system

Abstract

An object of a system according to an embodiment is to enable a driver to efficiently grasp and enjoy sightseeing spots around a current location.SOLUTION: A system according to an embodiment includes a current location specification part, a sightseeing spot search part, a sightseeing information generation part, and a voice reading part. The current location identification unit identifies the current location of the driver using the GPS. A sightseeing spot retrieval part accesses a sightseeing information database on the basis of the present location specified by the present location specification part, and retrieves a sightseeing spot. The sightseeing information generation part generates information on the sightseeing spot retrieved by the sightseeing spot retrieval part. The voice reading unit reads the information on the sightseeing spot generated by the sightseeing information generation unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for drivers to efficiently understand and enjoy tourist spots around their current location.

[0005] The system according to the embodiment aims to enable drivers to efficiently understand and enjoy tourist spots around their current location. [Means for solving the problem]

[0006] The system according to the embodiment includes a current location determination unit, a tourist spot search unit, a tourist information generation unit, and a voice readout unit. The current location determination unit determines the driver's current location using GPS. The tourist spot search unit accesses a tourist information database based on the current location determined by the current location determination unit and searches for tourist spots. The tourist information generation unit generates information about the tourist spots searched for by the tourist spot search unit. The voice readout unit reads out the information about the tourist spots generated by the tourist information generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows drivers to efficiently understand and enjoy tourist spots around their current location. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automated tourist guide car navigation system according to an embodiment of the present invention is a system that guides the driver to tourist spots and geographically interesting places around the driver's current location and reads out the information aloud. This allows the driver to obtain tourist information while driving and to gain a deeper understanding of the attractions of the area.

[0029] An automated tourist guide car navigation system according to an embodiment includes a current location determination unit, a tourist spot search unit, a tourist information generation unit, and a voice readout unit. The current location determination unit determines the driver's current location using a GPS. For example, the current location determination unit receives a GPS signal and calculates the driver's exact location. The current location determination unit can update the location information in real time. The tourist spot search unit accesses a tourist information database based on the current location determined by the current location determination unit to search for tourist spots. For example, the tourist spot search unit sends a query to the tourist information database to obtain information about tourist spots near the current location. The tourist spot search unit can filter the search results to extract information useful to the driver. The tourist information generation unit generates information about the tourist spots searched by the tourist spot search unit. For example, the tourist information generation unit generates text including an overview of the tourist spot, its highlights, and how to access it. The tourist information generation unit can convert the generated text into voice using speech synthesis technology. The voice readout unit reads out the tourist spot information generated by the tourist information generation unit. For example, the voice reading unit transmits voice generated using voice synthesis technology to the driver through a speaker. The voice reading unit also adjusts the timing of the reading, allowing the driver to receive information at an appropriate time while driving. This allows the automated tourist guide car navigation system according to the embodiment to provide tourist information while driving, enabling the driver to gain a deeper understanding of the attractions of the area. For example, the driver can obtain information about tourist spots while driving, enabling the driver to gain a deeper understanding of the area's history and culture. Providing the latest information in real time also improves driver convenience. Furthermore, customization based on user preferences and multilingual support make it possible to accommodate a wide range of users.

[0030] The tourist information generation unit can generate text including an overview of a tourist spot, highlights, and access methods. The tourist information generation unit generates text including, for example, an overview of a tourist spot, highlights, and access methods. For example, the tourist information generation unit generates text including the historical background and main features of a tourist spot. The tourist information generation unit also generates text including particularly noteworthy points of a tourist spot and seasonal highlights. The tourist information generation unit also generates text including access methods to the tourist spot, such as how to use public transportation and whether parking is available. This makes it possible to provide detailed tourist information to drivers.

[0031] The tourist information generation unit can provide the latest tourist information by taking into consideration traffic conditions, weather, event status, etc. The tourist information generation unit can provide the latest tourist information by taking into consideration traffic conditions, weather, event status, etc. For example, the tourist information generation unit can acquire real-time traffic information and suggest tourist spots by taking into consideration information such as traffic congestion and road closures. The tourist information generation unit can also acquire weather forecast data and suggest tourist spots according to changes in the weather. The tourist information generation unit can also acquire information from event calendars and official websites and suggest tourist spots based on the event status. This allows the driver to be provided with the latest tourist information.

[0032] The tourist information generation unit can analyze the driver's preferences and past visit history and provide individually customized tourist information. The tourist information generation unit can, for example, analyze the driver's preferences and past visit history and provide individually customized tourist information. For example, the tourist information generation unit can analyze the driver's past visit history and identify frequently visited places and routes. The tourist information generation unit can also identify the driver's preferences from survey results and past selection history and suggest tourist spots based on that. The tourist information generation unit can also suggest tourist spots for children in the case of families. This makes it possible to provide individually customized tourist information to the driver.

