system

Abstract

The system according to this embodiment aims to provide customized route search that reflects the specific needs of the user. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a coordination unit. The reception unit receives the user's departure point, destination, and detailed needs. The analysis unit analyzes the information received by the reception unit. The generation unit generates a route based on the information analyzed by the analysis unit. The coordination unit coordinates with other services based on the route generated by the generation 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, there is a problem that it is difficult to perform route search reflecting the detailed needs of users, and there is a limit in customizing the moving experience.

[0005] The system according to the embodiment aims to provide a customized route search reflecting the detailed needs of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a coordination unit. The reception unit receives the user's origin, destination, and detailed needs. The analysis unit analyzes the information received by the reception unit. The generation unit generates a route based on the information analyzed by the analysis unit. The coordination unit coordinates with other services based on the route generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized route search that reflects the user's specific needs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The route search system according to an embodiment of the present invention is a system that utilizes generative AI to provide route searches that meet the detailed needs of users. This route search system accepts the user's departure point, destination, and detailed needs, analyzes them, and generates the optimal route. For example, it can handle requests such as using highways for part of the journey and taking a meal break after one hour. By using generative AI, the user's needs are divided into multiple parts and combined, enabling more detailed route searches. First, the user inputs their departure point, destination, and detailed needs (for example, wanting to use highways for part of the journey, wanting to take a break every hour, etc.). This information is input into the generative AI. The generative AI analyzes the input information and generates routes that correspond to each need. For example, it divides the route from the departure point to the destination into multiple segments and sets the optimal means of transport and rest points for each segment. Next, it connects the routes of each generated segment to construct the overall route. This provides a customized route that meets the user's detailed needs. For example, a route is generated that travels using highways from the departure point, takes a break at a service area after one hour, and then reaches the destination using ordinary roads. Furthermore, the present invention can provide gourmet and accommodation information in conjunction with other services to address purchasing behavior while on the move. For example, it can provide information on nearby restaurants and hotels while the user is traveling, enabling seamless use. This makes the travel experience more enjoyable and provides a more personalized service. In this way, the present invention provides route search that meets the detailed needs of users, making the travel experience more enjoyable and personalized. In addition, by coordinating with other services, it provides a seamless user experience, achieving an experience that is overwhelmingly differentiated from other companies' services. As a result, the route search system can provide route search that meets the detailed needs of users.

[0029] The route search system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a collaboration unit. The reception unit receives the user's departure point, destination, and detailed needs. For example, the reception unit can receive the departure point, destination, and detailed needs entered by the user in natural language. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the user's input and extracts information for generating a route that corresponds to each need. The generation unit generates a route based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to divide the route from the departure point to the destination into multiple segments and sets the optimal means of transport and rest stops for each segment. The collaboration unit collaborates with other services based on the route generated by the generation unit. For example, the collaboration unit collaborates with gourmet information services and accommodation reservation services to provide information on nearby restaurants and hotels while the user is traveling. As a result, the route search system according to this embodiment can provide a route search that corresponds to the user's detailed needs. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's natural language input into a generation AI, which can then perform natural language processing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input into a generation AI, which can then perform analysis. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit uses a generation AI to generate routes based on the user's needs. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can use a generation AI to collaborate with other services based on the generated routes. As a result, the route search system according to this embodiment can provide route searches that meet the specific needs of the user.

[0030] The reception desk receives the user's departure point, destination, and specific needs. For example, the reception desk can receive the departure point, destination, and specific needs entered by the user in natural language. Specifically, when a user enters their departure point and destination using a smartphone or computer, the reception desk uses natural language processing technology to understand the content. For example, it can accept input such as, "I want to go from Tokyo Station to Osaka Station at 8 AM tomorrow. It would be great if there was a cafe where I could take a break along the way." In this case, the reception desk sends the user's input to a generating AI, which performs natural language processing. The generating AI analyzes the entered text and extracts information such as the departure point, destination, departure time, and rest points. Furthermore, it can also accept special requests as specific user needs, such as "places where pets are allowed" or "facilities that are wheelchair accessible." This allows the reception desk to accurately collect information that meets the diverse needs of users and prepare it for passing on to the analysis department.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit uses, for example, natural language processing technology to analyze the user's input and extract information to generate routes that meet each user's needs. Specifically, it uses a generation AI to analyze the user's input in detail. The generation AI tokenizes the input text and analyzes the context to understand the meaning of each token. For example, in response to the input "I want to go from Tokyo Station to Osaka Station," it identifies that the departure point is "Tokyo Station" and the destination is "Osaka Station." Also, in response to the request "It would be nice to have a cafe where I can take a break along the way," it recognizes that a cafe should be included as a rest stop. Furthermore, the analysis unit considers the user's special requests (for example, places where pets are allowed or facilities that are wheelchair accessible) and extracts information to address these requests. As a result, the analysis unit can provide the generation unit with detailed information based on the user's needs and build a foundation for generating the optimal route.

[0032] The generation unit generates routes based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to divide the route from the starting point to the destination into multiple segments and sets the optimal mode of transport and rest stops for each segment. Specifically, the generation AI calculates the shortest route from the starting point to the destination and divides that route into multiple segments. For each segment, it selects the optimal mode of transport (e.g., train, bus, taxi) according to the user's needs. It also sets cafes and restaurants as rest stops along the way based on the user's requests. For example, it generates a route that "travels by train from Tokyo Station to Osaka Station, with a rest stop at Nagoya Station along the way." Furthermore, to accommodate the user's special requests (e.g., pet-friendly places or wheelchair-accessible facilities), the generation unit includes facilities that meet these requests in the route. In this way, the generation unit can generate an optimal route that meets the user's specific needs and prepare it for handover to the next coordinating unit.

