system

Abstract

The system according to this embodiment aims to understand the administrative regulations of each local government and the complex menus for electricity, gas, etc., and to automatically perform application procedures with the most appropriate content. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a selection unit, and a procedure unit. The reception unit receives requests from users. The analysis unit analyzes the information received by the reception unit. The selection unit selects the optimal plan based on the information analyzed by the analysis unit. The procedure unit automatically performs the application procedure based on the plan selected by the selection unit.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to understand administrative regulations for each local government and complex menus such as electricity and gas, and to perform an automatic application procedure with optimal content.

[0005] The system according to the embodiment aims to understand administrative regulations for each local government and complex menus such as electricity and gas, and to perform an automatic application procedure with optimal content.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a procedure unit. The reception unit receives requests from users. The analysis unit analyzes the information received by the reception unit. The selection unit selects the optimal plan based on the information analyzed by the analysis unit. The procedure unit automatically performs the application procedure based on the plan selected by the selection unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the administrative regulations of each local government and the complex menus for electricity, gas, etc., and can automatically perform application procedures with the most appropriate content. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that understands the administrative regulations of each municipality and the complex menus of electricity, gas, etc., and automatically applies for and completes the procedures with the most suitable content. When a user moves or applies for a new service, this system allows them to consult with the AI ​​agent, which will propose the most suitable plan and automatically complete the procedures. For example, if a user makes a request such as "Please tell me the best electricity and gas plans for my new address," this request is input into the generating AI. The generating AI analyzes the input request and understands the administrative regulations of each municipality and the menus of electricity, gas, etc. Based on the latest information, the generating AI selects the most suitable plan and proposes it to the user. For example, the generating AI may suggest, "In this area, Company A's electricity plan is the most advantageous." Furthermore, the generating AI automatically carries out the application procedures based on the proposed plan. For example, the generating AI completes the electricity and gas contract procedures online on behalf of the user. In this process, the submission and verification of necessary documents are also handled automatically. This mechanism allows users to skip complicated procedures and efficiently use the most suitable services. The generating AI also supports consultations in a chat format, allowing users to easily consult with it. For example, if a user asks, "How do I obtain a resident registration certificate?", the AI ​​will guide the user through the process based on the regulations of that municipality and automatically complete the necessary procedures. In this way, the AI ​​agent can automate complex procedures and service applications for residents, reducing the burden on users. This allows residents to save time and use services efficiently. As a result, the AI ​​agent system can efficiently receive and analyze user requests, select the optimal plan, and automatically complete the application process.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a procedure unit. The reception unit receives requests from users. User requests include, but are not limited to, text, voice, and images. The reception unit can, for example, receive requests in text format. The reception unit can also receive requests in voice format. Furthermore, the reception unit can also receive requests in image format. For example, the reception unit can receive images taken by the user using a smartphone. The analysis unit analyzes the information received by the reception unit using a generative AI. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to these examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze voice data using speech recognition technology. Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the content of the user's request. Voice data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. The selection unit may also select the optimal plan based on efficiency. The selection unit may also select the optimal plan based on user needs. For example, the selection unit may select the most cost-effective plan based on the user's request. The procedure unit uses generative AI to automatically perform the application procedure based on the plan selected by the selection unit. Methods for automatically performing the application procedure include, but are not limited to, automatic completion of online forms and the use of electronic signatures. For example, the procedure unit automatically enters the necessary information into online forms. The procedure unit may also complete the contract procedure using electronic signatures.Furthermore, the procedures department can automatically submit necessary documents. For example, the procedures department can submit the user's identification documents online. This allows the AI ​​agent system according to the embodiment to efficiently receive and analyze user requests, select the optimal plan, and automatically carry out the application process.

[0030] The reception desk receives requests from users. These requests may include, but are not limited to, text, audio, and images. For example, the reception desk accepts text-based requests, audio requests, and image requests. For instance, it accepts images taken by users using their smartphones. The reception desk provides multiple interfaces for receiving user requests. For example, users can easily submit requests through websites or mobile applications. Text-based requests are accepted via chatbots or form input, while audio requests are converted to text in real time using speech recognition technology. Image requests are accepted through an image upload function, allowing users to easily submit photos and screenshots. Furthermore, to enhance user convenience, the reception desk has a 24 / 7 automated response system. This allows users to submit requests anytime, anywhere, and enables a quick response. The reception desk also plays a role in converting received requests into the appropriate format and sending them to the analysis department. For example, audio data is converted into text data using speech recognition technology, and image data is converted into an analyzable format using image recognition technology. This allows the reception unit to efficiently process a variety of user requests and ensure a smooth data transfer to the next step, the analysis unit.

[0031] The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses a generative AI to analyze the information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. The analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request content. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses a generative AI to analyze the information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples.For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. Furthermore, the analysis unit can also analyze audio data using speech recognition technology.Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. Furthermore, the analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For instance, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content.

[0032] The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request.The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request.The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Furthermore, the selection unit may select the optimal plan based on user needs. For example, the selection unit may select the most cost-effective plan based on the user's request.

[0033] The Procedures Department uses a generation AI to automatically process applications based on plans selected by the Selection Department. Methods for automated application processing include, but are not limited to, automatic online form completion and the use of electronic signatures. For example, the Procedures Department can automatically fill in the necessary information on online forms. It can also complete contract procedures using electronic signatures. Furthermore, the Procedures Department can automatically submit necessary documents. For example, it can submit the user's identification documents online. This significantly reduces the user's workload and ensures fast and accurate processing. The Procedures Department also has a function to notify the user of the progress of the process in real time. For example, it sends emails or app notifications to the user each time a step of the process is completed, informing them of the progress. This allows the user to always know the progress of the process and proceed with confidence. The Procedures Department also has a function to respond to user inquiries. For example, if users have questions or uncertainties about the process, they can contact the chatbot or customer support. This allows the Procedures Department to respond quickly to user needs and support a smooth process. The procedures department aims to maximize user convenience and achieve efficient procedures through these functions.

