System

Abstract

An object of the system according to the embodiment is to simplify the guidance of the inquiry window so that the user can quickly reach the target menu.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a connection unit. The reception unit receives an input of a user who orally declares the inquiry content. The analysis unit analyzes the inquiry content received by the reception unit and determines a corresponding menu. The providing unit provides the menu determined by the analyzing unit to the user by voice. The connection unit connects the user to the corresponding menu based on the menu provided by the providing 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that the guidance provided at the inquiry desk is complicated and it takes time to reach the desired menu.

[0005] The system according to the embodiment aims to simplify the guidance provided at the inquiry desk and enable the user to quickly reach the desired menu. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a connection unit. The reception unit receives input from a user verbally reporting the content of an inquiry. The analysis unit analyzes the content of the inquiry received by the reception unit and determines a corresponding menu. The provision unit provides the menu determined by the analysis unit to the user by voice. The connection unit connects the user to the corresponding menu based on the menu provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can simplify the guidance provided to the inquiry desk and enable the user to quickly reach the desired menu. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A guidance system according to an embodiment of the present invention is a system that improves the efficiency of customer service guidance at customer service desks. In this guidance system, a user verbally declares their inquiry, and a generation AI analyzes the inquiry, determines the appropriate menu, notifies the user via voice, and connects them to the appropriate menu. For example, if a user says, "Please tell me how to return a product," the generation AI analyzes the content, identifies the menu related to returns, and notifies the user via voice, "Connecting to the menu related to returns." If the user requests this, the AI ​​automatically connects to the appropriate menu. This allows the user to quickly reach the desired menu without having to navigate through complicated guidance. This allows the guidance system to improve the efficiency of customer service guidance at customer service desks and reduce user stress. For example, the user can quickly and accurately communicate their inquiry and be quickly connected to the desired menu, improving user convenience.

[0029] A guidance system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a connection unit. The reception unit accepts input in which a user verbally declares an inquiry. For example, the user can verbally declare the inquiry via telephone or a voice recognition system. The analysis unit uses a generation AI to analyze the inquiry received by the reception unit and determine the appropriate menu. For example, the generation AI uses natural language processing technology to understand the user's utterance and identify the appropriate menu. The generation AI can perform analysis based on data it has learned in advance. The provision unit provides the menu determined by the analysis unit to the user via voice. For example, the menu is notified to the user via voice using synthetic voice or recorded voice. The connection unit connects the user to the appropriate menu based on the menu provided by the provision unit. For example, if the user requests it, the generation AI automatically connects to the appropriate menu. This allows the guidance system according to an embodiment to improve the efficiency of guidance at the inquiry desk and reduce user stress.

[0030] The analysis unit can analyze the query content based on data that the generation AI has learned in advance. The generation AI analyzes the query content based on the data that it has learned in advance, using a natural language processing model such as GPT-4 (registered trademark) or Gemini. For example, the generation AI has the ability to learn large amounts of text data and understand what the user says. The generation AI analyzes the user's statement and identifies the relevant menu. This improves the accuracy of the analysis by having the generation AI analyze based on the data that it has learned in advance.

[0031] The analysis unit can understand the user's utterances using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. For example, the analysis unit uses morphological analysis to break down the user's utterances into words, uses grammatical analysis to analyze the structure of the sentence, and uses semantic analysis to understand the meaning of the sentence. In this way, the analysis unit can accurately understand the user's utterances by using natural language processing technology.

[0032] The providing unit can provide the menu to the user by voice. The providing unit can inform the user of the menu by voice, for example, using synthesized voice or recorded voice. For example, the providing unit can use synthesized voice to inform the user, for example, "You will be directed to the menu for returns." The providing unit can also use recorded voice to inform the user of the menu by pre-recorded voice. In this way, the providing unit can provide the menu by voice, allowing the user to quickly understand the menu.

[0033] The connection unit can automatically connect to a corresponding menu based on the user's preference. The connection unit automatically connects the user to a corresponding menu, for example, by using an automatic connection by script or a connection by AI. For example, if the user answers "Yes" to the confirmation "I will connect to the menu regarding returns. Is this OK?", the connection unit automatically connects to the corresponding menu. This allows the connection unit to automatically connect based on the user's preference, improving user convenience.

[0034] The reception unit can analyze the user's past inquiry history and select the optimal reception method. The reception unit can, for example, use a generation AI to analyze the user's past inquiry history. For example, it can prioritize reception of inquiries that the user has made frequently in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the reception method to be used during a specific time period from the user's past inquiry history. In this way, by analyzing the past inquiry history, it is possible to provide the optimal reception method for the user.

[0035] The reception unit can filter inquiry content based on the user's current situation and areas of interest when receiving the inquiry content. The reception unit, for example, uses a generation AI to analyze the user's current situation and areas of interest. For example, when the user describes their current situation, it can preferentially receive inquiry content related to that situation. It can also filter related inquiry content based on the user's areas of interest. Furthermore, when the user is in a specific situation, it can suggest inquiry content that is optimal for that situation. In this way, by filtering based on the user's current situation and areas of interest, it is possible to preferentially receive inquiry content that is highly relevant.

