system

Abstract

The system according to the embodiment aims to enable interactive ticket purchase, change, and refund. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a procedure unit. The reception unit accepts natural language input from a user. The analysis unit analyzes the natural language input accepted by the reception unit and provides options. The provision unit presents the options offered by the analysis unit to the user and accepts the user's selection. The procedure unit carries out procedures for purchasing, changing, and refunding tickets based on the selection accepted by the provision unit.

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] With conventional technology, purchasing tickets from automatic ticket vending machines or online was difficult for elderly people and others to use.

[0005] The system according to the embodiment aims to enable interactive ticket purchase, change, and refund. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a procedure unit. The reception unit accepts natural language input from a user. The analysis unit analyzes the natural language input accepted by the reception unit and provides options. The provision unit presents the options offered by the analysis unit to the user and accepts the user's selection. The procedure unit carries out procedures for purchasing, changing, and refunding tickets based on the selection accepted by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for interactive ticket purchase, change and refund. [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 system according to an embodiment of the present invention allows users to purchase, change, and refund train and airline tickets in a conversational manner, just like at a manned ticket counter. This conversational system can be used by elderly people and others who are unfamiliar with purchasing tickets from automated ticket machines or online, allowing them to use the system just as easily as at a manned ticket counter. This system reduces congestion, improves passenger convenience, and allows railway and airline companies to reduce the number of staff at ticket counters. For example, a user enters their ticket purchase, change, or refund request into the system in a conversational manner. For example, a user might enter a request such as, "I would like to purchase a Shinkansen ticket from Tokyo to Osaka." This request is then input into a generation AI. The generation AI then analyzes the input request and provides appropriate ticket information. The generation AI then presents optimal ticket options based on the user's request. For example, it might ask, "What time would you like to take the Shinkansen ticket from Tokyo to Osaka?" and provides options based on the user's request. After the user selects an option, the generation AI then processes the ticket purchase, change, and refund. For example, if a user responds, "I'll choose the 10 o'clock flight," the AI ​​will process the ticket purchase for that flight. This allows users, such as elderly people, to purchase, change, and refund tickets in a conversational manner, just like at a manned ticket counter. This reduces congestion and improves passenger convenience. It also allows railway companies and airlines to reduce counter staffing. For example, if an elderly person inputs into the system, "I'd like to purchase a Shinkansen ticket from Tokyo to Osaka," the AI ​​will ask, "What time would you like the flight?" If the elderly person responds, "I'll choose the 10 o'clock flight," the AI ​​will process the ticket purchase for that flight. In this way, even elderly people can easily purchase tickets. Reducing congestion also improves passenger convenience. For example, by using the system, congestion at ticket counters can be avoided, even during busy periods. This allows passengers to purchase, change, and refund tickets smoothly. Furthermore, railway companies and airlines can reduce counter staffing. For example, introducing the system can reduce counter staffing and reduce costs. This will improve the company's management efficiency.In this way, the present invention is a system that allows users to purchase, change, and refund train and airline tickets in an interactive manner, just like at a manned counter. This system is highly convenient for users such as the elderly, and is expected to improve management efficiency by easing congestion and reducing personnel.

[0029] A ticket purchasing system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a procedure unit. The reception unit accepts natural language input from a user. Examples of natural language input include, but are not limited to, voice input and text input. The reception unit converts the user's voice input into text data using, for example, voice recognition technology. The reception unit can also directly accept text input. For example, the reception unit accepts text entered by a user using a keyboard. The reception unit can also convert handwritten characters into text data using image recognition technology. For example, the reception unit can capture handwritten characters entered by a user with a camera and convert them into text data using image recognition technology. The analysis unit uses a generation AI to analyze the natural language input accepted by the reception unit and provide appropriate options. The analysis can be performed using, for example, morphological analysis, grammatical analysis, semantic analysis, or other methods, but is not limited to these examples. For example, the generation AI can divide the input text into words using morphological analysis and analyze the sentence structure using grammatical analysis. The generation AI can also understand the meaning of the input text using semantic analysis and provide appropriate options. Furthermore, the generation AI can analyze the user's intentions using natural language processing technology and provide optimal options. The provision unit presents the options provided by the analysis unit to the user and accepts the user's selection. The provision unit, for example, displays the options using a display. The provision unit can also present the options aloud using speech synthesis technology. For example, the provision unit reads the options aloud using the generation AI. The provision unit can also use input devices such as a touch panel, mouse, or keyboard to accept the user's selection. For example, the provision unit accepts the selection by the user tapping an option using a touch panel. The procedure unit performs ticket purchase, change, and refund procedures based on the selection accepted by the provision unit. Examples of procedures include, but are not limited to, ticket purchase procedures, ticket change procedures, and ticket refund procedures. For example, the procedure unit sends a confirmation message to the user when performing a ticket purchase procedure. The procedure unit can also confirm the changes with the user when performing a ticket change procedure.Furthermore, the procedure unit can also notify the user of the refund amount when performing ticket refund procedures. This allows the ticket purchasing system according to the embodiment to allow the user to purchase, change, and refund tickets in natural language. Some or all of the above-described processing in the procedure unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure unit can perform procedures using a generation AI model that takes a user's selection as input and outputs procedures for purchasing, changing, and refunding tickets. This allows the user to purchase, change, and refund tickets in natural language.

