system

Abstract

The system according to this embodiment aims to suggest the optimal route considering multiple destinations and desired time slots. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a calculation unit, a presentation unit, and a suggestion unit. The reception unit receives input from the user of multiple destinations and desired time slots. The collection unit collects transportation operation information or data on transportation costs, walking distance, and travel time based on the information received by the reception unit. The calculation unit calculates a route based on the data collected by the collection unit. The presentation unit presents the route calculated by the calculation unit to the user. The suggestion unit proposes specific means of transportation based on the route presented by the presentation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to find an optimal route considering a plurality of destinations and the time zones when wanting to go.

[0005] The system according to the embodiment aims to present an optimal route considering a plurality of destinations and the time zones when wanting to go.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a data collection unit, a calculation unit, a presentation unit, and a suggestion unit. The reception unit receives input from the user of multiple destinations and desired time slots. The data collection unit collects data on transportation services, transportation costs, walking distance, and travel time based on the information received by the reception unit. The calculation unit calculates a route based on the data collected by the data collection unit. The presentation unit presents the route calculated by the calculation unit to the user. The suggestion unit proposes specific means of transportation based on the route presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal route considering multiple destinations and desired time slots. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The route suggestion system according to an embodiment of the present invention is a system in which an AI presents the optimal route when a user specifies multiple destinations and desired time slots. This route suggestion system works by having the user input multiple destinations and desired time slots, and the AI ​​analyzes the input information to calculate the optimal route. The calculated routes are presented according to the user's preferences, such as in order of lowest transportation cost, shortest walking distance, or shortest travel time. The user can choose the route that best suits their preferences from the presented routes. This service allows users to easily find the optimal route that suits their preferences. Furthermore, because the AI ​​analyzes traffic information in real time, routes based on the latest operational information are provided. For example, when a user inputs multiple destinations and desired time slots, the system collects data such as transportation operation information, transportation costs, walking distance, and travel time. Next, it calculates the optimal route based on the collected data and presents it to the user. The user can choose the route that best suits their preferences from the presented routes. This allows the user to travel efficiently and save time and costs. Thus, the route suggestion system can present the optimal route and suggest specific means of transportation when a user specifies multiple destinations and desired time slots.

[0029] The route suggestion system according to this embodiment comprises a reception unit, a data collection unit, a calculation unit, a presentation unit, and a suggestion unit. The reception unit receives input from the user of multiple destinations and desired time slots. Destinations entered by the user include, but are not limited to, tourist destinations, business destinations, or friends' houses. Desired time slots include, but are not limited to, morning, afternoon, evening, or specific times. The data collection unit collects data such as transportation operation information, transportation costs, walking distance, and travel time based on the information received by the reception unit. Transportation operation information includes, but is not limited to, bus, train, and airplane schedules. Transportation costs include, but are not limited to, one-way fares, round-trip fares, and discounted fares. Walking distance is measured in, but is not limited to, meters, kilometers, or steps. Travel time is measured in, but is not limited to, minutes or hours. The calculation unit calculates the optimal route based on the data collected by the data collection unit. Route calculation may use algorithms such as the shortest distance, shortest time, or cheapest fare, but is not limited to such examples. The presentation unit presents the route calculated by the calculation unit to the user. The calculated route may be presented, for example, as a route on a map or as instructions in text format, but is not limited to such examples. The suggestion unit proposes specific means of transportation based on the route presented by the presentation unit. Means of transportation may include, for example, walking, cycling, taxi, or public transport, but is not limited to such examples. Thus, the route suggestion system according to the embodiment can present the optimal route and propose specific means of transportation when the user specifies multiple destinations and desired time slots.

[0030] The data collection unit can collect information on the operation of public transportation, as well as data on transportation costs, walking distance, and travel time. For example, the data collection unit can collect the operating schedules of buses, trains, airplanes, etc. It can also collect transportation costs such as one-way fares, round-trip fares, and discounted fares. Furthermore, the data collection unit can collect walking distance measured in meters, kilometers, steps, etc. It can also collect travel time measured in minutes, hours, etc. As a result, the data collection unit can provide information for calculating the optimal route by collecting data such as information on the operation of public transportation, transportation costs, walking distance, and travel time.

[0031] The calculation unit can calculate routes based on collected data. The calculation unit calculates routes using algorithms such as shortest distance, shortest time, and cheapest fare. For example, the calculation unit can calculate routes based on the shortest distance. It can also calculate routes based on the shortest time. Furthermore, it can calculate routes based on the cheapest fare. This allows the calculation unit to provide users with the most suitable mode of transportation by calculating the optimal route based on the collected data.

[0032] The display unit can present the calculated route to the user. For example, the display unit can display the route on a map to the user. The display unit can also present instructions in text format to the user. For example, the display unit can display the route on a map so that the user can visually confirm the route. Alternatively, the display unit can display route instructions in text format so that the user can confirm the specific steps of travel. In this way, by presenting the calculated route to the user, the display unit can enable the user to select the optimal route.

