system

Abstract

The system according to the embodiment aims to enable users to interactively decide on travel plans and complete reservations. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a concretization unit, and a reservation unit. The reception unit accepts a user's travel requests in a conversational format. The analysis unit analyzes the conversation accepted by the reception unit and generates a travel plan. The concretization unit concretizes the travel plan generated by the analysis unit. The reservation unit reserves the travel plan concretized by the concretization 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] In conventional technology, travel plans are proposed on a web-based basis, which means that users cannot decide on travel plans interactively.

[0005] The system according to the embodiment aims to enable users to interactively decide on travel plans and complete reservations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a concretization unit, and a reservation unit. The reception unit receives a user's travel requests in a conversational format. The analysis unit analyzes the conversation received by the reception unit and generates a travel plan. The concretization unit concretizes the travel plan generated by the analysis unit. The reservation unit reserves the travel plan concretized by the concretization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to interactively decide on a travel plan and complete a reservation. [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 travel plan decision system according to an embodiment of the present invention accepts a user's travel wishes in a conversational format, and a generation AI analyzes, concretizes, and even makes reservations. In the travel plan decision system, a user inputs their travel wishes in a conversational format through a speaker / microphone. The generation AI analyzes the conversation and proposes a travel plan based on the user's wishes. The proposed travel plan is then concretized and booked in collaboration with an external travel site. For example, in the travel plan decision system, a user inputs a request, such as "I want to go on a hot spring trip next weekend," in a conversational format through a speaker / microphone. This information is input to the generation AI. The generation AI then analyzes the input conversation and proposes a travel plan based on the user's wishes. The generation AI understands the user's wishes and generates an optimal travel plan. For example, in response to a request, "I want to go on a hot spring trip next weekend," the system suggests potential hot spring resorts, accommodations, transportation options, etc. The proposed travel plan is concretized in collaboration with an external travel site. For example, reservations for the proposed accommodations and transportation options are made through the travel site. The generation AI then obtains information from the travel site and provides the user with the optimal plan. Furthermore, the reservation is completed. The user can check the proposed plan through the generation AI and make the reservation. For example, reservations for accommodation and transportation are completed, and the trip details are provided to the user. This allows the travel plan decision system to accept, analyze, specify, and even make reservations for the user's travel wishes in a conversational format. This allows the travel plan decision system to accept, analyze, specify, and even make reservations for the user's travel wishes in a conversational format. For example, the user simply needs to enter their travel wishes in a conversational format, and the generation AI can propose the optimal plan and even make the reservation. This simplifies travel planning and improves user convenience.

[0029] The travel plan decision system according to the embodiment includes a reception unit, an analysis unit, a specification unit, and a reservation unit. The reception unit receives a user's travel requests in a conversational format. The user's travel requests include, but are not limited to, a request such as "I want to go on a hot spring trip next weekend." The reception unit receives the user's travel requests, for example, through a speaker / microphone. The reception unit can also receive the user's travel requests through text chat. The reception unit can also receive the user's travel requests in a voice dialogue format. For example, the reception unit receives the user's travel requests through a speaker / microphone and inputs the information into a generation AI. The analysis unit uses a generation AI to analyze the conversation received by the reception unit and generate a travel plan. The analysis unit can, for example, use natural language processing technology to analyze the user's conversation and generate a travel plan. The analysis unit can also use a machine learning algorithm to analyze the user's conversation and generate a travel plan. The analysis unit can also use a generation AI to analyze the user's conversation and generate a travel plan. For example, the analysis unit inputs a conversation such as "I want to go on a hot spring trip next weekend" into the generation AI, and suggests potential hot spring resorts, accommodations, transportation options, etc. The concretization unit uses the generation AI to concretize the travel plan generated by the analysis unit. The concretization unit, for example, collaborates with an external travel site to concretize the travel plan. The concretization unit can also use the generation AI to obtain information from the travel site and provide the user with an optimal plan. The concretization unit can also use the generation AI to concretize the details of the travel plan. For example, the concretization unit uses the generation AI to make reservations for the proposed accommodations and transportation options. The reservation unit makes reservations for the travel plan concretized by the concretization unit. The reservation unit can also use the generation AI to make reservations for the concretized travel plan. The reservation unit can also make reservations for accommodations and transportation options through a travel site. The reservation unit can also use the generation AI to provide the user with an optimal reservation method. For example, the reservation unit uses the generation AI to make reservations for accommodations and transportation options and provide the user with trip details.As a result, the travel plan decision system according to the embodiment can accept, analyze, and concretize a user's travel wishes in a conversational format, and even make a reservation. Some or all of the above-described processes in the reception unit, analysis unit, concretization unit, and reservation unit may be performed using or without the generation AI. For example, the reception unit accepts the user's travel wishes through a speaker / microphone and inputs the information into the generation AI, the analysis unit analyzes the user's conversation using the generation AI, the concretization unit concretizes a travel plan using the generation AI, and the reservation unit reserves the travel plan concretized by the generation AI.

[0030] The travel plan decision system includes a reception unit that uses a generation AI to accept a user's travel requests in a conversational format. The reception unit uses the generation AI to accept the user's travel requests in a conversational format. The generation AI can be, for example, a text generation AI such as GPT-4 (registered trademark). The reception unit accepts the user's travel requests, for example, through a speaker / microphone and inputs the information to the generation AI. The generation AI analyzes the user's conversation and understands the travel requests. For example, if a user says, "I want to go on a hot spring trip next weekend," the generation AI understands the request and provides information to the analysis unit. In this way, the generation AI can accept the user's travel requests in a conversational format. 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 accepts the user's travel requests through a speaker / microphone and inputs the information to the generation AI, which then analyzes the user's conversation.

[0031] The travel plan decision system includes an analysis unit that uses a generation AI to analyze a user's conversation and generate a travel plan. The analysis unit uses the generation AI to analyze the user's conversation and generate a travel plan. The generation AI can use, for example, a text generation AI such as GPT-4. The analysis unit uses, for example, natural language processing technology to analyze the user's conversation and generate a travel plan. The generation AI understands the user's conversation and generates an optimal travel plan. For example, if a user says, "I want to go on a hot spring trip next weekend," the generation AI suggests potential hot spring resorts, accommodations, transportation options, etc. In this way, the generation AI can analyze the user's conversation and generate a travel plan. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit analyzes the user's conversation using the generation AI and generates a travel plan.