[0030] The procedure unit can send a confirmation message to the user when purchasing, changing, or refunding a ticket. Examples of confirmation messages include, but are not limited to, text messages and voice messages. The procedure unit can send a confirmation message to the user when purchasing a ticket. For example, the procedure unit can send a message to the user asking, "Do you want to confirm your ticket purchase?" The procedure unit can also confirm the changes to the ticket when changing the ticket. For example, the procedure unit can send a message to the user saying, "Please confirm the changes." Furthermore, the procedure unit can notify the user of the refund amount when refunding a ticket. For example, the procedure unit can send a message to the user saying, "The refund amount is XXX yen." By sending a confirmation message to the user, the accuracy of the procedure is improved. Some or all of the above-described processing in the procedure unit can be performed using, for example, a generation AI. For example, the procedure unit can send a confirmation message using a generation AI model that takes a user's selection as input and outputs a confirmation message.

[0031] The reception unit can analyze the user's past input history and select the optimal input reception method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also suggest similar input methods by referring to content entered by the user in the past. In this way, by analyzing the past input history, the optimal input reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input reception method.

[0032] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving natural language input. For example, if the user is traveling, the reception unit can prioritize receiving travel-related information. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving business-related information. Furthermore, if the user is in a tourist spot, the reception unit can prioritize receiving tourism-related information. This makes it possible to provide highly relevant information by filtering based on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's current situation data into the generation AI and have the generation AI perform filtering.

[0033] When accepting natural language input, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit accepts the input using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the input using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the input using image recognition technology. This improves the accuracy of the input by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to the generation AI and have the generation AI select the optimal acceptance means.

[0034] When accepting natural language input, the reception unit can prioritize accepting highly relevant input by taking into account the user's geographical location information. For example, if the user is at a specific station, the reception unit can prioritize accepting information related to the station. Furthermore, if the user is at an airport, the reception unit can prioritize accepting information related to the airport. Furthermore, if the user is at a tourist destination, the reception unit can prioritize accepting information related to the tourist destination. In this way, by taking the user's geographical location information into account, highly relevant information can be provided preferentially. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize accepting highly relevant input.

[0035] When receiving natural language input, the reception unit can analyze the user's social media activity and receive related input. For example, the reception unit can prioritize receiving information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related information. The reception unit can also refer to the activities of the user's friends on social media and prioritize receiving related information. In this way, by analyzing social media activity, it is possible to provide information related to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to receive related input.

[0036] The reception unit can customize the reception method by reflecting the user's past feedback when receiving natural language input. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the optimal reception method. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input. For example, the analysis unit may have the generation AI perform a detailed analysis of important input. The analysis unit may also have the generation AI perform a standard analysis of general input. The analysis unit may also have the generation AI perform a simplified analysis of less important input. In this way, by adjusting the level of detail of the analysis based on the importance of the input, detailed analysis results can be provided for important input. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input input data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input. For example, in the analysis unit, for input related to ticket purchases, the generation AI applies an analysis algorithm specialized for purchases. In addition, in the analysis unit, for input related to ticket changes, the generation AI can apply an analysis algorithm specialized for changes. In addition, in the analysis unit, for input related to ticket refunds, the generation AI can apply an analysis algorithm specialized for refunds. In this way, by applying different analysis algorithms depending on the input category, it is possible to provide optimal analysis results for each category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input input data to the generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on analysis results previously provided by the user. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and apply an optimal analysis algorithm. By referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of input submission. For example, the analysis unit may assign the highest priority to the generation AI for analysis of urgent input. The analysis unit may also assign the generation AI a standard priority for analysis of general input. The analysis unit may also assign the generation AI a later priority for analysis of late-submitted input. In this way, by determining the priority of analysis based on the time of input submission, urgent input can be dealt with promptly. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input input data to the generation AI and have the generation AI determine the priority of analysis based on the time of submission.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input. For example, the analysis unit allows the generation AI to perform analysis with the highest priority for inputs with important relevance. The analysis unit can also allow the generation AI to analyze inputs with general relevance in a standard order. The analysis unit can also allow the generation AI to analyze inputs with low relevance later. In this way, by adjusting the order of analysis based on the relevance of the input, it is possible to respond quickly to inputs with important relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input input data to the generation AI and have the generation AI adjust the order of analysis based on the relevance.

[0042] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can also cause the generation AI to provide analysis results that use standard terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also cause the generation AI to provide analysis results that use simple terminology. By adjusting the use of technical terminology according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0043] The providing unit can adjust the level of detail to be provided based on the importance of the option at the time of providing. For example, the providing unit causes the generation AI to provide detailed information for important options. The providing unit can also cause the generation AI to provide standard information for common options. The providing unit can also cause the generation AI to provide simplified information for options with low importance. In this way, by adjusting the level of detail to be provided based on the importance of the option, detailed information can be provided for important options. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input option data to the generation AI and cause the generation AI to adjust the level of detail to be provided based on the importance.

[0044] When providing the information, the providing unit can apply different providing algorithms depending on the category of the option. For example, in the providing unit, the generation AI applies a providing algorithm specialized for purchases to options related to ticket purchases. In addition, in the providing unit, the generation AI can apply a providing algorithm specialized for changes to options related to ticket changes. In addition, in the providing unit, the generation AI can apply a providing algorithm specialized for refunds to options related to ticket refunds. In this way, by applying different providing algorithms depending on the category of the option, it is possible to provide optimal information for each category. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input option data to the generation AI and cause the generation AI to apply a providing algorithm according to the category.

[0045] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. For example, the providing unit causes the generation AI to improve the accuracy of the provision based on the results provided by the user in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the accuracy of the provision. The providing unit can also analyze the user's past provision results and apply an optimal provision algorithm. This improves the accuracy of the provision by referring to the past provision results. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0046] The providing unit can determine the priority of provision based on the time of submission of options when providing them. For example, the providing unit allows the generation AI to provide urgent options with the highest priority. The providing unit can also allow the generation AI to provide general options with standard priority. The providing unit can also allow the generation AI to provide options that are submitted later later. In this way, by determining the priority of provision based on the time of submission of options, urgent options can be dealt with quickly. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input option data into the generation AI and cause the generation AI to determine the priority of provision based on the time of submission.

