System

Abstract

An object of a system according to an embodiment is to efficiently plan a travel schedule and flexibly respond to a situation on the spot.SOLUTION: A system according to an embodiment includes a reception unit, a proposal unit, a change proposal unit, and a notification unit. The receiving unit receives information of a destination. The proposal unit proposes a travel schedule based on the information input by the reception unit. The change suggestion unit predicts weather and traffic conditions during the trip on the basis of the schedule suggested by the suggestion unit, and suggests a change in the schedule. The notification unit collects information on the site and notifies the user of the information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently plan travel schedules and respond flexibly to local conditions.

[0005] The system according to the embodiment aims to efficiently plan a travel schedule and flexibly respond to local conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a change proposal unit, and a notification unit. The reception unit inputs destination information. The proposal unit proposes a travel schedule based on the information input by the reception unit. The change proposal unit predicts weather and traffic conditions during the trip based on the schedule proposed by the proposal unit and proposes changes to the schedule. The notification unit collects local information and notifies the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently plan a travel schedule and flexibly respond to local conditions. [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 is a system that streamlines travel planning and execution. The travel planning support system includes a reception unit for inputting destination information, a proposal unit for proposing an optimal travel schedule based on the information input by the reception unit, a change proposal unit for predicting weather and traffic conditions during the trip based on the schedule proposed by the proposal unit and proposing schedule changes, and a notification unit for collecting local information and notifying the user. For example, in the travel planning support system, when a user inputs destination information, a generation AI proposes multiple optimal travel schedules. Then, on the day of the trip, the travel planning support system predicts weather and traffic conditions and proposes schedule changes as necessary. The travel planning support system also collects local information in a timely manner and notifies the user of the information as "recommended information." This streamlines travel planning and execution and reduces wasted time. For example, the travel planning support system allows users to receive optimal schedule proposals from the generation AI without having to make complex plans. Furthermore, during the trip, real-time information is provided, allowing users to change the schedule and obtain local recommended information. This will improve travel satisfaction and allow you to enjoy a more fulfilling trip.

[0029] A travel planning support system according to an embodiment includes a reception unit, a proposal unit, a change proposal unit, and a notification unit. The reception unit inputs destination information. The destination information includes, but is not limited to, city names, tourist attraction names, and addresses. For example, the reception unit allows a user to input "travel from Tokyo to Kyoto." The proposal unit uses a generation AI to propose an optimal travel schedule based on the information input by the reception unit. The proposal unit generates an optimal schedule for the user, taking into account, for example, the congestion status of tourist attractions, traffic conditions, and weather forecasts. For example, tourist attraction A is less crowded in the morning, so the proposal unit proposes a schedule to visit in the morning. The change proposal unit predicts weather and traffic conditions during the trip based on the schedule proposed by the proposal unit and proposes a change to the schedule. For example, if a planned tourist attraction is crowded, the change proposal unit proposes an alternative tourist attraction. Furthermore, if the weather worsens, the proposal unit proposes an indoor tourist attraction. The notification unit collects local information and notifies the user. The notification unit notifies, for example, local event information and restaurant recommendations. This allows users to obtain the latest local information and enjoy their trip more. As a result, the travel planning support system according to the embodiment can improve the efficiency of planning and executing a trip and reduce wasted time.

[0030] The suggestion unit can propose a schedule based on the congestion status of tourist destinations, traffic conditions, and weather forecasts. For example, the suggestion unit obtains the congestion status of tourist destinations based on real-time data and past data and proposes a schedule. The suggestion unit can also obtain traffic conditions based on traffic congestion information and the operation status of public transportation and propose a schedule. Furthermore, the suggestion unit can obtain weather forecasts based on data from the Japan Meteorological Agency and data from private weather services and propose a schedule. This makes it possible to propose an optimal schedule that takes into account the congestion status of tourist destinations, traffic conditions, weather forecasts, etc.

[0031] The change suggestion unit can suggest schedule changes according to changes in weather and traffic conditions during the trip. For example, the change suggestion unit detects changes in weather and traffic conditions based on the update frequency of real-time data and a change threshold, and suggests schedule changes. For example, if the weather worsens, the change suggestion unit can suggest indoor tourist spots. Also, if traffic congestion occurs, the change suggestion unit can suggest an alternative route. This makes it possible to suggest schedule changes according to changes in weather and traffic conditions during the trip.

[0032] The notification unit can collect local event information and restaurant information and notify the user. For example, the notification unit can collect local event information based on a local event calendar or online event information and notify the user. The notification unit can also collect restaurant information based on restaurant ratings, menus, and business hours and notify the user. This improves travel satisfaction by notifying the user of the latest local information.

[0033] The suggestion unit can propose multiple different schedules based on the information input by the user. For example, the suggestion unit proposes schedules with different visiting orders or different time periods based on the information input by the user. For example, if the user inputs "travel from Tokyo to Kyoto," the suggestion unit can propose both a schedule to visit tourist spot A in the morning and a schedule to visit tourist spot A in the afternoon. In this way, by proposing multiple schedules based on the information input by the user, the number of options increases.

[0034] The change proposal unit can collect weather forecast data and traffic condition data and propose schedule changes. For example, the change proposal unit collects weather forecast data based on data from the Japan Meteorological Agency or data from private weather services and proposes schedule changes. The change proposal unit can also collect traffic condition data based on traffic congestion information and the operation status of public transportation and propose schedule changes. This makes it possible to propose schedule changes based on data collection in real time.

[0035] The reception unit can analyze the user's past travel history and select the optimal method for inputting destination information. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destination information to be used during a specific time period based on the user's past travel history. This makes it possible to select the optimal input method based on the past travel history. Analysis of the past travel history is performed using data such as past travel destinations, length of stay, and frequency of visits. This allows the reception unit to analyze the user's past travel history and select the optimal method for inputting destination information.