[0033] The tourist information generation unit generates tourist information based on the driver's language setting, and the voice reading unit can read out the tourist information generated by the tourist information generation unit. The tourist information generation unit generates tourist information based on the driver's language setting, for example. For example, the tourist information generation unit can generate tourist information in multiple languages, such as Japanese, English, Chinese, and Korean. The tourist information generation unit can also translate the tourist information according to the driver's language setting and provide it in an appropriate language. The voice reading unit, for example, reads out the tourist information generated by the tourist information generation unit aloud. For example, the voice reading unit transmits audio generated using voice synthesis technology to the driver through a speaker. The voice reading unit can also adjust the timing of the reading out, allowing the driver to receive the information at an appropriate time while driving. This makes it possible to provide the driver with tourist information based on the language setting.

[0034] The tourist information generation unit can analyze the driver's driving patterns and past movement history, and predict and provide guidance to places the driver is likely to visit next. The tourist information generation unit can, for example, analyze the driver's driving patterns and past movement history, and predict and provide guidance to places the driver is likely to visit next. For example, the tourist information generation unit can analyze the driver's past movement history and identify frequently visited places and routes. The tourist information generation unit can also analyze the driver's driving speed and driving route trends, and predict places the driver is likely to visit next. The tourist information generation unit can also suggest tourist spots based on the predicted places. This makes it possible to predict and provide guidance to places the driver is likely to visit next.

[0035] The tourist information generation unit analyzes the voice and conversation content of the driver inside the car and can suggest tourist spots that the driver might be interested in. The tourist information generation unit, for example, analyzes the voice and conversation content of the driver inside the car and suggests tourist spots that the driver might be interested in. For example, the tourist information generation unit collects the driver's conversation content using an in-car microphone and analyzes it using voice recognition technology. The tourist information generation unit can also extract keywords from the conversation content and identify the driver's interests. The tourist information generation unit can also suggest tourist spots based on the extracted keywords. This makes it possible to suggest tourist spots that the driver might be interested in.

[0036] The tourist information generation unit can simultaneously search for nearby restaurants and shopping spots in addition to identifying the current location and suggest them in conjunction with sightseeing. The tourist information generation unit, for example, can simultaneously search for nearby restaurants and shopping spots in addition to identifying the current location and suggest them in conjunction with sightseeing. For example, the tourist information generation unit searches for nearby restaurants and shopping spots based on the identification of the current location. The tourist information generation unit can also suggest restaurants and shopping spots in conjunction with tourist spots. For example, it can suggest popular restaurants and shopping malls near tourist spots. This makes it possible to suggest restaurants and shopping spots in conjunction with sightseeing to the driver.

[0037] The tourist information generation unit can propose optimal routes and tourist spots by taking into consideration the driver's vehicle type and fuel efficiency information. The tourist information generation unit can propose optimal routes and tourist spots by taking into consideration, for example, the driver's vehicle type and fuel efficiency information. For example, the tourist information generation unit can analyze the driver's vehicle type and fuel efficiency information and select a route with good fuel efficiency. The tourist information generation unit can also propose tourist spots along the selected route. For example, it can select a route with good fuel efficiency and propose tourist spots along that route. This makes it possible to propose optimal routes and tourist spots to the driver by taking into consideration the vehicle type and fuel efficiency information.

[0038] The tourist spot search unit can provide the latest tourist information by extracting information from user-generated content such as social media and blogs in addition to the tourist information database. The tourist spot search unit can provide the latest tourist information by extracting information from user-generated content such as social media and blogs in addition to the tourist information database. For example, the tourist spot search unit can analyze posts on Twitter or Instagram to obtain the latest tourist spot information. The tourist spot search unit can also analyze blog articles to obtain the latest reviews and ratings of tourist spots. This allows the latest tourist information to be provided to drivers.

[0039] The tourist spot search unit can analyze past tourist reviews and ratings for the information in the tourist information database and preferentially extract highly reliable information. The tourist spot search unit can, for example, analyze past tourist reviews and ratings for the information in the tourist information database and preferentially extract highly reliable information. For example, the tourist spot search unit can analyze online reviews and survey results and preferentially suggest highly rated tourist spots. The tourist spot search unit can also evaluate the number and quality of reviews and extract highly reliable information. This makes it possible to provide drivers with highly reliable tourist information.