[0033] The integration unit collaborates with other services based on the routes generated by the generation unit. For example, the integration unit collaborates with gourmet information services and accommodation booking services to provide information on nearby restaurants and hotels while the user is traveling. Specifically, it collaborates with external information services via APIs to provide services that users can use while traveling, based on the generated route. For example, when a user takes a break at Nagoya Station, it collaborates with a gourmet information service to provide information on nearby cafes and restaurants. Also, if the user wishes to stay overnight, it collaborates with an accommodation booking service to provide information on the availability and booking status of nearby hotels. Furthermore, the integration unit provides information on facilities that meet the user's special requests (for example, places that allow pets or facilities that are wheelchair accessible). In this way, the integration unit can provide information that meets the user's needs while traveling and support the user in traveling comfortably. In addition, the integration unit can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can collect evaluations and comments on services used by users and reflect them in the next route generation and information provision. In this way, the integration unit can provide better services to users and improve the overall performance of the system.

[0034] The reception desk can receive the user's departure point, destination, and specific needs in natural language. For example, the reception desk can receive the departure point, destination, and specific needs entered by the user in natural language. For example, the reception desk can receive natural language input from the user such as "Departure point is Tokyo, destination is Osaka, and I would like to use the expressway along the way." The reception desk can also receive specific needs from the user in natural language such as "I would like to take a break every hour." This allows the user to input specific needs in natural language. The specific range of natural language and the languages ​​it supports include, but is not limited to, Japanese, English, and other languages. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's natural language input into a generating AI, which can then perform natural language processing.

[0035] The analysis unit can analyze the information received by the reception unit and generate routes that meet each user's needs. For example, the analysis unit can use natural language processing technology to analyze the user's input and extract information to generate routes that meet each user's needs. For example, the analysis unit can analyze the user's need to "use a highway along the way" and extract information to generate a route that uses a highway. The analysis unit can also analyze the user's need to "take a break every hour" and extract information to set rest points. This allows the system to generate routes that meet the user's specific needs. Routes that meet each user's needs may include, but are not limited to, the selection of transportation methods, the specification of intermediate stops, and time constraints. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input into a generation AI, which can then perform the analysis.

[0036] The generation unit can generate routes divided into multiple segments based on the information analyzed by the analysis unit. For example, the generation unit can use a generation AI to divide a route from the starting point to the destination into multiple segments and set the optimal mode of transport and rest stops for each segment. For example, the generation unit can divide a route from the starting point to the destination into a highway segment and a general road segment and set the optimal mode of transport for each. The generation unit can also divide a route into time segments to set rest stops every hour. This allows the generation of routes divided into multiple segments. Routes divided into multiple segments include, but are not limited to, divisions by section or by time of day. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate routes based on user needs.

[0037] The integration unit can integrate with other services based on the route generated by the generation unit. For example, the integration unit can integrate with a gourmet information service or a lodging reservation service to provide information on nearby restaurants and hotels while the user is traveling. For example, if the user is looking for a nearby restaurant while traveling, the integration unit will integrate with a gourmet information service to provide information on nearby restaurants. Also, if the user is looking for lodging while traveling, the integration unit will integrate with a lodging reservation service to provide information on nearby hotels. This allows for the provision of information on nearby restaurants and hotels while the user is traveling. Nearby restaurants and hotels include, but are not limited to, a radius of a certain number of kilometers or a specific area. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can use generation AI to integrate with other services based on the generated route.

[0038] The reception desk can analyze the user's past route search history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the departure and destination points that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest departure and destination points to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past history. The optimal input method includes, but is not limited to, ease of input, accuracy, and user preference. Some or all of the processing described above in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past route search history into a generating AI, which can then suggest the optimal input method.

[0039] The reception unit can complete inputs by considering the user's current traffic conditions and weather information when the user enters their departure and destination locations. For example, when the user enters their departure location, the reception unit can suggest the optimal departure time based on current traffic congestion information. The reception unit can also suggest the optimal route based on current weather information when the user enters their destination. Furthermore, if the weather changes while the user is traveling, the reception unit can resuggest a route in real time. This allows the system to complete inputs by considering current traffic conditions and weather information. Traffic conditions and weather information include, but are not limited to, real-time data and forecast data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input current traffic conditions and weather information into a generating AI, which can then complete the inputs.

[0040] The reception unit can suggest highly relevant locations by considering the user's geographical location information when the user enters their departure and destination points. For example, the reception unit can automatically display nearby departure point candidates based on the user's current location. It can also suggest the most suitable destination by considering the distance from the user's current location when the user enters their destination. Furthermore, if the user is using the app while on the move, the reception unit can update the user's current location in real time and suggest the most suitable destination. This allows the reception unit to suggest highly relevant locations by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not. For example, the reception unit can input the user's geographical location information into a generating AI, which can then suggest highly relevant locations.

[0041] The reception desk can analyze the user's social media activity when they input their departure and destination locations and suggest relevant candidate locations. For example, the reception desk can suggest departure and destination locations based on places the user has checked in to on social media. It can also suggest relevant candidate locations based on places and events the user follows on social media. Furthermore, the reception desk can suggest places of interest as candidate locations based on photos and posts the user has shared on social media. This allows the reception desk to suggest relevant candidate locations based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into a generating AI, which can then suggest relevant candidate locations.

[0042] The analysis unit can select the optimal route analysis method by referring to the user's past travel history during analysis. For example, the analysis unit can select the optimal route analysis method based on routes previously used by the user. The analysis unit can also select a route analysis method that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and select the most efficient route analysis method. This allows the analysis unit to select the optimal route analysis method based on the user's past travel history. The optimal route analysis method includes, but is not limited to, analysis based on past data and use of real-time data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history into a generating AI, which can then select the optimal route analysis method.

