system

Abstract

The system according to this embodiment aims to make effective use of travel time and provide an attractive travel experience. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a navigation unit, and a suggestion unit. The reception unit receives input of the departure point and destination. The generation unit generates the optimal route based on the information received by the reception unit. The navigation unit performs navigation based on the route generated by the generation unit. The suggestion unit makes suggestions for route changes and additions in real time based on the navigation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal route proposal for effectively utilizing travel time has not been sufficiently made, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize travel time and provide an attractive travel experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a navigation unit, and a suggestion unit. The reception unit receives input of the departure point and destination. The generation unit generates the optimal route based on the information received by the reception unit. The navigation unit performs navigation based on the route generated by the generation unit. The suggestion unit makes real-time suggestions for route changes and additions based on the navigation unit. [Effects of the Invention]

[0007] The system according to this embodiment can make effective use of travel time and provide an attractive travel experience. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system for positively transforming travel time according to an embodiment of the present invention is a system that uses AI to generate an optimal route in order to make travel time by car more enjoyable. This system is based on a car navigation app and can generate business revenue and advertising revenue by linking with tourism services such as restaurants and accommodations. Furthermore, it can complete all aspects of the trip, from planning to staying at the destination, within a single service. First, the user inputs the departure point and destination. Next, the AI ​​generates an optimal route based on the user's preferences and interests. This route includes tourist attractions, restaurants, and accommodations. For example, if the user is planning a trip from Tokyo to Kyoto, the AI ​​will suggest tourist attractions and restaurants along the way, providing the user with an enjoyable travel experience. Furthermore, the car navigation app will provide navigation based on the suggested route. The user can receive real-time suggestions for route changes and additions through the app. For example, if the user requests to "eat delicious ramen" along the way, the AI ​​will suggest a nearby ramen restaurant and update the route. This mechanism transforms travel time into a positive experience, allowing the user to enjoy the entire trip. In addition, business revenue and advertising revenue can be generated by linking with tourism services. For example, advertising revenue can be generated by displaying advertisements for suggested tourist attractions and restaurants. In this way, by using AI to transform travel time into something more engaging, it is possible to improve user satisfaction while also generating business and advertising revenue. Thus, systems that positively transform travel time can turn users' travel time into an attractive experience.

[0029] The system for positively changing travel time according to the embodiment comprises a reception unit, a generation unit, a navigation unit, and a suggestion unit. The reception unit receives input of the departure point and destination. The reception unit provides, for example, an interface for the user to input the departure point and destination. The reception unit receives the information entered by the user and passes it to the generation unit. The generation unit generates the optimal route based on the information received by the reception unit. The generation unit generates the optimal route based on the user's preferences and interests, for example, using AI. The generation unit suggests the optimal route for the user by referring to the user's past behavior history, survey results, social media data, etc. If the user is planning a trip from Tokyo to Kyoto, the generation unit suggests tourist spots and gourmet spots along the way. The navigation unit performs navigation based on the route generated by the generation unit. The navigation unit guides the user along the route, for example, using a car navigation app. The navigation unit makes suggestions for route changes and additions in real time. The suggestion unit makes suggestions for route changes and additions in real time based on the navigation unit. For example, if a user requests to "eat some delicious ramen" along the way, the suggestion unit will suggest a nearby ramen restaurant and update the route. The suggestion unit can also collect user feedback and incorporate it into future suggestions. In this way, the system that positively transforms travel time according to the embodiment can turn the user's travel time into an engaging experience.

[0030] The reception unit accepts input of the departure and destination locations. For example, the reception unit provides an interface for users to input their departure and destination locations. Specifically, it enables users to easily input their departure and destination locations through applications or websites running on devices such as smartphones, tablets, and personal computers. The interface employs an intuitive and user-friendly design, providing functions for users to select their departure and destination locations on a map, or to specify addresses or landmarks via text input. Furthermore, a voice input function allows users to specify their departure and destination locations by voice. The reception unit receives the information entered by the user and passes it to the generation unit. At this time, the entered information is stored in a database and used to learn the user's past input history and preferences. In addition, the reception unit also has a function to verify the accuracy of the information entered by the user and to complete or correct it as needed. For example, if a user enters an ambiguous place name, it will suggest alternative locations and prompt the user to select one. Also, if the departure or destination locations do not meet certain conditions, such as a non-existent address or an inaccessible location, a warning will be displayed and the user will be prompted to re-enter the information. This allows the reception desk to support users in accurately and quickly entering their departure and destination locations, improving the overall accuracy and reliability of the system.

[0031] The generation unit generates the optimal route based on the information received by the reception unit. For example, the generation unit uses AI to generate the optimal route based on the user's preferences and interests. Specifically, the generation unit refers to the user's past behavior history, survey results, and social media data to propose the best route for the user. The AI ​​uses machine learning algorithms to analyze the user's preferences and interests and generate the optimal route. For example, if a user is planning a trip from Tokyo to Kyoto, it will suggest tourist spots and restaurants along the way. The generation unit retrieves information on tourist spots and restaurants from a database and selects places that match the user's preferences. Furthermore, the generation unit generates the optimal route considering real-time traffic and weather information. For example, it will suggest alternative routes to avoid delays due to traffic jams or construction. Also, if the weather deteriorates, it will prioritize suggesting indoor tourist spots and restaurants. The generation unit can also collect user feedback and reflect it in future suggestions. This allows the generation unit to provide the optimal route to make the user's travel time more comfortable and enjoyable.

[0032] The navigation unit provides navigation based on the route generated by the generation unit. For example, the navigation unit guides the user along a route using a car navigation app. Specifically, the navigation unit operates on a smartphone or car navigation device and guides the user along a route through voice guidance and visual guidance. The navigation unit makes real-time suggestions for route changes and additions. For example, it recalculates the optimal route in response to changes in traffic conditions or user requests and notifies the user. The navigation unit uses GPS data to accurately determine the user's current location and provides information such as points along the route, distance to the destination, and estimated travel time. The navigation unit also has a function to allow the user to add places they want to stop at or spots of interest along the way. For example, if the user requests "I want to eat delicious ramen," it will suggest nearby ramen restaurants and update the route. The navigation unit can also collect user feedback and reflect it in future navigations. This allows the navigation unit to provide the user with the best possible route guidance, making travel time more comfortable and enjoyable.

[0033] The suggestion unit, guided by the navigation unit, makes real-time route changes and additional suggestions. For example, if a user requests to "eat some delicious ramen" along the way, the suggestion unit will suggest a nearby ramen restaurant and update the route. Specifically, the suggestion unit searches its database for relevant information based on the user's request and makes the best suggestion. The suggestion unit selects the best places to stop by, taking into account the user's current location, destination, and travel time. Furthermore, the suggestion unit analyzes the user's past behavior history and preferences to provide more personalized suggestions. For example, based on places the user has visited and rated in the past, it suggests new places that the user might be interested in. The suggestion unit can also collect user feedback and incorporate it into future suggestions. For example, by having the user rate suggested places, the suggestion unit learns from that rating and improves the accuracy of future suggestions. In addition, the suggestion unit can provide multiple suggestions in response to the user's request, allowing the user to choose. This enables the suggestion unit to provide optimal suggestions to make the user's travel time more engaging and fulfilling.

