System

Abstract

An object of a system according to an embodiment is to calculate an optimum moving route based on a destination and consistently perform purchase and settlement of a necessary ticket.SOLUTION: A system includes a destination input part, an information collection part, a route calculation part, and a settlement part. The destination input unit inputs a destination. The information collector collects necessary data based on the destination input by the destination inputter. The route calculation unit calculates an optimal moving route based on the data collected by the information collection unit. The settlement unit performs purchase and settlement of a necessary ticket on the basis of the moving route calculated by the route calculation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of being difficult to calculate the optimal travel route based on the destination and to consistently purchase and pay for the necessary tickets.

[0005] The system according to the embodiment aims to calculate the optimal travel route based on the destination and to consistently purchase and pay for the necessary tickets. [Means for solving the problem]

[0006] The system according to the embodiment includes a destination input unit, an information collection unit, a route calculation unit, and a payment unit. The destination input unit inputs a destination. The information collection unit collects necessary data based on the destination input by the destination input unit. The route calculation unit calculates an optimal travel route based on the data collected by the information collection unit. The payment unit purchases and pays for necessary tickets based on the travel route calculated by the route calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can calculate the optimal travel route based on the destination and can purchase and pay for the necessary tickets in an integrated manner. [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) The travel route planning system according to an embodiment of the present invention is a system that instantly calculates information such as train delays, weather, and timetables by simply inputting a destination, and instantly plans the optimal public transportation route. Furthermore, by linking with electronic payment services, it is also possible to purchase and pay for Shinkansen, airplane, limited express, and passenger tickets. This allows the travel route planning system to handle everything from inputting a destination to calculating a travel route and purchasing and paying for tickets all in one place.

[0029] A travel route planning system according to an embodiment includes a destination input unit, an information collection unit, a route calculation unit, and a payment unit. The destination input unit inputs a user's destination. For example, the user inputs "I want to go to Tokyo Station." The information collection unit collects necessary data based on the destination input by the destination input unit. For example, it collects data such as train delay information, weather, and timetables. The route calculation unit calculates an optimal travel route based on the data collected by the information collection unit. For example, it proposes a route that will allow the user to reach the destination most quickly, taking into account train delay information and weather. The payment unit purchases and pays for necessary tickets based on the travel route calculated by the route calculation unit. For example, Shinkansen, airplane, and express tickets and passenger tickets are purchased through an electronic payment service. This allows the travel route planning system to perform all processes, from inputting the destination to calculating the travel route and purchasing and paying for tickets, all in one place.

[0030] The destination input unit can predict and suggest destinations based on the user's past travel history. For example, the destination input unit analyzes the user's past travel history to identify frequently visited places and time periods. For example, if the user tends to go to a specific station every Monday morning, the destination input unit automatically suggests that destination. The destination input unit also works with the user's calendar app to predict destinations based on plans. For example, if a "meeting" is written on the calendar, the destination input unit suggests the meeting location as a destination. The destination input unit also predicts destinations based on the user's past search history and behavioral patterns. For example, it suggests tourist spots or restaurants that have been searched for in the past as destinations. This reduces the effort required for input by predicting and suggesting destinations based on the user's past travel history.

[0031] The destination input unit uses voice recognition technology to enable voice input, enabling hands-free operation. The destination input unit uses, for example, voice recognition technology to allow the user to input their destination by voice. For example, simply saying "I want to go to Tokyo Station" will set the destination. The destination input unit also uses noise canceling technology when inputting voice, accurately recognizing voice even in noisy environments. For example, smooth operation is possible on a station platform or inside a train. The destination input unit also uses voice recognition technology to support multiple languages. For example, it allows foreign tourists to input their destination in their native language. This makes hands-free operation possible using voice recognition technology.

[0032] The information collection unit can work in conjunction with the user's calendar app to automatically suggest destinations based on plans. The information collection unit, for example, works in conjunction with the user's calendar app to automatically suggest destinations based on plans. For example, if "meeting" is written on the calendar, the information collection unit will suggest the meeting location as a destination. The information collection unit also analyzes the calendar plans to suggest destinations that take travel time into consideration. For example, it will suggest the optimal departure time to arrive on time for the meeting start time. The information collection unit also successively suggests multiple destinations based on the user's calendar. For example, it will successively suggest a morning meeting location and an afternoon lunch location. In this way, by working in conjunction with the calendar app, destinations can be automatically suggested based on plans.

[0033] The information collection unit can simultaneously collect and suggest information about nearby tourist spots and restaurants when a destination is input. For example, when a destination is input, the information collection unit automatically collects information about nearby tourist spots and restaurants and suggests it to the user. For example, if the user inputs "I want to go to Tokyo Station," nearby tourist spots and restaurants are also displayed. The information collection unit also filters nearby tourist spots and restaurants based on the user's preferences. For example, it prioritizes suggesting restaurants in the user's favorite genre. Furthermore, when a destination is input, the information collection unit also simultaneously collects and suggests information about nearby events. For example, it displays events being held around the destination. In this way, by simultaneously collecting and suggesting information about nearby tourist spots and restaurants, convenience for the user is improved.

[0034] The route calculation unit can propose an optimal route by taking into account the user's health condition (number of steps, heart rate, etc.). The route calculation unit, for example, collects the user's health data (number of steps, heart rate, etc.) and reflects this in the calculation of the travel route. For example, it proposes a route that causes less fatigue. The route calculation unit also adjusts the difficulty of the travel route based on the user's health condition. For example, if the heart rate is high, it proposes a route that avoids stairs. The route calculation unit also monitors the user's health data in real time and recalculates the route if the user's health condition changes during travel. For example, it proposes rest points if the number of steps becomes too high. In this way, the optimal route can be proposed by taking the user's health condition into consideration.

[0035] The route calculation unit can take into account the user's preferences (for example, a scenic route or a quiet route). For example, the route calculation unit sets the user's preferences in advance and proposes a travel route based on them. For example, it selects a scenic route or a quiet route. The route calculation unit also analyzes the user's past travel history and proposes a route that suits the user's preferences. For example, it proposes a new route based on the characteristics of routes selected in the past. The route calculation unit also dynamically adjusts a route that suits the user's preferences based on real-time feedback from the user. For example, if feedback is received during travel that "a quiet route is preferable," the route will be recalculated. In this way, the optimal route can be proposed by taking the user's preferences into consideration.