[0043] The analysis unit can customize the analysis results based on the user's current lifestyle and areas of interest during the analysis process. For example, if the user is health-conscious, the analysis unit will prioritize suggesting walking or cycling routes. It can also suggest routes that include tourist spots if the user wants to enjoy sightseeing. Furthermore, if the user is traveling for business purposes, the analysis unit can suggest the shortest possible route. This allows for the customization of analysis results based on the user's current lifestyle and areas of interest. These lifestyle and areas of interest include, but are not limited to, survey data and analysis of behavioral history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then customize the analysis results.

[0044] The analysis unit can optimize the analysis results by considering the user's geographical location information during analysis. For example, the analysis unit can suggest the optimal route based on the user's current location. Furthermore, when the user enters a destination, the analysis unit can suggest the optimal route by considering the distance from the current location. Additionally, if the user is using the app while on the move, the analysis unit can update the current location in real time and suggest the optimal route. This allows for the optimization of the analysis results by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then optimize the analysis results.

[0045] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can suggest the optimal route based on the locations the user has checked into on social media. It can also provide relevant analysis results based on the locations and events the user follows on social media. Furthermore, the analysis unit can include locations of interest in the route based on photos and posts the user has shared on social media. This allows the analysis unit to provide relevant analysis results based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into a generating AI, which can then provide relevant analysis results.

[0046] The generation unit can select the optimal route generation method by referring to the user's past travel history when generating a route. For example, the generation unit can select the optimal route generation method based on routes previously used by the user. Furthermore, the generation unit can also select a route generation method that avoids congestion based on the user's past travel history. In addition, the generation unit can analyze the user's past travel history and select the most efficient route generation method. This allows the generation unit to select the optimal route generation method based on the user's past travel history. The optimal route generation method includes, but is not limited to, generation based on historical data or the use of real-time data. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can then select the optimal route generation method.

[0047] The generation unit can customize routes based on the user's current lifestyle and areas of interest during route generation. For example, if the user is health-conscious, the generation unit will prioritize suggesting walking or cycling routes. It can also suggest routes that include tourist attractions if the user wants to enjoy sightseeing. Furthermore, if the user is traveling for business purposes, the generation unit can suggest the shortest possible route. This allows for route customization based on the user's current lifestyle and areas of interest. These lifestyle and areas of interest include, but are not limited to, survey data and analysis of behavioral history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the user's lifestyle and areas of interest into the generation AI, which can then customize the route.

[0048] The generation unit can generate the optimal route by considering the user's geographical location information during route generation. For example, the generation unit can suggest the optimal route based on the user's current location. Furthermore, when the user enters a destination, the generation unit can suggest the optimal route by considering the distance from the current location. Additionally, if the user is using the app while on the move, the generation unit can update the current location in real time and suggest the optimal route. This allows the generation of the optimal route to take the user's geographical location information into account. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate the optimal route.

[0049] The generation unit can analyze the user's social media activity and suggest relevant routes when generating routes. For example, the generation unit can suggest the optimal route based on the locations the user has checked into on social media. It can also suggest relevant routes based on the places and events the user follows on social media. Furthermore, the generation unit can include places of interest in the route based on photos and posts the user has shared on social media. This allows the generation unit to suggest relevant routes based on the user's social media activity. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's social media activity into the generation AI, which can then suggest relevant routes.

[0050] The integration unit can suggest the most suitable service by referring to the user's past purchase history during integration. For example, the integration unit can suggest nearby restaurants based on information about restaurants the user has visited in the past. It can also suggest nearby hotels based on information about hotels the user has stayed at in the past. Furthermore, the integration unit can suggest shops of interest based on the user's past purchase history. This allows the integration unit to suggest the most suitable service based on the user's past purchase history. The most suitable service includes, but is not limited to, the user's preferences and past usage history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past purchase history into a generating AI, which can then suggest the most suitable service.

[0051] The integration unit can customize the integrated service based on the user's current lifestyle and areas of interest during integration. For example, if the user is health-conscious, the integration unit can provide information on healthy restaurants. It can also provide information on tourist spots if the user wants to enjoy sightseeing. Furthermore, if the user is traveling for business purposes, the integration unit can provide information on business hotels. This allows for the customization of integrated services based on the user's current lifestyle and areas of interest. These lifestyle and areas of interest include, but are not limited to, surveys and analysis of behavioral history. Some or all of the processing described above in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then customize the integrated service.

[0052] The integration unit can suggest the most suitable service when integrating, taking into account the user's geographical location information. For example, the integration unit can suggest nearby restaurants based on the user's current location. It can also suggest the most suitable service when the user enters a destination, taking into account the distance from the current location. Furthermore, if the user is using the app while on the move, the integration unit can update the user's current location in real time and suggest the most suitable service. This allows the integration unit to suggest the most suitable service considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not. For example, the integration unit can input the user's geographical location information into a generating AI, which can then suggest the most suitable service.

[0053] The integration unit can analyze the user's social media activity during integration and provide relevant services. For example, the integration unit can suggest relevant services based on the locations the user has checked into on social media. It can also provide relevant services based on the locations and events the user follows on social media. Furthermore, the integration unit can provide services of interest based on the photos and posts the user has shared on social media. In this way, relevant services can be provided based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media activity into a generating AI, which can then provide relevant services.

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

[0055] The analysis unit can analyze the user's past route search history and select the optimal route analysis method. For example, it can select the optimal route analysis method based on routes the user has used in the past. It can also select a route analysis method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and select the most efficient route analysis method. This allows the system to select the optimal route analysis method based on the user's past travel history. The optimal route analysis method includes, but is not limited to, analysis based on past data and use of real-time data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history into a generating AI, which can then select the optimal route analysis method.