[0032] The travel plan decision system includes a concretization unit that uses a generation AI to concretize a travel plan in cooperation with an external travel site. The concretization unit uses a generation AI to concretize a travel plan in cooperation with an external travel site. The generation AI can use, for example, a text generation AI such as GPT-4. The concretization unit, for example, concretizes a travel plan in cooperation with an external travel site. The generation AI acquires information from a travel site and provides an optimal plan to the user. For example, the generation AI makes reservations for suggested accommodations and transportation. In this way, the generation AI can be used to concretize a travel plan in cooperation with an external travel site. Some or all of the above-described processing in the concretization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the concretization unit acquires information from a travel site using the generation AI and provides an optimal plan to the user.

[0033] The travel plan determination system includes a reservation unit that reserves a travel plan realized using the generation AI. The reservation unit reserves a travel plan realized using the generation AI. The generation AI can use, for example, a text generation AI such as GPT-4. The reservation unit reserves a travel plan realized using the generation AI. The generation AI provides an optimal reservation method to a user. For example, the generation AI reserves accommodation and transportation and provides the user with travel details. In this way, the realized travel plan can be reserved using the generation AI. Some or all of the above-described processing in the reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reservation unit reserves a travel plan realized by the generation AI.

[0034] The reception unit can analyze the user's past travel history and select the optimal reception method. For example, the reception unit prioritizes suggesting travel destinations that the user has frequently used in the past. The reception unit can also select a reception method based on the user's preferred travel style in the past (resort, adventure, etc.). Furthermore, the reception unit can also suggest travel destinations preferred in specific seasons from the user's past travel history. In this way, the optimal reception method can be selected by analyzing the user's past travel history. Some or all of the above-mentioned 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 analyzes the user's past travel history using a generation AI and selects the optimal reception method.

[0035] The reception unit can filter travel destinations based on the user's current lifestyle and areas of interest when receiving a travel request. For example, if the user is seeking relaxation in their current lifestyle, the reception unit can suggest relaxing travel destinations. Furthermore, if the user leads an active lifestyle, the reception unit can also suggest active travel destinations. Furthermore, the reception unit can filter travel destinations based on the user's areas of interest (history, nature, etc.). By filtering travel destinations based on the user's current lifestyle and areas of interest, more appropriate travel destinations can be suggested. 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 uses a generation AI to analyze the user's current lifestyle and areas of interest and filter travel destinations.

[0036] When accepting a travel request, the reception unit can select the optimal reception means according to the user's input method. For example, if the user prefers voice input, the reception unit can preferentially accept voice input. Furthermore, if the user prefers text input, the reception unit can also preferentially accept text input. Furthermore, if the user prefers image input, the reception unit can also preferentially accept image input. This improves user convenience by selecting the optimal reception means according to the user's input method. 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 analyzes the user's input method using a generation AI and selects the optimal reception means.

[0037] When accepting travel requests, the reception unit can prioritize accepting highly relevant requests by taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. Furthermore, if the user is interested in a specific region, the reception unit can prioritize suggesting travel destinations in that region. Furthermore, the reception unit can prioritize suggesting travel destinations that are easily accessible from the user's current location. In this way, by taking the user's geographical location information into account, highly relevant travel requests can be prioritized. 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 analyzes the user's geographical location information using a generation AI, and prioritizes accepting highly relevant requests.

[0038] The reception unit can analyze the user's social media activity when receiving a travel request and receive related requests. For example, the reception unit can suggest travel destinations based on the locations 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 suggest related travel destinations. Furthermore, the reception unit can also suggest travel destinations based on the activities of the user's friends on social media. In this way, related travel requests can be received by analyzing the user's social media activity. 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 analyzes the user's social media activity using a generation AI and receives related requests.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a travel request. For example, the reception unit preferentially suggests travel destinations that the user has previously preferred. The reception unit can also select the optimal reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception interface by reflecting the user's past feedback. In this way, the optimal reception method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned 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 analyzes the user's past feedback using a generation AI and customizes the reception method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the travel plan. For example, the analysis unit performs a detailed analysis on a highly important travel plan. The analysis unit can also perform a concise analysis on a less important travel plan. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the travel plan. This allows optimal allocation of resources by adjusting the level of detail of the analysis based on the importance of the travel plan. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the importance of the travel plan using a generation AI and adjusts the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the travel plan. For example, in the case of a resort trip, the analysis unit applies an analysis algorithm that emphasizes relaxing elements. In addition, in the case of an adventure trip, the analysis unit can apply an analysis algorithm that emphasizes active elements. Furthermore, in the case of a cultural trip, the analysis unit can apply an analysis algorithm that emphasizes historical elements. In this way, by applying different analysis algorithms depending on the category of the travel plan, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the category of the travel plan using a generation AI and applies an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimally allocate analysis resources by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit analyzes the user's past analysis results using a generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the travel plan. For example, the analysis unit prioritizes analysis of travel plans submitted earlier. The analysis unit can also postpone analysis of travel plans submitted later. Furthermore, the analysis unit can optimally allocate analysis resources according to the time of submission. Thus, by determining the analysis priority based on the time of submission of the travel plan, resources can be optimally allocated. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit evaluates the time of submission of the travel plan using the generation AI and determines the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the travel plans. For example, the analysis unit prioritizes analysis of highly relevant travel plans. The analysis unit can also postpone analysis of less relevant travel plans. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the travel plans. This allows optimal allocation of resources by adjusting the order of analysis based on the relevance of the travel plans. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the relevance of the travel plans using a generation AI and adjusts the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can explain in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the user's level of expertise using a generation AI and adjusts the use of technical terms in the analysis.

[0046] At the time of concretization, the concretization unit can select the optimal concretization method by analyzing the user's past travel behavior. The concretization unit selects the optimal concretization method based on, for example, the user's past travel behavior. The concretization unit can also extract specific patterns from the user's past travel behavior and adjust the concretization method. Furthermore, the concretization unit can also optimally allocate concretization resources by referring to the user's past travel behavior. In this way, the optimal concretization method can be selected by analyzing the user's past travel behavior. Some or all of the above-mentioned processing in the concretization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the concretization unit analyzes the user's past travel behavior using a generation AI and selects the optimal concretization method.