[0036] The route calculation unit can suggest popular routes by referring to the reviews and ratings of other users. For example, the route calculation unit collects reviews and ratings of other users and reflects them in the calculation of a travel route. For example, it preferentially suggests routes with high ratings. The route calculation unit also filters reviews and ratings of other users based on the user's preferences. For example, it refers to ratings of users with the same hobbies or interests. The route calculation unit also dynamically adjusts the travel route based on real-time feedback from other users. For example, it suggests a route that reflects current traffic conditions and congestion. In this way, popular routes can be suggested by referring to the reviews and ratings of other users.

[0037] The route calculation unit can propose an eco-friendly route (for example, a route using an electric bus or bicycle). For example, the route calculation unit proposes a route that gives priority to using eco-friendly means of transportation (such as an electric bus or bicycle). For example, it calculates a travel route that takes into account the route of an electric bus. The route calculation unit also proposes an eco-friendly route based on the user's environmental awareness. For example, it selects an environmentally friendly means of transportation. The route calculation unit also considers CO2 emissions when selecting an eco-friendly route. For example, it proposes a route with the lowest CO2 emissions. In this way, proposing an eco-friendly route enables environmentally friendly travel.

[0038] The payment unit can automatically apply discounts and coupons based on the user's past purchase history. For example, the payment unit analyzes the user's past purchase history and automatically applies applicable discounts and coupons. For example, it automatically applies discount coupons for tickets purchased in the past. The payment unit also suggests specific promotions and campaigns based on the user's purchase history at the time of electronic payment. For example, it presents discounts that can be used during a specific period. The payment unit also provides benefits and services for repeat customers based on the user's purchase history. For example, it grants benefits after a certain number of uses. In this way, automatic application of discounts and coupons based on the user's past purchase history improves user convenience.

[0039] The payment unit can manage multiple payment methods (e.g., credit card, electronic money, QR code payment) all at once. For example, the payment unit can build a system that manages multiple payment methods all at once, allowing users to make payments smoothly using the payment method they select. For example, credit cards and electronic money can be managed in a single app. The payment unit also provides a function that allows users to register multiple payment methods and automatically select the optimal method when making a payment. For example, electronic money with a large balance can be used preferentially. The payment unit can also integrate multiple payment methods, allowing users to complete multiple payments with a single operation. For example, QR code payment and credit card payment can be performed simultaneously. This improves user convenience by managing multiple payment methods all at once.

[0040] The payment unit can work in conjunction with a point program to enable the automatic granting and use of points. The payment unit, for example, works in conjunction with a user's point program when making an electronic payment to automatically grant points. For example, points are automatically added according to the payment amount. The payment unit also enables users to make payments using points. For example, it provides a function to pay part of a ticket price using points. The payment unit also works in conjunction with a point program to enable users to check their point balance in real time. For example, it displays the point balance at the time of payment. In this way, by working in conjunction with a point program, it becomes possible to automatically grant and use points.

[0041] The payment unit works in conjunction with the user's bank account or electronic wallet, and can automatically check balances and charge the account. The payment unit, for example, works in conjunction with the user's bank account or electronic wallet to provide a function that allows balances to be checked in real time. For example, it notifies the user if the balance is insufficient at the time of payment. The payment unit also adds a function that automatically charges the account if the balance is insufficient. For example, it automatically charges the account from the bank account, ensuring smooth payments. The payment unit also builds a system that automatically charges the account based on conditions set by the user. For example, it automatically charges the account if the balance falls below a certain amount. This allows balances to be checked and charged automatically by working in conjunction with the bank account or electronic wallet.

[0042] The information collection unit can predict delays based on past delay data and notify users in advance. The information collection unit, for example, analyzes past delay data and identifies delay patterns. For example, it finds trends in which delays are more likely to occur during certain time periods or days of the week. The information collection unit also predicts delays and builds a system that notifies users in advance. For example, it notifies users by push notification when a delay is predicted. The information collection unit also suggests alternative routes to users based on delay prediction data. For example, it presents routes that avoid lines where delays are predicted. In this way, delays are predicted based on past delay data and users are notified in advance, improving convenience for users.

[0043] The information collection unit can propose an alternative route by taking into account the operation status of other means of transportation (such as buses and taxis). For example, the information collection unit collects the operation status of other means of transportation in real time and reflects this in the analysis of delay information. For example, the information collection unit proposes an alternative route that takes into account the operation status of buses and taxis. The information collection unit also calculates the optimal alternative route based on the user's current location and destination. For example, if a train is delayed, the information collection unit presents a route that uses a bus or taxi. The information collection unit also proposes the optimal means of travel to the user based on the operation status of other means of transportation. For example, if a train is delayed, the information collection unit provides information on the nearest bus stop or taxi stand. In this way, the optimal alternative route can be proposed by taking into account the operation status of other means of transportation.

[0044] The information collection unit can provide the operation status of the nearest public transportation facility in real time based on the user's location information. The information collection unit, for example, acquires the user's location information in real time and provides the operation status of the nearest public transportation facility. For example, it displays the operation status of the nearest station or bus stop from the current location. The information collection unit also suggests the optimal means of transportation based on the user's location information. For example, if the nearest station is crowded, it guides the user to another station. The information collection unit also uses the user's location information to update the operation status of the nearest public transportation facility in real time. For example, it notifies the user every time delay information is updated. In this way, the operation status of the nearest public transportation facility is provided in real time based on the user's location information, thereby improving user convenience.

[0045] The information collection unit can work in conjunction with the user's schedule to propose adjustments to minimize the impact of delays. The information collection unit, for example, works in conjunction with the user's schedule to propose adjustments to minimize the impact of delays. For example, it proposes delaying the start time of a meeting. The information collection unit also proposes an alternative plan in the event of a delay based on the user's schedule. For example, it proposes using a different means of transportation. The information collection unit also works in conjunction with the user's schedule to notify the impact of delays in real time. For example, it proposes a change to the schedule if a delay occurs. In this way, by working in conjunction with the user's schedule, it is possible to propose adjustments to minimize the impact of delays.

[0046] The information collection unit can predict weather based on past weather data and notify the user in advance. The information collection unit, for example, analyzes past weather data and identifies weather patterns. For example, it finds trends in weather changes during specific seasons or time periods. The information collection unit also builds a system that predicts weather and notifies the user in advance. For example, if bad weather is predicted, it notifies the user by push notification. The information collection unit also suggests an alternative route to the user based on the weather forecast data. For example, if bad weather is predicted, it presents an indoor route. In this way, weather predictions based on past weather data and advance notification improves user convenience.