[0034] The reception desk can receive requests from users in a chat format. For example, the reception desk can receive requests from users using text chat. For example, the reception desk can receive text entered by the user in the chat window. The reception desk can also receive requests from users using voice chat. For example, the reception desk can receive audio entered by the user using a microphone. The reception desk can also receive requests from users using video chat. For example, the reception desk can receive video entered by the user using a camera. This allows users to make requests in a chat format.

[0035] The analysis unit can understand the administrative regulations and electricity, gas, and other service plans of each municipality based on the latest information. For example, the analysis unit can acquire real-time data and perform analysis based on the latest information. For example, the analysis unit can acquire the latest administrative regulations from databases on the internet. The analysis unit can also periodically update data and perform analysis based on the latest information. For example, the analysis unit can update the database daily and acquire the latest electricity rate plans. Furthermore, the analysis unit can use specific analysis algorithms to understand the administrative regulations and electricity, gas, and other service plans of each municipality. For example, the analysis unit can analyze administrative regulations using natural language processing technology. The analysis unit can also analyze electricity rate plans using machine learning algorithms. This allows the analysis unit to understand the administrative regulations and electricity, gas, and other service plans of each municipality based on the latest information.

[0036] The selection unit can select the optimal plan. For example, the selection unit can select the optimal plan based on cost. For example, the selection unit can select the electricity rate plan with the best cost performance. The selection unit can also select the optimal plan based on efficiency. For example, the selection unit can select the gas rate plan with the best efficiency. The selection unit can also select the optimal plan based on user needs. For example, the selection unit can select the most suitable plan based on the user's request. This allows the selection of the optimal plan to be made.

[0037] The application department can automatically process applications based on the selected plan. For example, it can automatically fill in necessary information on online forms. For instance, it can enter the user's name and address into the online form. The application department can also complete contract procedures using electronic signatures. For example, it can obtain the user's electronic signature and sign the contract. Furthermore, the application department can automatically submit necessary documents. For example, it can submit the user's identification documents online. This allows the application process to be automated based on the selected plan.

[0038] The procedures department can automatically submit and verify necessary documents. For example, the procedures department can submit a user's identification documents online. For example, the procedures department can scan and submit a user's passport or driver's license online. The procedures department can also automatically verify submitted documents. For example, the procedures department can automatically check the contents of submitted documents and verify that all necessary information is included. The procedures department can also verify the validity of submitted documents. For example, the procedures department can verify that submitted documents are within their validity period. This enables the automatic submission and verification of necessary documents.

[0039] 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 made in the past as candidates. For example, the reception desk can display related requests as candidates based on the content of requests the user has made in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input to make a request in the past, it will prioritize suggesting voice input. The reception desk can also predict and suggest requests to be made during specific time periods based on the user's past request history. For example, the reception desk can suggest similar requests based on the content of requests the user has made during specific time periods in the past. This allows the reception desk to select the optimal reception method based on 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 using AI. For example, the reception desk can input the user's past request history data into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception desk can filter requests based on the user's current situation and areas of interest when receiving them. For example, if the reception desk determines that the user is planning to move, it will prioritize requests related to moving. The reception desk can also prioritize requests for services related to new services if the user wishes to subscribe to new services. For example, if the reception desk determines that the user wishes to subscribe to new services, it will prioritize requests for services related to new services. The reception desk can also filter and suggest relevant requests based on the user's areas of interest. For example, the reception desk can filter and suggest relevant requests based on the user's areas of interest. This allows requests to be filtered based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation data into a generating AI and have the generating AI perform the request filtering.

[0041] The reception unit can prioritize requests based on the user's geographical location when receiving requests. For example, if the reception unit determines that the user is in a specific region, it will prioritize requests related to that region. The reception unit can also suggest the most appropriate requests based on the user's current location if the user is on the move. For example, if the reception unit determines that the user is on the move, it will suggest the most appropriate requests based on the user's current location. The reception unit can also prioritize requests related to a specific location if the user is in that location. For example, if the reception unit determines that the user is in a specific location, it will prioritize requests related to that location. This allows the reception unit to prioritize 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 without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the process of determining the priority of requests.

[0042] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, if the reception unit has mentioned a specific topic on social media, it will prioritize accepting requests related to that topic. The reception unit can also predict services of interest based on the user's social media activity and suggest requests. For example, the reception unit analyzes the user's social media activity, predicts services of interest, and suggests requests. The reception unit can also accept relevant requests based on information shared by the user on social media. For example, the reception unit accepts relevant requests based on information shared by the user on social media. This allows the reception unit to accept relevant requests based on 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. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of accepting relevant requests.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a detailed analysis on information of high importance and provide it to the user. The analysis unit can also perform a concise analysis on information of low importance. For example, the analysis unit can perform a concise analysis on information of low importance and provide it to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit can prioritize the analysis of information of high importance and postpone the analysis of information of low importance. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific analysis algorithm to information concerning administrative regulations. For example, the analysis unit can apply a specific natural language processing algorithm to analyze information concerning administrative regulations. The analysis unit can also apply a different analysis algorithm to information concerning electricity and gas menus. For example, the analysis unit can apply a specific machine learning algorithm to analyze information concerning electricity and gas menus. The analysis unit can also select the optimal analysis algorithm depending on the category of information. For example, the analysis unit can select and apply the optimal analysis algorithm based on the category of information. This allows the analysis unit to apply the optimal analysis algorithm according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI select and apply the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may prioritize the analysis of the most recent information and provide it to the user. The analysis unit can also postpone the analysis of older information. For example, the analysis unit may postpone the analysis of older information and prioritize the analysis of the most recent information. The analysis unit can also determine the priority of analysis based on the information submission date. For example, the analysis unit may determine the priority of analysis based on the information submission date. This allows the analysis priority to be determined based on the information submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may prioritize the analysis of highly relevant information and provide it to the user. The analysis unit can also postpone the analysis of less relevant information. For example, the analysis unit may postpone the analysis of less relevant information and prioritize the analysis of highly relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. For example, the analysis unit may determine the order of analysis based on the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The selection unit can improve the accuracy of its selection by considering the interrelationships of information during the selection process. For example, the selection unit can select the optimal plan by considering the interrelationships between administrative regulations and electricity and gas menus. The selection unit can also select the optimal plan based on the interrelationships of information. For example, the selection unit can select the optimal plan based on the interrelationships of information. The selection unit can also improve the accuracy of its selection by analyzing the interrelationships of information. For example, the selection unit can improve the accuracy of its selection by analyzing the interrelationships of information. This allows the selection unit to improve the accuracy of its selection by considering the interrelationships of information. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input interrelationship data of information into a generating AI and have the generating AI perform the task of improving the accuracy of the selection.