[0034] The suggestion section includes an advertising section that displays advertisements for suggested tourist destinations and gourmet spots. The advertising section displays, for example, banner ads for suggested tourist destinations and gourmet spots. The advertising section can also display pop-up ads and inline ads. The advertising section displays visually appealing ads to encourage users to take an interest in the suggested tourist destinations and gourmet spots. The advertising section can also customize how ads are displayed to suit the user's preferences. For example, the advertising section displays highly relevant ads based on the user's past behavior history and interests. This allows for the generation of advertising revenue. Some or all of the above-described processes in the advertising section may be performed using AI or not. For example, the advertising section can input the user's behavior history into an AI and display ads using an AI model that generates highly relevant ads.

[0035] The proposal unit includes a feedback unit that collects user feedback and incorporates it into future proposals. The feedback unit collects user opinions, for example, through surveys. The feedback unit can also collect user reviews and rating scores. The feedback unit analyzes the collected feedback and incorporates it into future proposals. The feedback unit improves the accuracy of proposals based on user feedback. For example, the feedback unit collects user ratings of suggested tourist destinations and gourmet spots and uses those ratings to improve future proposals. This improves the accuracy of proposals by incorporating user feedback. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user feedback data into an AI and make proposals using an AI model that generates future proposals.

[0036] The generation unit generates the optimal route based on the user's preferences and interests. For example, the generation unit suggests the best route for the user by referencing the user's past behavior history, survey results, and social media data. The generation unit can use AI to identify the user's preferences and interests. For example, the generation unit inputs the user's past search history and social media posts into the AI ​​and generates the optimal route using an AI model that identifies the user's preferences and interests. The generation unit suggests tourist destinations and restaurants based on the user's preferences. For example, if the user is planning a trip from Tokyo to Kyoto, the generation unit suggests tourist destinations and restaurants along the way. This allows for the provision of an optimal route based on the user's preferences. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can suggest a route using an AI model that generates the optimal route based on the user's preferences and interests.

[0037] The navigation unit provides navigation based on the proposed route. For example, the navigation unit guides the user to a route using a car navigation app. The navigation unit provides navigation to the user until they reach their destination based on the proposed route. The navigation unit can also make real-time changes to the route or additional suggestions. For example, if the user requests to "eat some delicious ramen" along the way, the navigation unit will suggest a nearby ramen restaurant and update the route. This makes the user's journey smoother by providing navigation based on the proposed route. Some or all of the above processes in the navigation unit may be performed using AI or not. For example, the navigation unit can input the proposed route into an AI and provide route guidance using an AI model that performs navigation.

[0038] The suggestion unit makes real-time suggestions for route changes and additions. For example, if a user requests to "eat delicious ramen" along the way, the suggestion unit will suggest a nearby ramen restaurant and update the route. The suggestion unit makes real-time suggestions for route changes and additions in response to user requests. The suggestion unit can also collect user feedback and incorporate it into future suggestions. For example, the suggestion unit can collect user ratings for suggested tourist destinations and gourmet spots and use that feedback to improve future suggestions. This allows for flexible user travel by making real-time suggestions for route changes and additions. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user requests into an AI and make suggestions using an AI model that makes real-time suggestions for route changes and additions.

[0039] The reception desk analyzes the user's past travel history and suggests the optimal input method. For example, the reception desk automatically displays departure and destination locations that the user has frequently entered in the past as suggestions. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk predicts and suggests departure and destination locations to be used at specific times based on the user's past travel history. This improves user convenience by suggesting the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into an AI and make suggestions using an AI model that suggests the optimal input method.

[0040] The reception desk completes the input when the user enters their departure and destination locations, taking into account the user's current traffic conditions and weather information. For example, when the user enters their departure location, the reception desk suggests the optimal departure time considering current traffic congestion information. When the user enters their destination, the reception desk suggests the optimal route based on current weather information. If the weather changes while the user is traveling, the reception desk updates the weather information in real time and suggests the optimal route again. This allows for more accurate input by taking into account current traffic conditions and weather information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input traffic conditions and weather information into an AI and make suggestions using an AI model that suggests the optimal departure time and route.

[0041] The reception desk, when the user enters their departure and destination locations, considers the user's geographical location information to suggest highly relevant locations. For example, when the user enters their current location, the reception desk suggests nearby major landmarks as potential locations. When the user enters their destination, the reception desk suggests places the user has visited in the past as potential locations. When the user enters a new location while traveling, the reception desk suggests the most suitable location considering the distance from the current location. In this way, by considering geographical location information, highly relevant locations can be suggested. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into an AI and suggest locations using an AI model that suggests highly relevant locations.

[0042] The reception desk analyzes the user's social media activity when the user enters their departure and destination locations and suggests relevant locations. For example, the reception desk may suggest locations where the user has checked in on social media, places the user follows on social media, or places the user has shared on social media. This allows the reception desk to suggest relevant locations by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media activity data into an AI and suggest locations using an AI model that proposes relevant locations.

[0043] The generation unit proposes the optimal route by referring to the user's past travel history when generating a route. For example, the generation unit proposes the optimal route based on places the user has visited in the past. The generation unit proposes a route that avoids congestion based on the user's past travel history. The generation unit analyzes the user's past travel history and proposes the most efficient route. This improves user convenience by proposing the optimal route based on past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into an AI and propose a route using an AI model that proposes the optimal route.

[0044] The generation unit customizes routes based on the user's current interests and preferences when generating them. For example, the generation unit includes tourist destinations that the user is currently interested in, restaurants that the user is currently interested in, and activities that the user is currently interested in. This improves user satisfaction by providing routes based on current interests and preferences. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's current interests and preferences data into an AI and propose a route using an AI model that customizes routes.

[0045] The generation unit proposes the optimal route when generating a route, taking into account the user's geographical location information. For example, the generation unit proposes the optimal route based on the user's current location. The generation unit proposes the optimal route based on the user's destination. The generation unit re-proposes the optimal route based on the user's current location while they are traveling. In this way, the optimal route can be provided by taking geographical location information into consideration. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into an AI and propose a route using an AI model that proposes the optimal route.

[0046] The generation unit analyzes the user's social media activity when generating routes and proposes relevant routes. For example, the generation unit includes locations where the user has checked in on social media in the route. The generation unit includes locations where the user follows on social media in the route. The generation unit includes locations where the user has shared on social media in the route. In this way, relevant routes can be proposed by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into an AI and propose routes using an AI model that proposes relevant routes.