[0033] The suggestion function can propose routes in order of user preference, such as from lowest to highest transportation cost, shortest walking distance, or shortest travel time. For example, the suggestion function can suggest routes in order of lowest transportation cost. It can also suggest routes in order of shortest walking distance. It can also suggest routes in order of shortest travel time. For example, by suggesting routes in order of lowest transportation cost, the user can save money. It can also suggest routes in order of shortest walking distance, so that the user can travel without getting tired. It can also suggest routes in order of shortest travel time, so that the user can travel efficiently. In this way, the suggestion function can propose routes according to the user's preferences, allowing the user to choose the optimal route.

[0034] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display destinations and time slots that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations to be used during specific time slots based on the user's past input history. In this way, the reception desk can provide the optimal input interface by analyzing the user's past input history.

[0035] The reception system can complete inputs when users enter multiple destinations and desired time slots, taking into account their current schedule and appointments. For example, the reception system can refer to appointments registered in the user's calendar and automatically set destinations and time slots. It can also suggest locations related to specific events as potential destinations based on the user's calendar information. Furthermore, the reception system can suggest the optimal route based on the user's calendar information. In this way, the reception system can complete inputs by considering the user's current schedule and appointments.

[0036] The reception system can present highly relevant options when the user inputs multiple destinations and desired time slots, taking into account the user's geographical location. For example, the reception system can prioritize displaying destinations close to the user's current location. It can also suggest the optimal time slot, considering the distance from the user's current location. Furthermore, the reception system can automatically display highly relevant destinations as options based on the user's current location. In this way, the reception system can present highly relevant options by considering the user's geographical location.

[0037] The reception desk can analyze the user's social media activity when they input multiple destinations and desired time slots, and then suggest relevant options. For example, the reception desk can automatically display places the user has checked into on social media as suggested locations. It can also suggest relevant destinations based on the user's social media posts. Furthermore, it can suggest locations based on places visited by the user's social media friends. In this way, the reception desk can suggest relevant options by analyzing the user's social media activity.

[0038] The data collection unit can evaluate the reliability of current data by referring to past traffic data during collection. For example, the data collection unit compares past traffic data with current data and prioritizes the collection of highly reliable data. The data collection unit can also detect and correct anomalies in current data based on past traffic data. Furthermore, the data collection unit can evaluate the reliability of current data by referring to past traffic data and supplement the data as needed. In this way, the data collection unit can evaluate the reliability of current data by referring to past traffic data.

[0039] The data collection unit can analyze transportation operation patterns during collection to optimize the data collection frequency. For example, the unit can analyze transportation operation patterns and increase the data collection frequency during peak times. It can also reduce the data collection frequency during off-peak times based on transportation operation patterns. Furthermore, the unit can set an optimal data collection schedule considering transportation operation patterns. In this way, the data collection unit can optimize the data collection frequency by analyzing transportation operation patterns.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of highly relevant traffic information based on the user's current location. Furthermore, the data collection unit can also collect optimal traffic information by considering the distance from the user's current location. Additionally, the data collection unit can automatically collect highly relevant data based on the user's current location. This allows the data collection unit to prioritize the collection of highly relevant data by considering the user's geographical location information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect relevant transportation information from the user's social media posts. It can also collect information on the transportation used by the user's social media friends. Furthermore, the data collection unit can collect relevant data based on the user's social media check-in information. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity.

[0042] The calculation unit can optimize its calculation algorithm by referring to past square root calculation results during the calculation process. For example, the calculation unit can select the optimal square root calculation algorithm based on past square root calculation results. Furthermore, the calculation unit can analyze past square root calculation results and improve its calculation algorithm. It can also improve the accuracy of its calculation algorithm by referring to past square root calculation results. In this way, the calculation unit can optimize its calculation algorithm by referring to past square root calculation results.

[0043] The calculation unit can improve the accuracy of calculations by analyzing the operating patterns of transportation systems during calculations. For example, the calculation unit can analyze the operating patterns of transportation systems and calculate the optimal route. Furthermore, the calculation unit can optimize the calculation algorithm based on the operating patterns of transportation systems. It can also improve the accuracy of calculations by considering the operating patterns of transportation systems. In this way, the calculation unit can improve the accuracy of calculations by analyzing the operating patterns of transportation systems.

[0044] The calculation unit can calculate the optimal route by considering the user's geographical location information during calculation. For example, the calculation unit can calculate the optimal route based on the user's current location. It can also calculate the optimal route by considering the distance from the user's current location. Furthermore, the calculation unit can calculate highly relevant routes based on the user's current location. In this way, the calculation unit can calculate the optimal route by considering the user's geographical location information.