[0040] The tourist spot search unit can integrate information from local tourist information centers and guidebooks in addition to the tourist information database to provide comprehensive tourist information. The tourist spot search unit can, for example, integrate information from local tourist information centers and guidebooks in addition to the tourist information database to provide comprehensive tourist information. For example, the tourist spot search unit digitizes information from tourist information center brochures and guidebooks and integrates it into the tourist information database. The tourist spot search unit can also obtain information from local tourist associations and official guidebooks to complement the tourist spot information. This makes it possible to provide comprehensive tourist information to drivers.

[0041] The tourist spot search unit can filter the information in the tourist information database according to the driver's age group and family composition, and suggest the most suitable tourist spot. The tourist spot search unit can, for example, filter the information in the tourist information database according to the driver's age group and family composition, and suggest the most suitable tourist spot. For example, the tourist spot search unit can suggest tourist spots suitable for children to families with children. The tourist spot search unit can also suggest activities for young people and historical sites for middle-aged and elderly people. This makes it possible to suggest the most suitable tourist spot to the driver according to their age group and family composition.

[0042] The tourist information generation unit provides guidance in a storytelling format based on the driver's interests and concerns, thereby providing more attractive information. The tourist information generation unit provides guidance in a storytelling format based on the driver's interests and concerns, thereby providing more attractive information. For example, the tourist information generation unit provides guidance that incorporates historical background and anecdotes about tourist spots. The tourist information generation unit can also devise a story structure and narration style to attract the driver's interest. This makes it possible to provide the driver with attractive information based on their interests and concerns.

[0043] The tourist information generation unit can incorporate the experiences and anecdotes of past visitors to provide realistic information. The tourist information generation unit can incorporate the experiences and anecdotes of past visitors to provide realistic information. For example, the tourist information generation unit can analyze visitor reviews and blog articles to provide realistic information about tourist spots. The tourist information generation unit can also generate tourist information based on visitor experiences and specific events. This makes it possible to provide realistic tourist information to drivers.

[0044] When generating tourist information, the tourist information generation unit simultaneously provides visual information (images and videos) to attract the driver's interest. When generating tourist information, the tourist information generation unit simultaneously provides visual information (images and videos) to attract the driver's interest. For example, the tourist information generation unit provides guidance while displaying photos and videos of tourist spots. Furthermore, the tourist information generation unit can attract the driver's interest by providing information that incorporates visual elements. In this way, visual information can be provided to the driver to attract his or her interest.

[0045] When generating tourist information, the tourist information generation unit can provide information tailored to different cultural backgrounds depending on the driver's language setting. For example, when generating tourist information, the tourist information generation unit provides information tailored to different cultural backgrounds depending on the driver's language setting. For example, the tourist information generation unit generates tourist information in languages ​​such as Japanese, English, and Chinese, and provides information tailored to each cultural background. The tourist information generation unit can also provide information based on the history, traditions, and customs of the region. This makes it possible to provide the driver with information tailored to the cultural background depending on the language setting.

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

[0047] The tourist information generation unit can monitor the driver's health condition and suggest appropriate rest spots and health-conscious tourist spots. For example, the tourist information generation unit can measure the driver's heart rate and stress level with a sensor and suggest rest spots if fatigue accumulates. The tourist information generation unit can also suggest natural landscapes and walking courses that are considered good for health. Furthermore, the tourist information generation unit can also suggest health-conscious restaurants and spa facilities. This makes it possible to provide tourist information tailored to the driver's health condition.

[0048] The tourist information generation unit can suggest music-related tourist spots based on the driver's in-car music preferences. For example, the tourist information generation unit can analyze the driver's favorite music genre and suggest live music venues and music museums related to that genre. The tourist information generation unit can also provide information on upcoming music festivals and concerts. Furthermore, the tourist information generation unit can suggest historical places related to music and places associated with artists. This makes it possible to provide tourist information tailored to the driver's musical preferences.

[0049] The tourist information generation unit analyzes the driver's driving patterns and past travel history, and can predict and guide the driver to places he or she is likely to visit next. For example, the tourist information generation unit analyzes the driver's past travel history and identifies frequently visited places and routes. The tourist information generation unit can also analyze the driver's driving speed and driving route tendencies, and predict places he or she is likely to visit next. The tourist information generation unit can also suggest tourist spots based on the predicted places. This allows the driver to predict and guide the driver to places he or she is likely to visit next.

[0050] The tourist information generation unit can analyze the driver's voice and conversation content in the car and suggest tourist spots that may be of interest to the driver. For example, the tourist information generation unit collects the driver's conversation content using an in-car microphone and analyzes it using voice recognition technology. The tourist information generation unit can also extract keywords from the conversation content and identify the driver's interests. The tourist information generation unit can also suggest tourist spots based on the extracted keywords. This makes it possible to suggest tourist spots that may be of interest to the driver.