[0047] The information collection unit can propose an optimal route taking into account the user's health condition (e.g., allergies or chronic illnesses). The information collection unit, for example, collects the user's health data (such as allergies or chronic illnesses) and reflects this in the analysis of weather information. For example, it proposes a route suitable for a user with allergies during pollen season. The information collection unit also proposes a route that minimizes the impact of weather based on the user's health condition. For example, it selects a route with stable weather for a user with a chronic illness. The information collection unit also monitors the user's health data in real time and recalculates the route if the weather changes. For example, it proposes an indoor route if the weather worsens. In this way, it is possible to propose an optimal route by taking the user's health condition into consideration.

[0048] The information collection unit can suggest the nearest evacuation site or rest area based on the user's location information. The information collection unit, for example, acquires the user's location information in real time and suggests the nearest evacuation site or rest area. For example, it displays the nearest evacuation site from the current location. The information collection unit also provides guidance to safe evacuation sites when weather worsens based on the user's location information. For example, it suggests the nearest indoor facility when heavy rain or strong winds are predicted. The information collection unit also uses the user's location information to provide guidance to the nearest rest area or cafe in real time. For example, it suggests a place where you can relax when the weather worsens. In this way, suggesting the nearest evacuation site or rest area based on the user's location information improves the user's safety.

[0049] The information collection unit can work in conjunction with the user's schedule to suggest adjustments to minimize the impact of weather. The information collection unit, for example, works in conjunction with the user's schedule to suggest adjustments to minimize the impact of weather. For example, it suggests delaying the start time of a meeting. The information collection unit also suggests an alternative plan in case the weather worsens, based on the user's schedule. For example, it suggests using a different means of transportation. The information collection unit also works in conjunction with the user's schedule to notify the user of the impact of weather in real time. For example, it suggests changing the schedule if the weather worsens. In this way, by working in conjunction with the user's schedule, it is possible to suggest adjustments to minimize the impact of weather.

[0050] The information collection unit can predict operations based on past operation data and notify the user in advance. The information collection unit, for example, analyzes past operation data and identifies operation patterns. For example, it finds a tendency for operation delays during specific time periods or days of the week. The information collection unit also predicts operations and builds a system that notifies the user in advance. For example, if there is a possibility of operation delays, it notifies the user by push notification. The information collection unit also suggests alternative routes to the user based on operation prediction data. For example, if there is a possibility of operation delays, it presents another means of transportation. In this way, operation predictions based on past operation data and advance notification improves user convenience.

[0051] The information collection unit can suggest optimal transfer timing based on the user's movement history. The information collection unit, for example, analyzes the user's movement history and suggests optimal transfer timing. For example, it optimizes transfer times based on past movement patterns. The information collection unit also adjusts transfer timing in real time based on the user's movement history. For example, it recalculates transfer times if a delay occurs. The information collection unit also uses the user's movement history to build a system that optimizes transfer timing. For example, it suggests optimal transfer points based on past data. This improves user convenience by suggesting optimal transfer timing based on the user's movement history.

[0052] The information collection unit can also integrate timetables for other modes of transportation (such as buses and taxis) to propose a comprehensive travel plan. The information collection unit, for example, builds a system that integrates timetables for other modes of transportation to propose a comprehensive travel plan. For example, it proposes a route that combines train and bus timetables. The information collection unit also updates the timetables for other modes of transportation in real time to optimize the user's travel plan. For example, it presents a route that reflects bus delay information. The information collection unit also proposes the optimal means of travel for the user based on the timetables for other modes of transportation. For example, it presents a route that uses a bus or taxi if a train is delayed. In this way, a comprehensive travel plan can be proposed by integrating timetables for other modes of transportation.

[0053] The information collection unit works in conjunction with the user's schedule to suggest the optimal travel time. The information collection unit, for example, works in conjunction with the user's schedule to build a system that suggests the optimal travel time. For example, it suggests the optimal departure time to arrive on time for the start of a meeting. The information collection unit also adjusts the travel time in real time based on the user's schedule. For example, it recalculates the departure time if a delay occurs. The information collection unit also works in conjunction with the user's schedule to build a system that optimizes travel time. For example, it automatically adjusts the travel plan in response to changes in the schedule. In this way, it is possible to suggest the optimal travel time by working in conjunction with the user's schedule.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The destination input unit can automatically detect the user's current location and suggest the nearest means of transportation. For example, it can use a GPS function to identify the user's current location and display the nearest train station or bus stop. The destination input unit can also suggest the optimal departure time based on the user's current location. For example, it can calculate the travel time from the current location to the destination and notify the user of the optimal departure time. The destination input unit can also provide real-time information on surrounding traffic conditions based on the user's current location. For example, it can display information on traffic congestion and traffic accidents and suggest the optimal route. This makes it possible to provide the user with an optimal travel plan based on their current location.

[0056] The information collection unit can suggest the optimal means of transportation based on the user's past travel history. For example, it can analyze data on transportation methods used in the past and prioritize suggestions for transportation methods preferred by the user. The information collection unit can also optimize travel time based on the user's past travel history. For example, it can analyze past travel patterns and suggest optimal departure and arrival times. The information collection unit can also customize travel routes based on the user's past travel history. For example, it can suggest new routes based on routes used in the past. This makes it possible to provide an optimal travel plan based on the user's past travel history.

[0057] The route calculation unit can take into account the user's preferences (for example, a scenic route or a quiet route). For example, the user's preferences can be set in advance and a travel route can be proposed based on those preferences. For example, a scenic route or a quiet route can be selected. The route calculation unit can also analyze the user's past travel history to propose a route that suits the user's preferences. For example, a new route can be proposed based on the characteristics of previously selected routes. The route calculation unit can also dynamically adjust a route that suits the user's preferences based on real-time feedback from the user. For example, if feedback is received during travel that "a quiet route is preferable," the route can be recalculated. This allows the optimal route to be proposed by taking the user's preferences into consideration.

[0058] The route calculation unit can propose an optimal route by taking into account the user's health condition (number of steps, heart rate, etc.). For example, it collects the user's health data (number of steps, heart rate, etc.) and reflects it in the calculation of the travel route. For example, it proposes a route that causes less fatigue. The route calculation unit also adjusts the difficulty of the travel route based on the user's health condition. For example, if the heart rate is high, it proposes a route that avoids stairs. The route calculation unit also monitors the user's health data in real time and recalculates the route if the user's health condition changes during travel. For example, it proposes rest points if the number of steps becomes too high. In this way, it is possible to propose an optimal route by taking the user's health condition into consideration.