[0047] The providing unit can adjust the order of providing options based on the relevance of the options when providing them. For example, the providing unit allows the generation AI to provide options with high relevance as the highest priority. The providing unit can also allow the generation AI to provide options with general relevance in a standard order. The providing unit can also allow the generation AI to provide options with low relevance later. In this way, by adjusting the order of providing options based on the relevance of the options, options with high relevance can be dealt with quickly. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input option data to the generation AI and cause the generation AI to adjust the order of providing options based on the relevance.

[0048] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can cause the generation AI to provide information that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the providing unit can also cause the generation AI to provide information that uses standard terminology. Furthermore, if the user does not have technical expertise, the providing unit can also cause the generation AI to provide information that uses simple terminology. This allows the use of technical terminology to be adjusted according to the user's level of expertise, thereby providing information that is easy for the user to understand. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0049] At the time of a procedure, the procedure unit can analyze the user's past procedure history and select the optimal procedure method. For example, the procedure unit can prioritize procedural methods that the user has used in the past. The procedure unit can also predict and suggest a procedural method to be used at a specific time period based on the user's past procedure history. The procedure unit can also suggest a similar procedural method by referring to the procedures the user has performed in the past. In this way, by analyzing the past procedural history, the optimal procedural method can be provided to the user. Some or all of the above-mentioned processing in the procedure unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure unit can input the user's past procedural history data into the generation AI and have the generation AI select the optimal procedural method.

[0050] The procedure unit can customize the procedure means based on the user's current situation during the procedure. For example, if the user is traveling, the procedure unit can prioritize proposing travel-related procedures. Also, if the user is on a business trip, the procedure unit can prioritize proposing business-related procedures. Also, if the user is in a tourist destination, the procedure unit can prioritize proposing tourism-related procedures. This allows the procedure means to be customized based on the user's current situation, thereby providing the optimal procedure for the user. Some or all of the above-described processing in the procedure unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the procedure unit can input the user's current situation data into the generation AI and have the generation AI customize the procedure means.

[0051] The procedure unit can improve the procedure method by reflecting user feedback during the procedure. For example, the procedure unit allows the generation AI to improve the procedure method based on feedback provided by the user in the past. The procedure unit can also preferentially suggest a specific procedure method based on the user's past feedback. The procedure unit can also analyze the user's past feedback and customize the optimal procedure method. In this way, the procedure method can be improved by reflecting the feedback, and a better procedure method can be provided for the user. Some or all of the above-mentioned processing in the procedure unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the procedure unit can input user feedback data into the generation AI and have the generation AI execute improvements to the procedure method.

[0052] The procedure unit can select the optimal procedure during the procedure, taking into account the user's geographical location information. For example, if the user is at a specific station, the procedure unit can prioritize proposing procedures related to that station. Furthermore, if the user is at an airport, the procedure unit can prioritize proposing procedures related to the airport. Furthermore, if the user is at a tourist destination, the procedure unit can prioritize proposing procedures related to the tourist destination. This allows for providing procedures that are highly relevant to the user by taking the geographical location information into consideration. Some or all of the above-described processing in the procedure unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the procedure unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal procedure.

[0053] At the time of processing, the procedure unit can analyze the user's social media activity and suggest a procedure. For example, the procedure unit can suggest a procedure related to a location where the user has checked in on social media. The procedure unit can also analyze the content of the user's social media posts and suggest a relevant procedure. The procedure unit can also suggest a relevant procedure based on the activity of the user's friends on social media. In this way, by analyzing social media activity, a relevant procedure can be provided to the user. Some or all of the above-described processing in the procedure unit may be performed using, or without, a generation AI. For example, the procedure unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of a procedure.

[0054] The procedure unit can customize the procedure method by reflecting the user's past feedback during the procedure. For example, the procedure unit customizes the procedure method using a generation AI based on feedback provided by the user in the past. The procedure unit can also preferentially suggest a specific procedure method based on the user's past feedback. The procedure unit can also analyze the user's past feedback and customize the optimal procedure method. This makes it possible to provide the user with the optimal procedure method by reflecting past feedback. Some or all of the above-mentioned processing in the procedure unit may be performed using, or without, the generation AI. For example, the procedure unit can input the user's past feedback data into the generation AI and have the generation AI customize the procedure method.

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

[0056] The reception unit can analyze the user's preferences and past selection history based on the user's input and suggest optimal options. For example, if the user has frequently selected flights during a specific time period in the past, the reception unit can prioritize suggestions of flights during that time period. Also, if the user prefers a specific seat type, the reception unit can prioritize suggestions of options including that seat type. Furthermore, the reception unit can suggest optimal routes taking into account the routes and transfer patterns the user has used in the past. This enables more personalized suggestions by utilizing the user's past selection history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's selection history data into the generation AI and have the generation AI suggest optimal options.

[0057] The procedure unit can display the progress of the procedure in real time based on the user's input. For example, if a ticket purchase procedure is in progress, the unit notifies the user of the current progress. The unit can also update and display the progress to the user each time each step of the procedure is completed. Furthermore, when the procedure is completed, a completion notification can be sent to the user. This allows the user to understand the progress of the procedure in real time and proceed with the procedure with peace of mind. Some or all of the above-mentioned processing in the procedure unit may be performed using or without the generation AI. For example, the procedure unit can input procedure progress data to the generation AI and have the generation AI display the progress in real time.

[0058] The reception unit can analyze the user's past input history and select the optimal input reception method. For example, it can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also suggest similar input methods by referring to content the user has previously input. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input reception method. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input reception method.

