system

Abstract

The system according to the embodiment aims to enable a user to efficiently make a travel plan. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, a transfer unit, and a linking unit. The reception unit receives information from a user. The proposal unit makes proposals based on the information received by the reception unit. The transfer unit automatically transfers the information proposed by the proposal unit to a car navigation system. The linking unit links the information proposed by the proposal unit with a route transfer search.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that planning a trip requires a lot of effort and is difficult to do efficiently.

[0005] The system according to the embodiment aims to enable a user to efficiently make a travel plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a transfer unit, and a linking unit. The reception unit receives information from a user. The proposal unit makes proposals based on the information received by the reception unit. The transfer unit automatically transfers the information proposed by the proposal unit to a car navigation system. The linking unit links the information proposed by the proposal unit with a transfer search. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to efficiently plan a trip. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning assistance system according to an embodiment of the present invention is a system in which a user inputs their residential area, desired travel period, and general interests and preferences, and the system proposes recommended travel itineraries and events. The travel planning assistance system analyzes the user's input of their residential area, desired travel period, and general interests and preferences using a generation AI to propose recommended travel itineraries and events. For example, if a user loves nature and is planning a week-long trip, the generation AI proposes natural tourist spots and events. When traveling by car, the travel planning assistance system automatically transfers and connects with the car navigation system. It also suggests places to stop and eat. For example, if a user wants to go to a convenience store or restroom along the way, the user can ask the travel planning assistance system, which suggests the best place. When traveling by train, the travel planning assistance system connects with transfer search and provides navigation assistance and guidance within the station. It also allows reserved seat reservations. For example, if a user wants to reserve a reserved seat, the travel planning assistance system proposes the best seat and makes the reservation. This allows the travel planning assistance system to easily plan a trip even when the user is busy, allowing the user to enjoy their trip. This allows the travel planning assistance system to easily plan a trip even when the user is busy, and to enjoy the trip.

[0029] A travel planning assistance system according to an embodiment includes a reception unit, a proposal unit, a transfer unit, and a linking unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, a travel destination, a budget, and a schedule. The reception unit accepts, for example, information such as a travel destination, a budget, and a schedule input by the user. The reception unit can also accept the user's interests and preferences. The proposal unit uses a generation AI to make proposals based on the information accepted by the reception unit. The proposals include, but are not limited to, travel plans and tourist spot recommendations. For example, the proposal unit uses the generation AI to propose recommended travel itineraries and events based on the user's interests and preferences. The proposal unit can also analyze the user's input information and propose an optimal travel plan. The transfer unit automatically transfers the information proposed by the proposal unit to a car navigation system. The automatic transfer can be performed, for example, via Bluetooth (registered trademark), Wi-Fi, cloud services, or other methods. For example, the transfer unit automatically transfers the travel itinerary proposed by the proposal unit to a car navigation system. The forwarding unit can also suggest places to stop by and meals. The linking unit links the information suggested by the suggestion unit with the transfer search. Linking can be performed, for example, using an API or a data synchronization method, but is not limited to these examples. For example, the linking unit links the travel itinerary suggested by the suggestion unit with the transfer search. The linking unit can also provide travel assistance and guidance within stations. As a result, the travel planning assistance system according to the embodiment efficiently assists with travel planning by accepting and suggesting user information and linking with car navigation systems and transfer searches.

[0030] The suggestion unit can suggest recommended travel itineraries and events based on the user's interests or preferences. For example, the suggestion unit suggests recommended travel itineraries and events based on the user's interests and preferences. For example, the suggestion unit uses a generation AI to analyze the user's interests and preferences and suggest optimal travel itineraries and events. The suggestion unit can also identify and suggest interests and preferences based on the user's past search history and survey results. For example, the suggestion unit can suggest related travel itineraries and events based on tourist spots and events that the user has searched for in the past. The suggestion unit can also suggest optimal travel itineraries and events based on the interests and preferences that the user has answered in a survey. In this way, the suggestion unit improves user satisfaction by suggesting optimal travel itineraries and events based on the user's interests and preferences.

[0031] The forwarding unit can suggest places to stop by and meals to eat. For example, the forwarding unit suggests tourist spots, restaurants, cafes, etc. that can be stopped by based on the user's travel itinerary using the generation AI. The forwarding unit can also suggest optimal places to stop by based on the user's current location and travel route. For example, the forwarding unit suggests tourist spots and restaurants that the user can stop by while traveling by car. The forwarding unit can also suggest cafes and shops that the user can stop by while traveling by train. In this way, the forwarding unit improves convenience during travel by suggesting places to stop by and meals to eat.

[0032] The transfer unit can suggest locations if the user wants to go to a convenience store or restroom along the way. For example, the transfer unit suggests the best location if the user wants to go to a convenience store or restroom along the way. For example, the transfer unit uses the generation AI to suggest the best location of a convenience store or restroom based on the user's current location and travel route. The transfer unit can also suggest the best place to stop based on the user's input information and location information. For example, if the user inputs "I want to go to a convenience store" into the system, the transfer unit can suggest the nearest convenience store. Also, if the user inputs "I want to go to the restroom," the transfer unit can suggest the nearest restroom. In this way, the transfer unit improves the comfort of the user's trip by suggesting the best location if the user wants to go to a convenience store or restroom along the way.

[0033] The coordination unit can provide assistance and guidance for movement within a station. For example, the coordination unit provides assistance and guidance for movement within a station. For example, the generation AI guides the user along a route within the station based on the user's transfer information. The coordination unit can also guide the user to the optimal route based on the user's current location and destination. For example, the coordination unit provides maps and route guidance to prevent the user from getting lost within the station. The coordination unit can also guide the user to the locations of elevators and escalators so that the user can move smoothly within the station. In this way, the coordination unit provides assistance and guidance for movement within the station, making train travel smoother.

[0034] The linking unit can make reserved seat reservations. For example, the linking unit makes reserved seat reservations. For example, the generation AI in the linking unit suggests the most suitable reserved seat based on the user's travel plans. The linking unit can also reserve a reserved seat based on the user's input information and preferences. For example, if the user inputs "I would like to reserve a reserved seat" into the system, the linking unit will suggest the most suitable seat and make the reservation. The linking unit can also reserve a reserved seat based on the type and location of the seat desired by the user. In this way, the linking unit can make reserved seat reservations, making train travel more comfortable.

[0035] The reception unit can analyze the user's past travel history and provide an optimal information input format. The reception unit, for example, analyzes the user's past travel history and provides an optimal information input format. For example, the reception unit uses a generation AI to analyze the user's past travel history and automatically display related input fields. The reception unit can also prioritize frequently visited places as input candidates based on the user's past travel history. For example, the reception unit can automatically display related input fields based on travel destinations the user has visited in the past. The reception unit can also analyze the user's past travel history and suggest input fields related to specific seasons or events. In this way, the reception unit analyzes the user's past travel history to provide an optimal information input format and improve input efficiency.