[0047] The navigation unit, during navigation, refers to the user's past driving history to propose the optimal navigation method. For example, the navigation unit proposes the optimal navigation method based on routes the user has used in the past. The navigation unit proposes a navigation method that avoids congestion based on the user's past driving history. The navigation unit analyzes the user's past driving history and proposes the most efficient navigation method. This improves user convenience by proposing the optimal navigation method based on past driving history. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's past driving history data into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0048] The navigation unit customizes the navigation method based on the user's current driving situation during navigation. For example, if the user is using a highway, the navigation unit provides navigation optimized for highways. If the user is driving in an urban area, the navigation unit provides navigation optimized for urban areas. If the user is driving on a mountain road, the navigation unit provides navigation optimized for mountain roads. This improves user convenience by providing navigation methods based on the current driving situation. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's current driving situation data into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0049] The navigation unit proposes the optimal navigation method during navigation, taking into account the user's geographical location information. For example, the navigation unit proposes the optimal navigation method based on the user's current location. The navigation unit proposes the optimal navigation method based on the user's destination. The navigation unit re-proposes the optimal navigation method based on the user's current location while they are moving. In this way, the system can provide the optimal navigation method by taking geographical location information into consideration. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's geographical location information into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0050] The navigation unit analyzes the user's social media activity during navigation and provides relevant navigation information. For example, the navigation unit includes locations the user has checked into on social media in the navigation. The navigation unit includes locations the user follows on social media in the navigation. The navigation unit includes locations the user has shared on social media in the navigation. This allows the navigation unit to provide relevant navigation information by analyzing social media activity. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's social media activity data into an AI and perform navigation using an AI model that provides relevant navigation information.

[0051] The suggestion unit makes optimal suggestions by referring to the user's past travel history. For example, the suggestion unit suggests new tourist destinations based on tourist destinations the user has visited in the past. The suggestion unit suggests similar accommodations based on accommodations the user has used in the past. The suggestion unit suggests activities that the user might be interested in based on their past travel history. This improves user convenience by making optimal suggestions based on past travel history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past travel history data into an AI and make suggestions using an AI model that makes optimal suggestions.

[0052] The suggestion function customizes suggestions based on the user's current interests. For example, the suggestion function might suggest gourmet spots the user is currently interested in, tourist destinations the user is currently interested in, or activities the user is currently interested in. This improves user satisfaction by providing suggestions based on current interests. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input data on the user's current interests into an AI and use an AI model to customize the suggestions.

[0053] The suggestion unit makes optimal suggestions by considering the user's geographical location information. For example, the suggestion unit suggests nearby tourist destinations based on the user's current location. The suggestion unit suggests tourist destinations along the way based on the user's destination. The suggestion unit then re-suggests optimal tourist destinations based on the user's current location while they are traveling. In this way, the system can provide optimal suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into an AI and make suggestions using an AI model that makes optimal suggestions.

[0054] The suggestion department analyzes the user's social media activity and makes relevant suggestions when making a suggestion. For example, the suggestion department suggests relevant tourist destinations based on places the user has checked in to on social media. The suggestion department suggests relevant tourist destinations based on places the user follows on social media. The suggestion department suggests relevant tourist destinations based on places the user has shared on social media. In this way, relevant suggestions can be provided by analyzing social media activity. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input the user's social media activity data into an AI and make suggestions using an AI model that makes relevant suggestions.

[0055] The advertising department displays the most relevant ads by referencing the user's past purchase history when displaying ads. For example, the advertising department advertises related products based on products the user has purchased in the past. The advertising department advertises products that the user is likely to be interested in based on the user's past purchase history. The advertising department analyzes the user's past purchase history and displays the most effective ads. This improves the effectiveness of advertising by displaying the most relevant ads based on past purchase history. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's past purchase history data into an AI and display ads using an AI model that displays the most relevant ads.

[0056] The advertising department customizes ads based on the user's current interests when displaying them. For example, the advertising department advertises products the user is currently interested in, services the user is currently interested in, or events the user is currently interested in. This improves the effectiveness of advertising by providing ads based on current interests. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's current interests into an AI and display ads using an AI model that customizes the ads.

[0057] The advertising department displays the most suitable advertisements when showing them, taking into account the user's geographical location. For example, the advertising department displays advertisements for nearby stores based on the user's current location. The advertising department displays advertisements for stores along the way based on the user's destination. The advertising department redisplays advertisements for the most suitable stores based on the user's current location while they are on the move. In this way, the advertising department can provide the most suitable advertisements by taking geographical location into consideration. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's geographical location information into an AI and display advertisements using an AI model that displays the most suitable advertisements.

[0058] The advertising department analyzes the user's social media activity when displaying ads and displays relevant ads. For example, the advertising department displays ads for relevant stores based on the locations the user has checked into on social media. The advertising department displays ads for stores the user follows on social media. The advertising department displays ads for stores the user has shared on social media. In this way, relevant ads can be provided by analyzing social media activity. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's social media activity data into an AI and display ads using an AI model that displays relevant ads.

[0059] The feedback unit, when collecting feedback, refers to the user's past feedback history to propose the optimal collection method. For example, the feedback unit suggests relevant questions based on feedback the user has provided in the past. The feedback unit suggests questions that the user might be interested in based on the user's past feedback history. The feedback unit analyzes the user's past feedback history and proposes the most effective questions. This improves the accuracy of feedback by suggesting the optimal collection method based on past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's past feedback history data into an AI and collect feedback using an AI model that suggests the optimal collection method.

[0060] The feedback unit customizes questions based on the user's current interests when collecting feedback. For example, the feedback unit suggests questions about topics the user is currently interested in, services the user is currently interested in, or events the user is currently interested in. This improves the accuracy of feedback by providing questions based on current interests. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can collect feedback using an AI model that inputs the user's current interests into the AI ​​and customizes the questions.

[0061] The feedback unit proposes the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback unit requests feedback on nearby stores based on the user's current location. The feedback unit requests feedback on stores along the way based on the user's destination. The feedback unit then re-proposes optimal store feedback based on the user's current location while they are traveling. In this way, the system can provide the optimal feedback collection method by taking geographical location information into account. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into an AI and collect feedback using an AI model that proposes the optimal collection method.

[0062] The feedback unit analyzes the user's social media activity when collecting feedback and requests relevant feedback. For example, the feedback unit requests feedback on places the user has checked in to on social media. The feedback unit requests feedback on stores the user follows on social media. The feedback unit requests feedback on stores the user has shared on social media. This allows the system to provide relevant feedback by analyzing social media activity. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity data into an AI and collect feedback using an AI model that requests relevant feedback.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception desk analyzes the user's past travel history and suggests the optimal input method. For example, the reception desk automatically displays departure and destination locations that the user has frequently entered in the past as suggestions. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk predicts and suggests departure and destination locations to be used at specific times based on the user's past travel history. This improves user convenience by suggesting the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into an AI and make suggestions using an AI model that suggests the optimal input method.