[0036] When receiving an inquiry, the reception unit can select the optimal reception means depending on the user's input method. The reception unit, for example, uses a generation AI to analyze the user's input method (voice, text, image, etc.). For example, if the user inputs the inquiry by voice, the reception unit can prioritize the reception of the voice input. Also, if the user inputs the inquiry by text, the reception unit can prioritize the reception of the text input. Furthermore, if the user inputs the inquiry by image, the reception unit can prioritize the reception of the image input. This improves user convenience by selecting the optimal reception means depending on the user's input method.

[0037] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. The reception unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving inquiries related to that area. Also, if the user is traveling, the reception unit can prioritize receiving inquiries related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving inquiries related to the user's home. In this way, by taking into account the user's geographical location information, the reception unit can prioritize receiving highly relevant inquiries.

[0038] The reception unit can analyze the user's social media activity when receiving an inquiry and receive related inquiries. The reception unit can, for example, analyze the user's social media activity using a generative AI. For example, the reception unit can preferentially receive related inquiry content based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity and suggest related inquiry content. Furthermore, the reception unit can also refer to the activity of the user's friends on social media to receive related inquiry content. In this way, by analyzing social media activity, it is possible to preferentially receive related inquiry content.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the inquiry content. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the reception unit can propose the optimal reception method based on the feedback the user has provided in the past. The reception unit can also analyze the user's past feedback and improve the reception method. Furthermore, it can avoid reception methods that the user has been dissatisfied with in the past and provide the optimal reception method. In this way, the reception unit can provide the optimal reception method for the user by reflecting past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the query content. The analysis unit, for example, uses a generation AI to evaluate the importance of the query content. For example, the importance can be evaluated based on the urgency and scope of impact of the query content. The analysis unit adjusts the level of detail of the analysis based on the importance. For example, the generation AI performs a detailed analysis of query content with high importance. The generation AI can also perform a simplified analysis of query content with low importance. Furthermore, the generation AI can dynamically adjust the level of detail of the analysis depending on the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the query content, detailed analysis can be performed on important query content.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry content. The analysis unit, for example, uses a generation AI to classify the category of the inquiry content. For example, it can classify the inquiry into categories such as technical inquiries and support inquiries. The analysis unit applies different analysis algorithms depending on the category. For example, for inquiries about products, the generation AI can apply an analysis algorithm dedicated to products. Furthermore, for inquiries about services, the generation AI can apply an analysis algorithm dedicated to services. Furthermore, for technical inquiries, the generation AI can apply an analysis algorithm dedicated to technology. In this way, by applying different analysis algorithms depending on the category of the inquiry content, the accuracy of analysis is improved.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the generation AI can improve the accuracy of the analysis by referring to the analysis results of queries made by the user in the past. The generation AI can also analyze the user's past analysis results and propose the optimal analysis method. Furthermore, the generation AI can adjust the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the inquiry content was submitted. The analysis unit, for example, uses a generation AI to analyze the time when the inquiry content was submitted. For example, the analysis unit can determine the priority of analysis based on the submission date and time or the time elapsed since submission. The analysis unit determines the priority of analysis based on the time of submission. For example, it can prioritize analysis of the most recently submitted inquiry content. It can also postpone analysis of inquiries submitted earlier. Furthermore, it can dynamically adjust the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time when the inquiry content was submitted, it is possible to prioritize analysis of the most recent inquiry content.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the query content. The analysis unit, for example, uses a generative AI to evaluate the relevance of the query content. For example, the analysis unit can evaluate the relevance based on the similarity of the query content or related topics. The analysis unit adjusts the order of analysis based on the relevance. For example, it can prioritize analysis of query content with high relevance. It can also postpone query content with low relevance. Furthermore, it can dynamically adjust the order of analysis based on the relevance. In this way, by adjusting the order of analysis based on the relevance of the query content, it is possible to prioritize analysis of query content with high relevance.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the expertise level can be evaluated based on the user's occupation and past inquiries. The analysis unit adjusts the use of technical terms in the analysis according to the level of expertise. For example, the generation AI can provide analysis results that use a lot of technical terms to a user with high expertise. The generation AI can also provide analysis results that avoid technical terms to a user with low expertise. Furthermore, the generation AI can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0046] The providing unit can adjust the level of detail provided based on the importance of the menu when providing it. The providing unit, for example, uses a generation AI to evaluate the importance of the menu. For example, the importance can be evaluated based on the urgency or scope of impact of the menu. The providing unit adjusts the level of detail provided based on the importance. For example, the generation AI provides detailed information for menus with high importance. The generation AI can also provide simplified information for menus with low importance. Furthermore, the generation AI can dynamically adjust the level of detail provided depending on the importance. In this way, by adjusting the level of detail provided based on the importance of the menu, detailed information can be provided for important menus.

[0047] The provision unit can apply different provision algorithms depending on the menu category when providing the menu. The provision unit, for example, uses a generation AI to classify menu categories. For example, the menus can be classified into categories such as technical menus and support menus. The provision unit applies different provision algorithms depending on the category. For example, the generation AI can apply a provision algorithm dedicated to products to menus related to products. Furthermore, the generation AI can apply a provision algorithm dedicated to services to menus related to services. Furthermore, the generation AI can apply a provision algorithm dedicated to technology to technical menus. In this way, by applying different provision algorithms depending on the menu category, provision accuracy is improved.