[0036] The reception unit can filter input content based on the user's current living situation and areas of interest when receiving information. For example, the reception unit filters input content based on the user's current living situation and areas of interest when receiving information. For example, the reception unit uses a generation AI to analyze the user's current living situation and areas of interest and prioritize displaying related input items. The reception unit can also identify areas of interest based on the user's input information and social media activity and filter the input content. For example, when a user inputs their current living situation, the reception unit suggests travel destinations and events that match that situation. The reception unit can also prioritize displaying related input items based on the user's areas of interest (e.g., nature, history, gourmet food, etc.). Furthermore, the reception unit automatically omits unnecessary input items based on the user's current living situation and areas of interest. As a result, the reception unit filters input content based on the user's current living situation and areas of interest, omitting unnecessary input and realizing efficient information input.

[0037] The reception unit can select an input means according to the user's input method when receiving information. For example, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.) when receiving information. For example, the reception unit uses a generation AI to analyze the user's input method and propose the optimal input means. The reception unit can also select the optimal input means based on the user's device and input environment. For example, if the user selects voice input, the reception unit can input information using voice recognition technology. If the user selects text input, the reception unit can also input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. In this way, the reception unit can select the optimal input means according to the user's input method, allowing for smooth information input.

[0038] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving highly relevant information by taking the user's geographical location information into consideration when receiving information. For example, the reception unit uses a generation AI to analyze the user's geographical location information and automatically filter relevant information. The reception unit can also prioritize receiving highly relevant information based on the user's current location and destination. For example, when a user inputs their current location, the reception unit prioritizes suggesting travel destinations and events in the vicinity. The reception unit can also automatically filter relevant information based on the user's geographical location information. As a result, the reception unit can prioritize receiving highly relevant information by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity and accept related information when receiving information. For example, the reception unit analyzes the user's social media activity and accepts related information when receiving information. For example, the reception unit uses a generation AI to analyze the user's social media activity and preferentially display related information. The reception unit can also accept related information based on the user's posted content and check-in information. For example, the reception unit can suggest related travel destinations and events based on the location where the user checked in on social media. The reception unit can also analyze the user's posted content on social media and preferentially accept related information. Furthermore, the reception unit accepts related information with reference to the activity of the user's friends on social media. In this way, the reception unit can efficiently accept related information by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when receiving information. For example, the reception unit customizes the input method by reflecting the user's past feedback when receiving information. For example, the reception unit uses a generation AI to analyze the user's past feedback and suggest the optimal input method. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also automatically omit unnecessary input items by reflecting the user's past feedback. In this way, the reception unit provides the optimal input method by reflecting the user's past feedback, improving input efficiency.

[0041] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel itinerary when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the travel itinerary when making a suggestion. For example, the suggestion unit uses a generation AI to analyze the importance of the travel itinerary and make an optimal suggestion. The suggestion unit can also adjust the level of detail of the suggestion based on the user's priorities and the purpose of the trip. For example, the suggestion unit makes detailed suggestions for important travel itineraries. The suggestion unit can also make concise suggestions for less important travel itineraries. Furthermore, the suggestion unit dynamically adjusts the level of detail of the suggestion according to the importance of the travel itinerary. In this way, the suggestion unit can make an optimal suggestion for the user by adjusting the level of detail of the suggestion based on the importance of the travel itinerary.

[0042] The suggestion unit can apply different suggestion algorithms depending on the travel category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the travel category when making a suggestion. For example, the suggestion unit uses a generation AI to analyze the travel category and select the optimal suggestion algorithm. The suggestion unit can also apply different suggestion algorithms based on the user's interests and preferences. For example, in the case of nature tourism, the suggestion unit applies an algorithm that suggests natural landscapes and hiking trails. In addition, in the case of history tourism, the suggestion unit can apply an algorithm that suggests historical sites and museums. Furthermore, in the case of gourmet tourism, the suggestion unit applies an algorithm that suggests local specialty dishes and restaurants. In this way, the suggestion unit can make optimal suggestions for the user by applying different suggestion algorithms depending on the travel category.

[0043] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit uses a generation AI to analyze the user's past suggestion results and make the optimal suggestion. The suggestion unit can also improve the accuracy of the suggestion based on the user's feedback and proposal history. For example, the suggestion unit makes similar suggestions based on proposals that the user has accepted in the past. The suggestion unit can also omit unnecessary suggestions based on proposals that the user has rejected in the past. Furthermore, the suggestion unit analyzes the user's past suggestion results and improves the accuracy of the suggestion. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0044] The suggestion unit can determine the priority of suggestions based on the travel period when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the travel period when making suggestions. For example, the suggestion unit uses a generation AI to analyze the travel period and make optimal suggestions. The suggestion unit can also determine the priority of suggestions based on the user's wishes and the purpose of the trip. For example, the suggestion unit prioritizes seasonal events and tourist spots depending on the travel period. The suggestion unit can also make suggestions to avoid crowds based on the travel period. Furthermore, the suggestion unit suggests optimal travel destinations and events depending on the travel period. In this way, the suggestion unit can make optimal suggestions for the user by determining the priority of suggestions based on the travel period.

[0045] The suggestion unit can adjust the order of suggestions based on travel relevance when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on travel relevance when making suggestions. For example, the suggestion unit uses a generation AI to analyze travel relevance and determine an optimal order of suggestions. The suggestion unit can also adjust the order of suggestions based on the user's interests and preferences. For example, the suggestion unit prioritizes suggesting information with high travel relevance. The suggestion unit can also dynamically adjust the order of suggestions based on travel relevance. Furthermore, the suggestion unit postpones information with low travel relevance. In this way, the suggestion unit can make optimal suggestions for the user by adjusting the order of suggestions based on travel relevance.

[0046] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit uses a generation AI to analyze the user's level of expertise and make optimal suggestions. The suggestion unit can also identify the user's level of expertise based on survey results and past behavioral history of the user and adjust the use of technical terms in the proposal. For example, the suggestion unit makes suggestions using technical terms when the user has specialized knowledge. The suggestion unit can also make suggestions in simple language when the user does not have specialized knowledge. Furthermore, the suggestion unit dynamically adjusts the content of the proposal according to the user's level of expertise. As a result, the suggestion unit can make suggestions that are easy for the user to understand by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0047] The forwarding unit can select the optimal forwarding method by analyzing the user's past forwarding history at the time of forwarding. For example, the forwarding unit selects the optimal forwarding method by analyzing the user's past forwarding history at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's past forwarding history and propose the optimal forwarding method. The forwarding unit can also select the optimal forwarding method based on the user's feedback and forwarding history. For example, the forwarding unit proposes the optimal forwarding method based on the forwarding methods the user has used in the past. The forwarding unit can also prioritize and propose frequently used forwarding methods based on the user's past forwarding history. Furthermore, the forwarding unit analyzes the user's past forwarding history and proposes the most efficient forwarding method. In this way, the forwarding unit selects the optimal forwarding method by analyzing the user's past forwarding history, thereby achieving efficient information transfer.

[0048] The forwarding unit can customize the forwarding content based on the user's current living situation at the time of forwarding. For example, the forwarding unit customizes the forwarding content based on the user's current living situation at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's current living situation and prioritize forwarding relevant information. The forwarding unit can also identify the current living situation and customize the forwarding content based on the user's input information and social media activity. For example, when the user inputs their current living situation, the forwarding unit suggests forwarding content that matches that situation. The forwarding unit can also prioritize forwarding relevant information based on the user's current living situation. Furthermore, the forwarding unit automatically omits unnecessary information, taking the user's current living situation into consideration. As a result, the forwarding unit can provide highly relevant information by customizing the forwarding content based on the user's current living situation.