[0065] The generation unit proposes the optimal route by referring to the user's past travel history when generating a route. For example, the generation unit proposes the optimal route based on places the user has visited in the past. The generation unit proposes a route that avoids congestion based on the user's past travel history. The generation unit analyzes the user's past travel history and proposes the most efficient route. This improves user convenience by proposing the optimal route based on past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into an AI and propose a route using an AI model that proposes the optimal route.

[0066] The navigation unit, during navigation, refers to the user's past driving history to propose the optimal navigation method. For example, the navigation unit proposes the optimal navigation method based on routes the user has used in the past. The navigation unit proposes a navigation method that avoids congestion based on the user's past driving history. The navigation unit analyzes the user's past driving history and proposes the most efficient navigation method. This improves user convenience by proposing the optimal navigation method based on past driving history. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's past driving history data into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0067] The suggestion unit makes optimal suggestions by referring to the user's past travel history. For example, the suggestion unit suggests new tourist destinations based on tourist destinations the user has visited in the past. The suggestion unit suggests similar accommodations based on accommodations the user has used in the past. The suggestion unit suggests activities that the user might be interested in based on their past travel history. This improves user convenience by making optimal suggestions based on past travel history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past travel history data into an AI and make suggestions using an AI model that makes optimal suggestions.

[0068] The advertising department displays the most relevant ads by referencing the user's past purchase history when displaying ads. For example, the advertising department advertises related products based on products the user has purchased in the past. The advertising department advertises products that the user is likely to be interested in based on the user's past purchase history. The advertising department analyzes the user's past purchase history and displays the most effective ads. This improves the effectiveness of advertising by displaying the most relevant ads based on past purchase history. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's past purchase history data into an AI and display ads using an AI model that displays the most relevant ads.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception unit accepts input of the departure and destination locations. The reception unit provides an interface for the user to enter the departure and destination locations, receives the information entered by the user, and passes it to the generation unit. Step 2: The generation unit generates the optimal route based on the information received by the reception unit. The generation unit uses AI to generate the optimal route based on the user's preferences and interests, and proposes the best route for the user by referring to the user's past behavior history, survey results, social media data, etc. Step 3: The navigation unit performs navigation based on the route generated by the generation unit. The navigation unit guides the user along the route using a car navigation app and makes real-time suggestions for route changes or additions. Step 4: The suggestion unit makes real-time route changes and additional suggestions based on the navigation unit's input. The suggestion unit also suggests nearby destinations and updates the route if requested by the user along the way. It can also collect user feedback and incorporate it into future suggestions.

[0071] (Example of form 2) The system for positively transforming travel time according to an embodiment of the present invention is a system that uses AI to generate an optimal route in order to make travel time by car more enjoyable. This system is based on a car navigation app and can generate business revenue and advertising revenue by linking with tourism services such as restaurants and accommodations. Furthermore, it can complete all aspects of the trip, from planning to staying at the destination, within a single service. First, the user inputs the departure point and destination. Next, the AI ​​generates an optimal route based on the user's preferences and interests. This route includes tourist attractions, restaurants, accommodations, etc. For example, if the user is planning a trip from Tokyo to Kyoto, the AI ​​will suggest tourist attractions and restaurants along the way, providing the user with an enjoyable travel experience. Furthermore, the car navigation app will provide navigation based on the suggested route. The user can receive real-time suggestions for route changes and additions through the app. For example, if the user requests to "eat delicious ramen" along the way, the AI ​​will suggest a nearby ramen restaurant and update the route. This mechanism transforms travel time into a positive experience, allowing the user to enjoy the entire trip. In addition, business revenue and advertising revenue can be generated by linking with tourism services. For example, advertising revenue can be generated by displaying advertisements for suggested tourist attractions and restaurants. In this way, by using AI to transform travel time into something more engaging, it is possible to improve user satisfaction while also generating business and advertising revenue. Thus, a system that positively transforms travel time can turn users' travel time into an attractive experience.

[0072] The system for positively changing travel time according to the embodiment comprises a reception unit, a generation unit, a navigation unit, and a suggestion unit. The reception unit receives input of the departure point and destination. The reception unit provides, for example, an interface for the user to input the departure point and destination. The reception unit receives the information entered by the user and passes it to the generation unit. The generation unit generates the optimal route based on the information received by the reception unit. The generation unit generates the optimal route based on the user's preferences and interests, for example, using AI. The generation unit suggests the optimal route for the user by referring to the user's past behavior history, survey results, social media data, etc. If the user is planning a trip from Tokyo to Kyoto, the generation unit suggests tourist spots and gourmet spots along the way. The navigation unit performs navigation based on the route generated by the generation unit. The navigation unit guides the user along the route, for example, using a car navigation app. The navigation unit makes suggestions for route changes and additions in real time. The suggestion unit makes suggestions for route changes and additions in real time based on the navigation unit. For example, if a user requests to "eat some delicious ramen" along the way, the suggestion unit will suggest a nearby ramen restaurant and update the route. The suggestion unit can also collect user feedback and incorporate it into future suggestions. In this way, the system that positively transforms travel time according to the embodiment can turn the user's travel time into an engaging experience.

[0073] The reception unit accepts input of the departure and destination locations. For example, the reception unit provides an interface for users to input their departure and destination locations. Specifically, it enables users to easily input their departure and destination locations through applications or websites running on devices such as smartphones, tablets, and personal computers. The interface employs an intuitive and user-friendly design, providing functions for users to select their departure and destination locations on a map, or to specify addresses or landmarks via text input. Furthermore, a voice input function allows users to specify their departure and destination locations by voice. The reception unit receives the information entered by the user and passes it to the generation unit. At this time, the entered information is stored in a database and used to learn the user's past input history and preferences. In addition, the reception unit also has a function to verify the accuracy of the information entered by the user and to complete or correct it as needed. For example, if a user enters an ambiguous place name, it will suggest alternative locations and prompt the user to select one. Also, if the departure or destination locations do not meet certain conditions, such as a non-existent address or an inaccessible location, a warning will be displayed and the user will be prompted to re-enter the information. This allows the reception desk to support users in accurately and quickly entering their departure and destination locations, improving the overall accuracy and reliability of the system.

[0074] The generation unit generates the optimal route based on the information received by the reception unit. For example, the generation unit uses AI to generate the optimal route based on the user's preferences and interests. Specifically, the generation unit refers to the user's past behavior history, survey results, and social media data to propose the best route for the user. The AI ​​uses machine learning algorithms to analyze the user's preferences and interests and generate the optimal route. For example, if a user is planning a trip from Tokyo to Kyoto, it will suggest tourist spots and restaurants along the way. The generation unit retrieves information on tourist spots and restaurants from a database and selects places that match the user's preferences. Furthermore, the generation unit generates the optimal route considering real-time traffic and weather information. For example, it will suggest alternative routes to avoid delays due to traffic jams or construction. Also, if the weather deteriorates, it will prioritize suggesting indoor tourist spots and restaurants. The generation unit can also collect user feedback and reflect it in future suggestions. This allows the generation unit to provide the optimal route to make the user's travel time more comfortable and enjoyable.

