system

Abstract

The system according to this embodiment aims to suggest the optimal destination and route based on the user's individual needs. [Solution] The system according to the embodiment comprises a dialogue unit, an integration unit, a notification unit, and a suggestion unit. The dialogue unit suggests the optimal destination and route through dialogue based on the user's travel purpose. The integration unit integrates weather and event information using external tools such as a weather information provision API and an event calendar, based on the information acquired by the dialogue unit. The notification unit learns past travel history and dialogue data based on the information integrated by the integration unit and notifies the user of landmarks and events of interest. The suggestion unit analyzes commuting time and peak times based on the information notified by the notification unit and suggests an efficient travel time.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, 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 conventional technology, general route guidance that does not correspond to individual needs or route selection that does not consider external factors such as weather and events is performed, which may increase the stress of users.

[0005] The system according to the embodiment aims to propose an optimal destination and route based on the individual needs of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dialogue unit, an integration unit, a notification unit, and a suggestion unit. The dialogue unit suggests the optimal destination and route through dialogue based on the user's travel purpose. The integration unit integrates weather and event information using external tools such as a weather information provision API and an event calendar, based on the information acquired by the dialogue unit. The notification unit learns past travel history and dialogue data based on the information integrated by the integration unit and notifies the user of landmarks and events of interest. The suggestion unit analyzes commuting time and peak times based on the information notified by the notification unit and suggests an efficient travel time. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal destination and route based on the user's individual needs. [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, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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) An interactive navigation system according to an embodiment of the present invention is a system that provides route guidance that responds to the individual needs of the user and takes external factors into consideration. The interactive navigation system proposes the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generating AI proposes the optimal route. The interactive navigation system utilizes external tools such as weather information provision APIs and event calendars to integrate weather and event information. For example, considering weather information, it can propose a route with a roof on rainy days. The interactive navigation system learns past travel history and dialogue data and notifies the user of landmarks and events that may be of interest to them. For example, when the user passes near a cafe they have visited in the past, it notifies them of information about that cafe. The interactive navigation system analyzes commuting times and peak times to propose efficient travel times that avoid congestion. For example, it may suggest leaving a little earlier to avoid rush hour. As a result, the interactive navigation system can customize the travel experience based on the user's individual needs and utilize environmental information to enable comfortable and safe route selection. It can also make daily travel richer and stress-free. This enables interactive navigation systems to respond to individual user needs and provide route guidance that takes external factors into consideration.

[0029] The interactive navigation system according to this embodiment comprises a dialogue unit, an integration unit, a notification unit, and a suggestion unit. The dialogue unit suggests the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the dialogue unit's generating AI will suggest the optimal route. The dialogue unit can use the generating AI to engage in dialogue based on the user's travel purpose and suggest the optimal destination and route. For example, the generating AI analyzes the user's input and generates prompts to suggest the optimal route. The integration unit integrates weather and event information using external tools such as a weather information provision API and an event calendar. For example, the integration unit can consider weather information and suggest a route with a roof on a rainy day. The integration unit can integrate weather and event information using external tools. For example, it can use a weather information provision API to obtain current weather information and reflect it in route guidance. The notification unit learns past travel history and dialogue data and notifies the user of landmarks and events of interest. For example, the notification unit notifies the user of information about a cafe when the user passes near a cafe they have visited in the past. The notification unit learns past travel history and dialogue data and can notify the user of landmarks and events of interest. For example, the notification unit analyzes the user's travel history and identifies landmarks and events of interest. The suggestion unit analyzes commuting time and peak times to suggest efficient travel times. For example, the suggestion unit suggests leaving a little earlier to avoid rush hour. The suggestion unit can analyze commuting time and peak times to suggest efficient travel times. For example, the suggestion unit analyzes commuting time data and suggests the optimal departure time. As a result, the interactive navigation system according to the embodiment can respond to the user's individual needs and provide route guidance that takes external factors into consideration.

[0030] The dialogue unit proposes the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generative AI will propose the optimal route. The generative AI analyzes the user's input and generates prompts that suggest the optimal route. Specifically, the generative AI uses natural language processing technology to understand the user's intent and generate appropriate responses. For example, if the user inputs "I want to go to the station on a rainy day," the generative AI will consider weather information and propose routes with roofs or routes that minimize exposure to rain. The generative AI can also learn from the user's past dialogue and travel history, enabling it to make suggestions based on the user's preferences and habits. For example, if the user has visited a particular cafe in the past, it can suggest a route that passes near that cafe. Furthermore, the dialogue unit can modify the route in real time through dialogue with the user. For example, if the user changes their destination midway, the generative AI will recalculate and propose the optimal route to the new destination. This allows the dialogue unit to flexibly respond to user needs and provide optimal route guidance.