[0048] The selection unit can make selections by considering the attribute information of the information submitter. For example, if the information submitter is a local government, the selection unit will consider its attribute information when making selections. For example, if the selection unit determines that the information submitter is a local government, it will consider its attribute information when making selections. The selection unit can also make selections by considering the attribute information of the information submitter if the information submitter is a power company. For example, if the selection unit determines that the information submitter is a power company, it will consider its attribute information when making selections. The selection unit can also select the optimal plan based on the attribute information of the information submitter. For example, the selection unit can select the optimal plan based on the attribute information of the information submitter. This allows the selection of the optimal plan based on the attribute information of the information submitter. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the attribute information data of the information submitter into a generating AI and have the generating AI perform the selection of the optimal plan.

[0049] The selection unit can perform selections while considering the geographical distribution of information. For example, the selection unit can select the optimal plan based on the geographical distribution of information. The selection unit can also improve the accuracy of selection by considering geographical distribution. The selection unit can also determine the priority of selection according to geographical distribution. This makes it possible to select the optimal plan based on the geographical distribution of information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of information into a generating AI and have the generating AI perform the selection of the optimal plan.

[0050] The selection unit can improve the accuracy of its selection by referring to relevant literature during the selection process. For example, the selection unit can select the optimal plan by referring to relevant literature. The selection unit can also improve the accuracy of its selection based on relevant literature. The selection unit can also analyze relevant literature and determine selection priorities. This allows for improved selection accuracy by referring to relevant literature. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving selection accuracy.

[0051] The procedure unit can analyze the user's past procedure history and select the optimal procedure method during the procedure. For example, the procedure unit can propose the optimal procedure method based on the procedure methods the user has performed in the past. The procedure unit can also select an efficient procedure method from the user's past procedure history. For example, the procedure unit analyzes the user's past procedure history and selects an efficient procedure method. The procedure unit can also analyze the user's past procedure history and propose the simplest procedure method. For example, the procedure unit analyzes the user's past procedure history and proposes the simplest procedure method. This allows the procedure unit to select the optimal procedure method based on the user's past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's past procedure history data into a generating AI and have the generating AI select the optimal procedure method.

[0052] The procedure unit can customize the means of the procedure based on the user's current situation during the procedure. For example, if the user is on the move, the procedure unit can provide a procedure method optimized for a mobile device. For example, if the procedure unit determines that the user is on the move, it can provide a procedure method optimized for a mobile device. The procedure unit can also provide a procedure method optimized for a desktop device if the user is at home. For example, if the procedure unit determines that the user is at home, it can provide a procedure method optimized for a desktop device. The procedure unit can also suggest the most suitable means of the procedure depending on the user's current situation. For example, the procedure unit can suggest the most suitable means of the procedure depending on the user's current situation. This allows the means of the procedure to be customized according to the user's current situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of the procedure.

[0053] The procedure unit can select the optimal procedure method by considering the user's geographical location information during the procedure. For example, if the procedure unit determines that the user is in a specific region, it will prioritize procedures related to that region. The procedure unit can also suggest the optimal procedure method based on the user's current location if the user is on the move. For example, if the procedure unit determines that the user is on the move, it will suggest the optimal procedure method based on the user's current location. The procedure unit can also prioritize procedures related to a specific location if the user is in that location. For example, if the procedure unit determines that the user is in a specific location, it will prioritize procedures related to that location. This allows the procedure unit to select the optimal procedure method based on the user's geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal procedure method.

[0054] The procedure unit can analyze the user's social media activity during a procedure and propose a course of action. For example, if the procedure unit determines that the user has mentioned a specific topic on social media, it will prioritize procedures related to that topic. The procedure unit can also predict and propose procedures of interest based on the user's social media activity. For example, the procedure unit analyzes the user's social media activity, predicts and proposes procedures of interest. The procedure unit can also propose relevant procedures based on information shared by the user on social media. For example, the procedure unit proposes relevant procedures based on information shared by the user on social media. This allows the procedure unit to propose a course of action based on the user's social media activity. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's social media activity data into a generating AI and have the generating AI propose a course of action.

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

[0056] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, it can automatically display requests that the user has frequently made in the past as suggestions. It can also prioritize suggesting reception methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will make during specific time periods based on their past request history. This allows the system to select the most suitable reception method based on the user's past request history.

[0057] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information and a concise analysis on less important information. It can also determine the priority of the analysis based on the importance of the information. This allows for adjusting the level of detail of the analysis based on the importance of the information.

[0058] The selection process can improve the accuracy of selections by considering the interrelationships of information. For example, it can select options by considering the interrelationships between administrative regulations and electricity and gas service plans. It can also select the optimal plan based on the interrelationships of information. Furthermore, it can analyze the interrelationships of information to improve the accuracy of selections. This allows for improved selection accuracy by considering the interrelationships of information.

