system

Abstract

An object of a system according to an embodiment is to collect the latest trend information at the time of travel planning and perform route guidance according to individual needs.SOLUTION: A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a guidance unit. The reception unit receives input of a destination of a trip and a word of interest from a user. The collection unit collects the latest trend information from the SNS based on the information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and counts photographs and places. The guide unit guides the route based on the movement time and the method based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently collect the latest trend information when planning a trip and provide route guidance tailored to individual needs.

[0005] The system according to the embodiment aims to collect the latest trend information when planning a trip and provide route guidance tailored to individual needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a guidance unit. The reception unit receives input of travel destinations and words of interest from a user. The collection unit collects the latest trend information from social networking sites based on the information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and compiles photos and locations. The guidance unit provides route guidance based on travel time and method based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect the latest trend information when planning a trip and provide route guidance tailored to individual needs. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A travel planning support system according to an embodiment of the present invention utilizes a generation AI to provide a sense of excitement when planning a trip. The system allows users to input their travel destination and keywords of interest. The generation AI then collects the latest trend information from social media and aggregates photos and locations. Furthermore, when users specify their departure and return locations, the generation AI then guides them to the optimal route, taking travel time and methods into consideration. This system allows users to create their own personalized guidebooks. For example, in a travel planning support system, a user inputs specific keywords such as "cafe hopping in Tokyo" or "museums in Paris." The generation AI then analyzes the input information and collects the latest trend information from social media. The generation AI then aggregates related photos and location information and provides it to the user. Furthermore, when a user specifies a specific route, such as "starting from Shinjuku Station, visiting cafes in Shibuya, and returning to Roppongi in the evening," the generation AI automatically generates travel times and methods for that route. This allows users to create travel plans based on the latest trend information and reduce the hassle of traveling. The travel planning support system can also distribute coupons from stores and commercial facilities through the app, earning revenue from advertising fees and performance-based publishing fees. This allows users to create their own original guidebooks and plan trips based on the latest trend information. For example, users input their travel destinations and words of interest, and the generation AI collects the latest trend information from social media and compiles photos and locations. Furthermore, when users specify their departure location, time, and return location, the generation AI takes travel time and method into consideration to guide them to the optimal route. This allows users to create their own original guidebooks and saves them the trouble of traveling. The travel planning support system can also distribute coupons from stores and commercial facilities through the app, earning revenue from advertising fees and performance-based publishing fees.

[0029] A travel planning support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a guidance unit. The reception unit receives input from a user of a travel destination and words of interest. For example, the user inputs specific words such as "cafe hopping in Tokyo" or "art museums in Paris." The collection unit collects the latest trend information from social media based on the information received by the reception unit. For example, the collection unit collects the latest posts about cafe hopping in Tokyo and information about art museums in Paris from social media. The analysis unit analyzes the information collected by the collection unit and compiles photos and locations. For example, the analysis unit analyzes the collected information using image analysis or text analysis and compiles related photo and location information. The guidance unit provides guidance on an optimal route, taking travel time and method into consideration, based on the information analyzed by the analysis unit. For example, when a user specifies a specific route, such as "starting from Shinjuku Station, visiting cafes in Shibuya, and returning to Roppongi in the evening," the guidance unit automatically generates travel time and methods along the route. As a result, the travel planning support system according to the embodiment allows users to create their own original guidebook and plan their trip based on the latest trend information. For example, the reception unit inputs information entered by the user into the generation AI, which then performs analysis based on that information. The collection unit allows the generation AI to collect the latest trend information from social media, and the analysis unit allows the generation AI to analyze the collected information and compile photos and locations. The guidance unit provides guidance on the optimal route, taking into account travel time and method based on the information analyzed by the generation AI. This allows users to create their own original guidebook and also saves the effort of traveling.

[0030] The collection unit can collect the latest trend information from the SNS. The collection unit, for example, collects the latest trend information from the SNS. For example, the collection unit collects information about popular tourist spots and trendy restaurants from the SNS. The collection unit can also collect information about activities in which the user is interested from the SNS. For example, the collection unit collects the latest posts about activities in which the user is interested from the SNS. In this way, the collection unit can provide the user with the latest information by collecting the latest trend information from the SNS. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the information collected from the SNS into the generation AI, and the generation AI can analyze the information and extract trend information.

[0031] The analysis unit can analyze the collected information and tally up photos and locations. The analysis unit can analyze the collected information using, for example, image analysis or text analysis. For example, the analysis unit can analyze the collected information using image analysis technology to extract related photos. The analysis unit can also analyze the collected information using text analysis technology to extract related location information. For example, the analysis unit can analyze the collected information using data mining technology to extract related photo and location information. In this way, the analysis unit can analyze the collected information and tally up photos and locations, thereby providing visual information to the user. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected information into a generation AI, which can analyze the information and extract photo and location information.

[0032] The guidance unit can provide route guidance based on the travel time and method specified by the user based on the departure location, time, and return location. For example, the guidance unit provides guidance on the optimal route by taking into account the travel time and method specified by the user based on the departure location, time, and return location. For example, when the user specifies a specific route, such as "start from Shinjuku Station, visit cafes in Shibuya, and return to Roppongi in the evening," the guidance unit automatically generates the travel time and method along that route. The guidance unit can also provide guidance on routes based on the user's mode of transportation. For example, the guidance unit can provide guidance on a route suitable for walking if the user is traveling on foot, and on a route using public transportation if the user is traveling by public transportation. Furthermore, the guidance unit can provide navigation based on the user's walking speed. For example, the guidance unit can provide navigation based on the user's walking speed, providing a more comfortable journey. This allows the guidance unit to reduce the effort required for travel by providing guidance on the optimal route based on the conditions specified by the user. Some or all of the above-described processing in the guidance unit may be performed using a generation AI or without a generation AI. For example, the guidance unit can input information about the departure location, time, and return location specified by the user into the generation AI, and the generation AI can generate the optimal route based on that information.

