System

Abstract

An object of a system according to an embodiment is to enable an elderly person to easily find an optimal travel plan on the basis of the elderly person's preference or request.SOLUTION: A system includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception part inputs a request item. The analysis unit analyzes the request item input by the reception unit. The proposing section proposes a travel plan based on the information analyzed by the analyzing section. The providing unit makes a reservation or provides information based on the plan proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for seniors to find the best travel plan based on their preferences and requests.

[0005] The system according to the embodiment aims to enable elderly people to easily find the best travel plan based on their preferences and requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit inputs request items. The analysis unit analyzes the request items input by the reception unit. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. The provision unit makes reservations and provides information based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows seniors to easily find the best travel plan based on their preferences and requests. [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 proposal system according to an embodiment of the present invention utilizes a generation AI to provide travel plans for seniors aged 60 and over. In this system, users input request items such as their budget, interests, and restrictions, and the generation AI analyzes these request items to automatically propose optimal travel plans. The travel plans include hotel reservations, tourist attraction information, and local activity reservations. Revenues can be secured by charging fees and plan provision fees. This allows the travel plan proposal system to easily provide travel plans for seniors and realize personalized travel experiences tailored to users' needs. For example, users input request items such as their budget, interests, and restrictions, and the generation AI analyzes these request items to automatically propose optimal travel plans. Furthermore, the generation AI then makes hotel reservations, tourist attraction information, and local activity reservations based on the proposed travel plans. This allows users to prepare for their trips hassle-free. Revenues can also be secured by charging fees and plan provision fees. For example, a system can earn revenue from users by setting fees for travel plan proposals and reservation services. It is also possible to secure additional revenue by offering more detailed travel plans and special services as premium plans.

[0029] A travel plan proposal system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit allows a user to input request items such as their budget, interests, and restrictions. For example, the user can input a specific budget, tourist attractions of interest, health restrictions, and the like. The analysis unit uses a generation AI to analyze the request items input by the reception unit. For example, the generation AI automatically proposes an optimal travel plan based on the user's budget, interests, and restrictions. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. For example, the generation AI searches for hotels available within the user's budget and proposes tourist attractions and activities. The provision unit makes reservations and provides information based on the plan proposed by the proposal unit. For example, if the user agrees to the proposed plan, the generation AI automatically makes hotel reservations and provides information about tourist attractions. The generation AI can also make reservations for local activities on the user's behalf. As a result, the travel plan proposal system according to an embodiment can propose an optimal travel plan based on the user's request, and make reservations and provide information.

[0030] The travel plan proposal system includes a revenue section for securing revenue. The revenue section secures revenue by charging fees and for providing plans. For example, revenue can be obtained from users by setting fees for proposing travel plans and acting as a booking agent. It is also possible to secure additional revenue by offering more detailed travel plans and special services as premium plans. This makes it possible to provide functions for securing revenue.

[0031] The reception unit can analyze the user's past request history and suggest an input method. For example, the reception unit can automatically display request items that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest request items that will be used in a specific time period based on the user's past request history. This can improve input efficiency by suggesting the optimal input method based on the past request history.

[0032] The reception unit can filter the request items based on the user's current health condition and living situation when the request items are input. For example, if the user inputs their health condition, the reception unit can propose an appropriate travel plan based on that information. The reception unit can also propose a plan that takes into account specific constraints based on the user's living situation (e.g., if care is required). The reception unit can also propose a reasonable schedule based on the user's health condition. In this way, by filtering according to the user's health condition and living situation, more appropriate travel plans can be proposed.

[0033] The reception unit can select an input means according to the user's input method when inputting a request item. For example, when the user inputs the request item by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the request item by text, the reception unit can provide a predictive conversion function to simplify the input. Furthermore, when the user inputs the request item using an image, the reception unit can analyze the input content using image recognition technology. This allows the efficiency of input to be improved by selecting the optimal input means according to the user's input method.

[0034] When inputting request items, the reception unit can prioritize inputting highly relevant items based on the user's geographical location information. For example, when the user inputs their current location, the reception unit can prioritize suggesting tourist attractions and hotels in the vicinity. The reception unit can also propose an optimal travel plan based on the user's geographical location information. The reception unit can also prioritize inputting highly relevant request items by taking into account the distance from the user's current location. In this way, by prioritized input of highly relevant items based on the user's geographical location information, a more appropriate travel plan can be proposed.

