system

Abstract

The system according to this embodiment aims to provide information that is tailored to the user's emotions and interests. [Solution] The system according to the embodiment comprises an acquisition unit, a provision unit, and an emotion estimation unit. The acquisition unit acquires location information, which is the current location of the user and the planned locations that the user intends to visit. The provision unit provides historical information of the region corresponding to the location information acquired by the acquisition unit. The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions of the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the provision of information regarding the user's current location and planned visit location is uniform, and the provision of information according to the user's feelings and interests is insufficient.

[0005] The system according to the embodiment aims to provide information according to the user's feelings and interests.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a provision unit, and an emotion estimation unit. The acquisition unit acquires location information, which is the current location of the user and the planned locations the user intends to visit. The provision unit provides historical information about the region corresponding to the location information acquired by the acquisition unit. The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions of the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide information tailored to the user's emotions and interests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides an audio guide system that acquires information about the user's current location or planned location and provides historical information related to that location in audio. The audio guide system recommends tourist destinations based on the user's interests and past travel history and advises on the optimal sightseeing route and method of transportation. For example, the audio guide system acquires information about the user's current location or planned location. For example, the system can use GPS to identify the user's current location or the user can input the tourist destinations they plan to visit. This information is acquired by the acquisition unit. Next, the audio guide system provides historical information about the area based on the acquired location information. For example, if the user is in Kyoto, it provides information about Kyoto's history and famous places in audio. This information is provided by the provision unit. Furthermore, the audio guide system recommends tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it recommends tourist destinations related to temples. This recommendation is made by the provision unit. The audio guide system also provides the optimal route for visiting the recommended tourist destinations. For example, it proposes a route for efficiently visiting multiple tourist destinations. This route is provided by the service provider. Furthermore, the audio guide system provides the information in audio format. For example, users can listen to information about tourist spots while walking. This audio information is provided by the service provider. Finally, the audio guide system estimates the user's emotions and adjusts the content and tone of the information it provides. For example, if the user is tired, it will recommend relaxing tourist spots and provide the information in a gentle tone. This emotion estimation and information adjustment is performed by the service provider. In this way, the audio guide system provides information about the user's current location and planned destinations in audio format, recommends suitable tourist spots based on the user's interests and past travel history, and advises on the optimal sightseeing route and transportation method. In this way, the audio guide system can improve the user's sightseeing experience.

[0029] The voice guide system according to this embodiment comprises an acquisition unit, a provision unit, and an emotion estimation unit. The acquisition unit acquires location information, which is the user's current location and the locations the user plans to visit. The acquisition unit can, for example, use GPS to identify the user's current location. The acquisition unit can also take input of tourist destinations the user plans to visit. For example, the acquisition unit acquires the information when the user enters the planned destinations into a tourist app. The provision unit provides historical information about the region corresponding to the location information acquired by the acquisition unit. For example, if the user is in Kyoto, the provision unit can provide information about Kyoto's history and famous places in voice. The provision unit can also recommend tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it will recommend tourist destinations related to temples. Furthermore, the provision unit can also provide an optimal route for visiting the recommended tourist destinations. For example, it can suggest a route for efficiently visiting multiple tourist destinations. The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions of the user. The emotion estimation unit can, for example, recommend relaxing tourist destinations if the user is tired, and provide information in a gentle tone. As a result, the audio guide system according to this embodiment can provide information about the user's current location and planned destinations in audio, recommend tourist destinations based on the user's interests and past travel history, and advise on the optimal sightseeing route and transportation method.

[0030] The acquisition unit obtains location information, including the user's current location and planned destinations. For example, the acquisition unit can use GPS to pinpoint the user's current location. Specifically, it utilizes the GPS function of smartphones and mobile devices to obtain the user's latitude and longitude information in real time. The acquisition unit can also input tourist destinations the user plans to visit. For example, the user can input their planned destinations into a travel app, and the acquisition unit will retrieve that information. The travel app stores the user's entered destinations in a database, and the acquisition unit retrieves the information from that database. Furthermore, the acquisition unit can collect the user's travel history and past visit information. This allows for understanding the user's behavior patterns and providing more accurate location information. For example, it can record previously visited tourist destinations and travel routes, which can be used as a reference for future visits. The acquisition unit centrally manages this location information and can collaborate with other departments and systems. For example, the acquired location information can be stored on a cloud server, allowing access by the provision unit and sentiment estimation unit. Furthermore, the acquisition unit can utilize auxiliary location information technologies such as Wi-Fi and Bluetooth® beacons to improve the accuracy of location information. This allows the acquisition unit to accurately and quickly acquire information about the user's current location and planned destinations, thereby improving the overall system performance.

[0031] The information provider unit provides historical information about the region corresponding to the location information acquired by the information acquisition unit. For example, if the user is in Kyoto, the information provider unit can provide information about Kyoto's history and famous places in audio format. Specifically, based on the user's current location, the information provider unit retrieves information about historical events and famous places related to that region from its database and provides it as an audio guide. The audio guide is played through the user's smartphone or mobile device, allowing the user to listen to the information even while walking. The information provider unit can also recommend tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it will recommend tourist destinations related to temples. The information provider unit analyzes the user's travel history and selects tourist destinations that match the user's interests and preferences. Furthermore, the information provider unit can also provide the optimal route for visiting the recommended tourist destinations. For example, it can suggest a route to efficiently visit multiple tourist destinations. The information provider unit utilizes map information and traffic information to calculate a route that allows the user to visit multiple tourist destinations in the shortest possible time. This allows the service provider to provide users with voice-based information about their current location and planned destinations, recommend tourist spots based on their interests and past travel history, and advise on optimal travel routes and transportation methods. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and content of the information provided. For example, by having users provide ratings and comments on the information provided, the service provider can use that feedback to improve the quality of the information. This enables the service provider to provide users with a more satisfying service.