[0033] The guidance unit can guide the user along a route that corresponds to the user's means of transportation. For example, the guidance unit guides the user along a route that corresponds to the user's means of transportation. For example, if the user travels on foot, the guidance unit guides the user along a route that is suitable for walking. Furthermore, if the user travels by public transportation, the guidance unit can also guide the user along a route that uses public transportation. Furthermore, if the user travels by bicycle, the guidance unit can also guide the user along a route that is suitable for cycling. In this way, the guidance unit can provide a more appropriate method of travel by guiding the user along a route that corresponds to the user's means of transportation. Some or all of the above-described processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input information about the user's means of transportation into the generation AI, and the generation AI can generate an optimal route based on that information.

[0034] The guidance unit can perform navigation according to the user's walking speed. For example, the guidance unit performs navigation according to the user's walking speed. For example, the guidance unit performs navigation according to the user's walking speed, providing a more comfortable journey. The guidance unit can also monitor the user's walking speed in real time and adjust navigation. For example, the guidance unit detects the user's walking speed with a sensor and performs navigation according to that speed. Furthermore, the guidance unit can calculate travel time based on the user's walking speed and provide guidance on an optimal route. For example, the guidance unit calculates travel time based on the user's walking speed and provides guidance on a route according to that time. In this way, the guidance unit can provide a more comfortable journey by performing navigation according to the user's walking speed. Some or all of the above-described processing in the guidance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input information about the user's walking speed into the generation AI, and the generation AI can adjust navigation based on that information.

[0035] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit can automatically display related destinations and words of interest as candidates based on places the user has visited in the past. The reception unit can also prioritize suggesting input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations related to specific seasons or events based on the user's past travel history. This allows the reception unit to suggest the optimal input method based on the user's past travel history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past travel history data into the generation AI, which can then suggest the optimal input method based on that data.

[0036] The reception unit can present input candidates based on the user's current areas of interest and trends upon reception. For example, the reception unit presents input candidates based on the user's current areas of interest and trends upon reception. For example, the reception unit can suggest related destinations and words of interest based on topics and keywords recently searched by the user. The reception unit can also analyze the content of posts on social media accounts the user follows and present related input candidates. Furthermore, the reception unit can suggest related destinations and words of interest based on trends in online communities in which the user participates. This allows the reception unit to provide more appropriate information by presenting input candidates based on the user's current areas of interest and trends. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's areas of interest and trends into the generation AI, and the generation AI can present input candidates based on that data.

[0037] The reception unit can select the optimal input means according to the user's input method when receiving the input. For example, the reception unit can select the optimal input means according to the user's input method when receiving the input. For example, if the user selects voice input, the reception unit can input a destination or words of interest using voice recognition technology. Also, if the user selects text input, the reception unit can input using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can suggest related destinations or words of interest using image recognition technology. This allows the reception unit to select the optimal input means according to the user's input method, thereby improving input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data of the user's input method into the generation AI, and the generation AI can select the optimal input means based on that data.

[0038] The reception unit can present highly relevant input candidates taking into account the user's geographical location information at the time of reception. For example, the reception unit presents highly relevant input candidates taking into account the user's geographical location information at the time of reception. For example, the reception unit may preferentially suggest tourist spots and stores close to the user's current location. The reception unit may also preferentially suggest places that are easily accessible from the user's current location. Furthermore, the reception unit may also suggest destinations and words of interest that are suitable for the weather and season of the user's current location. In this way, the reception unit can provide more appropriate information by presenting input candidates taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's geographical location information to the generation AI, and the generation AI may present highly relevant input candidates based on that information.

[0039] The reception unit can analyze the user's social media activity and present related input candidates at the time of reception. For example, the reception unit can analyze the user's social media activity and present related input candidates at the time of reception. For example, the reception unit can suggest related destinations and words of interest based on the content of posts from accounts the user follows on social media. The reception unit can also present related input candidates based on locations where the user has checked in on social media. Furthermore, the reception unit can suggest related destinations and words of interest by referring to the activities of the user's friends on social media. This allows the reception unit to provide more appropriate information by analyzing the user's social media activity and presenting input candidates. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's social media activity into the generation AI, and the generation AI can present related input candidates based on the data.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when receiving the input. For example, the reception unit customizes the input method by reflecting the user's past feedback when receiving the input. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has previously preferred. Furthermore, the reception unit can improve and suggest a specific input method based on the user's past feedback. In this way, the reception unit can provide a more appropriate input method by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, and the generation AI can customize the input method based on that data.

[0041] The collection unit can prioritize collecting highly relevant information by referring to the user's past travel history when collecting data. For example, the collection unit can prioritize collecting highly relevant information by referring to the user's past travel history when collecting data. For example, the collection unit can prioritize collecting the latest trend information related to places the user has visited in the past. The collection unit can also prioritize collecting information related to specific seasons or events from the user's past travel history. Furthermore, the collection unit can prioritize collecting information related to genres in which the user has been interested in the past. This allows the collection unit to prioritize collecting highly relevant information by referring to the user's past travel history, thereby providing more appropriate information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past travel history data into the generation AI, and the generation AI can prioritize collecting highly relevant information based on that data.

[0042] The collection unit can select collection targets based on the user's current areas of interest and trends at the time of collection. For example, the collection unit selects collection targets based on the user's current areas of interest and trends at the time of collection. For example, the collection unit prioritizes collecting information related to topics and keywords recently searched by the user. The collection unit can also prioritize collecting information related to posts on social media accounts followed by the user. Furthermore, the collection unit can prioritize collecting information related to trends in online communities in which the user participates. This allows the collection unit to provide more appropriate information by selecting collection targets based on the user's current areas of interest and trends. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's areas of interest and trends into the generation AI, and the generation AI can select collection targets based on that data.

[0043] The collection unit can select the optimal collection means depending on the user's input method during collection. For example, the collection unit selects the optimal collection means depending on the user's input method during collection. For example, when the user uses voice input, the collection unit can collect related information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect related information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect related information using image recognition technology. This allows the collection unit to select the optimal collection means depending on the user's input method, thereby improving collection efficiency. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the user's input method into the generation AI, and the generation AI can select the optimal collection means based on that data.

[0044] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting information about tourist spots and stores close to the user's current location. The collection unit can also prioritize collecting information about places that are easily accessible from the user's current location. Furthermore, the collection unit can prioritize collecting information that is suitable for the weather and season of the user's current location. In this way, the collection unit can provide more appropriate information by collecting information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, and the generation AI can prioritize collecting highly relevant information based on that information.