[0036] The reception unit can perform filtering based on the user's current interests and concerns when inputting destination information. For example, the reception unit can preferentially display tourist spots recently searched for by the user. The reception unit can also analyze the user's social media activities and suggest related destination information. The reception unit can also filter destination information of interest by reflecting the user's past feedback. This makes it possible to filter destination information based on the user's interests and concerns. The user's current interests and concerns are acquired using, for example, recent search history, social media activity, etc. This allows the reception unit to perform filtering based on the user's current interests and concerns when inputting destination information.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting destination information. For example, if the user prefers voice input, the reception unit can provide voice input preferentially. Furthermore, if the user prefers text input, the reception unit can also provide text input preferentially. Furthermore, if the user prefers image input, the reception unit can also provide image input preferentially. This makes it possible to select the optimal input means depending on the user's input method. The input method is selected using data such as voice input, text input, and image input. This allows the reception unit to select the optimal input means depending on the user's input method when inputting destination information.

[0038] When inputting destination information, the reception unit can prioritize inputting highly relevant destination information in consideration of the user's geographical location information. The reception unit, for example, prioritizes displaying tourist spots close to the user's current location. The reception unit can also suggest optimal destination information in consideration of travel time from the user's current location. The reception unit can also prioritize displaying event information in the vicinity of the user's current location. This makes it possible to prioritize input of destination information based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or data from a location information service. This allows the reception unit, when inputting destination information, to prioritize input of highly relevant destination information in consideration of the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when inputting destination information and input related destination information. The reception unit, for example, suggests places where the user has checked in on social media as candidate destinations. The reception unit can also analyze the content of the user's social media posts and suggest related destination information. The reception unit can also suggest related destination information by referring to the activity of the user's friends on social media. This makes it possible to input destination information based on the user's social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of likes, and the number of followers. This makes it possible for the reception unit to analyze the user's social media activity when inputting destination information and input related destination information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting destination information. For example, the reception unit preferentially provides an input method that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Past feedback is collected using data such as survey results, reviews, and comments. This allows the reception unit to customize the input method by reflecting the user's past feedback when inputting destination information.

[0041] The suggestion unit can adjust the level of detail of the proposal based on the importance of the tourist destination when making a suggestion. For example, the suggestion unit provides detailed information for important tourist destinations. The suggestion unit can also provide concise information for less important tourist destinations. The suggestion unit can also adjust the level of detail of the proposal according to the importance of the tourist destination. This makes it possible to adjust the level of detail of the proposal based on the importance of the tourist destination. The importance of the tourist destination is evaluated using data such as the popularity, number of visitors, and ratings of the tourist destination. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the tourist destination when making a suggestion.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of tourist destination when making a suggestion. For example, for natural tourist destinations, the suggestion unit makes suggestions that emphasize natural scenery. Furthermore, for historical tourist destinations, the suggestion unit can also make suggestions that emphasize historical background. Furthermore, for activity tourist destinations, the suggestion unit can also make suggestions that emphasize the content of the activity. This makes it possible to apply suggestion algorithms depending on the category of tourist destination. Tourist destinations are classified into categories using criteria such as historical landmarks, natural scenery, and theme parks. This allows the suggestion unit to apply different suggestion algorithms depending on the category of tourist destination when making a suggestion.

[0043] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. For example, the suggestion unit makes similar suggestions based on proposals that the user has accepted in the past. The suggestion unit can also avoid proposals that the user has rejected in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results. The analysis of the past suggestion results is performed using data such as the proposal adoption rate, user satisfaction, and feedback. This makes it possible for the suggestion unit to improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions.

[0044] The suggestion unit can determine the priority of the suggestions based on the congestion status of tourist destinations when making suggestions. The suggestion unit, for example, makes suggestions to avoid crowded tourist destinations. The suggestion unit can also preferentially suggest less crowded tourist destinations. The suggestion unit can also adjust the priority of the suggestions according to the congestion status of the tourist destinations. This makes it possible to determine the priority of the suggestions based on the congestion status of the tourist destinations. The congestion status of the tourist destinations is acquired using, for example, real-time data or past data. This allows the suggestion unit to determine the priority of the suggestions based on the congestion status of the tourist destinations when making suggestions.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of tourist destinations when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant tourist destinations. The suggestion unit can also postpone suggesting less relevant tourist destinations. The suggestion unit can also adjust the order of suggestions according to the relevance of tourist destinations. This makes it possible to adjust the order of suggestions based on the relevance of tourist destinations. The relevance of tourist destinations is evaluated using data such as geographical proximity and commonality of themes. This allows the suggestion unit to adjust the order of suggestions based on the relevance of tourist destinations when making suggestions.

[0046] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit uses technical terms when the user has technical expertise. The suggestion unit can also explain in simple terms when the user does not have technical expertise. The suggestion unit can also adjust the use of technical terms in the proposal according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the proposal according to the user's level of expertise. The expertise level is evaluated using data such as questionnaire results and past learning history. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal.

[0047] The change suggestion unit can adjust the level of detail of the proposal based on the accuracy of the weather forecast data when proposing a change. For example, if the weather forecast is accurate, the change suggestion unit makes a detailed change suggestion. Furthermore, if the weather forecast is uncertain, the change suggestion unit can also make a concise change suggestion. The change suggestion unit can also adjust the level of detail of the proposal according to the accuracy of the weather forecast data. This makes it possible to adjust the level of detail of the proposal based on the accuracy of the weather forecast data. The accuracy of the weather forecast data is evaluated using criteria such as the accuracy rate of the forecast and the frequency of data updates. This allows the change suggestion unit to adjust the level of detail of the proposal based on the accuracy of the weather forecast data when proposing a change.

[0048] The change proposing unit can apply different proposed algorithms depending on changes in traffic conditions when proposing a change. For example, the change proposing unit can propose a detour route when traffic congestion occurs. The change proposing unit can also propose the shortest route when traffic conditions are good. The change proposing unit can also adjust the proposed algorithm depending on changes in traffic conditions. This makes it possible to apply the proposed algorithm depending on changes in traffic conditions. Changes in traffic conditions are detected using criteria such as the update frequency of real-time data and a change threshold. This makes it possible for the change proposing unit to apply different proposed algorithms depending on changes in traffic conditions when proposing a change.

[0049] When proposing changes, the change suggestion unit can improve the accuracy of the proposal by referring to the user's past change history. For example, the change suggestion unit makes similar proposals based on change proposals that the user has accepted in the past. The change suggestion unit can also avoid change proposals that the user has rejected in the past. The change suggestion unit can also analyze the user's past change history and make optimal change proposals. This makes it possible to improve the accuracy of proposals based on the user's past change history. The analysis of the past change history is performed using data such as the frequency of changes, the reasons for the changes, and the satisfaction level after the changes. This makes it possible for the change suggestion unit to improve the accuracy of proposals by referring to the user's past change history when proposing changes.