[0048] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past providing results. The providing unit, for example, uses the generation AI to analyze the user's past providing results. For example, the generation AI can improve the accuracy of the information provided by referring to the providing results of inquiries made by the user in the past. The generation AI can also analyze the user's past providing results and propose the optimal providing method. Furthermore, the generation AI can adjust the providing algorithm based on the user's past providing results. In this way, the accuracy of the information provided is improved by referring to the past providing results.

[0049] The provision unit can determine the provision priority based on the time of submission of the menu when it is provided. The provision unit, for example, uses generation AI to analyze the time of submission of the menu. For example, it can determine the provision priority based on the submission date and time or the time elapsed since submission. The provision unit determines the provision priority based on the time of submission. For example, it can provide the most recently submitted menu with priority. It can also postpone menus that were submitted earlier. Furthermore, it can dynamically adjust the provision priority according to the time of submission. In this way, by determining the provision priority based on the time of submission of the menu, it is possible to provide the latest menu with priority.

[0050] The providing unit can adjust the order of provision based on the relevance of the menus when providing them. The providing unit can, for example, use a generation AI to evaluate the relevance of the menus. For example, the relevance can be evaluated based on the similarity of the menu contents or related topics. The providing unit adjusts the order of provision based on the relevance. For example, highly relevant menus can be provided preferentially. It is also possible to postpone less relevant menus. Furthermore, the order of provision can be dynamically adjusted according to the relevance. In this way, by adjusting the order of provision based on the relevance of the menus, highly relevant menus can be provided preferentially.

[0051] The providing unit can adjust the use of provided terminology according to the user's level of expertise when providing the menu. The providing unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the expertise level can be evaluated based on the user's occupation and past inquiries. The providing unit adjusts the use of provided terminology according to the level of expertise. For example, the generation AI can provide a menu that uses a lot of terminology to a user with high expertise. The generation AI can also provide a menu that avoids terminology to a user with low expertise. Furthermore, the generation AI can dynamically adjust the use of provided terminology according to the user's level of expertise. In this way, by adjusting the use of terminology according to the user's level of expertise, a menu that is easy for the user to understand can be provided.

[0052] When connecting, the connection unit can analyze the user's past connection history and select the optimal connection method. The connection unit can, for example, use a generation AI to analyze the user's past connection history. For example, it can prioritize the application of connection methods that the user has used in the past. It can also suggest the optimal connection method based on the user's past connection history. Furthermore, the generation AI can adjust the connection method based on the user's past connection history. In this way, it is possible to provide the user with the optimal connection method by analyzing the past connection history.

[0053] When connecting, the connection unit can customize the connection means based on the user's current situation. The connection unit, for example, uses a generation AI to analyze the user's current situation. For example, if the user is on the move, the generation AI can prioritize a mobile connection. Also, if the user is at home, the generation AI can prioritize a Wi-Fi connection. Furthermore, the generation AI can suggest the optimal connection means depending on the user's current situation. In this way, by customizing the connection means based on the user's current situation, the optimal connection means for the user can be provided.

[0054] The connection unit can improve the connection method by reflecting user feedback at the time of connection. The connection unit, for example, uses a generation AI to analyze user feedback. For example, the generation AI can improve the connection method based on feedback provided by the user in the past. The generation AI can also analyze user feedback and suggest an optimal connection method. Furthermore, the generation AI can adjust the connection algorithm based on user feedback. In this way, the connection method can be improved by reflecting user feedback.

[0055] When connecting, the connection unit can select the optimal connection method by taking into account the user's geographical location information. The connection unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the connection unit can suggest the optimal connection method for that area. Also, if the user is traveling, the connection unit can suggest the optimal connection method for the user's destination. Furthermore, if the user is at home, the connection unit can suggest the optimal connection method for the user's home. In this way, the optimal connection method can be provided by taking into account the user's geographical location information.

[0056] At the time of connection, the connection unit can analyze the user's social media activity and suggest a means of connection. The connection unit can, for example, use generative AI to analyze the user's social media activity. For example, it can suggest the optimal connection means based on the content posted by the user on social media. It can also analyze the user's social media activity and suggest the optimal connection means. It can also suggest the optimal connection means by taking into account the activity of the user's friends on social media. In this way, it is possible to provide the user with the optimal connection means by analyzing social media activity.

[0057] The connection unit can customize the connection method by reflecting the user's past feedback when connecting. The connection unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI can customize the connection method based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and suggest the optimal connection method. Furthermore, the generation AI can adjust the connection algorithm based on the user's past feedback. In this way, the optimal connection method can be provided to the user by reflecting past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit learns the content of past inquiries made by the user and can respond quickly to similar inquiries. For example, if a user has previously inquired about how to return a product, the next time a similar inquiry is made, the analysis unit can respond quickly. Also, if a user has previously requested technical support, the analysis unit can provide similar support quickly the next time. Furthermore, if a user has previously inquired about a specific service, the analysis unit can also respond quickly to inquiries about that service. This allows the analysis unit to efficiently respond by utilizing the content of past inquiries made by the user.

[0060] The connection unit can analyze the user's past connection history and select the optimal connection method. For example, if the user has preferred voice connection in the past, it can provide voice connection preferentially the next time as well. Also, if the user has preferred text connection in the past, it can provide text connection preferentially the next time as well. Furthermore, if the user has connected during a specific time period in the past, it can suggest the optimal connection method for that time period. In this way, the connection unit can provide the optimal connection method by utilizing the user's past connection history.

