g., LLM). Furthermore, the analysis unit can analyze the content of the request using a multimodal generation AI. The analysis unit can also extract and analyze important parts of the request using a generation AI. For example, the text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The multimodal generation AI can handle multiple modals, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the request and performs analysis based on that information. The execution unit selects and executes the optimal plugin based on the request analyzed by the analysis unit. Execution includes, but is not limited to, the selection and execution of a plugin. For example, the execution unit uses the generation AI to select the optimal plugin for the request. The execution unit can also execute the selected plugin. Furthermore, the execution unit can collect the execution results of the plugin and pass them on to the next process.For example, the execution unit uses a generative AI to select the most suitable plugin for a request and executes that plugin. Depending on the content of the request, the selected plugin may include, for example, a restaurant search plugin, a weather forecast plugin, or a traffic information plugin. The delivery unit provides the user with the results executed by the execution unit. Delivery includes, but is not limited to, displaying or notifying the user of the execution results. For example, the delivery unit displays the execution results on the user's device. The delivery unit may also notify the user of the execution results via email or messaging apps. The delivery unit may also notify the user of the execution results by voice. For example, the delivery unit displays the execution results on the user's smartphone or tablet. The execution results are displayed in text format, image format, graph format, etc. The execution results may also be notified to the user via email or messaging apps. The execution results may also be notified by voice, for example, through a smart speaker. The analysis unit asks the user about their satisfaction level based on the results provided by the delivery unit, and the generative AI analyzes the answer. Analysis includes, but is not limited to, evaluating satisfaction levels and optimizing for future requests. For example, the analysis department can ask users about their satisfaction level in a questionnaire format and collect the responses. The analysis department can also analyze the collected satisfaction data using a generative AI to optimize for future requests. Furthermore, the analysis department can provide feedback based on the satisfaction data to improve the accuracy of requests. For example, the analysis department can ask users about their satisfaction level in a questionnaire format and collect the responses. The collected satisfaction data is analyzed using a generative AI to optimize for future requests. The satisfaction data is provided as feedback to improve the accuracy of requests. As a result, the total solution-compatible concierge system according to this embodiment can respond quickly and accurately to user requests and improve user satisfaction.

[0070] The reception desk receives requests from users. These requests may include, but are not limited to, text, audio, and image formats. For example, the reception desk analyzes text requests using natural language processing technology. Specifically, it uses natural language processing technology to perform grammatical and semantic analysis of the text and accurately understand the intent of the request. For example, if a user requests "Tell me about nearby restaurants," the natural language processing technology extracts the keyword "nearby restaurants" and recognizes the intent of the request as "restaurant search." The reception desk can also convert audio requests into text data using speech recognition technology. Speech recognition technology analyzes the audio signal, recognizes phonemes and words, and converts them into text. For example, if a user requests "What's the weather like tomorrow?" using voice, the speech recognition technology analyzes the audio signal and converts it into the text data "Tomorrow's weather." Furthermore, the reception desk can also analyze image requests using image recognition technology. Image recognition technology analyzes image data and recognizes objects and text within the image. For example, if a user uploads an image of a restaurant menu, image recognition technology analyzes the text and images of the dishes in the menu to understand the request. This allows the reception desk to accept and appropriately analyze a variety of requests from users.

[0071] The analysis unit uses generative AI to analyze requests received by the reception unit. Analysis can, for example, understand the content of a request and determine which plugin to use, but is not limited to such examples. Specifically, the generative AI uses text generation AI (e.g., LLM) to analyze the content of requests. Text generation AI has learned from large amounts of text data and possesses advanced natural language processing capabilities. For example, if a user requests "Tell me about nearby restaurants," the text generation AI understands the intent of the request and decides to use a restaurant search plugin. The analysis unit can also analyze the content of requests using multimodal generative AI. Multimodal generative AI can handle multiple modals, including not only text but also images and audio. For example, if a user uploads a restaurant menu image, the multimodal generative AI analyzes the text and images of the dishes within the image to understand the intent of the request. Furthermore, the analysis unit can use generative AI to extract and analyze important parts of a request. The generative AI uses keyword extraction technology to pick out particularly important information from the request and performs analysis based on that information. For example, if a user requests "What's the weather like tomorrow?", the generating AI extracts the keywords "tomorrow" and "weather" and decides to use a weather forecast plugin. This allows the analysis unit to quickly and accurately analyze the request received by the reception unit and select the most suitable plugin.