[0045] The collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can collect information related to the content of posts from accounts the user follows on social media. The collection unit can also collect information related to places the user has checked in to on social media. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect information to provide more appropriate information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's social media activities into the generation AI, and the generation AI can collect related information based on the data.

[0046] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit suggests an optimal collection method based on feedback provided by the user in the past. The collection unit can also preferentially suggest collection methods (voice, text, etc.) that the user has previously preferred. Furthermore, the collection unit can improve and suggest a specific collection method based on the user's past feedback. In this way, the collection unit can provide more appropriate information by customizing the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI, and the generation AI can customize the collection method based on that data.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a brief analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. As a result, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input importance data of the collected information to the generation AI, and the generation AI can adjust the level of detail of the analysis based on that data.

[0048] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a tourism-specific analysis algorithm to information about tourist spots. The analysis unit can also apply a dining-specific analysis algorithm to information about restaurants. The analysis unit can also apply a shopping-specific analysis algorithm to information about shopping. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input information category data into the generation AI, and the generation AI can select an appropriate analysis algorithm based on that data.

[0049] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can determine the priority of the analysis based on the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the accuracy of the analysis based on that data.

[0050] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. The analysis unit can, for example, determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the time of submission. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the time of submission of information into the generation AI, and the generation AI can determine the priority of analysis based on that data.

[0051] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also lower the priority of analysis of less relevant information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance of the information. In this way, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input information relevance data into the generation AI, and the generation AI can adjust the order of analysis based on that data.

[0052] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the level of detail in the analysis results according to the user's level of expertise. This allows the analysis unit to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the analysis based on that data.

[0053] The guidance unit can adjust the level of detail of the guidance based on the importance of the means of transportation when providing guidance. For example, the guidance unit can adjust the level of detail of the guidance based on the importance of the means of transportation when providing guidance. For example, the guidance unit provides detailed guidance for means of transportation with high importance. The guidance unit can also provide concise guidance for means of transportation with low importance. Furthermore, the guidance unit can determine the priority of the guidance according to the importance of the means of transportation. As a result, the guidance unit can provide more appropriate guidance by adjusting the level of detail of the guidance based on the importance of the means of transportation. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input importance data of means of transportation into the generation AI, and the generation AI can adjust the level of detail of the guidance based on that data.

[0054] The guidance unit can apply different guidance algorithms depending on the category of the means of transportation when providing guidance. For example, the guidance unit applies different guidance algorithms depending on the category of the means of transportation when providing guidance. For example, the guidance unit applies a public transportation-specialized guidance algorithm to guidance regarding public transportation. The guidance unit can also apply an automobile-specialized guidance algorithm to guidance regarding automobiles. Furthermore, the guidance unit can apply a walking-specialized guidance algorithm to guidance regarding walking. In this way, the guidance unit can provide more appropriate guidance by applying different guidance algorithms depending on the category of the means of transportation. Some or all of the above-mentioned processing in the guidance unit may be performed using or without using the generation AI. For example, the guidance unit can input category data of the means of transportation into the generation AI, and the generation AI can select an appropriate guidance algorithm based on that data.

[0055] The guidance unit can improve the accuracy of guidance by referring to the user's past guidance results when providing guidance. For example, the guidance unit can improve the accuracy of guidance by referring to the user's past guidance results when providing guidance. For example, the guidance unit can adjust the guidance algorithm based on feedback provided by the user in the past. The guidance unit can also extract specific patterns from the user's past guidance results to improve the accuracy of guidance. Furthermore, the guidance unit can determine the priority of guidance based on the user's past guidance results. In this way, the guidance unit can provide more appropriate guidance by improving the accuracy of guidance by referring to the user's past guidance results. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input the user's past guidance result data into the generation AI, and the generation AI can improve the accuracy of guidance based on that data.

[0056] The guidance unit can determine the priority of guidance based on the time of submission of the transportation means when providing guidance. The guidance unit, for example, determines the priority of guidance based on the time of submission of the transportation means when providing guidance. For example, the guidance unit prioritizes providing guidance related to the most recent transportation means. The guidance unit can also lower the priority of providing guidance related to transportation means that have been submitted less recently. Furthermore, the guidance unit can adjust the level of detail of the guidance depending on the time of submission. This allows the guidance unit to provide more appropriate guidance by determining the priority of guidance based on the time of submission of the transportation means. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input data on the time of submission of the transportation means into the generation AI, and the generation AI can determine the priority of guidance based on that data.

[0057] The guidance unit can adjust the order of guidance based on the relevance of transportation means when providing guidance. The guidance unit, for example, adjusts the order of guidance based on the relevance of transportation means when providing guidance. For example, the guidance unit prioritizes providing guidance related to highly relevant transportation means. The guidance unit can also lower the priority of providing guidance related to less relevant transportation means. Furthermore, the guidance unit can adjust the level of detail of the guidance according to the relevance of transportation means. In this way, the guidance unit can provide more appropriate guidance by adjusting the order of guidance based on the relevance of transportation means. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input relevance data of transportation means into the generation AI, and the generation AI can adjust the order of guidance based on that data.

[0058] The guidance unit can adjust the use of technical terms in the guidance according to the user's level of expertise when providing guidance. For example, the guidance unit can adjust the use of technical terms in the guidance according to the user's level of expertise when providing guidance. For example, if the user has technical expertise, the guidance unit can provide guidance that uses a lot of technical terms. Also, if the user does not have technical expertise, the guidance unit can provide concise and easy-to-understand guidance. Furthermore, the guidance unit can adjust the level of detail in the guidance according to the user's level of expertise. This allows the guidance unit to provide more appropriate guidance by adjusting the use of technical terms in the guidance according to the user's level of expertise. Some or all of the above-described processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the guidance based on that data.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's past travel history and automatically suggest related destinations and words of interest based on the places the user has visited and activities in which the user has been interested. For example, the reception unit can suggest other places of similar interest based on cities and tourist spots the user has visited in the past. The reception unit can also suggest new related events and activities based on events and activities the user has participated in in the past. Furthermore, the reception unit can make new suggestions that meet similar conditions based on the means of transportation and accommodations the user has used in the past. In this way, the reception unit can utilize the user's past travel history to support more personalized travel planning.