[0051] The tourist information generation unit generates tourist information based on the driver's language setting, and the voice reading unit reads out the tourist information generated by the tourist information generation unit. For example, the tourist information generation unit can generate tourist information in multiple languages, such as Japanese, English, Chinese, and Korean. The tourist information generation unit can also translate the tourist information according to the driver's language setting and provide it in an appropriate language. The voice reading unit, for example, reads out the tourist information generated by the tourist information generation unit aloud. For example, the voice reading unit transmits the generated voice to the driver through a speaker using voice synthesis technology. The voice reading unit can also adjust the timing of the reading out, allowing the driver to receive the information at an appropriate time while driving. This makes it possible to provide the driver with tourist information based on the language setting.

[0052] The tourist information generation unit can propose optimal routes and tourist spots by taking into consideration the driver's vehicle type and fuel efficiency information. For example, the tourist information generation unit can analyze the driver's vehicle type and fuel efficiency information and select a route with good fuel efficiency. The tourist information generation unit can also propose tourist spots along the selected route. For example, it can select a route with good fuel efficiency and propose tourist spots along that route. This makes it possible to propose optimal routes and tourist spots to the driver by taking into consideration the vehicle type and fuel efficiency information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The current location determination unit determines the driver's current location using a GPS. For example, the current location determination unit receives a GPS signal and calculates the driver's exact location. The current location determination unit can also update the location information in real time. Step 2: The tourist spot search unit accesses the tourist information database based on the current location identified by the current location identification unit and searches for tourist spots. For example, the tourist spot search unit sends a query to the tourist information database to obtain information about tourist spots around the current location. The tourist spot search unit can also filter the search results to extract information that is useful to the driver. Step 3: The tourist information generation unit generates information about the tourist spots found by the tourist spot search unit. For example, the tourist information generation unit generates text that includes an overview of the tourist spot, highlights, and access methods. The tourist information generation unit can also convert the generated text into speech using speech synthesis technology. Step 4: The voice reading unit reads out the tourist spot information generated by the tourist information generation unit. For example, the voice reading unit may use voice synthesis technology to generate a voice and transmit it to the driver through a speaker. The voice reading unit may also adjust the timing of the reading so that the driver receives the information at the appropriate time while driving.

[0055] (Example 2) The automated tourist guide car navigation system according to an embodiment of the present invention is a system that guides the driver to tourist spots and geographically interesting places around the driver's current location and reads out the information aloud. This allows the driver to obtain tourist information while driving and to gain a deeper understanding of the attractions of the area.

[0056] An automated tourist guide car navigation system according to an embodiment includes a current location determination unit, a tourist spot search unit, a tourist information generation unit, and a voice readout unit. The current location determination unit determines the driver's current location using a GPS. For example, the current location determination unit receives a GPS signal and calculates the driver's exact location. The current location determination unit can update the location information in real time. The tourist spot search unit accesses a tourist information database based on the current location determined by the current location determination unit to search for tourist spots. For example, the tourist spot search unit sends a query to the tourist information database to obtain information about tourist spots near the current location. The tourist spot search unit can filter the search results to extract information useful to the driver. The tourist information generation unit generates information about the tourist spots searched by the tourist spot search unit. For example, the tourist information generation unit generates text including an overview of the tourist spot, its highlights, and how to access it. The tourist information generation unit can convert the generated text into voice using speech synthesis technology. The voice readout unit reads out the tourist spot information generated by the tourist information generation unit. For example, the voice reading unit transmits voice generated using voice synthesis technology to the driver through a speaker. The voice reading unit also adjusts the timing of the reading, allowing the driver to receive information at an appropriate time while driving. This allows the automated tourist guide car navigation system according to the embodiment to provide tourist information while driving, enabling the driver to gain a deeper understanding of the attractions of the area. For example, the driver can obtain information about tourist spots while driving, enabling the driver to gain a deeper understanding of the area's history and culture. Providing the latest information in real time also improves driver convenience. Furthermore, customization based on user preferences and multilingual support make it possible to accommodate a wide range of users.

[0057] The tourist information generation unit can generate text including an overview of a tourist spot, highlights, and access methods. The tourist information generation unit generates text including, for example, an overview of a tourist spot, highlights, and access methods. For example, the tourist information generation unit generates text including the historical background and main features of a tourist spot. The tourist information generation unit also generates text including particularly noteworthy points of a tourist spot and seasonal highlights. The tourist information generation unit also generates text including access methods to the tourist spot, such as how to use public transportation and whether parking is available. This makes it possible to provide detailed tourist information to drivers.