[0072] The execution unit selects and executes the most suitable plugin based on the request analyzed by the analysis unit. Execution includes, but is not limited to, the selection and execution of a plugin. Specifically, the execution unit uses a generation AI to select the most suitable plugin for a request. The generation AI has an algorithm for analyzing the content of the request and selecting the most suitable plugin. For example, if a user requests "Tell me about nearby restaurants," the generation AI selects a restaurant search plugin. The execution unit can also execute the selected plugin. Depending on the content of the request, the selected plugin may include, for example, a restaurant search plugin, a weather forecast plugin, or a traffic information plugin. For example, the restaurant search plugin searches for nearby restaurants based on the user's current location and provides the results. The weather forecast plugin obtains the weather forecast for a specified area and provides the results. The traffic information plugin obtains traffic information for a specified route and provides the results. The execution unit can also collect the execution results of the plugins and pass them on to the next process. For example, as an execution result of the restaurant search plugin, a list of nearby restaurants is generated. This list is passed on to the next providing unit and provided to the user. This allows the execution unit to select and execute the optimal plugin based on the request analyzed by the analysis unit, enabling it to respond quickly and accurately to user requests.

[0073] The service provider provides the user with the results executed by the execution unit. This includes, but is not limited to, displaying or notifying the user of the results. Specifically, the service provider displays the results on the user's device. The results can be displayed in text, image, or graph format. For example, a restaurant search plugin might generate a list of nearby restaurants. This list is displayed on the user's smartphone or tablet. The service provider can also notify the user of the results via email or messaging apps. For example, a weather forecast plugin might generate a weather forecast for a specified area. This forecast is notified via email or messaging apps. The service provider can also notify the user of the results via voice. For example, the weather forecast can be delivered to the user via a smart speaker. Furthermore, the service provider can customize the results according to the user's preferences. For example, if a user prefers a particular type of restaurant, the service provider can customize the restaurant search results based on that information. This allows the service provider to quickly and appropriately deliver the results executed by the execution unit to the user, improving user satisfaction.

[0074] The analysis department asks users about their satisfaction based on the results provided by the service department, and the generative AI analyzes the responses. The analysis includes, but is not limited to, evaluating satisfaction and optimizing for future requests. Specifically, the analysis department asks users about their satisfaction in a questionnaire format and collects the responses. The questionnaire includes, for example, questions about satisfaction with the results and areas for improvement. For example, users rate restaurant search results as "satisfied" or "dissatisfied." The analysis department can also use the generative AI to analyze the collected satisfaction data and optimize for future requests. The generative AI analyzes the satisfaction data and learns the user's preferences and tendencies. For example, if a user prefers a particular type of restaurant, the AI ​​customizes the next restaurant search results based on that information. The analysis department can also provide feedback based on the satisfaction data to improve the accuracy of requests. For example, if a user rates a weather forecast result as "dissatisfied," the AI ​​analyzes the reason and identifies areas for improvement to enhance the accuracy of the weather forecast plugin. This allows the analysis department to improve the overall system performance by evaluating user satisfaction based on the results provided by the service department and optimizing for future requests.

[0075] The execution unit can search for necessary plugins if they are not installed and confirm with the user whether to install them. For example, if a necessary plugin is not installed, the execution unit can search for the plugin on the internet. The execution unit can also present the search results to the user and confirm whether to install them. For example, if a necessary plugin is not installed, the execution unit can search for the plugin on the internet and present the results to the user. The user can then select a plugin to install from the presented plugins. The execution unit can also automatically install the selected plugin. This allows for installation after confirming with the user, even if a necessary plugin is not installed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, if a necessary plugin is not installed, the execution unit can use an AI model to search for the plugin and confirm its installation.