[0056] The reception unit can complete inputs by considering the user's current traffic conditions and weather information when the user enters their departure and destination locations. For example, when the user enters their departure location, it can suggest the optimal departure time based on current traffic congestion information. Similarly, when the user enters their destination, it can suggest the optimal route based on current weather information. Furthermore, if the weather changes while the user is traveling, it can resuggest a route in real time. This allows the system to complete inputs by considering current traffic conditions and weather information. Traffic conditions and weather information include, but are not limited to, real-time data and forecast data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input current traffic conditions and weather information into a generating AI, which can then complete the inputs.

[0057] The generation unit can select the optimal route generation method by referring to the user's past travel history when generating a route. For example, it can select the optimal route generation method based on routes the user has used in the past. It can also select a route generation method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and select the most efficient route generation method. This allows the optimal route generation method to be selected based on the user's past travel history. The optimal route generation method includes, but is not limited to, generation based on past data and use of real-time data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can then select the optimal route generation method.

[0058] The integration unit can suggest the most suitable service by referring to the user's past purchase history during integration. For example, it can suggest nearby restaurants based on information about restaurants the user has visited in the past. It can also suggest nearby hotels based on information about hotels the user has stayed at in the past. Furthermore, it can suggest shops of interest based on the user's past purchase history. In this way, the integration unit can suggest the most suitable service based on the user's past purchase history. The most suitable service includes, but is not limited to, the user's preferences and past usage history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past purchase history into a generating AI, which can then suggest the most suitable service.

[0059] The reception desk can analyze the user's past route search history and suggest the optimal input method. For example, it can automatically display as suggestions the departure and destination points that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest departure and destination points to be used during specific time periods based on the user's past input history. This allows the system to suggest the optimal input method based on the user's past history. The optimal input method includes, but is not limited to, ease of input, accuracy, and user preference. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past route search history into a generating AI, which can then suggest the optimal input method.

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

[0061] Step 1: The reception desk receives the user's departure point, destination, and specific needs. For example, it can accept the departure point, destination, and specific needs entered by the user in natural language. The processing at the reception desk may or may not be performed using AI. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it uses natural language processing technology to analyze the user's input and extract information to generate routes that meet each user's needs. The processing in the analysis unit may be performed using AI or not. Step 3: The generation unit generates a route based on the information analyzed by the analysis unit. For example, using the generation AI, it divides the route from the starting point to the destination into multiple segments and sets the optimal mode of transport and rest stops for each segment. The processing in the generation unit is performed using the generation AI. Step 4: The integration unit integrates with other services based on the route generated by the generation unit. For example, it integrates with gourmet information services and accommodation booking services to provide information on nearby restaurants and hotels while the user is traveling. The processing in the integration unit may or may not be performed using AI.

[0062] (Example of form 2) The route search system according to an embodiment of the present invention is a system that utilizes generative AI to provide route searches that meet the detailed needs of users. This route search system accepts the user's departure point, destination, and detailed needs, analyzes them, and generates the optimal route. For example, it can handle requests such as using highways for part of the journey and taking a meal break after one hour. By using generative AI, the user's needs are divided into multiple parts and combined, enabling more detailed route searches. First, the user inputs their departure point, destination, and detailed needs (for example, wanting to use highways for part of the journey, wanting to take a break every hour, etc.). This information is input into the generative AI. The generative AI analyzes the input information and generates routes that correspond to each need. For example, it divides the route from the departure point to the destination into multiple segments and sets the optimal means of transport and rest points for each segment. Next, it connects the routes of each generated segment to construct the overall route. This provides a customized route that meets the user's detailed needs. For example, a route is generated that travels using highways from the departure point, takes a break at a service area after one hour, and then reaches the destination using ordinary roads. Furthermore, the present invention can provide gourmet and accommodation information in conjunction with other services to address purchasing behavior while on the move. For example, it can provide information on nearby restaurants and hotels while the user is traveling, enabling seamless use. This makes the travel experience more enjoyable and provides a more personalized service. In this way, the present invention provides route search that meets the detailed needs of users, making the travel experience more enjoyable and personalized. In addition, by coordinating with other services, it provides a seamless user experience, achieving an experience that is overwhelmingly differentiated from other companies' services. As a result, the route search system can provide route search that meets the detailed needs of users.

[0063] The route search system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a collaboration unit. The reception unit receives the user's departure point, destination, and detailed needs. For example, the reception unit can receive the departure point, destination, and detailed needs entered by the user in natural language. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses natural language processing technology to analyze the user's input and extracts information for generating a route that corresponds to each need. The generation unit generates a route based on the information analyzed by the analysis unit. For example, the generation unit uses generation AI to divide the route from the departure point to the destination into multiple segments and sets the optimal means of transport and rest stops for each segment. The collaboration unit collaborates with other services based on the route generated by the generation unit. For example, the collaboration unit collaborates with gourmet information services and accommodation reservation services to provide information on nearby restaurants and hotels while the user is traveling. As a result, the route search system according to this embodiment can provide a route search that corresponds to the user's detailed needs. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's natural language input into a generation AI, which can then perform natural language processing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input into a generation AI, which can then perform analysis. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit uses a generation AI to generate routes based on the user's needs. Some or all of the above-described processes in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can use a generation AI to collaborate with other services based on the generated routes. As a result, the route search system according to this embodiment can provide route searches that meet the specific needs of the user.