[0049] The forwarding unit can improve the forwarding method by reflecting user feedback at the time of forwarding. For example, the forwarding unit improves the forwarding method by reflecting user feedback at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze user feedback and propose an optimal forwarding method. The forwarding unit can also customize the forwarding interface based on the user's past feedback. For example, the forwarding unit proposes an optimal forwarding method based on feedback provided by the user in the past. The forwarding unit can also reflect the user's past feedback and automatically omit unnecessary forwarding items. In this way, the forwarding unit improves the forwarding method by reflecting user feedback and achieves optimal information transfer for the user.

[0050] The forwarding unit can select the optimal forwarding method by taking into account the user's geographical location information when forwarding. For example, the forwarding unit selects the optimal forwarding method by taking into account the user's geographical location information when forwarding. For example, the forwarding unit uses a generation AI to analyze the user's geographical location information and propose the optimal forwarding method. The forwarding unit can also select the optimal forwarding method based on the user's current location and destination. For example, when the user inputs their current location, the forwarding unit prioritizes forwarding information about the surrounding area. The forwarding unit can also automatically filter related information based on the user's geographical location information. Furthermore, the forwarding unit proposes the optimal forwarding method by taking into account the user's geographical location information. In this way, the forwarding unit selects the optimal forwarding method by taking into account the user's geographical location information, thereby achieving efficient information transfer.

[0051] The forwarding unit can analyze the user's social media activity and suggest the content to be forwarded at the time of forwarding. For example, the forwarding unit analyzes the user's social media activity and suggests the content to be forwarded at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's social media activity and prioritizes forwarding relevant information. The forwarding unit can also suggest related information based on the user's posted content and check-in information. For example, the forwarding unit can forward related information based on the location where the user checked in on social media. The forwarding unit can also analyze the user's posted content on social media and prioritize forwarding related information. Furthermore, the forwarding unit can transfer related information with reference to the activity of the user's friends on social media. In this way, the forwarding unit can efficiently transfer highly relevant information by analyzing the user's social media activity.

[0052] The forwarding unit can customize the forwarding method by reflecting the user's past feedback at the time of forwarding. For example, the forwarding unit customizes the forwarding method by reflecting the user's past feedback at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's past feedback and propose an optimal forwarding method. The forwarding unit can also customize the forwarding interface based on the user's past feedback. For example, the forwarding unit proposes an optimal forwarding method based on feedback provided by the user in the past. The forwarding unit can also reflect the user's past feedback and automatically omit unnecessary forwarding items. In this way, the forwarding unit provides an optimal forwarding method by reflecting the user's past feedback, thereby realizing efficient information transfer for the user.

[0053] The collaboration unit can select the optimal collaboration method by analyzing the user's past collaboration history at the time of collaboration. For example, the collaboration unit analyzes the user's past collaboration history to select the optimal collaboration method at the time of collaboration. For example, the collaboration unit uses a generation AI to analyze the user's past collaboration history and propose the optimal collaboration method. The collaboration unit can also select the optimal collaboration method based on the user's feedback and collaboration history. For example, the collaboration unit proposes the optimal collaboration method based on collaboration methods used by the user in the past. The collaboration unit can also prioritize and propose frequently used collaboration methods based on the user's past collaboration history. Furthermore, the collaboration unit analyzes the user's past collaboration history and proposes the most efficient collaboration method. In this way, the collaboration unit selects the optimal collaboration method by analyzing the user's past collaboration history, thereby achieving efficient information collaboration.

[0054] The linking unit can customize the linking content based on the user's current living situation at the time of linking. For example, the linking unit customizes the linking content based on the user's current living situation at the time of linking. For example, the linking unit uses a generation AI to analyze the user's current living situation and prioritize linking related information. The linking unit can also identify the current living situation based on the user's input information and social media activity and customize the linking content. For example, when the user inputs their current living situation, the linking unit suggests linking content that matches the situation. The linking unit can also prioritize linking related information based on the user's current living situation. Furthermore, the linking unit automatically omits unnecessary information in consideration of the user's current living situation. As a result, the linking unit can provide highly relevant information by customizing the linking content based on the user's current living situation.

[0055] The collaboration unit can improve the collaboration method by reflecting user feedback at the time of collaboration. For example, the collaboration unit improves the collaboration method by reflecting user feedback at the time of collaboration. For example, the collaboration unit uses a generation AI to analyze user feedback and propose the optimal collaboration method. The collaboration unit can also customize the collaboration interface based on the user's past feedback. For example, the collaboration unit proposes the optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also automatically omit unnecessary collaboration items by reflecting the user's past feedback. In this way, the collaboration unit improves the collaboration method by reflecting user feedback and realizes the optimal information collaboration for the user.

[0056] The collaboration unit can select the optimal collaboration method by taking into account the user's geographical location information when collaborating. For example, the collaboration unit selects the optimal collaboration method by taking into account the user's geographical location information when collaborating. For example, the collaboration unit uses a generation AI to analyze the user's geographical location information and propose the optimal collaboration method. The collaboration unit can also select the optimal collaboration method based on the user's current location and destination. For example, when the user inputs their current location, the collaboration unit prioritizes linking information about the surrounding area. The collaboration unit can also automatically filter related information based on the user's geographical location information. Furthermore, the collaboration unit proposes the optimal collaboration method by taking into account the user's geographical location information. In this way, the collaboration unit selects the optimal collaboration method by taking into account the user's geographical location information and achieves efficient information collaboration.

[0057] The linking unit can analyze the user's social media activity at the time of linking and suggest linking content. For example, the linking unit analyzes the user's social media activity and suggests linking content at the time of linking. For example, the linking unit uses a generation AI to analyze the user's social media activity and prioritize linking related information. The linking unit can also suggest related information based on the user's posted content and check-in information. For example, the linking unit can link related information based on the location where the user checked in on social media. The linking unit can also analyze the user's posted content on social media and prioritize linking related information. Furthermore, the linking unit links related information with reference to the activity of the user's friends on social media. In this way, the linking unit can efficiently link highly relevant information by analyzing the user's social media activity.

[0058] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit customizes the collaboration method by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit uses a generation AI to analyze the user's past feedback and propose the optimal collaboration method. The collaboration unit can also customize the collaboration interface based on the user's past feedback. For example, the collaboration unit proposes the optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also reflect the user's past feedback and automatically omit unnecessary collaboration items. In this way, the collaboration unit provides the optimal collaboration method by reflecting the user's past feedback, and achieves efficient information collaboration for the user.

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

[0060] The suggestion unit can also suggest travel itineraries taking into account the user's health condition. For example, the suggestion unit can analyze the user's health data (e.g., heart rate, number of steps, sleep data, etc.) and suggest tourist spots and activities according to the user's physical strength. If the user has a chronic illness, the suggestion unit can also suggest travel plans that take into account the user's medical condition. Furthermore, the suggestion unit can also suggest rest times and snacks based on the user's health condition. In this way, the suggestion unit supports a safer and more comfortable trip by suggesting travel itineraries that take into account the user's health condition.

[0061] The suggestion unit can also make suggestions taking into account the weather forecast at the user's travel destination. For example, the suggestion unit can analyze weather data at the travel destination and suggest indoor tourist spots when it rains. The suggestion unit can also suggest outdoor activities when it is sunny. Furthermore, the suggestion unit can also suggest a packing list depending on the weather. In this way, the suggestion unit can make suggestions taking into account the weather forecast, allowing the user to enjoy their trip regardless of the weather.

