System

Abstract

To provide a system that enables especially the elderly to easily grasp an appropriate counter and future flow upon visiting a city office or the like.SOLUTION: A system according to the embodiment comprises a reception unit, an analysis unit, and a guide unit. The reception unit receives input of content a user wants to resolve. The analysis unit analyzes the content received by the reception unit, and identifies a counter and future flow. The guide unit provides guidance to the user based on the information identified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that it was difficult for people, especially the elderly, to understand the appropriate counter to visit at city hall and other offices and what the next steps would be.

[0005] The system according to the embodiment is intended to enable elderly people in particular to easily understand the appropriate counter to go to and the next steps at counters such as city halls. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a guide unit. The reception unit receives input of the content to be resolved from the user. The analysis unit analyzes the content received by the reception unit and identifies the contact point and future course of action. The guide unit provides guidance to the user based on the information identified by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can enable elderly people in particular to easily understand the appropriate counter to go to and the next steps at a counter in a city hall or the like. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The guide system according to the embodiment of the present invention is a system that, when a user inputs the content he or she wants to solve, easily guides the user to the appropriate counter and the future flow. In this guide system, the user inputs the content he or she wants to solve, and the generation AI analyzes the content and guides the user to the appropriate counter and the future flow. This mechanism allows all citizens, including the elderly, to easily use city hall services. It also contributes to the promotion of digital transformation in government offices. For example, when a user inputs "I want to apply for pension," the generation AI guides the user to the location of the pension counter, the necessary documents, and the procedure flow. The generation AI provides the user with an appropriate guide based on the analysis results. For example, the location of the pension counter is displayed on a map and a list of necessary documents is provided. In addition, the procedure flow is explained step by step. In this way, the user can easily obtain the necessary information. This mechanism allows all citizens, including the elderly, to easily use city hall services. In particular, it becomes easier for the elderly to understand complicated procedures, and waiting time at the counter is shortened. It also contributes to the promotion of digital transformation in government offices, and improves the efficiency of work. For example, if an elderly person inputs "I want to apply for nursing care insurance," the generation AI guides the user to the location of the nursing care insurance counter, the necessary documents, and the procedure flow. This allows elderly people to go through the procedures without getting lost. The generative AI can also handle multiple languages, so it can also accommodate foreign citizens. For example, it can provide appropriate guidance for input in English or Chinese. This makes it easier for foreign citizens to use city hall services. In this way, the use of generative AI can make city hall counter operations more efficient and improve citizen services. This allows the guidance system to input the details the user wants to resolve, and guide them to the appropriate counter and next steps.

[0029] The guide system according to the embodiment includes a reception unit, an analysis unit, and a guide unit. The reception unit receives input of the content to be resolved from the user. The content to be resolved by the user includes, for example, pension procedures, nursing care insurance applications, address changes, etc., but is not limited to such examples. The reception unit receives, for example, the content input by the user in text format. The reception unit can also receive the content to be resolved by the user using voice input. For example, the user can input by voice using a microphone. The analysis unit uses a generation AI to analyze the content received by the reception unit and identify an appropriate counter and future flow. The analysis is performed, for example, using natural language processing technology, but is not limited to such examples. For example, the generation AI analyzes the content input by the user and identifies an appropriate counter, necessary documents, and procedure flow. The guide unit provides a guide to the user based on the information identified by the analysis unit. The guide is provided, for example, in text format, but is not limited to such examples. For example, the guide unit displays the location of an appropriate counter on a map for the user and provides a list of necessary documents. The guide unit can also explain the procedure flow step by step. As a result, the guide system according to the embodiment can guide the user to the appropriate contact point and future procedures by inputting the content that the user wants to resolve. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can provide guidance using an AI model that inputs information specified by the analysis unit and outputs guidance.

[0030] The reception unit receives input of the contents to be resolved from the user. The contents to be resolved by the user include, but are not limited to, pension procedures, nursing care insurance applications, and address changes. For example, the reception unit receives the contents input by the user in text format. Specifically, the reception unit receives text data input by the user using a keyboard and stores it in the system. The reception unit can also receive the contents to be resolved by the user using voice input. For example, the user can input by voice using a microphone. In the case of voice input, the voice data is converted into text data using voice recognition technology and stored in the system. Furthermore, the reception unit can also input by the user using a mobile device such as a smartphone or tablet, so that the user can input the contents to be resolved regardless of location. The reception unit provides an interface for centrally managing the input data and transmitting it to the analysis unit. As a result, the reception unit can respond to various input methods from the user and efficiently collect data.

[0031] The analysis unit uses the generation AI to analyze the content received by the reception unit and identify the appropriate window and future flow. The analysis is performed, for example, using natural language processing technology, but is not limited to such an example. Specifically, the generation AI analyzes the content entered by the user and identifies the appropriate window, necessary documents, and procedure flow. The generation AI first tokenizes the text data entered by the user and performs preprocessing to understand the context. Next, using a natural language processing model, it analyzes the intention of the entered content and identifies the related procedures and window. For example, if the user enters "I want to apply for a pension," the generation AI identifies the procedure flow and necessary documents related to pensions and presents the appropriate window. The generation AI can also refer to past data and cases and propose optimal solutions based on similar cases. Furthermore, the analysis unit can evaluate the priority and urgency of the procedure based on the user's input content and identify cases that should be dealt with quickly. This allows the analysis unit to build a foundation for providing appropriate guides according to the user's needs.

[0032] The guide unit provides a guide to the user based on the information specified by the analysis unit. The guide is provided in, for example, a text format, but is not limited to such an example. Specifically, the guide unit displays the location of an appropriate counter on a map for the user and provides a list of necessary documents. For example, when a user wishes to apply for a pension, the guide unit displays the location of the nearest pension office on a map and provides a list of necessary documents in text format. The guide unit can also explain the procedure step by step. For example, when a user wishes to apply for nursing care insurance, the guide unit explains step by step how to fill out an application form, where to submit it, how to prepare the necessary documents, etc. Furthermore, the guide unit can also provide audio guides and video guides, so that the user can receive guidance in a format that makes it easy to understand information visually or audibly. Part or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit can provide a guide using an AI model that inputs information specified by the analysis unit and outputs a guide. This allows the guide unit to provide a quick and accurate guide to the user and support the smooth progress of the procedure.

[0033] The guide unit may include a provision unit that provides a procedure flow and a list of necessary documents. The provision unit provides a specific procedure flow and a list of necessary documents based on the information received from the guide unit. The procedure flow includes, for example, application submission, screening, approval, etc., but is not limited to such examples. For example, the provision unit explains to the user how to submit an application form and provides a list of necessary documents. The provision unit can also explain each step of the procedure step by step. For example, the provision unit explains to the user how to fill out the application form and guides the user to the window to submit the form. In this way, the provision unit supports the user to smoothly proceed with the procedure. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide information using an AI model that uses information received from the guide unit as input and outputs the procedure flow and a list of necessary documents.

