system

Abstract

The system according to the embodiment aims to enable a user to intuitively understand the route to the destination through audio guidance. According to an embodiment, the system includes a reception unit, a calculation unit, and a guidance unit. The reception unit receives user input. The calculation unit calculates a route based on the information received by the reception unit. The guidance unit provides audio guidance based on the route calculated by the calculation unit.

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 that users often rely on visual information when trying to understand the route to their destination, and audio guidance is not provided sufficiently.

[0005] The system according to the embodiment aims to enable a user to intuitively understand the route to the destination through audio guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a calculation unit, and a guidance unit. The reception unit receives user input. The calculation unit calculates a route based on the information received by the reception unit. The guidance unit provides audio guidance based on the route calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to intuitively understand the route to a destination through audio guidance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A guidance system according to an embodiment of the present invention provides guidance to a user's desired location. The system receives a user's input of a desired location, calculates the optimal route to the location, and begins guidance. During this process, the system provides audio guidance while providing information about recommended shops, tourist attractions, and history of the area. This allows the user to experience the experience of being guided by a rickshaw driver. For example, a user inputs a desired location. The user simply inputs a starting point and a destination. For example, the user might input, "I want to go from the station to the museum." This information is entered into the system. The system then analyzes the input information and calculates the optimal route from the user's current location to the destination. The system calculates the optimal route based on map data and provides guidance along that route. For example, if the user inputs a route from the station to the museum, the system provides guidance along that route. Once guidance begins, the system provides audio guidance while providing information about recommended shops, tourist attractions, and history of the area. For example, while the user is on his or her way to the museum, the system might provide audio information such as, "There is a famous restaurant on this street" or "This place is a historical landmark." This allows the user to experience the experience of being guided by a rickshaw driver. With this system, users can not only receive guidance to their desired destinations, but also obtain information on recommended shops, tourist attractions, history, and so on in that area. For example, when visiting a tourist spot, the system can provide voice guidance on the history of the area and recommended tourist attractions, allowing the user to have a deeper sightseeing experience. Furthermore, because the system provides guidance in real time, the user can reach their destination without getting lost. In this way, the present invention is a system that guides users to their desired destinations, just like a rickshaw driver, providing voice guidance on recommended shops, tourist attractions, history, and so on in that area. This allows the guidance system to guide users to their desired destinations, providing voice guidance on recommended shops, tourist attractions, history, and so on in that area.

[0029] A guidance system according to an embodiment includes a reception unit, a calculation unit, and a guidance unit. The reception unit receives user input. User input includes, but is not limited to, touch input, voice input, keyboard input, and the like. The reception unit receives, for example, input of a starting point and a destination from the user. For example, the user inputs, "I want to go from the station to the museum." This information is input to the reception unit. The calculation unit calculates a route based on the information received by the reception unit. Route calculation includes, but is not limited to, criteria such as shortest distance, shortest time, and traffic conditions. The calculation unit calculates an optimal route based on map data, for example. For example, if a user inputs a route from the station to the museum, guidance is provided along that route. The guidance unit provides audio guidance based on the route calculated by the calculation unit. Audio guidance includes, but is not limited to, the type of audio, timing of the guidance, and volume adjustment. The guidance unit provides, for example, audio information about recommended shops, tourist attractions, history, and the like. For example, when a user is on his way to a museum, the guidance unit may provide audio information such as "There is a famous restaurant on this street" or "This place is a historical landmark." This allows the user to have an experience similar to being guided by a rickshaw driver. This allows the guidance system according to the embodiment to calculate the optimal route based on the user's input and provide audio guidance.

[0030] The guidance unit can provide information about recommended shops, tourist attractions, and history of a local area by voice. For example, when a user is on their way to a museum, the guidance unit can provide information by voice, such as "There is a famous restaurant on this street" or "This place is a historical landmark." The guidance unit must also clarify the specific content and criteria of the information to be provided. For example, this may include evaluation criteria, how often the information is updated, and the source of the information. This allows the user to obtain information about recommended shops, tourist attractions, history, and other information by voice. Some or all of the above-described processing by the guidance unit may be performed using, or without, AI. For example, the guidance unit can input the information provided by voice into a generation AI, which can then generate the information.

[0031] The reception unit allows the user to input a departure point and a destination. The reception unit, for example, accepts the user's input of the departure point and the destination. For example, the user inputs "I want to go from the station to the museum." This information is input to the reception unit. Methods and formats for inputting the departure point and the destination include, but are not limited to, address input, selection on a map, and voice input. This allows the user to receive guidance simply by inputting the departure point and the destination. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's input to a generation AI, which may analyze the input content.

[0032] The calculation unit can calculate a route based on map data. The calculation unit calculates a route based on, for example, the map data. Route calculation criteria include, but are not limited to, the shortest distance, the shortest time, and traffic conditions. The calculation unit calculates an optimal route based on, for example, the map data. For example, if a user inputs a route from a station to a museum, guidance is provided along that route. The type and update frequency of the map data used include, but are not limited to, online maps, offline maps, real-time updates, and the like. This allows the calculation of an optimal route based on the map data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input map data into a generation AI and have the generation AI perform route calculation.