[0058] The tourist information generation unit can provide the latest tourist information by taking into consideration traffic conditions, weather, event status, etc. The tourist information generation unit can provide the latest tourist information by taking into consideration traffic conditions, weather, event status, etc. For example, the tourist information generation unit can acquire real-time traffic information and suggest tourist spots by taking into consideration information such as traffic congestion and road closures. The tourist information generation unit can also acquire weather forecast data and suggest tourist spots according to changes in the weather. The tourist information generation unit can also acquire information from event calendars and official websites and suggest tourist spots based on the event status. This allows the driver to be provided with the latest tourist information.

[0059] The tourist information generation unit can analyze the driver's preferences and past visit history and provide individually customized tourist information. The tourist information generation unit can, for example, analyze the driver's preferences and past visit history and provide individually customized tourist information. For example, the tourist information generation unit can analyze the driver's past visit history and identify frequently visited places and routes. The tourist information generation unit can also identify the driver's preferences from survey results and past selection history and suggest tourist spots based on that. The tourist information generation unit can also suggest tourist spots for children in the case of families. This makes it possible to provide individually customized tourist information to the driver.

[0060] The tourist information generation unit generates tourist information based on the driver's language setting, and the voice reading unit can read out the tourist information generated by the tourist information generation unit. The tourist information generation unit generates tourist information based on the driver's language setting, for example. For example, the tourist information generation unit can generate tourist information in multiple languages, such as Japanese, English, Chinese, and Korean. The tourist information generation unit can also translate the tourist information according to the driver's language setting and provide it in an appropriate language. The voice reading unit, for example, reads out the tourist information generated by the tourist information generation unit aloud. For example, the voice reading unit transmits audio generated using voice synthesis technology to the driver through a speaker. The voice reading unit can also adjust the timing of the reading out, allowing the driver to receive the information at an appropriate time while driving. This makes it possible to provide the driver with tourist information based on the language setting.

[0061] The tourist information generation unit can analyze the driver's driving patterns and past movement history, and predict and provide guidance to places the driver is likely to visit next. The tourist information generation unit can, for example, analyze the driver's driving patterns and past movement history, and predict and provide guidance to places the driver is likely to visit next. For example, the tourist information generation unit can analyze the driver's past movement history and identify frequently visited places and routes. The tourist information generation unit can also analyze the driver's driving speed and driving route trends, and predict places the driver is likely to visit next. The tourist information generation unit can also suggest tourist spots based on the predicted places. This makes it possible to predict and provide guidance to places the driver is likely to visit next.

[0062] The tourist information generation unit analyzes the voice and conversation content of the driver inside the car and can suggest tourist spots that the driver might be interested in. The tourist information generation unit, for example, analyzes the voice and conversation content of the driver inside the car and suggests tourist spots that the driver might be interested in. For example, the tourist information generation unit collects the driver's conversation content using an in-car microphone and analyzes it using voice recognition technology. The tourist information generation unit can also extract keywords from the conversation content and identify the driver's interests. The tourist information generation unit can also suggest tourist spots based on the extracted keywords. This makes it possible to suggest tourist spots that the driver might be interested in.

[0063] The tourist information generation unit can analyze the driver's emotional state and suggest natural scenery if the driver wants to relax, or activity spots if the driver wants to get excited. The tourist information generation unit can, for example, analyze the driver's emotional state and suggest natural scenery if the driver wants to relax, or activity spots if the driver wants to get excited. For example, the tourist information generation unit uses an emotion estimation function to analyze the driver's facial expressions and voice to estimate the emotional state. Furthermore, the tourist information generation unit can suggest tourist spots with natural scenery if the driver wants to relax. Furthermore, the tourist information generation unit can suggest activity spots if the driver wants to get excited. In this way, tourist spots can be suggested according to the driver's emotional state.

[0064] The tourist information generation unit can simultaneously search for nearby restaurants and shopping spots in addition to identifying the current location and suggest them in conjunction with sightseeing. The tourist information generation unit, for example, can simultaneously search for nearby restaurants and shopping spots in addition to identifying the current location and suggest them in conjunction with sightseeing. For example, the tourist information generation unit searches for nearby restaurants and shopping spots based on the identification of the current location. The tourist information generation unit can also suggest restaurants and shopping spots in conjunction with tourist spots. For example, it can suggest popular restaurants and shopping malls near tourist spots. This makes it possible to suggest restaurants and shopping spots in conjunction with sightseeing to the driver.