[0061] The reception unit can analyze the user's current situation and suggest the optimal reception method. For example, if the user is on the move, it can suggest a reception method using a mobile device. If the user is at home, it can suggest a reception method using a PC. Furthermore, if the user is in an office, it can suggest a reception method that is suitable for the office environment. In this way, the reception unit can provide the optimal reception method according to the user's current situation.

[0062] The providing unit can analyze the user's past provision results and select the optimal provision method. For example, if the user has preferred audio provision in the past, audio provision can be given priority the next time as well. Also, if the user has preferred text provision in the past, text provision can be given priority the next time as well. Furthermore, if the user has received provision during a specific time period in the past, the providing unit can suggest the optimal provision method for that time period. In this way, the providing unit can utilize the user's past provision results to provide the optimal provision method.

[0063] The reception unit can analyze the user's social media activity and prioritize the reception of related inquiries. For example, if the user mentions a specific product on social media, it can prioritize the reception of inquiries about that product. Also, if the user mentions a specific service on social media, it can prioritize the reception of inquiries about that service. Furthermore, it can also refer to the activities of the user's friends on social media to receive related inquiries. In this way, the reception unit can utilize social media activity to prioritize the reception of related inquiries.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives an input in which the user verbally declares the content of the inquiry. For example, the user can verbally declare the content of the inquiry over the telephone or through a voice recognition system. Step 2: The analysis unit uses the generation AI to analyze the inquiry received by the reception unit and determine the appropriate menu. For example, the generation AI uses natural language processing technology to understand the user's utterance and identify the appropriate menu. The generation AI can perform analysis based on data it has learned in advance. Step 3: The providing unit provides the menu determined by the analyzing unit to the user by voice, for example, by using synthesized voice or recorded voice to inform the user of the menu. Step 4: The connection unit connects the user to the corresponding menu based on the menu provided by the provision unit. For example, if the user requests it, the generation AI automatically connects to the corresponding menu.

[0066] (Example 2) A guidance system according to an embodiment of the present invention is a system that improves the efficiency of customer service guidance at customer service desks. In this guidance system, a user verbally declares their inquiry, and a generation AI analyzes the inquiry, determines the appropriate menu, notifies the user via voice, and connects them to the appropriate menu. For example, if a user says, "Please tell me how to return a product," the generation AI analyzes the content, identifies the menu related to returns, and notifies the user via voice, "Connecting to the menu related to returns." If the user requests this, the AI ​​automatically connects to the appropriate menu. This allows the user to quickly reach the desired menu without having to navigate through complicated guidance. This allows the guidance system to improve the efficiency of customer service guidance at customer service desks and reduce user stress. For example, the user can quickly and accurately communicate their inquiry and be quickly connected to the desired menu, improving user convenience.

[0067] A guidance system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a connection unit. The reception unit accepts input in which a user verbally declares an inquiry. For example, the user can verbally declare the inquiry via telephone or a voice recognition system. The analysis unit uses a generation AI to analyze the inquiry received by the reception unit and determine the appropriate menu. For example, the generation AI uses natural language processing technology to understand the user's utterance and identify the appropriate menu. The generation AI can perform analysis based on data it has learned in advance. The provision unit provides the menu determined by the analysis unit to the user via voice. For example, the menu is notified to the user via voice using synthetic voice or recorded voice. The connection unit connects the user to the appropriate menu based on the menu provided by the provision unit. For example, if the user requests it, the generation AI automatically connects to the appropriate menu. This allows the guidance system according to an embodiment to improve the efficiency of guidance at the inquiry desk and reduce user stress.

[0068] The analysis unit can analyze the query content based on data that the generation AI has learned in advance. The generation AI analyzes the query content based on data that it has learned in advance, using natural language processing models such as GPT-4 or Gemini. For example, the generation AI has the ability to learn large amounts of text data and understand what the user is saying. The generation AI analyzes the user's statement and identifies the relevant menu. This improves the accuracy of the analysis by having the generation AI analyze based on data that it has learned in advance.

[0069] The analysis unit can understand the user's utterances using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. For example, the analysis unit uses morphological analysis to break down the user's utterances into words, uses grammatical analysis to analyze the structure of the sentence, and uses semantic analysis to understand the meaning of the sentence. In this way, the analysis unit can accurately understand the user's utterances by using natural language processing technology.

[0070] The providing unit can provide the menu to the user by voice. The providing unit can inform the user of the menu by voice, for example, using synthesized voice or recorded voice. For example, the providing unit can use synthesized voice to inform the user, for example, "You will be directed to the menu for returns." The providing unit can also use recorded voice to inform the user of the menu by pre-recorded voice. In this way, the providing unit can provide the menu by voice, allowing the user to quickly understand the menu.

[0071] The connection unit can automatically connect to a corresponding menu based on the user's preference. The connection unit automatically connects the user to a corresponding menu, for example, by using an automatic connection by script or a connection by AI. For example, if the user answers "Yes" to the confirmation "I will connect to the menu regarding returns. Is this OK?", the connection unit automatically connects to the corresponding menu. This allows the connection unit to automatically connect based on the user's preference, improving user convenience.

[0072] The reception unit can estimate the user's emotions and adjust the timing of receiving the inquiry content based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, voice analysis or facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The reception unit adjusts the timing of receiving the inquiry content based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can immediately accept the inquiry content. Alternatively, if the user is relaxed, the generation AI can take a little more time to accept the inquiry content. Furthermore, if the user is in a hurry, the generation AI can quickly accept the inquiry content. In this way, by adjusting the acceptance timing according to the user's emotions, the inquiry content can be accepted at a more appropriate time.

