System

Abstract

An object of a system according to an embodiment is to analyze sightseeing data and review data and automatically generate an optimal sightseeing plan matching a desired schedule of a user.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects sightseeing data and review data. The analysis unit analyzes the data collected by the collection unit and extracts an evaluation and a feature of the tourist spot. The generation unit automatically generates a sightseeing plan according to a desired schedule of the user based on the evaluation and the feature obtained by the analysis unit. The proposal unit searches for information on a new or less popular tourist spot based on the plan generated by the generation unit, and proposes to incorporate the information into the tour route.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 technologies do not adequately utilize tourism data and review data to propose optimal travel plans for individual travelers, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze tourism data and review data and automatically generate an optimal sightseeing plan that matches the user's desired schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects tourist data and review data. The analysis unit analyzes the data collected by the collection unit and extracts ratings and characteristics of tourist destinations. The generation unit automatically generates a sightseeing plan tailored to the user's desired schedule based on the ratings and characteristics obtained by the analysis unit. The proposal unit searches for information on new or lesser-known tourist destinations based on the plan generated by the generation unit and proposes incorporating them into the sightseeing route. [Effects of the Invention]

[0007] The system according to the embodiment can analyze tourism data and review data and automatically generate an optimal sightseeing plan tailored to the user's desired schedule. [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 tourist information suggestion system according to an embodiment of the present invention collects and analyzes tourist data and review data, automatically generates a sightseeing plan based on a user's desired itinerary, and suggests information on unexplored or lesser-known tourist destinations. The tourist information suggestion system analyzes the tourist data and review data, extracts ratings and characteristics of tourist destinations, and automatically generates a sightseeing plan tailored to the user's desired itinerary. Furthermore, the tourist information suggestion system searches for information on unexplored or lesser-known tourist destinations near the area and suggests incorporating them into a sightseeing route. For example, the tourist information suggestion system collects tourist data and review data, such as the popularity of tourist destinations, visitor ratings, and characteristics of specific tourist destinations (historical background, natural scenery, etc.). Next, the tourist information suggestion system analyzes the collected data and extracts ratings and characteristics of tourist destinations. For example, the tourist information suggestion system analyzes the popularity of tourist destinations, visitor ratings, and characteristics of specific tourist destinations. Next, once the user's desired itinerary is confirmed, the tourist information suggestion system automatically generates a plan incorporating local information (such as festivals and events) of that period. For example, the system suggests a sightseeing route incorporating information on local festivals and events held during the period the user is planning to travel. Next, based on the generated plan, the tourist information suggestion system searches for information on unexplored or little-known tourist destinations near the area and suggests incorporating them into the tourist route. For example, it suggests tourist destinations that are popular with locals but not listed in tourist guidebooks, or new tourist spots that are not yet known to tourists. In this way, the tourist information suggestion system can suggest optimal tourist routes that make use of the culture and characteristics of the area to the user based on high-quality tourist information. In addition, by suggesting plans that incorporate special information such as local festivals and events, or tourist routes that include information on unexplored tourist destinations, the user can enjoy a more fulfilling tourist experience. In this way, the tourist information suggestion system can suggest optimal tourist routes that make use of the culture and characteristics of the area to the user based on high-quality tourist information. In addition, by suggesting plans that incorporate special information such as local festivals and events, or tourist routes that include information on unexplored tourist destinations, the user can enjoy a more fulfilling tourist experience.

[0029] A tourism information suggestion system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects tourism data and review data. Tourism data includes, but is not limited to, tourist destination ratings, visitor feedback, and tourist destination characteristics, for example. The collection unit collects, for example, the popularity of tourist destinations, visitor ratings, and characteristics of specific tourist destinations (such as historical backgrounds and natural landscapes). The collection unit can also collect review data such as online reviews and survey results. For example, the collection unit collects tourist destination ratings using star ratings and comment analysis. Visitor feedback is collected using survey results and online reviews. Tourist destination characteristics are collected based on natural landscapes, historical backgrounds, and the like. The analysis unit analyzes the data collected by the collection unit and extracts tourist destination ratings and characteristics. For example, the analysis unit analyzes the collected data and extracts tourist destination ratings and characteristics. For example, the analysis unit analyzes tourist destination ratings using star ratings and comment analysis. Tourist destination characteristics are analyzed based on natural landscapes, historical backgrounds, and the like. The generation unit automatically generates a sightseeing plan tailored to the user's desired itinerary based on the evaluations and features obtained by the analysis unit. For example, the generation unit automatically generates a sightseeing plan that incorporates local information (such as festivals and events) for that period based on the user's desired itinerary. For example, the generation unit proposes a sightseeing route that incorporates information about local festivals and events that will be held during the period when the user is planning to travel. Based on the plan generated by the generation unit, the suggestion unit searches for information about unexplored or little-known tourist destinations near the area and suggests incorporating the destination into the sightseeing route. For example, based on the generated plan, the suggestion unit searches for information about unexplored or little-known tourist destinations near the area and suggests incorporating the destination into the sightseeing route. For example, the suggestion unit suggests tourist destinations that are not listed in tourist guidebooks but are popular with locals, or new tourist spots that are not yet known to tourists. This allows the tourist information suggestion system according to the embodiment to propose optimal sightseeing routes to users based on tourism data and review data.

[0030] The collection unit can collect tourist destination ratings, visitor feedback, and specific tourist destination characteristics. The collection unit, for example, collects tourist destination ratings using star ratings and comment analysis. For example, the collection unit collects tourist destination ratings based on star ratings. The collection unit can also collect tourist destination ratings based on comment analysis. The collection unit can also collect visitor feedback using survey results and online reviews. For example, the collection unit collects visitor feedback based on survey results. The collection unit can also collect visitor feedback based on online reviews. The collection unit can also collect tourist destination characteristics based on natural scenery, historical background, etc. For example, the collection unit collects tourist destination characteristics based on natural scenery. The collection unit can also collect tourist destination characteristics based on historical background. In this way, by collecting the popularity, ratings, and characteristics of tourist destinations, high-quality tourist information can be provided.

[0031] The analysis unit can analyze the collected data and extract the ratings and characteristics of tourist destinations. The analysis unit, for example, analyzes the collected data and extracts the ratings and characteristics of tourist destinations. For example, the analysis unit analyzes the ratings of tourist destinations using star ratings and comment analysis. The analysis unit can also analyze the characteristics of tourist destinations based on natural scenery, historical background, etc. For example, the analysis unit analyzes the ratings of tourist destinations based on star ratings. The analysis unit can also analyze the ratings of tourist destinations based on comment analysis. The analysis unit can also analyze the characteristics of tourist destinations based on natural scenery. The analysis unit can also analyze the characteristics of tourist destinations based on historical background. In this way, the ratings and characteristics of tourist destinations can be understood by analyzing the collected data.