[0050] When proposing a change, the change proposing unit can adjust the timing of the proposal based on the update frequency of the weather forecast data. For example, if the weather forecast is updated frequently, the change proposing unit makes change proposals in real time. Furthermore, if the weather forecast is not updated often, the change proposing unit can also make change proposals at regular intervals. Furthermore, the change proposing unit can adjust the timing of the proposal based on the update frequency of the weather forecast data. This makes it possible to adjust the timing of the proposal based on the update frequency of the weather forecast data. The update frequency of the weather forecast data is evaluated using, for example, data from the Japan Meteorological Agency or data from private weather services. This allows the change proposing unit, when proposing a change, to adjust the timing of the proposal based on the update frequency of the weather forecast data.

[0051] The change proposing unit can adjust the order of proposals by referring to real-time data on traffic conditions when proposing a change. For example, when traffic congestion occurs, the change proposing unit preferentially proposes a detour route. Furthermore, when traffic conditions are good, the change proposing unit can also preferentially propose the shortest route. Furthermore, the change proposing unit can adjust the order of proposals based on the real-time data on traffic conditions. This makes it possible to adjust the order of proposals based on the real-time data on traffic conditions. The real-time data on traffic conditions is acquired using, for example, data such as traffic congestion information and the operation status of public transportation. This allows the change proposing unit to adjust the order of proposals by referring to the real-time data on traffic conditions when proposing a change.

[0052] The change suggestion unit can customize the content of the suggestion based on the user's current location information when proposing changes. For example, the change suggestion unit prioritizes suggesting tourist spots close to the user's current location. The change suggestion unit can also make optimal change suggestions taking into account travel time from the user's current location. The change suggestion unit can also prioritize suggesting event information near the user's current location. This makes it possible to customize the content of the suggestion based on the user's current location information. The current location information is acquired using, for example, GPS data or data from a location information service. This makes it possible for the change suggestion unit to customize the content of the suggestion based on the user's current location information when proposing changes.

[0053] The notification unit can adjust the level of detail of the notification based on the importance of local event information at the time of notification. For example, the notification unit provides detailed notification for important events. The notification unit can also provide brief notification for events with low importance. The notification unit can also adjust the level of detail of the notification depending on the importance of the event. This makes it possible to adjust the level of detail of the notification based on the importance of local event information. The importance of the event information is evaluated using data such as the scale of the event, the number of participants, and ratings. This allows the notification unit to adjust the level of detail of the notification based on the importance of local event information at the time of notification.

[0054] The notification unit can apply different notification algorithms depending on the category of local restaurant information when providing notification. For example, the notification unit can provide detailed menu information for high-end restaurants. The notification unit can also provide simple menu information for casual restaurants. The notification unit can also adjust the notification algorithm depending on the restaurant category. This makes it possible to apply a notification algorithm depending on the category of local restaurant information. Restaurant information categories are classified using criteria such as type of cuisine, price range, and rating. This allows the notification unit to apply different notification algorithms depending on the category of local restaurant information when providing notification.

[0055] The notification unit can improve the accuracy of notifications by referring to the user's past notification history when making a notification. The notification unit, for example, provides similar notifications based on notifications that the user has previously accepted. The notification unit can also avoid notifications that the user has previously rejected. The notification unit can also analyze the user's past notification history and provide optimal notifications. This makes it possible to improve the accuracy of notifications based on the user's past notification history. The analysis of the past notification history is performed using data such as the frequency of notifications, the content of notifications, and reactions after notifications. This allows the notification unit to improve the accuracy of notifications by referring to the user's past notification history when making a notification.

[0056] The notification unit can adjust the timing of notification based on the update frequency of local event information at the time of notification. For example, if the event information is updated frequently, the notification unit can provide notification in real time. Furthermore, if the event information is not updated often, the notification unit can provide notification at regular intervals. Furthermore, the notification unit can adjust the timing of notification according to the update frequency of the event information. This makes it possible to adjust the timing of notification based on the update frequency of local event information. The update frequency of the event information is evaluated using criteria such as the update frequency of an event calendar or the update frequency of online event information. This allows the notification unit to adjust the timing of notification based on the update frequency of local event information at the time of notification.

[0057] The notification unit can adjust the order of notifications based on the relevance of local restaurant information at the time of notification. For example, the notification unit prioritizes notification of highly relevant restaurant information. The notification unit can also postpone notification of less relevant restaurant information. The notification unit can also adjust the order of notifications according to the relevance of the restaurant information. This makes it possible to adjust the order of notifications based on the relevance of local restaurant information. The evaluation of the relevance of restaurant information is performed using data such as the type of cuisine, price range, and rating. This allows the notification unit to adjust the order of notifications based on the relevance of local restaurant information at the time of notification.

[0058] The notification unit can customize the content of the notification based on the user's current location information when providing a notification. For example, the notification unit prioritizes notifying the user of event information close to the user's current location. The notification unit can also provide optimal notifications taking into account travel time from the user's current location. The notification unit can also prioritize notifying the user of restaurant information near the user's current location. This makes it possible to customize the content of the notification based on the user's current location information. The current location information is obtained using, for example, GPS data or data from a location information service. This allows the notification unit to customize the content of the notification based on the user's current location information when providing a notification.

[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 suggestion unit can analyze the user's past travel history and suggest similar travel destinations. For example, it can suggest new tourist destinations with similar characteristics based on the user's ratings and length of stay at tourist destinations visited in the past. The suggestion unit can also suggest tourist destinations that are good to visit in the same season as the tourist destinations visited by the user in the past, taking into account the season and event information of the tourist destinations visited in the past. Furthermore, the suggestion unit can suggest travel destinations based on a specific theme (e.g., history, nature, activity, etc.) from the user's past travel history. This makes it possible to suggest optimal travel destinations based on the user's past travel history.