[0073] The reception unit can analyze the user's past inquiry history and select the optimal reception method. The reception unit can, for example, use a generation AI to analyze the user's past inquiry history. For example, it can prioritize reception of inquiries that the user has made frequently in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the reception method to be used during a specific time period from the user's past inquiry history. In this way, by analyzing the past inquiry history, it is possible to provide the optimal reception method for the user.

[0074] The reception unit can filter inquiry content based on the user's current situation and areas of interest when receiving the inquiry content. The reception unit, for example, uses a generation AI to analyze the user's current situation and areas of interest. For example, when the user describes their current situation, it can preferentially receive inquiry content related to that situation. It can also filter related inquiry content based on the user's areas of interest. Furthermore, when the user is in a specific situation, it can suggest inquiry content that is optimal for that situation. In this way, by filtering based on the user's current situation and areas of interest, it is possible to preferentially receive inquiry content that is highly relevant.

[0075] When receiving an inquiry, the reception unit can select the optimal reception means depending on the user's input method. The reception unit, for example, uses a generation AI to analyze the user's input method (voice, text, image, etc.). For example, if the user inputs the inquiry by voice, the reception unit can prioritize the reception of the voice input. Also, if the user inputs the inquiry by text, the reception unit can prioritize the reception of the text input. Furthermore, if the user inputs the inquiry by image, the reception unit can prioritize the reception of the image input. This improves user convenience by selecting the optimal reception means depending on the user's input method.

[0076] The reception unit can estimate the user's emotions and determine the priority of the inquiries to be received based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, voice analysis or facial expression recognition. For example, the reception unit can use voice analysis to analyze the tone and speed of the user's voice to estimate the user's stress or relaxation state. It can also use facial expression recognition to analyze the user's facial expression and estimate the user's emotions. The reception unit determines the priority of the inquiries to be received based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize receiving that inquiry. Also, if the user is relaxed, the generation AI can prioritize receiving that inquiry as well as other inquiries. Furthermore, if the user is in a hurry, the generation AI can prioritize receiving that inquiry. In this way, by determining the priority of the inquiries according to the user's emotions, important inquiries can be prioritized.

[0077] When receiving an inquiry, the reception unit can prioritize receiving highly relevant inquiries by taking into account the user's geographical location information. The reception unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving inquiries related to that area. Also, if the user is traveling, the reception unit can prioritize receiving inquiries related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving inquiries related to the user's home. In this way, by taking into account the user's geographical location information, the reception unit can prioritize receiving highly relevant inquiries.

[0078] The reception unit can analyze the user's social media activity when receiving an inquiry and receive related inquiries. The reception unit can, for example, analyze the user's social media activity using a generative AI. For example, the reception unit can preferentially receive related inquiry content based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity and suggest related inquiry content. Furthermore, the reception unit can also refer to the activity of the user's friends on social media to receive related inquiry content. In this way, by analyzing social media activity, it is possible to preferentially receive related inquiry content.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the inquiry content. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the reception unit can propose the optimal reception method based on the feedback the user has provided in the past. The reception unit can also analyze the user's past feedback and improve the reception method. Furthermore, it can avoid reception methods that the user has been dissatisfied with in the past and provide the optimal reception method. In this way, the reception unit can provide the optimal reception method for the user by reflecting past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, voice analysis and facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The analysis unit adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide analysis results in a simple manner. If the user is relaxed, the generation AI can provide detailed analysis results. Furthermore, if the user is in a hurry, the generation AI can provide analysis results that are concise. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the query content. The analysis unit, for example, uses a generation AI to evaluate the importance of the query content. For example, the importance can be evaluated based on the urgency and scope of impact of the query content. The analysis unit adjusts the level of detail of the analysis based on the importance. For example, the generation AI performs a detailed analysis of query content with high importance. The generation AI can also perform a simplified analysis of query content with low importance. Furthermore, the generation AI can dynamically adjust the level of detail of the analysis depending on the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the query content, detailed analysis can be performed on important query content.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the inquiry content. The analysis unit, for example, uses a generation AI to classify the category of the inquiry content. For example, it can classify the inquiry into categories such as technical inquiries and support inquiries. The analysis unit applies different analysis algorithms depending on the category. For example, for inquiries about products, the generation AI can apply an analysis algorithm dedicated to products. Furthermore, for inquiries about services, the generation AI can apply an analysis algorithm dedicated to services. Furthermore, for technical inquiries, the generation AI can apply an analysis algorithm dedicated to technology. In this way, by applying different analysis algorithms depending on the category of the inquiry content, the accuracy of analysis is improved.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the generation AI can improve the accuracy of the analysis by referring to the analysis results of queries made by the user in the past. The generation AI can also analyze the user's past analysis results and propose the optimal analysis method. Furthermore, the generation AI can adjust the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit estimates the user's emotions using, for example, voice analysis and facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide a short analysis result. Alternatively, if the user is relaxed, the generation AI can provide a detailed analysis result. Furthermore, if the user is in a hurry, the generation AI can provide an analysis result that focuses on the main points. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an appropriate length for the user.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time when the inquiry content was submitted. The analysis unit, for example, uses a generation AI to analyze the time when the inquiry content was submitted. For example, the analysis unit can determine the priority of analysis based on the submission date and time or the time elapsed since submission. The analysis unit determines the priority of analysis based on the time of submission. For example, it can prioritize analysis of the most recently submitted inquiry content. It can also postpone analysis of inquiries submitted earlier. Furthermore, it can dynamically adjust the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time when the inquiry content was submitted, it is possible to prioritize analysis of the most recent inquiry content.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the query content. The analysis unit, for example, uses a generative AI to evaluate the relevance of the query content. For example, the analysis unit can evaluate the relevance based on the similarity of the query content or related topics. The analysis unit adjusts the order of analysis based on the relevance. For example, it can prioritize analysis of query content with high relevance. It can also postpone query content with low relevance. Furthermore, it can dynamically adjust the order of analysis based on the relevance. In this way, by adjusting the order of analysis based on the relevance of the query content, it is possible to prioritize analysis of query content with high relevance.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the expertise level can be evaluated based on the user's occupation and past inquiries. The analysis unit adjusts the use of technical terms in the analysis according to the level of expertise. For example, the generation AI can provide analysis results that use a lot of technical terms to a user with high expertise. The generation AI can also provide analysis results that avoid technical terms to a user with low expertise. Furthermore, the generation AI can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0088] The providing unit can estimate the user's emotions and adjust the way the menu is presented based on the estimated user's emotions. The providing unit estimates the user's emotions using, for example, voice analysis or facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The providing unit adjusts the way the menu is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can present a menu using a simple presentation. Alternatively, if the user is relaxed, the generation AI can present a detailed menu. Furthermore, if the user is in a hurry, the generation AI can present a menu that focuses on the main points. In this way, by adjusting the way the menu is presented according to the user's emotions, it is possible to present a menu that is easy for the user to understand.