[0045] The calculation unit can analyze the user's social media activity during calculations and calculate relevant routes. For example, the calculation unit can calculate relevant routes from the content of the user's social media posts. It can also calculate the optimal route by referring to routes used by the user's social media friends. Furthermore, the calculation unit can calculate relevant routes based on the user's social media check-in information. In this way, the calculation unit can calculate relevant routes by analyzing the user's social media activity.

[0046] The presentation unit can select the optimal presentation method by referring to past presentation history when presenting information. For example, the presentation unit can prioritize displaying presentation methods that the user has previously preferred. The presentation unit can also analyze the user's past presentation history and select the most effective presentation method. Furthermore, the presentation unit can suggest the optimal presentation method based on the user's past presentation history. Thus, the presentation unit can select the optimal presentation method by referring to past presentation history.

[0047] The display unit can suggest routes while considering the user's current schedule and appointments. For example, it can refer to appointments registered in the user's calendar and suggest the optimal route. It can also suggest routes related to specific events based on the user's calendar information. Furthermore, it can suggest the optimal route tailored to the appointment based on the user's calendar information. In this way, the display unit can suggest the optimal route by considering the user's current schedule and appointments.

[0048] The display unit can prioritize presenting highly relevant routes by considering the user's geographical location information. For example, the display unit can prioritize presenting the optimal route based on the user's current location. It can also present the optimal route by considering the distance from the user's current location. Furthermore, the display unit can automatically present highly relevant routes based on the user's current location. This allows the display unit to prioritize presenting highly relevant routes by considering the user's geographical location information.

[0049] The display unit can analyze the user's social media activity and suggest relevant routes at the time of presentation. For example, the display unit can suggest relevant routes based on the content of the user's social media posts. It can also suggest the optimal route by referring to routes used by the user's social media friends. Furthermore, the display unit can suggest relevant routes based on the user's social media check-in information. In this way, the display unit can suggest relevant routes by analyzing the user's social media activity.

[0050] The proposal unit can select the optimal proposal method by referring to past proposal history when making a proposal. For example, the proposal unit can prioritize suggesting modes of transportation that the user has previously preferred. The proposal unit can also analyze the user's past proposal history and select the most effective proposal method. Furthermore, the proposal unit can suggest the optimal mode of transportation based on the user's past proposal history. In this way, the proposal unit can select the optimal proposal method by referring to past proposal history.

[0051] The suggestion function can propose transportation options while considering the user's current schedule and appointments. For example, it can refer to appointments registered in the user's calendar and propose the most suitable transportation option. It can also propose transportation options related to specific events based on the user's calendar information. Furthermore, it can propose the most suitable transportation option tailored to the appointment based on the user's calendar information. In this way, the suggestion function can propose the most suitable transportation option by considering the user's current schedule and appointments.

[0052] The suggestion unit can prioritize suggesting the most relevant mode of transportation by considering the user's geographical location information when making suggestions. For example, the suggestion unit can prioritize suggesting the optimal mode of transportation based on the user's current location. It can also suggest the optimal mode of transportation by considering the distance from the user's current location. Furthermore, the suggestion unit can automatically suggest the most relevant mode of transportation based on the user's current location. In this way, the suggestion unit can prioritize suggesting the most relevant mode of transportation by considering the user's geographical location information.

[0053] The suggestion department can analyze the user's social media activity and suggest relevant modes of transportation when making suggestions. For example, the suggestion department can suggest relevant modes of transportation based on the content of the user's social media posts. It can also suggest the most suitable modes of transportation by referring to the modes of transportation used by the user's social media friends. Furthermore, the suggestion department can suggest relevant modes of transportation based on the user's social media check-in information. In this way, the suggestion department can suggest relevant modes of transportation by analyzing the user's social media activity.

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

[0055] The route suggestion system can also include a health monitoring unit that monitors the user's health status. The health monitoring unit collects data such as the user's heart rate, steps taken, and calorie consumption, and adjusts route suggestions based on this data. For example, if the user is tired, it can prioritize suggesting shorter routes. If the user is not getting enough exercise, it can suggest routes that allow for moderate exercise. Furthermore, the health monitoring unit can suggest routes that include rest stops, depending on the user's health condition. This allows users to travel efficiently while maintaining their health.

[0056] The data collection unit can further collect data while considering the user's preferences and past behavioral history. For example, it can collect data on places the user has visited and modes of transportation used in the past, and then suggest the optimal route based on this data. The data collection unit can also collect information on restaurants and tourist spots that the user likes and incorporate it into the route. Furthermore, the data collection unit can analyze the user's past travel history and suggest the most efficient travel pattern. This allows the user to choose a route that suits their preferences.

[0057] The calculation unit can further consider weather information when calculating routes. For example, in rainy weather, it can prioritize routes that pass through indoors or routes that allow travel without an umbrella. On hot days, it can suggest routes that pass through shaded areas or routes that go through air-conditioned facilities. Furthermore, on snowy days, it can calculate the optimal route according to the weather, such as choosing less slippery paths. This allows users to travel comfortably regardless of the weather.