[0075] The navigation unit provides navigation based on the route generated by the generation unit. For example, the navigation unit guides the user along a route using a car navigation app. Specifically, the navigation unit operates on a smartphone or car navigation device and guides the user along a route through voice guidance and visual guidance. The navigation unit makes real-time suggestions for route changes and additions. For example, it recalculates the optimal route in response to changes in traffic conditions or user requests and notifies the user. The navigation unit uses GPS data to accurately determine the user's current location and provides information such as points along the route, distance to the destination, and estimated travel time. The navigation unit also has a function to allow the user to add places they want to stop at or spots of interest along the way. For example, if the user requests "I want to eat delicious ramen," it will suggest nearby ramen restaurants and update the route. The navigation unit can also collect user feedback and reflect it in future navigations. This allows the navigation unit to provide the user with the best possible route guidance, making travel time more comfortable and enjoyable.

[0076] The suggestion unit, guided by the navigation unit, makes real-time route changes and additional suggestions. For example, if a user requests to "eat some delicious ramen" along the way, the suggestion unit will suggest a nearby ramen restaurant and update the route. Specifically, the suggestion unit searches its database for relevant information based on the user's request and makes the best suggestion. The suggestion unit selects the best places to stop by, taking into account the user's current location, destination, and travel time. Furthermore, the suggestion unit analyzes the user's past behavior history and preferences to provide more personalized suggestions. For example, based on places the user has visited and rated in the past, it suggests new places that the user might be interested in. The suggestion unit can also collect user feedback and incorporate it into future suggestions. For example, by having the user rate suggested places, the suggestion unit learns from that rating and improves the accuracy of future suggestions. In addition, the suggestion unit can provide multiple suggestions in response to the user's request, allowing the user to choose. This enables the suggestion unit to provide optimal suggestions to make the user's travel time more engaging and fulfilling.

[0077] The suggestion section includes an advertising section that displays advertisements for suggested tourist destinations and gourmet spots. The advertising section displays, for example, banner ads for suggested tourist destinations and gourmet spots. The advertising section can also display pop-up ads and inline ads. The advertising section displays visually appealing ads to encourage users to take an interest in the suggested tourist destinations and gourmet spots. The advertising section can also customize how ads are displayed to suit the user's preferences. For example, the advertising section displays highly relevant ads based on the user's past behavior history and interests. This allows for the generation of advertising revenue. Some or all of the above-described processes in the advertising section may be performed using AI or not. For example, the advertising section can input the user's behavior history into an AI and display ads using an AI model that generates highly relevant ads.

[0078] The proposal unit includes a feedback unit that collects user feedback and incorporates it into future proposals. The feedback unit collects user opinions, for example, through surveys. The feedback unit can also collect user reviews and rating scores. The feedback unit analyzes the collected feedback and incorporates it into future proposals. The feedback unit improves the accuracy of proposals based on user feedback. For example, the feedback unit collects user ratings of suggested tourist destinations and gourmet spots and uses those ratings to improve future proposals. This improves the accuracy of proposals by incorporating user feedback. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user feedback data into an AI and make proposals using an AI model that generates future proposals.

[0079] The generation unit generates the optimal route based on the user's preferences and interests. For example, the generation unit suggests the best route for the user by referencing the user's past behavior history, survey results, and social media data. The generation unit can use AI to identify the user's preferences and interests. For example, the generation unit inputs the user's past search history and social media posts into the AI ​​and generates the optimal route using an AI model that identifies the user's preferences and interests. The generation unit suggests tourist destinations and restaurants based on the user's preferences. For example, if the user is planning a trip from Tokyo to Kyoto, the generation unit suggests tourist destinations and restaurants along the way. This allows for the provision of an optimal route based on the user's preferences. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can suggest a route using an AI model that generates the optimal route based on the user's preferences and interests.

[0080] The navigation unit provides navigation based on the proposed route. For example, the navigation unit guides the user to a route using a car navigation app. The navigation unit provides navigation to the user until they reach their destination based on the proposed route. The navigation unit can also make real-time changes to the route or additional suggestions. For example, if the user requests to "eat some delicious ramen" along the way, the navigation unit will suggest a nearby ramen restaurant and update the route. This makes the user's journey smoother by providing navigation based on the proposed route. Some or all of the above processes in the navigation unit may be performed using AI or not. For example, the navigation unit can input the proposed route into an AI and provide route guidance using an AI model that performs navigation.

[0081] The suggestion unit makes real-time suggestions for route changes and additions. For example, if a user requests to "eat delicious ramen" along the way, the suggestion unit will suggest a nearby ramen restaurant and update the route. The suggestion unit makes real-time suggestions for route changes and additions in response to user requests. The suggestion unit can also collect user feedback and incorporate it into future suggestions. For example, the suggestion unit can collect user ratings for suggested tourist destinations and gourmet spots and use that feedback to improve future suggestions. This allows for flexible user travel by making real-time suggestions for route changes and additions. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user requests into an AI and make suggestions using an AI model that makes real-time suggestions for route changes and additions.

[0082] The reception desk estimates the user's emotions and adjusts the input method for the departure and destination based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of the departure and destination. This reduces user stress by providing an input method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception desk analyzes the user's past travel history and suggests the optimal input method. For example, the reception desk automatically displays departure and destination locations that the user has frequently entered in the past as suggestions. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk predicts and suggests departure and destination locations to be used at specific times based on the user's past travel history. This improves user convenience by suggesting the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into an AI and make suggestions using an AI model that suggests the optimal input method.

[0084] The reception desk completes the input when the user enters their departure and destination locations, taking into account the user's current traffic conditions and weather information. For example, when the user enters their departure location, the reception desk suggests the optimal departure time considering current traffic congestion information. When the user enters their destination, the reception desk suggests the optimal route based on current weather information. If the weather changes while the user is traveling, the reception desk updates the weather information in real time and suggests the optimal route again. This allows for more accurate input by taking into account current traffic conditions and weather information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input traffic conditions and weather information into an AI and make suggestions using an AI model that suggests the optimal departure time and route.

[0085] The reception desk estimates the user's emotions and determines the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk prioritizes inputting only the most important information. If the user is relaxed, the reception desk requests detailed information. If the user is in a hurry, the reception desk requests only the minimum necessary information. This reduces user stress by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reception desk, when the user enters their departure and destination locations, considers the user's geographical location information to suggest highly relevant locations. For example, when the user enters their current location, the reception desk suggests nearby major landmarks as potential locations. When the user enters their destination, the reception desk suggests places the user has visited in the past as potential locations. When the user enters a new location while traveling, the reception desk suggests the most suitable location considering the distance from the current location. In this way, by considering geographical location information, highly relevant locations can be suggested. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location information into an AI and suggest locations using an AI model that suggests highly relevant locations.