[0032] The generation unit can automatically generate a sightseeing plan that incorporates local information for that period based on the user's desired itinerary. For example, the generation unit automatically generates a sightseeing plan that incorporates local information for that period (festivals, events, etc.) based on the user's desired itinerary. For example, the generation unit proposes a sightseeing route that incorporates information about local festivals and events that will be held during the period when the user is planning to travel. For example, the generation unit proposes a sightseeing route that incorporates information about local festivals that will be held during the period when the user is planning to travel. The generation unit can also propose a sightseeing route that incorporates information about local events that will be held during the period when the user is planning to travel. This makes it possible to provide a special sightseeing experience based on the user's desired itinerary.

[0033] The suggestion unit can search for information on new or lesser-known tourist spots near the area based on the generated plan and suggest incorporating them into the tourist route. The suggestion unit can search for information on unexplored or little-known tourist spots near the area based on the generated plan and suggest incorporating them into the tourist route. For example, the suggestion unit can suggest tourist spots that are not listed in tourist guidebooks but are popular with locals, or new tourist spots that are not yet known to tourists. For example, the suggestion unit can suggest tourist spots that are not listed in tourist guidebooks but are popular with locals. The suggestion unit can also suggest new tourist spots that are not yet known to tourists. This allows users to enjoy new discoveries and surprises by providing information on unexplored tourist spots.

[0034] The collection unit can analyze the user's past travel history and select the optimal collection method. The collection unit, for example, prioritizes collecting data on tourist spots that the user has visited in the past. For example, the collection unit prioritizes collecting data on tourist spots that the user has visited in the past. The collection unit can also collect data on favorite tourist spots from the user's past travel history. For example, the collection unit collects data on favorite tourist spots from the user's past travel history. The collection unit can also focus on collecting data on tourist spots that the user has given high ratings in the past. For example, the collection unit focuses on collecting data on tourist spots that the user has given high ratings in the past. In this way, optimal tourist data can be collected by analyzing the user's past travel history.

[0035] When collecting tourism data, the collection unit can filter the tourism data based on the user's current interests and areas of interest. For example, if the user is interested in natural landscapes, the collection unit prioritizes collecting data related to natural landscapes. For example, if the user is interested in natural landscapes, the collection unit prioritizes collecting data related to natural landscapes. Furthermore, if the user is interested in historical buildings, the collection unit can also prioritize collecting data related to historical buildings. For example, if the user is interested in historical buildings, the collection unit prioritizes collecting data related to historical buildings. Furthermore, if the user is interested in gourmet food, the collection unit can also prioritize collecting local gourmet food information. For example, if the user is interested in gourmet food, the collection unit prioritizes collecting local gourmet food information. In this way, by filtering tourism data based on the user's interests and areas of interest, more relevant data can be collected.

[0036] When collecting tourism data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects tourism data using voice recognition technology. For example, when the user uses voice input, the collection unit collects tourism data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect tourism data using text analysis technology. For example, when the user uses text input, the collection unit collects tourism data using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect tourism data using image recognition technology. For example, when the user uses image input, the collection unit collects tourism data using image recognition technology. In this way, by selecting the optimal collection means depending on the user's input method, tourism data can be collected efficiently.

[0037] When collecting tourism data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting data on tourist spots close to the user's current location. For example, the collection unit prioritizes collecting data on tourist spots close to the user's current location. The collection unit can also prioritize collecting tourism data on areas the user plans to visit. For example, the collection unit prioritizes collecting tourism data on areas the user plans to visit. The collection unit can also prioritize collecting tourism data on areas the user has visited in the past. For example, the collection unit prioritizes collecting tourism data on areas the user has visited in the past. In this way, highly relevant tourism data can be collected by taking into account the user's geographical location information.

[0038] When collecting tourism data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects data on places where the user has checked in on social media. For example, the collection unit collects data on places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related tourism data. For example, the collection unit analyzes the content of the user's posts on social media and collects related tourism data. The collection unit can also collect related tourism data by referring to the activities of the user's friends on social media. For example, the collection unit collects related tourism data by referring to the activities of the user's friends on social media. In this way, highly relevant tourism data can be collected by analyzing the user's social media activities.

[0039] When collecting tourism data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, prioritizes collecting data on tourist destinations that the user has given a high rating in the past. For example, the collection unit prioritizes collecting data on tourist destinations that the user has given a high rating in the past. The collection unit can also adjust the type of data to collect based on the user's past feedback. For example, the collection unit adjusts the type of data to collect based on the user's past feedback. The collection unit can also adjust the amount of data to collect by referring to the user's past feedback. For example, the collection unit adjusts the amount of data to collect by referring to the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the tourist destination. For example, the analysis unit provides detailed analysis results for important tourist destinations. For example, the analysis unit provides detailed analysis results for important tourist destinations. The analysis unit can also provide concise analysis results for less important tourist destinations. For example, the analysis unit provides concise analysis results for less important tourist destinations. The analysis unit can also adjust the level of detail of the analysis based on the rating of the tourist destination. For example, the analysis unit adjusts the level of detail of the analysis based on the rating of the tourist destination. In this way, by adjusting the level of detail of the analysis based on the importance of the tourist destination, more appropriate analysis results can be provided.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the tourist destination. For example, the analysis unit applies a landscape analysis algorithm to a tourist destination with natural scenery. For example, the analysis unit applies a landscape analysis algorithm to a tourist destination with natural scenery. The analysis unit can also apply a history analysis algorithm to a tourist destination with historical buildings. For example, the analysis unit applies a history analysis algorithm to a tourist destination with historical buildings. The analysis unit can also apply a gourmet analysis algorithm to a gourmet spot. For example, the analysis unit applies a gourmet analysis algorithm to a gourmet spot. In this way, by applying different analysis algorithms depending on the category of the tourist destination, more appropriate analysis results can be provided.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to analysis results that the user has given a high rating to in the past. For example, the analysis unit improves the accuracy of the analysis by referring to analysis results that the user has given a high rating to in the past. The analysis unit can also adjust the analysis algorithm based on 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 analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit analyzes the user's past analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of the analysis based on the time of visiting tourist spots. For example, the analysis unit prioritizes the analysis of tourist spots that the user plans to visit. For example, the analysis unit prioritizes the analysis of tourist spots that the user plans to visit. The analysis unit can also postpone the analysis of tourist spots that the user has visited in the past. For example, the analysis unit postpones the analysis of tourist spots that the user has visited in the past. The analysis unit can also give top priority to the analysis of tourist spots that the user plans to visit. For example, the analysis unit gives top priority to the analysis of tourist spots that the user plans to visit. In this way, by determining the priority of the analysis based on the time of visiting tourist spots, more appropriate analysis results can be provided.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of tourist destinations. For example, the analysis unit prioritizes the analysis of highly relevant tourist destinations. For example, the analysis unit prioritizes the analysis of highly relevant tourist destinations. The analysis unit can also postpone the analysis of less relevant tourist destinations. For example, the analysis unit postpones the analysis of less relevant tourist destinations. The analysis unit can also adjust the order of analysis based on the relevance of tourist destinations. For example, the analysis unit adjusts the order of analysis based on the relevance of tourist destinations. In this way, by adjusting the order of analysis based on the relevance of tourist destinations, more appropriate analysis results can be provided.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides an analysis result that makes extensive use of technical terms. For example, if the user has technical expertise, the analysis unit provides an analysis result that makes extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide a concise and easy-to-understand analysis result. For example, if the user does not have technical expertise, the analysis unit provides a concise and easy-to-understand analysis result. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0046] The generation unit can adjust the level of detail of the plan based on the importance of the tourist destination when generating the plan. The generation unit, for example, provides a detailed plan for an important tourist destination. For example, the generation unit provides a detailed plan for an important tourist destination. The generation unit can also provide a concise plan for a less important tourist destination. For example, the generation unit provides a concise plan for a less important tourist destination. The generation unit can also adjust the level of detail of the plan based on the rating of the tourist destination. For example, the generation unit adjusts the level of detail of the plan based on the rating of the tourist destination. In this way, by adjusting the level of detail of the plan based on the importance of the tourist destination, a more appropriate plan can be provided.