[0058] The display unit can further customize how the route is displayed according to the user's visual preferences. For example, the map's colors and design can be changed to suit the user's taste. The display unit can also display the route using fonts and icons that are easy for the user to visually understand. Furthermore, the display unit can allow the user to select their preferred display format (e.g., 3D display or AR display). This allows the user to check the route in a visually comfortable way.

[0059] The suggestion function can further propose modes of transportation based on the user's environmental awareness. For example, if a user prefers environmentally friendly modes of transportation, it can prioritize suggesting eco-friendly options such as public transport, bicycles, or walking. The suggestion function can also suggest routes that help the user reduce their carbon footprint. Furthermore, if a user is environmentally conscious, the suggestion function can suggest routes that include eco-friendly facilities and services. This allows users to travel while being mindful of the environment.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk accepts the user's input of multiple destinations and desired time slots. Destinations entered by the user may include tourist spots, business destinations, friends' houses, etc., and desired time slots may include morning, afternoon, evening, or specific times. Step 2: The data collection unit collects data such as transportation service information, transportation costs, walking distance, and travel time based on the information received by the reception unit. Transportation service information includes the schedules of buses, trains, and airplanes, and transportation costs include one-way fares, round-trip fares, and discounted fares. Walking distance is measured in meters, kilometers, or steps, and travel time is measured in minutes or hours. Step 3: The calculation unit calculates the optimal route based on the data collected by the collection unit. The route calculation uses algorithms such as shortest distance, shortest time, and cheapest fare. Step 4: The presentation unit presents the route calculated by the calculation unit to the user. The calculated route is presented as a route on a map, as instructions in text format, etc. Step 5: The proposal team proposes specific means of transportation based on the route presented by the presentation team. These means of transportation may include walking, cycling, taxis, and public transport.

[0062] (Example of form 2) The route suggestion system according to an embodiment of the present invention is a system in which an AI presents the optimal route when a user specifies multiple destinations and desired time slots. This route suggestion system works by having the user input multiple destinations and desired time slots, and the AI ​​analyzes the input information to calculate the optimal route. The calculated routes are presented according to the user's preferences, such as in order of lowest transportation cost, shortest walking distance, or shortest travel time. The user can choose the route that best suits their preferences from the presented routes. This service allows users to easily find the optimal route that suits their preferences. Furthermore, because the AI ​​analyzes traffic information in real time, routes based on the latest operational information are provided. For example, when a user inputs multiple destinations and desired time slots, the system collects data such as transportation operation information, transportation costs, walking distance, and travel time. Next, it calculates the optimal route based on the collected data and presents it to the user. The user can choose the route that best suits their preferences from the presented routes. This allows the user to travel efficiently and save time and costs. Thus, the route suggestion system can present the optimal route and suggest specific means of transportation when a user specifies multiple destinations and desired time slots.

[0063] The route suggestion system according to this embodiment comprises a reception unit, a data collection unit, a calculation unit, a presentation unit, and a suggestion unit. The reception unit receives input from the user of multiple destinations and desired time slots. Destinations entered by the user include, but are not limited to, tourist destinations, business destinations, or friends' houses. Desired time slots include, but are not limited to, morning, afternoon, evening, or specific times. The data collection unit collects data such as transportation operation information, transportation costs, walking distance, and travel time based on the information received by the reception unit. Transportation operation information includes, but is not limited to, bus, train, and airplane schedules. Transportation costs include, but are not limited to, one-way fares, round-trip fares, and discounted fares. Walking distance is measured in, but is not limited to, meters, kilometers, or steps. Travel time is measured in, but is not limited to, minutes or hours. The calculation unit calculates the optimal route based on the data collected by the data collection unit. Route calculation may use algorithms such as the shortest distance, shortest time, or cheapest fare, but is not limited to such examples. The presentation unit presents the route calculated by the calculation unit to the user. The calculated route may be presented, for example, as a route on a map or as instructions in text format, but is not limited to such examples. The suggestion unit proposes specific means of transportation based on the route presented by the presentation unit. Means of transportation may include, for example, walking, cycling, taxi, or public transport, but is not limited to such examples. Thus, the route suggestion system according to the embodiment can present the optimal route and propose specific means of transportation when the user specifies multiple destinations and desired time slots.

[0064] The data collection unit can collect information on the operation of public transportation, as well as data on transportation costs, walking distance, and travel time. For example, the data collection unit can collect the operating schedules of buses, trains, airplanes, etc. It can also collect transportation costs such as one-way fares, round-trip fares, and discounted fares. Furthermore, the data collection unit can collect walking distance measured in meters, kilometers, steps, etc. It can also collect travel time measured in minutes, hours, etc. As a result, the data collection unit can provide information for calculating the optimal route by collecting data such as information on the operation of public transportation, transportation costs, walking distance, and travel time.