[0034] The guide unit may include a multilingual unit that supports multiple languages. The multilingual unit translates the information received from the analysis unit into multiple languages ​​and provides the translation to the guide unit. The supported languages ​​include, but are not limited to, English, Chinese, Spanish, and the like. For example, the multilingual unit analyzes the content input by the user in English and provides guidance in English on the appropriate counter and procedure flow. The multilingual unit may also analyze the content input by the user in Chinese and provide guidance in Chinese on the appropriate counter and procedure flow. This makes it easier for foreign citizens to use city hall services. Some or all of the above-mentioned processing in the multilingual unit may be performed, for example, using AI, or may be performed without using AI. For example, the multilingual unit may translate information using an AI model that receives information from the analysis unit as input and outputs a translation result.

[0035] The guide unit may include an exemplifying unit that provides examples of other procedures. The exemplifying unit provides specific examples of other procedures based on information received from the guide unit. Examples of procedures provided include, but are not limited to, passport applications, visa applications, and address change procedures. For example, the exemplifying unit may explain the procedure flow of a passport application to the user and provide a list of required documents. The exemplifying unit may also explain the procedure flow of a visa application and provide a list of required documents. This makes it easier for the user to understand other procedures. Some or all of the above-mentioned processing in the exemplifying unit may be performed, for example, using AI or may be performed without using AI. For example, the exemplifying unit may provide information using an AI model that uses information received from the guide unit as input and outputs specific examples of procedures.

[0036] The analysis unit can analyze the input contents and identify the counter and future flow. The analysis unit uses the generation AI to analyze the contents received by the reception unit and identify the appropriate counter and future flow. The analysis is performed, for example, using natural language processing technology, but is not limited to such an example. For example, the generation AI analyzes the contents input by the user and identifies the appropriate counter, necessary documents, and procedure flow. For example, when the user inputs "I want to apply for a pension," the analysis unit identifies the location of the pension counter, the necessary documents, and the procedure flow. In addition, when the user inputs "I want to apply for nursing care insurance," the analysis unit can also identify the location of the nursing care insurance counter, the necessary documents, and the procedure flow. This allows the analysis unit to identify the appropriate counter and future flow depending on the content that the user wants to solve. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the contents input by the user to the generation AI and output the analysis result from the generation AI.

[0037] The providing unit can provide a procedure flow and a list of necessary documents based on the information received from the guide unit. The providing unit provides a specific procedure flow and a list of necessary documents based on the information received from the guide unit. The procedure flow includes, for example, application submission, screening, approval, etc., but is not limited to such examples. For example, the providing unit explains to the user how to submit an application form and provides a list of necessary documents. The providing unit can also explain each step of the procedure step by step. For example, the providing unit explains to the user how to fill out the application form and guides the user to the window to submit the form. In this way, the providing unit supports the user to smoothly proceed with the procedure. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that uses information received from the guide unit as input and outputs a procedure flow and a list of necessary documents.

[0038] The multilingual unit can translate the information received from the analysis unit into multiple languages ​​and provide it to the guide unit. The multilingual unit translates the information received from the analysis unit into multiple languages ​​and provides it to the guide unit. Supported languages ​​include, for example, English, Chinese, Spanish, etc., but are not limited to such examples. For example, the multilingual unit analyzes the content entered by the user in English and guides the appropriate counter and procedure flow in English. The multilingual unit can also analyze the content entered by the user in Chinese and guides the appropriate counter and procedure flow in Chinese. This makes it easier for foreign citizens to use the city hall services. Part or all of the above-mentioned processing in the multilingual unit may be performed, for example, using AI, or may be performed without using AI. For example, the multilingual unit can translate information using an AI model that uses information received from the analysis unit as input and outputs a translation result.

[0039] The reception unit can analyze the user's past inquiry history and suggest an input method. For example, the reception unit automatically displays the contents of inquiries frequently made by the user in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past inquiry history. In this way, the reception unit can suggest an optimal input method by analyzing the user's past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past inquiry history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input method.

[0040] The reception unit can select information based on the user's current situation and field of interest when acquiring the input contents. For example, when the user inputs the current situation, the reception unit displays only related information. The reception unit can also filter unnecessary information based on the user's field of interest. Furthermore, the reception unit can also suggest optimal input contents according to the user's current situation. As a result, the reception unit can provide highly relevant information by filtering based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and field of interest to the generation AI and cause the generation AI to select information.

[0041] The reception unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring the input contents. For example, when the user inputs the current location, the reception unit preferentially displays nearby counter information. The reception unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the reception unit can also suggest the optimal counter in consideration of the geographical location information of the user. In this way, the reception unit can provide highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to acquire highly relevant information.

[0042] The reception unit can analyze the user's social media activity and acquire related information when acquiring the input contents. The reception unit can acquire procedure information of interest from the user's social media activity, for example. The reception unit can also analyze the user's social media activity and provide related contact information. Furthermore, the reception unit can also suggest an optimal procedure method based on the user's social media activity. In this way, the reception unit can provide related information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related information.

[0043] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, selects an optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data and optimize the analysis algorithm. As a result, the analysis unit can optimize the analysis algorithm and improve the accuracy of the analysis by referring to past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data to the generation AI and cause the generation AI to optimize the analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the type of input content during analysis. For example, in the case of pension procedures, the analysis unit applies a specific analysis method to identify the optimal window. In addition, in the case of nursing care insurance applications, the analysis unit can also apply different analysis methods to provide necessary information. Furthermore, the analysis unit can select an appropriate analysis method depending on other procedure categories. As a result, the analysis unit can provide more appropriate analysis results by applying different analysis methods depending on the type of input content. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the type of input content to the generation AI and cause the generation AI to select an appropriate analysis method.

[0045] The analysis unit can set the analysis priority based on the submission time of the input contents during analysis. For example, in the case of an urgent procedure, the analysis unit can increase the analysis priority to quickly respond. In addition, in the case of a normal procedure, the analysis unit can adjust the analysis priority to efficiently respond. Furthermore, the analysis unit can dynamically change the analysis priority according to the submission time. As a result, the analysis unit can determine the analysis priority based on the submission time of the input contents, thereby enabling a response according to the urgency. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the submission time data of the input contents to the generation AI and cause the generation AI to execute the setting of the analysis priority.

[0046] The analysis unit can improve the accuracy of the analysis by referring to related laws and regulations during the analysis. The analysis unit can improve the accuracy of the analysis by referring to, for example, related laws and regulations. The analysis unit can also improve the accuracy of the analysis based on the rules. Furthermore, the analysis unit can dynamically refer to laws and regulations and optimize the accuracy of the analysis. As a result, the analysis unit can improve the accuracy of the analysis by referring to related laws and regulations. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input related laws and regulations data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] When providing a guide, the guide unit can provide an optimal guide based on the user's past inquiry history. The guide unit provides an optimal guide based on, for example, the content of a user's past inquiry. The guide unit can also provide related information from the user's past inquiry history. Furthermore, the guide unit can analyze the user's past inquiry history and propose an optimal guide. Thereby, the guide unit can provide an optimal guide by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit can input the user's past inquiry history data to the generation AI and cause the generation AI to provide an optimal guide.