[0064] The reception desk receives the user's departure point, destination, and specific needs. For example, the reception desk can receive the departure point, destination, and specific needs entered by the user in natural language. Specifically, when a user enters their departure point and destination using a smartphone or computer, the reception desk uses natural language processing technology to understand the content. For example, it can accept input such as, "I want to go from Tokyo Station to Osaka Station at 8 AM tomorrow. It would be great if there was a cafe where I could take a break along the way." In this case, the reception desk sends the user's input to a generating AI, which performs natural language processing. The generating AI analyzes the entered text and extracts information such as the departure point, destination, departure time, and rest points. Furthermore, it can also accept special requests as specific user needs, such as "places where pets are allowed" or "facilities that are wheelchair accessible." This allows the reception desk to accurately collect information that meets the diverse needs of users and prepare it for passing on to the analysis department.

[0065] The analysis unit analyzes the information received by the reception unit. The analysis unit uses, for example, natural language processing technology to analyze the user's input and extract information to generate routes that meet each user's needs. Specifically, it uses a generation AI to analyze the user's input in detail. The generation AI tokenizes the input text and analyzes the context to understand the meaning of each token. For example, in response to the input "I want to go from Tokyo Station to Osaka Station," it identifies that the departure point is "Tokyo Station" and the destination is "Osaka Station." Also, in response to the request "It would be nice to have a cafe where I can take a break along the way," it recognizes that a cafe should be included as a rest stop. Furthermore, the analysis unit considers the user's special requests (for example, places where pets are allowed or facilities that are wheelchair accessible) and extracts information to address these requests. As a result, the analysis unit can provide the generation unit with detailed information based on the user's needs and build a foundation for generating the optimal route.

[0066] The generation unit generates routes based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to divide the route from the starting point to the destination into multiple segments and sets the optimal mode of transport and rest stops for each segment. Specifically, the generation AI calculates the shortest route from the starting point to the destination and divides that route into multiple segments. For each segment, it selects the optimal mode of transport (e.g., train, bus, taxi) according to the user's needs. It also sets cafes and restaurants as rest stops along the way based on the user's requests. For example, it generates a route that "travels by train from Tokyo Station to Osaka Station, with a rest stop at Nagoya Station along the way." Furthermore, to accommodate the user's special requests (e.g., pet-friendly places or wheelchair-accessible facilities), the generation unit includes facilities that meet these requests in the route. In this way, the generation unit can generate an optimal route that meets the user's specific needs and prepare it for handover to the next coordinating unit.

[0067] The integration unit collaborates with other services based on the routes generated by the generation unit. For example, the integration unit collaborates with gourmet information services and accommodation booking services to provide information on nearby restaurants and hotels while the user is traveling. Specifically, it collaborates with external information services via APIs to provide services that users can use while traveling, based on the generated route. For example, when a user takes a break at Nagoya Station, it collaborates with a gourmet information service to provide information on nearby cafes and restaurants. Also, if the user wishes to stay overnight, it collaborates with an accommodation booking service to provide information on the availability and booking status of nearby hotels. Furthermore, the integration unit provides information on facilities that meet the user's special requests (for example, places that allow pets or facilities that are wheelchair accessible). In this way, the integration unit can provide information that meets the user's needs while traveling and support the user in traveling comfortably. In addition, the integration unit can collect user feedback and continuously improve the accuracy and effectiveness of the information it provides. For example, it can collect evaluations and comments on services used by users and reflect them in the next route generation and information provision. In this way, the integration unit can provide better services to users and improve the overall performance of the system.

[0068] The reception desk can receive the user's departure point, destination, and specific needs in natural language. For example, the reception desk can receive the departure point, destination, and specific needs entered by the user in natural language. For example, the reception desk can receive natural language input from the user such as "Departure point is Tokyo, destination is Osaka, and I would like to use the expressway along the way." The reception desk can also receive specific needs from the user in natural language such as "I would like to take a break every hour." This allows the user to input specific needs in natural language. The specific range of natural language and the languages ​​it supports include, but is not limited to, Japanese, English, and other languages. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's natural language input into a generating AI, which can then perform natural language processing.

[0069] The analysis unit can analyze the information received by the reception unit and generate routes that meet each user's needs. For example, the analysis unit can use natural language processing technology to analyze the user's input and extract information to generate routes that meet each user's needs. For example, the analysis unit can analyze the user's need to "use a highway along the way" and extract information to generate a route that uses a highway. The analysis unit can also analyze the user's need to "take a break every hour" and extract information to set rest points. This allows the system to generate routes that meet the user's specific needs. Routes that meet each user's needs may include, but are not limited to, the selection of transportation methods, the specification of intermediate stops, and time constraints. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input into a generation AI, which can then perform the analysis.

[0070] The generation unit can generate routes divided into multiple segments based on the information analyzed by the analysis unit. For example, the generation unit can use a generation AI to divide a route from the starting point to the destination into multiple segments and set the optimal mode of transport and rest stops for each segment. For example, the generation unit can divide a route from the starting point to the destination into a highway segment and a general road segment and set the optimal mode of transport for each. The generation unit can also divide a route into time segments to set rest stops every hour. This allows the generation of routes divided into multiple segments. Routes divided into multiple segments include, but are not limited to, divisions by section or by time of day. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate routes based on user needs.

[0071] The integration unit can integrate with other services based on the route generated by the generation unit. For example, the integration unit can integrate with a gourmet information service or a lodging reservation service to provide information on nearby restaurants and hotels while the user is traveling. For example, if the user is looking for a nearby restaurant while traveling, the integration unit will integrate with a gourmet information service to provide information on nearby restaurants. Also, if the user is looking for lodging while traveling, the integration unit will integrate with a lodging reservation service to provide information on nearby hotels. This allows for the provision of information on nearby restaurants and hotels while the user is traveling. Nearby restaurants and hotels include, but are not limited to, a radius of a certain number of kilometers or a specific area. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can use generation AI to integrate with other services based on the generated route.

[0072] The reception desk can estimate the user's emotions and adjust the input method for the departure and destination based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the departure and destination. This provides 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 using AI. For example, the reception desk can input user emotion data into a generative AI, which can then perform emotion estimation.