[0061] The change suggestion unit can monitor the user's current health condition and suggest schedule changes based on the health condition. For example, if the user is tired, it can suggest increasing rest time. Also, if the user is not feeling well, it can suggest changes to a more reasonable schedule. Furthermore, if the user is in good health, it can suggest a schedule with more activity. This makes it possible to suggest schedule changes based on the user's health condition. Health condition monitoring is performed using data from a wearable device or a health app, for example. This allows the change suggestion unit to suggest schedule changes based on the user's health condition.

[0062] The suggestion unit can customize the suggestion content based on the user's current interests and concerns. For example, it can prioritize suggesting tourist attractions and activities that the user has recently searched for. It can also analyze the user's social media activities to suggest related tourist attractions and events. It can also suggest tourist attractions and activities that interest the user by reflecting the user's past feedback. This makes it possible to customize the suggestion content based on the user's current interests and concerns. The user's current interests and concerns can be acquired using, for example, recent search history, social media activity, etc. This allows the suggestion unit to customize the suggestion content based on the user's current interests and concerns.

[0063] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has technical expertise, technical terms are used. Also, if the user does not have technical expertise, explanations can be provided in simple terms. Furthermore, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the proposal according to the user's level of expertise. The expertise level is evaluated using data such as questionnaire results and past learning history. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal.

[0064] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, it can make similar suggestions based on suggestions that the user has accepted in the past. It can also avoid suggestions that the user has rejected in the past. Furthermore, the suggestion unit can analyze the user's past suggestion results and make optimal suggestions. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results. The analysis of the past suggestion results is performed using data such as the proposal adoption rate, user satisfaction, and feedback. This allows the suggestion unit to improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions.

[0065] The notification unit can adjust the timing of notification based on the update frequency of local event information at the time of notification. For example, if the event information is updated frequently, notification can be performed in real time. Alternatively, if the event information is not updated often, notification can be performed at regular intervals. Furthermore, the notification unit can adjust the timing of notification according to the update frequency of the event information. This makes it possible to adjust the timing of notification based on the update frequency of local event information. The update frequency of the event information is evaluated using criteria such as the update frequency of an event calendar or the update frequency of online event information. This allows the notification unit to adjust the timing of notification based on the update frequency of local event information at the time of notification.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs destination information. The destination information includes city names, tourist spot names, addresses, etc. For example, the user can input "Travel from Tokyo to Kyoto." Step 2: The suggestion unit proposes an optimal travel schedule based on the information input by the reception unit. The suggestion unit generates an optimal schedule for the user, taking into account the congestion status of tourist spots, traffic conditions, weather forecasts, etc. For example, tourist spot A is less crowded if visited in the morning, so the suggestion unit proposes a schedule to visit in the morning. Step 3: The change suggestion unit predicts weather and traffic conditions during the trip based on the schedule proposed by the suggestion unit and suggests changes to the schedule. For example, if the planned tourist spot is crowded, it suggests an alternative tourist spot. Also, if the weather worsens, it suggests an indoor tourist spot. Step 4: The notification unit collects local information and notifies the user. For example, it may notify the user of local event information or restaurant recommendations. This allows the user to obtain the latest local information and enjoy their trip even more.

[0068] (Example 2) A travel planning support system according to an embodiment of the present invention is a system that streamlines travel planning and execution. The travel planning support system includes a reception unit for inputting destination information, a proposal unit for proposing an optimal travel schedule based on the information input by the reception unit, a change proposal unit for predicting weather and traffic conditions during the trip based on the schedule proposed by the proposal unit and proposing schedule changes, and a notification unit for collecting local information and notifying the user. For example, in the travel planning support system, when a user inputs destination information, a generation AI proposes multiple optimal travel schedules. Then, on the day of the trip, the travel planning support system predicts weather and traffic conditions and proposes schedule changes as necessary. The travel planning support system also collects local information in a timely manner and notifies the user of the information as "recommended information." This streamlines travel planning and execution and reduces wasted time. For example, the travel planning support system allows users to receive optimal schedule proposals from the generation AI without having to make complex plans. Furthermore, during the trip, real-time information is provided, allowing users to change the schedule and obtain local recommended information. This will improve travel satisfaction and allow you to enjoy a more fulfilling trip.

[0069] A travel planning support system according to an embodiment includes a reception unit, a proposal unit, a change proposal unit, and a notification unit. The reception unit inputs destination information. The destination information includes, but is not limited to, city names, tourist attraction names, and addresses. For example, the reception unit allows a user to input "travel from Tokyo to Kyoto." The proposal unit uses a generation AI to propose an optimal travel schedule based on the information input by the reception unit. The proposal unit generates an optimal schedule for the user, taking into account, for example, the congestion status of tourist attractions, traffic conditions, and weather forecasts. For example, tourist attraction A is less crowded in the morning, so the proposal unit proposes a schedule to visit in the morning. The change proposal unit predicts weather and traffic conditions during the trip based on the schedule proposed by the proposal unit and proposes a change to the schedule. For example, if a planned tourist attraction is crowded, the change proposal unit proposes an alternative tourist attraction. Furthermore, if the weather worsens, the proposal unit proposes an indoor tourist attraction. The notification unit collects local information and notifies the user. The notification unit notifies, for example, local event information and restaurant recommendations. This allows users to obtain the latest local information and enjoy their trip more. As a result, the travel planning support system according to the embodiment can improve the efficiency of planning and executing a trip and reduce wasted time.

[0070] The suggestion unit can propose a schedule based on the congestion status of tourist destinations, traffic conditions, and weather forecasts. For example, the suggestion unit obtains the congestion status of tourist destinations based on real-time data and past data and proposes a schedule. The suggestion unit can also obtain traffic conditions based on traffic congestion information and the operation status of public transportation and propose a schedule. Furthermore, the suggestion unit can obtain weather forecasts based on data from the Japan Meteorological Agency and data from private weather services and propose a schedule. This makes it possible to propose an optimal schedule that takes into account the congestion status of tourist destinations, traffic conditions, weather forecasts, etc.

[0071] The change suggestion unit can suggest schedule changes according to changes in weather and traffic conditions during the trip. For example, the change suggestion unit detects changes in weather and traffic conditions based on the update frequency of real-time data and a change threshold, and suggests schedule changes. For example, if the weather worsens, the change suggestion unit can suggest indoor tourist spots. Also, if traffic congestion occurs, the change suggestion unit can suggest an alternative route. This makes it possible to suggest schedule changes according to changes in weather and traffic conditions during the trip.