[0061] The collection unit can prioritize collecting highly relevant trend information by taking into account the user's current geographical location information. For example, the collection unit can prioritize collecting information about tourist spots and restaurants close to the user's current location. The collection unit can also prioritize collecting information about places that are easily accessible from the user's current location. Furthermore, the collection unit can prioritize collecting information about activities and events that are suitable for the weather and season of the user's current location. This allows the collection unit to utilize the user's current geographical location information to provide more appropriate trend information.

[0062] The analysis unit can evaluate the reliability of the collected information and prioritize analysis of highly reliable information. For example, the analysis unit evaluates the reliability of the source of the information and the poster, and prioritizes analysis of highly reliable information. The analysis unit can also evaluate the content and degree of consistency of the information and prioritize analysis of highly reliable information. Furthermore, the analysis unit can evaluate the update frequency and recency of the information and prioritize analysis of highly reliable information. This allows the analysis unit to evaluate the reliability of the collected information and provide more appropriate analysis results.

[0063] The guidance unit can suggest services and facilities that can be used during travel, depending on the user's mode of transportation. For example, if the user uses public transportation, it can suggest cafes and restaurants near stations and bus stops. If the user uses a car, it can also provide information on parking lots and gas stations. Furthermore, if the user uses a bicycle, it can also suggest bicycle-only paths and rest spots. In this way, the guidance unit can support a more comfortable travel experience by providing services and facilities that can be used during travel, depending on the user's mode of transportation.

[0064] The guidance unit can adjust the details of the travel route based on the user's walking speed and travel pace. For example, if the user walks slowly, the guidance unit can suggest a route that includes many tourist spots and rest areas. Alternatively, if the user moves quickly, the guidance unit can suggest an efficient route. Furthermore, the guidance unit can adjust the travel time and estimated arrival time according to the user's travel pace. This allows the guidance unit to provide a more appropriate travel route based on the user's walking speed and travel pace.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit accepts input from the user of travel destinations and words of interest. For example, the user inputs specific words such as "visiting cafes in Tokyo" or "art museums in Paris." Step 2: The collection unit collects the latest trend information from the social networking site based on the information received by the reception unit. For example, the collection unit collects the latest posts about cafe hopping in Tokyo and information about art museums in Paris from the social networking site. Step 3: The analysis unit analyzes the information collected by the collection unit and compiles photos and locations. For example, the analysis unit analyzes the collected information using image analysis or text analysis and compiles related photo and location information. Step 4: The guidance unit provides the optimal route, taking into account travel time and method based on the information analyzed by the analysis unit. For example, if the user specifies a specific route such as "start from Shinjuku Station, visit cafes in Shibuya, and return to Roppongi in the evening," the guidance unit automatically generates travel time and method along that route.

[0067] (Example 2) A travel planning support system according to an embodiment of the present invention utilizes a generation AI to provide a sense of excitement when planning a trip. The system allows users to input their travel destination and keywords of interest. The generation AI then collects the latest trend information from social media and aggregates photos and locations. Furthermore, when users specify their departure and return locations, the generation AI then guides them to the optimal route, taking travel time and methods into consideration. This system allows users to create their own personalized guidebooks. For example, in a travel planning support system, a user inputs specific keywords such as "cafe hopping in Tokyo" or "museums in Paris." The generation AI then analyzes the input information and collects the latest trend information from social media. The generation AI then aggregates related photos and location information and provides it to the user. Furthermore, when a user specifies a specific route, such as "starting from Shinjuku Station, visiting cafes in Shibuya, and returning to Roppongi in the evening," the generation AI automatically generates travel times and methods for that route. This allows users to create travel plans based on the latest trend information and reduce the hassle of traveling. The travel planning support system can also distribute coupons from stores and commercial facilities through the app, earning revenue from advertising fees and performance-based publishing fees. This allows users to create their own original guidebooks and plan trips based on the latest trend information. For example, users input their travel destinations and words of interest, and the generation AI collects the latest trend information from social media and compiles photos and locations. Furthermore, when users specify their departure location, time, and return location, the generation AI takes travel time and method into consideration to guide them to the optimal route. This allows users to create their own original guidebooks and saves them the trouble of traveling. The travel planning support system can also distribute coupons from stores and commercial facilities through the app, earning revenue from advertising fees and performance-based publishing fees.

[0068] A travel planning support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a guidance unit. The reception unit receives input from a user of a travel destination and words of interest. For example, the user inputs specific words such as "cafe hopping in Tokyo" or "art museums in Paris." The collection unit collects the latest trend information from social media based on the information received by the reception unit. For example, the collection unit collects the latest posts about cafe hopping in Tokyo and information about art museums in Paris from social media. The analysis unit analyzes the information collected by the collection unit and compiles photos and locations. For example, the analysis unit analyzes the collected information using image analysis or text analysis and compiles related photo and location information. The guidance unit provides guidance on an optimal route, taking travel time and method into consideration, based on the information analyzed by the analysis unit. For example, when a user specifies a specific route, such as "starting from Shinjuku Station, visiting cafes in Shibuya, and returning to Roppongi in the evening," the guidance unit automatically generates travel time and methods along the route. As a result, the travel planning support system according to the embodiment allows users to create their own original guidebook and plan their trip based on the latest trend information. For example, the reception unit inputs information entered by the user into the generation AI, which then performs analysis based on that information. The collection unit allows the generation AI to collect the latest trend information from social media, and the analysis unit allows the generation AI to analyze the collected information and compile photos and locations. The guidance unit provides guidance on the optimal route, taking into account travel time and method based on the information analyzed by the generation AI. This allows users to create their own original guidebook and also saves the effort of traveling.

[0069] The collection unit can collect the latest trend information from the SNS. The collection unit, for example, collects the latest trend information from the SNS. For example, the collection unit collects information about popular tourist spots and trendy restaurants from the SNS. The collection unit can also collect information about activities in which the user is interested from the SNS. For example, the collection unit collects the latest posts about activities in which the user is interested from the SNS. In this way, the collection unit can provide the user with the latest information by collecting the latest trend information from the SNS. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the information collected from the SNS into the generation AI, and the generation AI can analyze the information and extract trend information.