[0035] When inputting a request item, the reception unit can analyze the user's social media activity and input related items. For example, the reception unit can suggest locations where the user has checked in on social media as request items. The reception unit can also analyze the content of the user's posts on social media and suggest related request items. The reception unit can also suggest related request items by referring to the activities of the user's friends on social media. In this way, related request items can be suggested by analyzing the user's social media activity.

[0036] The reception unit can customize the input method by reflecting the user's past feedback when inputting a request item. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's feedback. In this way, the input method can be optimized by reflecting the user's past feedback.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request item. For example, the analysis unit performs a detailed analysis on a request item with a high level of importance. The analysis unit can also perform a concise analysis on a request item with a low level of importance. The analysis unit can also determine the priority of the analysis according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the request item, more appropriate analysis results can be provided.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request item. For example, the analysis unit selects the optimal analysis algorithm depending on the category of the travel plan. The analysis unit can also apply different analysis methods based on the category of the request item. The analysis unit can also set the optimal analysis algorithm for each category and perform analysis. This allows the accuracy of analysis to be improved by applying the optimal analysis algorithm depending on the category of the request item.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing data obtained from the past analysis results. The analysis unit can also optimize the analysis parameters by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the request items. For example, the analysis unit prioritizes analysis of request items submitted earlier. The analysis unit can also postpone request items submitted later. The analysis unit can also adjust the analysis schedule based on the submission time. In this way, by determining the priority of analysis based on the submission time of the request items, more efficient analysis can be performed.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the request items. For example, the analysis unit prioritizes analysis of highly relevant request items. The analysis unit can also postpone analysis of less relevant request items. The analysis unit can also adjust the order of analysis based on the relevance of the request items. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the request items.

[0042] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to a user with a high level of expertise. The analysis unit can also provide analysis results in easy-to-understand language to a user with a low level of expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0043] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel plan when making the suggestion. For example, the suggestion unit makes a detailed suggestion for a travel plan with a high level of importance. The suggestion unit can also make a concise suggestion for a travel plan with a low level of importance. The suggestion unit can also determine the priority of the suggestion based on the importance. In this way, by adjusting the level of detail of the suggestion based on the importance of the travel plan, it is possible to provide a more appropriate suggestion.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making a suggestion. For example, the suggestion unit selects the optimal suggestion algorithm depending on the category of the travel plan. The suggestion unit can also apply different suggestion methods based on the category of the travel plan. The suggestion unit can also set the optimal suggestion algorithm for each category and make a suggestion. This allows the accuracy of suggestions to be improved by applying the optimal suggestion algorithm depending on the category of the travel plan.

[0045] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by utilizing data obtained from the past proposal results. The suggestion unit can also optimize the proposal parameters by referring to the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the past proposal results.

[0046] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the travel plan. For example, the proposal unit gives priority to the travel plan submitted earlier. The proposal unit can also postpone the travel plan submitted later. The proposal unit can also adjust the proposal schedule based on the time of submission. Thus, by determining the priority of the proposal based on the time of submission of the travel plan, more efficient proposals can be made.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant travel plans. The suggestion unit can also postpone less relevant travel plans. The suggestion unit can also adjust the order of suggestions based on the relevance of the travel plans. This allows for more efficient suggestions by adjusting the order of suggestions based on the relevance of the travel plans.

[0048] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit provides a proposal using detailed technical terminology to a user with a high level of expertise. The suggestion unit can also provide a proposal in easy-to-understand language to a user with a low level of expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate proposals can be provided.

[0049] At the time of provision, the provision unit can analyze the user's past consumption behavior and select the optimal provision method. The provision unit, for example, proposes the optimal service based on the user's past consumption behavior. The provision unit can also optimize the provision method by utilizing data obtained from the user's past consumption behavior. The provision unit can also analyze the user's consumption behavior and select the most effective provision method. In this way, the optimal provision method can be selected by analyzing the user's past consumption behavior.

[0050] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. The providing unit provides a service that takes into account specific constraints, for example, based on the user's living situation (e.g., when nursing care is required). The providing unit can also select the optimal means of provision in accordance with the user's current living situation. The providing unit can also customize the means of provision taking the user's living situation into consideration. In this way, by customizing the means of provision based on the user's current living situation, it is possible to provide a more appropriate service.