[0073] The reception desk can analyze the user's past route search history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the departure and destination points that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest departure and destination points to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past history. The optimal input method includes, but is not limited to, ease of input, accuracy, and user preference. Some or all of the processing described above in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past route search history into a generating AI, which can then suggest the optimal input method.

[0074] The reception unit can complete inputs by considering the user's current traffic conditions and weather information when the user enters their departure and destination locations. For example, when the user enters their departure location, the reception unit can suggest the optimal departure time based on current traffic congestion information. The reception unit can also suggest the optimal route based on current weather information when the user enters their destination. Furthermore, if the weather changes while the user is traveling, the reception unit can resuggest a route in real time. This allows the system to complete inputs by considering current traffic conditions and weather information. Traffic conditions and weather information include, but are not limited to, real-time data and forecast data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input current traffic conditions and weather information into a generating AI, which can then complete the inputs.

[0075] The reception desk can estimate the user's emotions and prioritize specific needs based on those emotions. For example, if the user is tired, the reception desk will prioritize suggesting rest stops. If the user is in a hurry, the reception desk can also prioritize suggesting the shortest route. Furthermore, if the user is relaxed, the reception desk can prioritize suggesting a scenic route. This allows for the prioritization of specific needs based on 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 user emotion data into a generative AI, which can then perform emotion estimation.

[0076] The reception unit can suggest highly relevant locations by considering the user's geographical location information when the user enters their departure and destination points. For example, the reception unit can automatically display nearby departure point candidates based on the user's current location. It can also suggest the most suitable destination by considering the distance from the user's current location when the user enters their destination. Furthermore, if the user is using the app while on the move, the reception unit can update the user's current location in real time and suggest the most suitable destination. This allows the reception unit to suggest highly relevant locations by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not. For example, the reception unit can input the user's geographical location information into a generating AI, which can then suggest highly relevant locations.

[0077] The reception desk can analyze the user's social media activity when they input their departure and destination locations and suggest relevant candidate locations. For example, the reception desk can suggest departure and destination locations based on places the user has checked in to on social media. It can also suggest relevant candidate locations based on places and events the user follows on social media. Furthermore, the reception desk can suggest places of interest as candidate locations based on photos and posts the user has shared on social media. This allows the reception desk to suggest relevant candidate locations based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into a generating AI, which can then suggest relevant candidate locations.

[0078] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide multiple route options. If the user is in a hurry, the analysis unit can perform a quick analysis and prioritize providing the shortest route. Furthermore, if the user is stressed, the analysis unit can perform a simple analysis and provide an intuitive route. This allows the accuracy of the analysis to be adjusted based on 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0079] The analysis unit can select the optimal route analysis method by referring to the user's past travel history during analysis. For example, the analysis unit can select the optimal route analysis method based on routes previously used by the user. The analysis unit can also select a route analysis method that avoids congestion based on the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and select the most efficient route analysis method. This allows the analysis unit to select the optimal route analysis method based on the user's past travel history. The optimal route analysis method includes, but is not limited to, analysis based on past data and use of real-time data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history into a generating AI, which can then select the optimal route analysis method.

[0080] The analysis unit can customize the analysis results based on the user's current lifestyle and areas of interest during the analysis process. For example, if the user is health-conscious, the analysis unit will prioritize suggesting walking or cycling routes. It can also suggest routes that include tourist spots if the user wants to enjoy sightseeing. Furthermore, if the user is traveling for business purposes, the analysis unit can suggest the shortest possible route. This allows for the customization of analysis results based on the user's current lifestyle and areas of interest. These lifestyle and areas of interest include, but are not limited to, survey data and analysis of behavioral history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then customize the analysis results.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0082] The analysis unit can optimize the analysis results by considering the user's geographical location information during analysis. For example, the analysis unit can suggest the optimal route based on the user's current location. Furthermore, when the user enters a destination, the analysis unit can suggest the optimal route by considering the distance from the current location. Additionally, if the user is using the app while on the move, the analysis unit can update the current location in real time and suggest the optimal route. This allows for the optimization of the analysis results by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then optimize the analysis results.

[0083] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can suggest the optimal route based on the locations the user has checked into on social media. It can also provide relevant analysis results based on the locations and events the user follows on social media. Furthermore, the analysis unit can include locations of interest in the route based on photos and posts the user has shared on social media. This allows the analysis unit to provide relevant analysis results based on the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity into a generating AI, which can then provide relevant analysis results.

[0084] The generation unit can estimate the user's emotions and adjust the route generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a route that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a route that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a route with visually stimulating effects. This allows the route generation method to be adjusted based on 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 is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotions.

[0085] The generation unit can select the optimal route generation method by referring to the user's past travel history when generating a route. For example, the generation unit can select the optimal route generation method based on routes previously used by the user. Furthermore, the generation unit can also select a route generation method that avoids congestion based on the user's past travel history. In addition, the generation unit can analyze the user's past travel history and select the most efficient route generation method. This allows the generation unit to select the optimal route generation method based on the user's past travel history. The optimal route generation method includes, but is not limited to, generation based on historical data or the use of real-time data. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can then select the optimal route generation method.

[0086] The generation unit can customize routes based on the user's current lifestyle and areas of interest during route generation. For example, if the user is health-conscious, the generation unit will prioritize suggesting walking or cycling routes. It can also suggest routes that include tourist attractions if the user wants to enjoy sightseeing. Furthermore, if the user is traveling for business purposes, the generation unit can suggest the shortest possible route. This allows for route customization based on the user's current lifestyle and areas of interest. These lifestyle and areas of interest include, but are not limited to, survey data and analysis of behavioral history. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs data on the user's lifestyle and areas of interest into the generation AI, which can then customize the route.