[0070] The analysis unit can analyze the collected information and tally up photos and locations. The analysis unit can analyze the collected information using, for example, image analysis or text analysis. For example, the analysis unit can analyze the collected information using image analysis technology to extract related photos. The analysis unit can also analyze the collected information using text analysis technology to extract related location information. For example, the analysis unit can analyze the collected information using data mining technology to extract related photo and location information. In this way, the analysis unit can analyze the collected information and tally up photos and locations, thereby providing visual information to the user. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected information into a generation AI, which can analyze the information and extract photo and location information.

[0071] The guidance unit can provide route guidance based on the travel time and method specified by the user based on the departure location, time, and return location. For example, the guidance unit provides guidance on the optimal route by taking into account the travel time and method specified by the user based on the departure location, time, and return location. For example, when the user specifies a specific route, such as "start from Shinjuku Station, visit cafes in Shibuya, and return to Roppongi in the evening," the guidance unit automatically generates the travel time and method along that route. The guidance unit can also provide guidance on routes based on the user's mode of transportation. For example, the guidance unit can provide guidance on a route suitable for walking if the user is traveling on foot, and on a route using public transportation if the user is traveling by public transportation. Furthermore, the guidance unit can provide navigation based on the user's walking speed. For example, the guidance unit can provide navigation based on the user's walking speed, providing a more comfortable journey. This allows the guidance unit to reduce the effort required for travel by providing guidance on the optimal route based on the conditions specified by the user. Some or all of the above-described processing in the guidance unit may be performed using a generation AI or without a generation AI. For example, the guidance unit can input information about the departure location, time, and return location specified by the user into the generation AI, and the generation AI can generate the optimal route based on that information.

[0072] The guidance unit can guide the user along a route that corresponds to the user's means of transportation. For example, the guidance unit guides the user along a route that corresponds to the user's means of transportation. For example, if the user travels on foot, the guidance unit guides the user along a route that is suitable for walking. Furthermore, if the user travels by public transportation, the guidance unit can also guide the user along a route that uses public transportation. Furthermore, if the user travels by bicycle, the guidance unit can also guide the user along a route that is suitable for cycling. In this way, the guidance unit can provide a more appropriate method of travel by guiding the user along a route that corresponds to the user's means of transportation. Some or all of the above-described processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input information about the user's means of transportation into the generation AI, and the generation AI can generate an optimal route based on that information.

[0073] The guidance unit can perform navigation according to the user's walking speed. For example, the guidance unit performs navigation according to the user's walking speed. For example, the guidance unit performs navigation according to the user's walking speed, providing a more comfortable journey. The guidance unit can also monitor the user's walking speed in real time and adjust navigation. For example, the guidance unit detects the user's walking speed with a sensor and performs navigation according to that speed. Furthermore, the guidance unit can calculate travel time based on the user's walking speed and provide guidance on an optimal route. For example, the guidance unit calculates travel time based on the user's walking speed and provides guidance on a route according to that time. In this way, the guidance unit can provide a more comfortable journey by performing navigation according to the user's walking speed. Some or all of the above-described processing in the guidance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the guidance unit can input information about the user's walking speed into the generation AI, and the generation AI can adjust navigation based on that information.

[0074] The reception unit can estimate the user's emotions and adjust the input method for travel destinations and words of interest based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the input method for travel destinations and words of interest based on the estimated user emotions. For example, if the user is excited, the reception unit can provide a colorful and interactive interface to make inputting more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to make inputting easier. Furthermore, if the user is stressed, the reception unit can provide an interface with relaxing colors to minimize input steps. This allows the reception unit to adjust the input method according to the user's emotions, thereby providing a more comfortable input experience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using the generation AI, or without the generation AI. For example, the reception unit can input the user's emotional data into the generation AI, which can then estimate the emotion based on that data and adjust the input method.

[0075] The reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit can analyze the user's past travel history and suggest the optimal input method. For example, the reception unit can automatically display related destinations and words of interest as candidates based on places the user has visited in the past. The reception unit can also prioritize suggesting input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations related to specific seasons or events based on the user's past travel history. This allows the reception unit to suggest the optimal input method based on the user's past travel history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past travel history data into the generation AI, which can then suggest the optimal input method based on that data.

[0076] The reception unit can present input candidates based on the user's current areas of interest and trends upon reception. For example, the reception unit presents input candidates based on the user's current areas of interest and trends upon reception. For example, the reception unit can suggest related destinations and words of interest based on topics and keywords recently searched by the user. The reception unit can also analyze the content of posts on social media accounts the user follows and present related input candidates. Furthermore, the reception unit can suggest related destinations and words of interest based on trends in online communities in which the user participates. This allows the reception unit to provide more appropriate information by presenting input candidates based on the user's current areas of interest and trends. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's areas of interest and trends into the generation AI, and the generation AI can present input candidates based on that data.

[0077] The reception unit can select the optimal input means according to the user's input method when receiving the input. For example, the reception unit can select the optimal input means according to the user's input method when receiving the input. For example, if the user selects voice input, the reception unit can input a destination or words of interest using voice recognition technology. Also, if the user selects text input, the reception unit can input using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can suggest related destinations or words of interest using image recognition technology. This allows the reception unit to select the optimal input means according to the user's input method, thereby improving input efficiency. Some or all of the above-mentioned processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data of the user's input method into the generation AI, and the generation AI can select the optimal input means based on that data.

[0078] The reception unit can estimate the user's emotions and prioritize the input information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input information based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying the latest trend information. Furthermore, if the user is relaxed, the reception unit can prioritize displaying detailed information. Furthermore, if the user is stressed, the reception unit can prioritize displaying concise, to-the-point information. This allows the reception unit to prioritize information according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then prioritize the information based on the data.

[0079] The reception unit can present highly relevant input candidates taking into account the user's geographical location information at the time of reception. For example, the reception unit presents highly relevant input candidates taking into account the user's geographical location information at the time of reception. For example, the reception unit may preferentially suggest tourist spots and stores close to the user's current location. The reception unit may also preferentially suggest places that are easily accessible from the user's current location. Furthermore, the reception unit may also suggest destinations and words of interest that are suitable for the weather and season of the user's current location. In this way, the reception unit can provide more appropriate information by presenting input candidates taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit may input the user's geographical location information to the generation AI, and the generation AI may present highly relevant input candidates based on that information.