[0059] The route calculation unit can suggest popular routes by referring to the reviews and ratings of other users. For example, it collects reviews and ratings from other users and reflects them in the calculation of a travel route. For example, it preferentially suggests routes with high ratings. The route calculation unit also filters reviews and ratings from other users based on the user's preferences. For example, it refers to ratings from users with the same hobbies and interests. The route calculation unit also dynamically adjusts the travel route based on real-time feedback from other users. For example, it suggests a route that reflects current traffic conditions and congestion. This makes it possible to suggest popular routes by referring to the reviews and ratings of other users.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The destination input unit inputs the user's destination. For example, the user inputs "I want to go to Tokyo Station." Step 2: The information collection unit collects necessary data based on the destination input by the destination input unit, such as train delay information, weather, timetables, etc. Step 3: The route calculation unit calculates the optimal travel route based on the data collected by the information collection unit. For example, it proposes the fastest route to the destination, taking into account train delay information and weather conditions. Step 4: The payment unit purchases and pays for the necessary tickets based on the travel route calculated by the route calculation unit. For example, Shinkansen, airplane, express, and passenger tickets are purchased through an electronic payment service.

[0062] (Example 2) The travel route planning system according to an embodiment of the present invention is a system that instantly calculates information such as train delays, weather, and timetables by simply inputting a destination, and instantly plans the optimal public transportation route. Furthermore, by linking with electronic payment services, it is also possible to purchase and pay for Shinkansen, airplane, limited express, and passenger tickets. This allows the travel route planning system to handle everything from inputting a destination to calculating a travel route and purchasing and paying for tickets all in one place.

[0063] A travel route planning system according to an embodiment includes a destination input unit, an information collection unit, a route calculation unit, and a payment unit. The destination input unit inputs a user's destination. For example, the user inputs "I want to go to Tokyo Station." The information collection unit collects necessary data based on the destination input by the destination input unit. For example, it collects data such as train delay information, weather, and timetables. The route calculation unit calculates an optimal travel route based on the data collected by the information collection unit. For example, it proposes a route that will allow the user to reach the destination most quickly, taking into account train delay information and weather. The payment unit purchases and pays for necessary tickets based on the travel route calculated by the route calculation unit. For example, Shinkansen, airplane, and express tickets and passenger tickets are purchased through an electronic payment service. This allows the travel route planning system to perform all processes, from inputting the destination to calculating the travel route and purchasing and paying for tickets, all in one place.

[0064] The destination input unit can predict and suggest destinations based on the user's past travel history. For example, the destination input unit analyzes the user's past travel history to identify frequently visited places and time periods. For example, if the user tends to go to a specific station every Monday morning, the destination input unit automatically suggests that destination. The destination input unit also works with the user's calendar app to predict destinations based on plans. For example, if a "meeting" is written on the calendar, the destination input unit suggests the meeting location as a destination. The destination input unit also predicts destinations based on the user's past search history and behavioral patterns. For example, it suggests tourist spots or restaurants that have been searched for in the past as destinations. This reduces the effort required for input by predicting and suggesting destinations based on the user's past travel history.

[0065] The destination input unit uses voice recognition technology to enable voice input, enabling hands-free operation. The destination input unit uses, for example, voice recognition technology to allow the user to input their destination by voice. For example, simply saying "I want to go to Tokyo Station" will set the destination. The destination input unit also uses noise canceling technology when inputting voice, accurately recognizing voice even in noisy environments. For example, smooth operation is possible on a station platform or inside a train. The destination input unit also uses voice recognition technology to support multiple languages. For example, it allows foreign tourists to input their destination in their native language. This makes hands-free operation possible using voice recognition technology.

[0066] The destination input unit can use the emotion estimation function to analyze the user's current emotional state and suggest a less stressful route. The destination input unit, for example, analyzes the user's facial expression or voice tone to estimate the user's current emotional state. For example, if the user is feeling stressed, it suggests a less crowded route. The destination input unit also suggests a route that will help the user relax based on the emotion estimation data. For example, it selects a scenic route or a quiet route. The destination input unit also suggests music or podcasts that will help the user relax while traveling, depending on the user's emotional state. For example, if the user is feeling stressed, it plays relaxing music. In this way, the emotion estimation function can suggest a route that will reduce the user's stress.

[0067] The information collection unit can work in conjunction with the user's calendar app to automatically suggest destinations based on plans. The information collection unit, for example, works in conjunction with the user's calendar app to automatically suggest destinations based on plans. For example, if "meeting" is written on the calendar, the information collection unit will suggest the meeting location as a destination. The information collection unit also analyzes the calendar plans to suggest destinations that take travel time into consideration. For example, it will suggest the optimal departure time to arrive on time for the meeting start time. The information collection unit also successively suggests multiple destinations based on the user's calendar. For example, it will successively suggest a morning meeting location and an afternoon lunch location. In this way, by working in conjunction with the calendar app, destinations can be automatically suggested based on plans.

[0068] The information collection unit can simultaneously collect and suggest information about nearby tourist spots and restaurants when a destination is input. For example, when a destination is input, the information collection unit automatically collects information about nearby tourist spots and restaurants and suggests it to the user. For example, if the user inputs "I want to go to Tokyo Station," nearby tourist spots and restaurants are also displayed. The information collection unit also filters nearby tourist spots and restaurants based on the user's preferences. For example, it prioritizes suggesting restaurants in the user's favorite genre. Furthermore, when a destination is input, the information collection unit also simultaneously collects and suggests information about nearby events. For example, it displays events being held around the destination. In this way, by simultaneously collecting and suggesting information about nearby tourist spots and restaurants, convenience for the user is improved.

[0069] The information collection unit can use the emotion estimation function to analyze the user's emotions regarding places they want to go and suggest destinations that will elicit positive emotions. The information collection unit, for example, analyzes the user's emotional state and suggests destinations that will elicit positive emotions. For example, it suggests places where the user can relax or have fun. The information collection unit also re-suggests places that the user visited in the past and felt positive emotions about, based on the emotion estimation data. For example, it suggests places that the user has fond memories of in the past as destinations. The information collection unit also customizes the suggested destinations according to the user's emotional state. For example, if the user is feeling stressed, it suggests places where they can relax. In this way, the emotion estimation function can be used to suggest destinations that will elicit positive emotions from the user.