[0059] The procedure unit can analyze the user's past procedure history to select the optimal procedure method. For example, it can propose the optimal procedure method based on the procedures the user has performed in the past. It can also select the most efficient procedure method from the user's past procedure history. Furthermore, it can analyze the user's past procedure history and propose the simplest procedure method. This allows for the selection of the optimal procedure method based on the user's past procedure history.

[0060] The procedure unit can select the optimal procedure method by considering the user's geographical location. For example, if the user is in a specific region, procedures related to that region will be prioritized. If the user is on the move, the system can also suggest the optimal procedure method based on their current location. Furthermore, if the user is in a specific location, procedures related to that location can be prioritized. This allows the system to select the optimal procedure method based on the user's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives requests from users. User requests can include text, audio, and images. For example, the reception desk can accept requests in text format. It can also accept requests in audio and image formats. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis is performed based on the data analysis method and the algorithm used. For example, the analysis unit analyzes text data using natural language processing technology, analyzes audio data using speech recognition technology, and analyzes image data using image recognition technology. Step 3: The selection unit uses generation AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs. For example, the selection unit may select the optimal plan based on cost, efficiency, and user needs. Step 4: The Procedures Department uses a Generative AI to automatically complete the application process based on the plan selected by the Selection Department. Methods for automatically completing the application process include automatic completion of online forms and the use of electronic signatures. For example, the Procedures Department automatically enters the necessary information into online forms, completes the contract procedures using electronic signatures, and automatically submits the required documents.

[0063] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that understands the administrative regulations of each municipality and the complex menus of electricity, gas, etc., and automatically applies for and completes the procedures with the most suitable content. When a user moves or applies for a new service, this system allows them to consult with the AI ​​agent, which will propose the most suitable plan and automatically complete the procedures. For example, if a user makes a request such as "Please tell me the best electricity and gas plans for my new address," this request is input into the generating AI. The generating AI analyzes the input request and understands the administrative regulations of each municipality and the menus of electricity, gas, etc. Based on the latest information, the generating AI selects the most suitable plan and proposes it to the user. For example, the generating AI may suggest, "In this area, Company A's electricity plan is the most advantageous." Furthermore, the generating AI automatically carries out the application procedures based on the proposed plan. For example, the generating AI completes the electricity and gas contract procedures online on behalf of the user. In this process, the submission and verification of necessary documents are also handled automatically. This mechanism allows users to skip complicated procedures and efficiently use the most suitable services. The generating AI also supports consultations in a chat format, allowing users to easily consult with it. For example, if a user asks, "How do I obtain a resident registration certificate?", the AI ​​will guide the user through the process based on the regulations of that municipality and automatically complete the necessary procedures. In this way, the AI ​​agent can automate complex procedures and service applications for residents, reducing the burden on users. This allows residents to save time and use services efficiently. As a result, the AI ​​agent system can efficiently receive and analyze user requests, select the optimal plan, and automatically complete the application process.

[0064] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a procedure unit. The reception unit receives requests from users. User requests include, but are not limited to, text, voice, and images. The reception unit can, for example, receive requests in text format. The reception unit can also receive requests in voice format. Furthermore, the reception unit can also receive requests in image format. For example, the reception unit can receive images taken by the user using a smartphone. The analysis unit analyzes the information received by the reception unit using a generative AI. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to these examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze voice data using speech recognition technology. Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the content of the user's request. Voice data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. The selection unit may also select the optimal plan based on efficiency. The selection unit may also select the optimal plan based on user needs. For example, the selection unit may select the most cost-effective plan based on the user's request. The procedure unit uses generative AI to automatically perform the application procedure based on the plan selected by the selection unit. Methods for automatically performing the application procedure include, but are not limited to, automatic completion of online forms and the use of electronic signatures. For example, the procedure unit automatically enters the necessary information into online forms. The procedure unit may also complete the contract procedure using electronic signatures.Furthermore, the procedures department can automatically submit necessary documents. For example, the procedures department can submit the user's identification documents online. This allows the AI ​​agent system according to the embodiment to efficiently receive and analyze user requests, select the optimal plan, and automatically carry out the application process.

[0065] The reception desk receives requests from users. These requests may include, but are not limited to, text, audio, and images. For example, the reception desk accepts text-based requests, audio requests, and image requests. For instance, it accepts images taken by users using their smartphones. The reception desk provides multiple interfaces for receiving user requests. For example, users can easily submit requests through websites or mobile applications. Text-based requests are accepted via chatbots or form input, while audio requests are converted to text in real time using speech recognition technology. Image requests are accepted through an image upload function, allowing users to easily submit photos and screenshots. Furthermore, to enhance user convenience, the reception desk has a 24 / 7 automated response system. This allows users to submit requests anytime, anywhere, and enables a quick response. The reception desk also plays a role in converting received requests into the appropriate format and sending them to the analysis department. For example, audio data is converted into text data using speech recognition technology, and image data is converted into an analyzable format using image recognition technology. This allows the reception unit to efficiently process a variety of user requests and ensure a smooth data transfer to the next step, the analysis unit.

[0066] The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses a generative AI to analyze the information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. The analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request content. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses a generative AI to analyze the information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples.For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. The analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit uses natural language processing technology to analyze text data. Furthermore, the analysis unit can also analyze audio data using speech recognition technology.Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. Furthermore, the analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can also analyze image data using image recognition technology. For example, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content. The analysis unit uses generative AI to analyze information received by the reception unit. Analysis is performed based on, for example, data analysis methods and algorithms used, but is not limited to such examples. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze audio data using speech recognition technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For instance, the analysis unit analyzes text data to understand the user's request. Audio data is converted to text data using speech recognition technology and then analyzed. Image data is analyzed using image recognition technology to extract the request content.