[0076] The reception unit can receive user requests in natural language. For example, the reception unit can receive requests entered by users in natural language. The reception unit can also receive natural language requests using voice input. For example, the reception unit can receive requests entered by users in natural language as text data. Requests using voice input are converted into text data using speech recognition technology and then accepted. This improves usability by allowing user requests to be received in natural language. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can analyze natural language requests using an AI model and then accept them.

[0077] The analysis unit can understand the content of the request and determine which plugin to use. For example, the analysis unit analyzes the content of the request and selects the optimal plugin. The analysis unit can also use a generative AI to understand the content of the request. For example, the analysis unit analyzes the content of the request and selects the optimal plugin. The generative AI understands the content of the request and determines which plugin to use. This allows the analysis unit to understand the content of the request and select the appropriate plugin. Some or all of the above processing in the analysis unit is performed using the generative AI. For example, the analysis unit can input the content of the request into the generative AI and have the generative AI select the optimal plugin.

[0078] The service provider can provide the user with the results of the request execution. For example, the service provider can display the execution results on the user's device. The service provider can also notify the user of the execution results via email or messaging apps. For example, the service provider can display the execution results on the user's smartphone or tablet. The execution results can be displayed in text format, image format, graph format, etc. The execution results may also be notified to the user via email or messaging apps. The execution results may also be notified by voice, for example, by a smart speaker. By providing the user with the results of the request execution, user satisfaction is improved. 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 analyze the execution results using an AI model and provide them to the user.

[0079] The analysis department can ask users about their satisfaction levels and perform analysis to improve the accuracy of requests. For example, the analysis department can ask users about their satisfaction levels in a questionnaire format and collect the responses. The analysis department can also analyze the collected satisfaction data using a generative AI to optimize future requests. For example, the analysis department can ask users about their satisfaction levels in a questionnaire format and collect the responses. The collected satisfaction data is analyzed using a generative AI to optimize future requests. The satisfaction data is provided as feedback to improve the accuracy of requests. In this way, the accuracy of requests can be improved by analyzing user satisfaction. Some or all of the above processing in the analysis department is performed using a generative AI. For example, the analysis department can input user satisfaction data into a generative AI and have the generative AI perform analysis to improve the accuracy of requests.

[0080] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. 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 process the request quickly. 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, it can provide detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to process the request quickly. This allows for more appropriate request processing by adjusting the request processing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 input user emotion data into a generating AI and have the AI ​​adjust how requests are received.

[0081] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. 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 requests to be used during specific time periods based on the user's past request history. For example, the reception desk can automatically display requests that the user has frequently entered in the past as candidates. It can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can predict and suggest requests to be used during specific time periods based on the user's past request history. In this way, the optimal reception method can be selected by analyzing the user's past request 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 analyze the user's past request history using an AI model and select the optimal reception method.

[0082] The reception unit can filter requests based on the user's current situation and areas of interest when receiving them. For example, when a user enters their current situation, the reception unit will prioritize displaying requests related to that situation. The reception unit can also filter and display relevant requests based on the user's areas of interest. Furthermore, the reception unit can narrow down the list of potential requests based on the user's current situation and areas of interest. For example, when a user enters their current situation, the reception unit will prioritize displaying requests related to that situation. It will filter and display relevant requests based on the user's areas of interest. It will narrow down the list of potential requests based on the user's current situation and areas of interest. This allows the reception unit to receive more relevant requests by filtering requests based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's current situation and areas of interest into an AI model and have the AI ​​model perform the request filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize high-priority requests. If the user is relaxed, the reception desk can also accept requests with normal priority. Furthermore, if the user is in a hurry, the reception desk can also prioritize high-urgency requests. For example, if the user is nervous, the reception desk will prioritize high-priority requests. If the user is relaxed, it will accept requests with normal priority. If the user is in a hurry, it will prioritize high-urgency requests. This allows for more appropriate request handling by determining the priority of requests 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 without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​determine the priority of requests.

[0084] The reception unit can prioritize receiving requests based on the user's geographical location when receiving requests. For example, if the user is in a specific region, the reception unit will prioritize requests related to that region. The reception unit can also prioritize requests related to locations close to the user's current location. Furthermore, the reception unit can filter and receive requests based on the user's geographical location. For example, if the reception unit is in a specific region, it will prioritize requests related to that region. It will prioritize requests related to locations close to the user's current location. It will filter and receive requests based on the user's geographical location. This allows the reception unit to receive more relevant requests by prioritizing requests based on the user's geographical location. 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 input the user's geographical location into an AI model and have the AI ​​model perform the request filtering.