[0033] The guidance unit can provide guidance in real time. The guidance unit, for example, provides guidance in real time. Real-time guidance includes, for example, the update frequency of location information, a communication means, and the like, but is not limited to these examples. The guidance unit, for example, tracks the user's current location in real time and provides guidance based on that location. This allows real-time guidance to be provided. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's location information to a generation AI and have the generation AI perform real-time guidance.

[0034] The guidance unit can use information registered in a database in advance or acquire information in real time. The guidance unit, for example, uses information registered in a database in advance. The guidance unit can also acquire information in real time. Information registered in a database in advance includes, for example, store information, tourist attraction information, and event information, but is not limited to these examples. Specific methods and standards for acquiring information in real time include, for example, using an API or a data source, but are not limited to these examples. This allows guidance to be provided using information registered in advance or information acquired in real time. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input information registered in a database or information acquired in real time into a generation AI, and have the generation AI generate information.

[0035] The reception unit can analyze the user's past input history and automatically suggest frequently visited places. The reception unit, for example, analyzes the user's past input history and automatically suggests frequently visited places. Specific methods and criteria for analyzing the past input history include, but are not limited to, frequency analysis, pattern recognition, and machine learning algorithms. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidate locations. It can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. It can also analyze the user's past travel patterns and suggest optimal candidate locations. This can reduce the effort required for input by suggesting frequently visited places based on the user's past input history. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest frequently visited places.

[0036] The reception unit can analyze the user's voice input and recognize the destination using natural language processing. The reception unit, for example, analyzes the user's voice input and recognizes the destination using natural language processing. Specific methods and criteria for analyzing the voice input include, but are not limited to, speech recognition technology, noise reduction, language models, etc. Specific methods and criteria for recognizing a destination using natural language processing include, but are not limited to, entity recognition, context analysis, and intent understanding, etc. For example, if a user voice inputs "I want to go to the station," natural language processing can be used to recognize "station" as the destination. Also, if a user voice inputs "Find a nearby cafe," natural language processing can be used to recognize "cafe" as the destination. Also, if a user voice inputs "I want to go to the museum," natural language processing can be used to recognize "museum" as the destination. By analyzing the voice input and recognizing the destination, the user can easily set the destination using voice. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's voice input data into the generation AI, allowing the generation AI to recognize the destination.

[0037] The reception unit can automatically suggest nearby popular spots based on the user's geographical location information. The reception unit, for example, automatically suggests nearby popular spots taking into account the user's geographical location information. Methods and accuracy of acquiring geographical location information include, but are not limited to, GPS, Wi-Fi location information, beacons, etc. Specific criteria and sources of nearby popular spots include, but are not limited to, user ratings, visitor counts, and reviews. For example, popular spots within a 1-kilometer radius of the user's current location can be automatically suggested. If the user is in a specific area, popular spots within that area can be prioritized. If the user is in a tourist destination, popular spots within that tourist destination can be automatically suggested. This allows the user to discover new places by suggesting nearby popular spots based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI, causing the generation AI to suggest popular spots.

[0038] The reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit can analyze the user's social media activity and suggest related destinations. Specific methods and criteria for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. Specific methods and criteria for suggesting related destinations include, but are not limited to, common interests, past visit history, and friend recommendations. For example, if a user posts "cafe" on social media, a nearby cafe can be suggested. Also, if a user posts "museum" on social media, a nearby museum can be suggested. Also, if a user posts "restaurant" on social media, a nearby restaurant can be suggested. By suggesting related destinations based on social media activity, places that match the user's interests can be suggested. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related destinations.

[0039] The calculation unit can predict a route by referring to past traffic data. The calculation unit, for example, refers to past traffic data to predict an optimal route. Specific methods and criteria for referring to past traffic data include, but are not limited to, traffic congestion information, accident information, and public transportation operation status. For example, the optimal route can be predicted based on past traffic congestion data. The optimal route can also be predicted based on past public transportation operation data. The optimal route can also be predicted based on past road construction data. This enables efficient travel by predicting an optimal route based on past traffic data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past traffic data into a generation AI and have the generation AI predict a route.

[0040] The calculation unit can apply different route calculation algorithms depending on the user's mode of transportation. The calculation unit applies different route calculation algorithms depending on, for example, the user's mode of transportation (walking, bicycle, car, etc.). Specific types and criteria of modes of transportation include, but are not limited to, walking, bicycle, car, public transportation, etc. Specific types and implementation methods of route calculation algorithms include, but are not limited to, Dijkstra's algorithm, A* algorithm, genetic algorithm, etc. For example, when the user travels by foot, a route calculation algorithm dedicated to pedestrians is applied. When the user travels by bicycle, a route calculation algorithm dedicated to bicycles can be applied. When the user travels by car, a route calculation algorithm dedicated to cars can be applied. This makes it possible to provide an optimal route depending on the user's mode of transportation. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's mode of transportation data into a generation AI, which can then apply a route calculation algorithm.