[0047] The generation unit can apply different generation algorithms depending on the category of tourist attraction during generation. For example, the generation unit applies a landscape generation algorithm to tourist attractions of natural scenery. For example, the generation unit applies a landscape generation algorithm to tourist attractions of natural scenery. The generation unit can also apply a history generation algorithm to tourist attractions of historical buildings. For example, the generation unit applies a history generation algorithm to tourist attractions of historical buildings. The generation unit can also apply a gourmet generation algorithm to gourmet spots. For example, the generation unit applies a gourmet generation algorithm to gourmet spots. In this way, by applying different generation algorithms depending on the category of tourist attraction, a more appropriate plan can be provided.

[0048] The generation unit can improve the accuracy of generation by referring to the user's past plan results during generation. The generation unit can improve the accuracy of generation by referring to, for example, plans that the user has given high ratings to in the past. For example, the generation unit can improve the accuracy of generation by referring to plans that the user has given high ratings to in the past. The generation unit can also adjust the generation algorithm based on the user's past plan results. For example, the generation unit can adjust the generation algorithm based on the user's past plan results. The generation unit can also analyze the user's past plan results and improve the accuracy of generation. For example, the generation unit can analyze the user's past plan results and improve the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past plan results.

[0049] At the time of generation, the generation unit can determine the priority of the plans based on the time of visiting the tourist destinations. For example, the generation unit generates preferentially plans for tourist destinations that the user plans to visit. For example, the generation unit generates preferentially plans for tourist destinations that the user plans to visit. The generation unit can also postpone plans for tourist destinations that the user has visited in the past. For example, the generation unit postpones plans for tourist destinations that the user has visited in the past. The generation unit can also generate plans for tourist destinations that the user plans to visit with the highest priority. For example, the generation unit generates plans for tourist destinations that the user plans to visit with the highest priority. In this way, by determining the priority of the plans based on the time of visiting the tourist destinations, a more appropriate plan can be provided.

[0050] The generation unit can adjust the order of the plans based on the relevance of the tourist destinations when generating them. The generation unit, for example, generates plans with high relevance as a priority. For example, the generation unit generates plans with high relevance as a priority. The generation unit can also postpone plans with low relevance as a priority. For example, the generation unit postpones plans with low relevance as a priority. The generation unit can also adjust the order of the plans based on the relevance of the tourist destinations. For example, the generation unit adjusts the order of the plans based on the relevance of the tourist destinations. In this way, by adjusting the order of the plans based on the relevance of the tourist destinations, a more appropriate plan can be provided.

[0051] The generation unit may adjust the use of technical terminology in the plan according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit provides a plan that uses a lot of technical terminology. For example, if the user has technical expertise, the generation unit provides a plan that uses a lot of technical terminology. The generation unit may also provide a concise and easy-to-understand plan if the user does not have technical expertise. For example, if the user does not have technical expertise, the generation unit provides a concise and easy-to-understand plan. The generation unit may also adjust the use of technical terminology in the plan according to the user's level of expertise. For example, the generation unit adjusts the use of technical terminology in the plan according to the user's level of expertise. This makes it possible to provide a more appropriate plan by adjusting the use of technical terminology in the plan according to the user's level of expertise.

[0052] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the tourist destination. For example, the suggestion unit provides a detailed proposal for an important tourist destination. For example, the suggestion unit provides a detailed proposal for an important tourist destination. The suggestion unit can also provide a concise proposal for a less important tourist destination. For example, the suggestion unit provides a concise proposal for a less important tourist destination. The suggestion unit can also adjust the level of detail of the proposal based on an evaluation of the tourist destination. For example, the suggestion unit adjusts the level of detail of the proposal based on the evaluation of the tourist destination. In this way, by adjusting the level of detail of the proposal based on the importance of the tourist destination, it is possible to provide a more appropriate proposal.

[0053] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist destination. For example, the suggestion unit applies a scenery suggestion algorithm to tourist destinations with natural scenery. For example, the suggestion unit applies a scenery suggestion algorithm to tourist destinations with natural scenery. The suggestion unit can also apply a history suggestion algorithm to tourist destinations with historical buildings. For example, the suggestion unit applies a history suggestion algorithm to tourist destinations with historical buildings. The suggestion unit can also apply a gourmet suggestion algorithm to gourmet spots. For example, the suggestion unit applies a gourmet suggestion algorithm to gourmet spots. In this way, by applying different suggestion algorithms depending on the category of the tourist destination, more appropriate suggestions can be provided.

[0054] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, improves the accuracy of the suggestion by referring to proposals that the user has given high ratings to in the past. For example, the suggestion unit improves the accuracy of the suggestion by referring to proposals that the user has given high ratings to in the past. The suggestion unit can also adjust the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results to improve the accuracy of the suggestion. For example, the suggestion unit analyzes the user's past suggestion results to improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0055] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of visiting the tourist destination. For example, the suggestion unit gives priority to proposing tourist destinations that the user plans to visit. For example, the suggestion unit gives priority to proposing tourist destinations that the user plans to visit. The suggestion unit can also postpone proposing tourist destinations that the user has visited in the past. For example, the suggestion unit postpones proposing tourist destinations that the user has visited in the past. The suggestion unit can also give top priority to proposing tourist destinations that the user plans to visit. For example, the suggestion unit gives top priority to proposing tourist destinations that the user plans to visit. In this way, by determining the priority of the proposal based on the time of visiting the tourist destination, more appropriate suggestions can be provided.