[0087] The reception desk analyzes the user's social media activity when the user enters their departure and destination locations and suggests relevant locations. For example, the reception desk may suggest locations where the user has checked in on social media, places the user follows on social media, or places the user has shared on social media. This allows the reception desk to suggest relevant locations by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's social media activity data into an AI and suggest locations using an AI model that proposes relevant locations.

[0088] The generation unit estimates the user's emotions and adjusts the route generation algorithm based on the estimated emotions. For example, if the user is relaxed, the generation unit uses an algorithm that prioritizes scenic routes. If the user is in a hurry, the generation unit uses an algorithm that prioritizes the shortest route. If the user is excited, the generation unit uses an algorithm that prioritizes routes with more activity. This improves user satisfaction by providing a route generation algorithm that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit proposes the optimal route by referring to the user's past travel history when generating a route. For example, the generation unit proposes the optimal route based on places the user has visited in the past. The generation unit proposes a route that avoids congestion based on the user's past travel history. The generation unit analyzes the user's past travel history and proposes the most efficient route. This improves user convenience by proposing the optimal route based on past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into an AI and propose a route using an AI model that proposes the optimal route.

[0090] The generation unit customizes routes based on the user's current interests and preferences when generating them. For example, the generation unit includes tourist destinations that the user is currently interested in, restaurants that the user is currently interested in, and activities that the user is currently interested in. This improves user satisfaction by providing routes based on current interests and preferences. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's current interests and preferences data into an AI and propose a route using an AI model that customizes routes.

[0091] The generation unit estimates the user's emotions and determines the priority of the routes to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit prioritizes routes with good scenery. If the user is in a hurry, the generation unit prioritizes the shortest route. If the user is excited, the generation unit prioritizes routes with more activity. This improves user satisfaction by providing route priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The generation unit proposes the optimal route when generating a route, taking into account the user's geographical location information. For example, the generation unit proposes the optimal route based on the user's current location. The generation unit proposes the optimal route based on the user's destination. The generation unit re-proposes the optimal route based on the user's current location while they are traveling. In this way, the optimal route can be provided by taking geographical location information into consideration. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into an AI and propose a route using an AI model that proposes the optimal route.

[0093] The generation unit analyzes the user's social media activity when generating routes and proposes relevant routes. For example, the generation unit includes locations where the user has checked in on social media in the route. The generation unit includes locations where the user follows on social media in the route. The generation unit includes locations where the user has shared on social media in the route. In this way, relevant routes can be proposed by analyzing social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity data into an AI and propose routes using an AI model that proposes relevant routes.

[0094] The navigation unit estimates the user's emotions and adjusts the navigation display method based on the estimated emotions. For example, if the user is tense, the navigation unit provides a simple and highly visible display method. If the user is relaxed, the navigation unit provides a display method that includes detailed information. If the user is in a hurry, the navigation unit provides a display method that gets straight to the point. By providing a navigation display method that responds to the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The navigation unit, during navigation, refers to the user's past driving history to propose the optimal navigation method. For example, the navigation unit proposes the optimal navigation method based on routes the user has used in the past. The navigation unit proposes a navigation method that avoids congestion based on the user's past driving history. The navigation unit analyzes the user's past driving history and proposes the most efficient navigation method. This improves user convenience by proposing the optimal navigation method based on past driving history. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's past driving history data into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0096] The navigation unit customizes the navigation method based on the user's current driving situation during navigation. For example, if the user is using a highway, the navigation unit provides navigation optimized for highways. If the user is driving in an urban area, the navigation unit provides navigation optimized for urban areas. If the user is driving on a mountain road, the navigation unit provides navigation optimized for mountain roads. This improves user convenience by providing navigation methods based on the current driving situation. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's current driving situation data into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0097] The navigation unit estimates the user's emotions and determines navigation priorities based on the estimated emotions. For example, if the user is tense, the navigation unit prioritizes displaying only the most important information. If the user is relaxed, the navigation unit displays detailed information. If the user is in a hurry, the navigation unit displays only the minimum necessary information. This improves user satisfaction by providing navigation priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The navigation unit proposes the optimal navigation method during navigation, taking into account the user's geographical location information. For example, the navigation unit proposes the optimal navigation method based on the user's current location. The navigation unit proposes the optimal navigation method based on the user's destination. The navigation unit re-proposes the optimal navigation method based on the user's current location while they are moving. In this way, the system can provide the optimal navigation method by taking geographical location information into consideration. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's geographical location information into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0099] The navigation unit analyzes the user's social media activity during navigation and provides relevant navigation information. For example, the navigation unit includes locations the user has checked into on social media in the navigation. The navigation unit includes locations the user follows on social media in the navigation. The navigation unit includes locations the user has shared on social media in the navigation. This allows the navigation unit to provide relevant navigation information by analyzing social media activity. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's social media activity data into an AI and perform navigation using an AI model that provides relevant navigation information.

[0100] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, it provides concise suggestions. If the user is excited, it provides visually stimulating suggestions. This improves user satisfaction by providing suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The suggestion unit makes optimal suggestions by referring to the user's past travel history. For example, the suggestion unit suggests new tourist destinations based on tourist destinations the user has visited in the past. The suggestion unit suggests similar accommodations based on accommodations the user has used in the past. The suggestion unit suggests activities that the user might be interested in based on their past travel history. This improves user convenience by making optimal suggestions based on past travel history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past travel history data into an AI and make suggestions using an AI model that makes optimal suggestions.

[0102] The suggestion function customizes suggestions based on the user's current interests. For example, the suggestion function might suggest gourmet spots the user is currently interested in, tourist destinations the user is currently interested in, or activities the user is currently interested in. This improves user satisfaction by providing suggestions based on current interests. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input data on the user's current interests into an AI and use an AI model to customize the suggestions.

[0103] The suggestion unit estimates the user's emotions and determines the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit prioritizes detailed suggestions. If the user is in a hurry, the suggestion unit prioritizes concise suggestions. If the user is excited, the suggestion unit prioritizes visually stimulating suggestions. This improves user satisfaction by providing suggestion priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The suggestion unit makes optimal suggestions by considering the user's geographical location information. For example, the suggestion unit suggests nearby tourist destinations based on the user's current location. The suggestion unit suggests tourist destinations along the way based on the user's destination. The suggestion unit then re-suggests optimal tourist destinations based on the user's current location while they are traveling. In this way, the system can provide optimal suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into an AI and make suggestions using an AI model that makes optimal suggestions.

[0105] The suggestion department analyzes the user's social media activity and makes relevant suggestions when making a suggestion. For example, the suggestion department suggests relevant tourist destinations based on places the user has checked in to on social media. The suggestion department suggests relevant tourist destinations based on places the user follows on social media. The suggestion department suggests relevant tourist destinations based on places the user has shared on social media. In this way, relevant suggestions can be provided by analyzing social media activity. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input the user's social media activity data into an AI and make suggestions using an AI model that makes relevant suggestions.