[0041] The calculation unit can calculate a route taking into account geographical obstacles and construction information. The calculation unit, for example, calculates a route taking into account geographical obstacles and construction information. Methods and standards for acquiring geographical obstacles and construction information include, but are not limited to, real-time traffic information, construction schedule information, and terrain data. For example, a route that avoids geographical obstacles can be calculated. A route that avoids roads under construction can also be calculated. An optimal route can also be calculated taking into account the undulations of the terrain. This makes it possible to provide an optimal route that takes into account geographical obstacles and construction information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input geographical obstacle and construction information data into a generation AI and have the generation AI perform route calculation.

[0042] The calculation unit can analyze the user's past travel history and prioritize suggest a preferred route. For example, the calculation unit analyzes the user's past travel history and prioritize suggest a preferred route. Specific methods and criteria for analyzing the past travel history include, but are not limited to, visit frequency, stay duration, and travel patterns. Specific methods and criteria for suggesting a preferred route include, but are not limited to, past selection history, user ratings, and feedback. For example, the calculation unit can suggest a preferred route based on routes the user has used in the past. It can also suggest a route that avoids congestion based on the user's past travel history. It can also analyze the user's past travel history and suggest the most efficient route. This enables more comfortable travel by suggesting a preferred route based on the user's past travel history. Some or all of the above-described processing in the calculation unit may be performed using, or without, AI. For example, the calculation unit can input the user's past travel history data into a generation AI and have the generation AI suggest a preferred route.

[0043] The guidance unit can analyze the user's reaction in real time during guidance and dynamically change the guidance content. For example, the guidance unit can analyze the user's reaction in real time during guidance and dynamically change the guidance content. Specific methods and criteria for analyzing the user's reaction include, but are not limited to, facial expression recognition, voice analysis, and behavioral pattern analysis. Specific methods and criteria for dynamically changing the guidance content include, but are not limited to, adding or deleting content based on the user's reaction, changing the order of the guidance, and the like. For example, if the user shows interest, detailed information about the location can be provided. Furthermore, if the user expresses dissatisfaction, the guidance content can be simplified. Furthermore, if the user asks a question, information corresponding to the question can be provided. This enables more appropriate guidance by dynamically changing the guidance content according to the user's reaction. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input user reaction data into a generation AI and have the generation AI dynamically change the guidance content.

[0044] The guidance unit can add related episodes and anecdotes to attract the user's interest during the guidance. For example, the guidance unit adds related episodes and anecdotes to attract the user's interest during the guidance. Specific content and criteria for related episodes and anecdotes include, but are not limited to, historical background, anecdotes about famous people, and local legends. For example, when guiding a historical landmark, an anecdote related to the location can be introduced. Also, when guiding a famous restaurant, an anecdote about the restaurant's founder can be introduced. Also, when guiding a tourist attraction, a scene from a movie or drama related to the location can be introduced. By adding related episodes and anecdotes to attract the user's interest, the guidance can be more engaging. Some or all of the above-described processing in the guidance unit may be performed using, or without, AI. For example, the guidance unit can input data of related episodes and anecdotes into a generation AI, causing the generation AI to generate episodes and anecdotes.

[0045] The guidance unit can provide nearby event information based on the user's location information during guidance. For example, the guidance unit provides nearby event information based on the user's location information during guidance. Specific content and criteria for the nearby event information include, but are not limited to, the type of event, the event date and time, and the location. For example, if the user is in a specific area, information about events being held in that area can be provided. Also, if the user is in a tourist destination, information about events being held in the tourist destination can be provided. Also, if the user is in a shopping mall, information about sales being held in the mall can be provided. By providing nearby event information based on the user's location information, the user can obtain the latest event information. Some or all of the above-described processing by the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's location information data into a generation AI and have the generation AI provide the event information.

[0046] The guidance unit can provide related information by referring to the user's past visit history during guidance. For example, the guidance unit can provide related information by referring to the user's past visit history during guidance. Specific methods and criteria for referencing the past visit history include, but are not limited to, the number of visits, the length of stay, and ratings. Specific content and criteria for the related information include, but are not limited to, historical information, tourist attractions, and event information related to the previously visited places. For example, information related to places the user has previously visited can be provided. Information related to restaurants the user has previously visited can also be provided. Information related to tourist attractions the user has previously visited can also be provided. By providing related information based on the user's past visit history, the user can obtain more in-depth information. Some or all of the above-described processing by the guidance unit can be performed using, or without, AI. For example, the guidance unit can input the user's past visit history data into a generation AI and have the generation AI provide related information.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The guidance system can also monitor the user's health status and adjust the guidance content based on the user's health status. For example, if the user is tired, the system can suggest rest spots or cafes. If the user is looking for healthy meals, the system can recommend health-oriented restaurants. Furthermore, if the user needs exercise, the system can suggest walking trails or parks. This allows guidance based on the user's health status, providing a more personalized experience.

[0049] The guidance system can also provide customized guidance based on the user's hobbies and interests. For example, if the user is interested in art, the system can suggest nearby art museums and galleries. If the user is interested in sports, the system can also recommend sporting events and facilities. If the user is interested in music, the system can also suggest live music venues and music events. This allows guidance based on the user's hobbies and interests, providing a more fulfilling experience.