[0048] The guide unit can adjust the content of the guide based on the user's current situation when providing the guide. For example, the guide unit displays only related information when the user inputs the current situation. The guide unit can also provide an optimal guide according to the user's current situation. Furthermore, the guide unit can dynamically change the content of the guide in consideration of the user's current situation. In this way, the guide unit can provide more appropriate information by customizing the content of the guide based on the user's current situation. A part or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit can input the user's current situation data to the generation AI and cause the generation AI to adjust the content of the guide.

[0049] The guide unit can provide an optimal guide based on the geographical location information of the user when providing the guide. For example, when the user inputs the current location, the guide unit preferentially displays nearby counter information. The guide unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the guide unit can also suggest an optimal counter by taking into account the geographical location information of the user. In this way, the guide unit can provide highly relevant information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide the optimal guide.

[0050] When providing a guide, the guide unit can analyze the user's social media activity and suggest the contents of the guide. The guide unit, for example, acquires procedure information of interest from the user's social media activity. The guide unit can also analyze the user's social media activity and provide related contact information. Furthermore, the guide unit can also suggest an optimal procedure method based on the user's social media activity. In this way, the guide unit can provide related information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a suggestion of the contents of the guide.

[0051] The providing unit can provide optimal information based on the user's past inquiry history at the time of providing. The providing unit provides optimal information based on, for example, the content of a user's past inquiry. The providing unit can also provide related information from the user's past inquiry history. Furthermore, the providing unit can analyze the user's past inquiry history and suggest optimal information. As a result, the providing unit can provide optimal information by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history data to the generating AI and cause the generating AI to provide optimal information.

[0052] The providing unit can provide optimal information based on the geographical location information of the user at the time of providing. For example, when the user inputs the current location, the providing unit preferentially displays nearby counter information. The providing unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the providing unit can also suggest an optimal counter by taking into account the geographical location information of the user. In this way, the providing unit can provide highly relevant information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide optimal information.

[0053] The multilingual unit can optimize the translation algorithm based on past translation data during translation. The multilingual unit, for example, selects an optimal translation algorithm based on past translation data. The multilingual unit can also improve the accuracy of the translation by referring to the past translation data. Furthermore, the multilingual unit can analyze the past translation data and optimize the translation algorithm. As a result, the multilingual unit can optimize the translation algorithm and improve the accuracy of the translation by referring to the past translation data. Some or all of the above-mentioned processing in the multilingual unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the multilingual unit can input the past translation data to the generation AI and cause the generation AI to optimize the translation algorithm.

[0054] The multilingual unit can provide an optimal translation based on the geographical location information of the user during translation. For example, when the user inputs the current location, the multilingual unit preferentially displays nearby counter information. The multilingual unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the multilingual unit can provide an optimal translation taking into account the geographical location information of the user. In this way, the multilingual unit can provide highly relevant information by taking into account the geographical location information of the user. Some or all of the above-mentioned processing in the multilingual unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide an optimal translation.

[0055] The exemplifying unit can provide optimal examples based on the user's past inquiry history at the time of exemplification. The exemplifying unit provides optimal examples based on, for example, the content of a user's past inquiry. The exemplifying unit can also provide related examples from the user's past inquiry history. Furthermore, the exemplifying unit can analyze the user's past inquiry history and propose optimal examples. In this way, the exemplifying unit can provide optimal examples by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the exemplifying unit may be performed, for example, using AI, or may be performed without using AI. For example, the exemplifying unit can input the user's past inquiry history data to the generating AI and cause the generating AI to provide optimal examples.

[0056] The exemplifying unit can provide optimal examples based on the geographical location information of the user at the time of exemplification. For example, when the user inputs the current location, the exemplifying unit preferentially displays nearby counter information. The exemplifying unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the exemplifying unit can provide optimal examples by taking into account the geographical location information of the user. In this way, the exemplifying unit can provide highly relevant information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the exemplifying unit may be performed, for example, using AI, or may be performed without using AI. For example, the exemplifying unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide optimal examples.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The guide system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit collects and analyzes behavior data such as what procedures the user has performed in the past and which counters they have used. For example, if the user has performed pension procedures in the past, the next time the user performs pension procedures, the necessary documents and procedure flow can be automatically suggested by referring to the contents of the previous procedures. The behavior analysis unit can also grasp the trends of counters and procedures that the user frequently uses and provide the most suitable guide for the user. This allows the user to proceed with procedures more smoothly based on the past behavior history.

[0059] The guide system can further include a health management unit that takes into account the user's health condition. The health management unit collects and analyzes the user's health data. For example, if the user has a chronic illness such as high blood pressure or diabetes, the health management unit can use that information to guide the user on the necessary considerations and precautions to take when completing a procedure. The health management unit can also adjust the priority of procedures according to the user's health condition. This allows the user to receive guidance that takes into account their own health condition, allowing them to proceed with the procedure with peace of mind.

[0060] The guidance system may further include a lifestyle analysis unit that takes into account the user's lifestyle. The lifestyle analysis unit analyzes the user's lifestyle and preferences to provide optimal guidance. For example, if the user lives a nocturnal lifestyle, information on counters and online procedures that are available at night may be provided preferentially. The lifestyle analysis unit may also suggest related procedures and services based on the user's hobbies and interests. This allows the user to receive guidance that matches their lifestyle and to proceed with procedures more efficiently.

[0061] The guide system may further include a family information management unit that takes into account the user's family structure. The family information management unit collects and analyzes information such as the user's family structure, the health condition of the family members, and their ages. For example, if the user is caring for elderly parents, information on nursing care insurance procedures and nursing care services may be provided preferentially. The family information management unit may also suggest necessary procedures and services according to the user's family structure. This allows the user to receive guidance according to the situation of their family, allowing the procedures to proceed more smoothly.

[0062] The guide system may further include an economic situation analysis unit that takes into account the user's economic situation. The economic situation analysis unit collects and analyzes economic data such as the user's income, expenses, and asset status. For example, if the user has a low income, the economic situation analysis unit may provide preferentially information on available subsidies and support systems. The economic situation analysis unit may also adjust the priority of procedures according to the user's economic situation. This allows the user to receive guidance that takes into account his or her own economic situation, and allows the procedures to proceed more efficiently.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives input of the matter that the user wants to resolve. Matters that the user wants to resolve include, for example, pension procedures, nursing care insurance applications, and address changes. The reception unit not only receives the matter that the user inputs in text format, but can also receive the matter that the user wants to resolve by using voice input. For example, the user can input the matter by voice using a microphone. Step 2: The analysis unit uses the generation AI to analyze the content received by the reception unit and identify the appropriate contact point and future steps. The analysis is performed using natural language processing technology. For example, the generation AI analyzes the content entered by the user and identifies the appropriate contact point, necessary documents, and procedure steps. Step 3: The guide unit provides a guide to the user based on the information identified by the analysis unit. The guide can be provided in text format, or the location of the appropriate counter can be displayed on a map and a list of required documents can be provided. It can also explain the procedure step by step. Some or all of the processing in the guide unit may be performed using AI or may be performed without using AI.