[0067] The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request.The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request. The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Alternatively, the selection unit may select the optimal plan based on user needs. For example, the selection unit selects the most cost-effective plan based on the user's request.The selection unit uses generative AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs, but is not limited to these examples. For example, the selection unit may select the optimal plan based on cost. Alternatively, the selection unit may select the optimal plan based on efficiency. Furthermore, the selection unit may select the optimal plan based on user needs. For example, the selection unit may select the most cost-effective plan based on the user's request.

[0068] The Procedures Department uses a generation AI to automatically process applications based on plans selected by the Selection Department. Methods for automated application processing include, but are not limited to, automatic online form completion and the use of electronic signatures. For example, the Procedures Department can automatically fill in the necessary information on online forms. It can also complete contract procedures using electronic signatures. Furthermore, the Procedures Department can automatically submit necessary documents. For example, it can submit the user's identification documents online. This significantly reduces the user's workload and ensures fast and accurate processing. The Procedures Department also has a function to notify the user of the progress of the process in real time. For example, it sends emails or app notifications to the user each time a step of the process is completed, informing them of the progress. This allows the user to always know the progress of the process and proceed with confidence. The Procedures Department also has a function to respond to user inquiries. For example, if users have questions or uncertainties about the process, they can contact the chatbot or customer support. This allows the Procedures Department to respond quickly to user needs and support a smooth process. The procedures department aims to maximize user convenience and achieve efficient procedures through these functions.

[0069] The reception desk can receive requests from users in a chat format. For example, the reception desk can receive requests from users using text chat. For example, the reception desk can receive text entered by the user in the chat window. The reception desk can also receive requests from users using voice chat. For example, the reception desk can receive audio entered by the user using a microphone. The reception desk can also receive requests from users using video chat. For example, the reception desk can receive video entered by the user using a camera. This allows users to make requests in a chat format.

[0070] The analysis unit can understand the administrative regulations and electricity, gas, and other service plans of each municipality based on the latest information. For example, the analysis unit can acquire real-time data and perform analysis based on the latest information. For example, the analysis unit can acquire the latest administrative regulations from databases on the internet. The analysis unit can also periodically update data and perform analysis based on the latest information. For example, the analysis unit can update the database daily and acquire the latest electricity rate plans. Furthermore, the analysis unit can use specific analysis algorithms to understand the administrative regulations and electricity, gas, and other service plans of each municipality. For example, the analysis unit can analyze administrative regulations using natural language processing technology. The analysis unit can also analyze electricity rate plans using machine learning algorithms. This allows the analysis unit to understand the administrative regulations and electricity, gas, and other service plans of each municipality based on the latest information.

[0071] The selection unit can select the optimal plan. For example, the selection unit can select the optimal plan based on cost. For example, the selection unit can select the electricity rate plan with the best cost performance. The selection unit can also select the optimal plan based on efficiency. For example, the selection unit can select the gas rate plan with the best efficiency. The selection unit can also select the optimal plan based on user needs. For example, the selection unit can select the most suitable plan based on the user's request. This allows the selection of the optimal plan to be made.

[0072] The application department can automatically process applications based on the selected plan. For example, it can automatically fill in necessary information on online forms. For instance, it can enter the user's name and address into the online form. The application department can also complete contract procedures using electronic signatures. For example, it can obtain the user's electronic signature and sign the contract. Furthermore, the application department can automatically submit necessary documents. For example, it can submit the user's identification documents online. This allows the application process to be automated based on the selected plan.

[0073] The procedures department can automatically submit and verify necessary documents. For example, the procedures department can submit a user's identification documents online. For example, the procedures department can scan and submit a user's passport or driver's license online. The procedures department can also automatically verify submitted documents. For example, the procedures department can automatically check the contents of submitted documents and verify that all necessary information is included. The procedures department can also verify the validity of submitted documents. For example, the procedures department can verify that submitted documents are within their validity period. This enables the automatic submission and verification of necessary documents.

[0074] The reception desk can estimate the user's emotions and adjust how requests are processed based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the reception desk estimates that the user is stressed, it can accept requests in the form of simple questions. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. For example, if the reception desk estimates that the user is relaxed, it can present multiple options and allow the user to choose freely. If the user is in a hurry, the reception desk can prioritize voice input to process requests quickly. For example, if the reception desk estimates that the user is in a hurry, it can accept requests using voice input. This allows the request processing method to be adjusted 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 facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0075] 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 made in the past as candidates. For example, the reception desk can display related requests as candidates based on the content of requests the user has made in the past. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has used voice input to make a request in the past, it will prioritize suggesting voice input. The reception desk can also predict and suggest requests to be made during specific time periods based on the user's past request history. For example, the reception desk can suggest similar requests based on the content of requests the user has made during specific time periods in the past. This allows the reception desk to select the optimal reception method based on 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 using AI. For example, the reception desk can input the user's past request history data into a generating AI and have the generating AI select the optimal reception method.

[0076] The reception desk can filter requests based on the user's current situation and areas of interest when receiving them. For example, if the reception desk determines that the user is planning to move, it will prioritize requests related to moving. The reception desk can also prioritize requests for services related to new services if the user wishes to subscribe to new services. For example, if the reception desk determines that the user wishes to subscribe to new services, it will prioritize requests for services related to new services. The reception desk can also filter and suggest relevant requests based on the user's areas of interest. For example, the reception desk can filter and suggest relevant requests based on the user's areas of interest. This allows requests to be filtered based on the user's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current situation data into a generating AI and have the generating AI perform the request filtering.

[0077] 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 reception desk estimates that the user has made an urgent request, it will prioritize that request. The reception desk can also accept requests with normal priority if the user is relaxed. For example, if the reception desk estimates that the user is relaxed, it will accept the request with normal priority. The reception desk can also raise the priority of requests to respond quickly if the user is stressed. For example, if the reception desk estimates that the user is stressed, it will raise the priority of the request. This allows for the prioritization 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 the user's voice data into a generating AI and have the AI ​​perform emotion estimation.