[0065] The calculation unit can calculate routes based on collected data. The calculation unit calculates routes using algorithms such as shortest distance, shortest time, and cheapest fare. For example, the calculation unit can calculate routes based on the shortest distance. It can also calculate routes based on the shortest time. Furthermore, it can calculate routes based on the cheapest fare. This allows the calculation unit to provide users with the most suitable mode of transportation by calculating the optimal route based on the collected data.

[0066] The display unit can present the calculated route to the user. For example, the display unit can display the route on a map to the user. The display unit can also present instructions in text format to the user. For example, the display unit can display the route on a map so that the user can visually confirm the route. Alternatively, the display unit can display route instructions in text format so that the user can confirm the specific steps of travel. In this way, by presenting the calculated route to the user, the display unit can enable the user to select the optimal route.

[0067] The suggestion function can propose routes in order of user preference, such as from lowest to highest transportation cost, shortest walking distance, or shortest travel time. For example, the suggestion function can suggest routes in order of lowest transportation cost. It can also suggest routes in order of shortest walking distance. It can also suggest routes in order of shortest travel time. For example, by suggesting routes in order of lowest transportation cost, the user can save money. It can also suggest routes in order of shortest walking distance, so that the user can travel without getting tired. It can also suggest routes in order of shortest travel time, so that the user can travel efficiently. In this way, the suggestion function can propose routes according to the user's preferences, allowing the user to choose the optimal route.

[0068] The reception system can estimate the user's emotions and adjust the input method for multiple destinations and desired time slots based on the estimated emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception system can prioritize voice input, allowing for quick input of multiple destinations and desired time slots. In this way, the reception system can ensure that users can input information without stress by adjusting the input method according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The reception desk can analyze the user's past input history and provide the optimal input interface. For example, the reception desk can automatically display destinations and time slots that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations to be used during specific time slots based on the user's past input history. In this way, the reception desk can provide the optimal input interface by analyzing the user's past input history.

[0070] The reception system can complete inputs when users enter multiple destinations and desired time slots, taking into account their current schedule and appointments. For example, the reception system can refer to appointments registered in the user's calendar and automatically set destinations and time slots. It can also suggest locations related to specific events as potential destinations based on the user's calendar information. Furthermore, the reception system can suggest the optimal route based on the user's calendar information. In this way, the reception system can complete inputs by considering the user's current schedule and appointments.

[0071] The reception desk can estimate the user's emotions and prioritize inputs based on those emotions. For example, if the user is nervous, the reception desk can prioritize displaying important input fields and simplify the input process. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize displaying the most important input fields to allow for quick input. In this way, the reception desk can prioritize important input fields by determining input priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The reception system can present highly relevant options when the user inputs multiple destinations and desired time slots, taking into account the user's geographical location. For example, the reception system can prioritize displaying destinations close to the user's current location. It can also suggest the optimal time slot, considering the distance from the user's current location. Furthermore, the reception system can automatically display highly relevant destinations as options based on the user's current location. In this way, the reception system can present highly relevant options by considering the user's geographical location.

[0073] The reception desk can analyze the user's social media activity when they input multiple destinations and desired time slots, and then suggest relevant options. For example, the reception desk can automatically display places the user has checked into on social media as suggested locations. It can also suggest relevant destinations based on the user's social media posts. Furthermore, it can suggest locations based on places visited by the user's social media friends. In this way, the reception desk can suggest relevant options by analyzing the user's social media activity.

[0074] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect detailed traffic information. If the user is in a hurry, the data collection unit can prioritize collecting the most important traffic information. If the user is stressed, the data collection unit can collect simple, minimal traffic information. In this way, the data collection unit can efficiently collect the necessary information by adjusting the type of data collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The data collection unit can evaluate the reliability of current data by referring to past traffic data during collection. For example, the data collection unit compares past traffic data with current data and prioritizes the collection of highly reliable data. The data collection unit can also detect and correct anomalies in current data based on past traffic data. Furthermore, the data collection unit can evaluate the reliability of current data by referring to past traffic data and supplement the data as needed. In this way, the data collection unit can evaluate the reliability of current data by referring to past traffic data.

[0076] The data collection unit can analyze transportation operation patterns during collection to optimize the data collection frequency. For example, the unit can analyze transportation operation patterns and increase the data collection frequency during peak times. It can also reduce the data collection frequency during off-peak times based on transportation operation patterns. Furthermore, the unit can set an optimal data collection schedule considering transportation operation patterns. In this way, the data collection unit can optimize the data collection frequency by analyzing transportation operation patterns.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important traffic information. If the user is relaxed, the data collection unit can also collect detailed traffic information. If the user is in a hurry, the data collection unit can also prioritize collecting the most important traffic information. In this way, the data collection unit can prioritize the collection of important information by prioritizing the data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of highly relevant traffic information based on the user's current location. Furthermore, the data collection unit can also collect optimal traffic information by considering the distance from the user's current location. Additionally, the data collection unit can automatically collect highly relevant data based on the user's current location. This allows the data collection unit to prioritize the collection of highly relevant data by considering the user's geographical location information.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect relevant transportation information from the user's social media posts. It can also collect information on the transportation used by the user's social media friends. Furthermore, the data collection unit can collect relevant data based on the user's social media check-in information. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity.