[0056] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of tourist destinations. For example, the proposal unit gives priority to proposing highly relevant tourist destinations. For example, the proposal unit gives priority to proposing highly relevant tourist destinations. The proposal unit can also postpone proposing less relevant tourist destinations. For example, the proposal unit postpones proposing less relevant tourist destinations. The proposal unit can also adjust the order of proposals based on the relevance of tourist destinations. For example, the proposal unit adjusts the order of proposals based on the relevance of tourist destinations. In this way, by adjusting the order of proposals based on the relevance of tourist destinations, more appropriate proposals can be provided.

[0057] 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, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a concise and easy-to-understand proposal. For example, if the user does not have technical expertise, the suggestion unit provides a concise and easy-to-understand proposal. Furthermore, 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 adjusts the use of technical terminology in the proposal 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.

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

[0059] The collection unit can analyze the user's past travel history and select the optimal collection method. For example, it can prioritize collection of data on tourist spots that the user has visited in the past. It can also collect data on the user's favorite tourist spots from the user's past travel history. It can also prioritize collection of data on tourist spots that the user has given high ratings in the past. In this way, it is possible to collect optimal tourist data by analyzing the user's past travel history.

[0060] When collecting tourism data, the collection unit can filter the data based on the user's current interests and areas of interest. For example, if the user is interested in natural landscapes, data related to natural landscapes can be collected preferentially. Also, if the user is interested in historical buildings, data related to historical buildings can be collected preferentially. Furthermore, if the user is interested in gourmet food, local gourmet information can be collected preferentially. In this way, by filtering tourism data based on the user's interests and areas of interest, more relevant data can be collected.

[0061] When collecting tourism data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the tourism data can be collected using voice recognition technology. If the user uses text input, the tourism data can also be collected using text analysis technology. Furthermore, if the user uses image input, the tourism data can also be collected using image recognition technology. This allows tourism data to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0062] When collecting tourism data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting data on tourist spots close to the user's current location. It can also prioritize collecting tourism data on areas the user plans to visit. It can also prioritize collecting tourism data on areas the user has visited in the past. In this way, it is possible to collect highly relevant tourism data by taking into account the user's geographical location information.

[0063] When collecting tourism data, the collection unit can analyze the user's social media activities and collect related data. For example, data on places where the user has checked in on social media can be collected. The collection unit can also analyze the content of the user's social media posts to collect related tourism data. Furthermore, the collection unit can also collect related tourism data by referring to the activities of the user's friends on social media. In this way, highly relevant tourism data can be collected by analyzing the user's social media activities.

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

[0065] Step 1: The collection unit collects tourism data and review data. Tourism data includes tourist destination ratings, visitor feedback, and tourist destination characteristics (historical background, natural scenery, etc.). The collection unit also collects review data such as online reviews and survey results. For example, the collection unit collects tourist destination ratings using star ratings and comment analysis, and collects visitor feedback using survey results and online reviews. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the ratings and characteristics of the tourist destinations. For example, the analysis unit analyzes the ratings of the tourist destinations using star ratings and comment analysis, and analyzes the characteristics of the tourist destinations based on their natural scenery and historical background. Step 3: The generation unit automatically generates a sightseeing plan tailored to the user's desired schedule based on the evaluations and features obtained by the analysis unit. For example, the generation unit automatically generates a sightseeing plan that incorporates local information (festivals, events, etc.) for the time period based on the user's desired schedule. Step 4: The suggestion unit searches for information on new or lesser-known tourist destinations based on the plan generated by the generation unit and suggests them for incorporation into the tourist route. For example, the suggestion unit suggests tourist destinations that are not listed in tourist guidebooks but are popular among locals, or new tourist spots that are not yet known to tourists.

[0066] (Example 2) A tourist information suggestion system according to an embodiment of the present invention collects and analyzes tourist data and review data, automatically generates a sightseeing plan based on a user's desired itinerary, and suggests information on unexplored or lesser-known tourist destinations. The tourist information suggestion system analyzes the tourist data and review data, extracts ratings and characteristics of tourist destinations, and automatically generates a sightseeing plan tailored to the user's desired itinerary. Furthermore, the tourist information suggestion system searches for information on unexplored or lesser-known tourist destinations near the area and suggests incorporating them into a sightseeing route. For example, the tourist information suggestion system collects tourist data and review data, such as the popularity of tourist destinations, visitor ratings, and characteristics of specific tourist destinations (historical background, natural scenery, etc.). Next, the tourist information suggestion system analyzes the collected data and extracts ratings and characteristics of tourist destinations. For example, the tourist information suggestion system analyzes the popularity of tourist destinations, visitor ratings, and characteristics of specific tourist destinations. Next, once the user's desired itinerary is confirmed, the tourist information suggestion system automatically generates a plan incorporating local information (such as festivals and events) of that period. For example, the system suggests a sightseeing route incorporating information on local festivals and events held during the period the user is planning to travel. Next, based on the generated plan, the tourist information suggestion system searches for information on unexplored or little-known tourist destinations near the area and suggests incorporating them into the tourist route. For example, it suggests tourist destinations that are popular with locals but not listed in tourist guidebooks, or new tourist spots that are not yet known to tourists. In this way, the tourist information suggestion system can suggest optimal tourist routes that make use of the culture and characteristics of the area to the user based on high-quality tourist information. In addition, by suggesting plans that incorporate special information such as local festivals and events, or tourist routes that include information on unexplored tourist destinations, the user can enjoy a more fulfilling tourist experience. In this way, the tourist information suggestion system can suggest optimal tourist routes that make use of the culture and characteristics of the area to the user based on high-quality tourist information. In addition, by suggesting plans that incorporate special information such as local festivals and events, or tourist routes that include information on unexplored tourist destinations, the user can enjoy a more fulfilling tourist experience.

[0067] A tourism information suggestion system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects tourism data and review data. Tourism data includes, but is not limited to, tourist destination ratings, visitor feedback, and tourist destination characteristics, for example. The collection unit collects, for example, the popularity of tourist destinations, visitor ratings, and characteristics of specific tourist destinations (such as historical backgrounds and natural landscapes). The collection unit can also collect review data such as online reviews and survey results. For example, the collection unit collects tourist destination ratings using star ratings and comment analysis. Visitor feedback is collected using survey results and online reviews. Tourist destination characteristics are collected based on natural landscapes, historical backgrounds, and the like. The analysis unit analyzes the data collected by the collection unit and extracts tourist destination ratings and characteristics. For example, the analysis unit analyzes the collected data and extracts tourist destination ratings and characteristics. For example, the analysis unit analyzes tourist destination ratings using star ratings and comment analysis. Tourist destination characteristics are analyzed based on natural landscapes, historical backgrounds, and the like. The generation unit automatically generates a sightseeing plan tailored to the user's desired itinerary based on the evaluations and features obtained by the analysis unit. For example, the generation unit automatically generates a sightseeing plan that incorporates local information (such as festivals and events) for that period based on the user's desired itinerary. For example, the generation unit proposes a sightseeing route that incorporates information about local festivals and events that will be held during the period when the user is planning to travel. Based on the plan generated by the generation unit, the suggestion unit searches for information about unexplored or little-known tourist destinations near the area and suggests incorporating the destination into the sightseeing route. For example, based on the generated plan, the suggestion unit searches for information about unexplored or little-known tourist destinations near the area and suggests incorporating the destination into the sightseeing route. For example, the suggestion unit suggests tourist destinations that are not listed in tourist guidebooks but are popular with locals, or new tourist spots that are not yet known to tourists. This allows the tourist information suggestion system according to the embodiment to propose optimal sightseeing routes to users based on tourism data and review data.