[0031] The integration department integrates weather and event information using external tools such as weather information APIs and event calendars. Specifically, it uses weather information APIs to obtain current weather information and reflects it in route guidance. For example, it can suggest routes with roofs on rainy days and routes with scenic views on sunny days. It can also use event calendars to provide route guidance that takes into account the location and time of specific events. For example, if a user wants to attend a particular event, it can suggest the optimal route that matches the event's location and time. Furthermore, the integration department can obtain traffic information and congestion status in real time and reflect it in route guidance. For example, if there is traffic congestion, it can suggest an alternative route to avoid the congestion. In this way, the integration department can use external tools to integrate diverse information and provide users with optimal route guidance.

[0032] The notification unit learns from past travel history and conversation data to notify users of landmarks and events that may interest them. Specifically, when a user passes near a cafe they have visited in the past, it will notify them of information about that cafe. The notification unit analyzes the user's travel history to identify landmarks and events of interest. For example, based on information about tourist spots or restaurants the user has visited in the past, it can notify the user when they pass nearby. The notification unit also learns from the user's conversation data and can provide information based on the user's interests and preferences. For example, if a user has previously shown interest in a particular genre of event, it can notify the user when an event of that genre is being held. Furthermore, the notification unit can provide information in real time based on the user's current location and travel route. For example, when a user passes near a particular landmark, it can notify them of information about the history and characteristics of that landmark. In this way, the notification unit can provide information based on the user's interests and preferences, enriching the travel experience.

[0033] The suggestion department analyzes commute times and peak times to propose efficient travel times. Specifically, it suggests leaving a little earlier to avoid rush hour. The suggestion department can analyze commute time data and propose the optimal departure time. For example, based on past commute data, it can predict congestion levels during specific time periods and propose the optimal departure time to avoid congestion. Furthermore, the suggestion department can propose efficient travel times based on the user's schedule and plans. For example, if a user needs to attend a meeting or event at a specific time, it can propose the optimal departure time and route to suit that time. In addition, the suggestion department can optimize travel times by considering real-time updated traffic and weather information. For example, if traffic congestion or bad weather is predicted, it can support efficient travel by suggesting an earlier departure. In this way, the suggestion department can propose efficient travel times that take into account the user's schedule and external factors, enabling comfortable travel.

[0034] The dialogue unit can use generative AI to suggest the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generative AI will suggest the optimal route. The dialogue unit can use generative AI to engage in dialogue based on the user's travel purpose and suggest the optimal destination and route. For example, the generative AI analyzes the user's input and generates prompts to suggest the optimal route. This makes it possible to suggest the optimal route based on the user's travel purpose by using generative AI. The generative AI, for example, uses text generation AI (e.g., a large-scale language model) to analyze the user's input and generate prompts to suggest the optimal route. The generative AI generates information to suggest the optimal route based on the user's input. The generative AI generates prompts to suggest the optimal route based on the user's travel purpose. The generative AI analyzes the user's input and generates information to suggest the optimal route. The generative AI generates prompts to suggest the optimal route based on the user's travel purpose. This makes it possible to suggest the optimal route based on the user's travel purpose by using generative AI.

[0035] The integration unit can integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. For example, the integration unit can consider weather information and suggest routes with roofs on rainy days. The integration unit can integrate weather and event information by utilizing external tools. For example, it can use a weather information API to obtain current weather information and reflect it in route guidance. The integration unit can integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. For example, the integration unit can use a weather information API to obtain current weather information and reflect it in route guidance. The integration unit can use an event calendar to obtain current event information and reflect it in route guidance. The integration unit can integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. For example, the integration unit can use a weather information API to obtain current weather information and reflect it in route guidance. The integration unit can use an event calendar to obtain current event information and reflect it in route guidance. In this way, weather and event information can be integrated and reflected in route guidance by utilizing external tools. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input weather information obtained using a weather information provision API into the AI ​​and have the AI ​​perform the analysis of the weather information.

[0036] The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, when the notification unit passes near a cafe that the user has visited in the past, it can notify the user of information about that cafe. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. This makes it possible to provide notifications based on user interests by learning from past travel history and conversation data. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's movement history data into a generating AI and have the generating AI identify landmarks and events of interest.

[0037] The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can suggest leaving a little earlier to avoid rush hour. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. This makes it possible to propose efficient travel times by analyzing commute times and peak times. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input commute time data into a generation AI and have the AI ​​generate suggestions for the optimal departure time.

[0038] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can use a generation AI to conduct a dialogue in a similar style based on the dialogue style the user has preferred in the past. The dialogue unit can also use the generation AI to predict and prepare answers based on questions the user has frequently asked in the past. The dialogue unit can also avoid dialogue content that the user has expressed dissatisfaction with in the past and provide more appropriate content through the generation AI. In this way, by analyzing past dialogue history, the dialogue unit can provide the user with the most suitable dialogue method. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history data into the generation AI and have the generation AI select the optimal dialogue method.