[0047] The materialization unit can customize the materialization means based on the user's current living situation during materialization. For example, if the user is seeking relaxation in their current living situation, the materialization unit can suggest materialization means that allow them to relax. Furthermore, if the user is leading an active lifestyle, the materialization unit can also suggest active materialization means. Furthermore, the materialization unit can customize the materialization means based on the user's current living situation. This allows for more appropriate materialization by customizing the materialization means based on the user's current living situation. Some or all of the above-described processing in the materialization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the materialization unit analyzes the user's current living situation using a generation AI and customizes the materialization means.

[0048] The concretization unit can improve the concretization method by reflecting user feedback during concretization. For example, the concretization unit improves the concretization method based on user feedback. The concretization unit can also extract specific patterns from user feedback and adjust the concretization method. Furthermore, the concretization unit can optimally allocate concretization resources by referring to user feedback. In this way, the concretization method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the concretization unit may be performed using a generation AI or may be performed without using a generation AI. For example, the concretization unit analyzes user feedback using a generation AI and improves the concretization method.

[0049] During concretization, the concretization unit can select the optimal concretization method by taking into account the user's geographical location information. For example, the concretization unit prioritizes travel destinations close to the user's current location. Furthermore, if the user is interested in a specific region, the concretization unit can also prioritize travel destinations in that region. Furthermore, the concretization unit can prioritize travel destinations that are easily accessible from the user's current location. In this way, the optimal concretization method can be selected by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the concretization unit may be performed using or without the generation AI. For example, the concretization unit analyzes the user's geographical location information using the generation AI and selects the optimal concretization method.

[0050] During the concretization, the concretization unit can analyze the user's social media activity and suggest a means of concretization. For example, the concretization unit can suggest a means of concretization based on the location where the user checked in on social media. The concretization unit can also analyze the content of the user's social media posts and suggest related means of concretization. Furthermore, the concretization unit can also suggest a means of concretization based on the activities of the user's friends on social media. In this way, related means of concretization can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the concretization unit may be performed using a generation AI or may be performed without using a generation AI. For example, the concretization unit analyzes the user's social media activity using a generation AI and suggests a means of concretization.

[0051] The materialization unit can customize the materialization method by reflecting the user's past feedback during materialization. The materialization unit customizes the materialization method based on, for example, the user's past feedback. The materialization unit can also extract specific patterns from the user's past feedback and adjust the materialization method. Furthermore, the materialization unit can optimally allocate materialization resources by referring to the user's past feedback. In this way, the materialization method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the materialization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the materialization unit analyzes the user's past feedback using a generation AI and customizes the materialization method.

[0052] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. The reservation unit selects the optimal reservation method based on, for example, the user's past reservation history. The reservation unit can also extract specific patterns from the user's past reservation history and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to the user's past reservation history. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's past reservation history using a generation AI and selects the optimal reservation method.

[0053] The reservation unit can customize the reservation method based on the user's current lifestyle at the time of reservation. For example, if the user is seeking relaxation in their current lifestyle, the reservation unit can suggest a reservation method that allows relaxation. Also, if the user leads an active lifestyle, the reservation unit can suggest an active reservation method. Furthermore, the reservation unit can customize the reservation method based on the user's current lifestyle. This makes it possible to provide a more appropriate reservation procedure by customizing the reservation method based on the user's current lifestyle. Some or all of the above-described processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's current lifestyle using a generation AI and customizes the reservation method.

[0054] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit improves the reservation method based on user feedback, for example. The reservation unit can also extract specific patterns from user feedback and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to user feedback. In this way, the reservation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes user feedback using a generation AI and improves the reservation method.

[0055] When making a reservation, the reservation unit can select the optimal reservation method by taking into account the user's geographical location information. For example, the reservation unit prioritizes reserving travel destinations close to the user's current location. Furthermore, if the user is interested in a particular area, the reservation unit can also prioritize reserving travel destinations in that area. Furthermore, the reservation unit can also prioritize reserving travel destinations that are easily accessible from the user's current location. In this way, the optimal reservation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's geographical location information using a generation AI and selects the optimal reservation method.

[0056] At the time of reservation, the reservation unit can analyze the user's social media activity to suggest a reservation method. For example, the reservation unit can suggest a reservation method based on the location where the user checked in on social media. The reservation unit can also analyze the content of the user's social media posts to suggest a related reservation method. Furthermore, the reservation unit can also suggest a reservation method based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related reservation methods can be suggested. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's social media activity using a generation AI to suggest a reservation method.

[0057] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit customizes the reservation method based on, for example, the user's past feedback. The reservation unit can also extract specific patterns from the user's past feedback and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to the user's past feedback. In this way, the reservation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's past feedback using a generation AI and customizes the reservation method.

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

[0059] The reception unit can analyze the user's past travel history and select the optimal reception method. For example, it can prioritize and suggest travel destinations that the user has frequently used in the past. The reception unit can also select the reception method based on the user's preferred travel style in the past (resort, adventure, etc.). Furthermore, the reception unit can also suggest travel destinations preferred in specific seasons from the user's past travel history. In this way, the optimal reception method can be selected by analyzing the user's past travel history. Some or all of the above-mentioned 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 analyzes the user's past travel history using a generation AI and selects the optimal reception method.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the travel plan. For example, a detailed analysis is performed for a highly important travel plan. The analysis unit can also perform a brief analysis for a less important travel plan. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the travel plan. By adjusting the level of detail of the analysis based on the importance of the travel plan, resources can be optimally allocated. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the importance of the travel plan using a generation AI and adjusts the level of detail of the analysis.

[0061] At the time of concretization, the concretization unit can select the optimal concretization method by analyzing the user's past travel behavior. For example, the concretization unit selects the optimal concretization method based on the user's past travel behavior. The concretization unit can also extract specific patterns from the user's past travel behavior and adjust the concretization method. Furthermore, the concretization unit can also optimally allocate concretization resources by referring to the user's past travel behavior. In this way, the optimal concretization method can be selected by analyzing the user's past travel behavior. Some or all of the above-mentioned processing in the concretization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the concretization unit analyzes the user's past travel behavior using a generation AI and selects the optimal concretization method.