[0065] The tourist information generation unit can propose optimal routes and tourist spots by taking into consideration the driver's vehicle type and fuel efficiency information. The tourist information generation unit can propose optimal routes and tourist spots by taking into consideration, for example, the driver's vehicle type and fuel efficiency information. For example, the tourist information generation unit can analyze the driver's vehicle type and fuel efficiency information and select a route with good fuel efficiency. The tourist information generation unit can also propose tourist spots along the selected route. For example, it can select a route with good fuel efficiency and propose tourist spots along that route. This makes it possible to propose optimal routes and tourist spots to the driver by taking into consideration the vehicle type and fuel efficiency information.

[0066] The tourist spot search unit can provide the latest tourist information by extracting information from user-generated content such as social media and blogs in addition to the tourist information database. The tourist spot search unit can provide the latest tourist information by extracting information from user-generated content such as social media and blogs in addition to the tourist information database. For example, the tourist spot search unit can analyze posts on Twitter or Instagram to obtain the latest tourist spot information. The tourist spot search unit can also analyze blog articles to obtain the latest reviews and ratings of tourist spots. This allows the latest tourist information to be provided to drivers.

[0067] The tourist spot search unit can analyze past tourist reviews and ratings for the information in the tourist information database and preferentially extract highly reliable information. The tourist spot search unit can, for example, analyze past tourist reviews and ratings for the information in the tourist information database and preferentially extract highly reliable information. For example, the tourist spot search unit can analyze online reviews and survey results and preferentially suggest highly rated tourist spots. The tourist spot search unit can also evaluate the number and quality of reviews and extract highly reliable information. This makes it possible to provide drivers with highly reliable tourist information.

[0068] The tourist spot search unit can use the emotion estimation function to analyze the emotional responses of past visitors to tourist spots and prioritize guidance to spots with many positive reviews. The tourist spot search unit can, for example, use the emotion estimation function to analyze the emotional responses of past visitors to tourist spots and prioritize guidance to spots with many positive reviews. For example, the tourist spot search unit can analyze the emotional responses of past visitors using facial expression recognition or voice analysis. The tourist spot search unit can also rate tourist spots based on positive comments and highly rated reviews. This allows the driver to be guided to tourist spots with many positive reviews.

[0069] The tourist spot search unit can integrate information from local tourist information centers and guidebooks in addition to the tourist information database to provide comprehensive tourist information. The tourist spot search unit can, for example, integrate information from local tourist information centers and guidebooks in addition to the tourist information database to provide comprehensive tourist information. For example, the tourist spot search unit digitizes information from tourist information center brochures and guidebooks and integrates it into the tourist information database. The tourist spot search unit can also obtain information from local tourist associations and official guidebooks to complement the tourist spot information. This makes it possible to provide comprehensive tourist information to drivers.

[0070] The tourist spot search unit can filter the information in the tourist information database according to the driver's age group and family composition, and suggest the most suitable tourist spot. The tourist spot search unit can, for example, filter the information in the tourist information database according to the driver's age group and family composition, and suggest the most suitable tourist spot. For example, the tourist spot search unit can suggest tourist spots suitable for children to families with children. The tourist spot search unit can also suggest activities for young people and historical sites for middle-aged and elderly people. This makes it possible to suggest the most suitable tourist spot to the driver according to their age group and family composition.

[0071] The tourist spot search unit can use the emotion estimation function to customize tourist spot information according to the driver's emotional state, thereby providing a more personalized sightseeing experience. The tourist spot search unit can, for example, use the emotion estimation function to customize tourist spot information according to the driver's emotional state, thereby providing a more personalized sightseeing experience. For example, the tourist spot search unit can use the emotion estimation function to analyze the driver's facial expressions and voice to estimate the driver's emotional state. The tourist spot search unit can also customize tourist spot information based on the estimated emotional state. For example, if the driver wants to relax, tourist spots with natural scenery can be suggested, and if the driver wants to get excited, activity spots can be suggested. This makes it possible to provide the driver with a personalized sightseeing experience according to their emotional state.

[0072] The tourist information generation unit provides guidance in a storytelling format based on the driver's interests and concerns, thereby providing more attractive information. The tourist information generation unit provides guidance in a storytelling format based on the driver's interests and concerns, thereby providing more attractive information. For example, the tourist information generation unit provides guidance that incorporates historical background and anecdotes about tourist spots. The tourist information generation unit can also devise a story structure and narration style to attract the driver's interest. This makes it possible to provide the driver with attractive information based on their interests and concerns.

[0073] The tourist information generation unit can incorporate the experiences and anecdotes of past visitors to provide realistic information. The tourist information generation unit can incorporate the experiences and anecdotes of past visitors to provide realistic information. For example, the tourist information generation unit can analyze visitor reviews and blog articles to provide realistic information about tourist spots. The tourist information generation unit can also generate tourist information based on visitor experiences and specific events. This makes it possible to provide realistic tourist information to drivers.