[0051] The providing unit can improve the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method based on user feedback. The providing unit can also utilize data obtained from the feedback to optimize the providing means. The providing unit can also customize the providing method by reflecting user feedback. In this way, the providing method can be improved by reflecting user feedback.

[0052] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information at the time of providing the service. The providing unit provides the optimal service, for example, based on the user's current location. The providing unit can also select the optimal providing means based on the geographical location information. The providing unit can also provide the optimal service by taking into consideration the distance from the user's current location. In this way, the optimal providing method can be selected by taking into consideration the user's geographical location information.

[0053] At the time of provision, the provision unit can analyze the user's social media activity and suggest a means of provision. The provision unit can, for example, suggest the most appropriate service based on the user's social media activity. The provision unit can also analyze the content posted on social media and provide related services. The provision unit can also suggest the most appropriate service by taking into account the activity of the user's friends on social media. In this way, the most appropriate means of provision can be suggested by analyzing the user's social media activity.

[0054] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method based on, for example, the user's past feedback. The providing unit can also optimize the delivery means by utilizing data obtained from the feedback. The providing unit can also improve the delivery method by reflecting the user's past feedback. In this way, the delivery method can be customized by reflecting the user's past feedback.

[0055] The revenue department can optimize the revenue model by referring to past revenue data when securing revenue. The revenue department, for example, sets an optimal revenue model based on past revenue data. The revenue department can also optimize the revenue model by utilizing information obtained from the revenue data. The revenue department can also adjust the parameters of the revenue model by referring to past revenue data. In this way, the revenue model can be optimized by referring to past revenue data.

[0056] The revenue department can update the revenue model by reflecting user feedback when securing revenue. The revenue department updates the revenue model based on user feedback, for example. The revenue department can also utilize data obtained from the feedback to optimize the revenue model. The revenue department can also improve the revenue model by reflecting user feedback. In this way, the revenue model can be updated by reflecting user feedback.

[0057] The revenue department can weight the revenue model based on the time of submission of the travel plan when securing revenue. For example, the revenue department weights the revenue model for travel plans submitted early. The revenue department can also adjust the revenue model weight for travel plans submitted late. The revenue department can also set revenue model parameters based on the time of submission. By weighting the revenue model based on the time of submission of the travel plan, more effective revenue can be secured.

[0058] The revenue department can integrate information from different data sources to enhance the revenue model when securing revenue. For example, the revenue department integrates information from different data sources to enhance the revenue model. The revenue department can also utilize information obtained from the data sources to optimize the revenue model. The revenue department can also adjust the parameters of the revenue model by referring to the different data sources. In this way, the revenue model can be enhanced by integrating information from different data sources.

[0059] The revenue department can update the revenue model by reflecting the user's past feedback when securing revenue. The revenue department updates the revenue model, for example, based on the user's past feedback. The revenue department can also utilize data obtained from the feedback to optimize the revenue model. The revenue department can also improve the revenue model by reflecting the user's past feedback. In this way, the revenue model can be updated by reflecting the user's past feedback.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request item. For example, a detailed analysis can be performed for a request item with a high importance. A simple analysis can also be performed for a request item with a low importance. Furthermore, the analysis priority can be determined according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the request item, more appropriate analysis results can be provided.

[0062] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel plan when making the suggestion. For example, a detailed suggestion can be made for a highly important travel plan. A concise suggestion can also be made for a less important travel plan. Furthermore, the priority of the suggestion can be determined according to the importance. In this way, by adjusting the level of detail of the suggestion based on the importance of the travel plan, more appropriate suggestions can be provided.

[0063] At the time of provision, the provision unit can analyze the user's past consumption behavior and select the optimal provision method. For example, the optimal service can be proposed based on the user's past consumption behavior. The provision method can also be optimized by utilizing data obtained from past consumption behavior. Furthermore, the user's consumption behavior can be analyzed and the most effective provision method can be selected. In this way, the optimal provision method can be selected by analyzing past consumption behavior.