[0039] The dialogue unit can customize the content of the conversation based on the user's current situation and interests during the conversation. For example, if the user is interested in the current weather, the generating AI will provide weather information. If the user shows interest in a specific event, the generating AI can also provide information about that event. The dialogue unit can also provide information about nearby landmarks and facilities based on the user's current location. This allows for the provision of more appropriate information by customizing the conversation content based on the user's current situation and interests. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's current situation and interest data into the generating AI and have the generating AI customize the conversation content.

[0040] The integration unit can select the optimal integration method by referring to past integration data during the integration process. For example, the integration unit integrates information using a similar method based on information integration methods that users have preferred to use in the past. The integration unit can also prioritize the integration of information that users frequently used from past integration data. The integration unit can also analyze past integration data and select the most efficient information integration method. This makes it possible to perform optimal information integration by referring to past integration data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI and have the generating AI select the optimal integration method.

[0041] The integration unit can customize the integration content based on the user's current situation and interests during the integration process. For example, if the user is interested in the current weather, the integration unit will prioritize integrating weather information. If the user is interested in a specific event, the integration unit can also prioritize integrating information about that event. Based on the user's current location, the integration unit can also prioritize integrating information about nearby landmarks and facilities. This allows for more appropriate information integration by customizing the integration content based on the user's current situation and interests. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's current situation and interest data into a generating AI and have the generating AI perform the customization of the integration content.

[0042] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit can send notifications using a similar method based on the notification method the user preferred to receive in the past. The notification unit can also prioritize notifying information that the user frequently used based on past notification history. The notification unit can also analyze past notification history and select the most efficient notification method. This makes it possible to use the optimal notification method by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0043] The notification unit can customize notification content based on the user's current situation and interests. For example, if the user is interested in the current weather, the notification unit will notify the user of weather information. If the user has shown interest in a specific event, the notification unit can also notify the user of information about that event. Based on the user's current location, the notification unit can also notify the user of information about nearby landmarks and facilities. This allows for more appropriate notifications by customizing notification content based on the user's current situation and interests. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation and interest data into a generating AI and have the generating AI perform the customization of the notification content.

[0044] 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 make proposals using a similar method based on proposal methods that users have previously preferred and accepted. The proposal unit can also prioritize information that users have frequently used based on past proposal history. The proposal unit can also analyze past proposal history and select the most efficient proposal method. This makes it possible to make the optimal proposal method by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input past proposal history data into a generating AI and have the generating AI select the optimal proposal method.

[0045] The suggestion unit can customize the suggested content based on the user's current situation and interests. For example, if the user is interested in the current weather, the suggestion unit will make weather-related suggestions. If the user has shown interest in a specific event, the suggestion unit can also make suggestions related to that event. The suggestion unit can also make suggestions about nearby landmarks and facilities based on the user's current location. This allows for more appropriate suggestions by customizing the suggested content based on the user's current situation and interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's current situation and interest data into a generating AI and have the generating AI perform the customization of the suggested content.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The interactive unit can monitor the user's health status and suggest routes and destinations based on that status. For example, if the user is tired, the interactive unit can suggest a route that includes rest areas. If the user is not getting enough exercise, the interactive unit can suggest routes suitable for walking or jogging. Furthermore, if the user has specific health goals, the interactive unit can suggest routes and destinations that align with those goals. This enables optimal route suggestions tailored to the user's health condition.

[0048] The integration unit can consolidate information based on the user's hobbies and preferences and reflect this in route guidance. For example, if a user enjoys visiting art museums, the integration unit can suggest a route that includes museums. Similarly, if a user is interested in gourmet food, the integration unit can suggest a route that includes popular restaurants. Furthermore, if a user enjoys nature walks, the integration unit can suggest a route that includes parks and nature reserves. This enables information integration tailored to the user's hobbies and preferences.

[0049] The suggestion function can analyze a user's past behavior patterns and provide optimal suggestions. For example, if a user has visited a specific place at a specific time in the past, the suggestion function can suggest that place again at that time. Similarly, if a user has preferred a particular route in the past, the suggestion function can suggest that route again. Furthermore, if a user has participated in a specific event in the past, the suggestion function can suggest similar events. This enables optimal suggestions based on the user's past behavior patterns.

[0050] The integration unit can consolidate information based on the user's current situation and interests and reflect this in route guidance. For example, if a user is interested in the current weather, the integration unit can prioritize integrating weather information. Similarly, if a user is interested in a specific event, the integration unit can prioritize integrating information related to that event. Furthermore, based on the user's current location, it can prioritize integrating information about nearby landmarks and facilities. This enables optimal information integration based on the user's current situation and interests.

[0051] The suggestion unit can monitor the user's health status and propose routes and destinations based on that status. For example, if the user is tired, the suggestion unit can suggest a route that includes rest areas. If the user is not getting enough exercise, the suggestion unit can also suggest routes suitable for walking or jogging. Furthermore, if the user has specific health goals, the suggestion unit can suggest routes and destinations that align with those goals. This enables the system to propose optimal routes tailored to the user's health status.