[0062] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. For example, the reservation unit selects the optimal reservation method based on the user's past reservation history. The reservation unit can also extract specific patterns from the user's past reservation history and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to the user's past reservation history. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's past reservation history using a generation AI and selects the optimal reservation method.

[0063] When receiving a travel request, the reception unit can filter travel destinations based on the user's current lifestyle and areas of interest. For example, if the user is seeking relaxation in their current lifestyle, the reception unit can suggest relaxing travel destinations. Furthermore, if the user leads an active lifestyle, the reception unit can also suggest active travel destinations. Furthermore, the reception unit can filter travel destinations based on the user's areas of interest (history, nature, etc.). By filtering travel destinations based on the user's current lifestyle and areas of interest, more appropriate travel destinations can be suggested. 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 uses a generation AI to analyze the user's current lifestyle and areas of interest and filter travel destinations.

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

[0065] Step 1: The reception unit accepts the user's travel wishes in a conversational format. The user's travel wishes include, but are not limited to, an example such as "I want to go on a hot spring trip next weekend." The reception unit can accept the user's travel wishes through a speaker / microphone. It can also accept the wishes in a text chat or voice dialogue format. Step 2: The analysis unit uses generation AI to analyze the conversation received by the reception unit and generate a travel plan. The analysis unit can analyze the user's conversation using natural language processing technology and machine learning algorithms and generate a travel plan. For example, if the conversation is input as "I want to go on a hot spring trip next weekend," the analysis unit will suggest possible hot spring resorts, accommodations, transportation options, etc. Step 3: The concretization unit uses the generation AI to concretize the travel plan generated by the analysis unit. The concretization unit concretizes the travel plan in cooperation with external travel sites, and can obtain information from the travel sites to provide the user with the optimal plan. For example, it makes reservations for suggested accommodations and transportation. Step 4: The reservation unit reserves the travel plan realized by the realization unit. The reservation unit reserves the travel plan realized using the generation AI and can make reservations for accommodation and transportation through a travel site. For example, it makes reservations for accommodation and transportation and provides travel details to the user.

[0066] (Example 2) A travel plan decision system according to an embodiment of the present invention accepts a user's travel wishes in a conversational format, and a generation AI analyzes, concretizes, and even makes reservations. In the travel plan decision system, a user inputs their travel wishes in a conversational format through a speaker / microphone. The generation AI analyzes the conversation and proposes a travel plan based on the user's wishes. The proposed travel plan is then concretized and booked in collaboration with an external travel site. For example, in the travel plan decision system, a user inputs a request, such as "I want to go on a hot spring trip next weekend," in a conversational format through a speaker / microphone. This information is input to the generation AI. The generation AI then analyzes the input conversation and proposes a travel plan based on the user's wishes. The generation AI understands the user's wishes and generates an optimal travel plan. For example, in response to a request, "I want to go on a hot spring trip next weekend," the system suggests potential hot spring resorts, accommodations, transportation options, etc. The proposed travel plan is concretized in collaboration with an external travel site. For example, reservations for the proposed accommodations and transportation options are made through the travel site. The generation AI then obtains information from the travel site and provides the user with the optimal plan. Furthermore, the reservation is completed. The user can check the proposed plan through the generation AI and make the reservation. For example, reservations for accommodation and transportation are completed, and the trip details are provided to the user. This allows the travel plan decision system to accept, analyze, specify, and even make reservations for the user's travel wishes in a conversational format. This allows the travel plan decision system to accept, analyze, specify, and even make reservations for the user's travel wishes in a conversational format. For example, the user simply needs to enter their travel wishes in a conversational format, and the generation AI can propose the optimal plan and even make the reservation. This simplifies travel planning and improves user convenience.

[0067] The travel plan decision system according to the embodiment includes a reception unit, an analysis unit, a specification unit, and a reservation unit. The reception unit receives a user's travel requests in a conversational format. The user's travel requests include, but are not limited to, a request such as "I want to go on a hot spring trip next weekend." The reception unit receives the user's travel requests, for example, through a speaker / microphone. The reception unit can also receive the user's travel requests through text chat. The reception unit can also receive the user's travel requests in a voice dialogue format. For example, the reception unit receives the user's travel requests through a speaker / microphone and inputs the information into a generation AI. The analysis unit uses a generation AI to analyze the conversation received by the reception unit and generate a travel plan. The analysis unit can, for example, use natural language processing technology to analyze the user's conversation and generate a travel plan. The analysis unit can also use a machine learning algorithm to analyze the user's conversation and generate a travel plan. The analysis unit can also use a generation AI to analyze the user's conversation and generate a travel plan. For example, the analysis unit inputs a conversation such as "I want to go on a hot spring trip next weekend" into the generation AI, and suggests potential hot spring resorts, accommodations, transportation options, etc. The concretization unit uses the generation AI to concretize the travel plan generated by the analysis unit. The concretization unit, for example, collaborates with an external travel site to concretize the travel plan. The concretization unit can also use the generation AI to obtain information from the travel site and provide the user with an optimal plan. The concretization unit can also use the generation AI to concretize the details of the travel plan. For example, the concretization unit uses the generation AI to make reservations for the proposed accommodations and transportation options. The reservation unit makes reservations for the travel plan concretized by the concretization unit. The reservation unit can also use the generation AI to make reservations for the concretized travel plan. The reservation unit can also make reservations for accommodations and transportation options through a travel site. The reservation unit can also use the generation AI to provide the user with an optimal reservation method. For example, the reservation unit uses the generation AI to make reservations for accommodations and transportation options and provide the user with trip details.As a result, the travel plan decision system according to the embodiment can accept, analyze, and concretize a user's travel wishes in a conversational format, and even make a reservation. Some or all of the above-described processes in the reception unit, analysis unit, concretization unit, and reservation unit may be performed using or without the generation AI. For example, the reception unit accepts the user's travel wishes through a speaker / microphone and inputs the information into the generation AI, the analysis unit analyzes the user's conversation using the generation AI, the concretization unit concretizes a travel plan using the generation AI, and the reservation unit reserves the travel plan concretized by the generation AI.