[0072] The notification unit can collect local event information and restaurant information and notify the user. For example, the notification unit can collect local event information based on a local event calendar or online event information and notify the user. The notification unit can also collect restaurant information based on restaurant ratings, menus, and business hours and notify the user. This improves travel satisfaction by notifying the user of the latest local information.

[0073] The suggestion unit can propose multiple different schedules based on the information input by the user. For example, the suggestion unit proposes schedules with different visiting orders or different time periods based on the information input by the user. For example, if the user inputs "travel from Tokyo to Kyoto," the suggestion unit can propose both a schedule to visit tourist spot A in the morning and a schedule to visit tourist spot A in the afternoon. In this way, by proposing multiple schedules based on the information input by the user, the number of options increases.

[0074] The change proposal unit can collect weather forecast data and traffic condition data and propose schedule changes. For example, the change proposal unit collects weather forecast data based on data from the Japan Meteorological Agency or data from private weather services and proposes schedule changes. The change proposal unit can also collect traffic condition data based on traffic congestion information and the operation status of public transportation and propose schedule changes. This makes it possible to propose schedule changes based on data collection in real time.

[0075] The reception unit can estimate the user's emotions and adjust the timing of inputting destination information based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide time for the user to relax. Furthermore, if the user is excited, the reception unit can advance the input timing to prompt the user to input information quickly. Furthermore, if the user is tired, the reception unit can adjust the input timing to ensure time for a break. This makes it possible to adjust the input timing according to the user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. This allows the reception unit to adjust the input timing of destination information based on the user's emotions.

[0076] The reception unit can analyze the user's past travel history and select the optimal method for inputting destination information. For example, the reception unit automatically displays places that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destination information to be used during a specific time period based on the user's past travel history. This makes it possible to select the optimal input method based on the past travel history. Analysis of the past travel history is performed using data such as past travel destinations, length of stay, and frequency of visits. This allows the reception unit to analyze the user's past travel history and select the optimal method for inputting destination information.

[0077] The reception unit can perform filtering based on the user's current interests and concerns when inputting destination information. For example, the reception unit can preferentially display tourist spots recently searched for by the user. The reception unit can also analyze the user's social media activities and suggest related destination information. The reception unit can also filter destination information of interest by reflecting the user's past feedback. This makes it possible to filter destination information based on the user's interests and concerns. The user's current interests and concerns are acquired using, for example, recent search history, social media activity, etc. This allows the reception unit to perform filtering based on the user's current interests and concerns when inputting destination information.

[0078] The reception unit can select the optimal input means depending on the user's input method when inputting destination information. For example, if the user prefers voice input, the reception unit can provide voice input preferentially. Furthermore, if the user prefers text input, the reception unit can also provide text input preferentially. Furthermore, if the user prefers image input, the reception unit can also provide image input preferentially. This makes it possible to select the optimal input means depending on the user's input method. The input method is selected using data such as voice input, text input, and image input. This allows the reception unit to select the optimal input means depending on the user's input method when inputting destination information.

[0079] The reception unit can estimate the user's emotions and determine the priority of destination information to be input based on the estimated user's emotions. For example, when the user is relaxed, the reception unit sets a high priority to tourist destinations. Furthermore, when the user is in a hurry, the reception unit can also preferentially display destination information with short travel times. Furthermore, when the user is excited, the reception unit can also preferentially display destination information with high activity. This makes it possible to determine the priority of destination information based on the user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. This allows the reception unit to estimate the user's emotions and determine the priority of destination information to be input based on the estimated user's emotions.

[0080] When inputting destination information, the reception unit can prioritize inputting highly relevant destination information in consideration of the user's geographical location information. The reception unit, for example, prioritizes displaying tourist spots close to the user's current location. The reception unit can also suggest optimal destination information in consideration of travel time from the user's current location. The reception unit can also prioritize displaying event information in the vicinity of the user's current location. This makes it possible to prioritize input of destination information based on the user's geographical location information. The geographical location information is acquired using, for example, GPS data or data from a location information service. This allows the reception unit, when inputting destination information, to prioritize input of highly relevant destination information in consideration of the user's geographical location information.

[0081] The reception unit can analyze the user's social media activity when inputting destination information and input related destination information. The reception unit, for example, suggests places where the user has checked in on social media as candidate destinations. The reception unit can also analyze the content of the user's social media posts and suggest related destination information. The reception unit can also suggest related destination information by referring to the activity of the user's friends on social media. This makes it possible to input destination information based on the user's social media activity. The analysis of social media activity is performed using data such as the content of posts, the number of likes, and the number of followers. This makes it possible for the reception unit to analyze the user's social media activity when inputting destination information and input related destination information.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting destination information. For example, the reception unit preferentially provides an input method that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's past feedback. This makes it possible to customize the input method based on the user's past feedback. Past feedback is collected using data such as survey results, reviews, and comments. This allows the reception unit to customize the input method by reflecting the user's past feedback when inputting destination information.

[0083] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can provide visually appealing suggestions when the user is excited. This makes it possible to adjust the way in which suggestions are expressed based on the user's emotion. The user's emotion is estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. This makes it possible for the suggestion unit to estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion.

[0084] The suggestion unit can adjust the level of detail of the proposal based on the importance of the tourist destination when making a suggestion. For example, the suggestion unit provides detailed information for important tourist destinations. The suggestion unit can also provide concise information for less important tourist destinations. The suggestion unit can also adjust the level of detail of the proposal according to the importance of the tourist destination. This makes it possible to adjust the level of detail of the proposal based on the importance of the tourist destination. The importance of the tourist destination is evaluated using data such as the popularity, number of visitors, and ratings of the tourist destination. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the tourist destination when making a suggestion.

[0085] The suggestion unit can apply different suggestion algorithms depending on the category of tourist destination when making a suggestion. For example, for natural tourist destinations, the suggestion unit makes suggestions that emphasize natural scenery. Furthermore, for historical tourist destinations, the suggestion unit can also make suggestions that emphasize historical background. Furthermore, for activity tourist destinations, the suggestion unit can also make suggestions that emphasize the content of the activity. This makes it possible to apply suggestion algorithms depending on the category of tourist destination. Tourist destinations are classified into categories using criteria such as historical landmarks, natural scenery, and theme parks. This allows the suggestion unit to apply different suggestion algorithms depending on the category of tourist destination when making a suggestion.