[0052] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, based on the dialogue style the user has preferred in the past, the dialogue unit can conduct the conversation in a similar style. Furthermore, based on questions the user has frequently asked in the past, the dialogue unit can predict and prepare answers. In addition, the dialogue unit can avoid dialogue content that the user has previously expressed dissatisfaction with and provide more appropriate content. In this way, by analyzing past dialogue history, the dialogue unit can provide the user with the most suitable dialogue method.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The dialogue unit proposes the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generating AI will propose the optimal route. The dialogue unit can use the generating AI to engage in dialogue based on the user's travel purpose and propose the optimal destination and route. Step 2: The integration unit integrates weather and event information using external tools such as weather information APIs and event calendars. For example, it can suggest routes with roofs on rainy days, taking weather information into consideration. The integration unit can integrate weather and event information using external tools. Step 3: The notification unit learns from past travel history and conversation data and notifies the user of landmarks and events that may be of interest to them. For example, when the user passes near a cafe they have visited in the past, it will notify them of information about that cafe. The notification unit can learn from past travel history and conversation data and notify the user of landmarks and events that may be of interest to them. Step 4: The proposal team analyzes commute times and peak times to suggest efficient travel times. For example, they might suggest leaving a little earlier to avoid rush hour. The proposal team can analyze commute times and peak times to suggest efficient travel times.

[0055] (Example of form 2) An interactive navigation system according to an embodiment of the present invention is a system that provides route guidance that responds to the individual needs of the user and takes external factors into consideration. The interactive navigation system proposes the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generating AI proposes the optimal route. The interactive navigation system utilizes external tools such as weather information provision APIs and event calendars to integrate weather and event information. For example, considering weather information, it can propose a route with a roof on rainy days. The interactive navigation system learns past travel history and dialogue data and notifies the user of landmarks and events that may be of interest to them. For example, when the user passes near a cafe they have visited in the past, it notifies them of information about that cafe. The interactive navigation system analyzes commuting times and peak times to propose efficient travel times that avoid congestion. For example, it may suggest leaving a little earlier to avoid rush hour. As a result, the interactive navigation system can customize the travel experience based on the user's individual needs and utilize environmental information to enable comfortable and safe route selection. It can also make daily travel richer and stress-free. This enables interactive navigation systems to respond to individual user needs and provide route guidance that takes external factors into consideration.

[0056] The interactive navigation system according to this embodiment comprises a dialogue unit, an integration unit, a notification unit, and a suggestion unit. The dialogue unit suggests the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the dialogue unit's generating AI will suggest the optimal route. The dialogue unit can use the generating AI to engage in dialogue based on the user's travel purpose and suggest the optimal destination and route. For example, the generating AI analyzes the user's input and generates prompts to suggest the optimal route. The integration unit integrates weather and event information using external tools such as a weather information provision API and an event calendar. For example, the integration unit can consider weather information and suggest a route with a roof on a rainy day. The integration unit can integrate weather and event information using external tools. For example, it can use a weather information provision API to obtain current weather information and reflect it in route guidance. The notification unit learns past travel history and dialogue data and notifies the user of landmarks and events of interest. For example, the notification unit notifies the user of information about a cafe when the user passes near a cafe they have visited in the past. The notification unit learns past travel history and dialogue data and can notify the user of landmarks and events of interest. For example, the notification unit analyzes the user's travel history and identifies landmarks and events of interest. The suggestion unit analyzes commuting time and peak times to suggest efficient travel times. For example, the suggestion unit suggests leaving a little earlier to avoid rush hour. The suggestion unit can analyze commuting time and peak times to suggest efficient travel times. For example, the suggestion unit analyzes commuting time data and suggests the optimal departure time. As a result, the interactive navigation system according to the embodiment can respond to the user's individual needs and provide route guidance that takes external factors into consideration.

[0057] The dialogue unit proposes the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generative AI will propose the optimal route. The generative AI analyzes the user's input and generates prompts that suggest the optimal route. Specifically, the generative AI uses natural language processing technology to understand the user's intent and generate appropriate responses. For example, if the user inputs "I want to go to the station on a rainy day," the generative AI will consider weather information and propose routes with roofs or routes that minimize exposure to rain. The generative AI can also learn from the user's past dialogue and travel history, enabling it to make suggestions based on the user's preferences and habits. For example, if the user has visited a particular cafe in the past, it can suggest a route that passes near that cafe. Furthermore, the dialogue unit can modify the route in real time through dialogue with the user. For example, if the user changes their destination midway, the generative AI will recalculate and propose the optimal route to the new destination. This allows the dialogue unit to flexibly respond to user needs and provide optimal route guidance.