[0085] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and accept relevant requests preferentially. The reception unit can also filter and accept relevant requests based on the user's social media activity history. Furthermore, the reception unit can analyze the user's interests on social media and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and accept relevant requests preferentially. It can filter and accept relevant requests based on the user's social media activity history. It can analyze the user's interests on social media and accept relevant requests. In this way, relevant requests can be accepted by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into an AI model and have the AI ​​model perform the request filtering.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, it provides concise analysis results that get straight to the point. If the user is excited, it provides analysis results with visually stimulating effects. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the request during the analysis. For example, the analysis unit performs a detailed analysis for high-importance requests. It can also perform a concise analysis for low-importance requests. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the request. For example, the analysis unit performs a detailed analysis for high-importance requests, a concise analysis for low-importance requests, and adjusts the depth of the analysis according to the importance of the request. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the request. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input request importance data into the generating AI and have the generating AI adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the request during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical requests. It can also apply a business-oriented analysis algorithm to business-related requests. Furthermore, it can apply an entertainment-oriented analysis algorithm to entertainment-related requests. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the request. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input request category data into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, it provides a longer analysis with detailed explanations. If the user is excited, it provides an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analyses based on when the requests were submitted. For example, the analysis unit can determine the priority of analyses based on the time period in which the requests were submitted. The analysis unit can also adjust the order of analyses according to when the requests were submitted. Furthermore, the analysis unit can adjust the analysis schedule based on when the requests were submitted. For example, the analysis unit can determine the priority of analyses based on the time period in which the requests were submitted. It can adjust the order of analyses according to when the requests were submitted. It can adjust the analysis schedule according to when the requests were submitted. This allows for the provision of more appropriate analysis results by determining the priority of analyses based on when the requests were submitted. Some or all of the above processes in the analysis unit are performed using a generating AI. For example, the analysis unit can input request submission time data into the generating AI and have the generating AI perform the determination of analysis priorities.

[0091] The analysis unit can adjust the order of analysis based on the relevance of requests during analysis. For example, the analysis unit prioritizes analyzing requests with high relevance. The analysis unit can also adjust the order of analysis based on the relevance of requests. Furthermore, the analysis unit can adjust the analysis schedule considering the relevance of requests. For example, the analysis unit prioritizes analyzing requests with high relevance. It adjusts the order of analysis based on the relevance of requests. It adjusts the analysis schedule considering the relevance of requests. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of requests. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input request relevance data into the generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is relaxed, the execution unit will execute at a leisurely pace. If the user is in a hurry, the execution unit can also execute quickly. Furthermore, if the user is excited, the execution unit can execute with visually stimulating effects. For example, if the user is relaxed, the execution unit will execute at a leisurely pace. If the user is in a hurry, it will execute quickly. If the user is excited, it will execute with visually stimulating effects. This allows for more appropriate execution by adjusting the execution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit is performed using generative AI. For example, the execution unit can input user emotion data into the generative AI and have the generative AI adjust the execution method.

[0093] The execution unit can improve execution accuracy by considering the interrelationships of requests during execution. For example, if multiple requests are related, the execution unit will integrate and execute them. The execution unit can also analyze the interrelationships of requests and select the optimal execution method. Furthermore, the execution unit can adjust the execution order by considering the interrelationships of requests. For example, if multiple requests are related, the execution unit will integrate and execute them. It will analyze the interrelationships of requests and select the optimal execution method. It will adjust the execution order by considering the interrelationships of requests. In this way, the execution accuracy can be improved by considering the interrelationships of requests. Some or all of the above processing in the execution unit is performed using a generation AI. For example, the execution unit can input request interrelationship data into the generation AI and have the generation AI perform adjustments to improve execution accuracy.