[0065] (Example 2) The guide system according to the embodiment of the present invention is a system that, when a user inputs the content he or she wants to solve, easily guides the user to the appropriate counter and the future flow. In this guide system, the user inputs the content he or she wants to solve, and the generation AI analyzes the content and guides the user to the appropriate counter and the future flow. This mechanism allows all citizens, including the elderly, to easily use city hall services. It also contributes to the promotion of digital transformation in government offices. For example, when a user inputs "I want to apply for pension," the generation AI guides the user to the location of the pension counter, the necessary documents, and the procedure flow. The generation AI provides the user with an appropriate guide based on the analysis results. For example, the location of the pension counter is displayed on a map and a list of necessary documents is provided. In addition, the procedure flow is explained step by step. In this way, the user can easily obtain the necessary information. This mechanism allows all citizens, including the elderly, to easily use city hall services. In particular, it becomes easier for the elderly to understand complicated procedures, and waiting time at the counter is shortened. It also contributes to the promotion of digital transformation in government offices, and improves the efficiency of work. For example, if an elderly person inputs "I want to apply for nursing care insurance," the generation AI guides the user to the location of the nursing care insurance counter, the necessary documents, and the procedure flow. This allows elderly people to go through the procedures without getting lost. The generative AI can also handle multiple languages, so it can also accommodate foreign citizens. For example, it can provide appropriate guidance for input in English or Chinese. This makes it easier for foreign citizens to use city hall services. In this way, the use of generative AI can make city hall counter operations more efficient and improve citizen services. This allows the guidance system to input the details the user wants to resolve, and guide them to the appropriate counter and next steps.

[0066] The guide system according to the embodiment includes a reception unit, an analysis unit, and a guide unit. The reception unit receives input of the content to be resolved from the user. The content to be resolved by the user includes, for example, pension procedures, nursing care insurance applications, address changes, etc., but is not limited to such examples. The reception unit receives, for example, the content input by the user in text format. The reception unit can also receive the content to be resolved by the user using voice input. For example, the user can input by voice using a microphone. The analysis unit uses a generation AI to analyze the content received by the reception unit and identify an appropriate counter and future flow. The analysis is performed, for example, using natural language processing technology, but is not limited to such examples. For example, the generation AI analyzes the content input by the user and identifies an appropriate counter, necessary documents, and procedure flow. The guide unit provides a guide to the user based on the information identified by the analysis unit. The guide is provided, for example, in text format, but is not limited to such examples. For example, the guide unit displays the location of an appropriate counter on a map for the user and provides a list of necessary documents. The guide unit can also explain the procedure flow step by step. As a result, the guide system according to the embodiment can guide the user to the appropriate contact point and future procedures by inputting the content that the user wants to resolve. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can provide guidance using an AI model that inputs information specified by the analysis unit and outputs guidance.

[0067] The reception unit receives input of the contents to be resolved from the user. The contents to be resolved by the user include, but are not limited to, pension procedures, nursing care insurance applications, and address changes. For example, the reception unit receives the contents input by the user in text format. Specifically, the reception unit receives text data input by the user using a keyboard and stores it in the system. The reception unit can also receive the contents to be resolved by the user using voice input. For example, the user can input by voice using a microphone. In the case of voice input, the voice data is converted into text data using voice recognition technology and stored in the system. Furthermore, the reception unit can also input by the user using a mobile device such as a smartphone or tablet, so that the user can input the contents to be resolved regardless of location. The reception unit provides an interface for centrally managing the input data and transmitting it to the analysis unit. As a result, the reception unit can respond to various input methods from the user and efficiently collect data.

[0068] The analysis unit uses the generation AI to analyze the content received by the reception unit and identify the appropriate window and future flow. The analysis is performed, for example, using natural language processing technology, but is not limited to such an example. Specifically, the generation AI analyzes the content entered by the user and identifies the appropriate window, necessary documents, and procedure flow. The generation AI first tokenizes the text data entered by the user and performs preprocessing to understand the context. Next, using a natural language processing model, it analyzes the intention of the entered content and identifies the related procedures and window. For example, if the user enters "I want to apply for a pension," the generation AI identifies the procedure flow and necessary documents related to pensions and presents the appropriate window. The generation AI can also refer to past data and cases and propose optimal solutions based on similar cases. Furthermore, the analysis unit can evaluate the priority and urgency of the procedure based on the user's input content and identify cases that should be dealt with quickly. This allows the analysis unit to build a foundation for providing appropriate guides according to the user's needs.

[0069] The guide unit provides a guide to the user based on the information specified by the analysis unit. The guide is provided in, for example, a text format, but is not limited to such an example. Specifically, the guide unit displays the location of an appropriate counter on a map for the user and provides a list of necessary documents. For example, when a user wishes to apply for a pension, the guide unit displays the location of the nearest pension office on a map and provides a list of necessary documents in text format. The guide unit can also explain the procedure step by step. For example, when a user wishes to apply for nursing care insurance, the guide unit explains step by step how to fill out an application form, where to submit it, how to prepare the necessary documents, etc. Furthermore, the guide unit can also provide audio guides and video guides, so that the user can receive guidance in a format that makes it easy to understand information visually or audibly. Part or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit can provide a guide using an AI model that inputs information specified by the analysis unit and outputs a guide. This allows the guide unit to provide a quick and accurate guide to the user and support the smooth progress of the procedure.

[0070] The guide unit may include a provision unit that provides a procedure flow and a list of necessary documents. The provision unit provides a specific procedure flow and a list of necessary documents based on the information received from the guide unit. The procedure flow includes, for example, application submission, screening, approval, etc., but is not limited to such examples. For example, the provision unit explains to the user how to submit an application form and provides a list of necessary documents. The provision unit can also explain each step of the procedure step by step. For example, the provision unit explains to the user how to fill out the application form and guides the user to the window to submit the form. In this way, the provision unit supports the user to smoothly proceed with the procedure. A part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can provide information using an AI model that uses information received from the guide unit as input and outputs the procedure flow and a list of necessary documents.

[0071] The guide unit may include a multilingual unit that supports multiple languages. The multilingual unit translates the information received from the analysis unit into multiple languages ​​and provides the translation to the guide unit. The supported languages ​​include, but are not limited to, English, Chinese, Spanish, and the like. For example, the multilingual unit analyzes the content input by the user in English and provides guidance in English on the appropriate counter and procedure flow. The multilingual unit may also analyze the content input by the user in Chinese and provide guidance in Chinese on the appropriate counter and procedure flow. This makes it easier for foreign citizens to use city hall services. Some or all of the above-mentioned processing in the multilingual unit may be performed, for example, using AI, or may be performed without using AI. For example, the multilingual unit may translate information using an AI model that receives information from the analysis unit as input and outputs a translation result.