[0058] The integration department integrates weather and event information using external tools such as weather information APIs and event calendars. Specifically, it uses weather information APIs to obtain current weather information and reflects it in route guidance. For example, it can suggest routes with roofs on rainy days and routes with scenic views on sunny days. It can also use event calendars to provide route guidance that takes into account the location and time of specific events. For example, if a user wants to attend a particular event, it can suggest the optimal route that matches the event's location and time. Furthermore, the integration department can obtain traffic information and congestion status in real time and reflect it in route guidance. For example, if there is traffic congestion, it can suggest an alternative route to avoid the congestion. In this way, the integration department can use external tools to integrate diverse information and provide users with optimal route guidance.

[0059] The notification unit learns from past travel history and conversation data to notify users of landmarks and events that may interest them. Specifically, when a user passes near a cafe they have visited in the past, it will notify them of information about that cafe. The notification unit analyzes the user's travel history to identify landmarks and events of interest. For example, based on information about tourist spots or restaurants the user has visited in the past, it can notify the user when they pass nearby. The notification unit also learns from the user's conversation data and can provide information based on the user's interests and preferences. For example, if a user has previously shown interest in a particular genre of event, it can notify the user when an event of that genre is being held. Furthermore, the notification unit can provide information in real time based on the user's current location and travel route. For example, when a user passes near a particular landmark, it can notify them of information about the history and characteristics of that landmark. In this way, the notification unit can provide information based on the user's interests and preferences, enriching the travel experience.

[0060] The suggestion department analyzes commute times and peak times to propose efficient travel times. Specifically, it suggests leaving a little earlier to avoid rush hour. The suggestion department can analyze commute time data and propose the optimal departure time. For example, based on past commute data, it can predict congestion levels during specific time periods and propose the optimal departure time to avoid congestion. Furthermore, the suggestion department can propose efficient travel times based on the user's schedule and plans. For example, if a user needs to attend a meeting or event at a specific time, it can propose the optimal departure time and route to suit that time. In addition, the suggestion department can optimize travel times by considering real-time updated traffic and weather information. For example, if traffic congestion or bad weather is predicted, it can support efficient travel by suggesting an earlier departure. In this way, the suggestion department can propose efficient travel times that take into account the user's schedule and external factors, enabling comfortable travel.

[0061] The dialogue unit can use generative AI to suggest the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generative AI will suggest the optimal route. The dialogue unit can use generative AI to engage in dialogue based on the user's travel purpose and suggest the optimal destination and route. For example, the generative AI analyzes the user's input and generates prompts to suggest the optimal route. This makes it possible to suggest the optimal route based on the user's travel purpose by using generative AI. The generative AI, for example, uses text generation AI (e.g., a large-scale language model) to analyze the user's input and generate prompts to suggest the optimal route. The generative AI generates information to suggest the optimal route based on the user's input. The generative AI generates prompts to suggest the optimal route based on the user's travel purpose. The generative AI analyzes the user's input and generates information to suggest the optimal route. The generative AI generates prompts to suggest the optimal route based on the user's travel purpose. This makes it possible to suggest the optimal route based on the user's travel purpose by using generative AI.

[0062] The integration unit can integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. For example, the integration unit can consider weather information and suggest routes with roofs on rainy days. The integration unit can integrate weather and event information by utilizing external tools. For example, it can use a weather information API to obtain current weather information and reflect it in route guidance. The integration unit can integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. For example, the integration unit can use a weather information API to obtain current weather information and reflect it in route guidance. The integration unit can use an event calendar to obtain current event information and reflect it in route guidance. The integration unit can integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. For example, the integration unit can use a weather information API to obtain current weather information and reflect it in route guidance. The integration unit can use an event calendar to obtain current event information and reflect it in route guidance. In this way, weather and event information can be integrated and reflected in route guidance by utilizing external tools. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input weather information obtained using a weather information provision API into the AI ​​and have the AI ​​perform the analysis of the weather information.

[0063] The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, when the notification unit passes near a cafe that the user has visited in the past, it can notify the user of information about that cafe. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. The notification unit learns from past travel history and conversation data and can notify users of landmarks and events that may be of interest to them. For example, the notification unit analyzes the user's travel history and identifies landmarks and events that may be of interest to them. This makes it possible to provide notifications based on user interests by learning from past travel history and conversation data. Some or all of the processing described above in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's movement history data into a generating AI and have the generating AI identify landmarks and events of interest.

[0064] The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can suggest leaving a little earlier to avoid rush hour. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. The suggestion unit can analyze commute times and peak times to propose efficient travel times. For example, the suggestion unit can analyze commute time data and propose the optimal departure time. This makes it possible to propose efficient travel times by analyzing commute times and peak times. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input commute time data into a generation AI and have the AI ​​generate suggestions for the optimal departure time.