[0087] The generation unit can estimate the user's emotions and determine the priority of generated routes based on the estimated emotions. For example, if the user is tired, the generation unit will prioritize suggesting rest stops. It can also prioritize suggesting the shortest route if the user is in a hurry. Furthermore, if the user is relaxed, it can prioritize suggesting scenic routes. This allows the generation unit to determine the priority of generated routes based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, 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 is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotions.

[0088] The generation unit can generate the optimal route by considering the user's geographical location information during route generation. For example, the generation unit can suggest the optimal route based on the user's current location. Furthermore, when the user enters a destination, the generation unit can suggest the optimal route by considering the distance from the current location. Additionally, if the user is using the app while on the move, the generation unit can update the current location in real time and suggest the optimal route. This allows the generation of the optimal route to take the user's geographical location information into account. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate the optimal route.

[0089] The generation unit can analyze the user's social media activity and suggest relevant routes when generating routes. For example, the generation unit can suggest the optimal route based on the locations the user has checked into on social media. It can also suggest relevant routes based on the places and events the user follows on social media. Furthermore, the generation unit can include places of interest in the route based on photos and posts the user has shared on social media. This allows the generation unit to suggest relevant routes based on the user's social media activity. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's social media activity into the generation AI, which can then suggest relevant routes.

[0090] The integration unit can estimate the user's emotions and select services to integrate with based on the estimated emotions. For example, if the user is relaxed, the integration unit can provide information on relaxation facilities. If the user is in a hurry, the integration unit can also provide information on fast food restaurants. Furthermore, if the user is excited, the integration unit can provide information on activity facilities. This allows for the selection of the most suitable service based on 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 integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0091] The integration unit can suggest the most suitable service by referring to the user's past purchase history during integration. For example, the integration unit can suggest nearby restaurants based on information about restaurants the user has visited in the past. It can also suggest nearby hotels based on information about hotels the user has stayed at in the past. Furthermore, the integration unit can suggest shops of interest based on the user's past purchase history. This allows the integration unit to suggest the most suitable service based on the user's past purchase history. The most suitable service includes, but is not limited to, the user's preferences and past usage history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past purchase history into a generating AI, which can then suggest the most suitable service.

[0092] The integration unit can customize the integrated service based on the user's current lifestyle and areas of interest during integration. For example, if the user is health-conscious, the integration unit can provide information on healthy restaurants. It can also provide information on tourist spots if the user wants to enjoy sightseeing. Furthermore, if the user is traveling for business purposes, the integration unit can provide information on business hotels. This allows for the customization of integrated services based on the user's current lifestyle and areas of interest. These lifestyle and areas of interest include, but are not limited to, surveys and analysis of behavioral history. Some or all of the processing described above in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then customize the integrated service.

[0093] The integration unit can estimate the user's emotions and prioritize integrated services based on the estimated emotions. For example, if the user is tired, the integration unit will prioritize providing information on rest facilities. It can also prioritize providing information on services that can be used quickly if the user is in a hurry. Furthermore, if the user is relaxed, it can prioritize providing information on relaxation facilities. This allows for the prioritization of integrated services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 integration unit may be performed using AI, or not. For example, the integration unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0094] The integration unit can suggest the most suitable service when integrating, taking into account the user's geographical location information. For example, the integration unit can suggest nearby restaurants based on the user's current location. It can also suggest the most suitable service when the user enters a destination, taking into account the distance from the current location. Furthermore, if the user is using the app while on the move, the integration unit can update the user's current location in real time and suggest the most suitable service. This allows the integration unit to suggest the most suitable service considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not. For example, the integration unit can input the user's geographical location information into a generating AI, which can then suggest the most suitable service.

[0095] The integration unit can analyze the user's social media activity during integration and provide relevant services. For example, the integration unit can suggest relevant services based on the locations the user has checked into on social media. It can also provide relevant services based on the locations and events the user follows on social media. Furthermore, the integration unit can provide services of interest based on the photos and posts the user has shared on social media. In this way, relevant services can be provided based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media activity into a generating AI, which can then provide relevant services.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The reception desk can estimate the user's emotions and adjust the input method for departure and destination based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of departure and destination. This provides an input 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can then perform emotion estimation.

[0098] The analysis unit can analyze the user's past route search history and select the optimal route analysis method. For example, it can select the optimal route analysis method based on routes the user has used in the past. It can also select a route analysis method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and select the most efficient route analysis method. This allows the system to select the optimal route analysis method based on the user's past travel history. The optimal route analysis method includes, but is not limited to, analysis based on past data and use of real-time data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history into a generating AI, which can then select the optimal route analysis method.

[0099] The generation unit can estimate the user's emotions and adjust the route generation method based on the estimated user emotions. For example, if the user is relaxed, it can generate a route that proceeds at a leisurely pace. If the user is in a hurry, it can generate a route that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a route with visually stimulating effects. This allows the route generation method to be adjusted based on 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 is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotions.

[0100] The integration unit can estimate the user's emotions and select services to integrate with based on the estimated emotions. For example, if the user is relaxed, it can provide information on relaxation facilities. If the user is in a hurry, it can provide information on fast food restaurants. Furthermore, if the user is excited, it can provide information on activity facilities. This allows for the selection of the most suitable service based on 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 integration unit may be performed using AI, or not using AI. For example, the integration unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0101] The reception unit can complete inputs by considering the user's current traffic conditions and weather information when the user enters their departure and destination locations. For example, when the user enters their departure location, it can suggest the optimal departure time based on current traffic congestion information. Similarly, when the user enters their destination, it can suggest the optimal route based on current weather information. Furthermore, if the weather changes while the user is traveling, it can resuggest a route in real time. This allows the system to complete inputs by considering current traffic conditions and weather information. Traffic conditions and weather information include, but are not limited to, real-time data and forecast data. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input current traffic conditions and weather information into a generating AI, which can then complete the inputs.