[0062] The forwarding unit can also monitor the user's energy consumption during the trip and suggest meals and rest breaks at appropriate times. For example, the forwarding unit can analyze the user's travel distance and activity level and suggest meals if energy consumption is high. The forwarding unit can also suggest appropriate rest locations if the user is traveling for a long period of time. Furthermore, the forwarding unit can also suggest snacks and drinks if calorie replenishment is necessary based on the user's energy consumption. In this way, the forwarding unit supports health management during travel by making suggestions that take the user's energy consumption into consideration.

[0063] The suggestion unit can also make suggestions taking into account the cultural background of the user's travel destination. For example, the suggestion unit can analyze the culture and customs of the travel destination and suggest events and places where the user can experience that culture. The suggestion unit can also suggest basic manners and greeting methods so that the user can adapt to the culture of the travel destination. Furthermore, the suggestion unit can also provide information on historical background and traditional cuisine so that the user can gain a deeper understanding of the culture of the travel destination. In this way, the suggestion unit supports the user's cultural experience at the travel destination.

[0064] The suggestion unit can also make suggestions taking into account safety information at the user's travel destination. For example, the suggestion unit can analyze public safety information at the travel destination and suggest safe tourist spots and accommodations. The suggestion unit can also provide information on the nearest hospitals and police stations so that the user can prepare for emergencies at the travel destination. Furthermore, the suggestion unit can also provide information on areas requiring caution and safe means of transportation so that the user can take safety measures at the travel destination. In this way, the suggestion unit supports the user's safety at the travel destination.

[0065] The suggestion unit can also suggest eco-friendly options for the user's travel destination. For example, the suggestion unit can suggest environmentally friendly accommodations and restaurants at the travel destination. The suggestion unit can also suggest eco-friendly means of transportation (e.g., bicycles and public transportation) so that the user can select them. Furthermore, the suggestion unit can provide information on volunteer activities and eco-tours so that the user can participate in environmental protection activities at the travel destination. In this way, the suggestion unit supports the user in making eco-friendly options for the user's travel destination.

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

[0067] Step 1: The reception unit receives information from the user. The information from the user includes travel destination, budget, schedule, interests, preferences, etc. For example, information such as travel destination, budget, schedule, etc. input by the user is received. Step 2: The suggestion unit uses the generation AI to make suggestions based on the information received by the reception unit. Suggestions include travel plans and tourist spot recommendations. For example, the generation AI suggests recommended travel itineraries and events based on the user's interests and preferences. Step 3: The transfer unit automatically transfers the information proposed by the suggestion unit to the car navigation system. The automatic transfer is performed using methods such as Bluetooth, Wi-Fi, or cloud services. For example, the travel itinerary proposed by the suggestion unit is automatically transferred to the car navigation system. Step 4: The linking unit links the information proposed by the suggestion unit with the transfer search. Linking is performed using an API or a data synchronization method. For example, the travel itinerary proposed by the suggestion unit is linked with the transfer search.

[0068] (Example 2) A travel planning assistance system according to an embodiment of the present invention is a system in which a user inputs their residential area, desired travel period, and general interests and preferences, and the system proposes recommended travel itineraries and events. The travel planning assistance system analyzes the user's input of their residential area, desired travel period, and general interests and preferences using a generation AI to propose recommended travel itineraries and events. For example, if a user loves nature and is planning a week-long trip, the generation AI proposes natural tourist spots and events. When traveling by car, the travel planning assistance system automatically transfers and connects with the car navigation system. It also suggests places to stop and eat. For example, if a user wants to go to a convenience store or restroom along the way, the user can ask the travel planning assistance system, which suggests the best place. When traveling by train, the travel planning assistance system connects with transfer search and provides navigation assistance and guidance within the station. It also allows reserved seat reservations. For example, if a user wants to reserve a reserved seat, the travel planning assistance system proposes the best seat and makes the reservation. This allows the travel planning assistance system to easily plan a trip even when the user is busy, allowing the user to enjoy their trip. This allows the travel planning assistance system to easily plan a trip even when the user is busy, and to enjoy the trip.

[0069] A travel planning assistance system according to an embodiment includes a reception unit, a proposal unit, a transfer unit, and a linking unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, a travel destination, a budget, and a schedule. The reception unit accepts, for example, information such as a travel destination, a budget, and a schedule input by the user. The reception unit can also accept the user's interests and preferences. The proposal unit uses a generation AI to make proposals based on the information accepted by the reception unit. The proposals include, for example, recommendations of travel plans and tourist spots, but are not limited to, examples. For example, the proposal unit uses the generation AI to propose recommended travel itineraries and events based on the user's interests and preferences. The proposal unit can also analyze the user's input information and propose an optimal travel plan. The transfer unit automatically transfers the information proposed by the proposal unit to a car navigation system. The automatic transfer can be performed, for example, via Bluetooth, Wi-Fi, a cloud service, or the like, but is not limited to, examples. For example, the transfer unit automatically transfers the travel itinerary proposed by the proposal unit to a car navigation system. The forwarding unit can also suggest places to stop by and meals. The linking unit links the information suggested by the suggestion unit with the transfer search. Linking can be performed, for example, using an API or a data synchronization method, but is not limited to these examples. For example, the linking unit links the travel itinerary suggested by the suggestion unit with the transfer search. The linking unit can also provide travel assistance and guidance within stations. As a result, the travel planning assistance system according to the embodiment efficiently assists with travel planning by accepting and suggesting user information and linking with car navigation systems and transfer searches.

[0070] The suggestion unit can suggest recommended travel itineraries and events based on the user's interests or preferences. For example, the suggestion unit suggests recommended travel itineraries and events based on the user's interests and preferences. For example, the suggestion unit uses a generation AI to analyze the user's interests and preferences and suggest optimal travel itineraries and events. The suggestion unit can also identify and suggest interests and preferences based on the user's past search history and survey results. For example, the suggestion unit can suggest related travel itineraries and events based on tourist spots and events that the user has searched for in the past. The suggestion unit can also suggest optimal travel itineraries and events based on the interests and preferences that the user has answered in a survey. In this way, the suggestion unit improves user satisfaction by suggesting optimal travel itineraries and events based on the user's interests and preferences.

[0071] The forwarding unit can suggest places to stop by and meals to eat. For example, the forwarding unit suggests tourist spots, restaurants, cafes, etc. that can be stopped by based on the user's travel itinerary using the generation AI. The forwarding unit can also suggest optimal places to stop by based on the user's current location and travel route. For example, the forwarding unit suggests tourist spots and restaurants that the user can stop by while traveling by car. The forwarding unit can also suggest cafes and shops that the user can stop by while traveling by train. In this way, the forwarding unit improves convenience during travel by suggesting places to stop by and meals to eat.

[0072] The transfer unit can suggest locations if the user wants to go to a convenience store or restroom along the way. For example, the transfer unit suggests the best location if the user wants to go to a convenience store or restroom along the way. For example, the transfer unit uses the generation AI to suggest the best location of a convenience store or restroom based on the user's current location and travel route. The transfer unit can also suggest the best place to stop based on the user's input information and location information. For example, if the user inputs "I want to go to a convenience store" into the system, the transfer unit can suggest the nearest convenience store. Also, if the user inputs "I want to go to the restroom," the transfer unit can suggest the nearest restroom. In this way, the transfer unit improves the comfort of the user's trip by suggesting the best location if the user wants to go to a convenience store or restroom along the way.