[0070] The route calculation unit can propose an optimal route by taking into account the user's health condition (number of steps, heart rate, etc.). The route calculation unit, for example, collects the user's health data (number of steps, heart rate, etc.) and reflects this in the calculation of the travel route. For example, it proposes a route that causes less fatigue. The route calculation unit also adjusts the difficulty of the travel route based on the user's health condition. For example, if the heart rate is high, it proposes a route that avoids stairs. The route calculation unit also monitors the user's health data in real time and recalculates the route if the user's health condition changes during travel. For example, it proposes rest points if the number of steps becomes too high. In this way, the optimal route can be proposed by taking the user's health condition into consideration.

[0071] The route calculation unit can take into account the user's preferences (for example, a scenic route or a quiet route). For example, the route calculation unit sets the user's preferences in advance and proposes a travel route based on them. For example, it selects a scenic route or a quiet route. The route calculation unit also analyzes the user's past travel history and proposes a route that suits the user's preferences. For example, it proposes a new route based on the characteristics of routes selected in the past. The route calculation unit also dynamically adjusts a route that suits the user's preferences based on real-time feedback from the user. For example, if feedback is received during travel that "a quiet route is preferable," the route will be recalculated. In this way, the optimal route can be proposed by taking the user's preferences into consideration.

[0072] The route calculation unit can use the emotion estimation function to suggest a relaxing route that matches the user's emotional state. The route calculation unit, for example, analyzes the user's emotional state in real time and suggests a relaxing route. For example, if the user is feeling stressed, a quiet route is selected. The route calculation unit also customizes a travel route that allows the user to relax based on the emotion estimation data. For example, it suggests a scenic route or a route that passes through a park. The route calculation unit also suggests music or podcasts that will help the user relax while traveling, depending on the user's emotional state. For example, if the user is feeling stressed, it plays relaxing music. In this way, by using the emotion estimation function, it is possible to suggest a relaxing route that matches the user's emotional state.

[0073] The route calculation unit can suggest popular routes by referring to the reviews and ratings of other users. For example, the route calculation unit collects reviews and ratings of other users and reflects them in the calculation of a travel route. For example, it preferentially suggests routes with high ratings. The route calculation unit also filters reviews and ratings of other users based on the user's preferences. For example, it refers to ratings of users with the same hobbies or interests. The route calculation unit also dynamically adjusts the travel route based on real-time feedback from other users. For example, it suggests a route that reflects current traffic conditions and congestion. In this way, popular routes can be suggested by referring to the reviews and ratings of other users.

[0074] The route calculation unit can propose an eco-friendly route (for example, a route using an electric bus or bicycle). For example, the route calculation unit proposes a route that gives priority to using eco-friendly means of transportation (such as an electric bus or bicycle). For example, it calculates a travel route that takes into account the route of an electric bus. The route calculation unit also proposes an eco-friendly route based on the user's environmental awareness. For example, it selects an environmentally friendly means of transportation. The route calculation unit also considers CO2 emissions when selecting an eco-friendly route. For example, it proposes a route with the lowest CO2 emissions. In this way, proposing an eco-friendly route enables environmentally friendly travel.

[0075] The route calculation unit uses the emotion estimation function to suggest the most enjoyable route for the user and can also provide entertainment information for the journey. The route calculation unit, for example, analyzes the user's emotional state and suggests the most enjoyable route. For example, it selects a route with many tourist attractions and events. The route calculation unit also provides entertainment information (music, podcasts, tourist information, etc.) that can be enjoyed while traveling. For example, it plays music that matches the user's preferences. The route calculation unit also suggests activities that can be enjoyed while traveling, depending on the user's emotional state. For example, it suggests cafes and shops that can be stopped at along the way. In this way, by using the emotion estimation function, it is possible to suggest the most enjoyable route for the user and provide entertainment information for the journey.

[0076] The payment unit can automatically apply discounts and coupons based on the user's past purchase history. For example, the payment unit analyzes the user's past purchase history and automatically applies applicable discounts and coupons. For example, it automatically applies discount coupons for tickets purchased in the past. The payment unit also suggests specific promotions and campaigns based on the user's purchase history at the time of electronic payment. For example, it presents discounts that can be used during a specific period. The payment unit also provides benefits and services for repeat customers based on the user's purchase history. For example, it grants benefits after a certain number of uses. In this way, automatic application of discounts and coupons based on the user's past purchase history improves user convenience.

[0077] The payment unit can manage multiple payment methods (e.g., credit card, electronic money, QR code payment) all at once. For example, the payment unit can build a system that manages multiple payment methods all at once, allowing users to make payments smoothly using the payment method they select. For example, credit cards and electronic money can be managed in a single app. The payment unit also provides a function that allows users to register multiple payment methods and automatically select the optimal method when making a payment. For example, electronic money with a large balance can be used preferentially. The payment unit can also integrate multiple payment methods, allowing users to complete multiple payments with a single operation. For example, QR code payment and credit card payment can be performed simultaneously. This improves user convenience by managing multiple payment methods all at once.

[0078] The payment unit can use the emotion estimation function to suggest a payment method that does not cause stress to the user. For example, the payment unit analyzes the user's emotional state and suggests a payment method that does not cause stress. For example, it selects the optimal payment method when the user is relaxed. The payment unit also re-suggests payment methods that the user has not experienced stress with in the past based on the emotion estimation data. For example, it prioritizes suggesting payment methods that the user has used smoothly in the past. The payment unit also customizes payment method options according to the user's emotional state. For example, if the user is experiencing stress, it suggests a simple and quick payment method. In this way, the emotion estimation function can suggest a payment method that does not cause stress to the user.

[0079] The payment unit can work in conjunction with a point program to enable the automatic granting and use of points. The payment unit, for example, works in conjunction with a user's point program when making an electronic payment to automatically grant points. For example, points are automatically added according to the payment amount. The payment unit also enables users to make payments using points. For example, it provides a function to pay part of a ticket price using points. The payment unit also works in conjunction with a point program to enable users to check their point balance in real time. For example, it displays the point balance at the time of payment. In this way, by working in conjunction with a point program, it becomes possible to automatically grant and use points.

[0080] The payment unit works in conjunction with the user's bank account or electronic wallet, and can automatically check balances and charge the account. The payment unit, for example, works in conjunction with the user's bank account or electronic wallet to provide a function that allows balances to be checked in real time. For example, it notifies the user if the balance is insufficient at the time of payment. The payment unit also adds a function that automatically charges the account if the balance is insufficient. For example, it automatically charges the account from the bank account, ensuring smooth payments. The payment unit also builds a system that automatically charges the account based on conditions set by the user. For example, it automatically charges the account if the balance falls below a certain amount. This allows balances to be checked and charged automatically by working in conjunction with the bank account or electronic wallet.