[0059] When receiving natural language input, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, if the user is traveling, the reception unit can prioritize receiving travel-related information. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving business-related information. Furthermore, if the user is in a tourist spot, the reception unit can prioritize receiving tourism-related information. In this way, by filtering based on the user's situation and areas of interest, highly relevant information can be provided. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's current situation data into the generation AI and have the generation AI perform filtering.

[0060] When accepting natural language input, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit accepts the input using voice recognition technology. If the user uses text input, the acceptance unit can also accept the input using text analysis technology. If the user uses image input, the acceptance unit can also accept the input using image recognition technology. This improves the accuracy of the input by selecting the optimal acceptance means according to the user's input method. Some or all of the above-mentioned processing in the acceptance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to the generation AI and have the generation AI select the optimal acceptance means.

[0061] When accepting natural language input, the reception unit can prioritize accepting highly relevant input by taking into account the user's geographical location information. For example, if the user is at a specific station, the reception unit can prioritize accepting information related to that station. Furthermore, if the user is at an airport, the reception unit can prioritize accepting information related to the airport. Furthermore, if the user is at a tourist destination, the reception unit can prioritize accepting information related to the tourist destination. In this way, by taking the user's geographical location information into account, highly relevant information can be provided preferentially. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize accepting highly relevant input.

[0062] When receiving natural language input, the reception unit can analyze the user's social media activity and receive related input. For example, the reception unit can prioritize receiving information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related information. The reception unit can also refer to the activities of the user's friends on social media and prioritize receiving related information. In this way, by analyzing social media activity, it is possible to provide information related to the user. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI receive related input.

[0063] When receiving natural language input, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the optimal reception method. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

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

[0065] Step 1: The reception unit receives natural language input from a user. Natural language input includes voice input, text input, and handwritten input. For example, voice input is converted into text data using voice recognition technology, text input is directly accepted, and handwritten characters are converted into text data using image recognition technology. Step 2: The analysis unit analyzes the natural language input received by the reception unit and provides appropriate options. Analysis is performed using methods such as morphological analysis, grammatical analysis, and semantic analysis, and a generation AI is used to analyze the user's intention and provide the optimal option. Step 3: The providing unit presents the options provided by the analysis unit to the user and accepts the user's selection. The providing unit presents the options using a display or voice synthesis technology, and accepts the user's selection using an input device such as a touch panel, mouse, or keyboard. Step 4: The transaction section processes the ticket purchase, change, or refund based on the selections received by the provision section. The transaction section can also use a generation AI to process the ticket, sending a confirmation message to the user, confirming the changes, and notifying the refund amount.

[0066] (Example 2) A system according to an embodiment of the present invention allows users to purchase, change, and refund train and airline tickets in a conversational manner, just like at a manned ticket counter. This conversational system can be used by elderly people and others who are unfamiliar with purchasing tickets from automated ticket machines or online, allowing them to use the system just as easily as at a manned ticket counter. This system reduces congestion, improves passenger convenience, and allows railway and airline companies to reduce the number of staff at ticket counters. For example, a user enters their ticket purchase, change, or refund request into the system in a conversational manner. For example, a user might enter a request such as, "I would like to purchase a Shinkansen ticket from Tokyo to Osaka." This request is then input into a generation AI. The generation AI then analyzes the input request and provides appropriate ticket information. The generation AI then presents optimal ticket options based on the user's request. For example, it might ask, "What time would you like to take the Shinkansen ticket from Tokyo to Osaka?" and provides options based on the user's request. After the user selects an option, the generation AI then processes the ticket purchase, change, and refund. For example, if a user responds, "I'll choose the 10 o'clock flight," the AI ​​will process the ticket purchase for that flight. This allows users, such as elderly people, to purchase, change, and refund tickets in a conversational manner, just like at a manned ticket counter. This reduces congestion and improves passenger convenience. It also allows railway companies and airlines to reduce counter staffing. For example, if an elderly person inputs into the system, "I'd like to purchase a Shinkansen ticket from Tokyo to Osaka," the AI ​​will ask, "What time would you like the flight?" If the elderly person responds, "I'll choose the 10 o'clock flight," the AI ​​will process the ticket purchase for that flight. In this way, even elderly people can easily purchase tickets. Reducing congestion also improves passenger convenience. For example, by using the system, congestion at ticket counters can be avoided, even during busy periods. This allows passengers to purchase, change, and refund tickets smoothly. Furthermore, railway companies and airlines can reduce counter staffing. For example, introducing the system can reduce counter staffing and reduce costs. This will improve the company's management efficiency.In this way, the present invention is a system that allows users to purchase, change, and refund train and airline tickets in an interactive manner, just like at a manned counter. This system is highly convenient for users such as the elderly, and is expected to improve management efficiency by easing congestion and reducing personnel.