[0086] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. For example, the suggestion unit makes similar suggestions based on proposals that the user has accepted in the past. The suggestion unit can also avoid proposals that the user has rejected in the past. The suggestion unit can also analyze the user's past suggestion results and make optimal suggestions. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results. The analysis of the past suggestion results is performed using data such as the proposal adoption rate, user satisfaction, and feedback. This makes it possible for the suggestion unit to improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can provide visually appealing suggestions when the user is excited. This makes it possible to adjust the length of the suggestion based on the user's emotion. The user's emotion is estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. This makes it possible for the suggestion unit to estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion.

[0088] The suggestion unit can determine the priority of the suggestions based on the congestion status of tourist destinations when making suggestions. The suggestion unit, for example, makes suggestions to avoid crowded tourist destinations. The suggestion unit can also preferentially suggest less crowded tourist destinations. The suggestion unit can also adjust the priority of the suggestions according to the congestion status of the tourist destinations. This makes it possible to determine the priority of the suggestions based on the congestion status of the tourist destinations. The congestion status of the tourist destinations is acquired using, for example, real-time data or past data. This allows the suggestion unit to determine the priority of the suggestions based on the congestion status of the tourist destinations when making suggestions.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of tourist destinations when making suggestions. For example, the suggestion unit prioritizes suggesting highly relevant tourist destinations. The suggestion unit can also postpone suggesting less relevant tourist destinations. The suggestion unit can also adjust the order of suggestions according to the relevance of tourist destinations. This makes it possible to adjust the order of suggestions based on the relevance of tourist destinations. The relevance of tourist destinations is evaluated using data such as geographical proximity and commonality of themes. This allows the suggestion unit to adjust the order of suggestions based on the relevance of tourist destinations when making suggestions.

[0090] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit uses technical terms when the user has technical expertise. The suggestion unit can also explain in simple terms when the user does not have technical expertise. The suggestion unit can also adjust the use of technical terms in the proposal according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the proposal according to the user's level of expertise. The expertise level is evaluated using data such as questionnaire results and past learning history. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal.

[0091] The change suggestion unit can estimate the user's emotions and adjust the method of proposing schedule changes based on the estimated user's emotions. For example, if the user is relaxed, the change suggestion unit can make detailed change suggestions. Furthermore, if the user is in a hurry, the change suggestion unit can make concise change suggestions. Furthermore, if the user is excited, the change suggestion unit can make visually appealing change suggestions. This makes it possible to adjust the method of proposing schedule changes based on the user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, questionnaire results, etc. This makes it possible for the change suggestion unit to estimate the user's emotions and adjust the method of proposing schedule changes based on the estimated user's emotions.

[0092] The change suggestion unit can adjust the level of detail of the proposal based on the accuracy of the weather forecast data when proposing a change. For example, if the weather forecast is accurate, the change suggestion unit makes a detailed change suggestion. Furthermore, if the weather forecast is uncertain, the change suggestion unit can also make a concise change suggestion. The change suggestion unit can also adjust the level of detail of the proposal according to the accuracy of the weather forecast data. This makes it possible to adjust the level of detail of the proposal based on the accuracy of the weather forecast data. The accuracy of the weather forecast data is evaluated using criteria such as the accuracy rate of the forecast and the frequency of data updates. This allows the change suggestion unit to adjust the level of detail of the proposal based on the accuracy of the weather forecast data when proposing a change.

[0093] The change proposing unit can apply different proposed algorithms depending on changes in traffic conditions when proposing a change. For example, the change proposing unit can propose a detour route when traffic congestion occurs. The change proposing unit can also propose the shortest route when traffic conditions are good. The change proposing unit can also adjust the proposed algorithm depending on changes in traffic conditions. This makes it possible to apply the proposed algorithm depending on changes in traffic conditions. Changes in traffic conditions are detected using criteria such as the update frequency of real-time data and a change threshold. This makes it possible for the change proposing unit to apply different proposed algorithms depending on changes in traffic conditions when proposing a change.

[0094] When proposing changes, the change suggestion unit can improve the accuracy of the proposal by referring to the user's past change history. For example, the change suggestion unit makes similar proposals based on change proposals that the user has accepted in the past. The change suggestion unit can also avoid change proposals that the user has rejected in the past. The change suggestion unit can also analyze the user's past change history and make optimal change proposals. This makes it possible to improve the accuracy of proposals based on the user's past change history. The analysis of the past change history is performed using data such as the frequency of changes, the reasons for the changes, and the satisfaction level after the changes. This makes it possible for the change suggestion unit to improve the accuracy of proposals by referring to the user's past change history when proposing changes.

[0095] The change suggestion unit can estimate the user's emotions and determine the priority of schedule changes based on the estimated user's emotions. For example, if the user is relaxed, the change suggestion unit can set a high priority for tourist spots. Furthermore, if the user is in a hurry, the change suggestion unit can also prioritize schedule changes that involve shorter travel times. Furthermore, if the user is excited, the change suggestion unit can also prioritize schedule changes that involve more activities. This makes it possible to determine the priority of schedule changes based on the user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This makes it possible for the change suggestion unit to estimate the user's emotions and determine the priority of schedule changes based on the estimated user's emotions.

[0096] When proposing a change, the change proposing unit can adjust the timing of the proposal based on the update frequency of the weather forecast data. For example, if the weather forecast is updated frequently, the change proposing unit makes change proposals in real time. Furthermore, if the weather forecast is not updated often, the change proposing unit can also make change proposals at regular intervals. Furthermore, the change proposing unit can adjust the timing of the proposal based on the update frequency of the weather forecast data. This makes it possible to adjust the timing of the proposal based on the update frequency of the weather forecast data. The update frequency of the weather forecast data is evaluated using, for example, data from the Japan Meteorological Agency or data from private weather services. This allows the change proposing unit, when proposing a change, to adjust the timing of the proposal based on the update frequency of the weather forecast data.