[0068] The travel plan decision system includes a reception unit that uses a generation AI to accept a user's travel requests in a conversational format. The reception unit uses the generation AI to accept the user's travel requests in a conversational format. The generation AI can be, for example, a text generation AI such as GPT-4. The reception unit accepts the user's travel requests, for example, through a speaker / microphone and inputs the information to the generation AI. The generation AI analyzes the user's conversation and understands the travel requests. For example, if a user says, "I want to go on a hot spring trip next weekend," the generation AI understands the request and provides information to the analysis unit. In this way, the generation AI can accept the user's travel requests in a conversational format. 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 accepts the user's travel requests through a speaker / microphone and inputs the information to the generation AI, which then analyzes the user's conversation.

[0069] The travel plan decision system includes an analysis unit that uses a generation AI to analyze a user's conversation and generate a travel plan. The analysis unit uses the generation AI to analyze the user's conversation and generate a travel plan. The generation AI can use, for example, a text generation AI such as GPT-4. The analysis unit uses, for example, natural language processing technology to analyze the user's conversation and generate a travel plan. The generation AI understands the user's conversation and generates an optimal travel plan. For example, if a user says, "I want to go on a hot spring trip next weekend," the generation AI suggests potential hot spring resorts, accommodations, transportation options, etc. In this way, the generation AI can analyze the user's conversation and generate a travel plan. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit analyzes the user's conversation using the generation AI and generates a travel plan.

[0070] The travel plan decision system includes a concretization unit that uses a generation AI to concretize a travel plan in cooperation with an external travel site. The concretization unit uses a generation AI to concretize a travel plan in cooperation with an external travel site. The generation AI can use, for example, a text generation AI such as GPT-4. The concretization unit, for example, concretizes a travel plan in cooperation with an external travel site. The generation AI acquires information from a travel site and provides an optimal plan to the user. For example, the generation AI makes reservations for suggested accommodations and transportation. In this way, the generation AI can be used to concretize a travel plan in cooperation with an external travel site. Some or all of the above-described processing in the concretization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the concretization unit acquires information from a travel site using the generation AI and provides an optimal plan to the user.

[0071] The travel plan determination system includes a reservation unit that reserves a travel plan realized using the generation AI. The reservation unit reserves a travel plan realized using the generation AI. The generation AI can use, for example, a text generation AI such as GPT-4. The reservation unit reserves a travel plan realized using the generation AI. The generation AI provides an optimal reservation method to a user. For example, the generation AI reserves accommodation and transportation and provides the user with travel details. In this way, the realized travel plan can be reserved using the generation AI. Some or all of the above-described processing in the reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reservation unit reserves a travel plan realized by the generation AI.

[0072] The reception unit can estimate the user's emotions and adjust the timing of accepting the travel requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can adjust the timing to accept the travel requests so that they are accepted during a time when the user is able to relax. Furthermore, if the user is excited, the reception unit can also adjust the timing to accept the travel requests immediately. Furthermore, if the user is tired, the reception unit can also adjust the timing to accept the travel requests after the user has rested. By adjusting the timing to accept the travel requests based on the user's emotions, the travel requests can be accepted at a more appropriate time. The emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit estimates the user's emotions using the generation AI, and adjusts the timing to accept the travel requests based on the estimated user emotions.

[0073] The reception unit can analyze the user's past travel history and select the optimal reception method. For example, the reception unit prioritizes suggesting travel destinations that the user has frequently used in the past. The reception unit can also select a reception method based on the user's preferred travel style in the past (resort, adventure, etc.). Furthermore, the reception unit can also suggest travel destinations preferred in specific seasons from the user's past travel history. In this way, the optimal reception method can be selected by analyzing the user's past travel history. Some or all of the above-mentioned 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 analyzes the user's past travel history using a generation AI and selects the optimal reception method.

[0074] The reception unit can filter travel destinations based on the user's current lifestyle and areas of interest when receiving a travel request. For example, if the user is seeking relaxation in their current lifestyle, the reception unit can suggest relaxing travel destinations. Furthermore, if the user leads an active lifestyle, the reception unit can also suggest active travel destinations. Furthermore, the reception unit can filter travel destinations based on the user's areas of interest (history, nature, etc.). By filtering travel destinations based on the user's current lifestyle and areas of interest, more appropriate travel destinations can be suggested. 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 uses a generation AI to analyze the user's current lifestyle and areas of interest and filter travel destinations.

[0075] When accepting a travel request, the reception unit can select the optimal reception means according to the user's input method. For example, if the user prefers voice input, the reception unit can preferentially accept voice input. Furthermore, if the user prefers text input, the reception unit can also preferentially accept text input. Furthermore, if the user prefers image input, the reception unit can also preferentially accept image input. This improves user convenience by selecting the optimal reception means according to the user's input method. 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 analyzes the user's input method using a generation AI and selects the optimal reception means.

[0076] The reception unit can estimate the user's emotions and prioritize the travel requests to be received based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize relaxing travel requests. Furthermore, if the user is excited, the reception unit can prioritize active travel requests. Furthermore, if the user is tired, the reception unit can prioritize restful travel requests. By prioritizing the travel requests based on the user's emotions, more appropriate travel requests can be prioritized. The emotion estimation is realized using an emotion estimation function, such as 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 reception unit may be performed using the generation AI, or may be performed without the generation AI. For example, the reception unit estimates the user's emotions using the generation AI, and prioritizes the travel requests based on the estimated user emotions.

[0077] When accepting travel requests, the reception unit can prioritize accepting highly relevant requests by taking into account the user's geographical location information. For example, the reception unit can prioritize suggesting travel destinations close to the user's current location. Furthermore, if the user is interested in a specific region, the reception unit can prioritize suggesting travel destinations in that region. Furthermore, the reception unit can prioritize suggesting travel destinations that are easily accessible from the user's current location. In this way, by taking the user's geographical location information into account, highly relevant travel requests can be prioritized. 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 analyzes the user's geographical location information using a generation AI, and prioritizes accepting highly relevant requests.