[0074] The tourist information generation unit can use the emotion estimation function to read out tourist information in a tone and tempo that corresponds to the driver's emotional state. The tourist information generation unit can use the emotion estimation function to read out tourist information in a tone and tempo that corresponds to the driver's emotional state. For example, the tourist information generation unit uses the emotion estimation function to analyze the driver's facial expressions and voice to estimate the emotional state. The tourist information generation unit can also adjust the tone and tempo of the voice based on the estimated emotional state. For example, if the driver wants to relax, the guidance can be provided in a calm tone, and if the driver wants to get excited, the guidance can be provided in a lively tone. This allows for effective information communication to the driver that corresponds to their emotional state.

[0075] When generating tourist information, the tourist information generation unit simultaneously provides visual information (images and videos) to attract the driver's interest. When generating tourist information, the tourist information generation unit simultaneously provides visual information (images and videos) to attract the driver's interest. For example, the tourist information generation unit provides guidance while displaying photos and videos of tourist spots. Furthermore, the tourist information generation unit can attract the driver's interest by providing information that incorporates visual elements. In this way, visual information can be provided to the driver to attract his or her interest.

[0076] When generating tourist information, the tourist information generation unit can provide information tailored to different cultural backgrounds depending on the driver's language setting. For example, when generating tourist information, the tourist information generation unit provides information tailored to different cultural backgrounds depending on the driver's language setting. For example, the tourist information generation unit generates tourist information in languages ​​such as Japanese, English, and Chinese, and provides information tailored to each cultural background. The tourist information generation unit can also provide information based on the history, traditions, and customs of the region. This makes it possible to provide the driver with information tailored to the cultural background depending on the language setting.

[0077] The tourist information generation unit uses the emotion estimation function to update the tourist information in real time according to the driver's emotional state, thereby always providing optimal information. The tourist information generation unit, for example, uses the emotion estimation function to update the tourist information in real time according to the driver's emotional state, thereby always providing optimal information. For example, the tourist information generation unit uses the emotion estimation function to analyze the driver's facial expressions and voice to estimate the emotional state. The tourist information generation unit can also adjust the tourist information based on the estimated emotional state and update it in real time. For example, the tourist information can be adjusted each time the driver's emotions change, thereby providing optimal information. This makes it possible to provide the driver with optimal tourist information in real time according to their emotional state.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The tourist information generation unit can monitor the driver's health condition and suggest appropriate rest spots and health-conscious tourist spots. For example, the tourist information generation unit can measure the driver's heart rate and stress level with a sensor and suggest rest spots if fatigue accumulates. The tourist information generation unit can also suggest natural landscapes and walking courses that are considered good for health. Furthermore, the tourist information generation unit can also suggest health-conscious restaurants and spa facilities. This makes it possible to provide tourist information tailored to the driver's health condition.

[0080] The tourist information generation unit can suggest music-related tourist spots based on the driver's in-car music preferences. For example, the tourist information generation unit can analyze the driver's favorite music genre and suggest live music venues and music museums related to that genre. The tourist information generation unit can also provide information on upcoming music festivals and concerts. Furthermore, the tourist information generation unit can suggest historical places related to music and places associated with artists. This makes it possible to provide tourist information tailored to the driver's musical preferences.

[0081] The tourist information generation unit analyzes the driver's emotional state and can suggest natural scenery if the driver wants to relax, or activity spots if the driver wants to get excited. For example, the tourist information generation unit uses the emotion estimation function to analyze the driver's facial expressions and voice to estimate the emotional state. Furthermore, the tourist information generation unit can suggest tourist spots with natural scenery if the driver wants to relax. Furthermore, the tourist information generation unit can suggest activity spots if the driver wants to get excited. In this way, tourist spots can be suggested according to the driver's emotional state.

[0082] The tourist information generation unit can adjust the tone and tempo of the tourist information according to the driver's emotional state. For example, the tourist information generation unit can use an emotion estimation function to analyze the driver's facial expressions and voice to estimate the driver's emotional state. The tourist information generation unit can also provide guidance in a calm tone if the driver wants to relax, and in a lively tone if the driver wants to get excited. This allows for effective information transmission according to the driver's emotional state.

[0083] The tourist information generation unit updates the tourist information in real time according to the driver's emotional state, and can always provide optimal information. For example, the tourist information generation unit uses an emotion estimation function to analyze the driver's facial expressions and voice to estimate the emotional state. The tourist information generation unit can also adjust the tourist information based on the estimated emotional state and update it in real time. For example, the tourist information can be adjusted every time the driver's emotions change, and optimal information can be provided. This makes it possible to provide the driver with optimal tourist information in real time according to their emotional state.