[0067] A ticket purchasing system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a procedure unit. The reception unit accepts natural language input from a user. Examples of natural language input include, but are not limited to, voice input and text input. The reception unit converts the user's voice input into text data using, for example, voice recognition technology. The reception unit can also directly accept text input. For example, the reception unit accepts text entered by a user using a keyboard. The reception unit can also convert handwritten characters into text data using image recognition technology. For example, the reception unit can capture handwritten characters entered by a user with a camera and convert them into text data using image recognition technology. The analysis unit uses a generation AI to analyze the natural language input accepted by the reception unit and provide appropriate options. The analysis can be performed using, for example, morphological analysis, grammatical analysis, semantic analysis, or other methods, but is not limited to these examples. For example, the generation AI can divide the input text into words using morphological analysis and analyze the sentence structure using grammatical analysis. The generation AI can also understand the meaning of the input text using semantic analysis and provide appropriate options. Furthermore, the generation AI can analyze the user's intentions using natural language processing technology and provide optimal options. The provision unit presents the options provided by the analysis unit to the user and accepts the user's selection. The provision unit, for example, displays the options using a display. The provision unit can also present the options aloud using speech synthesis technology. For example, the provision unit reads the options aloud using the generation AI. The provision unit can also use input devices such as a touch panel, mouse, or keyboard to accept the user's selection. For example, the provision unit accepts the selection by the user tapping an option using a touch panel. The procedure unit performs ticket purchase, change, and refund procedures based on the selection accepted by the provision unit. Examples of procedures include, but are not limited to, ticket purchase procedures, ticket change procedures, and ticket refund procedures. For example, the procedure unit sends a confirmation message to the user when performing a ticket purchase procedure. The procedure unit can also confirm the changes with the user when performing a ticket change procedure.Furthermore, the procedure unit can also notify the user of the refund amount when performing ticket refund procedures. This allows the ticket purchasing system according to the embodiment to allow the user to purchase, change, and refund tickets in natural language. Some or all of the above-described processing in the procedure unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure unit can perform procedures using a generation AI model that takes a user's selection as input and outputs procedures for purchasing, changing, and refunding tickets. This allows the user to purchase, change, and refund tickets in natural language.

[0068] The procedure unit can send a confirmation message to the user when purchasing, changing, or refunding a ticket. Examples of confirmation messages include, but are not limited to, text messages and voice messages. The procedure unit can send a confirmation message to the user when purchasing a ticket. For example, the procedure unit can send a message to the user asking, "Do you want to confirm your ticket purchase?" The procedure unit can also confirm the changes to the ticket when changing the ticket. For example, the procedure unit can send a message to the user saying, "Please confirm the changes." Furthermore, the procedure unit can notify the user of the refund amount when refunding a ticket. For example, the procedure unit can send a message to the user saying, "The refund amount is XXX yen." By sending a confirmation message to the user, the accuracy of the procedure is improved. Some or all of the above-described processing in the procedure unit can be performed using, for example, a generation AI. For example, the procedure unit can send a confirmation message using a generation AI model that takes a user's selection as input and outputs a confirmation message.

[0069] The reception unit can estimate the user's emotions and adjust the timing of accepting natural language input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can cause the generation AI to delay the timing of input acceptance and wait until the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can also cause the generation AI to accelerate the timing of input acceptance to respond quickly. Furthermore, if the user is feeling anxious, the reception unit can also cause the generation AI to adjust the timing of input acceptance to provide a sense of security. This adjusts the timing of input acceptance according to the user's emotions, thereby reducing the user's stress. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0070] The reception unit can analyze the user's past input history and select the optimal input reception method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also suggest similar input methods by referring to content entered by the user in the past. In this way, by analyzing the past input history, the optimal input reception method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input reception method.

[0071] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving natural language input. For example, if the user is traveling, the reception unit can prioritize receiving travel-related information. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving business-related information. Furthermore, if the user is in a tourist spot, the reception unit can prioritize receiving tourism-related information. This makes it possible to provide highly relevant information by filtering based on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's current situation data into the generation AI and have the generation AI perform filtering.

[0072] When accepting natural language input, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit accepts the input using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the input using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the input using image recognition technology. This improves the accuracy of the input by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to the generation AI and have the generation AI select the optimal acceptance means.

[0073] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user emotions. For example, when the user is in an emergency, the reception unit allows the generation AI to receive that input with the highest priority. Furthermore, when the user is relaxed, the reception unit can also allow the generation AI to receive that input with the same priority as other inputs. Furthermore, when the user is feeling anxious, the reception unit can also allow the generation AI to receive that input with the same priority. This allows for a prompt response to urgent inputs by determining the priority of inputs according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of inputs.

[0074] When accepting natural language input, the reception unit can prioritize accepting highly relevant input by taking into account the user's geographical location information. For example, if the user is at a specific station, the reception unit can prioritize accepting information related to the station. Furthermore, if the user is at an airport, the reception unit can prioritize accepting information related to the airport. Furthermore, if the user is at a tourist destination, the reception unit can prioritize accepting information related to the tourist destination. In this way, by taking the user's geographical location information into account, highly relevant information can be provided preferentially. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize accepting highly relevant input.

[0075] When receiving natural language input, the reception unit can analyze the user's social media activity and receive related input. For example, the reception unit can prioritize receiving information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related information. The reception unit can also refer to the activities of the user's friends on social media and prioritize receiving related information. In this way, by analyzing social media activity, it is possible to provide information related to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to receive related input.