[0064] The revenue department can optimize the revenue model by referring to past revenue data when securing revenue. For example, it can set an optimal revenue model based on past revenue data. It can also optimize the revenue model by utilizing information obtained from the revenue data. Furthermore, it can also adjust the revenue model parameters by referring to past revenue data. In this way, it is possible to optimize the revenue model by referring to past revenue data.

[0065] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information at the time of providing the service. For example, the optimal service is provided based on the user's current location. The optimal providing means can also be selected based on the geographical location information. Furthermore, the optimal service can be provided by taking into consideration the distance from the user's current location. In this way, the optimal providing method can be selected by taking into consideration the user's geographical location information.

[0066] When securing revenue, the revenue department can integrate information from different data sources to enhance the revenue model. For example, information from different data sources can be integrated to enhance the revenue model. The revenue model can also be optimized by utilizing information obtained from the data sources. Furthermore, the revenue model parameters can be adjusted by referring to the different data sources. In this way, the revenue model can be enhanced by integrating information from different data sources.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception unit receives requests from users, including their budget, interests, and restrictions. For example, users can enter specific budgets, tourist attractions they are interested in, and health restrictions. Step 2: The analysis unit uses the generation AI to analyze the request items entered by the reception unit. For example, the generation AI takes into account the user's budget, interests, restrictions, etc. and automatically proposes the optimal travel plan. Step 3: The suggestion unit proposes travel plans based on the information analyzed by the analysis unit. For example, the generation AI searches for hotels within a budget and suggests tourist attractions and activities. Step 4: The provision unit makes reservations and provides information based on the plan proposed by the proposal unit. For example, if the user agrees to the proposed plan, the generation AI automatically makes hotel reservations and provides information on tourist attractions. The generation AI can also make reservations for local activities on the user's behalf.

[0069] (Example 2) A travel plan proposal system according to an embodiment of the present invention utilizes a generation AI to provide travel plans for seniors aged 60 and over. In this system, users input request items such as their budget, interests, and restrictions, and the generation AI analyzes these request items to automatically propose optimal travel plans. The travel plans include hotel reservations, tourist attraction information, and local activity reservations. Revenues can be secured by charging fees and plan provision fees. This allows the travel plan proposal system to easily provide travel plans for seniors and realize personalized travel experiences tailored to users' needs. For example, users input request items such as their budget, interests, and restrictions, and the generation AI analyzes these request items to automatically propose optimal travel plans. Furthermore, the generation AI then makes hotel reservations, tourist attraction information, and local activity reservations based on the proposed travel plans. This allows users to prepare for their trips hassle-free. Revenues can also be secured by charging fees and plan provision fees. For example, a system can earn revenue from users by setting fees for travel plan proposals and reservation services. It is also possible to secure additional revenue by offering more detailed travel plans and special services as premium plans.

[0070] A travel plan proposal system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit allows a user to input request items such as their budget, interests, and restrictions. For example, the user can input a specific budget, tourist attractions of interest, health restrictions, and the like. The analysis unit uses a generation AI to analyze the request items input by the reception unit. For example, the generation AI automatically proposes an optimal travel plan based on the user's budget, interests, and restrictions. The proposal unit proposes a travel plan based on the information analyzed by the analysis unit. For example, the generation AI searches for hotels available within the user's budget and proposes tourist attractions and activities. The provision unit makes reservations and provides information based on the plan proposed by the proposal unit. For example, if the user agrees to the proposed plan, the generation AI automatically makes hotel reservations and provides information about tourist attractions. The generation AI can also make reservations for local activities on the user's behalf. As a result, the travel plan proposal system according to an embodiment can propose an optimal travel plan based on the user's request, and make reservations and provide information.

[0071] The travel plan proposal system includes a revenue section for securing revenue. The revenue section secures revenue by charging fees and for providing plans. For example, revenue can be obtained from users by setting fees for proposing travel plans and acting as a booking agent. It is also possible to secure additional revenue by offering more detailed travel plans and special services as premium plans. This makes it possible to provide functions for securing revenue.

[0072] The reception unit can estimate the user's emotions and adjust the input interface for the request items based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the request items. This allows the input interface to be adjusted according to the user's emotions, thereby providing a more user-friendly system. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can analyze the user's past request history and suggest an input method. For example, the reception unit can automatically display request items that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest request items that will be used in a specific time period based on the user's past request history. This can improve input efficiency by suggesting the optimal input method based on the past request history.