[0081] The payment unit can use the emotion estimation function to suggest the payment method that the user can use with the greatest confidence. For example, the payment unit analyzes the user's emotional state and suggests the payment method that the user can use with the greatest confidence. For example, it selects the optimal payment method when the user is relaxed. The payment unit also re-suggests payment methods that the user has used with confidence in the past based on the emotion estimation data. For example, it prioritizes suggesting payment methods that have had the fewest problems in the past. The payment unit also customizes payment method options according to the user's emotional state. For example, if the user is feeling stressed, it suggests a simple and quick payment method. In this way, the emotion estimation function can suggest the payment method that the user can use with the greatest confidence.

[0082] The information collection unit can predict delays based on past delay data and notify users in advance. The information collection unit, for example, analyzes past delay data and identifies delay patterns. For example, it finds trends in which delays are more likely to occur during certain time periods or days of the week. The information collection unit also predicts delays and builds a system that notifies users in advance. For example, it notifies users by push notification when a delay is predicted. The information collection unit also suggests alternative routes to users based on delay prediction data. For example, it presents routes that avoid lines where delays are predicted. In this way, delays are predicted based on past delay data and users are notified in advance, improving convenience for users.

[0083] The information collection unit can propose an alternative route by taking into account the operation status of other means of transportation (such as buses and taxis). For example, the information collection unit collects the operation status of other means of transportation in real time and reflects this in the analysis of delay information. For example, the information collection unit proposes an alternative route that takes into account the operation status of buses and taxis. The information collection unit also calculates the optimal alternative route based on the user's current location and destination. For example, if a train is delayed, the information collection unit presents a route that uses a bus or taxi. The information collection unit also proposes the optimal means of travel to the user based on the operation status of other means of transportation. For example, if a train is delayed, the information collection unit provides information on the nearest bus stop or taxi stand. In this way, the optimal alternative route can be proposed by taking into account the operation status of other means of transportation.

[0084] The information collection unit can use the emotion estimation function to suggest relaxation methods to reduce the user's stress caused by delays. The information collection unit, for example, analyzes the user's emotional state and suggests relaxation methods to reduce stress caused by delays. For example, it plays relaxing music or podcasts. The information collection unit also suggests activities that the user can enjoy to relax based on the emotion estimation data. For example, it suggests reading or games that can be enjoyed during delays. The information collection unit also provides an environment in which the user can relax during travel, depending on the user's emotional state. For example, it guides the user to a quiet place or a comfortable seat. In this way, the emotion estimation function can be used to suggest relaxation methods to reduce the user's stress caused by delays.

[0085] The information collection unit can provide the operation status of the nearest public transportation facility in real time based on the user's location information. The information collection unit, for example, acquires the user's location information in real time and provides the operation status of the nearest public transportation facility. For example, it displays the operation status of the nearest station or bus stop from the current location. The information collection unit also suggests the optimal means of transportation based on the user's location information. For example, if the nearest station is crowded, it guides the user to another station. The information collection unit also uses the user's location information to update the operation status of the nearest public transportation facility in real time. For example, it notifies the user every time delay information is updated. In this way, the operation status of the nearest public transportation facility is provided in real time based on the user's location information, thereby improving user convenience.

[0086] The information collection unit can work in conjunction with the user's schedule to propose adjustments to minimize the impact of delays. The information collection unit, for example, works in conjunction with the user's schedule to propose adjustments to minimize the impact of delays. For example, it proposes delaying the start time of a meeting. The information collection unit also proposes an alternative plan in the event of a delay based on the user's schedule. For example, it proposes using a different means of transportation. The information collection unit also works in conjunction with the user's schedule to notify the impact of delays in real time. For example, it proposes a change to the schedule if a delay occurs. In this way, by working in conjunction with the user's schedule, it is possible to propose adjustments to minimize the impact of delays.

[0087] The information collection unit can use the emotion estimation function to analyze the user's emotions when receiving delay information and provide a positive message. The information collection unit, for example, analyzes the user's emotional state and provides a positive message when receiving delay information. For example, it provides an encouraging message or a suggestion to help the user relax. The information collection unit also provides a message that helps the user maintain a positive mood based on the emotion estimation data. For example, it suggests activities that can be enjoyed during the delay. The information collection unit also customizes the positive message when receiving delay information according to the user's emotional state. For example, it provides a message that suits the user's preferences. In this way, by using the emotion estimation function, the user's emotions when receiving delay information can be analyzed and a positive message can be provided.

[0088] The information collection unit can predict weather based on past weather data and notify the user in advance. The information collection unit, for example, analyzes past weather data and identifies weather patterns. For example, it finds trends in weather changes during specific seasons or time periods. The information collection unit also builds a system that predicts weather and notifies the user in advance. For example, if bad weather is predicted, it notifies the user by push notification. The information collection unit also suggests an alternative route to the user based on the weather forecast data. For example, if bad weather is predicted, it presents an indoor route. In this way, weather predictions based on past weather data and advance notification improves user convenience.

[0089] The information collection unit can propose an optimal route taking into account the user's health condition (e.g., allergies or chronic illnesses). The information collection unit, for example, collects the user's health data (such as allergies or chronic illnesses) and reflects this in the analysis of weather information. For example, it proposes a route suitable for a user with allergies during pollen season. The information collection unit also proposes a route that minimizes the impact of weather based on the user's health condition. For example, it selects a route with stable weather for a user with a chronic illness. The information collection unit also monitors the user's health data in real time and recalculates the route if the weather changes. For example, it proposes an indoor route if the weather worsens. In this way, it is possible to propose an optimal route by taking the user's health condition into consideration.

[0090] The information collection unit can use the emotion estimation function to suggest ways for the user to relax during bad weather. For example, the information collection unit analyzes the user's emotional state and suggests ways for the user to relax during bad weather. For example, it plays relaxing music or podcasts. The information collection unit also suggests activities that the user can enjoy during bad weather based on the emotion estimation data. For example, it suggests reading or games that can be enjoyed during bad weather. The information collection unit also provides an environment in which the user can relax during bad weather according to the user's emotional state. For example, it guides the user to a quiet cafe or rest area. In this way, the emotion estimation function can suggest ways for the user to relax during bad weather.