[0078] The reception unit can analyze the user's social media activity when receiving a travel request and receive related requests. For example, the reception unit can suggest travel destinations based on the locations 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 suggest related travel destinations. Furthermore, the reception unit can also suggest travel destinations based on the activities of the user's friends on social media. In this way, related travel requests can be received by analyzing the user's social media activity. 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 analyzes the user's social media activity using a generation AI and receives related requests.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a travel request. For example, the reception unit preferentially suggests travel destinations that the user has previously preferred. The reception unit can also select the optimal reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception interface by reflecting the user's past feedback. In this way, the optimal reception method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned 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 analyzes the user's past feedback using a generation AI and customizes the reception method.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method for the travel plan based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest a relaxing travel plan. If the user is in a hurry, the analysis unit can also perform a concise analysis and suggest a quick travel plan. Furthermore, if the user is excited, the analysis unit can adjust the analysis method to suggest an active travel plan. This allows for more appropriate travel plans to be suggested by adjusting the analysis method for the travel plan based on the user's emotions. The 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 such examples. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can estimate the user's emotions using the generation AI and adjust the analysis method for the travel plan based on the estimated user emotions.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the travel plan. For example, the analysis unit performs a detailed analysis on a highly important travel plan. The analysis unit can also perform a concise analysis on a less important travel plan. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the travel plan. This allows optimal allocation of resources by adjusting the level of detail of the analysis based on the importance of the travel plan. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the importance of the travel plan using a generation AI and adjusts the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the travel plan. For example, in the case of a resort trip, the analysis unit applies an analysis algorithm that emphasizes relaxing elements. In addition, in the case of an adventure trip, the analysis unit can apply an analysis algorithm that emphasizes active elements. Furthermore, in the case of a cultural trip, the analysis unit can apply an analysis algorithm that emphasizes historical elements. In this way, by applying different analysis algorithms depending on the category of the travel plan, more appropriate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the category of the travel plan using a generation AI and applies an appropriate analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can also optimally allocate analysis resources by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit analyzes the user's past analysis results using a generation AI to improve the accuracy of the analysis.

[0084] 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 complete the analysis in a short time. Furthermore, if the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can also perform a quick and detailed analysis. This allows for more appropriate analysis by adjusting the length of the analysis based on 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 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 the generation AI, or can be performed without the generation AI. For example, the analysis unit estimates the user's emotions using the generation AI, and adjusts the length of the analysis based on the estimated user emotions.

[0085] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the travel plan. For example, the analysis unit prioritizes analysis of travel plans submitted earlier. The analysis unit can also postpone analysis of travel plans submitted later. Furthermore, the analysis unit can optimally allocate analysis resources according to the time of submission. Thus, by determining the analysis priority based on the time of submission of the travel plan, resources can be optimally allocated. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit evaluates the time of submission of the travel plan using the generation AI and determines the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the travel plans. For example, the analysis unit prioritizes analysis of highly relevant travel plans. The analysis unit can also postpone analysis of less relevant travel plans. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the travel plans. This allows optimal allocation of resources by adjusting the order of analysis based on the relevance of the travel plans. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the relevance of the travel plans using a generation AI and adjusts the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can explain in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the user's level of expertise using a generation AI and adjusts the use of technical terms in the analysis.

[0088] The concretization unit can estimate the user's emotion and adjust the concretization method based on the estimated user's emotion. For example, the concretization unit can perform detailed concretization when the user is relaxed. Furthermore, the concretization unit can perform concise concretization when the user is in a hurry. Furthermore, the concretization unit can perform visually appealing concretization when the user is excited. This allows for more appropriate concretization by adjusting the concretization method based on the user's emotion. 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-mentioned processing in the concretization unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the concretization unit can estimate the user's emotion using the generation AI and adjust the concretization method based on the estimated user's emotion.

[0089] At the time of concretization, the concretization unit can select the optimal concretization method by analyzing the user's past travel behavior. The concretization unit selects the optimal concretization method based on, for example, the user's past travel behavior. The concretization unit can also extract specific patterns from the user's past travel behavior and adjust the concretization method. Furthermore, the concretization unit can also optimally allocate concretization resources by referring to the user's past travel behavior. In this way, the optimal concretization method can be selected by analyzing the user's past travel behavior. Some or all of the above-mentioned processing in the concretization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the concretization unit analyzes the user's past travel behavior using a generation AI and selects the optimal concretization method.

[0090] The materialization unit can customize the materialization means based on the user's current living situation during materialization. For example, if the user is seeking relaxation in their current living situation, the materialization unit can suggest materialization means that allow them to relax. Furthermore, if the user is leading an active lifestyle, the materialization unit can also suggest active materialization means. Furthermore, the materialization unit can customize the materialization means based on the user's current living situation. This allows for more appropriate materialization by customizing the materialization means based on the user's current living situation. Some or all of the above-described processing in the materialization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the materialization unit analyzes the user's current living situation using a generation AI and customizes the materialization means.

[0091] The concretization unit can improve the concretization method by reflecting user feedback during concretization. For example, the concretization unit improves the concretization method based on user feedback. The concretization unit can also extract specific patterns from user feedback and adjust the concretization method. Furthermore, the concretization unit can optimally allocate concretization resources by referring to user feedback. In this way, the concretization method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the concretization unit may be performed using a generation AI or may be performed without using a generation AI. For example, the concretization unit analyzes user feedback using a generation AI and improves the concretization method.

[0092] The concretization unit can estimate the user's emotions and determine the priority of concretization based on the estimated user's emotions. For example, if the user is stressed, the concretization unit can prioritize concretization that allows relaxation. Furthermore, if the user is excited, the concretization unit can prioritize concretization that allows the user to rest. In this way, by determining the priority of concretization based on the user's emotions, more appropriate concretization can be prioritized. 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-mentioned processing in the concretization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the concretization unit estimates the user's emotions using the generation AI, and determines the priority of concretization based on the estimated user's emotions.

[0093] During concretization, the concretization unit can select the optimal concretization method by taking into account the user's geographical location information. For example, the concretization unit prioritizes travel destinations close to the user's current location. Furthermore, if the user is interested in a specific region, the concretization unit can also prioritize travel destinations in that region. Furthermore, the concretization unit can prioritize travel destinations that are easily accessible from the user's current location. In this way, the optimal concretization method can be selected by taking the user's geographical location information into account. Some or all of the above-mentioned processing in the concretization unit may be performed using or without the generation AI. For example, the concretization unit analyzes the user's geographical location information using the generation AI and selects the optimal concretization method.

[0094] During the concretization, the concretization unit can analyze the user's social media activity and suggest a means of concretization. For example, the concretization unit can suggest a means of concretization based on the location where the user checked in on social media. The concretization unit can also analyze the content of the user's social media posts and suggest related means of concretization. Furthermore, the concretization unit can also suggest a means of concretization based on the activities of the user's friends on social media. In this way, related means of concretization can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the concretization unit may be performed using a generation AI or may be performed without using a generation AI. For example, the concretization unit analyzes the user's social media activity using a generation AI and suggests a means of concretization.