[0073] The coordination unit can provide assistance and guidance for movement within a station. For example, the coordination unit provides assistance and guidance for movement within a station. For example, the generation AI guides the user along a route within the station based on the user's transfer information. The coordination unit can also guide the user to the optimal route based on the user's current location and destination. For example, the coordination unit provides maps and route guidance to prevent the user from getting lost within the station. The coordination unit can also guide the user to the locations of elevators and escalators so that the user can move smoothly within the station. In this way, the coordination unit provides assistance and guidance for movement within the station, making train travel smoother.

[0074] The linking unit can make reserved seat reservations. For example, the linking unit makes reserved seat reservations. For example, the generation AI in the linking unit suggests the most suitable reserved seat based on the user's travel plans. The linking unit can also reserve a reserved seat based on the user's input information and preferences. For example, if the user inputs "I would like to reserve a reserved seat" into the system, the linking unit will suggest the most suitable seat and make the reservation. The linking unit can also reserve a reserved seat based on the type and location of the seat desired by the user. In this way, the linking unit can make reserved seat reservations, making train travel more comfortable.

[0075] The reception unit can estimate the user's emotions and adjust the information input method based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the information input method based on the estimated user emotions. For example, the reception unit uses a generation AI to analyze the user's facial expressions and voice to estimate emotions. The reception unit can also estimate emotions based on the user's input information and behavioral patterns. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. In this way, the reception unit adjusts the information input method according to the user's emotions, thereby reducing the user's stress and improving input efficiency. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past travel history and provide an optimal information input format. The reception unit, for example, analyzes the user's past travel history and provides an optimal information input format. For example, the reception unit uses a generation AI to analyze the user's past travel history and automatically display related input fields. The reception unit can also prioritize frequently visited places as input candidates based on the user's past travel history. For example, the reception unit can automatically display related input fields based on travel destinations the user has visited in the past. The reception unit can also analyze the user's past travel history and suggest input fields related to specific seasons or events. In this way, the reception unit analyzes the user's past travel history to provide an optimal information input format and improve input efficiency.

[0077] The reception unit can filter input content based on the user's current living situation and areas of interest when receiving information. For example, the reception unit filters input content based on the user's current living situation and areas of interest when receiving information. For example, the reception unit uses a generation AI to analyze the user's current living situation and areas of interest and prioritize displaying related input items. The reception unit can also identify areas of interest based on the user's input information and social media activity and filter the input content. For example, when a user inputs their current living situation, the reception unit suggests travel destinations and events that match that situation. The reception unit can also prioritize displaying related input items based on the user's areas of interest (e.g., nature, history, gourmet food, etc.). Furthermore, the reception unit automatically omits unnecessary input items based on the user's current living situation and areas of interest. As a result, the reception unit filters input content based on the user's current living situation and areas of interest, omitting unnecessary input and realizing efficient information input.

[0078] The reception unit can select an input means according to the user's input method when receiving information. For example, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.) when receiving information. For example, the reception unit uses a generation AI to analyze the user's input method and propose the optimal input means. The reception unit can also select the optimal input means based on the user's device and input environment. For example, if the user selects voice input, the reception unit can input information using voice recognition technology. If the user selects text input, the reception unit can also input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. In this way, the reception unit can select the optimal input means according to the user's input method, allowing for smooth information input.

[0079] The reception unit can estimate the user's emotions and prioritize the input information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input information based on the estimated user emotions. For example, the reception unit uses a generation AI to analyze the user's facial expressions and voice and estimate emotions. The reception unit can also estimate emotions based on the user's input information and behavioral patterns. For example, the reception unit can prioritize input of important information when the user is stressed. The reception unit can also prioritize input of detailed information when the user is relaxed. Furthermore, the reception unit can prioritize input of the minimum necessary information when the user is in a hurry. In this way, the reception unit can prioritize input of important information by prioritizing input information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving highly relevant information by taking the user's geographical location information into consideration when receiving information. For example, the reception unit uses a generation AI to analyze the user's geographical location information and automatically filter relevant information. The reception unit can also prioritize receiving highly relevant information based on the user's current location and destination. For example, when a user inputs their current location, the reception unit prioritizes suggesting travel destinations and events in the vicinity. The reception unit can also automatically filter relevant information based on the user's geographical location information. As a result, the reception unit can prioritize receiving highly relevant information by taking the user's geographical location information into consideration.

[0081] The reception unit can analyze the user's social media activity and accept related information when receiving information. For example, the reception unit analyzes the user's social media activity and accepts related information when receiving information. For example, the reception unit uses a generation AI to analyze the user's social media activity and preferentially display related information. The reception unit can also accept related information based on the user's posted content and check-in information. For example, the reception unit can suggest related travel destinations and events based on the location where the user checked in on social media. The reception unit can also analyze the user's posted content on social media and preferentially accept related information. Furthermore, the reception unit accepts related information with reference to the activity of the user's friends on social media. In this way, the reception unit can efficiently accept related information by analyzing the user's social media activity.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when receiving information. For example, the reception unit customizes the input method by reflecting the user's past feedback when receiving information. For example, the reception unit uses a generation AI to analyze the user's past feedback and suggest the optimal input method. The reception unit can also customize the input interface based on the user's past feedback. For example, the reception unit suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also automatically omit unnecessary input items by reflecting the user's past feedback. In this way, the reception unit provides the optimal input method by reflecting the user's past feedback, improving input efficiency.

[0083] The suggestion unit can estimate the user's emotion and change the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit uses a generation AI to analyze the user's facial expressions and voice to estimate the emotion. The suggestion unit can also estimate the emotion based on the user's input information and behavioral patterns. For example, the suggestion unit can make detailed suggestions when the user is relaxed. The suggestion unit can also make concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can make visually appealing suggestions when the user is excited. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion, thereby making the most suitable suggestion for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the travel itinerary when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the importance of the travel itinerary when making a suggestion. For example, the suggestion unit uses a generation AI to analyze the importance of the travel itinerary and make an optimal suggestion. The suggestion unit can also adjust the level of detail of the suggestion based on the user's priorities and the purpose of the trip. For example, the suggestion unit makes detailed suggestions for important travel itineraries. The suggestion unit can also make concise suggestions for less important travel itineraries. Furthermore, the suggestion unit dynamically adjusts the level of detail of the suggestion according to the importance of the travel itinerary. In this way, the suggestion unit can make an optimal suggestion for the user by adjusting the level of detail of the suggestion based on the importance of the travel itinerary.

[0085] The suggestion unit can apply different suggestion algorithms depending on the travel category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the travel category when making a suggestion. For example, the suggestion unit uses a generation AI to analyze the travel category and select the optimal suggestion algorithm. The suggestion unit can also apply different suggestion algorithms based on the user's interests and preferences. For example, in the case of nature tourism, the suggestion unit applies an algorithm that suggests natural landscapes and hiking trails. In addition, in the case of history tourism, the suggestion unit can apply an algorithm that suggests historical sites and museums. Furthermore, in the case of gourmet tourism, the suggestion unit applies an algorithm that suggests local specialty dishes and restaurants. In this way, the suggestion unit can make optimal suggestions for the user by applying different suggestion algorithms depending on the travel category.