[0076] The reception unit can customize the reception method by reflecting the user's past feedback when receiving natural language input. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the optimal reception method. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is feeling anxious, the generation AI can also provide analysis results that give the user a sense of security. By adjusting the way the analysis is presented based on the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input. For example, the analysis unit may have the generation AI perform a detailed analysis of important input. The analysis unit may also have the generation AI perform a standard analysis of general input. The analysis unit may also have the generation AI perform a simplified analysis of less important input. In this way, by adjusting the level of detail of the analysis based on the importance of the input, detailed analysis results can be provided for important input. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input input data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input. For example, in the analysis unit, for input related to ticket purchases, the generation AI applies an analysis algorithm specialized for purchases. In addition, in the analysis unit, for input related to ticket changes, the generation AI can apply an analysis algorithm specialized for changes. In addition, in the analysis unit, for input related to ticket refunds, the generation AI can apply an analysis algorithm specialized for refunds. In this way, by applying different analysis algorithms depending on the input category, it is possible to provide optimal analysis results for each category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input input data to the generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit allows the generation AI to improve the accuracy of the analysis based on analysis results previously provided by the user. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and apply an optimal analysis algorithm. By referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can cause the generation AI to provide a short analysis result. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can cause the generation AI to provide an analysis result that gives a sense of security. By adjusting the length of the analysis according to the user's emotions, an analysis result of an appropriate length for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the time of input submission. For example, the analysis unit may assign the highest priority to the generation AI for analysis of urgent input. The analysis unit may also assign the generation AI a standard priority for analysis of general input. The analysis unit may also assign the generation AI a later priority for analysis of late-submitted input. In this way, by determining the priority of analysis based on the time of input submission, urgent input can be dealt with promptly. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input input data to the generation AI and have the generation AI determine the priority of analysis based on the time of submission.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input. For example, the analysis unit allows the generation AI to perform analysis with the highest priority for inputs with important relevance. The analysis unit can also allow the generation AI to analyze inputs with general relevance in a standard order. The analysis unit can also allow the generation AI to analyze inputs with low relevance later. In this way, by adjusting the order of analysis based on the relevance of the input, it is possible to respond quickly to inputs with important relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input input data to the generation AI and have the generation AI adjust the order of analysis based on the relevance.

[0084] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can cause the generation AI to provide analysis results that use a lot of technical terminology. Furthermore, if the user has general knowledge, the analysis unit can also cause the generation AI to provide analysis results that use standard terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also cause the generation AI to provide analysis results that use simple terminology. By adjusting the use of technical terminology according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0085] The providing unit can estimate the user's emotions and adjust the presentation style based on the estimated user emotions. For example, if the user is relaxed, the generating AI can provide detailed information. If the user is in a hurry, the providing unit can also provide concise information. If the user is feeling anxious, the generating AI can also provide information that gives the user a sense of security. This allows the presentation style to be adjusted according to the user's emotions, making it possible to provide information that is easy for the user to understand. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generating AI, or can be performed without using the generating AI. For example, the providing unit can input the user's emotion data into the generating AI and cause the generating AI to adjust the presentation style.

[0086] The providing unit can adjust the level of detail to be provided based on the importance of the option at the time of providing. For example, the providing unit causes the generation AI to provide detailed information for important options. The providing unit can also cause the generation AI to provide standard information for common options. The providing unit can also cause the generation AI to provide simplified information for options with low importance. In this way, by adjusting the level of detail to be provided based on the importance of the option, detailed information can be provided for important options. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input option data to the generation AI and cause the generation AI to adjust the level of detail to be provided based on the importance.

[0087] When providing the information, the providing unit can apply different providing algorithms depending on the category of the option. For example, in the providing unit, the generation AI applies a providing algorithm specialized for purchases to options related to ticket purchases. In addition, in the providing unit, the generation AI can apply a providing algorithm specialized for changes to options related to ticket changes. In addition, in the providing unit, the generation AI can apply a providing algorithm specialized for refunds to options related to ticket refunds. In this way, by applying different providing algorithms depending on the category of the option, it is possible to provide optimal information for each category. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input option data to the generation AI and cause the generation AI to apply a providing algorithm according to the category.

[0088] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. For example, the providing unit causes the generation AI to improve the accuracy of the provision based on the results provided by the user in the past. The providing unit can also extract specific patterns from the user's past provision results to improve the accuracy of the provision. The providing unit can also analyze the user's past provision results and apply an optimal provision algorithm. This improves the accuracy of the provision by referring to the past provision results. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0089] The providing unit can estimate the user's emotions and adjust the length of the information provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can have the generation AI provide short, concise information. Furthermore, if the user is relaxed, the providing unit can have the generation AI provide detailed information. Furthermore, if the user is feeling anxious, the providing unit can have the generation AI provide information that gives a sense of security. By adjusting the length of the information provided according to the user's emotions, it is possible to provide information of an appropriate length for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information provided.

[0090] The providing unit can determine the priority of provision based on the time of submission of options when providing them. For example, the providing unit allows the generation AI to provide urgent options with the highest priority. The providing unit can also allow the generation AI to provide general options with standard priority. The providing unit can also allow the generation AI to provide options that are submitted later later. In this way, by determining the priority of provision based on the time of submission of options, urgent options can be dealt with quickly. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input option data into the generation AI and cause the generation AI to determine the priority of provision based on the time of submission.

[0091] The providing unit can adjust the order of providing options based on the relevance of the options when providing them. For example, the providing unit allows the generation AI to provide options with high relevance as the highest priority. The providing unit can also allow the generation AI to provide options with general relevance in a standard order. The providing unit can also allow the generation AI to provide options with low relevance later. In this way, by adjusting the order of providing options based on the relevance of the options, options with high relevance can be dealt with quickly. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input option data to the generation AI and cause the generation AI to adjust the order of providing options based on the relevance.

[0092] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can cause the generation AI to provide information that uses a lot of technical terminology. Furthermore, if the user has general knowledge, the providing unit can also cause the generation AI to provide information that uses standard terminology. Furthermore, if the user does not have technical expertise, the providing unit can also cause the generation AI to provide information that uses simple terminology. This allows the use of technical terminology to be adjusted according to the user's level of expertise, thereby providing information that is easy for the user to understand. Some or all of the above-described processing in the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0093] The procedure unit can estimate the user's emotions and adjust the procedure method based on the estimated user emotions. For example, if the user is relaxed, the generation AI can provide detailed procedure methods. Also, if the user is in a hurry, the procedure unit can provide concise procedure methods. Also, if the user is feeling anxious, the generation AI can provide procedure methods that give the user a sense of security. By adjusting the procedure method according to the user's emotions, it is possible to provide an appropriate procedure method for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the procedure unit can be performed using, for example, the generation AI, or without the generation AI. For example, the procedure unit can input the user's emotion data into the generation AI and have the generation AI adjust the procedure method.