[0065] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated emotions. For example, if the user is stressed, the dialogue unit's generating AI can engage in dialogue in a calm tone and provide relaxing content. If the user is in a hurry, the dialogue unit's generating AI can engage in dialogue in a quick and concise tone and suggest the shortest route. If the user is having fun, the dialogue unit's generating AI can engage in dialogue in a cheerful tone and provide interesting content. This allows for more appropriate dialogue by adjusting the tone and content of the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0066] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, the dialogue unit can use a generation AI to conduct a dialogue in a similar style based on the dialogue style the user has preferred in the past. The dialogue unit can also use the generation AI to predict and prepare answers based on questions the user has frequently asked in the past. The dialogue unit can also avoid dialogue content that the user has expressed dissatisfaction with in the past and provide more appropriate content through the generation AI. In this way, by analyzing past dialogue history, the dialogue unit can provide the user with the most suitable dialogue method. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's past dialogue history data into the generation AI and have the generation AI select the optimal dialogue method.

[0067] The dialogue unit can customize the content of the conversation based on the user's current situation and interests during the conversation. For example, if the user is interested in the current weather, the generating AI will provide weather information. If the user shows interest in a specific event, the generating AI can also provide information about that event. The dialogue unit can also provide information about nearby landmarks and facilities based on the user's current location. This allows for the provision of more appropriate information by customizing the conversation content based on the user's current situation and interests. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the user's current situation and interest data into the generating AI and have the generating AI customize the conversation content.

[0068] The integration unit can estimate the user's emotions and determine the priority of information to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating information that promotes relaxation. If the user is in a hurry, the integration unit can also provide the necessary information quickly. If the user is enjoying themselves, the integration unit can also prioritize integrating information that interests them. This allows for more appropriate information integration by prioritizing information 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0069] The integration unit can select the optimal integration method by referring to past integration data during the integration process. For example, the integration unit integrates information using a similar method based on information integration methods that users have preferred to use in the past. The integration unit can also prioritize the integration of information that users frequently used from past integration data. The integration unit can also analyze past integration data and select the most efficient information integration method. This makes it possible to perform optimal information integration by referring to past integration data. Some or all of the above processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past integration data into a generating AI and have the generating AI select the optimal integration method.

[0070] The integration unit can customize the integration content based on the user's current situation and interests during the integration process. For example, if the user is interested in the current weather, the integration unit will prioritize integrating weather information. If the user is interested in a specific event, the integration unit can also prioritize integrating information about that event. Based on the user's current location, the integration unit can also prioritize integrating information about nearby landmarks and facilities. This allows for more appropriate information integration by customizing the integration content based on the user's current situation and interests. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's current situation and interest data into a generating AI and have the generating AI perform the customization of the integration content.

[0071] The notification unit can estimate the user's emotions and adjust the timing and content of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can send a relaxing notification. If the user is in a hurry, the notification unit can also send a quick and concise notification. If the user is enjoying themselves, the notification unit can also send an interesting notification. By adjusting the timing and content of notifications according to the user's emotions, more appropriate notifications can be made. 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. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the timing and content of notifications.

[0072] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit can send notifications using a similar method based on the notification method the user preferred to receive in the past. The notification unit can also prioritize notifying information that the user frequently used based on past notification history. The notification unit can also analyze past notification history and select the most efficient notification method. This makes it possible to use the optimal notification method by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0073] The notification unit can customize notification content based on the user's current situation and interests. For example, if the user is interested in the current weather, the notification unit will notify the user of weather information. If the user has shown interest in a specific event, the notification unit can also notify the user of information about that event. Based on the user's current location, the notification unit can also notify the user of information about nearby landmarks and facilities. This allows for more appropriate notifications by customizing notification content based on the user's current situation and interests. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's current situation and interest data into a generating AI and have the generating AI perform the customization of the notification content.

[0074] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit will present suggestions in a relaxing manner. If the user is in a hurry, the suggestion unit can present suggestions in a quick and concise manner. If the user is enjoying themselves, the suggestion unit can present suggestions in an engaging manner. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0075] 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 make proposals using a similar method based on proposal methods that users have previously preferred and accepted. The proposal unit can also prioritize information that users have frequently used based on past proposal history. The proposal unit can also analyze past proposal history and select the most efficient proposal method. This makes it possible to make the optimal proposal method by referring to past proposal history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input past proposal history data into a generating AI and have the generating AI select the optimal proposal method.

[0076] The suggestion unit can customize the suggested content based on the user's current situation and interests. For example, if the user is interested in the current weather, the suggestion unit will make weather-related suggestions. If the user has shown interest in a specific event, the suggestion unit can also make suggestions related to that event. The suggestion unit can also make suggestions about nearby landmarks and facilities based on the user's current location. This allows for more appropriate suggestions by customizing the suggested content based on the user's current situation and interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's current situation and interest data into a generating AI and have the generating AI perform the customization of the suggested content.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The interactive unit can monitor the user's health status and suggest routes and destinations based on that status. For example, if the user is tired, the interactive unit can suggest a route that includes rest areas. If the user is not getting enough exercise, the interactive unit can suggest routes suitable for walking or jogging. Furthermore, if the user has specific health goals, the interactive unit can suggest routes and destinations that align with those goals. This enables optimal route suggestions tailored to the user's health condition.