[0094] The execution unit can perform actions while considering the attribute information of the request submitter. For example, the execution unit can select the optimal execution method based on the request submitter's age and gender. The execution unit can also adjust the content of the execution based on the request submitter's occupation and hobbies. Furthermore, the execution unit can adjust the order of execution while considering the attribute information of the request submitter. For example, the execution unit selects the optimal execution method based on the request submitter's age and gender. It adjusts the content of the execution based on the request submitter's occupation and hobbies. It adjusts the order of execution while considering the attribute information of the request submitter. This makes it possible to perform more appropriate execution by considering the attribute information of the request submitter. Some or all of the above processing in the execution unit is performed using a generation AI. For example, the execution unit can input the request submitter's attribute information into the generation AI and have the generation AI perform the execution adjustments.

[0095] The execution unit can estimate the user's emotions and adjust the order in which the execution results are displayed based on the estimated emotions. For example, if the user is nervous, the execution unit will display important results first. If the user is relaxed, the execution unit can also display detailed results sequentially. Furthermore, if the user is in a hurry, the execution unit can display concise results first. For example, if the user is nervous, the execution unit will display important results first. If the user is relaxed, it will display detailed results sequentially. If the user is in a hurry, it will display concise results first. By adjusting the order in which the execution results are displayed according to the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the execution unit is performed using generative AI. For example, the execution unit can input user emotion data into the generative AI and have the generative AI adjust the order in which the results are displayed.

[0096] The execution unit can perform execution while considering the geographical distribution of requests. For example, the execution unit can analyze the geographical distribution of requests and select the optimal execution method. The execution unit can also adjust the execution order based on the geographical distribution of requests. Furthermore, the execution unit can adjust the execution schedule while considering the geographical distribution of requests. For example, the execution unit analyzes the geographical distribution of requests and selects the optimal execution method. It adjusts the execution order based on the geographical distribution of requests. It adjusts the execution schedule while considering the geographical distribution of requests. This makes it possible to perform more appropriate execution by considering the geographical distribution of requests. Some or all of the above processing in the execution unit is performed using a generative AI. For example, the execution unit can input geographical distribution data of requests into the generative AI and have the generative AI perform the execution adjustments.

[0097] The execution unit can improve the accuracy of execution by referring to the relevant literature for the request during execution. For example, the execution unit can refer to the relevant literature for the request and select the optimal execution method. The execution unit can also adjust the content of the execution based on the relevant literature for the request. Furthermore, the execution unit can adjust the order of execution considering the relevant literature for the request. For example, the execution unit can refer to the relevant literature for the request and select the optimal execution method. It can adjust the content of the execution based on the relevant literature for the request. It can adjust the order of execution considering the relevant literature for the request. In this way, the accuracy of execution can be improved by referring to the relevant literature for the request. Some or all of the above processing in the execution unit is performed using a generation AI. For example, the execution unit can input the relevant literature data for the request into the generation AI and have the generation AI perform the adjustments to the execution.

[0098] The service provider can estimate the user's emotions and adjust the display method of the service based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method of the service according to the user's emotions, a more appropriate display becomes possible. 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 is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0099] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can prioritize providing display methods that the user has used in the past. The service provider can also select the optimal display method based on the user's past operation history. Furthermore, the service provider can analyze the user's past operation history and provide the most efficient display method. For example, the service provider can prioritize providing display methods that the user has used in the past. It can select the optimal display method based on the user's past operation history. It can analyze the user's past operation history and provide the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past operation history. 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 input the user's past operation history data into an AI model and have the AI ​​model perform the selection of the display method.

[0100] The information provider can adjust the displayed content based on the user's current situation and areas of interest at the time of delivery. For example, when the user enters their current situation, the information provider will prioritize displaying information related to that situation. The information provider can also filter and display relevant information based on the user's areas of interest. Furthermore, the information provider can customize the displayed content based on the user's current situation and areas of interest. For example, when the user enters their current situation, the information provider will prioritize displaying information related to that situation. Based on the user's areas of interest, relevant information will be filtered and displayed. Based on the user's current situation and areas of interest, the displayed content will be customized. This allows for the provision of more appropriate information by adjusting the displayed content based on the user's current situation and areas of interest. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input data on the user's current situation and areas of interest into an AI model and have the AI ​​model perform the adjustment of the displayed content.