[0089] The providing unit can adjust the level of detail provided based on the importance of the menu when providing it. The providing unit, for example, uses a generation AI to evaluate the importance of the menu. For example, the importance can be evaluated based on the urgency or scope of impact of the menu. The providing unit adjusts the level of detail provided based on the importance. For example, the generation AI provides detailed information for menus with high importance. The generation AI can also provide simplified information for menus with low importance. Furthermore, the generation AI can dynamically adjust the level of detail provided depending on the importance. In this way, by adjusting the level of detail provided based on the importance of the menu, detailed information can be provided for important menus.

[0090] The provision unit can apply different provision algorithms depending on the menu category when providing the menu. The provision unit, for example, uses a generation AI to classify menu categories. For example, the menus can be classified into categories such as technical menus and support menus. The provision unit applies different provision algorithms depending on the category. For example, the generation AI can apply a provision algorithm dedicated to products to menus related to products. Furthermore, the generation AI can apply a provision algorithm dedicated to services to menus related to services. Furthermore, the generation AI can apply a provision algorithm dedicated to technology to technical menus. In this way, by applying different provision algorithms depending on the menu category, provision accuracy is improved.

[0091] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past providing results. The providing unit, for example, uses the generation AI to analyze the user's past providing results. For example, the generation AI can improve the accuracy of the information provided by referring to the providing results of inquiries made by the user in the past. The generation AI can also analyze the user's past providing results and propose the optimal providing method. Furthermore, the generation AI can adjust the providing algorithm based on the user's past providing results. In this way, the accuracy of the information provided is improved by referring to the past providing results.

[0092] The providing unit can estimate the user's emotions and adjust the length of the menu to be provided based on the estimated user's emotions. The providing unit estimates the user's emotions using, for example, voice analysis or facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The providing unit adjusts the length of the menu to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide a short menu. Alternatively, if the user is relaxed, the generation AI can provide a detailed menu. Furthermore, if the user is in a hurry, the generation AI can provide a menu that focuses on the main points. In this way, by adjusting the length of the menu according to the user's emotions, a menu of an appropriate length for the user can be provided.

[0093] The provision unit can determine the provision priority based on the time of submission of the menu when it is provided. The provision unit, for example, uses generation AI to analyze the time of submission of the menu. For example, it can determine the provision priority based on the submission date and time or the time elapsed since submission. The provision unit determines the provision priority based on the time of submission. For example, it can provide the most recently submitted menu with priority. It can also postpone menus that were submitted earlier. Furthermore, it can dynamically adjust the provision priority according to the time of submission. In this way, by determining the provision priority based on the time of submission of the menu, it is possible to provide the latest menu with priority.

[0094] The providing unit can adjust the order of provision based on the relevance of the menus when providing them. The providing unit can, for example, use a generation AI to evaluate the relevance of the menus. For example, the relevance can be evaluated based on the similarity of the menu contents or related topics. The providing unit adjusts the order of provision based on the relevance. For example, highly relevant menus can be provided preferentially. It is also possible to postpone less relevant menus. Furthermore, the order of provision can be dynamically adjusted according to the relevance. In this way, by adjusting the order of provision based on the relevance of the menus, highly relevant menus can be provided preferentially.

[0095] The providing unit can adjust the use of provided terminology according to the user's level of expertise when providing the menu. The providing unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the expertise level can be evaluated based on the user's occupation and past inquiries. The providing unit adjusts the use of provided terminology according to the level of expertise. For example, the generation AI can provide a menu that uses a lot of terminology to a user with high expertise. The generation AI can also provide a menu that avoids terminology to a user with low expertise. Furthermore, the generation AI can dynamically adjust the use of provided terminology according to the user's level of expertise. In this way, by adjusting the use of terminology according to the user's level of expertise, a menu that is easy for the user to understand can be provided.