[0095] The materialization unit can customize the materialization method by reflecting the user's past feedback during materialization. The materialization unit customizes the materialization method based on, for example, the user's past feedback. The materialization unit can also extract specific patterns from the user's past feedback and adjust the materialization method. Furthermore, the materialization unit can optimally allocate materialization resources by referring to the user's past feedback. In this way, the materialization method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the materialization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the materialization unit analyzes the user's past feedback using a generation AI and customizes the materialization method.

[0096] The reservation unit can estimate a user's emotions and adjust the reservation method based on the estimated user emotions. For example, the reservation unit can provide a detailed reservation procedure when the user is relaxed. Furthermore, the reservation unit can provide a concise reservation procedure when the user is in a hurry. Furthermore, the reservation unit can provide a visually appealing reservation procedure when the user is excited. This allows for a more appropriate reservation method by adjusting the reservation method based on 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 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 reservation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the reservation unit estimates a user's emotions using the generation AI, and adjusts the reservation method based on the estimated user emotions.

[0097] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. The reservation unit selects the optimal reservation method based on, for example, the user's past reservation history. The reservation unit can also extract specific patterns from the user's past reservation history and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to the user's past reservation history. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's past reservation history using a generation AI and selects the optimal reservation method.

[0098] The reservation unit can customize the reservation method based on the user's current lifestyle at the time of reservation. For example, if the user is seeking relaxation in their current lifestyle, the reservation unit can suggest a reservation method that allows relaxation. Also, if the user leads an active lifestyle, the reservation unit can suggest an active reservation method. Furthermore, the reservation unit can customize the reservation method based on the user's current lifestyle. This makes it possible to provide a more appropriate reservation procedure by customizing the reservation method based on the user's current lifestyle. Some or all of the above-described processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's current lifestyle using a generation AI and customizes the reservation method.

[0099] The reservation unit can improve the reservation method by reflecting user feedback at the time of reservation. The reservation unit improves the reservation method based on user feedback, for example. The reservation unit can also extract specific patterns from user feedback and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to user feedback. In this way, the reservation method can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes user feedback using a generation AI and improves the reservation method.

[0100] The reservation unit can estimate the user's emotions and prioritize reservations based on the estimated user emotions. For example, if the user is feeling stressed, the reservation unit can prioritize reservations that allow relaxation. Furthermore, if the user is excited, the reservation unit can prioritize active reservations. Furthermore, if the user is tired, the reservation unit can prioritize reservations that allow rest. Thus, by prioritizing reservations based on the user's emotions, more appropriate reservations can be prioritized. The emotion estimation is realized using an emotion estimation function, such as 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 reservation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reservation unit estimates the user's emotions using the generation AI, and prioritizes reservations based on the estimated user emotions.

[0101] When making a reservation, the reservation unit can select the optimal reservation method by taking into account the user's geographical location information. For example, the reservation unit prioritizes reserving travel destinations close to the user's current location. Furthermore, if the user is interested in a particular area, the reservation unit can also prioritize reserving travel destinations in that area. Furthermore, the reservation unit can also prioritize reserving travel destinations that are easily accessible from the user's current location. In this way, the optimal reservation method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's geographical location information using a generation AI and selects the optimal reservation method.

[0102] At the time of reservation, the reservation unit can analyze the user's social media activity to suggest a reservation method. For example, the reservation unit can suggest a reservation method based on the location where the user checked in on social media. The reservation unit can also analyze the content of the user's social media posts to suggest a related reservation method. Furthermore, the reservation unit can also suggest a reservation method based on the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related reservation methods can be suggested. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's social media activity using a generation AI to suggest a reservation method.

[0103] The reservation unit can customize the reservation method by reflecting the user's past feedback when making a reservation. The reservation unit customizes the reservation method based on, for example, the user's past feedback. The reservation unit can also extract specific patterns from the user's past feedback and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to the user's past feedback. In this way, the reservation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's past feedback using a generation AI and customizes the reservation method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, realization unit, and reservation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's travel requests through the speaker / microphone of the smart device 14 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to generate a travel plan. The realization unit is realized by the specific processing unit 290 of the data processing device 12 and realizes the travel plan in cooperation with an external travel site. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a reservation for the realized travel plan. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, realization unit, and reservation 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 travel requests through the microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to generate a travel plan. The realization unit is realized by the specific processing unit 290 of the data processing device 12 and realizes the travel plan in cooperation with an external travel site. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a reservation for the realized travel plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, realization unit, and reservation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives the user's travel requests through the microphone 238 of the headset-type terminal 314 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to generate a travel plan. The realization unit is realized by the specific processing unit 290 of the data processing device 12 and realizes the travel plan in cooperation with an external travel site. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a reservation for the realized travel plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, realization unit, and reservation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the user's travel requests through the microphone 238 of the robot 414 and transmits the information to the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's conversation using a generation AI to generate a travel plan. The realization unit is realized by the specific processing unit 290 of the data processing device 12 and realizes the travel plan in cooperation with an external travel site. The reservation unit is realized by the specific processing unit 290 of the data processing device 12 and makes a reservation for the realized travel plan.

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

[0105] The analysis unit can estimate the user's emotions and adjust the proposed travel plan based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize suggested travel destinations and activities that allow relaxation. If the user is excited, it can suggest active travel destinations and activities. Furthermore, if the user is tired, it can suggest travel destinations and activities that allow rest. By adjusting the proposed travel plan based on the user's emotions, it is possible to provide a more appropriate travel plan. Emotion estimation is achieved using an emotion estimation function, such as 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 the generation AI, or can be performed without the generation AI. For example, the analysis unit estimates the user's emotions using the generation AI, and adjusts the proposed travel plan based on the estimated user emotions.

[0106] The concretization unit can estimate the user's emotion and adjust the concretization means based on the estimated user's emotion. For example, if the user is relaxed, detailed concretization can be performed. If the user is in a hurry, concretization can be performed. Furthermore, if the user is excited, visually appealing concretization can be performed. This allows for more appropriate concretization by adjusting the concretization means based on the user's emotion. 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-mentioned processing in the concretization unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the concretization unit estimates the user's emotion using the generation AI, and adjusts the concretization means based on the estimated user's emotion.