[0050] The guidance system can also provide multilingual guidance based on the user's language setting. For example, if the user selects English, the system will provide guidance in English. If the user selects Chinese, the system can provide guidance in Chinese. If the user selects French, the system can provide guidance in French. This makes it possible to provide guidance according to the user's language setting, making the system suitable for foreign tourists.

[0051] The guidance system can also suggest optimal routes depending on the user's means of transportation. For example, if the user travels by bicycle, a route exclusively for bicycles can be suggested. If the user travels by public transportation, a route that takes bus or train timetables into consideration can be suggested. Furthermore, if the user travels on foot, a route exclusively for pedestrians can be suggested. This makes it possible to provide optimal routes depending on the user's means of transportation.

[0052] The guidance system can also analyze the user's past visit history and suggest new places the user has never been to. For example, it can suggest restaurants or cafes the user has never visited before. It can also guide the user to tourist attractions or events the user has never visited. It can also suggest new spots that the user may be interested in. This allows the user to enjoy discovering new places.

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

[0054] Step 1: The reception unit accepts user input. User input includes touch input, voice input, keyboard input, etc. For example, the reception unit accepts the user's input of a departure point and a destination. Specifically, the user may enter "I want to go from the station to the museum." This information is input into the reception unit. Step 2: The calculation unit calculates a route based on the information received by the reception unit. The route calculation includes criteria such as the shortest distance, shortest time, and traffic conditions. For example, the optimal route is calculated based on map data. If the user inputs a route from the station to the museum, guidance will be provided along that route. Step 3: The guidance unit provides voice guidance based on the route calculated by the calculation unit. Voice guidance includes the type of voice, timing of the guidance, volume adjustment, etc. For example, voice guidance may provide information such as recommended shops, tourist attractions, and history of the area. When the user is on his way to a museum, the guidance unit may provide voice guidance such as "There is a famous restaurant on this street" or "This place is a historical landmark."

[0055] (Example 2) A guidance system according to an embodiment of the present invention provides guidance to a user's desired location. The system receives a user's input of a desired location, calculates the optimal route to the location, and begins guidance. During this process, the system provides audio guidance while providing information about recommended shops, tourist attractions, and history of the area. This allows the user to experience the experience of being guided by a rickshaw driver. For example, a user inputs a desired location. The user simply inputs a starting point and a destination. For example, the user might input, "I want to go from the station to the museum." This information is entered into the system. The system then analyzes the input information and calculates the optimal route from the user's current location to the destination. The system calculates the optimal route based on map data and provides guidance along that route. For example, if the user inputs a route from the station to the museum, the system provides guidance along that route. Once guidance begins, the system provides audio guidance while providing information about recommended shops, tourist attractions, and history of the area. For example, while the user is on his or her way to the museum, the system might provide audio information such as, "There is a famous restaurant on this street" or "This place is a historical landmark." This allows the user to experience the experience of being guided by a rickshaw driver. With this system, users can not only receive guidance to their desired destinations, but also obtain information on recommended shops, tourist attractions, history, and so on in that area. For example, when visiting a tourist spot, the system can provide voice guidance on the history of the area and recommended tourist attractions, allowing the user to have a deeper sightseeing experience. Furthermore, because the system provides guidance in real time, the user can reach their destination without getting lost. In this way, the present invention is a system that guides users to their desired destinations, just like a rickshaw driver, providing voice guidance on recommended shops, tourist attractions, history, and so on in that area. This allows the guidance system to guide users to their desired destinations, providing voice guidance on recommended shops, tourist attractions, history, and so on in that area.

[0056] A guidance system according to an embodiment includes a reception unit, a calculation unit, and a guidance unit. The reception unit receives user input. User input includes, but is not limited to, touch input, voice input, keyboard input, and the like. The reception unit receives, for example, input of a starting point and a destination from the user. For example, the user inputs, "I want to go from the station to the museum." This information is input to the reception unit. The calculation unit calculates a route based on the information received by the reception unit. Route calculation includes, but is not limited to, criteria such as shortest distance, shortest time, and traffic conditions. The calculation unit calculates an optimal route based on map data, for example. For example, if a user inputs a route from the station to the museum, guidance is provided along that route. The guidance unit provides audio guidance based on the route calculated by the calculation unit. Audio guidance includes, but is not limited to, the type of audio, timing of the guidance, and volume adjustment. The guidance unit provides, for example, audio information about recommended shops, tourist attractions, history, and the like. For example, when a user is on his way to a museum, the guidance unit may provide audio information such as "There is a famous restaurant on this street" or "This place is a historical landmark." This allows the user to have an experience similar to being guided by a rickshaw driver. This allows the guidance system according to the embodiment to calculate the optimal route based on the user's input and provide audio guidance.

[0057] The guidance unit can provide information about recommended shops, tourist attractions, and history of a local area by voice. For example, when a user is on their way to a museum, the guidance unit can provide information by voice, such as "There is a famous restaurant on this street" or "This place is a historical landmark." The guidance unit must also clarify the specific content and criteria of the information to be provided. For example, this may include evaluation criteria, how often the information is updated, and the source of the information. This allows the user to obtain information about recommended shops, tourist attractions, history, and other information by voice. Some or all of the above-described processing by the guidance unit may be performed using, or without, AI. For example, the guidance unit can input the information provided by voice into a generation AI, which can then generate the information.