[0072] The guide unit may include an exemplifying unit that provides examples of other procedures. The exemplifying unit provides specific examples of other procedures based on information received from the guide unit. Examples of procedures provided include, but are not limited to, passport applications, visa applications, and address change procedures. For example, the exemplifying unit may explain the procedure flow of a passport application to the user and provide a list of required documents. The exemplifying unit may also explain the procedure flow of a visa application and provide a list of required documents. This makes it easier for the user to understand other procedures. Some or all of the above-mentioned processing in the exemplifying unit may be performed, for example, using AI or may be performed without using AI. For example, the exemplifying unit may provide information using an AI model that uses information received from the guide unit as input and outputs specific examples of procedures.

[0073] The analysis unit can analyze the input contents and identify the counter and future flow. The analysis unit uses the generation AI to analyze the contents received by the reception unit and identify the appropriate counter and future flow. The analysis is performed, for example, using natural language processing technology, but is not limited to such an example. For example, the generation AI analyzes the contents input by the user and identifies the appropriate counter, necessary documents, and procedure flow. For example, when the user inputs "I want to apply for a pension," the analysis unit identifies the location of the pension counter, the necessary documents, and the procedure flow. In addition, when the user inputs "I want to apply for nursing care insurance," the analysis unit can also identify the location of the nursing care insurance counter, the necessary documents, and the procedure flow. This allows the analysis unit to identify the appropriate counter and future flow depending on the content that the user wants to solve. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the contents input by the user to the generation AI and output the analysis result from the generation AI.

[0074] The providing unit can provide a procedure flow and a list of necessary documents based on the information received from the guide unit. The providing unit provides a specific procedure flow and a list of necessary documents based on the information received from the guide unit. The procedure flow includes, for example, application submission, screening, approval, etc., but is not limited to such examples. For example, the providing unit explains to the user how to submit an application form and provides a list of necessary documents. The providing unit can also explain each step of the procedure step by step. For example, the providing unit explains to the user how to fill out the application form and guides the user to the window to submit the form. In this way, the providing unit supports the user to smoothly proceed with the procedure. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that uses information received from the guide unit as input and outputs a procedure flow and a list of necessary documents.

[0075] The multilingual unit can translate the information received from the analysis unit into multiple languages ​​and provide it to the guide unit. The multilingual unit translates the information received from the analysis unit into multiple languages ​​and provides it to the guide unit. Supported languages ​​include, for example, English, Chinese, Spanish, etc., but are not limited to such examples. For example, the multilingual unit analyzes the content entered by the user in English and guides the appropriate counter and procedure flow in English. The multilingual unit can also analyze the content entered by the user in Chinese and guides the appropriate counter and procedure flow in Chinese. This makes it easier for foreign citizens to use the city hall services. Part or all of the above-mentioned processing in the multilingual unit may be performed, for example, using AI, or may be performed without using AI. For example, the multilingual unit can translate information using an AI model that uses information received from the analysis unit as input and outputs a translation result.

[0076] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. For example, when the user feels anxious, the reception unit allows the user to input important information first, giving the user a sense of security. In addition, when the user is relaxed, the reception unit can postpone detailed information and start with simple questions. Furthermore, when the user is in a hurry, the reception unit can allow the most important information to be input preferentially, and quickly proceed with the process. In this way, the reception unit can reduce the user's stress by adjusting the priority of the input contents based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The reception unit can analyze the user's past inquiry history and suggest an input method. For example, the reception unit automatically displays the contents of inquiries frequently made by the user in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input method to be used in a specific time period from the user's past inquiry history. In this way, the reception unit can suggest an optimal input method by analyzing the user's past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past inquiry history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input method.

[0078] The reception unit can select information based on the user's current situation and field of interest when acquiring the input contents. For example, when the user inputs the current situation, the reception unit displays only related information. The reception unit can also filter unnecessary information based on the user's field of interest. Furthermore, the reception unit can also suggest optimal input contents according to the user's current situation. As a result, the reception unit can provide highly relevant information by filtering based on the user's current situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's current situation and field of interest to the generation AI and cause the generation AI to select information.

[0079] The reception unit can estimate the user's emotion and determine a display method of the input contents based on the estimated user's emotion. For example, when the user is nervous, the reception unit provides a display method that is simple and highly visible. Furthermore, when the user is relaxed, the reception unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the reception unit can also provide a display method that focuses on the main points. In this way, the reception unit can reduce the user's stress by adjusting the display method of the input contents based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the emotion estimation.

[0080] The reception unit can preferentially acquire highly relevant information in consideration of the geographical location information of the user when acquiring the input contents. For example, when the user inputs the current location, the reception unit preferentially displays nearby counter information. The reception unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the reception unit can also suggest the optimal counter in consideration of the geographical location information of the user. In this way, the reception unit can provide highly relevant information by considering the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to acquire highly relevant information.

[0081] The reception unit can analyze the user's social media activity and acquire related information when acquiring the input contents. The reception unit can acquire procedure information of interest from the user's social media activity, for example. The reception unit can also analyze the user's social media activity and provide related contact information. Furthermore, the reception unit can also suggest an optimal procedure method based on the user's social media activity. In this way, the reception unit can provide related information by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related information.

[0082] The analysis unit can estimate the user's emotion and determine the accuracy of the analysis based on the estimated user's emotion. For example, when the user feels anxious, the analysis unit increases the accuracy of the analysis to provide detailed information. In addition, when the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide concise information. Furthermore, when the user is in a hurry, the analysis unit can quickly perform the analysis and provide the necessary information. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the accuracy of the analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of the analysis.

[0083] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit, for example, selects an optimal analysis algorithm based on past analysis data. The analysis unit can also improve the accuracy of the analysis by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data and optimize the analysis algorithm. As a result, the analysis unit can optimize the analysis algorithm and improve the accuracy of the analysis by referring to past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data to the generation AI and cause the generation AI to optimize the analysis algorithm.

[0084] The analysis unit can apply different analysis methods depending on the type of input content during analysis. For example, in the case of pension procedures, the analysis unit applies a specific analysis method to identify the optimal window. In addition, in the case of nursing care insurance applications, the analysis unit can also apply different analysis methods to provide necessary information. Furthermore, the analysis unit can select an appropriate analysis method depending on other procedure categories. As a result, the analysis unit can provide more appropriate analysis results by applying different analysis methods depending on the type of input content. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the type of input content to the generation AI and cause the generation AI to select an appropriate analysis method.