[0068] The collection unit can collect tourist destination ratings, visitor feedback, and specific tourist destination characteristics. The collection unit, for example, collects tourist destination ratings using star ratings and comment analysis. For example, the collection unit collects tourist destination ratings based on star ratings. The collection unit can also collect tourist destination ratings based on comment analysis. The collection unit can also collect visitor feedback using survey results and online reviews. For example, the collection unit collects visitor feedback based on survey results. The collection unit can also collect visitor feedback based on online reviews. The collection unit can also collect tourist destination characteristics based on natural scenery, historical background, etc. For example, the collection unit collects tourist destination characteristics based on natural scenery. The collection unit can also collect tourist destination characteristics based on historical background. In this way, by collecting the popularity, ratings, and characteristics of tourist destinations, high-quality tourist information can be provided.

[0069] The analysis unit can analyze the collected data and extract the ratings and characteristics of tourist destinations. The analysis unit, for example, analyzes the collected data and extracts the ratings and characteristics of tourist destinations. For example, the analysis unit analyzes the ratings of tourist destinations using star ratings and comment analysis. The analysis unit can also analyze the characteristics of tourist destinations based on natural scenery, historical background, etc. For example, the analysis unit analyzes the ratings of tourist destinations based on star ratings. The analysis unit can also analyze the ratings of tourist destinations based on comment analysis. The analysis unit can also analyze the characteristics of tourist destinations based on natural scenery. The analysis unit can also analyze the characteristics of tourist destinations based on historical background. In this way, the ratings and characteristics of tourist destinations can be understood by analyzing the collected data.

[0070] The generation unit can automatically generate a sightseeing plan that incorporates local information for that period based on the user's desired itinerary. For example, the generation unit automatically generates a sightseeing plan that incorporates local information for that period (festivals, events, etc.) based on the user's desired itinerary. For example, the generation unit proposes a sightseeing route that incorporates information about local festivals and events that will be held during the period when the user is planning to travel. For example, the generation unit proposes a sightseeing route that incorporates information about local festivals that will be held during the period when the user is planning to travel. The generation unit can also propose a sightseeing route that incorporates information about local events that will be held during the period when the user is planning to travel. This makes it possible to provide a special sightseeing experience based on the user's desired itinerary.

[0071] The suggestion unit can search for information on new or lesser-known tourist spots near the area based on the generated plan and suggest incorporating them into the tourist route. The suggestion unit can search for information on unexplored or little-known tourist spots near the area based on the generated plan and suggest incorporating them into the tourist route. For example, the suggestion unit can suggest tourist spots that are not listed in tourist guidebooks but are popular with locals, or new tourist spots that are not yet known to tourists. For example, the suggestion unit can suggest tourist spots that are not listed in tourist guidebooks but are popular with locals. The suggestion unit can also suggest new tourist spots that are not yet known to tourists. This allows users to enjoy new discoveries and surprises by providing information on unexplored tourist spots.

[0072] The collection unit can estimate the user's emotions and adjust the timing of collecting tourist data based on the estimated user emotions. For example, when the user is excited, the collection unit collects the latest tourist data in real time. For example, when the user is excited, the collection unit collects the latest tourist spot data in real time. Furthermore, when the user is relaxed, the collection unit can collect a wide range of tourist information, including past data. For example, when the user is relaxed, the collection unit collects data on past popular spots. Furthermore, when the user is stressed, the collection unit can collect only simple and important tourist data. For example, when the user is stressed, the collection unit collects simple and important tourist data. This allows for more appropriate data to be collected by adjusting the timing of collecting tourist data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit can analyze the user's past travel history and select the optimal collection method. The collection unit, for example, prioritizes collecting data on tourist spots that the user has visited in the past. For example, the collection unit prioritizes collecting data on tourist spots that the user has visited in the past. The collection unit can also collect data on favorite tourist spots from the user's past travel history. For example, the collection unit collects data on favorite tourist spots from the user's past travel history. The collection unit can also focus on collecting data on tourist spots that the user has given high ratings in the past. For example, the collection unit focuses on collecting data on tourist spots that the user has given high ratings in the past. In this way, optimal tourist data can be collected by analyzing the user's past travel history.

[0074] When collecting tourism data, the collection unit can filter the tourism data based on the user's current interests and areas of interest. For example, if the user is interested in natural landscapes, the collection unit prioritizes collecting data related to natural landscapes. For example, if the user is interested in natural landscapes, the collection unit prioritizes collecting data related to natural landscapes. Furthermore, if the user is interested in historical buildings, the collection unit can also prioritize collecting data related to historical buildings. For example, if the user is interested in historical buildings, the collection unit prioritizes collecting data related to historical buildings. Furthermore, if the user is interested in gourmet food, the collection unit can also prioritize collecting local gourmet food information. For example, if the user is interested in gourmet food, the collection unit prioritizes collecting local gourmet food information. In this way, by filtering tourism data based on the user's interests and areas of interest, more relevant data can be collected.

[0075] When collecting tourism data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects tourism data using voice recognition technology. For example, when the user uses voice input, the collection unit collects tourism data using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect tourism data using text analysis technology. For example, when the user uses text input, the collection unit collects tourism data using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect tourism data using image recognition technology. For example, when the user uses image input, the collection unit collects tourism data using image recognition technology. In this way, by selecting the optimal collection means depending on the user's input method, tourism data can be collected efficiently.

[0076] The collection unit can estimate the user's emotions and determine the priority of tourist data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting data on the latest tourist spots. For example, if the user is excited, the collection unit prioritizes collecting data on the latest tourist spots. The collection unit can also prioritize collecting data on past popular tourist spots when the user is relaxed. For example, if the user is relaxed, the collection unit prioritizes collecting data on past popular spots. The collection unit can also prioritize collecting simple and important tourist data when the user is stressed. For example, if the user is stressed, the collection unit prioritizes collecting simple and important tourist data. This allows for more appropriate data to be collected by prioritizing tourist data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When collecting tourism data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, prioritizes collecting data on tourist spots close to the user's current location. For example, the collection unit prioritizes collecting data on tourist spots close to the user's current location. The collection unit can also prioritize collecting tourism data on areas the user plans to visit. For example, the collection unit prioritizes collecting tourism data on areas the user plans to visit. The collection unit can also prioritize collecting tourism data on areas the user has visited in the past. For example, the collection unit prioritizes collecting tourism data on areas the user has visited in the past. In this way, highly relevant tourism data can be collected by taking into account the user's geographical location information.