[0084] The tourist information generation unit analyzes the driver's driving patterns and past travel history, and can predict and guide the driver to places he or she is likely to visit next. For example, the tourist information generation unit analyzes the driver's past travel history and identifies frequently visited places and routes. The tourist information generation unit can also analyze the driver's driving speed and driving route tendencies, and predict places he or she is likely to visit next. The tourist information generation unit can also suggest tourist spots based on the predicted places. This allows the driver to predict and guide the driver to places he or she is likely to visit next.

[0085] The tourist information generation unit can analyze the driver's voice and conversation content in the car and suggest tourist spots that may be of interest to the driver. For example, the tourist information generation unit collects the driver's conversation content using an in-car microphone and analyzes it using voice recognition technology. The tourist information generation unit can also extract keywords from the conversation content and identify the driver's interests. The tourist information generation unit can also suggest tourist spots based on the extracted keywords. This makes it possible to suggest tourist spots that may be of interest to the driver.

[0086] The tourist information generation unit generates tourist information based on the driver's language setting, and the voice reading unit reads out the tourist information generated by the tourist information generation unit. For example, the tourist information generation unit can generate tourist information in multiple languages, such as Japanese, English, Chinese, and Korean. The tourist information generation unit can also translate the tourist information according to the driver's language setting and provide it in an appropriate language. The voice reading unit, for example, reads out the tourist information generated by the tourist information generation unit aloud. For example, the voice reading unit transmits the generated voice to the driver through a speaker using voice synthesis technology. The voice reading unit can also adjust the timing of the reading out, allowing the driver to receive the information at an appropriate time while driving. This makes it possible to provide the driver with tourist information based on the language setting.

[0087] The tourist information generation unit can adjust the tone and tempo of the tourist information according to the driver's emotional state. For example, the tourist information generation unit can use an emotion estimation function to analyze the driver's facial expressions and voice to estimate the driver's emotional state. The tourist information generation unit can also provide guidance in a calm tone if the driver wants to relax, and in a lively tone if the driver wants to get excited. This allows for effective information transmission according to the driver's emotional state.

[0088] The tourist information generation unit can propose optimal routes and tourist spots by taking into consideration the driver's vehicle type and fuel efficiency information. For example, the tourist information generation unit can analyze the driver's vehicle type and fuel efficiency information and select a route with good fuel efficiency. The tourist information generation unit can also propose tourist spots along the selected route. For example, it can select a route with good fuel efficiency and propose tourist spots along that route. This makes it possible to propose optimal routes and tourist spots to the driver by taking into consideration the vehicle type and fuel efficiency information.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The current location determination unit determines the driver's current location using a GPS. For example, the current location determination unit receives a GPS signal and calculates the driver's exact location. The current location determination unit can also update the location information in real time. Step 2: The tourist spot search unit accesses the tourist information database based on the current location identified by the current location identification unit and searches for tourist spots. For example, the tourist spot search unit sends a query to the tourist information database to obtain information about tourist spots around the current location. The tourist spot search unit can also filter the search results to extract information that is useful to the driver. Step 3: The tourist information generation unit generates information about the tourist spots found by the tourist spot search unit. For example, the tourist information generation unit generates text that includes an overview of the tourist spot, highlights, and access methods. The tourist information generation unit can also convert the generated text into speech using speech synthesis technology. Step 4: The voice reading unit reads out the tourist spot information generated by the tourist information generation unit. For example, the voice reading unit may use voice synthesis technology to generate a voice and transmit it to the driver through a speaker. The voice reading unit may also adjust the timing of the reading so that the driver receives the information at the appropriate time while driving.

[0091] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0095] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

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

[0127] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0131] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0132] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0141] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0147] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0148] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0149] 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.

[0150] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0151] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0152] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0153] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0154] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0155] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a current location determination unit that determines the current location of the driver using GPS; a tourist spot search unit that accesses a tourist information database based on the current location identified by the current location identification unit and searches for tourist spots; a tourist information generation unit that generates information about the tourist spots searched by the tourist spot search unit; a voice reading unit that reads out the tourist spot information generated by the tourist information generating unit. A system characterized by:

2. The tourist information generation unit Generate text that includes an overview of tourist attractions, highlights, and access methods 2. The system of claim 1.

3. The tourist spot search unit In addition to the tourism information database, information is extracted from user-generated content such as social media and blogs to provide the latest tourism information.

2. The system of claim 1.

4. The tourist information generation unit Providing more engaging information through storytelling based on the driver's interests 2. The system of claim 1.

5. The tourist information generation unit Analyzes the driver's emotional state and suggests natural scenery if they want to relax, or activity spots if they want to get excited.

2. The system of claim 1.

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