[0078] The reception unit can prioritize requests based on the user's geographical location when receiving requests. For example, if the reception unit determines that the user is in a specific region, it will prioritize requests related to that region. The reception unit can also suggest the most appropriate requests based on the user's current location if the user is on the move. For example, if the reception unit determines that the user is on the move, it will suggest the most appropriate requests based on the user's current location. The reception unit can also prioritize requests related to a specific location if the user is in that location. For example, if the reception unit determines that the user is in a specific location, it will prioritize requests related to that location. This allows the reception unit to prioritize 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 without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI perform the process of determining the priority of requests.

[0079] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, if the reception unit has mentioned a specific topic on social media, it will prioritize accepting requests related to that topic. The reception unit can also predict services of interest based on the user's social media activity and suggest requests. For example, the reception unit analyzes the user's social media activity, predicts services of interest, and suggests requests. The reception unit can also accept relevant requests based on information shared by the user on social media. For example, the reception unit accepts relevant requests based on information shared by the user on social media. This allows the reception unit to accept relevant requests based on 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. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of accepting relevant requests.

[0080] 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 can provide detailed analysis results. For example, if the analysis unit estimates that the user is relaxed, it can provide detailed analysis results. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. For example, if the analysis unit estimates that the user is in a hurry, it can provide concise analysis results that get straight to the point. The analysis unit can also provide visually easy-to-understand analysis results if the user is stressed. For example, if the analysis unit estimates that the user is stressed, it can provide visually easy-to-understand analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a detailed analysis on information of high importance and provide it to the user. The analysis unit can also perform a concise analysis on information of low importance. For example, the analysis unit can perform a concise analysis on information of low importance and provide it to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit can prioritize the analysis of information of high importance and postpone the analysis of information of low importance. This allows the level of detail of the analysis to be adjusted based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific analysis algorithm to information concerning administrative regulations. For example, the analysis unit can apply a specific natural language processing algorithm to analyze information concerning administrative regulations. The analysis unit can also apply a different analysis algorithm to information concerning electricity and gas menus. For example, the analysis unit can apply a specific machine learning algorithm to analyze information concerning electricity and gas menus. The analysis unit can also select the optimal analysis algorithm depending on the category of information. For example, the analysis unit can select and apply the optimal analysis algorithm based on the category of information. This allows the analysis unit to apply the optimal analysis algorithm according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI select and apply the optimal analysis algorithm.

[0083] 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 can provide a short, concise analysis result. For example, if the analysis unit estimates that the user is in a hurry, it can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates that the user is relaxed, it can provide a detailed analysis result. The analysis unit can also provide a visually easy-to-understand analysis result if the user is stressed. For example, if the analysis unit estimates that the user is stressed, it can provide a visually easy-to-understand analysis result. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user voice data into a generating AI and have the generating AI perform emotion estimation.

[0084] The analysis unit can determine the priority of analysis based on the information submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may prioritize the analysis of the most recent information and provide it to the user. The analysis unit can also postpone the analysis of older information. For example, the analysis unit may postpone the analysis of older information and prioritize the analysis of the most recent information. The analysis unit can also determine the priority of analysis based on the information submission date. For example, the analysis unit may determine the priority of analysis based on the information submission date. This allows the analysis priority to be determined based on the information submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant information. For example, the analysis unit may prioritize the analysis of highly relevant information and provide it to the user. The analysis unit can also postpone the analysis of less relevant information. For example, the analysis unit may postpone the analysis of less relevant information and prioritize the analysis of highly relevant information. The analysis unit can also determine the order of analysis based on the relevance of the information. For example, the analysis unit may determine the order of analysis based on the relevance of the information. This allows the order of analysis to be adjusted based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated emotions. For example, if the user is relaxed, the selection unit can apply detailed selection criteria. For example, if the selection unit estimates that the user is relaxed, it can apply detailed selection criteria. The selection unit can also apply concise selection criteria if the user is in a hurry. For example, if the selection unit estimates that the user is in a hurry, it can apply concise selection criteria. The selection unit can also apply visually easy-to-understand selection criteria if the user is stressed. For example, if the selection unit estimates that the user is stressed, it can apply visually easy-to-understand selection criteria. This allows the selection criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. 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 selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0087] The selection unit can improve the accuracy of its selection by considering the interrelationships of information during the selection process. For example, the selection unit can select the optimal plan by considering the interrelationships between administrative regulations and electricity and gas menus. The selection unit can also select the optimal plan based on the interrelationships of information. For example, the selection unit can select the optimal plan based on the interrelationships of information. The selection unit can also improve the accuracy of its selection by analyzing the interrelationships of information. For example, the selection unit can improve the accuracy of its selection by analyzing the interrelationships of information. This allows the selection unit to improve the accuracy of its selection by considering the interrelationships of information. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input interrelationship data of information into a generating AI and have the generating AI perform the task of improving the accuracy of the selection.

[0088] The selection unit can make selections by considering the attribute information of the information submitter. For example, if the information submitter is a local government, the selection unit will consider its attribute information when making selections. For example, if the selection unit determines that the information submitter is a local government, it will consider its attribute information when making selections. The selection unit can also make selections by considering the attribute information of the information submitter if the information submitter is a power company. For example, if the selection unit determines that the information submitter is a power company, it will consider its attribute information when making selections. The selection unit can also select the optimal plan based on the attribute information of the information submitter. For example, the selection unit can select the optimal plan based on the attribute information of the information submitter. This allows the selection of the optimal plan based on the attribute information of the information submitter. Some or all of the above processing in the selection unit may be performed using AI, for example, or without using AI. For example, the selection unit can input the attribute information data of the information submitter into a generating AI and have the generating AI perform the selection of the optimal plan.