[0074] The reception unit can filter the request items based on the user's current health condition and living situation when the request items are input. For example, if the user inputs their health condition, the reception unit can propose an appropriate travel plan based on that information. The reception unit can also propose a plan that takes into account specific constraints based on the user's living situation (e.g., if care is required). The reception unit can also propose a reasonable schedule based on the user's health condition. In this way, by filtering according to the user's health condition and living situation, more appropriate travel plans can be proposed.

[0075] The reception unit can select an input means according to the user's input method when inputting a request item. For example, when the user inputs the request item by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs the request item by text, the reception unit can provide a predictive conversion function to simplify the input. Furthermore, when the user inputs the request item using an image, the reception unit can analyze the input content using image recognition technology. This allows the efficiency of input to be improved by selecting the optimal input means according to the user's input method.

[0076] The reception unit can estimate the user's emotions and determine the priority of request items to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can cause the user to input important request items first. Furthermore, when the user is relaxed, the reception unit can cause the user to input detailed request items. Furthermore, when the user is in a hurry, the reception unit can cause the user to input only the most important request items. In this way, by determining the priority of request items according to the user's emotions, more appropriate input can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When inputting request items, the reception unit can prioritize inputting highly relevant items based on the user's geographical location information. For example, when the user inputs their current location, the reception unit can prioritize suggesting tourist attractions and hotels in the vicinity. The reception unit can also propose an optimal travel plan based on the user's geographical location information. The reception unit can also prioritize inputting highly relevant request items by taking into account the distance from the user's current location. In this way, by prioritized input of highly relevant items based on the user's geographical location information, a more appropriate travel plan can be proposed.

[0078] When inputting a request item, the reception unit can analyze the user's social media activity and input related items. For example, the reception unit can suggest locations where the user has checked in on social media as request items. The reception unit can also analyze the content of the user's posts on social media and suggest related request items. The reception unit can also suggest related request items by referring to the activities of the user's friends on social media. In this way, related request items can be suggested by analyzing the user's social media activity.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting a request item. The reception unit can, for example, suggest the optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also customize the input interface by reflecting the user's feedback. In this way, the input method can be optimized by reflecting the user's past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. If the user is feeling stressed, the analysis unit can also provide visually easy-to-understand analysis results. This allows for adjusting the way the analysis is presented according to the user's emotions, making it possible to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request item. For example, the analysis unit performs a detailed analysis on a request item with a high level of importance. The analysis unit can also perform a concise analysis on a request item with a low level of importance. The analysis unit can also determine the priority of the analysis according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the request item, more appropriate analysis results can be provided.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the request item. For example, the analysis unit selects the optimal analysis algorithm depending on the category of the travel plan. The analysis unit can also apply different analysis methods based on the category of the request item. The analysis unit can also set the optimal analysis algorithm for each category and perform analysis. This allows the accuracy of analysis to be improved by applying the optimal analysis algorithm depending on the category of the request item.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing data obtained from the past analysis results. The analysis unit can also optimize the analysis parameters by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the request items. For example, the analysis unit prioritizes analysis of request items submitted earlier. The analysis unit can also postpone request items submitted later. The analysis unit can also adjust the analysis schedule based on the submission time. In this way, by determining the priority of analysis based on the submission time of the request items, more efficient analysis can be performed.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the request items. For example, the analysis unit prioritizes analysis of highly relevant request items. The analysis unit can also postpone analysis of less relevant request items. The analysis unit can also adjust the order of analysis based on the relevance of the request items. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the request items.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to a user with a high level of expertise. The analysis unit can also provide analysis results in easy-to-understand language to a user with a low level of expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0088] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions. When the user is in a hurry, the suggestion unit can also provide concise suggestions that focus on the main points. When the user is stressed, the suggestion unit can also provide visually easy-to-understand suggestions. This allows for adjusting the way suggestions are expressed according to the user's emotions, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0089] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel plan when making the suggestion. For example, the suggestion unit makes a detailed suggestion for a travel plan with a high level of importance. The suggestion unit can also make a concise suggestion for a travel plan with a low level of importance. The suggestion unit can also determine the priority of the suggestion based on the importance. In this way, by adjusting the level of detail of the suggestion based on the importance of the travel plan, it is possible to provide a more appropriate suggestion.