[0107] The reservation unit can estimate the user's emotions and adjust the reservation method based on the estimated user emotions. For example, if the user is relaxed, a detailed reservation procedure can be provided. If the user is in a hurry, a concise reservation procedure can be provided. Furthermore, if the user is excited, a visually appealing reservation procedure can be provided. This allows for a more appropriate reservation method to be provided by adjusting the reservation method based on 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 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 reservation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the reservation unit estimates the user's emotions using the generation AI and adjusts the reservation method based on the estimated user emotions.

[0108] The reception unit can estimate the user's emotions and adjust the timing of accepting travel requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can adjust the timing to accept travel requests during a time when the user is able to relax. Furthermore, if the user is excited, the reception unit can adjust the timing to accept travel requests immediately. Furthermore, if the user is tired, the reception unit can adjust the timing to accept travel requests after the user has rested. By adjusting the reception timing of travel requests based on the user's emotions, travel requests can be accepted at a more appropriate time. The estimation of emotions is realized using an emotion estimation function, such as 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit estimates the user's emotions using the generation AI, and adjusts the reception timing of travel requests based on the estimated user emotions.

[0109] 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 can be completed in a short time. Furthermore, if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is excited, a quick and detailed analysis can be performed. This allows for more appropriate analysis by adjusting the length of the analysis based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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 analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit estimates the user's emotions using the generation AI, and adjusts the length of the analysis based on the estimated user emotions.

[0110] The reception unit can analyze the user's past travel history and select the optimal reception method. For example, it can prioritize and suggest travel destinations that the user has frequently used in the past. The reception unit can also select the reception method based on the user's preferred travel style in the past (resort, adventure, etc.). Furthermore, the reception unit can also suggest travel destinations preferred in specific seasons from the user's past travel history. In this way, the optimal reception method can be selected by analyzing the user's past travel history. Some or all of the above-mentioned 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 analyzes the user's past travel history using a generation AI and selects the optimal reception method.

[0111] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the travel plan. For example, a detailed analysis is performed for a highly important travel plan. The analysis unit can also perform a brief analysis for a less important travel plan. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the travel plan. By adjusting the level of detail of the analysis based on the importance of the travel plan, resources can be optimally allocated. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit evaluates the importance of the travel plan using a generation AI and adjusts the level of detail of the analysis.

[0112] At the time of concretization, the concretization unit can select the optimal concretization method by analyzing the user's past travel behavior. For example, the concretization unit selects the optimal concretization method based on the user's past travel behavior. The concretization unit can also extract specific patterns from the user's past travel behavior and adjust the concretization method. Furthermore, the concretization unit can also optimally allocate concretization resources by referring to the user's past travel behavior. In this way, the optimal concretization method can be selected by analyzing the user's past travel behavior. Some or all of the above-mentioned processing in the concretization unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the concretization unit analyzes the user's past travel behavior using a generation AI and selects the optimal concretization method.

[0113] When making a reservation, the reservation unit can analyze the user's past reservation history and select the optimal reservation method. For example, the reservation unit selects the optimal reservation method based on the user's past reservation history. The reservation unit can also extract specific patterns from the user's past reservation history and adjust the reservation method. Furthermore, the reservation unit can also optimally allocate reservation resources by referring to the user's past reservation history. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. Some or all of the above-mentioned processing in the reservation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reservation unit analyzes the user's past reservation history using a generation AI and selects the optimal reservation method.

[0114] When receiving a travel request, the reception unit can filter travel destinations based on the user's current lifestyle and areas of interest. For example, if the user is seeking relaxation in their current lifestyle, the reception unit can suggest relaxing travel destinations. Furthermore, if the user leads an active lifestyle, the reception unit can also suggest active travel destinations. Furthermore, the reception unit can filter travel destinations based on the user's areas of interest (history, nature, etc.). By filtering travel destinations based on the user's current lifestyle and areas of interest, more appropriate travel destinations can be suggested. 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 uses a generation AI to analyze the user's current lifestyle and areas of interest and filter travel destinations.

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

[0116] Step 1: The reception unit accepts the user's travel wishes in a conversational format. The user's travel wishes include, but are not limited to, an example such as "I want to go on a hot spring trip next weekend." The reception unit can accept the user's travel wishes through a speaker / microphone. It can also accept the wishes in a text chat or voice dialogue format. Step 2: The analysis unit uses generation AI to analyze the conversation received by the reception unit and generate a travel plan. The analysis unit can analyze the user's conversation using natural language processing technology and machine learning algorithms and generate a travel plan. For example, if the conversation is input as "I want to go on a hot spring trip next weekend," the analysis unit will suggest possible hot spring resorts, accommodations, transportation options, etc. Step 3: The concretization unit uses the generation AI to concretize the travel plan generated by the analysis unit. The concretization unit concretizes the travel plan in cooperation with external travel sites, and can obtain information from the travel sites to provide the user with the optimal plan. For example, it makes reservations for suggested accommodations and transportation. Step 4: The reservation unit reserves the travel plan realized by the realization unit. The reservation unit reserves the travel plan realized using the generation AI and can make reservations for accommodation and transportation through a travel site. For example, it makes reservations for accommodation and transportation and provides travel details to the user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

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

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

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

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

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

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

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

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

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

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives travel requests from users in a conversational format; an analysis unit that analyzes the conversation received by the reception unit and generates a travel plan; an embodiment unit that implements the travel plan generated by the analysis unit; a reservation unit that reserves the travel plan realized by the realization unit. A system characterized by:

2. The reception unit Using generative AI to accept users' travel requests in a conversational format 2. The system of claim 1.

3. The analysis unit Analyze user conversations using generative AI and generate travel plans 2. The system of claim 1.

4. The materialization unit Using generative AI to connect with external travel sites and create travel plans 2. The system of claim 1.

5. The reservation unit Book a travel plan that is materialized using generative AI 2. The system of claim 1.

6. The reception unit Estimate user emotions and adjust the timing of accepting travel requests based on the estimated user emotions 2. The system of claim 1.

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

8. The reception unit Filtering travel requests based on the user's current lifestyle and interests 2. The system of claim 1.

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