[0086] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit uses a generation AI to analyze the user's past suggestion results and make the optimal suggestion. The suggestion unit can also improve the accuracy of the suggestion based on the user's feedback and proposal history. For example, the suggestion unit makes similar suggestions based on proposals that the user has accepted in the past. The suggestion unit can also omit unnecessary suggestions based on proposals that the user has rejected in the past. Furthermore, the suggestion unit analyzes the user's past suggestion results and improves the accuracy of the suggestion. In this way, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0087] The suggestion unit can estimate the user's emotion and change the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit uses a generation AI to analyze the user's facial expressions and voice to estimate the emotion. The suggestion unit can also estimate the emotion based on the user's input information and behavioral patterns. For example, the suggestion unit can make detailed suggestions when the user is relaxed. The suggestion unit can also make concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can make visually appealing suggestions when the user is excited. In this way, the suggestion unit can make optimal suggestions for the user by adjusting the length of the suggestion according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The suggestion unit can determine the priority of suggestions based on the travel period when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the travel period when making suggestions. For example, the suggestion unit uses a generation AI to analyze the travel period and make optimal suggestions. The suggestion unit can also determine the priority of suggestions based on the user's wishes and the purpose of the trip. For example, the suggestion unit prioritizes seasonal events and tourist spots depending on the travel period. The suggestion unit can also make suggestions to avoid crowds based on the travel period. Furthermore, the suggestion unit suggests optimal travel destinations and events depending on the travel period. In this way, the suggestion unit can make optimal suggestions for the user by determining the priority of suggestions based on the travel period.

[0089] The suggestion unit can adjust the order of suggestions based on travel relevance when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on travel relevance when making suggestions. For example, the suggestion unit uses a generation AI to analyze travel relevance and determine an optimal order of suggestions. The suggestion unit can also adjust the order of suggestions based on the user's interests and preferences. For example, the suggestion unit prioritizes suggesting information with high travel relevance. The suggestion unit can also dynamically adjust the order of suggestions based on travel relevance. Furthermore, the suggestion unit postpones information with low travel relevance. In this way, the suggestion unit can make optimal suggestions for the user by adjusting the order of suggestions based on travel relevance.

[0090] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit uses a generation AI to analyze the user's level of expertise and make optimal suggestions. The suggestion unit can also identify the user's level of expertise based on survey results and past behavioral history of the user and adjust the use of technical terms in the proposal. For example, the suggestion unit makes suggestions using technical terms when the user has specialized knowledge. The suggestion unit can also make suggestions in simple language when the user does not have specialized knowledge. Furthermore, the suggestion unit dynamically adjusts the content of the proposal according to the user's level of expertise. As a result, the suggestion unit can make suggestions that are easy for the user to understand by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0091] The transfer unit can estimate the user's emotions and set a priority order for the information to be transferred based on the estimated user emotions. The transfer unit, for example, estimates the user's emotions and determines the priority order for the information to be transferred based on the estimated user emotions. For example, the transfer unit uses a generation AI to analyze the user's facial expressions and voice to estimate emotions. The transfer unit can also estimate emotions based on the user's input information and behavioral patterns. For example, the transfer unit prioritizes transferring important information when the user is stressed. The transfer unit can also transfer detailed information when the user is relaxed. Furthermore, the transfer unit prioritizes transferring the minimum necessary information when the user is in a hurry. In this way, the transfer unit can prioritize transferring important information by determining the priority order for the information to be transferred based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The forwarding unit can select the optimal forwarding method by analyzing the user's past forwarding history at the time of forwarding. For example, the forwarding unit selects the optimal forwarding method by analyzing the user's past forwarding history at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's past forwarding history and propose the optimal forwarding method. The forwarding unit can also select the optimal forwarding method based on the user's feedback and forwarding history. For example, the forwarding unit proposes the optimal forwarding method based on the forwarding methods the user has used in the past. The forwarding unit can also prioritize and propose frequently used forwarding methods based on the user's past forwarding history. Furthermore, the forwarding unit analyzes the user's past forwarding history and proposes the most efficient forwarding method. In this way, the forwarding unit selects the optimal forwarding method by analyzing the user's past forwarding history, thereby achieving efficient information transfer.

[0093] The forwarding unit can customize the forwarding content based on the user's current living situation at the time of forwarding. For example, the forwarding unit customizes the forwarding content based on the user's current living situation at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's current living situation and prioritize forwarding relevant information. The forwarding unit can also identify the current living situation and customize the forwarding content based on the user's input information and social media activity. For example, when the user inputs their current living situation, the forwarding unit suggests forwarding content that matches that situation. The forwarding unit can also prioritize forwarding relevant information based on the user's current living situation. Furthermore, the forwarding unit automatically omits unnecessary information, taking the user's current living situation into consideration. As a result, the forwarding unit can provide highly relevant information by customizing the forwarding content based on the user's current living situation.

[0094] The forwarding unit can improve the forwarding method by reflecting user feedback at the time of forwarding. For example, the forwarding unit improves the forwarding method by reflecting user feedback at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze user feedback and propose an optimal forwarding method. The forwarding unit can also customize the forwarding interface based on the user's past feedback. For example, the forwarding unit proposes an optimal forwarding method based on feedback provided by the user in the past. The forwarding unit can also reflect the user's past feedback and automatically omit unnecessary forwarding items. In this way, the forwarding unit improves the forwarding method by reflecting user feedback and achieves optimal information transfer for the user.

[0095] The transfer unit can estimate the user's emotions and change the display method of the information to be transferred based on the estimated user emotions. For example, the transfer unit estimates the user's emotions and adjusts the display method of the information to be transferred based on the estimated user emotions. For example, the transfer unit uses a generation AI to analyze the user's facial expressions and voice to estimate emotions. The transfer unit can also estimate emotions based on the user's input information and behavioral patterns. For example, if the user is nervous, the transfer unit provides a simple, highly visible display method. If the user is relaxed, the transfer unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the transfer unit provides a display method that focuses on the main points. In this way, the transfer unit can adjust the display method of information according to the user's emotions, thereby providing highly visible information to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The forwarding unit can select the optimal forwarding method by taking into account the user's geographical location information when forwarding. For example, the forwarding unit selects the optimal forwarding method by taking into account the user's geographical location information when forwarding. For example, the forwarding unit uses a generation AI to analyze the user's geographical location information and propose the optimal forwarding method. The forwarding unit can also select the optimal forwarding method based on the user's current location and destination. For example, when the user inputs their current location, the forwarding unit prioritizes forwarding information about the surrounding area. The forwarding unit can also automatically filter related information based on the user's geographical location information. Furthermore, the forwarding unit proposes the optimal forwarding method by taking into account the user's geographical location information. In this way, the forwarding unit selects the optimal forwarding method by taking into account the user's geographical location information, thereby achieving efficient information transfer.