[0085] The analysis unit can estimate the user's emotion and determine a display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, the analysis unit can provide information in a form that is easy for the user to understand by adjusting the display method of the analysis result based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0086] The analysis unit can set the analysis priority based on the submission time of the input contents during analysis. For example, in the case of an urgent procedure, the analysis unit can increase the analysis priority to quickly respond. In addition, in the case of a normal procedure, the analysis unit can adjust the analysis priority to efficiently respond. Furthermore, the analysis unit can dynamically change the analysis priority according to the submission time. As a result, the analysis unit can determine the analysis priority based on the submission time of the input contents, thereby enabling a response according to the urgency. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the submission time data of the input contents to the generation AI and cause the generation AI to execute the setting of the analysis priority.

[0087] The analysis unit can improve the accuracy of the analysis by referring to related laws and regulations during the analysis. The analysis unit can improve the accuracy of the analysis by referring to, for example, related laws and regulations. The analysis unit can also improve the accuracy of the analysis based on the rules. Furthermore, the analysis unit can dynamically refer to laws and regulations and optimize the accuracy of the analysis. As a result, the analysis unit can improve the accuracy of the analysis by referring to related laws and regulations. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input related laws and regulations data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0088] The guide unit can estimate the user's emotion and determine the method of expressing the guide based on the estimated user's emotion. For example, when the user feels anxious, the guide unit provides the guide in gentle language. Furthermore, when the user is relaxed, the guide unit can also provide a guide including detailed information. Furthermore, when the user is in a hurry, the guide unit can also provide a concise and quick guide. Thereby, the guide unit can provide information in a form that is easy for the user to understand by adjusting the method of expressing the guide based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the method of expressing the guide.

[0089] When providing a guide, the guide unit can provide an optimal guide based on the user's past inquiry history. The guide unit provides an optimal guide based on, for example, the content of a user's past inquiry. The guide unit can also provide related information from the user's past inquiry history. Furthermore, the guide unit can analyze the user's past inquiry history and propose an optimal guide. Thereby, the guide unit can provide an optimal guide by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit can input the user's past inquiry history data to the generation AI and cause the generation AI to provide an optimal guide.

[0090] The guide unit can adjust the content of the guide based on the user's current situation when providing the guide. For example, the guide unit displays only related information when the user inputs the current situation. The guide unit can also provide an optimal guide according to the user's current situation. Furthermore, the guide unit can dynamically change the content of the guide in consideration of the user's current situation. In this way, the guide unit can provide more appropriate information by customizing the content of the guide based on the user's current situation. A part or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit can input the user's current situation data to the generation AI and cause the generation AI to adjust the content of the guide.

[0091] The guide unit can estimate the user's emotion and set the priority of the guide based on the estimated user's emotion. For example, when the user feels anxious, the guide unit provides important information first. Also, when the user is relaxed, the guide unit can postpone detailed information. Furthermore, when the user is in a hurry, the guide unit can provide the most important information preferentially. In this way, the guide unit can reduce the user's stress by determining the priority of the guide based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit can input the user's emotion data to the generation AI and cause the generation AI to execute the setting of the priority of the guide.

[0092] The guide unit can provide an optimal guide based on the geographical location information of the user when providing the guide. For example, when the user inputs the current location, the guide unit preferentially displays nearby counter information. The guide unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the guide unit can also suggest an optimal counter by taking into account the geographical location information of the user. In this way, the guide unit can provide highly relevant information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the guide unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guide unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide the optimal guide.

[0093] When providing a guide, the guide unit can analyze the user's social media activity and suggest the contents of the guide. The guide unit, for example, acquires procedure information of interest from the user's social media activity. The guide unit can also analyze the user's social media activity and provide related contact information. Furthermore, the guide unit can also suggest an optimal procedure method based on the user's social media activity. In this way, the guide unit can provide related information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using AI, or may be performed without using AI. For example, the guide unit can input the user's social media activity data to the generation AI and cause the generation AI to execute a suggestion of the contents of the guide.

[0094] The providing unit can estimate the user's emotion and determine the level of detail of the information to be provided based on the estimated user's emotion. For example, when the user feels anxious, the providing unit provides detailed information to give a sense of security. In addition, when the user is relaxed, the providing unit can also provide concise information. Furthermore, when the user is in a hurry, the providing unit can also provide information that covers the main points. In this way, the providing unit can reduce the user's stress by adjusting the level of detail of the information to be provided based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0095] The providing unit can provide optimal information based on the user's past inquiry history at the time of providing. The providing unit provides optimal information based on, for example, the content of a user's past inquiry. The providing unit can also provide related information from the user's past inquiry history. Furthermore, the providing unit can analyze the user's past inquiry history and suggest optimal information. As a result, the providing unit can provide optimal information by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history data to the generating AI and cause the generating AI to provide optimal information.

[0096] The providing unit can estimate the user's emotion and determine a display method of the information to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a display method that is simple and highly visible. In addition, when the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, the providing unit can reduce the user's stress by adjusting the display method of the information to be provided based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the information.

[0097] The providing unit can provide optimal information based on the geographical location information of the user at the time of providing. For example, when the user inputs the current location, the providing unit preferentially displays nearby counter information. The providing unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the providing unit can also suggest an optimal counter by taking into account the geographical location information of the user. In this way, the providing unit can provide highly relevant information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide optimal information.

[0098] The multilingual unit can estimate the user's emotions and determine the accuracy of the translation based on the estimated user's emotions. For example, when the user feels anxious, the multilingual unit can improve the accuracy of the translation and provide detailed information. In addition, the multilingual unit can provide a concise translation when the user is relaxed. Furthermore, when the user is in a hurry, the multilingual unit can quickly translate and provide the necessary information. In this way, the multilingual unit can reduce the user's stress by adjusting the accuracy of the translation based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the multilingual unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of the translation.

[0099] The multilingual unit can optimize the translation algorithm based on past translation data during translation. The multilingual unit, for example, selects an optimal translation algorithm based on past translation data. The multilingual unit can also improve the accuracy of the translation by referring to the past translation data. Furthermore, the multilingual unit can analyze the past translation data and optimize the translation algorithm. As a result, the multilingual unit can optimize the translation algorithm and improve the accuracy of the translation by referring to the past translation data. Some or all of the above-mentioned processing in the multilingual unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the multilingual unit can input the past translation data to the generation AI and cause the generation AI to optimize the translation algorithm.

[0100] The multilingual unit can estimate the user's emotions and determine a display method of the translation result based on the estimated user's emotions. For example, when the user is nervous, the multilingual unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the multilingual unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the multilingual unit can also provide a display method that focuses on the main points. In this way, the multilingual unit can reduce the user's stress by adjusting the display method of the translation result based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the multilingual unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the translation result.