[0106] The advertising department estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. For example, if the user is relaxed, the advertising department displays a detailed ad. If the user is in a hurry, the advertising department displays a concise ad. If the user is excited, the advertising department displays a visually stimulating ad. This improves the effectiveness of ads by providing an advertising method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The advertising department displays the most relevant ads by referencing the user's past purchase history when displaying ads. For example, the advertising department advertises related products based on products the user has purchased in the past. The advertising department advertises products that the user is likely to be interested in based on the user's past purchase history. The advertising department analyzes the user's past purchase history and displays the most effective ads. This improves the effectiveness of advertising by displaying the most relevant ads based on past purchase history. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's past purchase history data into an AI and display ads using an AI model that displays the most relevant ads.

[0108] The advertising department customizes ads based on the user's current interests when displaying them. For example, the advertising department advertises products the user is currently interested in, services the user is currently interested in, or events the user is currently interested in. This improves the effectiveness of advertising by providing ads based on current interests. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's current interests into an AI and display ads using an AI model that customizes the ads.

[0109] The advertising department estimates the user's emotions and prioritizes ads based on those emotions. For example, if the user is relaxed, the advertising department prioritizes detailed ads. If the user is in a hurry, the advertising department prioritizes concise ads. If the user is excited, the advertising department prioritizes visually stimulating ads. This improves the effectiveness of ads by providing ad prioritization according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0110] The advertising department displays the most suitable advertisements when showing them, taking into account the user's geographical location. For example, the advertising department displays advertisements for nearby stores based on the user's current location. The advertising department displays advertisements for stores along the way based on the user's destination. The advertising department redisplays advertisements for the most suitable stores based on the user's current location while they are on the move. In this way, the advertising department can provide the most suitable advertisements by taking geographical location into consideration. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's geographical location information into an AI and display advertisements using an AI model that displays the most suitable advertisements.

[0111] The advertising department analyzes the user's social media activity when displaying ads and displays relevant ads. For example, the advertising department displays ads for relevant stores based on the locations the user has checked into on social media. The advertising department displays ads for stores the user follows on social media. The advertising department displays ads for stores the user has shared on social media. In this way, relevant ads can be provided by analyzing social media activity. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's social media activity data into an AI and display ads using an AI model that displays relevant ads.

[0112] The feedback unit estimates the user's emotions and adjusts the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the feedback unit requests detailed feedback. If the user is in a hurry, the feedback unit requests concise feedback. If the user is excited, the feedback unit requests visually stimulating feedback. This improves the accuracy of feedback by providing a feedback collection method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0113] The feedback unit, when collecting feedback, refers to the user's past feedback history to propose the optimal collection method. For example, the feedback unit suggests relevant questions based on feedback the user has provided in the past. The feedback unit suggests questions that the user might be interested in based on the user's past feedback history. The feedback unit analyzes the user's past feedback history and proposes the most effective questions. This improves the accuracy of feedback by suggesting the optimal collection method based on past feedback history. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's past feedback history data into an AI and collect feedback using an AI model that suggests the optimal collection method.

[0114] The feedback unit customizes questions based on the user's current interests when collecting feedback. For example, the feedback unit suggests questions about topics the user is currently interested in, services the user is currently interested in, or events the user is currently interested in. This improves the accuracy of feedback by providing questions based on current interests. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can collect feedback using an AI model that inputs the user's current interests into the AI ​​and customizes the questions.

[0115] The feedback unit estimates the user's emotions and determines the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit prioritizes detailed feedback. If the user is in a hurry, the feedback unit prioritizes concise feedback. If the user is excited, the feedback unit prioritizes visually stimulating feedback. This improves the accuracy of feedback by providing feedback priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The feedback unit proposes the optimal collection method when collecting feedback, taking into account the user's geographical location information. For example, the feedback unit requests feedback on nearby stores based on the user's current location. The feedback unit requests feedback on stores along the way based on the user's destination. The feedback unit then re-proposes optimal store feedback based on the user's current location while they are traveling. In this way, the system can provide the optimal feedback collection method by taking geographical location information into account. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's geographical location information into an AI and collect feedback using an AI model that proposes the optimal collection method.

[0117] The feedback unit analyzes the user's social media activity when collecting feedback and requests relevant feedback. For example, the feedback unit requests feedback on places the user has checked in to on social media. The feedback unit requests feedback on stores the user follows on social media. The feedback unit requests feedback on stores the user has shared on social media. This allows the system to provide relevant feedback by analyzing social media activity. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's social media activity data into an AI and collect feedback using an AI model that requests relevant feedback.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The reception desk estimates the user's emotions and adjusts the input method for the departure and destination based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of the departure and destination. This reduces user stress by providing an input method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0120] The generation unit estimates the user's emotions and adjusts the route generation algorithm based on the estimated emotions. For example, if the user is relaxed, the generation unit uses an algorithm that prioritizes scenic routes. If the user is in a hurry, the generation unit uses an algorithm that prioritizes the shortest route. If the user is excited, the generation unit uses an algorithm that prioritizes routes with more activity. This improves user satisfaction by providing a route generation algorithm that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0121] The navigation unit estimates the user's emotions and adjusts the navigation display method based on the estimated emotions. For example, if the user is tense, the navigation unit provides a simple and highly visible display method. If the user is relaxed, the navigation unit provides a display method that includes detailed information. If the user is in a hurry, the navigation unit provides a display method that gets straight to the point. By providing a navigation display method that responds to the user's emotions, user satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0122] The suggestion unit estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions. If the user is in a hurry, it provides concise suggestions. If the user is excited, it provides visually stimulating suggestions. This improves user satisfaction by providing suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0123] The advertising department estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. For example, if the user is relaxed, the advertising department displays a detailed ad. If the user is in a hurry, the advertising department displays a concise ad. If the user is excited, the advertising department displays a visually stimulating ad. This improves the effectiveness of ads by providing an advertising method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not. For example, the advertising department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0124] The reception desk analyzes the user's past travel history and suggests the optimal input method. For example, the reception desk automatically displays departure and destination locations that the user has frequently entered in the past as suggestions. The reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk predicts and suggests departure and destination locations to be used at specific times based on the user's past travel history. This improves user convenience by suggesting the optimal input method based on past travel history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past travel history data into an AI and make suggestions using an AI model that suggests the optimal input method.

[0125] The generation unit proposes the optimal route by referring to the user's past travel history when generating a route. For example, the generation unit proposes the optimal route based on places the user has visited in the past. The generation unit proposes a route that avoids congestion based on the user's past travel history. The generation unit analyzes the user's past travel history and proposes the most efficient route. This improves user convenience by proposing the optimal route based on past travel history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past travel history data into an AI and propose a route using an AI model that proposes the optimal route.