[0097] The forwarding unit can analyze the user's social media activity and suggest the content to be forwarded at the time of forwarding. For example, the forwarding unit analyzes the user's social media activity and suggests the content to be forwarded at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's social media activity and prioritizes forwarding relevant information. The forwarding unit can also suggest related information based on the user's posted content and check-in information. For example, the forwarding unit can forward related information based on the location where the user checked in on social media. The forwarding unit can also analyze the user's posted content on social media and prioritize forwarding related information. Furthermore, the forwarding unit can transfer related information with reference to the activity of the user's friends on social media. In this way, the forwarding unit can efficiently transfer highly relevant information by analyzing the user's social media activity.

[0098] The forwarding unit can customize the forwarding method by reflecting the user's past feedback at the time of forwarding. For example, the forwarding unit customizes the forwarding method by reflecting the user's past feedback at the time of forwarding. For example, the forwarding unit uses a generation AI to analyze the user's past feedback and propose an optimal forwarding method. The forwarding unit can also customize the forwarding interface based on the user's past feedback. For example, the forwarding unit proposes an optimal forwarding method based on feedback provided by the user in the past. The forwarding unit can also reflect the user's past feedback and automatically omit unnecessary forwarding items. In this way, the forwarding unit provides an optimal forwarding method by reflecting the user's past feedback, thereby realizing efficient information transfer for the user.

[0099] The linking unit can estimate the user's emotions and set a priority order for the information to be linked based on the estimated user emotions. The linking unit, for example, estimates the user's emotions and determines the priority order for the information to be linked based on the estimated user emotions. For example, the linking unit uses a generation AI to analyze the user's facial expressions and voice to estimate emotions. The linking unit can also estimate emotions based on the user's input information and behavioral patterns. For example, the linking unit prioritizes linking important information when the user is stressed. The linking unit can also prioritize linking detailed information when the user is relaxed. Furthermore, the linking unit prioritizes linking the minimum necessary information when the user is in a hurry. In this way, the linking unit can prioritize linking important information by determining the priority order for the information to be linked based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The collaboration unit can select the optimal collaboration method by analyzing the user's past collaboration history at the time of collaboration. For example, the collaboration unit analyzes the user's past collaboration history to select the optimal collaboration method at the time of collaboration. For example, the collaboration unit uses a generation AI to analyze the user's past collaboration history and propose the optimal collaboration method. The collaboration unit can also select the optimal collaboration method based on the user's feedback and collaboration history. For example, the collaboration unit proposes the optimal collaboration method based on collaboration methods used by the user in the past. The collaboration unit can also prioritize and propose frequently used collaboration methods based on the user's past collaboration history. Furthermore, the collaboration unit analyzes the user's past collaboration history and proposes the most efficient collaboration method. In this way, the collaboration unit selects the optimal collaboration method by analyzing the user's past collaboration history, thereby achieving efficient information collaboration.

[0101] The linking unit can customize the linking content based on the user's current living situation at the time of linking. For example, the linking unit customizes the linking content based on the user's current living situation at the time of linking. For example, the linking unit uses a generation AI to analyze the user's current living situation and prioritize linking related information. The linking unit can also identify the current living situation based on the user's input information and social media activity and customize the linking content. For example, when the user inputs their current living situation, the linking unit suggests linking content that matches the situation. The linking unit can also prioritize linking related information based on the user's current living situation. Furthermore, the linking unit automatically omits unnecessary information in consideration of the user's current living situation. As a result, the linking unit can provide highly relevant information by customizing the linking content based on the user's current living situation.

[0102] The collaboration unit can improve the collaboration method by reflecting user feedback at the time of collaboration. For example, the collaboration unit improves the collaboration method by reflecting user feedback at the time of collaboration. For example, the collaboration unit uses a generation AI to analyze user feedback and propose the optimal collaboration method. The collaboration unit can also customize the collaboration interface based on the user's past feedback. For example, the collaboration unit proposes the optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also automatically omit unnecessary collaboration items by reflecting the user's past feedback. In this way, the collaboration unit improves the collaboration method by reflecting user feedback and realizes the optimal information collaboration for the user.

[0103] The linking unit can estimate the user's emotion and change the display method of the linked information based on the estimated user emotion. For example, the linking unit estimates the user's emotion and adjusts the display method of the linked information based on the estimated user emotion. For example, the linking unit uses a generation AI to analyze the user's facial expressions and voice to estimate the emotion. The linking unit can also estimate the emotion based on the user's input information and behavioral patterns. For example, if the user is nervous, the linking unit provides a simple, highly visible display method. If the user is relaxed, the linking unit can provide a display method that includes detailed information. If the user is in a hurry, the linking unit provides a display method that focuses on the main points. In this way, the linking unit can adjust the display method of information according to the user's emotion, thereby providing highly visible information to the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The collaboration unit can select the optimal collaboration method by taking into account the user's geographical location information when collaborating. For example, the collaboration unit selects the optimal collaboration method by taking into account the user's geographical location information when collaborating. For example, the collaboration unit uses a generation AI to analyze the user's geographical location information and propose the optimal collaboration method. The collaboration unit can also select the optimal collaboration method based on the user's current location and destination. For example, when the user inputs their current location, the collaboration unit prioritizes linking information about the surrounding area. The collaboration unit can also automatically filter related information based on the user's geographical location information. Furthermore, the collaboration unit proposes the optimal collaboration method by taking into account the user's geographical location information. In this way, the collaboration unit selects the optimal collaboration method by taking into account the user's geographical location information and achieves efficient information collaboration.

[0105] The linking unit can analyze the user's social media activity at the time of linking and suggest linking content. For example, the linking unit analyzes the user's social media activity and suggests linking content at the time of linking. For example, the linking unit uses a generation AI to analyze the user's social media activity and prioritize linking related information. The linking unit can also suggest related information based on the user's posted content and check-in information. For example, the linking unit can link related information based on the location where the user checked in on social media. The linking unit can also analyze the user's posted content on social media and prioritize linking related information. Furthermore, the linking unit links related information with reference to the activity of the user's friends on social media. In this way, the linking unit can efficiently link highly relevant information by analyzing the user's social media activity.

[0106] The collaboration unit can customize the collaboration method by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit customizes the collaboration method by reflecting the user's past feedback at the time of collaboration. For example, the collaboration unit uses a generation AI to analyze the user's past feedback and propose the optimal collaboration method. The collaboration unit can also customize the collaboration interface based on the user's past feedback. For example, the collaboration unit proposes the optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also reflect the user's past feedback and automatically omit unnecessary collaboration items. In this way, the collaboration unit provides the optimal collaboration method by reflecting the user's past feedback, and achieves efficient information collaboration for the user. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, transfer unit, and linking unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives information from a user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes suggestions using a generation AI. The transfer unit is realized, for example, by the communication I / F 44 of the smart device 14 and automatically transfers the suggested information to the car navigation system. The linking unit is realized, for example, by the communication I / F 26 of the data processing device 12 and links the suggested information with a transfer search. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, suggestion unit, transfer unit, and linking unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives information from the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes suggestions using a generation AI. The transfer unit is realized, for example, by the communication I / F 44 of the smart glasses 214 and automatically transfers the suggested information to the car navigation system. The linking unit is realized, for example, by the communication I / F 26 of the data processing device 12 and links the suggested information with the route transfer search. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, transfer unit, and linking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives information from the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes suggestions using a generation AI. The transfer unit is realized, for example, by the communication I / F 44 of the headset type terminal 314 and automatically transfers the suggested information to the car navigation system. The linking unit is realized, for example, by the communication I / F 26 of the data processing device 12 and links the suggested information with the route transfer search. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, transfer unit, and linking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives information from the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and makes suggestions using a generation AI. The transfer unit is realized, for example, by the communication I / F 44 of the robot 414 and automatically transfers the suggested information to the car navigation system. The linking unit is realized, for example, by the communication I / F 26 of the data processing device 12 and links the suggested information with the transfer search.