[0058] The reception unit allows the user to input a departure point and a destination. The reception unit, for example, accepts the user's input of the departure point and the destination. For example, the user inputs "I want to go from the station to the museum." This information is input to the reception unit. Methods and formats for inputting the departure point and the destination include, but are not limited to, address input, selection on a map, and voice input. This allows the user to receive guidance simply by inputting the departure point and the destination. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's input to a generation AI, which may analyze the input content.

[0059] The calculation unit can calculate a route based on map data. The calculation unit calculates a route based on, for example, the map data. Route calculation criteria include, but are not limited to, the shortest distance, the shortest time, and traffic conditions. The calculation unit calculates an optimal route based on, for example, the map data. For example, if a user inputs a route from a station to a museum, guidance is provided along that route. The type and update frequency of the map data used include, but are not limited to, online maps, offline maps, real-time updates, and the like. This allows the calculation of an optimal route based on the map data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input map data into a generation AI and have the generation AI perform route calculation.

[0060] The guidance unit can provide guidance in real time. The guidance unit, for example, provides guidance in real time. Real-time guidance includes, for example, the update frequency of location information, a communication means, and the like, but is not limited to these examples. The guidance unit, for example, tracks the user's current location in real time and provides guidance based on that location. This allows real-time guidance to be provided. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's location information to a generation AI and have the generation AI perform real-time guidance.

[0061] The guidance unit can use information registered in a database in advance or acquire information in real time. The guidance unit, for example, uses information registered in a database in advance. The guidance unit can also acquire information in real time. Information registered in a database in advance includes, for example, store information, tourist attraction information, and event information, but is not limited to these examples. Specific methods and standards for acquiring information in real time include, for example, using an API or a data source, but are not limited to these examples. This allows guidance to be provided using information registered in advance or information acquired in real time. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input information registered in a database or information acquired in real time into a generation AI, and have the generation AI generate information.

[0062] The reception unit can estimate the user's emotion and dynamically change the design of the input interface based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and dynamically changes the design of the input interface based on the estimated user emotion. Specific methods and criteria for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, and behavioral pattern analysis. Changing the design of the input interface can include, but are not limited to, changing the color, layout, and font size. For example, if the user is nervous, a calm-colored interface can be provided to reduce visual stress. If the user is enjoying themselves, a bright-colored interface can be provided to make input tasks more enjoyable. If the user is tired, a simple, highly visible interface can be provided to make input tasks easier. Changing the design of the input interface according to the user's emotion enables more comfortable operation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0063] The reception unit can analyze the user's past input history and automatically suggest frequently visited places. The reception unit, for example, analyzes the user's past input history and automatically suggests frequently visited places. Specific methods and criteria for analyzing the past input history include, but are not limited to, frequency analysis, pattern recognition, and machine learning algorithms. For example, the reception unit can automatically display places that the user has frequently visited in the past as candidate locations. It can also predict places that the user will visit on specific days of the week or during specific time periods and suggest them as candidate locations. It can also analyze the user's past travel patterns and suggest optimal candidate locations. This can reduce the effort required for input by suggesting frequently visited places based on the user's past input history. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI suggest frequently visited places.

[0064] The reception unit can analyze the user's voice input and recognize the destination using natural language processing. The reception unit, for example, analyzes the user's voice input and recognizes the destination using natural language processing. Specific methods and criteria for analyzing the voice input include, but are not limited to, speech recognition technology, noise reduction, language models, etc. Specific methods and criteria for recognizing a destination using natural language processing include, but are not limited to, entity recognition, context analysis, and intent understanding, etc. For example, if a user voice inputs "I want to go to the station," natural language processing can be used to recognize "station" as the destination. Also, if a user voice inputs "Find a nearby cafe," natural language processing can be used to recognize "cafe" as the destination. Also, if a user voice inputs "I want to go to the museum," natural language processing can be used to recognize "museum" as the destination. By analyzing the voice input and recognizing the destination, the user can easily set the destination using voice. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's voice input data into the generation AI, allowing the generation AI to recognize the destination.

[0065] The reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes inputs based on the estimated user emotions. Specific methods and criteria for prioritizing inputs include, but are not limited to, urgency, importance, and user intent. For example, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter their departure and destination locations. Furthermore, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is stressed, a simple interface can be provided to minimize input steps. This allows input prioritization based on the user's emotions, thereby providing a more appropriate input method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0066] The reception unit can automatically suggest nearby popular spots based on the user's geographical location information. The reception unit, for example, automatically suggests nearby popular spots taking into account the user's geographical location information. Methods and accuracy of acquiring geographical location information include, but are not limited to, GPS, Wi-Fi location information, beacons, etc. Specific criteria and sources of nearby popular spots include, but are not limited to, user ratings, visitor counts, and reviews. For example, popular spots within a 1-kilometer radius of the user's current location can be automatically suggested. If the user is in a specific area, popular spots within that area can be prioritized. If the user is in a tourist destination, popular spots within that tourist destination can be automatically suggested. This allows the user to discover new places by suggesting nearby popular spots based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI, causing the generation AI to suggest popular spots.