[0080] The reception unit can analyze the user's social media activity and present related input candidates at the time of reception. For example, the reception unit can analyze the user's social media activity and present related input candidates at the time of reception. For example, the reception unit can suggest related destinations and words of interest based on the content of posts from accounts the user follows on social media. The reception unit can also present related input candidates based on locations where the user has checked in on social media. Furthermore, the reception unit can suggest related destinations and words of interest by referring to the activities of the user's friends on social media. This allows the reception unit to provide more appropriate information by analyzing the user's social media activity and presenting input candidates. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input data on the user's social media activity into the generation AI, and the generation AI can present related input candidates based on the data.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when receiving the input. For example, the reception unit customizes the input method by reflecting the user's past feedback when receiving the input. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has previously preferred. Furthermore, the reception unit can improve and suggest a specific input method based on the user's past feedback. In this way, the reception unit can provide a more appropriate input method by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, and the generation AI can customize the input method based on that data.

[0082] The collection unit can estimate the user's emotions and adjust the range of trend information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the range of trend information to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit can collect and provide extensive trend information. Furthermore, when the user is relaxed, the collection unit can collect and provide detailed trend information. Furthermore, when the user is stressed, the collection unit can collect and provide concise, to-the-point trend information. This allows the collection unit to provide more appropriate information by adjusting the range of trend information to be collected 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. Some or all of the above-described processing in the collection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and adjust the range of trend information to be collected by the generation AI based on the data.

[0083] The collection unit can prioritize collecting highly relevant information by referring to the user's past travel history when collecting data. For example, the collection unit can prioritize collecting highly relevant information by referring to the user's past travel history when collecting data. For example, the collection unit can prioritize collecting the latest trend information related to places the user has visited in the past. The collection unit can also prioritize collecting information related to specific seasons or events from the user's past travel history. Furthermore, the collection unit can prioritize collecting information related to genres in which the user has been interested in the past. This allows the collection unit to prioritize collecting highly relevant information by referring to the user's past travel history, thereby providing more appropriate information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past travel history data into the generation AI, and the generation AI can prioritize collecting highly relevant information based on that data.

[0084] The collection unit can select collection targets based on the user's current areas of interest and trends at the time of collection. For example, the collection unit selects collection targets based on the user's current areas of interest and trends at the time of collection. For example, the collection unit prioritizes collecting information related to topics and keywords recently searched by the user. The collection unit can also prioritize collecting information related to posts on social media accounts followed by the user. Furthermore, the collection unit can prioritize collecting information related to trends in online communities in which the user participates. This allows the collection unit to provide more appropriate information by selecting collection targets based on the user's current areas of interest and trends. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's areas of interest and trends into the generation AI, and the generation AI can select collection targets based on that data.

[0085] The collection unit can select the optimal collection means depending on the user's input method during collection. For example, the collection unit selects the optimal collection means depending on the user's input method during collection. For example, when the user uses voice input, the collection unit can collect related information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect related information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect related information using image recognition technology. This allows the collection unit to select the optimal collection means depending on the user's input method, thereby improving collection efficiency. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the user's input method into the generation AI, and the generation AI can select the optimal collection means based on that data.

[0086] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting the latest trend information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed information. Furthermore, if the user is stressed, the collection unit can prioritize collecting concise and to-the-point information. This allows the collection unit to prioritize information according to the user's emotions, thereby providing more appropriate information. 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. Some or all of the above-described processing in the collection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, and the generation AI can prioritize information based on the data.

[0087] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting information about tourist spots and stores close to the user's current location. The collection unit can also prioritize collecting information about places that are easily accessible from the user's current location. Furthermore, the collection unit can prioritize collecting information that is suitable for the weather and season of the user's current location. In this way, the collection unit can provide more appropriate information by collecting information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, and the generation AI can prioritize collecting highly relevant information based on that information.

[0088] The collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can analyze the user's social media activities and collect related information at the time of collection. For example, the collection unit can collect information related to the content of posts from accounts the user follows on social media. The collection unit can also collect information related to places the user has checked in to on social media. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, the collection unit can analyze the user's social media activities and collect information to provide more appropriate information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input data on the user's social media activities into the generation AI, and the generation AI can collect related information based on the data.

[0089] The collection unit can customize the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit customizes the collection method by reflecting the user's past feedback at the time of collection. For example, the collection unit suggests an optimal collection method based on feedback provided by the user in the past. The collection unit can also preferentially suggest collection methods (voice, text, etc.) that the user has previously preferred. Furthermore, the collection unit can improve and suggest a specific collection method based on the user's past feedback. In this way, the collection unit can provide more appropriate information by customizing the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI, and the generation AI can customize the collection method based on that data.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, the analysis unit can provide a quick and concise analysis method when the user is excited. The analysis unit can also provide a detailed analysis method when the user is relaxed. Furthermore, the analysis unit can provide a concise and to-the-point analysis method when the user is stressed. This allows the analysis unit to adjust the analysis method according to the user's emotions and 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input user emotion data into the generation AI, and the generation AI can adjust the analysis method based on the data.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a brief analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. As a result, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input importance data of the collected information to the generation AI, and the generation AI can adjust the level of detail of the analysis based on that data.

[0092] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a tourism-specific analysis algorithm to information about tourist spots. The analysis unit can also apply a dining-specific analysis algorithm to information about restaurants. The analysis unit can also apply a shopping-specific analysis algorithm to information about shopping. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input information category data into the generation AI, and the generation AI can select an appropriate analysis algorithm based on that data.

[0093] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can determine the priority of the analysis based on the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, and the generation AI can improve the accuracy of the analysis based on that data.

[0094] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priorities based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the latest trend information. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing detailed information. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing concise, to-the-point information. This allows the analysis unit to determine the analysis priorities based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then determine the analysis priorities based on the data.