[0080] The calculation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated emotions. For example, if the user is relaxed, the calculation unit will perform a detailed route calculation. If the user is in a hurry, the calculation unit can prioritize calculating the shortest route. If the user is stressed, the calculation unit can perform a simple and efficient route calculation. In this way, the calculation unit can calculate the optimal route by adjusting the route calculation algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The calculation unit can optimize its calculation algorithm by referring to past square root calculation results during the calculation process. For example, the calculation unit can select the optimal square root calculation algorithm based on past square root calculation results. Furthermore, the calculation unit can analyze past square root calculation results and improve its calculation algorithm. It can also improve the accuracy of its calculation algorithm by referring to past square root calculation results. In this way, the calculation unit can optimize its calculation algorithm by referring to past square root calculation results.

[0082] The calculation unit can improve the accuracy of calculations by analyzing the operating patterns of transportation systems during calculations. For example, the calculation unit can analyze the operating patterns of transportation systems and calculate the optimal route. Furthermore, the calculation unit can optimize the calculation algorithm based on the operating patterns of transportation systems. It can also improve the accuracy of calculations by considering the operating patterns of transportation systems. In this way, the calculation unit can improve the accuracy of calculations by analyzing the operating patterns of transportation systems.

[0083] The calculation unit can estimate the user's emotions and adjust the display method of the calculation results based on the estimated emotions. For example, if the user is tense, the calculation unit provides a simple and highly visible display method. If the user is relaxed, the calculation unit can also provide a display method that includes detailed information. If the user is in a hurry, the calculation unit can also provide a display method that gets straight to the point. In this way, the calculation unit can provide a highly visible display by adjusting the display method of the calculation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The calculation unit can calculate the optimal route by considering the user's geographical location information during calculation. For example, the calculation unit can calculate the optimal route based on the user's current location. It can also calculate the optimal route by considering the distance from the user's current location. Furthermore, the calculation unit can calculate highly relevant routes based on the user's current location. In this way, the calculation unit can calculate the optimal route by considering the user's geographical location information.

[0085] The calculation unit can analyze the user's social media activity during calculations and calculate relevant routes. For example, the calculation unit can calculate relevant routes from the content of the user's social media posts. It can also calculate the optimal route by referring to routes used by the user's social media friends. Furthermore, the calculation unit can calculate relevant routes based on the user's social media check-in information. In this way, the calculation unit can calculate relevant routes by analyzing the user's social media activity.

[0086] The presentation unit can estimate the user's emotions and adjust the route presentation method based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible display method. If the user is relaxed, the presentation unit can also provide a display method that includes detailed information. If the user is in a hurry, the presentation unit can provide a concise display method. In this way, the presentation unit can provide a highly visible display by adjusting the route presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The presentation unit can select the optimal presentation method by referring to past presentation history when presenting information. For example, the presentation unit can prioritize displaying presentation methods that the user has previously preferred. The presentation unit can also analyze the user's past presentation history and select the most effective presentation method. Furthermore, the presentation unit can suggest the optimal presentation method based on the user's past presentation history. Thus, the presentation unit can select the optimal presentation method by referring to past presentation history.

[0088] The display unit can suggest routes while considering the user's current schedule and appointments. For example, it can refer to appointments registered in the user's calendar and suggest the optimal route. It can also suggest routes related to specific events based on the user's calendar information. Furthermore, it can suggest the optimal route tailored to the appointment based on the user's calendar information. In this way, the display unit can suggest the optimal route by considering the user's current schedule and appointments.

[0089] The presentation unit can estimate the user's emotions and determine the priority of the routes to present based on the estimated emotions. For example, if the user is nervous, the presentation unit will prioritize the simplest and most efficient route. If the user is relaxed, the presentation unit may prioritize a route with more detailed information. If the user is in a hurry, the presentation unit may prioritize the shortest route. In this way, the presentation unit can present the optimal route by prioritizing the routes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The display unit can prioritize presenting highly relevant routes by considering the user's geographical location information. For example, the display unit can prioritize presenting the optimal route based on the user's current location. It can also present the optimal route by considering the distance from the user's current location. Furthermore, the display unit can automatically present highly relevant routes based on the user's current location. This allows the display unit to prioritize presenting highly relevant routes by considering the user's geographical location information.

[0091] The display unit can analyze the user's social media activity and suggest relevant routes at the time of presentation. For example, the display unit can suggest relevant routes based on the content of the user's social media posts. It can also suggest the optimal route by referring to routes used by the user's social media friends. Furthermore, the display unit can suggest relevant routes based on the user's social media check-in information. In this way, the display unit can suggest relevant routes by analyzing the user's social media activity.