[0067] The reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit can analyze the user's social media activity and suggest related destinations. Specific methods and criteria for analyzing social media activity include, but are not limited to, the content of posts, the number of likes, and the number of followers. Specific methods and criteria for suggesting related destinations include, but are not limited to, common interests, past visit history, and friend recommendations. For example, if a user posts "cafe" on social media, a nearby cafe can be suggested. Also, if a user posts "museum" on social media, a nearby museum can be suggested. Also, if a user posts "restaurant" on social media, a nearby restaurant can be suggested. By suggesting related destinations based on social media activity, places that match the user's interests can be suggested. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related destinations.

[0068] The calculation unit can estimate the user's emotions and adjust the route selection criteria based on the estimated user emotions. For example, the calculation unit can estimate the user's emotions and adjust the route selection criteria based on the estimated user emotions. Specific methods and criteria for adjusting the route selection criteria include, but are not limited to, prioritizing time, distance, and scenery. For example, if the user is relaxed, a route with good scenery can be prioritized. Also, if the user is in a hurry, the shortest route can be prioritized. Also, if the user is excited, a route with a lot of activity can be prioritized. This allows for adjusting the route selection criteria according to the user's emotions to provide a more appropriate route. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's emotion data into the generation AI and have the generation AI adjust the route selection criteria.

[0069] The calculation unit can predict a route by referring to past traffic data. The calculation unit, for example, refers to past traffic data to predict an optimal route. Specific methods and criteria for referring to past traffic data include, but are not limited to, traffic congestion information, accident information, and public transportation operation status. For example, the optimal route can be predicted based on past traffic congestion data. The optimal route can also be predicted based on past public transportation operation data. The optimal route can also be predicted based on past road construction data. This enables efficient travel by predicting an optimal route based on past traffic data. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input past traffic data into a generation AI and have the generation AI predict a route.

[0070] The calculation unit can apply different route calculation algorithms depending on the user's mode of transportation. The calculation unit applies different route calculation algorithms depending on, for example, the user's mode of transportation (walking, bicycle, car, etc.). Specific types and criteria of modes of transportation include, but are not limited to, walking, bicycle, car, public transportation, etc. Specific types and implementation methods of route calculation algorithms include, but are not limited to, Dijkstra's algorithm, A* algorithm, genetic algorithm, etc. For example, when the user travels by foot, a route calculation algorithm dedicated to pedestrians is applied. When the user travels by bicycle, a route calculation algorithm dedicated to bicycles can be applied. When the user travels by car, a route calculation algorithm dedicated to cars can be applied. This makes it possible to provide an optimal route depending on the user's mode of transportation. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's mode of transportation data into a generation AI, which can then apply a route calculation algorithm.

[0071] The calculation unit can estimate the user's emotions and determine the priority of routes based on the estimated user emotions. The calculation unit, for example, estimates the user's emotions and determines the priority of routes based on the estimated user emotions. Specific methods and criteria for determining route priorities include, but are not limited to, urgency, importance, and the user's intentions. For example, if the user is in a hurry, the shortest route can be prioritized. Also, if the user is relaxed, a scenic route can be prioritized. Also, if the user is excited, a route with a lot of activity can be prioritized. This allows for route prioritization based on the user's emotions, resulting in a more appropriate route being provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the calculation unit may be performed using AI, or without AI. For example, the calculation unit can input user emotion data into the generation AI and have the generation AI determine the priority of routes.

[0072] The calculation unit can calculate a route taking into account geographical obstacles and construction information. The calculation unit, for example, calculates a route taking into account geographical obstacles and construction information. Methods and standards for acquiring geographical obstacles and construction information include, but are not limited to, real-time traffic information, construction schedule information, and terrain data. For example, a route that avoids geographical obstacles can be calculated. A route that avoids roads under construction can also be calculated. An optimal route can also be calculated taking into account the undulations of the terrain. This makes it possible to provide an optimal route that takes into account geographical obstacles and construction information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input geographical obstacle and construction information data into a generation AI and have the generation AI perform route calculation.

[0073] The calculation unit can analyze the user's past travel history and prioritize suggest a preferred route. For example, the calculation unit analyzes the user's past travel history and prioritize suggest a preferred route. Specific methods and criteria for analyzing the past travel history include, but are not limited to, visit frequency, stay duration, and travel patterns. Specific methods and criteria for suggesting a preferred route include, but are not limited to, past selection history, user ratings, and feedback. For example, the calculation unit can suggest a preferred route based on routes the user has used in the past. It can also suggest a route that avoids congestion based on the user's past travel history. It can also analyze the user's past travel history and suggest the most efficient route. This enables more comfortable travel by suggesting a preferred route based on the user's past travel history. Some or all of the above-described processing in the calculation unit may be performed using, or without, AI. For example, the calculation unit can input the user's past travel history data into a generation AI and have the generation AI suggest a preferred route.