[0078] When collecting tourism data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, collects data on places where the user has checked in on social media. For example, the collection unit collects data on places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related tourism data. For example, the collection unit analyzes the content of the user's posts on social media and collects related tourism data. The collection unit can also collect related tourism data by referring to the activities of the user's friends on social media. For example, the collection unit collects related tourism data by referring to the activities of the user's friends on social media. In this way, highly relevant tourism data can be collected by analyzing the user's social media activities.

[0079] When collecting tourism data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, prioritizes collecting data on tourist destinations that the user has given a high rating in the past. For example, the collection unit prioritizes collecting data on tourist destinations that the user has given a high rating in the past. The collection unit can also adjust the type of data to collect based on the user's past feedback. For example, the collection unit adjusts the type of data to collect based on the user's past feedback. The collection unit can also adjust the amount of data to collect by referring to the user's past feedback. For example, the collection unit adjusts the amount of data to collect by referring to the user's past feedback. In this way, the collection method can be customized 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, the analysis unit provides detailed analysis results when the user is relaxed. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. For example, the analysis unit provides concise analysis results that focus on the main points when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. For example, the analysis unit provides visually stimulating analysis results when the user is excited. This allows for adjusting the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the tourist destination. For example, the analysis unit provides detailed analysis results for important tourist destinations. For example, the analysis unit provides detailed analysis results for important tourist destinations. The analysis unit can also provide concise analysis results for less important tourist destinations. For example, the analysis unit provides concise analysis results for less important tourist destinations. The analysis unit can also adjust the level of detail of the analysis based on the rating of the tourist destination. For example, the analysis unit adjusts the level of detail of the analysis based on the rating of the tourist destination. In this way, by adjusting the level of detail of the analysis based on the importance of the tourist destination, more appropriate analysis results can be provided.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the tourist destination. For example, the analysis unit applies a landscape analysis algorithm to a tourist destination with natural scenery. For example, the analysis unit applies a landscape analysis algorithm to a tourist destination with natural scenery. The analysis unit can also apply a history analysis algorithm to a tourist destination with historical buildings. For example, the analysis unit applies a history analysis algorithm to a tourist destination with historical buildings. The analysis unit can also apply a gourmet analysis algorithm to a gourmet spot. For example, the analysis unit applies a gourmet analysis algorithm to a gourmet spot. In this way, by applying different analysis algorithms depending on the category of the tourist destination, more appropriate analysis results can be provided.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to analysis results that the user has given a high rating to in the past. For example, the analysis unit improves the accuracy of the analysis by referring to analysis results that the user has given a high rating to in the past. The analysis unit can also adjust the analysis algorithm based on 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 analyze the user's past analysis results to improve the accuracy of the analysis. For example, the analysis unit analyzes the user's past analysis results to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's 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 provides a short and to-the-point analysis result. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows for adjusting the length of the analysis based on the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, 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.

[0085] During analysis, the analysis unit can determine the priority of the analysis based on the time of visiting tourist spots. For example, the analysis unit prioritizes the analysis of tourist spots that the user plans to visit. For example, the analysis unit prioritizes the analysis of tourist spots that the user plans to visit. The analysis unit can also postpone the analysis of tourist spots that the user has visited in the past. For example, the analysis unit postpones the analysis of tourist spots that the user has visited in the past. The analysis unit can also give top priority to the analysis of tourist spots that the user plans to visit. For example, the analysis unit gives top priority to the analysis of tourist spots that the user plans to visit. In this way, by determining the priority of the analysis based on the time of visiting tourist spots, more appropriate analysis results can be provided.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of tourist destinations. For example, the analysis unit prioritizes the analysis of highly relevant tourist destinations. For example, the analysis unit prioritizes the analysis of highly relevant tourist destinations. The analysis unit can also postpone the analysis of less relevant tourist destinations. For example, the analysis unit postpones the analysis of less relevant tourist destinations. The analysis unit can also adjust the order of analysis based on the relevance of tourist destinations. For example, the analysis unit adjusts the order of analysis based on the relevance of tourist destinations. In this way, by adjusting the order of analysis based on the relevance of tourist destinations, more appropriate analysis results can be provided.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides an analysis result that makes extensive use of technical terms. For example, if the user has technical expertise, the analysis unit provides an analysis result that makes extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide a concise and easy-to-understand analysis result. For example, if the user does not have technical expertise, the analysis unit provides a concise and easy-to-understand analysis result. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0088] The generation unit can estimate the user's emotions and adjust the expression method of the generated plan based on the estimated user's emotions. For example, when the user is relaxed, the generation unit provides a detailed plan. For example, when the user is relaxed, the generation unit provides a detailed plan. Furthermore, when the user is in a hurry, the generation unit can provide a concise plan that focuses on the main points. For example, when the user is in a hurry, the generation unit provides a concise plan that focuses on the main points. Furthermore, when the user is excited, the generation unit can provide a visually stimulating plan. For example, when the user is excited, the generation unit provides a visually stimulating plan. In this way, by adjusting the expression method of the plan based on the user's emotions, a more appropriate plan can be provided. 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.

[0089] The generation unit can adjust the level of detail of the plan based on the importance of the tourist destination when generating the plan. The generation unit, for example, provides a detailed plan for an important tourist destination. For example, the generation unit provides a detailed plan for an important tourist destination. The generation unit can also provide a concise plan for a less important tourist destination. For example, the generation unit provides a concise plan for a less important tourist destination. The generation unit can also adjust the level of detail of the plan based on the rating of the tourist destination. For example, the generation unit adjusts the level of detail of the plan based on the rating of the tourist destination. In this way, by adjusting the level of detail of the plan based on the importance of the tourist destination, a more appropriate plan can be provided.

[0090] The generation unit can apply different generation algorithms depending on the category of tourist attraction during generation. For example, the generation unit applies a landscape generation algorithm to tourist attractions of natural scenery. For example, the generation unit applies a landscape generation algorithm to tourist attractions of natural scenery. The generation unit can also apply a history generation algorithm to tourist attractions of historical buildings. For example, the generation unit applies a history generation algorithm to tourist attractions of historical buildings. The generation unit can also apply a gourmet generation algorithm to gourmet spots. For example, the generation unit applies a gourmet generation algorithm to gourmet spots. In this way, by applying different generation algorithms depending on the category of tourist attraction, a more appropriate plan can be provided.

[0091] The generation unit can improve the accuracy of generation by referring to the user's past plan results during generation. The generation unit can improve the accuracy of generation by referring to, for example, plans that the user has given high ratings to in the past. For example, the generation unit can improve the accuracy of generation by referring to plans that the user has given high ratings to in the past. The generation unit can also adjust the generation algorithm based on the user's past plan results. For example, the generation unit can adjust the generation algorithm based on the user's past plan results. The generation unit can also analyze the user's past plan results and improve the accuracy of generation. For example, the generation unit can analyze the user's past plan results and improve the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past plan results.