[0090] The suggestion unit can apply different suggestion algorithms depending on the category of the travel plan when making a suggestion. For example, the suggestion unit selects the optimal suggestion algorithm depending on the category of the travel plan. The suggestion unit can also apply different suggestion methods based on the category of the travel plan. The suggestion unit can also set the optimal suggestion algorithm for each category and make a suggestion. This allows the accuracy of suggestions to be improved by applying the optimal suggestion algorithm depending on the category of the travel plan.

[0091] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by utilizing data obtained from the past proposal results. The suggestion unit can also optimize the proposal parameters by referring to the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the past proposal results.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is stressed, the suggestion unit can also provide visually easy-to-understand suggestions. This allows for adjusting the length of the suggestions according to the user's emotions, thereby providing more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0093] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the travel plan. For example, the proposal unit gives priority to the travel plan submitted earlier. The proposal unit can also postpone the travel plan submitted later. The proposal unit can also adjust the proposal schedule based on the time of submission. Thus, by determining the priority of the proposal based on the time of submission of the travel plan, more efficient proposals can be made.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of the travel plans when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant travel plans. The suggestion unit can also postpone less relevant travel plans. The suggestion unit can also adjust the order of suggestions based on the relevance of the travel plans. This allows for more efficient suggestions by adjusting the order of suggestions based on the relevance of the travel plans.

[0095] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit provides a proposal using detailed technical terminology to a user with a high level of expertise. The suggestion unit can also provide a proposal in easy-to-understand language to a user with a low level of expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, more appropriate proposals can be provided.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing the service based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide concise information that focuses on the main points. Furthermore, if the user is feeling stressed, the providing unit can provide visually easy-to-understand information. This allows for adjusting the method of providing the service according to the user's emotions, thereby providing a more appropriate service. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0097] At the time of provision, the provision unit can analyze the user's past consumption behavior and select the optimal provision method. The provision unit, for example, proposes the optimal service based on the user's past consumption behavior. The provision unit can also optimize the provision method by utilizing data obtained from the user's past consumption behavior. The provision unit can also analyze the user's consumption behavior and select the most effective provision method. In this way, the optimal provision method can be selected by analyzing the user's past consumption behavior.

[0098] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. The providing unit provides a service that takes into account specific constraints, for example, based on the user's living situation (e.g., when nursing care is required). The providing unit can also select the optimal means of provision in accordance with the user's current living situation. The providing unit can also customize the means of provision taking the user's living situation into consideration. In this way, by customizing the means of provision based on the user's current living situation, it is possible to provide a more appropriate service.

[0099] The providing unit can improve the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method based on user feedback. The providing unit can also utilize data obtained from the feedback to optimize the providing means. The providing unit can also customize the providing method by reflecting user feedback. In this way, the providing method can be improved by reflecting user feedback.

[0100] The providing unit can estimate the user's emotions and determine the priority of services to be provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can provide important services with priority. Furthermore, when the user is relaxed, the providing unit can provide detailed services. Furthermore, when the user is in a hurry, the providing unit can provide only the most important services. This allows for more appropriate services to be provided by determining the priority of services according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information at the time of providing the service. The providing unit provides the optimal service, for example, based on the user's current location. The providing unit can also select the optimal providing means based on the geographical location information. The providing unit can also provide the optimal service by taking into consideration the distance from the user's current location. In this way, the optimal providing method can be selected by taking into consideration the user's geographical location information.

[0102] At the time of provision, the provision unit can analyze the user's social media activity and suggest a means of provision. The provision unit can, for example, suggest the most appropriate service based on the user's social media activity. The provision unit can also analyze the content posted on social media and provide related services. The provision unit can also suggest the most appropriate service by taking into account the activity of the user's friends on social media. In this way, the most appropriate means of provision can be suggested by analyzing the user's social media activity.

[0103] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. The providing unit customizes the delivery method based on, for example, the user's past feedback. The providing unit can also optimize the delivery means by utilizing data obtained from the feedback. The providing unit can also improve the delivery method by reflecting the user's past feedback. In this way, the delivery method can be customized by reflecting the user's past feedback.