[0095] The analysis unit can determine the priority of analysis based on the time of submission of information during analysis. The analysis unit can, for example, determine the priority of analysis based on the time of submission of information during analysis. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also lower the priority of information that was submitted earlier. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the time of submission. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the time of submission of information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input data on the time of submission of information into the generation AI, and the generation AI can determine the priority of analysis based on that data.

[0096] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also lower the priority of analysis of less relevant information. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance of the information. In this way, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input information relevance data into the generation AI, and the generation AI can adjust the order of analysis based on that data.

[0097] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the level of detail in the analysis results according to the user's level of expertise. This allows the analysis unit to provide more appropriate analysis results by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the analysis based on that data.

[0098] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, the guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, if the user is excited, the guidance unit can provide colorful and interactive guidance. Furthermore, if the user is relaxed, the guidance unit can provide guidance in calm colors. Furthermore, if the user is stressed, the guidance unit can provide simple and highly visible guidance. This allows the guidance unit to adjust the way the guidance is presented based on the user's emotions, thereby providing more appropriate guidance. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the guidance unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input user's emotion data into the generation AI, and the generation AI can adjust the way the guidance is presented based on the data.

[0099] The guidance unit can adjust the level of detail of the guidance based on the importance of the means of transportation when providing guidance. For example, the guidance unit can adjust the level of detail of the guidance based on the importance of the means of transportation when providing guidance. For example, the guidance unit provides detailed guidance for means of transportation with high importance. The guidance unit can also provide concise guidance for means of transportation with low importance. Furthermore, the guidance unit can determine the priority of the guidance according to the importance of the means of transportation. As a result, the guidance unit can provide more appropriate guidance by adjusting the level of detail of the guidance based on the importance of the means of transportation. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input importance data of means of transportation into the generation AI, and the generation AI can adjust the level of detail of the guidance based on that data.

[0100] The guidance unit can apply different guidance algorithms depending on the category of the means of transportation when providing guidance. For example, the guidance unit applies different guidance algorithms depending on the category of the means of transportation when providing guidance. For example, the guidance unit applies a public transportation-specialized guidance algorithm to guidance regarding public transportation. The guidance unit can also apply an automobile-specialized guidance algorithm to guidance regarding automobiles. Furthermore, the guidance unit can apply a walking-specialized guidance algorithm to guidance regarding walking. In this way, the guidance unit can provide more appropriate guidance by applying different guidance algorithms depending on the category of the means of transportation. Some or all of the above-mentioned processing in the guidance unit may be performed using or without using the generation AI. For example, the guidance unit can input category data of the means of transportation into the generation AI, and the generation AI can select an appropriate guidance algorithm based on that data.

[0101] The guidance unit can improve the accuracy of guidance by referring to the user's past guidance results when providing guidance. For example, the guidance unit can improve the accuracy of guidance by referring to the user's past guidance results when providing guidance. For example, the guidance unit can adjust the guidance algorithm based on feedback provided by the user in the past. The guidance unit can also extract specific patterns from the user's past guidance results to improve the accuracy of guidance. Furthermore, the guidance unit can determine the priority of guidance based on the user's past guidance results. In this way, the guidance unit can provide more appropriate guidance by improving the accuracy of guidance by referring to the user's past guidance results. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input the user's past guidance result data into the generation AI, and the generation AI can improve the accuracy of guidance based on that data.

[0102] The guidance unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user emotions. For example, the guidance unit can estimate the user's emotions and adjust the length of the guidance based on the estimated user emotions. For example, the guidance unit can provide detailed guidance when the user is excited. Furthermore, the guidance unit can provide guidance of appropriate length when the user is relaxed. Furthermore, the guidance unit can provide brief guidance that focuses on the main points when the user is stressed. This allows the guidance unit to adjust the length of the guidance according to the user's emotions and provide more appropriate guidance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the guidance unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the guidance unit can input user emotion data into the generation AI, and the generation AI can adjust the length of the guidance based on the data.

[0103] The guidance unit can determine the priority of guidance based on the time of submission of the transportation means when providing guidance. The guidance unit, for example, determines the priority of guidance based on the time of submission of the transportation means when providing guidance. For example, the guidance unit prioritizes providing guidance related to the most recent transportation means. The guidance unit can also lower the priority of providing guidance related to transportation means that have been submitted less recently. Furthermore, the guidance unit can adjust the level of detail of the guidance depending on the time of submission. This allows the guidance unit to provide more appropriate guidance by determining the priority of guidance based on the time of submission of the transportation means. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input data on the time of submission of the transportation means into the generation AI, and the generation AI can determine the priority of guidance based on that data.

[0104] The guidance unit can adjust the order of guidance based on the relevance of transportation means when providing guidance. The guidance unit, for example, adjusts the order of guidance based on the relevance of transportation means when providing guidance. For example, the guidance unit prioritizes providing guidance related to highly relevant transportation means. The guidance unit can also lower the priority of providing guidance related to less relevant transportation means. Furthermore, the guidance unit can adjust the level of detail of the guidance according to the relevance of transportation means. In this way, the guidance unit can provide more appropriate guidance by adjusting the order of guidance based on the relevance of transportation means. Some or all of the above-mentioned processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input relevance data of transportation means into the generation AI, and the generation AI can adjust the order of guidance based on that data.

[0105] The guidance unit can adjust the use of technical terms in the guidance according to the user's level of expertise when providing guidance. For example, the guidance unit can adjust the use of technical terms in the guidance according to the user's level of expertise when providing guidance. For example, if the user has technical expertise, the guidance unit can provide guidance that uses a lot of technical terms. Also, if the user does not have technical expertise, the guidance unit can provide concise and easy-to-understand guidance. Furthermore, the guidance unit can adjust the level of detail in the guidance according to the user's level of expertise. This allows the guidance unit to provide more appropriate guidance by adjusting the use of technical terms in the guidance according to the user's level of expertise. Some or all of the above-described processing in the guidance unit may be performed using or without the generation AI. For example, the guidance unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the guidance based on that data. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, and guidance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of travel destinations and words of interest from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest trend information from social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and compiles photos and locations. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance on the optimal route, taking into account travel time and method. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, collection unit, analysis unit, and guidance unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of travel destinations and words of interest from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest trend information from social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and compiles photos and locations. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance on the optimal route, taking into account travel time and method. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and guidance unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of travel destinations and words of interest from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest trend information from social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and tallying up photos and locations. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance on the optimal route taking into account travel time and method. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and guidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of travel destinations and words of interest from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the latest trend information from social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information and tallying up photos and locations. The guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides guidance on the optimal route taking into account travel time and method.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can analyze the user's past travel history and automatically suggest related destinations and words of interest based on the places the user has visited and activities in which the user has been interested. For example, the reception unit can suggest other places of similar interest based on cities and tourist spots the user has visited in the past. The reception unit can also suggest new related events and activities based on events and activities the user has participated in in the past. Furthermore, the reception unit can make new suggestions that meet similar conditions based on the means of transportation and accommodations the user has used in the past. In this way, the reception unit can utilize the user's past travel history to support more personalized travel planning.