[0094] At the time of a procedure, the procedure unit can analyze the user's past procedure history and select the optimal procedure method. For example, the procedure unit can prioritize procedural methods that the user has used in the past. The procedure unit can also predict and suggest a procedural method to be used at a specific time period based on the user's past procedure history. The procedure unit can also suggest a similar procedural method by referring to the procedures the user has performed in the past. In this way, by analyzing the past procedural history, the optimal procedural method can be provided to the user. Some or all of the above-mentioned processing in the procedure unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the procedure unit can input the user's past procedural history data into the generation AI and have the generation AI select the optimal procedural method.

[0095] The procedure unit can customize the procedure means based on the user's current situation during the procedure. For example, if the user is traveling, the procedure unit can prioritize proposing travel-related procedures. Also, if the user is on a business trip, the procedure unit can prioritize proposing business-related procedures. Also, if the user is in a tourist destination, the procedure unit can prioritize proposing tourism-related procedures. This allows the procedure means to be customized based on the user's current situation, thereby providing the optimal procedure for the user. Some or all of the above-described processing in the procedure unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the procedure unit can input the user's current situation data into the generation AI and have the generation AI customize the procedure means.

[0096] The procedure unit can improve the procedure method by reflecting user feedback during the procedure. For example, the procedure unit allows the generation AI to improve the procedure method based on feedback provided by the user in the past. The procedure unit can also preferentially suggest a specific procedure method based on the user's past feedback. The procedure unit can also analyze the user's past feedback and customize the optimal procedure method. In this way, the procedure method can be improved by reflecting the feedback, and a better procedure method can be provided for the user. Some or all of the above-mentioned processing in the procedure unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the procedure unit can input user feedback data into the generation AI and have the generation AI execute improvements to the procedure method.

[0097] The procedure unit can estimate the user's emotions and determine the priority of procedures based on the estimated user emotions. For example, if the user is in an emergency, the procedure unit causes the generation AI to perform that procedure as the highest priority. Furthermore, if the user is relaxed, the procedure unit can also cause the generation AI to perform that procedure with the same priority as other procedures. Furthermore, if the user is feeling anxious, the procedure unit can also cause the generation AI to perform that procedure with the same priority. This allows for rapid response to emergency procedures by determining the priority of procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the procedure unit may be performed using, for example, the generation AI, or without the generation AI. For example, the procedure unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of procedures.

[0098] The procedure unit can select the optimal procedure during the procedure, taking into account the user's geographical location information. For example, if the user is at a specific station, the procedure unit can prioritize proposing procedures related to that station. Furthermore, if the user is at an airport, the procedure unit can prioritize proposing procedures related to the airport. Furthermore, if the user is at a tourist destination, the procedure unit can prioritize proposing procedures related to the tourist destination. This allows for providing procedures that are highly relevant to the user by taking the geographical location information into consideration. Some or all of the above-described processing in the procedure unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the procedure unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal procedure.

[0099] At the time of processing, the procedure unit can analyze the user's social media activity and suggest a procedure. For example, the procedure unit can suggest a procedure related to a location where the user has checked in on social media. The procedure unit can also analyze the content of the user's social media posts and suggest a relevant procedure. The procedure unit can also suggest a relevant procedure based on the activity of the user's friends on social media. In this way, by analyzing social media activity, a relevant procedure can be provided to the user. Some or all of the above-described processing in the procedure unit may be performed using, or without, a generation AI. For example, the procedure unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of a procedure.

[0100] The procedure unit can customize the procedure method by reflecting the user's past feedback during the procedure. For example, the procedure unit customizes the procedure method using a generation AI based on feedback provided by the user in the past. The procedure unit can also preferentially suggest a specific procedure method based on the user's past feedback. The procedure unit can also analyze the user's past feedback and customize the optimal procedure method. This makes it possible to provide the user with the optimal procedure method by reflecting past feedback. Some or all of the above-mentioned processing in the procedure unit may be performed using, or without, the generation AI. For example, the procedure unit can input the user's past feedback data into the generation AI and have the generation AI customize the procedure method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and procedure 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 accepts user voice or text input 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 input natural language using a generation AI. The provision unit presents options using the display 40A or speaker 40B of the smart device 14 and accepts the user's selection. The procedure unit is realized by the specific processing unit 290 of the data processing device 12 and handles ticket purchase, change, and refund procedures. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and procedure 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 a user's voice input 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 input natural language using a generative AI. The provision unit presents options by voice using the speaker 240 of the smart glasses 214 and accepts the user's selection. The procedure unit is realized by the specific processing unit 290 of the data processing device 12 and performs procedures for purchasing, changing, and refunding tickets. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and procedure 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 voice input from the user 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 input natural language using a generation AI. The provision unit presents options by voice using the speaker 240 of the headset-type terminal 314 and accepts the user's selection. The procedure unit is realized by the specific processing unit 290 of the data processing device 12, and performs procedures for purchasing, changing, and refunding tickets. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and procedure 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 voice input from the user 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 input natural language using a generative AI. The provision unit presents options by voice using the speaker 240 of the robot 414 and accepts the user's selection. The procedure unit is realized by the specific processing unit 290 of the data processing device 12 and performs procedures for purchasing, changing, and refunding tickets.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's preferences and past selection history based on the user's input and suggest optimal options. For example, if the user has frequently selected flights during a specific time period in the past, the reception unit can prioritize suggestions of flights during that time period. Also, if the user prefers a specific seat type, the reception unit can prioritize suggestions of options including that seat type. Furthermore, the reception unit can suggest optimal routes taking into account the routes and transfer patterns the user has used in the past. This enables more personalized suggestions by utilizing the user's past selection history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's selection history data into the generation AI and have the generation AI suggest optimal options.