[0097] The change proposing unit can adjust the order of proposals by referring to real-time data on traffic conditions when proposing a change. For example, when traffic congestion occurs, the change proposing unit preferentially proposes a detour route. Furthermore, when traffic conditions are good, the change proposing unit can also preferentially propose the shortest route. Furthermore, the change proposing unit can adjust the order of proposals based on the real-time data on traffic conditions. This makes it possible to adjust the order of proposals based on the real-time data on traffic conditions. The real-time data on traffic conditions is acquired using, for example, data such as traffic congestion information and the operation status of public transportation. This allows the change proposing unit to adjust the order of proposals by referring to the real-time data on traffic conditions when proposing a change.

[0098] The change suggestion unit can customize the content of the suggestion based on the user's current location information when proposing changes. For example, the change suggestion unit prioritizes suggesting tourist spots close to the user's current location. The change suggestion unit can also make optimal change suggestions taking into account travel time from the user's current location. The change suggestion unit can also prioritize suggesting event information near the user's current location. This makes it possible to customize the content of the suggestion based on the user's current location information. The current location information is acquired using, for example, GPS data or data from a location information service. This makes it possible for the change suggestion unit to customize the content of the suggestion based on the user's current location information when proposing changes.

[0099] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. For example, the notification unit can provide a detailed notification when the user is relaxed. Furthermore, the notification unit can provide a concise notification when the user is in a hurry. Furthermore, the notification unit can provide a visually appealing notification when the user is excited. This makes it possible to adjust the notification expression method based on the user's emotion. The user's emotion is estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This makes it possible for the notification unit to estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion.

[0100] The notification unit can adjust the level of detail of the notification based on the importance of local event information at the time of notification. For example, the notification unit provides detailed notification for important events. The notification unit can also provide brief notification for events with low importance. The notification unit can also adjust the level of detail of the notification depending on the importance of the event. This makes it possible to adjust the level of detail of the notification based on the importance of local event information. The importance of the event information is evaluated using data such as the scale of the event, the number of participants, and ratings. This allows the notification unit to adjust the level of detail of the notification based on the importance of local event information at the time of notification.

[0101] The notification unit can apply different notification algorithms depending on the category of local restaurant information when providing notification. For example, the notification unit can provide detailed menu information for high-end restaurants. The notification unit can also provide simple menu information for casual restaurants. The notification unit can also adjust the notification algorithm depending on the restaurant category. This makes it possible to apply a notification algorithm depending on the category of local restaurant information. Restaurant information categories are classified using criteria such as type of cuisine, price range, and rating. This allows the notification unit to apply different notification algorithms depending on the category of local restaurant information when providing notification.

[0102] The notification unit can improve the accuracy of notifications by referring to the user's past notification history when making a notification. The notification unit, for example, provides similar notifications based on notifications that the user has previously accepted. The notification unit can also avoid notifications that the user has previously rejected. The notification unit can also analyze the user's past notification history and provide optimal notifications. This makes it possible to improve the accuracy of notifications based on the user's past notification history. The analysis of the past notification history is performed using data such as the frequency of notifications, the content of notifications, and reactions after notifications. This allows the notification unit to improve the accuracy of notifications by referring to the user's past notification history when making a notification.

[0103] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. For example, if the user is relaxed, the notification unit can prioritize notifications of tourist spots. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications of short travel times. Furthermore, if the user is excited, the notification unit can prioritize notifications of high activity. This makes it possible to determine the priority of notifications based on the user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This allows the notification unit to estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions.

[0104] The notification unit can adjust the timing of notification based on the update frequency of local event information at the time of notification. For example, if the event information is updated frequently, the notification unit can provide notification in real time. Furthermore, if the event information is not updated often, the notification unit can provide notification at regular intervals. Furthermore, the notification unit can adjust the timing of notification according to the update frequency of the event information. This makes it possible to adjust the timing of notification based on the update frequency of local event information. The update frequency of the event information is evaluated using criteria such as the update frequency of an event calendar or the update frequency of online event information. This allows the notification unit to adjust the timing of notification based on the update frequency of local event information at the time of notification.

[0105] The notification unit can adjust the order of notifications based on the relevance of local restaurant information at the time of notification. For example, the notification unit prioritizes notification of highly relevant restaurant information. The notification unit can also postpone notification of less relevant restaurant information. The notification unit can also adjust the order of notifications according to the relevance of the restaurant information. This makes it possible to adjust the order of notifications based on the relevance of local restaurant information. The evaluation of the relevance of restaurant information is performed using data such as the type of cuisine, price range, and rating. This allows the notification unit to adjust the order of notifications based on the relevance of local restaurant information at the time of notification.

[0106] The notification unit can customize the content of the notification based on the user's current location information when providing a notification. For example, the notification unit prioritizes notifying the user of event information close to the user's current location. The notification unit can also provide optimal notifications taking into account travel time from the user's current location. The notification unit can also prioritize notifying the user of restaurant information near the user's current location. This makes it possible to customize the content of the notification based on the user's current location information. The current location information is obtained using, for example, GPS data or data from a location information service. This allows the notification unit to customize the content of the notification based on the user's current location information when providing a notification. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, change proposal unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs destination information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal travel schedule using a generation AI. The change proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts weather and traffic conditions during the trip and proposes schedule changes. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and collects local information and notifies the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, proposal unit, change proposal unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs destination information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal travel schedule using a generation AI. The change proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts weather and traffic conditions during the trip and proposes schedule changes. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and collects local information and notifies the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, change proposal unit, and notification 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 the user inputs destination information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal travel schedule using a generation AI. The change proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts weather and traffic conditions during the trip and proposes schedule changes. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and collects local information and notifies the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, proposal unit, change proposal unit, and notification 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 the user inputs destination information. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal travel schedule using a generation AI. The change proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts weather and traffic conditions during the trip and proposes schedule changes. The notification unit is realized, for example, by the control unit 46A of the robot 414, and collects local information and notifies the user.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The suggestion unit can analyze the user's past travel history and suggest similar travel destinations. For example, it can suggest new tourist destinations with similar characteristics based on the user's ratings and length of stay at tourist destinations visited in the past. The suggestion unit can also suggest tourist destinations that are good to visit in the same season as the tourist destinations visited by the user in the past, taking into account the season and event information of the tourist destinations visited in the past. Furthermore, the suggestion unit can suggest travel destinations based on a specific theme (e.g., history, nature, activity, etc.) from the user's past travel history. This makes it possible to suggest optimal travel destinations based on the user's past travel history.