[0092] The generation unit can estimate the user's emotions and adjust the length of the plan to be generated based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit provides a short and to-the-point plan. For example, if the user is in a hurry, the generation unit provides a short and to-the-point plan. The generation unit can also provide a detailed plan if the user is relaxed. For example, if the user is relaxed, the generation unit provides a detailed plan. The generation unit can also provide a visually stimulating plan if the user is excited. For example, if the user is excited, the generation unit provides a visually stimulating plan. This allows for adjusting the length of the plan based on the user's emotions to provide a more appropriate plan. 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.

[0093] At the time of generation, the generation unit can determine the priority of the plans based on the time of visiting the tourist destinations. For example, the generation unit generates preferentially plans for tourist destinations that the user plans to visit. For example, the generation unit generates preferentially plans for tourist destinations that the user plans to visit. The generation unit can also postpone plans for tourist destinations that the user has visited in the past. For example, the generation unit postpones plans for tourist destinations that the user has visited in the past. The generation unit can also generate plans for tourist destinations that the user plans to visit with the highest priority. For example, the generation unit generates plans for tourist destinations that the user plans to visit with the highest priority. In this way, by determining the priority of the plans based on the time of visiting the tourist destinations, a more appropriate plan can be provided.

[0094] The generation unit can adjust the order of the plans based on the relevance of the tourist destinations when generating them. The generation unit, for example, generates plans with high relevance as a priority. For example, the generation unit generates plans with high relevance as a priority. The generation unit can also postpone plans with low relevance as a priority. For example, the generation unit postpones plans with low relevance as a priority. The generation unit can also adjust the order of the plans based on the relevance of the tourist destinations. For example, the generation unit adjusts the order of the plans based on the relevance of the tourist destinations. In this way, by adjusting the order of the plans based on the relevance of the tourist destinations, a more appropriate plan can be provided.

[0095] The generation unit may adjust the use of technical terminology in the plan according to the user's level of expertise during generation. For example, if the user has technical expertise, the generation unit provides a plan that uses a lot of technical terminology. For example, if the user has technical expertise, the generation unit provides a plan that uses a lot of technical terminology. The generation unit may also provide a concise and easy-to-understand plan if the user does not have technical expertise. For example, if the user does not have technical expertise, the generation unit provides a concise and easy-to-understand plan. The generation unit may also adjust the use of technical terminology in the plan according to the user's level of expertise. For example, the generation unit adjusts the use of technical terminology in the plan according to the user's level of expertise. This makes it possible to provide a more appropriate plan by adjusting the use of technical terminology in the plan according to the user's level of expertise.

[0096] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. Furthermore, when the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. For example, when the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the main points. Furthermore, when the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, when the user is excited, the suggestion unit provides visually stimulating suggestions. In this way, by adjusting the way in which suggestions are expressed based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0097] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the tourist destination. For example, the suggestion unit provides a detailed proposal for an important tourist destination. For example, the suggestion unit provides a detailed proposal for an important tourist destination. The suggestion unit can also provide a concise proposal for a less important tourist destination. For example, the suggestion unit provides a concise proposal for a less important tourist destination. The suggestion unit can also adjust the level of detail of the proposal based on an evaluation of the tourist destination. For example, the suggestion unit adjusts the level of detail of the proposal based on the evaluation of the tourist destination. In this way, by adjusting the level of detail of the proposal based on the importance of the tourist destination, it is possible to provide a more appropriate proposal.

[0098] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist destination. For example, the suggestion unit applies a scenery suggestion algorithm to tourist destinations with natural scenery. For example, the suggestion unit applies a scenery suggestion algorithm to tourist destinations with natural scenery. The suggestion unit can also apply a history suggestion algorithm to tourist destinations with historical buildings. For example, the suggestion unit applies a history suggestion algorithm to tourist destinations with historical buildings. The suggestion unit can also apply a gourmet suggestion algorithm to gourmet spots. For example, the suggestion unit applies a gourmet suggestion algorithm to gourmet spots. In this way, by applying different suggestion algorithms depending on the category of the tourist destination, more appropriate suggestions can be provided.

[0099] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, improves the accuracy of the suggestion by referring to proposals that the user has given high ratings to in the past. For example, the suggestion unit improves the accuracy of the suggestion by referring to proposals that the user has given high ratings to in the past. The suggestion unit can also adjust the suggestion algorithm based on the user's past suggestion results. For example, the suggestion unit adjusts the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results to improve the accuracy of the suggestion. For example, the suggestion unit analyzes the user's past suggestion results to improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. The suggestion unit can also provide visually stimulating suggestions if the user is excited. For example, if the user is excited, the suggestion unit provides visually stimulating suggestions. This allows for adjusting the length of the suggestions based on the user's emotions to provide more appropriate suggestions. The emotion estimation is achieved using an emotion estimation function, for example, 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.

[0101] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of visiting the tourist destination. For example, the suggestion unit gives priority to proposing tourist destinations that the user plans to visit. For example, the suggestion unit gives priority to proposing tourist destinations that the user plans to visit. The suggestion unit can also postpone proposing tourist destinations that the user has visited in the past. For example, the suggestion unit postpones proposing tourist destinations that the user has visited in the past. The suggestion unit can also give top priority to proposing tourist destinations that the user plans to visit. For example, the suggestion unit gives top priority to proposing tourist destinations that the user plans to visit. In this way, by determining the priority of the proposal based on the time of visiting the tourist destination, more appropriate suggestions can be provided.

[0102] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of tourist destinations. For example, the proposal unit gives priority to proposing highly relevant tourist destinations. For example, the proposal unit gives priority to proposing highly relevant tourist destinations. The proposal unit can also postpone proposing less relevant tourist destinations. For example, the proposal unit postpones proposing less relevant tourist destinations. The proposal unit can also adjust the order of proposals based on the relevance of tourist destinations. For example, the proposal unit adjusts the order of proposals based on the relevance of tourist destinations. In this way, by adjusting the order of proposals based on the relevance of tourist destinations, more appropriate proposals can be provided.