[0103] The procedure unit can display the progress of the procedure in real time based on the user's input. For example, if a ticket purchase procedure is in progress, the unit notifies the user of the current progress. The unit can also update and display the progress to the user each time each step of the procedure is completed. Furthermore, when the procedure is completed, a completion notification can be sent to the user. This allows the user to understand the progress of the procedure in real time and proceed with the procedure with peace of mind. Some or all of the above-mentioned processing in the procedure unit may be performed using or without the generation AI. For example, the procedure unit can input procedure progress data to the generation AI and have the generation AI display the progress in real time.

[0104] The reception unit can estimate the user's emotions and confirm the input content based on the estimated user emotions. For example, if the user is feeling anxious, the generation AI can carefully confirm the input content to reassure the user. If the user is in a hurry, the generation AI can simplify the confirmation process and respond quickly. Furthermore, if the user is relaxed, the generation AI can perform the standard confirmation process. This reduces user stress and ensures a smooth process by adjusting the confirmation method for the input content according to the user's emotions. The emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI confirm the input content.

[0105] The reception unit can analyze the user's past input history and select the optimal input reception method. For example, it can prioritize and suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also suggest similar input methods by referring to content the user has previously input. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input reception method. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input reception method.

[0106] When receiving natural language input, the reception unit can perform filtering based on the user's current situation and areas of interest. For example, if the user is traveling, the reception unit can prioritize receiving travel-related information. Furthermore, if the user is on a business trip, the reception unit can prioritize receiving business-related information. Furthermore, if the user is in a tourist spot, the reception unit can prioritize receiving tourism-related information. In this way, by filtering based on the user's situation and areas of interest, highly relevant information can be provided. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's current situation data into the generation AI and have the generation AI perform filtering.

[0107] When accepting natural language input, the acceptance unit can select an acceptance means according to the user's input method. For example, if the user uses voice input, the acceptance unit accepts the input using voice recognition technology. If the user uses text input, the acceptance unit can also accept the input using text analysis technology. If the user uses image input, the acceptance unit can also accept the input using image recognition technology. This improves the accuracy of the input by selecting the optimal acceptance means according to the user's input method. Some or all of the above-mentioned processing in the acceptance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data to the generation AI and have the generation AI select the optimal acceptance means.

[0108] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated user emotions. For example, if the user is in an emergency, the generation AI receives that input with the highest priority. Furthermore, if the user is relaxed, the reception unit can also cause the generation AI to receive that input with the same priority as other inputs. Furthermore, if the user is feeling anxious, the reception unit can also cause the generation AI to receive that input with the same priority. This allows for a prompt response to urgent inputs by determining the priority of inputs according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the inputs.

[0109] When accepting natural language input, the reception unit can prioritize accepting highly relevant input by taking into account the user's geographical location information. For example, if the user is at a specific station, the reception unit can prioritize accepting information related to that station. Furthermore, if the user is at an airport, the reception unit can prioritize accepting information related to the airport. Furthermore, if the user is at a tourist destination, the reception unit can prioritize accepting information related to the tourist destination. In this way, by taking the user's geographical location information into account, highly relevant information can be provided preferentially. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize accepting highly relevant input.

[0110] When receiving natural language input, the reception unit can analyze the user's social media activity and receive related input. For example, the reception unit can prioritize receiving information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related information. The reception unit can also refer to the activities of the user's friends on social media and prioritize receiving related information. In this way, by analyzing social media activity, it is possible to provide information related to the user. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI receive related input.

[0111] When receiving natural language input, the reception unit can customize the reception method by reflecting the user's past feedback. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the optimal reception method. In this way, the optimal reception method can be provided to the user by reflecting the past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives natural language input from a user. Natural language input includes voice input, text input, and handwritten input. For example, voice input is converted into text data using voice recognition technology, text input is directly accepted, and handwritten characters are converted into text data using image recognition technology. Step 2: The analysis unit analyzes the natural language input received by the reception unit and provides appropriate options. Analysis is performed using methods such as morphological analysis, grammatical analysis, and semantic analysis, and a generation AI is used to analyze the user's intention and provide the optimal option. Step 3: The providing unit presents the options provided by the analysis unit to the user and accepts the user's selection. The providing unit presents the options using a display or voice synthesis technology, and accepts the user's selection using an input device such as a touch panel, mouse, or keyboard. Step 4: The transaction section processes the ticket purchase, change, or refund based on the selections received by the provision section. The transaction section can also use a generation AI to process the ticket, sending a confirmation message to the user, confirming the changes, and notifying the refund amount.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 in natural language from a user; an analysis unit that analyzes the natural language input received by the reception unit and provides options; a providing unit that presents the options provided by the analysis unit to a user and accepts a selection from the user; and a procedure unit that carries out procedures for purchasing, changing, and refunding tickets based on the selections received by the provision unit. A system characterized by:

2. The procedural division: Sending confirmation messages to users when purchasing, changing, or refunding tickets 2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the timing of accepting natural language input based on the estimated user emotions.

2. The system of claim 1.

4. The reception unit Analyze the user's past input history and select the input reception method 2. The system of claim 1.

5. The reception unit Accept natural language input and filter it based on the user's current situation and interests.

2. The system of claim 1.

6. The reception unit When accepting natural language input, select the acceptance method according to the user's input method 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When accepting natural language input, the system prioritizes relevant input by taking into account the user's geographic location.

2. The system of claim 1.

9. The reception unit When accepting natural language input, analyze the user's social media activity and accept related input.

2. The system of claim 1.

Images