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

[0108] The suggestion unit can also suggest travel itineraries taking into account the user's health condition. For example, the suggestion unit can analyze the user's health data (e.g., heart rate, number of steps, sleep data, etc.) and suggest tourist spots and activities according to the user's physical strength. If the user has a chronic illness, the suggestion unit can also suggest travel plans that take into account the user's medical condition. Furthermore, the suggestion unit can also suggest rest times and snacks based on the user's health condition. In this way, the suggestion unit supports a safer and more comfortable trip by suggesting travel itineraries that take into account the user's health condition.

[0109] The suggestion unit can also make suggestions taking into account the weather forecast at the user's travel destination. For example, the suggestion unit can analyze weather data at the travel destination and suggest indoor tourist spots when it rains. The suggestion unit can also suggest outdoor activities when it is sunny. Furthermore, the suggestion unit can also suggest a packing list depending on the weather. In this way, the suggestion unit can make suggestions taking into account the weather forecast, allowing the user to enjoy their trip regardless of the weather.

[0110] The forwarding unit can also monitor the user's energy consumption during the trip and suggest meals and rest breaks at appropriate times. For example, the forwarding unit can analyze the user's travel distance and activity level and suggest meals if energy consumption is high. The forwarding unit can also suggest appropriate rest locations if the user is traveling for a long period of time. Furthermore, the forwarding unit can also suggest snacks and drinks if calorie replenishment is necessary based on the user's energy consumption. In this way, the forwarding unit supports health management during travel by making suggestions that take the user's energy consumption into consideration.

[0111] The forwarding unit can also monitor the user's stress level during the trip and suggest places and activities that will help them relax. For example, the forwarding unit can analyze the user's heart rate and breathing rate and suggest cafes or parks where they can relax if their stress level is high. The forwarding unit can also suggest music or meditation apps that will help the user relax. Furthermore, the forwarding unit can suggest relaxing massages or spas based on the user's stress level. In this way, the forwarding unit supports relaxation during the trip by making suggestions that take the user's stress level into consideration.

[0112] The linking unit can also monitor the user's emotions during the trip and suggest entertainment and activities according to the emotions. For example, the linking unit can analyze the user's facial expressions and voice and suggest active activities if the user is feeling excited. The linking unit can also suggest movies or music to lift the user's spirits if the user is feeling down. Furthermore, the linking unit can suggest relaxing activities and tourist spots based on the user's emotions. In this way, the linking unit supports the user's mood during the trip by making suggestions that take the user's emotions into consideration.

[0113] The suggestion unit can also make suggestions taking into account the cultural background of the user's travel destination. For example, the suggestion unit can analyze the culture and customs of the travel destination and suggest events and places where the user can experience that culture. The suggestion unit can also suggest basic manners and greeting methods so that the user can adapt to the culture of the travel destination. Furthermore, the suggestion unit can also provide information on historical background and traditional cuisine so that the user can gain a deeper understanding of the culture of the travel destination. In this way, the suggestion unit supports the user's cultural experience at the travel destination.

[0114] The suggestion unit can also make suggestions taking into account safety information at the user's travel destination. For example, the suggestion unit can analyze public safety information at the travel destination and suggest safe tourist spots and accommodations. The suggestion unit can also provide information on the nearest hospitals and police stations so that the user can prepare for emergencies at the travel destination. Furthermore, the suggestion unit can also provide information on areas requiring caution and safe means of transportation so that the user can take safety measures at the travel destination. In this way, the suggestion unit supports the user's safety at the travel destination.

[0115] The forwarding unit can also monitor the user's emotions during the trip and suggest meals according to the emotions. For example, the forwarding unit can analyze the user's facial expressions and voice and suggest energetic meals if the user is feeling excited. The forwarding unit can also suggest sweets or comfort foods to lift the user's spirits if the user is feeling down. Furthermore, the forwarding unit can also suggest relaxing meals and drinks based on the user's emotions. In this way, the forwarding unit supports the user's mood during the trip by suggesting meals that take the user's emotions into consideration.

[0116] The linking unit can also monitor the user's emotions during the trip and suggest transportation methods that correspond to the emotions. For example, the linking unit can analyze the user's facial expressions and voice and suggest active transportation methods (e.g., bicycles or walking) if the user is emotionally excited. The linking unit can also suggest comfortable transportation methods (e.g., taxis or reserved seats on trains) if the user is tired. Furthermore, the linking unit can suggest relaxing transportation methods (e.g., sightseeing buses or cruises) based on the user's emotions. In this way, the linking unit supports comfort during travel by suggesting transportation methods that take the user's emotions into consideration.

[0117] The suggestion unit can also suggest eco-friendly options for the user's travel destination. For example, the suggestion unit can suggest environmentally friendly accommodations and restaurants at the travel destination. The suggestion unit can also suggest eco-friendly means of transportation (e.g., bicycles and public transportation) so that the user can select them. Furthermore, the suggestion unit can provide information on volunteer activities and eco-tours so that the user can participate in environmental protection activities at the travel destination. In this way, the suggestion unit supports the user in making eco-friendly options for the user's travel destination.

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

[0119] Step 1: The reception unit receives information from the user. The information from the user includes travel destination, budget, schedule, interests, preferences, etc. For example, information such as travel destination, budget, schedule, etc. input by the user is received. Step 2: The suggestion unit uses the generation AI to make suggestions based on the information received by the reception unit. Suggestions include travel plans and tourist spot recommendations. For example, the generation AI suggests recommended travel itineraries and events based on the user's interests and preferences. Step 3: The transfer unit automatically transfers the information proposed by the suggestion unit to the car navigation system. The automatic transfer is performed using methods such as Bluetooth, Wi-Fi, or cloud services. For example, the travel itinerary proposed by the suggestion unit is automatically transferred to the car navigation system. Step 4: The linking unit links the information proposed by the suggestion unit with the transfer search. Linking is performed using an API or a data synchronization method. For example, the travel itinerary proposed by the suggestion unit is linked with the transfer search.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

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

Claims

1. a reception unit that receives information from a user; a proposal unit that makes a proposal based on the information received by the reception unit; a transfer unit that automatically transfers the information proposed by the proposal unit to a car navigation system; a linking unit that links the information proposed by the suggestion unit with a transfer search; Equipped with A system characterized by:

2. The proposal unit Suggest travel itineraries and activities based on your interests or preferences 2. The system of claim 1.

3. The transfer unit Suggest places to stop and eat 2. The system of claim 1.

4. The transfer unit Suggest locations if the user wants to go to a convenience store or toilet along the way 2. The system of claim 1.

5. The linking unit is Providing assistance and guidance within the station 2. The system of claim 1.

6. The linking unit is Make a seat reservation 2. The system of claim 1.

7. The reception unit Inferring user emotions and changing the way information is input based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past travel history and provide the optimal information input format 2. The system of claim 1.

9. The reception unit As information is received, filter input based on the user's current life situation and interests.

2. The system of claim 1.

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