[0101] The multilingual unit can provide an optimal translation based on the geographical location information of the user during translation. For example, when the user inputs the current location, the multilingual unit preferentially displays nearby counter information. The multilingual unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the multilingual unit can provide an optimal translation taking into account the geographical location information of the user. In this way, the multilingual unit can provide highly relevant information by taking into account the geographical location information of the user. Some or all of the above-mentioned processing in the multilingual unit may be performed using, for example, AI, or may be performed without using AI. For example, the multilingual unit can input the geographical location information of the user to the generation AI and cause the generation AI to provide an optimal translation.

[0102] The exemplifying unit can estimate the user's emotion and determine the content of the example based on the estimated user's emotion. For example, when the user feels anxious, the exemplifying unit provides a detailed example to give a sense of security. In addition, when the user is relaxed, the exemplifying unit can provide a concise example. Furthermore, when the user is in a hurry, the exemplifying unit can provide an example that covers the main points. In this way, the exemplifying unit can reduce the user's stress by adjusting the content of the example based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the exemplifying unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the exemplifying unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the example.

[0103] The exemplifying unit can provide optimal examples based on the user's past inquiry history at the time of exemplification. The exemplifying unit provides optimal examples based on, for example, the content of a user's past inquiry. The exemplifying unit can also provide related examples from the user's past inquiry history. Furthermore, the exemplifying unit can analyze the user's past inquiry history and propose optimal examples. In this way, the exemplifying unit can provide optimal examples by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the exemplifying unit may be performed, for example, using AI, or may be performed without using AI. For example, the exemplifying unit can input the user's past inquiry history data to the generating AI and cause the generating AI to provide optimal examples.

[0104] The exemplifying unit can estimate the user's emotion and determine the exemplary display method based on the estimated user's emotion. For example, when the user is nervous, the exemplifying unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the exemplifying unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the exemplifying unit can also provide a display method that focuses on the main points. In this way, the exemplifying unit can reduce the user's stress by adjusting the exemplary display method based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the exemplifying unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the exemplifying unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the exemplary display method.

[0105] The exemplifying unit can provide optimal examples based on the geographical location information of the user at the time of exemplification. For example, when the user inputs the current location, the exemplifying unit preferentially displays nearby counter information. The exemplifying unit can also provide related procedure information based on the geographical location information of the user. Furthermore, the exemplifying unit can provide optimal examples by taking into account the geographical location information of the user. In this way, the exemplifying unit can provide highly relevant information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the exemplifying unit may be performed, for example, using AI, or may be performed without using AI. For example, the exemplifying unit can input the geographical location information of the user to the generating AI and cause the generating AI to provide optimal examples.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The guide system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit collects and analyzes behavior data such as what procedures the user has performed in the past and which counters they have used. For example, if the user has performed pension procedures in the past, the next time the user performs pension procedures, the necessary documents and procedure flow can be automatically suggested by referring to the contents of the previous procedures. The behavior analysis unit can also grasp the trends of counters and procedures that the user frequently uses and provide the most suitable guide for the user. This allows the user to proceed with procedures more smoothly based on the past behavior history.

[0108] The guide system can further include a health management unit that takes into account the user's health condition. The health management unit collects and analyzes the user's health data. For example, if the user has a chronic illness such as high blood pressure or diabetes, the health management unit can use that information to guide the user on the necessary considerations and precautions to take when completing a procedure. The health management unit can also adjust the priority of procedures according to the user's health condition. This allows the user to receive guidance that takes into account their own health condition, allowing them to proceed with the procedure with peace of mind.

[0109] The guidance system may further include a lifestyle analysis unit that takes into account the user's lifestyle. The lifestyle analysis unit analyzes the user's lifestyle and preferences to provide optimal guidance. For example, if the user lives a nocturnal lifestyle, information on counters and online procedures that are available at night may be provided preferentially. The lifestyle analysis unit may also suggest related procedures and services based on the user's hobbies and interests. This allows the user to receive guidance that matches their lifestyle and to proceed with procedures more efficiently.

[0110] The guide system may further include a family information management unit that takes into account the user's family structure. The family information management unit collects and analyzes information such as the user's family structure, the health condition of the family members, and their ages. For example, if the user is caring for elderly parents, information on nursing care insurance procedures and nursing care services may be provided preferentially. The family information management unit may also suggest necessary procedures and services according to the user's family structure. This allows the user to receive guidance according to the situation of their family, allowing the procedures to proceed more smoothly.

[0111] The guide system may further include an economic situation analysis unit that takes into account the user's economic situation. The economic situation analysis unit collects and analyzes economic data such as the user's income, expenses, and asset status. For example, if the user has a low income, the economic situation analysis unit may provide preferentially information on available subsidies and support systems. The economic situation analysis unit may also adjust the priority of procedures according to the user's economic situation. This allows the user to receive guidance that takes into account his or her own economic situation, and allows the procedures to proceed more efficiently.

[0112] The guide system can estimate the user's emotions and adjust the guide's tone based on the estimated emotions. For example, if the user feels anxious, the guide's tone can be made gentler to give a sense of security. If the user feels relaxed, the guide's tone can be made friendly to give the impression of familiarity. Furthermore, if the user is in a hurry, the guide's tone can be made concise to provide information quickly. This allows the user to receive guidance that matches their emotions and allows the process to proceed more smoothly.

[0113] The guide system can estimate the user's emotions and customize the content of the guide based on the estimated emotions. For example, if the user feels anxious, detailed explanations and additional support information can be provided. If the user feels relaxed, concise information can be provided to allow the user to proceed quickly through the procedure. Furthermore, if the user is in a hurry, the most important information can be provided as a priority to allow the user to complete the procedure quickly. This allows the user to receive guidance according to their emotions and proceed through the procedure more efficiently.

[0114] The guide system can estimate the user's emotions and adjust the order of the guidance based on the estimated emotions. For example, if the user feels anxious, the most important information can be provided first, providing a sense of security. If the user feels relaxed, detailed information can be left for later, and the user can start with simple procedures. Furthermore, if the user is in a hurry, the most important information can be provided first, expediting the procedure. This allows the user to receive guidance that corresponds to their emotions, and to proceed with the procedure more smoothly.

[0115] The guide system can estimate the user's emotions and adjust the format of the guide based on the estimated emotions. For example, if the user feels anxious, the system can provide a guide using visually easy-to-understand diagrams and illustrations. If the user feels relaxed, the system can provide a detailed text-based guide. If the user is in a hurry, the system can provide a bullet-point guide with the main points. This allows the user to receive guidance that matches their emotions and progress through the process more efficiently.