[0074] The guidance unit can estimate the user's emotions and adjust the tone and speed of the guidance based on the estimated user's emotions. For example, the guidance unit estimates the user's emotions and adjusts the tone and speed of the guidance based on the estimated user's emotions. Specific methods and criteria for adjusting the tone and speed of the guidance include, but are not limited to, voice pitch, speaking speed, and emotional expression. For example, if the user is nervous, the guidance can be provided slowly in a calm tone. Alternatively, if the user is relaxed, the guidance can be provided in a bright tone. Alternatively, if the user is in a hurry, the guidance can be provided quickly and concisely. This allows for more appropriate guidance by adjusting the tone and speed of the guidance according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input the user's emotional data into the generation AI, which can then adjust the tone and speed of the guidance.

[0075] The guidance unit can analyze the user's reaction in real time during guidance and dynamically change the guidance content. For example, the guidance unit can analyze the user's reaction in real time during guidance and dynamically change the guidance content. Specific methods and criteria for analyzing the user's reaction include, but are not limited to, facial expression recognition, voice analysis, and behavioral pattern analysis. Specific methods and criteria for dynamically changing the guidance content include, but are not limited to, adding or deleting content based on the user's reaction, changing the order of the guidance, and the like. For example, if the user shows interest, detailed information about the location can be provided. Furthermore, if the user expresses dissatisfaction, the guidance content can be simplified. Furthermore, if the user asks a question, information corresponding to the question can be provided. This enables more appropriate guidance by dynamically changing the guidance content according to the user's reaction. Some or all of the above-described processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input user reaction data into a generation AI and have the generation AI dynamically change the guidance content.

[0076] The guidance unit can add related episodes and anecdotes to attract the user's interest during the guidance. For example, the guidance unit adds related episodes and anecdotes to attract the user's interest during the guidance. Specific content and criteria for related episodes and anecdotes include, but are not limited to, historical background, anecdotes about famous people, and local legends. For example, when guiding a historical landmark, an anecdote related to the location can be introduced. Also, when guiding a famous restaurant, an anecdote about the restaurant's founder can be introduced. Also, when guiding a tourist attraction, a scene from a movie or drama related to the location can be introduced. By adding related episodes and anecdotes to attract the user's interest, the guidance can be more engaging. Some or all of the above-described processing in the guidance unit may be performed using, or without, AI. For example, the guidance unit can input data of related episodes and anecdotes into a generation AI, causing the generation AI to generate episodes and anecdotes.

[0077] The guidance unit can estimate the user's emotions and adjust the order of guidance based on the estimated user emotions. The guidance unit, for example, estimates the user's emotions and adjusts the order of guidance based on the estimated user emotions. Specific methods and criteria for adjusting the order of guidance include, but are not limited to, the user's interests, time constraints, and priorities. For example, if the user is in a hurry, important information can be presented first. Also, if the user is relaxed, detailed information can be presented in order. Also, if the user is excited, information that is of interest can be presented first. This allows for more appropriate guidance by adjusting the order of guidance based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without AI. For example, the guidance unit can input the user's emotion data into the generation AI and have the generation AI adjust the order of guidance.

[0078] The guidance unit can provide nearby event information based on the user's location information during guidance. For example, the guidance unit provides nearby event information based on the user's location information during guidance. Specific content and criteria for the nearby event information include, but are not limited to, the type of event, the event date and time, and the location. For example, if the user is in a specific area, information about events being held in that area can be provided. Also, if the user is in a tourist destination, information about events being held in the tourist destination can be provided. Also, if the user is in a shopping mall, information about sales being held in the mall can be provided. By providing nearby event information based on the user's location information, the user can obtain the latest event information. Some or all of the above-described processing by the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit can input the user's location information data into a generation AI and have the generation AI provide the event information.

[0079] The guidance unit can provide related information by referring to the user's past visit history during guidance. For example, the guidance unit can provide related information by referring to the user's past visit history during guidance. Specific methods and criteria for referencing the past visit history include, but are not limited to, the number of visits, the length of stay, and ratings. Specific content and criteria for the related information include, but are not limited to, historical information, tourist attractions, and event information related to the previously visited places. For example, information related to places the user has previously visited can be provided. Information related to restaurants the user has previously visited can also be provided. Information related to tourist attractions the user has previously visited can also be provided. By providing related information based on the user's past visit history, the user can obtain more in-depth information. Some or all of the above-described processing by the guidance unit can be performed using, or without, AI. For example, the guidance unit can input the user's past visit history data into a generation AI and have the generation AI provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, and guidance unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using a touch panel 38A or a microphone 38B of the smart device 14. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route based on map data. The guidance unit provides audio guidance, for example, using a speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, and guidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route based on map data. The guidance unit provides audio guidance, for example, using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, and guidance unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the headset terminal 314. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route based on map data. The guidance unit provides audio guidance, for example, using the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, calculation unit, and guidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The calculation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates the optimal route based on map data. The guidance unit provides audio guidance, for example, using the speaker 240 of the robot 414.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The guidance system can also monitor the user's health status and adjust the guidance content based on the user's health status. For example, if the user is tired, the system can suggest rest spots or cafes. If the user is looking for healthy meals, the system can recommend health-oriented restaurants. Furthermore, if the user needs exercise, the system can suggest walking trails or parks. This allows guidance based on the user's health status, providing a more personalized experience.