[0089] The selection unit can estimate the user's emotions and adjust the order in which the selection results are displayed based on the estimated emotions. For example, if the user is relaxed, the selection unit can display detailed selection results. For example, if the selection unit estimates that the user is relaxed, it can display detailed selection results. The selection unit can also display concise selection results if the user is in a hurry. For example, if the selection unit estimates that the user is in a hurry, it can display concise selection results. The selection unit can also display visually easy-to-understand selection results if the user is stressed. For example, if the selection unit estimates that the user is stressed, it can display visually easy-to-understand selection results. This allows the order in which the selection results are displayed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. 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 selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0090] The selection unit can perform selections while considering the geographical distribution of information. For example, the selection unit can select the optimal plan based on the geographical distribution of information. The selection unit can also improve the accuracy of selection by considering geographical distribution. The selection unit can also determine the priority of selection according to geographical distribution. This makes it possible to select the optimal plan based on the geographical distribution of information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input geographical distribution data of information into a generating AI and have the generating AI perform the selection of the optimal plan.

[0091] The selection unit can improve the accuracy of its selection by referring to relevant literature during the selection process. For example, the selection unit can select the optimal plan by referring to relevant literature. The selection unit can also improve the accuracy of its selection based on relevant literature. The selection unit can also analyze relevant literature and determine selection priorities. This allows for improved selection accuracy by referring to relevant literature. Some or all of the above-described processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving selection accuracy.

[0092] The procedure unit can estimate the user's emotions and adjust the procedure based on the estimated emotions. For example, if the procedure unit estimates that the user is relaxed, it can provide a detailed procedure. For example, if the procedure unit estimates that the user is relaxed, it can provide a detailed procedure. The procedure unit can also provide a concise procedure if the user is in a hurry. For example, if the procedure unit estimates that the user is in a hurry, it can provide a concise procedure. The procedure unit can also provide a visually easy-to-understand procedure if the user is stressed. For example, if the procedure unit estimates that the user is stressed, it can provide a visually easy-to-understand procedure. This allows the procedure to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedural unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0093] The procedure unit can analyze the user's past procedure history and select the optimal procedure method during the procedure. For example, the procedure unit can propose the optimal procedure method based on the procedure methods the user has performed in the past. The procedure unit can also select an efficient procedure method from the user's past procedure history. For example, the procedure unit analyzes the user's past procedure history and selects an efficient procedure method. The procedure unit can also analyze the user's past procedure history and propose the simplest procedure method. For example, the procedure unit analyzes the user's past procedure history and proposes the simplest procedure method. This allows the procedure unit to select the optimal procedure method based on the user's past procedure history. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's past procedure history data into a generating AI and have the generating AI select the optimal procedure method.

[0094] The procedure unit can customize the means of the procedure based on the user's current situation during the procedure. For example, if the user is on the move, the procedure unit can provide a procedure method optimized for a mobile device. For example, if the procedure unit determines that the user is on the move, it can provide a procedure method optimized for a mobile device. The procedure unit can also provide a procedure method optimized for a desktop device if the user is at home. For example, if the procedure unit determines that the user is at home, it can provide a procedure method optimized for a desktop device. The procedure unit can also suggest the most suitable means of the procedure depending on the user's current situation. For example, the procedure unit can suggest the most suitable means of the procedure depending on the user's current situation. This allows the means of the procedure to be customized according to the user's current situation. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the means of the procedure.

[0095] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated emotions. For example, if the procedure unit estimates that the user is performing an urgent procedure, it will prioritize that procedure. The procedure unit can also process procedures with normal priority if the user is relaxed. For example, if the procedure unit estimates that the user is relaxed, it will process the procedure with normal priority. The procedure unit can also raise the priority of procedures to respond quickly if the user is stressed. For example, if the procedure unit estimates that the user is stressed, it will raise the priority of the procedure. This allows the procedure unit to determine the priority of procedures 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 procedure unit may be performed using AI, for example, or without AI. For example, the procedural unit can input user voice data into a generating AI and have the generating AI perform emotion estimation.

[0096] The procedure unit can select the optimal procedure method by considering the user's geographical location information during the procedure. For example, if the procedure unit determines that the user is in a specific region, it will prioritize procedures related to that region. The procedure unit can also suggest the optimal procedure method based on the user's current location if the user is on the move. For example, if the procedure unit determines that the user is on the move, it will suggest the optimal procedure method based on the user's current location. The procedure unit can also prioritize procedures related to a specific location if the user is in that location. For example, if the procedure unit determines that the user is in a specific location, it will prioritize procedures related to that location. This allows the procedure unit to select the optimal procedure method based on the user's geographical location information. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal procedure method.

[0097] The procedure unit can analyze the user's social media activity during a procedure and propose a course of action. For example, if the procedure unit determines that the user has mentioned a specific topic on social media, it will prioritize procedures related to that topic. The procedure unit can also predict and propose procedures of interest based on the user's social media activity. For example, the procedure unit analyzes the user's social media activity, predicts and proposes procedures of interest. The procedure unit can also propose relevant procedures based on information shared by the user on social media. For example, the procedure unit proposes relevant procedures based on information shared by the user on social media. This allows the procedure unit to propose a course of action based on the user's social media activity. Some or all of the above processing in the procedure unit may be performed using AI, for example, or without AI. For example, the procedure unit can input the user's social media activity data into a generating AI and have the generating AI propose a course of action.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception desk can estimate the user's emotions and adjust how requests are processed based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can offer detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to ensure quick request processing. This allows the system to adjust how requests are processed according to the user's emotions.

[0100] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, it can provide visually easy-to-understand analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions.

[0101] The selection unit can estimate the user's emotions and adjust the selection criteria based on those emotions. For example, if the user is relaxed, detailed selection criteria can be applied. If the user is in a hurry, concise selection criteria can be applied. Furthermore, if the user is stressed, visually easy-to-understand selection criteria can be applied. This allows the selection criteria to be adjusted according to the user's emotions.