[0091] The information collection unit can suggest the nearest evacuation site or rest area based on the user's location information. The information collection unit, for example, acquires the user's location information in real time and suggests the nearest evacuation site or rest area. For example, it displays the nearest evacuation site from the current location. The information collection unit also provides guidance to safe evacuation sites when weather worsens based on the user's location information. For example, it suggests the nearest indoor facility when heavy rain or strong winds are predicted. The information collection unit also uses the user's location information to provide guidance to the nearest rest area or cafe in real time. For example, it suggests a place where you can relax when the weather worsens. In this way, suggesting the nearest evacuation site or rest area based on the user's location information improves the user's safety.

[0092] The information collection unit can work in conjunction with the user's schedule to suggest adjustments to minimize the impact of weather. The information collection unit, for example, works in conjunction with the user's schedule to suggest adjustments to minimize the impact of weather. For example, it suggests delaying the start time of a meeting. The information collection unit also suggests an alternative plan in case the weather worsens, based on the user's schedule. For example, it suggests using a different means of transportation. The information collection unit also works in conjunction with the user's schedule to notify the user of the impact of weather in real time. For example, it suggests changing the schedule if the weather worsens. In this way, by working in conjunction with the user's schedule, it is possible to suggest adjustments to minimize the impact of weather.

[0093] The information collection unit can use the emotion estimation function to provide a message that helps the user maintain a positive mood during bad weather. The information collection unit, for example, analyzes the user's emotional state and provides a positive message during bad weather. For example, it provides an encouraging message or a suggestion to help the user relax. The information collection unit also provides a message that helps the user maintain a positive mood based on the emotion estimation data. For example, it suggests activities that can be enjoyed during bad weather. The information collection unit also customizes a positive message during bad weather according to the user's emotional state. For example, it provides a message that suits the user's preferences. In this way, by using the emotion estimation function, it is possible to provide a message that helps the user maintain a positive mood during bad weather.

[0094] The information collection unit can predict operations based on past operation data and notify the user in advance. The information collection unit, for example, analyzes past operation data and identifies operation patterns. For example, it finds a tendency for operation delays during specific time periods or days of the week. The information collection unit also predicts operations and builds a system that notifies the user in advance. For example, if there is a possibility of operation delays, it notifies the user by push notification. The information collection unit also suggests alternative routes to the user based on operation prediction data. For example, if there is a possibility of operation delays, it presents another means of transportation. In this way, operation predictions based on past operation data and advance notification improves user convenience.

[0095] The information collection unit can suggest optimal transfer timing based on the user's movement history. The information collection unit, for example, analyzes the user's movement history and suggests optimal transfer timing. For example, it optimizes transfer times based on past movement patterns. The information collection unit also adjusts transfer timing in real time based on the user's movement history. For example, it recalculates transfer times if a delay occurs. The information collection unit also uses the user's movement history to build a system that optimizes transfer timing. For example, it suggests optimal transfer points based on past data. This improves user convenience by suggesting optimal transfer timing based on the user's movement history.

[0096] The information collection unit can use the emotion estimation function to suggest a transfer method that does not cause stress to the user. The information collection unit, for example, analyzes the user's emotional state and suggests a transfer method that does not cause stress. For example, it selects a route with sufficient transfer time. The information collection unit also re-suggests transfer methods that the user has not experienced stress in the past based on the emotion estimation data. For example, it prioritizes suggesting routes that have allowed smooth transfers in the past. The information collection unit also customizes transfer method options according to the user's emotional state. For example, if the user is feeling stressed, it suggests a route with fewer transfers. In this way, the emotion estimation function can be used to suggest a transfer method that does not cause stress to the user.

[0097] The information collection unit can also integrate timetables for other modes of transportation (such as buses and taxis) to propose a comprehensive travel plan. The information collection unit, for example, builds a system that integrates timetables for other modes of transportation to propose a comprehensive travel plan. For example, it proposes a route that combines train and bus timetables. The information collection unit also updates the timetables for other modes of transportation in real time to optimize the user's travel plan. For example, it presents a route that reflects bus delay information. The information collection unit also proposes the optimal means of travel for the user based on the timetables for other modes of transportation. For example, it presents a route that uses a bus or taxi if a train is delayed. In this way, a comprehensive travel plan can be proposed by integrating timetables for other modes of transportation.

[0098] The information collection unit works in conjunction with the user's schedule to suggest the optimal travel time. The information collection unit, for example, works in conjunction with the user's schedule to build a system that suggests the optimal travel time. For example, it suggests the optimal departure time to arrive on time for the start of a meeting. The information collection unit also adjusts the travel time in real time based on the user's schedule. For example, it recalculates the departure time if a delay occurs. The information collection unit also works in conjunction with the user's schedule to build a system that optimizes travel time. For example, it automatically adjusts the travel plan in response to changes in the schedule. In this way, it is possible to suggest the optimal travel time by working in conjunction with the user's schedule.

[0099] The information collection unit can use the emotion estimation function to suggest the time when the user can travel most comfortably. The information collection unit, for example, analyzes the user's emotional state and suggests the time when the user can travel most comfortably. For example, it selects a time period with less congestion. The information collection unit also re-suggests times when the user has traveled comfortably in the past based on the emotion estimation data. For example, it prioritizes suggesting time periods when the user has not felt stressed in the past. The information collection unit also customizes travel time options according to the user's emotional state. For example, if the user is feeling stressed, it suggests a time period with less travel. In this way, the emotion estimation function can suggest the time when the user can travel most comfortably.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The destination input unit can automatically detect the user's current location and suggest the nearest means of transportation. For example, it can use a GPS function to identify the user's current location and display the nearest train station or bus stop. The destination input unit can also suggest the optimal departure time based on the user's current location. For example, it can calculate the travel time from the current location to the destination and notify the user of the optimal departure time. The destination input unit can also provide real-time information on surrounding traffic conditions based on the user's current location. For example, it can display information on traffic congestion and traffic accidents and suggest the optimal route. This makes it possible to provide the user with an optimal travel plan based on their current location.

[0102] The information collection unit can suggest the optimal means of transportation based on the user's past travel history. For example, it can analyze data on transportation methods used in the past and prioritize suggestions for transportation methods preferred by the user. The information collection unit can also optimize travel time based on the user's past travel history. For example, it can analyze past travel patterns and suggest optimal departure and arrival times. The information collection unit can also customize travel routes based on the user's past travel history. For example, it can suggest new routes based on routes used in the past. This makes it possible to provide an optimal travel plan based on the user's past travel history.