[0082] The guidance system can also provide customized guidance based on the user's hobbies and interests. For example, if the user is interested in art, the system can suggest nearby art museums and galleries. If the user is interested in sports, the system can also recommend sporting events and facilities. If the user is interested in music, the system can also suggest live music venues and music events. This allows guidance based on the user's hobbies and interests, providing a more fulfilling experience.

[0083] The guidance system can also provide multilingual guidance based on the user's language setting. For example, if the user selects English, the system will provide guidance in English. If the user selects Chinese, the system can provide guidance in Chinese. If the user selects French, the system can provide guidance in French. This makes it possible to provide guidance according to the user's language setting, making the system suitable for foreign tourists.

[0084] The guidance system can also suggest optimal routes depending on the user's means of transportation. For example, if the user travels by bicycle, a route exclusively for bicycles can be suggested. If the user travels by public transportation, a route that takes bus or train timetables into consideration can be suggested. Furthermore, if the user travels on foot, a route exclusively for pedestrians can be suggested. This makes it possible to provide optimal routes depending on the user's means of transportation.

[0085] The guidance system can also analyze the user's past visit history and suggest new places the user has never been to. For example, it can suggest restaurants or cafes the user has never visited before. It can also guide the user to tourist attractions or events the user has never visited. It can also suggest new spots that the user may be interested in. This allows the user to enjoy discovering new places.

[0086] The guidance system can also estimate the user's emotions and adjust the tone and content of the guidance based on the estimated emotions. For example, if the user is relaxed, the guidance can be given in a calm tone. If the user is excited, the guidance can be given in a lively tone. Furthermore, if the user is tired, the guidance system can suggest places where the user can relax. This makes it possible to provide guidance that is tailored to the user's emotions, providing a more comfortable experience.

[0087] The guidance system can further estimate the user's emotions and adjust the order of guidance based on the estimated emotions. For example, if the user is in a hurry, important information can be provided first. If the user is relaxed, detailed information can be provided step by step. Furthermore, if the user is excited, information that will interest the user can be provided first. This allows for more appropriate guidance by adjusting the order of guidance according to the user's emotions.

[0088] The guidance system can also estimate the user's emotions and dynamically change the content of the guidance based on the estimated emotions. For example, if the user shows interest, detailed information about the location can be provided. If the user shows dissatisfaction, the guidance content can be simplified. Furthermore, if the user asks a question, information corresponding to the question can be provided. This allows for more appropriate guidance by dynamically changing the guidance content according to the user's emotions.

[0089] The guidance system can further estimate the user's emotions and adjust the tone and speed of the guidance based on the estimated emotions. For example, if the user is nervous, the guidance can be provided slowly in a calm tone. If the user is relaxed, the guidance can be provided in a bright tone. Furthermore, if the user is in a hurry, the guidance can be provided quickly and concisely. This allows for more appropriate guidance by adjusting the tone and speed of the guidance according to the user's emotions.

[0090] The guidance system can also estimate the user's emotions and customize the guidance content based on the estimated emotions. For example, if the user is relaxed, it can suggest relaxing places and activities. If the user is excited, it can suggest active places and events. Furthermore, if the user is tired, it can suggest rest spots and cafes. In this way, by customizing the guidance content according to the user's emotions, it is possible to provide a more comfortable experience.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit accepts user input. User input includes touch input, voice input, keyboard input, etc. For example, the reception unit accepts the user's input of a departure point and a destination. Specifically, the user may enter "I want to go from the station to the museum." This information is input into the reception unit. Step 2: The calculation unit calculates a route based on the information received by the reception unit. The route calculation includes criteria such as the shortest distance, shortest time, and traffic conditions. For example, the optimal route is calculated based on map data. If the user inputs a route from the station to the museum, guidance will be provided along that route. Step 3: The guidance unit provides voice guidance based on the route calculated by the calculation unit. Voice guidance includes the type of voice, timing of the guidance, volume adjustment, etc. For example, voice guidance may provide information such as recommended shops, tourist attractions, and history of the area. When the user is on his way to a museum, the guidance unit may provide voice guidance such as "There is a famous restaurant on this street" or "This place is a historical landmark."

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a calculation unit that calculates a route based on the information received by the reception unit; a guidance unit that provides voice guidance based on the route calculated by the calculation unit; Equipped with A system characterized by:

2. The guide unit is Provides audio information about recommended shops, tourist attractions, and history of the area 2. The system of claim 1.

3. The reception unit The user enters the origin and destination 2. The system of claim 1.

4. The calculation unit Calculate routes based on map data 2. The system of claim 1.

5. The guide unit is Providing real-time guidance 2. The system of claim 1.

6. The guide unit is Use information pre-registered in a database or obtain information in real time 2. The system of claim 1.

7. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and automatically suggests frequently visited places 2. The system of claim 1.

9. The reception unit Analyzes user voice input and uses natural language processing to recognize destinations 2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

Images