[0102] The procedure unit can estimate the user's emotions and adjust the procedure based on those emotions. For example, if the user is relaxed, it can provide detailed instructions. If the user is in a hurry, it can provide concise instructions. Furthermore, if the user is stressed, it can provide visually easy-to-understand instructions. This allows the procedure to be adjusted according to the user's emotions.

[0103] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, it can automatically display requests that the user has frequently made in the past as suggestions. It can also prioritize suggesting reception methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will make during specific time periods based on their past request history. This allows the system to select the most suitable reception method based on the user's past request history.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information and a concise analysis on less important information. It can also determine the priority of the analysis based on the importance of the information. This allows for adjusting the level of detail of the analysis based on the importance of the information.

[0105] The selection process can improve the accuracy of selections by considering the interrelationships of information. For example, it can select options by considering the interrelationships between administrative regulations and electricity and gas service plans. It can also select the optimal plan based on the interrelationships of information. Furthermore, it can analyze the interrelationships of information to improve the accuracy of selections. This allows for improved selection accuracy by considering the interrelationships of information.

[0106] The procedure unit can analyze the user's past procedure history to select the optimal procedure method. For example, it can propose the optimal procedure method based on the procedures the user has performed in the past. It can also select the most efficient procedure method from the user's past procedure history. Furthermore, it can analyze the user's past procedure history and propose the simplest procedure method. This allows for the selection of the optimal procedure method based on the user's past procedure history.

[0107] The procedure unit can select the optimal procedure method by considering the user's geographical location. For example, if the user is in a specific region, procedures related to that region will be prioritized. If the user is on the move, the system can also suggest the optimal procedure method based on their current location. Furthermore, if the user is in a specific location, procedures related to that location can be prioritized. This allows the system to select the optimal procedure method based on the user's geographical location.

[0108] The procedure unit can estimate the user's emotions and determine the priority of procedures based on those emotions. For example, if the user is performing an urgent procedure, it will be processed with priority. If the user is relaxed, it can be processed with the normal priority. Also, if the user is stressed, the priority can be increased to allow for a quicker response. This allows procedures to be prioritized according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives requests from users. User requests can include text, audio, and images. For example, the reception desk can accept requests in text format. It can also accept requests in audio and image formats. Step 2: The analysis unit uses a generation AI to analyze the information received by the reception unit. The analysis is performed based on the data analysis method and the algorithm used. For example, the analysis unit analyzes text data using natural language processing technology, analyzes audio data using speech recognition technology, and analyzes image data using image recognition technology. Step 3: The selection unit uses generation AI to select the optimal plan based on the information analyzed by the analysis unit. The optimal plan is selected based on criteria such as cost, efficiency, and user needs. For example, the selection unit may select the optimal plan based on cost, efficiency, and user needs. Step 4: The Procedures Department uses a Generative AI to automatically complete the application process based on the plan selected by the Selection Department. Methods for automatically completing the application process include automatic completion of online forms and the use of electronic signatures. For example, the Procedures Department automatically enters the necessary information into online forms, completes the contract procedures using electronic signatures, and automatically submits the required documents.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, and procedure 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 control unit 46A of the smart device 14 and receives requests from users. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal plan based on the analyzed information. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically performs the application procedure based on the selected plan. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, and procedure unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives requests from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and selects the optimal plan based on the analyzed information. The procedure unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automatically performs the application procedure based on the selected plan. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, and procedure unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A 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 received information using a generation AI. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects the optimal plan based on the analyzed information. The procedure unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically performs the application procedure based on the selected plan. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, and procedure 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 control unit 46A of the robot 414 and receives requests from users. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information using a generating AI. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal plan based on the analyzed information. The procedure unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically performs the application procedure based on the selected plan. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives requests from users, An analysis unit that analyzes the information received by the reception unit, A selection unit selects the optimal plan based on the information analyzed by the aforementioned analysis unit, A procedure unit that automatically performs the application procedure based on the plan selected by the aforementioned selection unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept user requests in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Understand the administrative regulations and electricity / gas service plans of each local government based on the latest information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Select the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned procedural department, The application process will be automatically initiated based on the selected plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned procedural department, The system automatically handles the submission and verification of necessary documents. 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, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When 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 information. 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 category of information. 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 the analysis, the priority of the analysis is determined based on when the information 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 information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is It estimates user sentiment and adjusts selection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When making a selection, consider the interrelationships between the information to improve the accuracy of the selection process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When making a selection, the attribute information of the information submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates the user's emotions and adjusts the order in which the selection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is When making a selection, the geographical distribution of the information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is During the selection process, we refer to relevant literature to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned procedural department, It estimates the user's emotions and adjusts the procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned procedural department, During the process, the system analyzes the user's past procedure history to select the most suitable procedure. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned procedural department, During the process, the procedure is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned procedural department, The system estimates the user's emotions and determines the priority of procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned procedural department, During the process, the system will select the most appropriate procedure based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned procedural department, During the process, we analyze the user's social media activity and suggest appropriate procedures. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 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 that analyzes the information received by the reception unit, A selection unit selects the optimal plan based on the information analyzed by the aforementioned analysis unit, A procedure unit that automatically performs the application procedure based on the plan selected by the aforementioned selection unit, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is We accept user requests in a chat format. The system according to feature 1.

3. The aforementioned analysis unit, Understand the administrative regulations and electricity / gas service plans of each local government based on the latest information. The system according to feature 1.

4. The aforementioned selection unit is Select the optimal plan. The system according to feature 1.

5. The aforementioned procedural department, The application process will be automatically initiated based on the selected plan. The system according to feature 1.

6. The aforementioned procedural department, The system automatically handles the submission and verification of necessary documents. 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.

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 according to feature 1.

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