[0103] The route calculation unit can take into account the user's preferences (for example, a scenic route or a quiet route). For example, the user's preferences can be set in advance and a travel route can be proposed based on those preferences. For example, a scenic route or a quiet route can be selected. The route calculation unit can also analyze the user's past travel history to propose a route that suits the user's preferences. For example, a new route can be proposed based on the characteristics of previously selected routes. The route calculation unit can also dynamically adjust a route that suits the user's preferences based on real-time feedback from the user. For example, if feedback is received during travel that "a quiet route is preferable," the route can be recalculated. This allows the optimal route to be proposed by taking the user's preferences into consideration.

[0104] The route calculation unit can propose an optimal route by taking into account the user's health condition (number of steps, heart rate, etc.). For example, it collects the user's health data (number of steps, heart rate, etc.) and reflects it in the calculation of the travel route. For example, it proposes a route that causes less fatigue. The route calculation unit also adjusts the difficulty of the travel route based on the user's health condition. For example, if the heart rate is high, it proposes a route that avoids stairs. The route calculation unit also monitors the user's health data in real time and recalculates the route if the user's health condition changes during travel. For example, it proposes rest points if the number of steps becomes too high. In this way, it is possible to propose an optimal route by taking the user's health condition into consideration.

[0105] The route calculation unit can suggest popular routes by referring to the reviews and ratings of other users. For example, it collects reviews and ratings from other users and reflects them in the calculation of a travel route. For example, it preferentially suggests routes with high ratings. The route calculation unit also filters reviews and ratings from other users based on the user's preferences. For example, it refers to ratings from users with the same hobbies and interests. The route calculation unit also dynamically adjusts the travel route based on real-time feedback from other users. For example, it suggests a route that reflects current traffic conditions and congestion. This makes it possible to suggest popular routes by referring to the reviews and ratings of other users.

[0106] The destination input unit can use the emotion estimation function to analyze the user's current emotional state and suggest a less stressful route. For example, it can analyze the user's facial expressions and voice tone to estimate the user's current emotional state. For example, if the user is feeling stressed, it can suggest a less crowded route. The destination input unit can also suggest a route that will help the user relax based on the emotion estimation data. For example, it can select a scenic route or a quiet route. The destination input unit can also suggest music or podcasts that will help the user relax while traveling, depending on the user's emotional state. For example, if the user is feeling stressed, it can play relaxing music. In this way, the emotion estimation function can suggest a route that will reduce the user's stress.

[0107] The information collection unit can use the emotion estimation function to analyze the user's emotions regarding places they want to go and suggest destinations that will elicit positive emotions. For example, it can analyze the user's emotional state and suggest destinations that will elicit positive emotions. For example, it can suggest places where the user can relax or have fun. Furthermore, the information collection unit can re-suggest places that the user visited in the past and felt positive emotions, based on the emotion estimation data. For example, it can suggest places that the user has fond memories of in the past as destinations. Furthermore, the information collection unit customizes the suggested destinations according to the user's emotional state. For example, if the user is feeling stressed, it can suggest places where they can relax. In this way, the emotion estimation function can be used to suggest destinations that will elicit positive emotions from the user.

[0108] The route calculation unit can use the emotion estimation function to suggest a relaxing route that matches the user's emotional state. For example, it can analyze the user's emotional state in real time and suggest a relaxing route. For example, if the user is feeling stressed, it can select a quiet route. The route calculation unit also customizes a travel route that allows the user to relax based on the emotion estimation data. For example, it can suggest a scenic route or a route that passes through a park. The route calculation unit can also suggest music or podcasts that will help the user relax while traveling, depending on the user's emotional state. For example, if the user is feeling stressed, it can play relaxing music. In this way, the emotion estimation function can suggest a relaxing route that matches the user's emotional state.

[0109] The payment unit can use the emotion estimation function to suggest payment methods that do not cause stress to the user. For example, it can analyze the user's emotional state and suggest payment methods that do not cause stress. For example, it can select the optimal payment method when the user is relaxed. The payment unit can also re-suggest payment methods that the user has not experienced stress in the past based on the emotion estimation data. For example, it can prioritize suggesting payment methods that the user has used smoothly in the past. The payment unit can also customize payment method options according to the user's emotional state. For example, if the user is experiencing stress, it can suggest a simple and quick payment method. In this way, the emotion estimation function can suggest payment methods that do not cause stress to the user.

[0110] The information collection unit can use the emotion estimation function to suggest relaxation methods to reduce the user's stress caused by delays. For example, it analyzes the user's emotional state and suggests relaxation methods to reduce stress caused by delays. For example, it plays relaxing music or podcasts. The information collection unit also suggests activities that the user can enjoy to relax based on the emotion estimation data. For example, it suggests reading or games that can be enjoyed during delays. The information collection unit also provides an environment in which the user can relax during travel, depending on the user's emotional state. For example, it guides the user to a quiet place or a comfortable seat. In this way, the emotion estimation function can suggest relaxation methods to reduce the user's stress caused by delays.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The destination input unit inputs the user's destination. For example, the user inputs "I want to go to Tokyo Station." Step 2: The information collection unit collects necessary data based on the destination input by the destination input unit, such as train delay information, weather, timetables, etc. Step 3: The route calculation unit calculates the optimal travel route based on the data collected by the information collection unit. For example, it proposes the fastest route to the destination, taking into account train delay information and weather conditions. Step 4: The payment unit purchases and pays for the necessary tickets based on the travel route calculated by the route calculation unit. For example, Shinkansen, airplane, express, and passenger tickets are purchased through an electronic payment service.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, 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 robot 414 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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. [Explanation of symbols]

[0180] 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 destination input section for inputting a destination; an information collection unit that collects necessary data based on the destination input by the destination input unit; a route calculation unit that calculates an optimal travel route based on the data collected by the information collection unit; a payment unit that purchases and pays for necessary tickets based on the travel route calculated by the route calculation unit. A system characterized by:

2. The destination input unit Predicts and suggests destination input based on the user's past travel history 2. The system of claim 1.

3. The destination input unit Voice recognition technology enables voice input, enabling hands-free operation.

2. The system of claim 1.

4. The destination input unit Analyzes the user's current emotional state and suggests a less stressful route 2. The system of claim 1.

5. The information collecting unit Works with the user's calendar app to automatically suggest destinations based on their schedule 2. The system of claim 1.

6. The information collecting unit When entering a destination, information on nearby tourist spots and restaurants is also collected and suggested.

2. The system of claim 1.

7. The information collecting unit Analyzes the user's feelings about the place they want to go and suggests destinations that evoke positive emotions 2. The system of claim 1.

8. The route calculation unit Taking into account the user's health status (number of steps, heart rate, etc.) and proposing the optimal route 2. The system of claim 1.

Images