[0092] The suggestion unit can estimate the user's emotions and adjust its suggestion method of transportation based on those emotions. For example, if the user is relaxed, the suggestion unit will suggest detailed transportation options. If the user is in a hurry, the suggestion unit can prioritize suggesting the fastest transportation option. If the user is stressed, the suggestion unit can suggest simple and efficient transportation options. In this way, the suggestion unit can suggest the optimal transportation option by adjusting its suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The proposal unit can select the optimal proposal method by referring to past proposal history when making a proposal. For example, the proposal unit can prioritize suggesting modes of transportation that the user has previously preferred. The proposal unit can also analyze the user's past proposal history and select the most effective proposal method. Furthermore, the proposal unit can suggest the optimal mode of transportation based on the user's past proposal history. In this way, the proposal unit can select the optimal proposal method by referring to past proposal history.

[0094] The suggestion function can propose transportation options while considering the user's current schedule and appointments. For example, it can refer to appointments registered in the user's calendar and propose the most suitable transportation option. It can also propose transportation options related to specific events based on the user's calendar information. Furthermore, it can propose the most suitable transportation option tailored to the appointment based on the user's calendar information. In this way, the suggestion function can propose the most suitable transportation option by considering the user's current schedule and appointments.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggested modes of transportation based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting the simplest and most efficient mode of transportation. If the user is relaxed, the suggestion unit may prioritize suggesting modes of transportation that include detailed information. If the user is in a hurry, the suggestion unit may prioritize suggesting the fastest mode of transportation. In this way, the suggestion unit can suggest the optimal mode of transportation by determining the priority of suggested modes of transportation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The suggestion unit can prioritize suggesting the most relevant mode of transportation by considering the user's geographical location information when making suggestions. For example, the suggestion unit can prioritize suggesting the optimal mode of transportation based on the user's current location. It can also suggest the optimal mode of transportation by considering the distance from the user's current location. Furthermore, the suggestion unit can automatically suggest the most relevant mode of transportation based on the user's current location. In this way, the suggestion unit can prioritize suggesting the most relevant mode of transportation by considering the user's geographical location information.

[0097] The suggestion department can analyze the user's social media activity and suggest relevant modes of transportation when making suggestions. For example, the suggestion department can suggest relevant modes of transportation based on the content of the user's social media posts. It can also suggest the most suitable modes of transportation by referring to the modes of transportation used by the user's social media friends. Furthermore, the suggestion department can suggest relevant modes of transportation based on the user's social media check-in information. In this way, the suggestion department can suggest relevant modes of transportation by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, presentation unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs multiple destinations and desired time slots. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and collects data such as transportation operation information, transportation costs, walking distance, and travel time. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and calculates the optimal route based on the collected data. The presentation unit is implemented, for example, by the output device 40 of the smart device 14, and presents the calculated route to the user. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes specific means of transportation based on the presented route. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, presentation unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, where the user inputs multiple destinations and desired time slots by voice. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and collects data such as transportation operation information, transportation costs, walking distance, and travel time. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and calculates the optimal route based on the collected data. The presentation unit is implemented, for example, by the speaker 240 of the smart glasses 214, and presents the calculated route to the user by voice. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes specific means of transportation based on the presented route. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, presentation unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs multiple destinations and desired time slots by voice. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and collects data such as transportation operation information, transportation costs, walking distance, and travel time. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and calculates the optimal route based on the collected data. The presentation unit is implemented by, for example, the display 343 of the headset terminal 314, and presents the calculated route to the user. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes specific means of transportation based on the presented route. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, collection unit, calculation unit, presentation unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, where the user inputs multiple destinations and desired time slots by voice. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and collects data such as transportation operation information, transportation costs, walking distance, and travel time. The calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and calculates the optimal route based on the collected data. The presentation unit is implemented by, for example, the speaker 240 of the robot 414, and presents the calculated route to the user by voice. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and proposes specific means of transportation based on the presented route.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The route suggestion system can also include a health monitoring unit that monitors the user's health status. The health monitoring unit collects data such as the user's heart rate, steps taken, and calorie consumption, and adjusts route suggestions based on this data. For example, if the user is tired, it can prioritize suggesting shorter routes. If the user is not getting enough exercise, it can suggest routes that allow for moderate exercise. Furthermore, the health monitoring unit can suggest routes that include rest stops, depending on the user's health condition. This allows users to travel efficiently while maintaining their health.

[0100] The data collection unit can further collect data while considering the user's preferences and past behavioral history. For example, it can collect data on places the user has visited and modes of transportation used in the past, and then suggest the optimal route based on this data. The data collection unit can also collect information on restaurants and tourist spots that the user likes and incorporate it into the route. Furthermore, the data collection unit can analyze the user's past travel history and suggest the most efficient travel pattern. This allows the user to choose a route that suits their preferences.