[0104] The revenue unit can estimate the user's emotions and adjust the revenue model based on the estimated user emotions. For example, if the user is relaxed, the revenue unit can provide detailed services and set an additional fee. Furthermore, if the user is in a hurry, the revenue unit can provide simple services and set a fee. Furthermore, if the user is stressed, the revenue unit can provide visually easy-to-understand services and ensure revenue. This allows for more effective revenue generation by adjusting the revenue model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The revenue department can optimize the revenue model by referring to past revenue data when securing revenue. The revenue department, for example, sets an optimal revenue model based on past revenue data. The revenue department can also optimize the revenue model by utilizing information obtained from the revenue data. The revenue department can also adjust the parameters of the revenue model by referring to past revenue data. In this way, the revenue model can be optimized by referring to past revenue data.

[0106] The revenue department can update the revenue model by reflecting user feedback when securing revenue. The revenue department updates the revenue model based on user feedback, for example. The revenue department can also utilize data obtained from the feedback to optimize the revenue model. The revenue department can also improve the revenue model by reflecting user feedback. In this way, the revenue model can be updated by reflecting user feedback.

[0107] The revenue unit can estimate the user's emotions and adjust the frequency of revenue capture based on the estimated user emotions. For example, if the user is relaxed, the revenue unit can provide services frequently and capture revenue. If the user is in a hurry, the revenue unit can also provide simple services and set a fee. If the user is stressed, the revenue unit can also provide visually easy-to-understand services and capture revenue. This allows for more effective revenue capture by adjusting the frequency of revenue capture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The revenue department can weight the revenue model based on the time of submission of the travel plan when securing revenue. For example, the revenue department weights the revenue model for travel plans submitted early. The revenue department can also adjust the revenue model weight for travel plans submitted late. The revenue department can also set revenue model parameters based on the time of submission. By weighting the revenue model based on the time of submission of the travel plan, more effective revenue can be secured.

[0109] The revenue department can integrate information from different data sources to enhance the revenue model when securing revenue. For example, the revenue department integrates information from different data sources to enhance the revenue model. The revenue department can also utilize information obtained from the data sources to optimize the revenue model. The revenue department can also adjust the parameters of the revenue model by referring to the different data sources. In this way, the revenue model can be enhanced by integrating information from different data sources.

[0110] The revenue department can update the revenue model by reflecting the user's past feedback when securing revenue. The revenue department updates the revenue model, for example, based on the user's past feedback. The revenue department can also utilize data obtained from the feedback to optimize the revenue model. The revenue department can also improve the revenue model by reflecting the user's past feedback. In this way, the revenue model can be updated by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, provision unit, and revenue unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input user request items using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request items using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal travel plan based on the analysis results. The provision unit makes reservations and provides information based on the proposed plan using the output device 40 of the smart device 14. The revenue unit is realized by the specific processing unit 290 of the data processing device 12 and secures revenue by charging fees and providing plans. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, provision unit, and revenue unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input a user's request items using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request items using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal travel plan based on the analysis results. The provision unit makes reservations and provides information based on the proposed plan using the speaker 240 of the smart glasses 214. The revenue unit is realized by the specific processing unit 290 of the data processing device 12 and secures revenue by charging fees and providing plans. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, provision unit, and revenue 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 can input a user's request items using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request items using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal travel plan based on the analysis results. The provision unit makes reservations and provides information based on the proposed plan using the display 343 of the headset-type terminal 314. The revenue unit is realized by the specific processing unit 290 of the data processing device 12 and secures revenue by charging a fee or for providing the plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, provision unit, and revenue unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input a user's request items using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the request items using a generation AI. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal travel plan based on the analysis results. The provision unit makes reservations and provides information based on the proposed plan using the speaker 240 of the robot 414. The revenue unit is realized by the specific processing unit 290 of the data processing device 12 and secures revenue by charging a fee or for providing the plan.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is feeling stressed, analysis results that are visually easy to understand can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0113] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be provided. If the user is in a hurry, concise suggestions that focus on the main points can be provided. Furthermore, if the user is stressed, visually easy-to-understand suggestions can be provided. In this way, by adjusting the way suggestions are expressed according to the user's emotions, more appropriate suggestions can be provided.