[0116] The guidance system can estimate the user's emotions and adjust the frequency of guidance based on the estimated emotions. For example, if the user feels anxious, the system can provide frequent guidance to reassure the user. If the user feels relaxed, the system can provide the minimum necessary guidance to respect the user's autonomy. Furthermore, if the user is in a hurry, the system can provide the most important information at once to expedite the process. This allows the user to receive guidance according to their emotions and to proceed with the process more smoothly.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The reception unit receives input of the matter that the user wants to resolve. Matters that the user wants to resolve include, for example, pension procedures, nursing care insurance applications, and address changes. The reception unit not only receives the matter that the user inputs in text format, but can also receive the matter that the user wants to resolve by using voice input. For example, the user can input the matter by voice using a microphone. Step 2: The analysis unit uses the generation AI to analyze the content received by the reception unit and identify the appropriate contact point and future steps. The analysis is performed using natural language processing technology. For example, the generation AI analyzes the content entered by the user and identifies the appropriate contact point, necessary documents, and procedure steps. Step 3: The guide unit provides a guide to the user based on the information identified by the analysis unit. The guide can be provided in text format, or the location of the appropriate counter can be displayed on a map and a list of required documents can be provided. It can also explain the procedure step by step. Some or all of the processing in the guide unit may be performed using AI or may be performed without using AI.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and guide unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives the contents input by the user in text format or voice format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents input by the user using a generation AI and identifies an appropriate contact point and future flow. The guide unit is realized, for example, by the output device 40 of the smart device 14, and provides a guide to the user based on the information identified by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements including the reception unit, analysis unit, and guide unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the contents input by the user in voice format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the contents input by the user using a generation AI to identify the appropriate contact point and future flow. The guide unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides a guide to the user based on the information identified by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and guide unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives the contents input by the user in voice format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents input by the user using a generation AI and identifies the appropriate contact point and future flow. The guide unit is realized, for example, by the display 343 of the headset type terminal 314, and provides a guide to the user based on the information identified by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] Each of the multiple elements including the reception unit, analysis unit, and guide unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and receives the contents input by the user in voice format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents input by the user using a generation AI and identifies the appropriate contact point and future flow. The guide unit is realized, for example, by the speaker 240 of the robot 414, and provides a guide to the user based on the information identified by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0173] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] In addition, 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 upon request from the data processing device 12.

[0182] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0184] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) A reception unit that receives an input of a content to be resolved from a user; an analysis unit that analyzes the content received by the reception unit and identifies a contact point and a future course of action; A guide unit that provides a guide to a user based on the information identified by the analysis unit. A system characterized by: (Appendix 2) The guide portion is Equipped with a section that provides the procedure and a list of required documents 2. The system of claim 1, (Appendix 3) The guide portion is Equipped with a multilingual section that can handle multiple languages 2. The system of claim 1, (Appendix 4) The guide portion is Includes an example section that provides examples of other procedures 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze the input information and identify the contact point and future course of action 2. The system of claim 1, (Appendix 6) The providing unit is Provide a procedure and a list of required documents based on the information received from the Guidance Department 3. The system of claim 2, (Appendix 7) The multilingual unit includes: Translates the information received from the analysis section into multiple languages ​​and provides it to the guide section 4. The system of claim 3. (Appendix 8) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 9) The reception unit is Analyze the user's past inquiry history and suggest input methods 2. The system of claim 1, (Appendix 10) The reception unit is As input is captured, filter information based on the user's current context and interests 2. The system of claim 1, (Appendix 11) The reception unit is Infer the user's emotions and determine how to display input content based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The reception unit is When capturing input, prioritize the most relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 13) The reception unit is When input information is acquired, the user's social media activity is analyzed to acquire related information. 2. The system of claim 1, (Appendix 14) The analysis unit is Estimate the user's emotions and determine the accuracy of the analysis based on the estimated user's emotions. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, the analysis algorithm is optimized based on past analysis data. 2. The system of claim 1, (Appendix 16) The analysis unit is During parsing, apply different parsing techniques depending on the type of input 2. The system of claim 1, (Appendix 17) The analysis unit is Inferring user emotions and determining how to display analysis results based on the estimated user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when inputs were submitted 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, improve the accuracy of the analysis based on relevant laws and regulations 2. The system of claim 1, (Appendix 20) The guide portion is Estimate the user's emotion and determine the guide's expression based on the estimated user's emotion. 2. The system of claim 1, (Appendix 21) The guide portion is When providing a guide, the most appropriate guide is provided based on the user's past inquiry history. 2. The system of claim 1, (Appendix 22) The guide portion is When providing a guide, adjust the guide content based on the user's current situation 2. The system of claim 1, (Appendix 23) The guide portion is Inferring a user's emotions and prioritizing guides based on the estimated user's emotions 2. The system of claim 1, (Appendix 24) The guide portion is When providing a guide, provide the most suitable guide based on the user's geographic location information 2. The system of claim 1, (Appendix 25) The guide portion is When providing a guide, the content of the guide is suggested based on the user's SNS activity. 2. The system of claim 1, (Appendix 26) The providing unit is The user's emotions are estimated, and the level of detail of information to be provided is determined based on the estimated user's emotions. 3. The system of claim 2, (Appendix 27) The providing unit is At the time of provision, provide the most appropriate information based on the user's past inquiry history 3. The system of claim 2, (Appendix 28) The providing unit is To estimate a user's emotion and determine a method of displaying information based on the estimated user's emotion 3. The system of claim 2, (Appendix 29) The providing unit is At the time of serving, provide the most appropriate information based on the user's geographic location 3. The system of claim 2, (Appendix 30) The multilingual unit includes: Estimate the user's emotions and determine the accuracy of the translation based on the estimated user's emotions. 4. The system of claim 3. (Appendix 31) The multilingual unit includes: During translation, the translation algorithm is optimized based on past translation data 4. The system of claim 3. (Appendix 32) The multilingual unit includes: Estimate the user's emotions and determine how to display the translation results based on the estimated user's emotions. 4. The system of claim 3. (Appendix 33) The multilingual unit includes: During translation, provide optimal translations based on the user's geographic location 4. The system of claim 3. (Appendix 34) The example part is A user's emotion is estimated, and an example content is determined based on the estimated user's emotion. 5. The system of claim 4. (Appendix 35) The example part is When providing examples, provide the best examples based on the user's past inquiry history 5. The system of claim 4. (Appendix 36) The example part is A user's emotion is estimated, and a method of displaying the example is determined based on the estimated user's emotion. 5. The system of claim 4. (Appendix 37) The example part is During instantiation, the best examples are provided based on the user's geographic location. 5. The system of claim 4. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an input of a content to be resolved from a user; an analysis unit that analyzes the content received by the reception unit and identifies a contact point and a future course of action; A guide unit that provides a guide to a user based on the information identified by the analysis unit. A system characterized by:

2. The guide portion is Equipped with a section that provides the procedure and a list of required documents 2. The system of claim 1 .

3. The guide portion is Equipped with a multilingual section that can handle multiple languages 2. The system of claim 1 .

4. The guide portion is Includes an example section that provides examples of other procedures 2. The system of claim 1 .

5. The providing unit is Provide a procedure and a list of required documents based on the information received from the guidance department 3. The system of claim 2.

6. The multilingual unit includes: The information received from the analysis unit is translated into a plurality of languages ​​and provided to the guide unit.

4. The system of claim 3.

7. The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past inquiry history and suggest input methods 2. The system of claim 1 .

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