[0096] The connection unit can estimate the user's emotions and adjust the connection method based on the estimated user's emotions. The connection unit estimates the user's emotions using, for example, voice analysis or facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The connection unit adjusts the connection method based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can quickly establish a connection. Alternatively, if the user is relaxed, the generation AI can apply a normal connection method. Furthermore, if the user is in a hurry, the generation AI can establish a connection in the shortest time. In this way, by adjusting the connection method according to the user's emotions, it is possible to provide the optimal connection method for the user.

[0097] When connecting, the connection unit can analyze the user's past connection history and select the optimal connection method. The connection unit can, for example, use a generation AI to analyze the user's past connection history. For example, it can prioritize the application of connection methods that the user has used in the past. It can also suggest the optimal connection method based on the user's past connection history. Furthermore, the generation AI can adjust the connection method based on the user's past connection history. In this way, it is possible to provide the user with the optimal connection method by analyzing the past connection history.

[0098] When connecting, the connection unit can customize the connection means based on the user's current situation. The connection unit, for example, uses a generation AI to analyze the user's current situation. For example, if the user is on the move, the generation AI can prioritize a mobile connection. Also, if the user is at home, the generation AI can prioritize a Wi-Fi connection. Furthermore, the generation AI can suggest the optimal connection means depending on the user's current situation. In this way, by customizing the connection means based on the user's current situation, the optimal connection means for the user can be provided.

[0099] The connection unit can improve the connection method by reflecting user feedback at the time of connection. The connection unit, for example, uses a generation AI to analyze user feedback. For example, the generation AI can improve the connection method based on feedback provided by the user in the past. The generation AI can also analyze user feedback and suggest an optimal connection method. Furthermore, the generation AI can adjust the connection algorithm based on user feedback. In this way, the connection method can be improved by reflecting user feedback.

[0100] The connection unit can estimate the user's emotions and determine the priority of connections based on the estimated user's emotions. The connection unit estimates the user's emotions using, for example, voice analysis or facial expression recognition. For example, voice analysis can be used to analyze the tone and speed of the user's voice to estimate their state of stress or relaxation. Facial expression recognition can also be used to analyze the user's facial expressions to estimate their emotions. The connection unit determines the priority of connections based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize that connection. Also, if the user is relaxed, the generation AI can treat that connection equally with other connections. Furthermore, if the user is in a hurry, the generation AI can give that connection the highest priority. In this way, by determining the priority of connections according to the user's emotions, important connections can be prioritized.

[0101] When connecting, the connection unit can select the optimal connection method by taking into account the user's geographical location information. The connection unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the connection unit can suggest the optimal connection method for that area. Also, if the user is traveling, the connection unit can suggest the optimal connection method for the user's destination. Furthermore, if the user is at home, the connection unit can suggest the optimal connection method for the user's home. In this way, the optimal connection method can be provided by taking into account the user's geographical location information.

[0102] At the time of connection, the connection unit can analyze the user's social media activity and suggest a means of connection. The connection unit can, for example, use generative AI to analyze the user's social media activity. For example, it can suggest the optimal connection means based on the content posted by the user on social media. It can also analyze the user's social media activity and suggest the optimal connection means. It can also suggest the optimal connection means by taking into account the activity of the user's friends on social media. In this way, it is possible to provide the user with the optimal connection means by analyzing social media activity.

[0103] The connection unit can customize the connection method by reflecting the user's past feedback when connecting. The connection unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI can customize the connection method based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and suggest the optimal connection method. Furthermore, the generation AI can adjust the connection algorithm based on the user's past feedback. In this way, the optimal connection method can be provided to the user by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and connection 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 receives the user's inquiry using the microphone 38B or touch panel 38A of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry using a generation AI. The provision unit provides the analysis result by voice using the speaker 40B of the smart device 14. The connection unit is realized by the specific processing unit 290 of the data processing device 12 and connects the user to the corresponding menu. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and connection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives the user's inquiry using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the inquiry using a generation AI. The provision unit provides the analysis result by voice using the speaker 240 of the smart glasses 214. The connection unit is realized by the specific processing unit 290 of the data processing device 12 and connects the user to a corresponding menu. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and connection 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 receives the user's inquiry using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the inquiry using a generation AI. The provision unit provides the analysis result by voice using the speaker 240 of the headset type terminal 314. The connection unit is realized by the specific processing unit 290 of the data processing device 12, and connects the user to the corresponding menu. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and connection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the user's inquiry using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the inquiry using a generation AI. The provision unit provides the analysis result by voice using the speaker 240 of the robot 414. The connection unit is realized by the specific processing unit 290 of the data processing device 12, and connects the user to a corresponding menu.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the tone and speed of the user's voice and estimate the user's emotions. For example, if the user speaks quickly and in a high-pitched voice, it can be estimated that the user is stressed. If the user speaks slowly and in a low-pitched voice, it can be estimated that the user is relaxed. Furthermore, if the user speaks in a subdued voice, it can be estimated that the user is nervous. This allows the reception unit to take appropriate action based on the user's emotions.