[0079] The integration unit can consolidate information based on the user's hobbies and preferences and reflect this in route guidance. For example, if a user enjoys visiting art museums, the integration unit can suggest a route that includes museums. Similarly, if a user is interested in gourmet food, the integration unit can suggest a route that includes popular restaurants. Furthermore, if a user enjoys nature walks, the integration unit can suggest a route that includes parks and nature reserves. This enables information integration tailored to the user's hobbies and preferences.

[0080] The notification unit can estimate the user's emotions and adjust the content and timing of notifications based on those estimates. For example, if the user is stressed, the notification unit can provide information about places or activities where they can relax. If the user is in a hurry, the notification unit can provide information that allows them to act quickly. Furthermore, if the user is having fun, the notification unit can provide information about events or places that might interest them. This enables optimal notifications tailored to the user's emotions.

[0081] The suggestion function can analyze a user's past behavior patterns and provide optimal suggestions. For example, if a user has visited a specific place at a specific time in the past, the suggestion function can suggest that place again at that time. Similarly, if a user has preferred a particular route in the past, the suggestion function can suggest that route again. Furthermore, if a user has participated in a specific event in the past, the suggestion function can suggest similar events. This enables optimal suggestions based on the user's past behavior patterns.

[0082] The dialogue unit can estimate the user's emotions and adjust the tone and content of the conversation based on those estimates. For example, if the user is stressed, the dialogue unit can use a calm tone and provide relaxing content. If the user is in a hurry, the dialogue unit can use a quick and concise tone and suggest the shortest route. Furthermore, if the user is having fun, the dialogue unit can use a cheerful tone and provide engaging content. This enables optimal conversation tailored to the user's emotions.

[0083] The integration unit can consolidate information based on the user's current situation and interests and reflect this in route guidance. For example, if a user is interested in the current weather, the integration unit can prioritize integrating weather information. Similarly, if a user is interested in a specific event, the integration unit can prioritize integrating information related to that event. Furthermore, based on the user's current location, it can prioritize integrating information about nearby landmarks and facilities. This enables optimal information integration based on the user's current situation and interests.

[0084] The notification unit can estimate the user's emotions and adjust the content and timing of notifications based on those estimates. For example, if the user is stressed, the notification unit can provide information about places or activities where they can relax. If the user is in a hurry, the notification unit can provide information that allows them to act quickly. Furthermore, if the user is having fun, the notification unit can provide information about events or places that might interest them. This enables optimal notifications tailored to the user's emotions.

[0085] The suggestion unit can monitor the user's health status and propose routes and destinations based on that status. For example, if the user is tired, the suggestion unit can suggest a route that includes rest areas. If the user is not getting enough exercise, the suggestion unit can also suggest routes suitable for walking or jogging. Furthermore, if the user has specific health goals, the suggestion unit can suggest routes and destinations that align with those goals. This enables the system to propose optimal routes tailored to the user's health status.

[0086] The dialogue unit can analyze the user's past dialogue history and select the optimal dialogue method. For example, based on the dialogue style the user has preferred in the past, the dialogue unit can conduct the conversation in a similar style. Furthermore, based on questions the user has frequently asked in the past, the dialogue unit can predict and prepare answers. In addition, the dialogue unit can avoid dialogue content that the user has previously expressed dissatisfaction with and provide more appropriate content. In this way, by analyzing past dialogue history, the dialogue unit can provide the user with the most suitable dialogue method.

[0087] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function can present suggestions in a relaxing manner. If the user is in a hurry, the suggestion function can present suggestions in a quick and concise manner. Furthermore, if the user is enjoying themselves, the suggestion function can present suggestions in an engaging manner. This enables the provision of optimal suggestions tailored to the user's emotions.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The dialogue unit proposes the optimal destination and route through dialogue based on the user's travel purpose. For example, if the user inputs "I want to go to the station," the generating AI will propose the optimal route. The dialogue unit can use the generating AI to engage in dialogue based on the user's travel purpose and propose the optimal destination and route. Step 2: The integration unit integrates weather and event information using external tools such as weather information APIs and event calendars. For example, it can suggest routes with roofs on rainy days, taking weather information into consideration. The integration unit can integrate weather and event information using external tools. Step 3: The notification unit learns from past travel history and conversation data and notifies the user of landmarks and events that may be of interest to them. For example, when the user passes near a cafe they have visited in the past, it will notify them of information about that cafe. The notification unit can learn from past travel history and conversation data and notify the user of landmarks and events that may be of interest to them. Step 4: The proposal team analyzes commute times and peak times to suggest efficient travel times. For example, they might suggest leaving a little earlier to avoid rush hour. The proposal team can analyze commute times and peak times to suggest efficient travel times.