[0114] The providing unit can estimate the user's emotions and adjust the method of providing services based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, concise information that focuses on the main points can be provided. Furthermore, if the user is feeling stressed, visually easy-to-understand information can be provided. In this way, by adjusting the method of providing services according to the user's emotions, more appropriate services can be provided.

[0115] The revenue unit can estimate the user's emotions and adjust the revenue model based on the estimated user emotions. For example, if the user is relaxed, detailed services can be provided and an additional fee can be set. If the user is in a hurry, simple services can be provided and a fee can be set. Furthermore, if the user is stressed, visually easy-to-understand services can be provided to ensure revenue. This allows for more effective revenue generation by adjusting the revenue model according to the user's emotions.

[0116] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the request item. For example, a detailed analysis can be performed for a request item with a high importance. A simple analysis can also be performed for a request item with a low importance. Furthermore, the analysis priority can be determined according to the importance. In this way, by adjusting the level of detail of the analysis based on the importance of the request item, more appropriate analysis results can be provided.

[0117] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel plan when making the suggestion. For example, a detailed suggestion can be made for a highly important travel plan. A concise suggestion can also be made for a less important travel plan. Furthermore, the priority of the suggestion can be determined according to the importance. In this way, by adjusting the level of detail of the suggestion based on the importance of the travel plan, more appropriate suggestions can be provided.

[0118] At the time of provision, the provision unit can analyze the user's past consumption behavior and select the optimal provision method. For example, the optimal service can be proposed based on the user's past consumption behavior. The provision method can also be optimized by utilizing data obtained from past consumption behavior. Furthermore, the user's consumption behavior can be analyzed and the most effective provision method can be selected. In this way, the optimal provision method can be selected by analyzing past consumption behavior.

[0119] The revenue department can optimize the revenue model by referring to past revenue data when securing revenue. For example, it can set an optimal revenue model based on past revenue data. It can also optimize the revenue model by utilizing information obtained from the revenue data. Furthermore, it can also adjust the revenue model parameters by referring to past revenue data. In this way, it is possible to optimize the revenue model by referring to past revenue data.

[0120] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information at the time of providing the service. For example, the optimal service is provided based on the user's current location. The optimal providing means can also be selected based on the geographical location information. Furthermore, the optimal service can be provided by taking into consideration the distance from the user's current location. In this way, the optimal providing method can be selected by taking into consideration the user's geographical location information.

[0121] When securing revenue, the revenue department can integrate information from different data sources to enhance the revenue model. For example, information from different data sources can be integrated to enhance the revenue model. The revenue model can also be optimized by utilizing information obtained from the data sources. Furthermore, the revenue model parameters can be adjusted by referring to the different data sources. In this way, the revenue model can be enhanced by integrating information from different data sources.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The reception unit receives requests from users, including their budget, interests, and restrictions. For example, users can enter specific budgets, tourist attractions they are interested in, and health restrictions. Step 2: The analysis unit uses the generation AI to analyze the request items entered by the reception unit. For example, the generation AI takes into account the user's budget, interests, restrictions, etc. and automatically proposes the optimal travel plan. Step 3: The suggestion unit proposes travel plans based on the information analyzed by the analysis unit. For example, the generation AI searches for hotels within a budget and suggests tourist attractions and activities. Step 4: The provision unit makes reservations and provides information based on the plan proposed by the proposal unit. For example, if the user agrees to the proposed plan, the generation AI automatically makes hotel reservations and provides information on tourist attractions. The generation AI can also make reservations for local activities on the user's behalf.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 AI 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 AI 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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 section for inputting request items; an analysis unit that analyzes the request items input by the reception unit; a suggestion unit that proposes a travel plan based on the information analyzed by the analysis unit; a provision unit that makes reservations and provides information based on the plan proposed by the proposal unit; A system characterized by:

2. Establish a revenue department to ensure profits 2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the input interface for the request items based on the estimated user emotions.

2. The system of claim 1.

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

5. The reception unit Filtering requests based on the user's current health and living situation when entering them 2. The system of claim 1.

6. The reception unit When entering request items, select the input method according to the user's input method.

2. The system of claim 1.

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

2. The system of claim 1.

8. The reception unit When filling out a request, prioritize relevant items based on the user's geographic location.

2. The system of claim 1.

9. The reception unit When a user fills in a request, the system analyzes the user's social media activity and fills in relevant fields.

2. The system of claim 1.

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