[0108] The collection unit can prioritize collecting highly relevant trend information by taking into account the user's current geographical location information. For example, the collection unit can prioritize collecting information about tourist spots and restaurants close to the user's current location. The collection unit can also prioritize collecting information about places that are easily accessible from the user's current location. Furthermore, the collection unit can prioritize collecting information about activities and events that are suitable for the weather and season of the user's current location. This allows the collection unit to utilize the user's current geographical location information to provide more appropriate trend information.

[0109] The analysis unit can evaluate the reliability of the collected information and prioritize analysis of highly reliable information. For example, the analysis unit evaluates the reliability of the source of the information and the poster, and prioritizes analysis of highly reliable information. The analysis unit can also evaluate the content and degree of consistency of the information and prioritize analysis of highly reliable information. Furthermore, the analysis unit can evaluate the update frequency and recency of the information and prioritize analysis of highly reliable information. This allows the analysis unit to evaluate the reliability of the collected information and provide more appropriate analysis results.

[0110] The guidance unit can suggest services and facilities that can be used during travel, depending on the user's mode of transportation. For example, if the user uses public transportation, it can suggest cafes and restaurants near stations and bus stops. If the user uses a car, it can also provide information on parking lots and gas stations. Furthermore, if the user uses a bicycle, it can also suggest bicycle-only paths and rest spots. In this way, the guidance unit can support a more comfortable travel experience by providing services and facilities that can be used during travel, depending on the user's mode of transportation.

[0111] The guidance unit can adjust the details of the travel route based on the user's walking speed and travel pace. For example, if the user walks slowly, the guidance unit can suggest a route that includes many tourist spots and rest areas. Alternatively, if the user moves quickly, the guidance unit can suggest an efficient route. Furthermore, the guidance unit can adjust the travel time and estimated arrival time according to the user's travel pace. This allows the guidance unit to provide a more appropriate travel route based on the user's walking speed and travel pace.

[0112] The reception unit can estimate the user's emotions and adjust the input method for travel destinations and words of interest based on the estimated emotions. For example, if the user is excited, a colorful and interactive interface can be provided to make the input task more enjoyable. Alternatively, if the user is tired, a simple and highly visible interface can be provided to make the input task easier. Furthermore, if the user is stressed, an interface with relaxing colors can be provided to minimize the input steps. In this way, the reception unit can provide a more comfortable input experience by adjusting the input method according to the user's emotions.

[0113] The collection unit can estimate the user's emotions and adjust the range of trend information to be collected based on the estimated emotions. For example, when the user is excited, a wide range of trend information can be collected and provided. When the user is relaxed, detailed trend information can be collected and provided. Furthermore, when the user is stressed, concise and to the point trend information can be collected and provided. In this way, the collection unit can provide more appropriate information by adjusting the range of trend information to be collected according to the user's emotions.

[0114] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is excited, a quick and concise analysis method can be provided. If the user is relaxed, a detailed analysis method can be provided. Furthermore, if the user is stressed, a concise analysis method that focuses on the main points can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis method according to the user's emotions.

[0115] The guidance unit can estimate the user's emotions and adjust the way in which guidance is presented based on the estimated emotions. For example, if the user is excited, colorful and interactive guidance can be provided. If the user is relaxed, guidance in calm colors can be provided. Furthermore, if the user is stressed, simple and highly visible guidance can be provided. In this way, the guidance unit can provide more appropriate guidance by adjusting the way in which guidance is presented according to the user's emotions.

[0116] The guidance unit can estimate the user's emotions and adjust the length of the guidance based on the estimated emotions. For example, if the user is excited, detailed guidance can be provided. If the user is relaxed, guidance of an appropriate length can be provided. Furthermore, if the user is stressed, brief guidance that focuses on the main points can be provided. In this way, the guidance unit can provide more appropriate guidance by adjusting the length of the guidance according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit accepts input from the user of travel destinations and words of interest. For example, the user inputs specific words such as "visiting cafes in Tokyo" or "art museums in Paris." Step 2: The collection unit collects the latest trend information from the social networking site based on the information received by the reception unit. For example, the collection unit collects the latest posts about cafe hopping in Tokyo and information about art museums in Paris from the social networking site. Step 3: The analysis unit analyzes the information collected by the collection unit and compiles photos and locations. For example, the analysis unit analyzes the collected information using image analysis or text analysis and compiles related photo and location information. Step 4: The guidance unit provides the optimal route, taking into account travel time and method based on the information analyzed by the analysis unit. For example, if the user specifies a specific route such as "start from Shinjuku Station, visit cafes in Shibuya, and return to Roppongi in the evening," the guidance unit automatically generates travel time and method along that route.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of travel destinations and words of interest from users; a collection unit that collects the latest trend information from SNS based on the information received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and compiles photos and locations; a guidance unit that provides route guidance based on travel time and method on the basis of the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect the latest trend information from social media 2. The system of claim 1.

3. The analysis unit Analyze the collected information and compile photos and locations 2. The system of claim 1.

4. The guide unit is Route guidance based on travel time and method based on user-specified departure and return locations 2. The system of claim 1.

5. The guide unit is Provide route guidance based on the user's mode of transportation 2. The system of claim 1.

6. The guide unit is Navigation that matches the user's walking speed 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting input methods for travel destinations and words of interest based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past travel history and suggests the best input method 2. The system of claim 1.

Images