[0090] 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.

[0091] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0092] 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.

[0093] Each of the multiple elements described above, including the dialogue unit, integration unit, notification unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart device 14 and engages in dialogue based on the user's travel purpose to suggest the optimal destination and route. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates weather and event information using external tools such as a weather information provision API and an event calendar. The notification unit is implemented by the control unit 46A of the smart device 14 and learns past travel history and dialogue data to notify the user of landmarks and events of interest. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes commuting time and peak times to suggest efficient travel times. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.).

[0106] 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.

[0107] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the dialogue unit, integration unit, notification unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart glasses 214 and engages in dialogue based on the user's travel purpose to suggest the optimal destination and route. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates weather and event information using external tools such as a weather information provision API and an event calendar. The notification unit is implemented by the control unit 46A of the smart glasses 214 and learns past travel history and dialogue data to notify the user of landmarks and events of interest. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes commuting time and peak times to suggest efficient travel times. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the dialogue unit, integration unit, notification unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the headset terminal 314, and engages in dialogue based on the user's travel purpose to suggest the optimal destination and route. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12, and integrates weather and event information using external tools such as a weather information provision API and an event calendar. The notification unit is implemented by the control unit 46A of the headset terminal 314, and learns past travel history and dialogue data to notify the user of landmarks and events of interest. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes commuting time and peak times to suggest efficient travel times. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the dialogue unit, integration unit, notification unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the robot 414 and engages in dialogue based on the user's travel purpose and suggests the optimal destination and route. The integration unit is implemented by the specific processing unit 290 of the data processing unit 12 and integrates weather and event information using external tools such as a weather information provision API and an event calendar. The notification unit is implemented by the control unit 46A of the robot 414 and learns past travel history and dialogue data to notify the user of landmarks and events of interest. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes commuting time and peak times to suggest an efficient travel time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] (Note 1) A dialogue unit that proposes the optimal destination and route based on the user's travel purpose, Based on the information acquired by the aforementioned dialogue unit, the integration unit integrates weather and event information using external tools such as weather information provision APIs and event calendars. Based on the information integrated by the aforementioned integration unit, the notification unit learns past travel history and dialogue data and notifies the user of landmarks and events of interest. The system includes a suggestion unit that analyzes commuting time and peak times based on the information notified by the notification unit and proposes an efficient travel time. A system characterized by the following features. (Note 2) The aforementioned dialogue unit, Using generative AI, the system proposes optimal destinations and routes through dialogue based on the user's travel purpose. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned integration unit is Integrate weather and event information using external tools such as Weather APIs and event calendars. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, It learns from past travel history and conversation data to notify users of landmarks and events that they might be interested in. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We analyze commute times and peak hours to suggest efficient travel times. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the tone and content of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned dialogue unit, Analyze the user's past conversation history and select the optimal conversation method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dialogue unit, During conversations, the content of the dialogue is customized based on the user's current situation and interests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned integration unit is It estimates the user's emotions and determines the priority of information to integrate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned integration unit is During integration, the optimal integration method is selected by referring to past integration data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned integration unit is During integration, customize the integration based on the user's current situation and interests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing and content of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, When sending a notification, the system will refer to past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the user's current situation and interests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, refer to past proposal history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, customize the proposal content based on the user's current situation and interests. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0162] 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 dialogue unit that proposes the optimal destination and route based on the user's travel purpose, Based on the information acquired by the aforementioned dialogue unit, the integration unit integrates weather and event information using external tools such as weather information provision APIs and event calendars. Based on the information integrated by the aforementioned integration unit, the notification unit learns past travel history and dialogue data and notifies the user of landmarks and events of interest. The system includes a suggestion unit that analyzes commuting time and peak times based on the information notified by the notification unit and proposes an efficient travel time. A system characterized by the following features.

2. The aforementioned dialogue unit, Using generative AI, the system proposes the optimal destination and route through dialogue based on the user's travel purpose. The system according to feature 1.

3. The aforementioned integration unit is Integrate weather and event information by utilizing external tools such as weather information APIs and event calendars. The system according to feature 1.

4. The aforementioned notification unit, It learns from past travel history and conversation data to notify users of landmarks and events that they might be interested in. The system according to feature 1.

5. The aforementioned proposal section is, We analyze commute times and peak hours to suggest efficient travel times. The system according to feature 1.

6. The aforementioned dialogue unit, It estimates the user's emotions and adjusts the tone and content of the conversation based on those estimated emotions. The system according to feature 1.

7. The aforementioned dialogue unit, Analyze the user's past conversation history and select the optimal conversation method. The system according to feature 1.

8. The aforementioned dialogue unit, During conversations, the content of the dialogue is customized based on the user's current situation and interests. The system according to feature 1.

9. The aforementioned integration unit is It estimates the user's emotions and determines the priority of information to integrate based on the estimated user emotions. The system according to feature 1.

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