[0103] 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, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. For example, if the user has technical expertise, the suggestion unit provides a proposal that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide a concise and easy-to-understand proposal. For example, if the user does not have technical expertise, the suggestion unit provides a concise and easy-to-understand proposal. Furthermore, 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 adjusts the use of technical terminology in the proposal 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. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit, realized, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data and extracts ratings and characteristics of tourist destinations. The generation unit, realized, for example, by the identification processing unit 290 of the data processing device 12, automatically generates a sightseeing plan tailored to the user's desired itinerary. The suggestion unit, realized, for example, by the control unit 46A of the smart device 14, searches for information on unexplored or little-known tourist destinations and suggests incorporating them into a tourist route. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion 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 collection unit collects tourist data and review data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit, realized, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data and extracts ratings and characteristics of tourist destinations. The generation unit, realized, for example, by the identification processing unit 290 of the data processing device 12, automatically generates a sightseeing plan tailored to the user's desired itinerary. The suggestion unit, realized, for example, by the control unit 46A of the smart glasses 214, searches for information on unexplored or little-known tourist destinations and suggests incorporating them into a tourist route. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit, realized, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data and extracts ratings and characteristics of tourist destinations. The generation unit, realized, for example, by the identification processing unit 290 of the data processing device 12, automatically generates a sightseeing plan tailored to the user's desired itinerary. The suggestion unit, realized, for example, by the control unit 46A of the headset terminal 314, searches for information on unexplored or little-known tourist destinations and suggests incorporating them into a tourist route. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects tourist data and review data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts ratings and characteristics of tourist destinations. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically generates a sightseeing plan tailored to the user's desired itinerary. The suggestion unit, realized, for example, by the control unit 46A of the robot 414, searches for information on unexplored or little-known tourist destinations and suggests incorporating them into a tourist route.

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

[0105] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, if the user is excited, it can prioritize analyzing data on the latest tourist spots. Alternatively, if the user is relaxed, it can prioritize analyzing data on popular spots from the past. Furthermore, if the user is stressed, it can prioritize analyzing simple and important tourist data. In this way, by determining the analysis priorities based on the user's emotions, it is possible to provide more appropriate analysis results.

[0106] The generation unit can estimate the user's emotions and adjust the level of detail of the plan to be generated based on the estimated user's emotions. For example, if the user is relaxed, a detailed plan can be provided. If the user is in a hurry, a concise plan that focuses on the main points can be provided. Furthermore, if the user is excited, a visually stimulating plan can be provided. In this way, by adjusting the level of detail of the plan based on the user's emotions, a more appropriate plan can be provided.

[0107] The suggestion unit can estimate the user's emotions and adjust the order of suggestions based on the estimated user's emotions. For example, if the user is excited, it can prioritize suggestions of visually stimulating tourist spots. If the user is relaxed, it can prioritize suggestions of tourist spots that include detailed information. Furthermore, if the user is in a hurry, it can prioritize suggestions that are concise and get to the point. In this way, by adjusting the order of suggestions based on the user's emotions, it is possible to provide more appropriate suggestions.

[0108] The collection unit can estimate the user's emotions and adjust the type of tourist data to be collected based on the estimated user's emotions. For example, if the user is excited, data on the latest tourist spots can be collected with priority. If the user is relaxed, data on popular spots from the past can be collected with priority. Furthermore, if the user is stressed, simple and important tourist data can be collected with priority. In this way, by adjusting the type of tourist data to be collected based on the user's emotions, more appropriate data can be collected.

[0109] 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, 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 excited, visually stimulating analysis results can be provided. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided.

[0110] The collection unit can analyze the user's past travel history and select the optimal collection method. For example, it can prioritize collection of data on tourist spots that the user has visited in the past. It can also collect data on the user's favorite tourist spots from the user's past travel history. It can also prioritize collection of data on tourist spots that the user has given high ratings in the past. In this way, it is possible to collect optimal tourist data by analyzing the user's past travel history.

[0111] When collecting tourism data, the collection unit can filter the data based on the user's current interests and areas of interest. For example, if the user is interested in natural landscapes, data related to natural landscapes can be collected preferentially. Also, if the user is interested in historical buildings, data related to historical buildings can be collected preferentially. Furthermore, if the user is interested in gourmet food, local gourmet information can be collected preferentially. In this way, by filtering tourism data based on the user's interests and areas of interest, more relevant data can be collected.

[0112] When collecting tourism data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the tourism data can be collected using voice recognition technology. If the user uses text input, the tourism data can also be collected using text analysis technology. Furthermore, if the user uses image input, the tourism data can also be collected using image recognition technology. This allows tourism data to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0113] When collecting tourism data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting data on tourist spots close to the user's current location. It can also prioritize collecting tourism data on areas the user plans to visit. It can also prioritize collecting tourism data on areas the user has visited in the past. In this way, it is possible to collect highly relevant tourism data by taking into account the user's geographical location information.

[0114] When collecting tourism data, the collection unit can analyze the user's social media activities and collect related data. For example, data on places where the user has checked in on social media can be collected. The collection unit can also analyze the content of the user's social media posts to collect related tourism data. Furthermore, the collection unit can also collect related tourism data by referring to the activities of the user's friends on social media. In this way, highly relevant tourism data can be collected by analyzing the user's social media activities.

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

[0116] Step 1: The collection unit collects tourism data and review data. Tourism data includes tourist destination ratings, visitor feedback, and tourist destination characteristics (historical background, natural scenery, etc.). The collection unit also collects review data such as online reviews and survey results. For example, the collection unit collects tourist destination ratings using star ratings and comment analysis, and collects visitor feedback using survey results and online reviews. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the ratings and characteristics of the tourist destinations. For example, the analysis unit analyzes the ratings of the tourist destinations using star ratings and comment analysis, and analyzes the characteristics of the tourist destinations based on their natural scenery and historical background. Step 3: The generation unit automatically generates a sightseeing plan tailored to the user's desired schedule based on the evaluations and features obtained by the analysis unit. For example, the generation unit automatically generates a sightseeing plan that incorporates local information (festivals, events, etc.) for the time period based on the user's desired schedule. Step 4: The suggestion unit searches for information on new or lesser-known tourist destinations based on the plan generated by the generation unit and suggests them for incorporation into the tourist route. For example, the suggestion unit suggests tourist destinations that are not listed in tourist guidebooks but are popular among locals, or new tourist spots that are not yet known to tourists.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0188] [Explanation of symbols]

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

Claims

1. a collection department that collects tourist data and review data; an analysis unit that analyzes the data collected by the collection unit and extracts evaluations and characteristics of tourist spots; a generation unit that automatically generates a sightseeing plan tailored to the user's desired schedule based on the evaluations and features obtained by the analysis unit; a suggestion unit that searches for information on new or lesser-known tourist spots based on the plan generated by the generation unit and suggests incorporating them into the tourist route. A system characterized by:

2. The collecting unit Collect destination ratings, visitor feedback, and specific destination characteristics 2. The system of claim 1.

3. The analysis unit Analyze the collected data and extract evaluations and characteristics of tourist destinations 2. The system of claim 1.

4. The generation unit Automatically generate a sightseeing plan incorporating local information for that time based on the user's desired dates 2. The system of claim 1.

5. The proposal unit Based on the generated plan, the system searches for new or lesser-known tourist destinations in the area and suggests incorporating them into the tourist route.

2. The system of claim 1.

6. The collecting unit Estimating user emotions and adjusting the timing of collecting tourism data based on the estimated user emotions 2. The system of claim 1.

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

8. The collecting unit When collecting tourism data, filter it based on the user's current interests and areas of concern.

2. The system of claim 1.

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