[0101] The calculation unit can further consider weather information when calculating routes. For example, in rainy weather, it can prioritize routes that pass through indoors or routes that allow travel without an umbrella. On hot days, it can suggest routes that pass through shaded areas or routes that go through air-conditioned facilities. Furthermore, on snowy days, it can calculate the optimal route according to the weather, such as choosing less slippery paths. This allows users to travel comfortably regardless of the weather.

[0102] The display unit can further customize how the route is displayed according to the user's visual preferences. For example, the map's colors and design can be changed to suit the user's taste. The display unit can also display the route using fonts and icons that are easy for the user to visually understand. Furthermore, the display unit can allow the user to select their preferred display format (e.g., 3D display or AR display). This allows the user to check the route in a visually comfortable way.

[0103] The suggestion function can further propose modes of transportation based on the user's environmental awareness. For example, if a user prefers environmentally friendly modes of transportation, it can prioritize suggesting eco-friendly options such as public transport, bicycles, or walking. The suggestion function can also suggest routes that help the user reduce their carbon footprint. Furthermore, if a user is environmentally conscious, the suggestion function can suggest routes that include eco-friendly facilities and services. This allows users to travel while being mindful of the environment.

[0104] The reception desk can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to help them relax. If the user is excited, it can provide a bright and lively design. Furthermore, if the user is tired, it can provide a simple, highly visible design. In this way, the reception desk can provide the optimal input interface according to the user's emotions.

[0105] The reception desk can estimate the user's emotions and adjust the timing of input based on those estimates. For example, if the user is relaxed, it can select a time to request detailed input. If the user is in a hurry, it can prioritize displaying the most important input fields to allow for quick completion. Furthermore, if the user is stressed, it can simplify the input process and request only the minimum necessary information. In this way, the reception desk can provide the optimal input timing according to the user's emotions.

[0106] The data collection unit can estimate the user's emotions and adjust the accuracy of the data it collects based on those emotions. For example, if the user is relaxed, it can collect detailed data. If the user is in a hurry, it can prioritize collecting the most important data. Furthermore, if the user is stressed, it can collect simple, minimal data. In this way, the data collection unit can efficiently collect the necessary information by adjusting the accuracy of the data collected according to the user's emotions.

[0107] The calculation unit can estimate the user's emotions and adjust the priority of route calculations based on those emotions. For example, if the user is relaxed, it can perform a detailed route calculation. If the user is in a hurry, it can prioritize calculating the shortest route. Furthermore, if the user is stressed, it can perform a simple and efficient route calculation. In this way, the calculation unit can calculate the optimal route by adjusting the priority of route calculations according to the user's emotions.

[0108] The display unit can estimate the user's emotions and adjust the route presentation method based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the display unit can provide a highly visible display by adjusting the route presentation method according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk accepts the user's input of multiple destinations and desired time slots. Destinations entered by the user may include tourist spots, business destinations, friends' houses, etc., and desired time slots may include morning, afternoon, evening, or specific times. Step 2: The data collection unit collects data such as transportation service information, transportation costs, walking distance, and travel time based on the information received by the reception unit. Transportation service information includes the schedules of buses, trains, and airplanes, and transportation costs include one-way fares, round-trip fares, and discounted fares. Walking distance is measured in meters, kilometers, or steps, and travel time is measured in minutes or hours. Step 3: The calculation unit calculates the optimal route based on the data collected by the collection unit. The route calculation uses algorithms such as shortest distance, shortest time, and cheapest fare. Step 4: The presentation unit presents the route calculated by the calculation unit to the user. The calculated route is presented as a route on a map, as instructions in text format, etc. Step 5: The proposal team proposes specific means of transportation based on the route presented by the presentation team. These means of transportation may include walking, cycling, taxis, and public transport.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0132] As shown in Figure 5, the data processing system 310 includes a 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user input of multiple destinations and desired time slots, A collection unit collects data on transportation services, transportation costs, walking distance, and travel time based on the information received by the aforementioned reception unit. A calculation unit that calculates a route based on the data collected by the collection unit, A presentation unit that presents the route calculated by the calculation unit to the user, The system comprises a suggestion unit that proposes specific means of transportation based on the route presented by the presentation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on public transportation operations, transportation costs, walking distance, and travel time. The system according to feature 1.

3. The calculation unit, Calculate the route based on the collected data. The system according to feature 1.

4. The aforementioned display unit is, Present the calculated route to the user. The system according to feature 1.

5. The aforementioned proposal section is, The system suggests routes based on user preferences, sorted by lowest transportation cost, shortest walking distance, and shortest travel time. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for multiple destinations and desired time slots based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history and provide the optimal input interface. The system according to feature 1.

8. The aforementioned reception unit is When users enter multiple destinations and desired time slots, the system will complete the input by considering their current schedule and plans. The system according to feature 1.

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