[0126] The navigation unit, during navigation, refers to the user's past driving history to propose the optimal navigation method. For example, the navigation unit proposes the optimal navigation method based on routes the user has used in the past. The navigation unit proposes a navigation method that avoids congestion based on the user's past driving history. The navigation unit analyzes the user's past driving history and proposes the most efficient navigation method. This improves user convenience by proposing the optimal navigation method based on past driving history. Some or all of the above processing in the navigation unit may be performed using AI or not. For example, the navigation unit can input the user's past driving history data into an AI and perform navigation using an AI model that proposes the optimal navigation method.

[0127] The suggestion unit makes optimal suggestions by referring to the user's past travel history. For example, the suggestion unit suggests new tourist destinations based on tourist destinations the user has visited in the past. The suggestion unit suggests similar accommodations based on accommodations the user has used in the past. The suggestion unit suggests activities that the user might be interested in based on their past travel history. This improves user convenience by making optimal suggestions based on past travel history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past travel history data into an AI and make suggestions using an AI model that makes optimal suggestions.

[0128] The advertising department displays the most relevant ads by referencing the user's past purchase history when displaying ads. For example, the advertising department advertises related products based on products the user has purchased in the past. The advertising department advertises products that the user is likely to be interested in based on the user's past purchase history. The advertising department analyzes the user's past purchase history and displays the most effective ads. This improves the effectiveness of advertising by displaying the most relevant ads based on past purchase history. Some or all of the above processes in the advertising department may be performed using AI or not. For example, the advertising department can input the user's past purchase history data into an AI and display ads using an AI model that displays the most relevant ads.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception unit accepts input of the departure and destination locations. The reception unit provides an interface for the user to enter the departure and destination locations, receives the information entered by the user, and passes it to the generation unit. Step 2: The generation unit generates the optimal route based on the information received by the reception unit. The generation unit uses AI to generate the optimal route based on the user's preferences and interests, and proposes the best route for the user by referring to the user's past behavior history, survey results, social media data, etc. Step 3: The navigation unit performs navigation based on the route generated by the generation unit. The navigation unit guides the user along the route using a car navigation app and makes real-time suggestions for route changes or additions. Step 4: The suggestion unit makes real-time route changes and additional suggestions based on the navigation unit's input. The suggestion unit also suggests nearby destinations and updates the route if requested by the user along the way. It can also collect user feedback and incorporate it into future suggestions.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, suggestion unit, and advertising unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input their departure point and destination. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate the optimal route based on the user's preferences and interests. The navigation unit is implemented by the control unit 46A of the smart device 14 and provides navigation based on the generated route. The suggestion unit is implemented by the control unit 46A of the smart device 14 and makes suggestions for route changes and additions in real time. The advertising unit is implemented by the control unit 46A of the smart device 14 and displays advertisements for suggested tourist destinations and gourmet spots. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, suggestion unit, and advertising unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their starting point and destination. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate the optimal route based on the user's preferences and interests. The navigation unit is implemented by the control unit 46A of the smart glasses 214 and provides navigation based on the generated route. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and makes suggestions for route changes and additions in real time. The advertising unit is implemented by the control unit 46A of the smart glasses 214 and displays advertisements for suggested tourist destinations and gourmet spots. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, suggestion unit, and advertising unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input their departure point and destination. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses AI to generate the optimal route based on the user's preferences and interests. The navigation unit is implemented by the control unit 46A of the headset terminal 314 and provides navigation based on the generated route. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and makes suggestions for route changes and additions in real time. The advertising unit is implemented by the control unit 46A of the headset terminal 314 and displays advertisements for suggested tourist destinations and gourmet spots. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, suggestion unit, and advertising unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input the departure point and destination. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses AI to generate the optimal route based on the user's preferences and interests. The navigation unit is implemented by the control unit 46A of the robot 414 and performs navigation based on the generated route. The suggestion unit is implemented by the control unit 46A of the robot 414 and makes suggestions for route changes and additions in real time. The advertising unit is implemented by the control unit 46A of the robot 414 and displays advertisements for suggested tourist destinations and gourmet spots. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] 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.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) A reception desk that accepts input of departure and destination, A generation unit generates the optimal route based on the information received by the reception unit, A navigation unit that performs navigation based on the route generated by the generation unit, The navigation unit provides a proposal unit that makes real-time suggestions for route changes and additions. A system characterized by the following features. (Note 2) It includes an advertising section that displays advertisements for suggested tourist destinations and gourmet spots. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feedback section that collects user feedback and incorporates it into future proposals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generates the optimal route based on the user's preferences and interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned navigation unit is Navigate based on the proposed route. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Suggest route changes and additions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for origin and destination based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their departure and destination locations, the system will complete the input by considering the user's current traffic conditions and weather information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their departure and destination locations, the system considers their geographical location to suggest highly relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their departure and destination locations, the system analyzes their social media activity and suggests relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the route generation algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a route, the system suggests the optimal route by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a route, customize the route based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of routes to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a route, the system proposes the optimal route considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During route generation, the system analyzes the user's social media activity and suggests relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is During navigation, the system refers to the user's past driving history to suggest the optimal navigation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, the navigation method is customized based on the user's current driving situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned navigation unit is During navigation, the system suggests the optimal navigation method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and provides relevant navigation information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, we refer to the user's past travel history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, customize the proposal content based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advertising department, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned advertising department, When displaying ads, the system refers to the user's past purchase history to display the most relevant ads. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned advertising department, When displaying ads, customize them based on the user's current interests and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned advertising department, It estimates user sentiment and prioritizes ads based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned advertising department, When displaying ads, the system takes into account the user's geographical location to show the most relevant ads. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned advertising department, When displaying ads, we analyze the user's social media activity and display relevant ads. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned feedback unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When collecting feedback, we refer to the user's past feedback history to suggest the optimal collection method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned feedback unit is When collecting feedback, customize questions based on the user's current interests and concerns. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned feedback unit is When collecting feedback, we propose the optimal collection method considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned feedback unit is When collecting feedback, we analyze users' social media activity and seek relevant feedback. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of departure and destination, A generation unit generates the optimal route based on the information received by the reception unit, A navigation unit that performs navigation based on the route generated by the generation unit, The navigation unit provides a proposal unit that makes real-time suggestions for route changes and additions. A system characterized by the following features.

2. It includes an advertising section that displays advertisements for suggested tourist destinations and gourmet spots. The system according to feature 1.

3. It includes a feedback section that collects user feedback and incorporates it into future proposals. The system according to feature 1.

4. The generating unit is Generates the optimal route based on the user's preferences and interests. The system according to feature 1.

5. The aforementioned navigation unit is Navigate based on the proposed route. The system according to feature 1.

6. The aforementioned proposal section is, Suggest route changes and additions in real time. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for origin and destination based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past travel history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter their departure and destination locations, the system will complete the input by considering the user's current traffic conditions and weather information. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

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