[0102] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and provide multiple route options. If the user is in a hurry, it can perform a quick analysis and prioritize providing the shortest route. Furthermore, if the user is stressed, it can perform a simple analysis and provide an intuitive route. This allows the accuracy of the analysis to be adjusted based on 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0103] The generation unit can select the optimal route generation method by referring to the user's past travel history when generating a route. For example, it can select the optimal route generation method based on routes the user has used in the past. It can also select a route generation method that avoids congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and select the most efficient route generation method. This allows the optimal route generation method to be selected based on the user's past travel history. The optimal route generation method includes, but is not limited to, generation based on past data and use of real-time data. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the user's past travel history into the generation AI, which can then select the optimal route generation method.

[0104] The integration unit can suggest the most suitable service by referring to the user's past purchase history during integration. For example, it can suggest nearby restaurants based on information about restaurants the user has visited in the past. It can also suggest nearby hotels based on information about hotels the user has stayed at in the past. Furthermore, it can suggest shops of interest based on the user's past purchase history. In this way, the integration unit can suggest the most suitable service based on the user's past purchase history. The most suitable service includes, but is not limited to, the user's preferences and past usage history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past purchase history into a generating AI, which can then suggest the most suitable service.

[0105] The reception desk can analyze the user's past route search history and suggest the optimal input method. For example, it can automatically display as suggestions the departure and destination points that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest departure and destination points to be used during specific time periods based on the user's past input history. This allows the system to suggest the optimal input method based on the user's past history. The optimal input method includes, but is not limited to, ease of input, accuracy, and user preference. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past route search history into a generating AI, which can then suggest the optimal input method.

[0106] The integration unit can estimate the user's emotions and prioritize integrated services based on the estimated emotions. For example, if the user is tired, it can prioritize providing information on rest facilities. If the user is in a hurry, it can prioritize providing information on services that can be used quickly. Furthermore, if the user is relaxed, it can prioritize providing information on relaxation facilities. This allows for the prioritization of integrated services based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, or not using AI. For example, the integration unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives the user's departure point, destination, and specific needs. For example, it can accept the departure point, destination, and specific needs entered by the user in natural language. The processing at the reception desk may or may not be performed using AI. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it uses natural language processing technology to analyze the user's input and extract information to generate routes that meet each user's needs. The processing in the analysis unit may be performed using AI or not. Step 3: The generation unit generates a route based on the information analyzed by the analysis unit. For example, using the generation AI, it divides the route from the starting point to the destination into multiple segments and sets the optimal mode of transport and rest stops for each segment. The processing in the generation unit is performed using the generation AI. Step 4: The integration unit integrates with other services based on the route generated by the generation unit. For example, it integrates with gourmet information services and accommodation booking services to provide information on nearby restaurants and hotels while the user is traveling. The processing in the integration unit may or may not be performed using AI.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's departure point, destination, and specific needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a route based on the analyzed information. The collaboration unit collaborates with other services via the communication I / F 44 of the smart device 14 to provide gourmet information and accommodation information while traveling. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and coordination 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 microphone 238 of the smart glasses 214 and receives the user's origin, destination, and specific needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a route based on the analyzed information. The coordination unit cooperates with other services via the communication I / F 44 of the smart glasses 214 to provide gourmet information and accommodation information while traveling. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and coordination 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 microphone 238 of the headset terminal 314 and receives the user's origin, destination, and specific needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a route based on the analyzed information. The coordination unit coordinates with other services via the communication I / F 44 of the headset terminal 314 and provides information on restaurants and accommodations while traveling. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and coordination unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's departure point, destination, and specific needs. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a route based on the analyzed information. The coordination unit coordinates with other services via the communication I / F 44 of the robot 414 and provides gourmet information and accommodation information during travel. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A reception desk that handles the user's departure point, destination, and specific needs, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates a route based on the information analyzed by the analysis unit, The system includes a collaboration unit that collaborates with other services based on the routes generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts the user's origin, destination, and specific needs in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The reception desk analyzes the information received and generates routes tailored to each individual's needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the information analyzed by the analysis unit, a route divided into multiple segments is generated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Provides information on nearby restaurants and hotels while you're on the go. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned reception unit is It analyzes the user's past route search history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of specific needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) 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 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the system selects the optimal route analysis method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the results are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the analysis results are optimized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and provides relevant analytical results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the route generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a route, the system selects the optimal route generation method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During route generation, the route is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of routes generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a route, the system takes the user's geographical location into consideration to generate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned linkage unit is, It estimates the user's emotions and selects services to integrate with based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, When integrating, the system will refer to the user's past purchase history to suggest the most suitable services. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, During integration, the integrated services are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of linked services based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, When integrating, we propose the most suitable service considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, During integration, the system analyzes the user's social media activity and provides relevant services. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 handles the user's departure point, destination, and specific needs, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates a route based on the information analyzed by the analysis unit, The system includes a collaboration unit that collaborates with other services based on the routes generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is It accepts the user's origin, destination, and specific needs in natural language. The system according to feature 1.

3. The aforementioned analysis unit, The information received by the reception unit is analyzed, and routes corresponding to each need are generated. The system according to feature 1.

4. The generating unit is Based on the information analyzed by the analysis unit, the system generates a route divided into multiple segments. The system according to feature 1.

5. The aforementioned linkage unit is, Provides information on nearby restaurants and hotels while you're on the go. The system according to feature 1.

6. 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.

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

8. 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.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of specific needs based on those estimated emotions. The system according to feature 1.

10. 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 according to feature 1.

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