[0101] The service provider can estimate the user's emotions and adjust the operating procedures based on the estimated emotions. For example, if the user is nervous, the service provider can simplify the operating procedures. If the user is relaxed, the service provider can also provide detailed operating procedures. Furthermore, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. For example, if the user is nervous, the service provider can simplify the operating procedures. If the user is relaxed, it can provide detailed operating procedures. If the user is in a hurry, it can provide procedures that allow for quick operation. By adjusting the operating procedures according to the user's emotions, more appropriate operation becomes possible. 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 is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the operating procedures.

[0102] The service provider can select the optimal display method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to select the optimal display method by considering the user's device 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 input the user's device information into an AI model and have the AI ​​model select the display method.

[0103] The service provider can provide multilingual displays according to the user's language settings at the time of delivery. For example, the service provider can automatically set the display language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, the service provider can provide displays in a specific language if the user selects one. For example, the service provider can automatically set the display language based on the language settings of the user's device. If the user uses multiple languages, it can provide a language switching function. If the user selects a specific language, it can provide displays in that language. This enables the provision of more appropriate information by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's language setting data into an AI model and have the AI ​​model perform the display language setting.

[0104] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. If the user is in a hurry, the analysis unit can perform a concise analysis. Furthermore, if the user is excited, the analysis unit can perform an analysis with visually stimulating effects. For example, if the user is relaxed, the analysis unit performs a detailed analysis. If the user is in a hurry, it performs a concise analysis. If the user is excited, it performs an analysis with visually stimulating effects. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis method.

[0105] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to past analysis data. In addition, the analysis unit can adjust the analysis schedule based on past analysis data. For example, the analysis unit selects the optimal analysis algorithm based on past analysis data. It improves the accuracy of the analysis by referring to past analysis data. It adjusts the analysis schedule based on past analysis data. This allows the analysis algorithm to be optimized by referring to past analysis data. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input past analysis data into the generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0106] The analysis unit can improve the accuracy of its analysis based on user satisfaction data. For example, the analysis unit can adjust its analysis algorithm based on user satisfaction data. It can also improve the content of the analysis by referring to user satisfaction data. Furthermore, the analysis unit can adjust the analysis schedule based on user satisfaction data. For example, the analysis unit adjusts its analysis algorithm based on user satisfaction data. It improves the content of the analysis by referring to user satisfaction data. It adjusts the analysis schedule based on user satisfaction data. This allows the analysis unit to improve its accuracy based on user satisfaction data. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input user satisfaction data into the generative AI and have the generative AI perform adjustments to improve the accuracy of the analysis.

[0107] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will perform analyses frequently. If the user is in a hurry, the analysis unit can perform analyses at the minimum necessary frequency. Furthermore, if the user is excited, the analysis unit can perform analyses at a moderate frequency. For example, if the user is relaxed, the analysis unit will perform analyses frequently. If the user is in a hurry, it will perform analyses at the minimum necessary frequency. If the user is excited, it will perform analyses at a moderate frequency. By adjusting the frequency of analysis according to the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis frequency.

[0108] The analysis unit can weight the analysis data based on when the requests were submitted. For example, the analysis unit can weight the analysis data based on the time period in which the requests were submitted. The analysis unit can also determine the priority of the analysis data based on when the requests were submitted. Furthermore, the analysis unit can adjust the schedule of the analysis data based on when the requests were submitted. For example, the analysis unit weights the analysis data based on the time period in which the requests were submitted. It determines the priority of the analysis data based on when the requests were submitted. It adjusts the schedule of the analysis data based on when the requests were submitted. This allows for more appropriate analysis by weighting the analysis data based on when the requests were submitted. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit can input the request submission time data into the generative AI and have the generative AI perform the weighting of the analysis data.