[0109] The change suggestion unit can monitor the user's current health condition and suggest schedule changes based on the health condition. For example, if the user is tired, it can suggest increasing rest time. Also, if the user is not feeling well, it can suggest changes to a more reasonable schedule. Furthermore, if the user is in good health, it can suggest a schedule with more activity. This makes it possible to suggest schedule changes based on the user's health condition. Health condition monitoring is performed using data from a wearable device or a health app, for example. This allows the change suggestion unit to suggest schedule changes based on the user's health condition.

[0110] The notification unit can estimate the user's emotions and customize the content of the notification based on the estimated user's emotions. For example, if the user is relaxed, detailed information about tourist spots can be notified. If the user is in a hurry, concise information can be notified. Furthermore, if the user is excited, visually appealing information can be notified. This makes it possible to customize the content of the notification based on the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This allows the notification unit to customize the content of the notification based on the user's emotions.

[0111] The suggestion unit can customize the suggestion content based on the user's current interests and concerns. For example, it can prioritize suggesting tourist attractions and activities that the user has recently searched for. It can also analyze the user's social media activities to suggest related tourist attractions and events. It can also suggest tourist attractions and activities that interest the user by reflecting the user's past feedback. This makes it possible to customize the suggestion content based on the user's current interests and concerns. The user's current interests and concerns can be acquired using, for example, recent search history, social media activity, etc. This allows the suggestion unit to customize the suggestion content based on the user's current interests and concerns.

[0112] The change suggestion unit can estimate the user's emotions and determine the priority of schedule changes based on the estimated user's emotions. For example, if the user is relaxed, tourist spots can be given a higher priority. If the user is in a hurry, schedule changes with shorter travel times can be given priority. Furthermore, if the user is excited, schedule changes with more activities can be given priority. This makes it possible to determine the priority of schedule changes based on the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This allows the change suggestion unit to estimate the user's emotions and determine the priority of schedule changes based on the estimated user's emotions.

[0113] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. For example, if the user has technical expertise, technical terms are used. Also, if the user does not have technical expertise, explanations can be provided in simple terms. Furthermore, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the proposal according to the user's level of expertise. The expertise level is evaluated using data such as questionnaire results and past learning history. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal.

[0114] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. For example, if the user is relaxed, notifications of tourist spots can be given priority. Also, if the user is in a hurry, notifications of short travel times can be given priority. Furthermore, if the user is excited, notifications of high activity can be given priority. This makes it possible to determine the priority of notifications based on the user's emotions. The user's emotions are estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This allows the notification unit to estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions.

[0115] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. For example, it can make similar suggestions based on suggestions that the user has accepted in the past. It can also avoid suggestions that the user has rejected in the past. Furthermore, the suggestion unit can analyze the user's past suggestion results and make optimal suggestions. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results. The analysis of the past suggestion results is performed using data such as the proposal adoption rate, user satisfaction, and feedback. This allows the suggestion unit to improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions.

[0116] The change suggestion unit can estimate the user's emotions and adjust the method of suggesting schedule changes based on the estimated user's emotions. For example, if the user is relaxed, detailed change suggestions can be made. If the user is in a hurry, concise change suggestions can be made. Furthermore, if the user is excited, visually appealing change suggestions can be made. This makes it possible to adjust the method of suggesting schedule changes based on the user's emotions. The user's emotions can be estimated using, for example, facial expression recognition, voice analysis, survey results, etc. This allows the change suggestion unit to estimate the user's emotions and adjust the method of suggesting schedule changes based on the estimated user's emotions.

[0117] The notification unit can adjust the timing of notification based on the update frequency of local event information at the time of notification. For example, if the event information is updated frequently, notification can be performed in real time. Alternatively, if the event information is not updated often, notification can be performed at regular intervals. Furthermore, the notification unit can adjust the timing of notification according to the update frequency of the event information. This makes it possible to adjust the timing of notification based on the update frequency of local event information. The update frequency of the event information is evaluated using criteria such as the update frequency of an event calendar or the update frequency of online event information. This allows the notification unit to adjust the timing of notification based on the update frequency of local event information at the time of notification.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs destination information. The destination information includes city names, tourist spot names, addresses, etc. For example, the user can input "Travel from Tokyo to Kyoto." Step 2: The suggestion unit proposes an optimal travel schedule based on the information input by the reception unit. The suggestion unit generates an optimal schedule for the user, taking into account the congestion status of tourist spots, traffic conditions, weather forecasts, etc. For example, tourist spot A is less crowded if visited in the morning, so the suggestion unit proposes a schedule to visit in the morning. Step 3: The change suggestion unit predicts weather and traffic conditions during the trip based on the schedule proposed by the suggestion unit and suggests changes to the schedule. For example, if the planned tourist spot is crowded, it suggests an alternative tourist spot. Also, if the weather worsens, it suggests an indoor tourist spot. Step 4: The notification unit collects local information and notifies the user. For example, it may notify the user of local event information or restaurant recommendations. This allows the user to obtain the latest local information and enjoy their trip even more.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting destination information; a proposal unit that proposes a travel schedule based on the information input by the reception unit; a change suggestion unit that predicts weather and traffic conditions during the trip based on the schedule suggested by the suggestion unit and suggests changes to the schedule; a notification unit that collects local information and notifies the user of the information; A system characterized by:

2. The proposal unit Suggest a schedule based on the congestion status of tourist spots, traffic conditions, and weather forecasts 2. The system of claim 1.

3. The change suggestion unit Suggest changes to your schedule based on changing weather and traffic conditions during your trip 2. The system of claim 1.

4. The notification unit Collecting information about local events and restaurants and notifying users 2. The system of claim 1.

5. The proposal unit Proposes different schedules based on user input 2. The system of claim 1.

6. The change suggestion unit Collects weather and traffic data and suggests schedule changes 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the timing of inputting destination information based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past travel history and select the optimal way to enter destination information 2. The system of claim 1.

Images