[0106] The analysis unit learns the content of past inquiries made by the user and can respond quickly to similar inquiries. For example, if a user has previously inquired about how to return a product, the next time a similar inquiry is made, the analysis unit can respond quickly. Also, if a user has previously requested technical support, the analysis unit can provide similar support quickly the next time. Furthermore, if a user has previously inquired about a specific service, the analysis unit can also respond quickly to inquiries about that service. This allows the analysis unit to efficiently respond by utilizing the content of past inquiries made by the user.

[0107] The providing unit can estimate the user's emotions and adjust the order of menus to be provided based on the estimated emotions. For example, if the user is feeling stressed, important menus can be provided preferentially. Also, if the user is relaxed, a detailed menu can be provided. Furthermore, if the user is in a hurry, a menu that focuses on the main points can be provided. In this way, the providing unit can provide the optimal menu according to the user's emotions.

[0108] The connection unit can analyze the user's past connection history and select the optimal connection method. For example, if the user has preferred voice connection in the past, it can provide voice connection preferentially the next time as well. Also, if the user has preferred text connection in the past, it can provide text connection preferentially the next time as well. Furthermore, if the user has connected during a specific time period in the past, it can suggest the optimal connection method for that time period. In this way, the connection unit can provide the optimal connection method by utilizing the user's past connection history.

[0109] The reception unit can analyze the user's current situation and suggest the optimal reception method. For example, if the user is on the move, it can suggest a reception method using a mobile device. If the user is at home, it can suggest a reception method using a PC. Furthermore, if the user is in an office, it can suggest a reception method that is suitable for the office environment. In this way, the reception unit can provide the optimal reception method according to the user's current situation.

[0110] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, important analysis can be prioritized. If the user is relaxed, detailed analysis can be performed. Furthermore, if the user is in a hurry, analysis that focuses on the main points can be performed. This allows the analysis unit to provide the optimal analysis according to the user's emotions.

[0111] The providing unit can analyze the user's past provision results and select the optimal provision method. For example, if the user has preferred audio provision in the past, audio provision can be given priority the next time as well. Also, if the user has preferred text provision in the past, text provision can be given priority the next time as well. Furthermore, if the user has received provision during a specific time period in the past, the providing unit can suggest the optimal provision method for that time period. In this way, the providing unit can utilize the user's past provision results to provide the optimal provision method.

[0112] The connection unit can estimate the user's emotion and determine the priority of connections based on the estimated emotion. For example, if the user is feeling stressed, an important connection can be prioritized. If the user is relaxed, a normal connection can be made. Furthermore, if the user is in a hurry, a connection can be made in the shortest time. In this way, the connection unit can provide an optimal connection according to the user's emotion.

[0113] The reception unit can analyze the user's social media activity and prioritize the reception of related inquiries. For example, if the user mentions a specific product on social media, it can prioritize the reception of inquiries about that product. Also, if the user mentions a specific service on social media, it can prioritize the reception of inquiries about that service. Furthermore, it can also refer to the activities of the user's friends on social media to receive related inquiries. In this way, the reception unit can utilize social media activity to prioritize the reception of related inquiries.

[0114] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis results can be presented in a simple manner. If the user is relaxed, detailed analysis results can be presented. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be presented. This allows the analysis unit to present the analysis results in the most appropriate manner according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives an input in which the user verbally declares the content of the inquiry. For example, the user can verbally declare the content of the inquiry over the telephone or through a voice recognition system. Step 2: The analysis unit uses the generation AI to analyze the inquiry received by the reception unit and determine the appropriate menu. For example, the generation AI uses natural language processing technology to understand the user's utterance and identify the appropriate menu. The generation AI can perform analysis based on data it has learned in advance. Step 3: The providing unit provides the menu determined by the analyzing unit to the user by voice, for example, by using synthesized voice or recorded voice to inform the user of the menu. Step 4: The connection unit connects the user to the corresponding menu based on the menu provided by the provision unit. For example, if the user requests it, the generation AI automatically connects to the corresponding menu.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0118] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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, RAM 30, and 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.

[0124] 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.

[0125] 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 audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] 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 carried out in a secure state.

[0128] Fig. 4 shows an example of the 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.

[0129] 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.

[0130] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0134] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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, RAM 30, and 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.

[0140] 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.

[0141] 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 audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] 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 carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0148] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0150] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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, RAM 30, and 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.

[0156] 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] 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 audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] 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 carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0161] 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.

[0162] 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.

[0163] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0165] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] 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 voice indicating a user input regarding 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.

[0167] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] 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.

[0178] 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 USB (Universal Serial Bus) 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.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0183] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 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 input from a user who verbally declares the content of an inquiry; an analysis unit that analyzes the inquiry content received by the reception unit and determines a corresponding menu; a providing unit that provides the menu determined by the analyzing unit to the user by voice; a connection unit that connects a user to a corresponding menu based on the menu provided by the provision unit. A system characterized by:

2. The analysis unit Generative AI analyzes the query content based on pre-trained data 2. The system of claim 1.

3. The analysis unit Understanding user statements using natural language processing technology 2. The system of claim 1.

4. The providing unit Providing a menu to the user by voice 2. The system of claim 1.

5. The connection portion is Automatically connect to the appropriate menu based on the user's preference 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of accepting inquiries based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past inquiry history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When receiving inquiries, filter them based on the user's current situation and areas of interest.

2. The system of claim 1.

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