[0109] The analysis unit can optimize its analysis algorithm based on user feedback during analysis. For example, the analysis unit can adjust the analysis algorithm based on user feedback. The analysis unit can also improve the content of the analysis by referring to user feedback. Furthermore, the analysis unit can adjust the analysis schedule based on user feedback. For example, the analysis unit adjusts the analysis algorithm based on user feedback. It improves the content of the analysis by referring to user feedback. It adjusts the analysis schedule based on user feedback. This allows for more appropriate analysis by optimizing the analysis algorithm based on user feedback. Some or all of the above processes in the analysis unit are performed using generative AI. For example, the analysis unit can input user feedback data into the generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception unit can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is stressed, a simple interface and minimal input steps can be provided. If the user is relaxed, detailed input options and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to process the request quickly. This allows for more appropriate request processing by adjusting the request processing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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 unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the request processing method.

[0112] The analysis unit can understand the content of the request and determine which plugin to use. For example, it can analyze the content of the request and select the optimal plugin. Alternatively, a generative AI can be used to understand the content of the request. For example, it can analyze the content of the request and select the optimal plugin. The generative AI understands the content of the request and determines which plugin to use. This allows for understanding the content of the request and selecting the appropriate plugin. Some or all of the above-described processes in the analysis unit are performed using the generative AI. For example, the analysis unit can input the content of the request into the generative AI and have the generative AI select the optimal plugin.

[0113] The service provider can provide the user with the results of the request execution. For example, the results can be displayed on the user's device. The results can also be notified via email or messaging apps. For example, the results can be displayed on the user's smartphone or tablet. The results can be displayed in text format, image format, graph format, etc. The results may also be notified to the user via email or messaging apps. The results may also be notified by voice, for example, through a smart speaker. By providing the user with the results of the request execution, user satisfaction is improved. 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 analyze the results using an AI model and provide them to the user.

[0114] The analysis department can ask users about their satisfaction levels and perform analyses to improve the accuracy of requests. For example, it can ask users about their satisfaction levels in a questionnaire format and collect the responses. It can also analyze the collected satisfaction data using a generative AI to optimize future requests. For example, it can ask users about their satisfaction levels in a questionnaire format and collect the responses. The collected satisfaction data is analyzed using a generative AI to optimize future requests. The satisfaction data is provided as feedback to improve the accuracy of requests. In this way, the accuracy of requests can be improved by analyzing user satisfaction levels. Some or all of the above processes in the analysis department are performed using a generative AI. For example, the analysis department can input user satisfaction data into a generative AI and have the generative AI perform analyses to improve the accuracy of requests.

[0115] The reception unit can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if the user is stressed, high-priority requests will be given priority. If the user is relaxed, requests can be given the normal priority. Furthermore, if the user is in a hurry, urgent requests can be given priority. This allows for more appropriate request processing by prioritizing requests 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 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 unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of requests.

[0116] The analysis unit can adjust the level of detail of the analysis based on the importance of the request. For example, it can perform a detailed analysis for high-importance requests and a concise analysis for low-importance requests. Furthermore, it can adjust the depth of the analysis according to the importance of the request. By adjusting the level of detail of the analysis based on the importance of the request, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input request importance data into the generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0117] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is relaxed, the execution can be performed at a leisurely pace. If the user is in a hurry, the execution can be performed quickly. Furthermore, if the user is excited, visually stimulating effects can be added to the execution. This allows for more appropriate execution by adjusting the execution method 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 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 execution unit is performed using generative AI. For example, the execution unit can input user emotion data into the generative AI and have the generative AI adjust the execution method.

[0118] The service provider can estimate the user's emotions and adjust the display method of the service based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the display method of the service according to the user's emotions, a more appropriate display becomes possible. 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 is performed using generative AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0119] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, it can prioritize providing display methods previously used by the user. It can also select the optimal display method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and provide the most efficient display method. This allows the service provider to select the optimal display method by referring to the user's past operation history. 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 input the user's past operation history data into an AI model and have the AI ​​model perform the selection of the display method.

[0120] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a concise analysis can be performed. Furthermore, if the user is excited, an analysis with visually stimulating effects can be added. This allows for more appropriate analysis by adjusting the analysis method 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 analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives requests from users. User requests can be in text format, audio format, image format, etc. The reception desk analyzes text-format requests using natural language processing technology, converts audio-format requests into text data using speech recognition technology, and analyzes image-format requests using image recognition technology. Step 2: The analysis unit uses a generation AI to analyze the request received by the reception unit. The analysis understands the content of the request and determines which plugin to use. The generation AI uses a text generation AI and a multimodal generation AI to analyze the content of the request, extracting and analyzing the important parts. Step 3: The execution unit selects and executes the most suitable plugin based on the request analyzed by the analysis unit. The execution unit uses a generation AI to select the most suitable plugin for the request and executes that plugin. Depending on the content of the request, the selected plugin may include, for example, a restaurant search plugin, a weather forecast plugin, or a traffic information plugin. Step 4: The delivery unit provides the user with the results executed by the execution unit. The delivery unit can display the execution results on the user's device, notify them via email or messaging apps, and may also notify them by voice. Step 5: The analysis department asks users about their satisfaction based on the results provided by the service department, and the generative AI analyzes the responses. The analysis department asks users about their satisfaction in a questionnaire format, collects the responses, analyzes them using the generative AI, and optimizes for future requests.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, provision unit, and data processing 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 requests from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the request using generated AI. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects and executes the optimal plugin. The provision unit is implemented by the output device 40 of the smart device 14 and provides the execution results to the user. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user satisfaction. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, provision unit, and data processing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives requests from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the request using generated AI. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects and executes the optimal plugin. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the execution results to the user. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user satisfaction. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In 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.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 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.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, provision unit, and data processing unit, is implemented by 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 requests from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the request using a generation AI. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects and executes the optimal plugin. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the execution results to the user. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user satisfaction. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, analysis unit, execution unit, provision unit, and data processing unit, is implemented by, for example, 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 requests from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the request using generated AI. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects and executes the optimal plugin. The provision unit is implemented by the speaker 240 of the robot 414 and provides the execution results to the user. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user satisfaction. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception desk that receives requests from users, An analysis unit analyzes the requests received by the aforementioned reception unit, An execution unit selects and executes the optimal plugin based on the request analyzed by the aforementioned analysis unit, A providing unit that provides the user with the results executed by the execution unit, The system comprises: an analysis unit that asks about user satisfaction based on the results provided by the aforementioned provision unit, and an analysis unit that uses a generating AI to analyze the results. A system characterized by the following features. (Note 2) The execution unit is, If a required plugin is not installed, the system will search for the plugin and ask the user whether they want to install it. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Accepts user requests in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We will understand the request and determine which plugin to use. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the user with the results of the request execution. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We ask about user satisfaction and perform analysis to improve the accuracy of requests. 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 requests are processed 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 request history and select the optimal method of processing requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a request is received, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted 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 a request, the system prioritizes requests that are highly relevant based on 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 a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the request category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, At runtime, the system improves execution accuracy by considering the interrelationships between requests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, During execution, the request will be executed while taking into account the attribute information of the request submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, It estimates the user's emotions and adjusts the order in which the results of the execution are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, At runtime, the execution will take into account the geographical distribution of requests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, During execution, the system references relevant literature related to the request to improve execution accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the displayed content will be adjusted based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the operating procedures provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When provided, the display will support multiple languages ​​according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis based on user satisfaction data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit is It estimates the user's sentiment and adjusts the frequency of analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit is During analysis, the analysis data is weighted based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized based on user feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 receives requests from users, An analysis unit analyzes the requests received by the aforementioned reception unit, An execution unit selects and executes the optimal plugin based on the request analyzed by the aforementioned analysis unit, A providing unit that provides the user with the results executed by the execution unit, The system comprises: an analysis unit that asks about user satisfaction based on the results provided by the aforementioned provision unit, and an analysis unit that uses a generating AI to analyze the results. A system characterized by the following features.

2. The execution unit is, If a required plugin is not installed, the system will search for the plugin and ask the user whether they want to install it. The system according to feature 1.

3. The aforementioned reception unit is Accepts user requests in natural language. The system according to feature 1.

4. The aforementioned analysis unit, We will understand the request and determine which plugin to use. The system according to feature 1.

5. The aforementioned supply unit is, Provide the user with the results of the request execution. The system according to feature 1.

6. The aforementioned analysis unit is We ask about user satisfaction and perform analysis to improve the accuracy of requests. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system according to feature 1.

9. The aforementioned reception unit is When a request is received, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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