[0032] The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions. For example, if the user is tired, the emotion estimation unit can recommend relaxing tourist destinations and provide the information in a gentle tone. Specifically, the emotion estimation unit analyzes the user's voice, facial expressions, and behavioral patterns to estimate the user's emotional state. For example, it can determine if the user is tired from the tone and speed of the user's voice when speaking into the smartphone, and from facial expressions captured by the camera using facial recognition technology. It can also analyze behavioral data such as the user's walking speed and distance traveled to estimate the user's fatigue level. The emotion estimation unit comprehensively analyzes this data to grasp the user's emotional state in real time. Furthermore, the emotion estimation unit adjusts the content and tone of the information provided by the information provider based on the estimated emotional state. For example, if the user is tired, it can recommend relaxing tourist destinations and rest spots and provide the information in a gentle tone. Conversely, if the user is excited, it can provide information on active tourist destinations and events and convey the information in an energetic tone. This enables the emotion estimation unit to provide optimal information tailored to the user's emotional state, thereby improving user satisfaction. Furthermore, the emotion estimation unit can accumulate user emotional data and analyze long-term emotional trends. This allows for an understanding of user emotional patterns and the provision of more personalized services. For example, if a user consistently feels relaxed at a particular tourist destination, that destination can be recommended preferentially. In this way, the emotion estimation unit can provide personalized information based on the user's emotions, improving the overall user experience of the system.

[0033] The service provider can provide historical information about tourist destinations when recommending them to users. For example, if a user is in Kyoto, the service provider can provide audio information about Kyoto's history and famous places. Similarly, if a user is in Nara, the service provider can provide audio information about Nara's history and famous places. Furthermore, if a user is in Tokyo, the service provider can provide audio information about Tokyo's history and famous places. This enriches the user's travel experience by providing historical information about tourist destinations. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without one. For example, the service provider can input historical information about a tourist destination into a generative AI, which can then generate audio data based on that information.

[0034] The service provider can determine which tourist destinations to recommend to the user based on the user's interests and travel history. For example, if the user has visited many temples in the past, the service provider will recommend tourist destinations related to temples. Similarly, if the user has visited many museums in the past, the service provider can recommend tourist destinations related to museums. Furthermore, if the user has visited many natural landscapes in the past, the service provider can recommend tourist destinations related to natural landscapes. This allows for the recommendation of more appropriate tourist destinations based on the user's interests and past travel history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's interests and travel history into a generative AI, which can then recommend tourist destinations based on that information.

[0035] The service provider can provide users with an efficient route for visiting multiple recommended tourist destinations. For example, if a user is visiting multiple temples in Kyoto, the service provider can suggest an efficient route. Similarly, if a user is visiting multiple museums in Tokyo, the service provider can suggest an efficient route. Furthermore, if a user is visiting multiple natural landscapes in Nara, the service provider can suggest an efficient route. This allows the service provider to provide the optimal route for efficiently visiting multiple tourist destinations. Some or all of the processing described above in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input location information of multiple tourist destinations into a generative AI, which can then calculate the optimal route based on that information.

[0036] The information provider can provide historical information in audio format. For example, if the user is in Kyoto, the information provider can provide information about Kyoto's history and famous places in audio format. Similarly, if the user is in Nara, the information provider can provide information about Nara's history and famous places in audio format. Furthermore, if the user is in Tokyo, the information provider can provide information about Tokyo's history and famous places in audio format. This allows users to obtain information even while walking. Some or all of the processing described above in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input historical information into a generative AI, which can then generate audio data based on that information.

[0037] The acquisition unit can analyze the user's past location history and select an efficient acquisition method. For example, the acquisition unit can predict the next destination based on the user's past visits to tourist destinations and acquire location information. The acquisition unit can also analyze the user's past travel patterns and select an efficient method for acquiring location information. Furthermore, the acquisition unit can consider the frequency of places the user has visited in the past and prioritize the acquisition of important location information. This enables efficient acquisition of location information by analyzing past location history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without a generating AI. For example, the acquisition unit can input the user's past location history into a generating AI, and the generating AI can select an efficient acquisition method based on that data.

[0038] The acquisition unit can filter location information based on the user's current activities and areas of interest. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring location information related to tourist destinations. Similarly, if the user is shopping, the acquisition unit can prioritize acquiring location information for shopping areas. Furthermore, if the user is eating, the acquisition unit can prioritize acquiring location information for restaurants and cafes. This allows the acquisition of highly relevant location information based on the user's current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without one. For example, the acquisition unit can input data on the user's current activities and areas of interest into a generating AI, which can then filter the data.

[0039] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user approaches a tourist destination, the acquisition unit will prioritize the acquisition of detailed information about that tourist destination. Furthermore, if the user is in a specific area, the acquisition unit can prioritize the acquisition of information related to that area. Additionally, if the user approaches a specific landmark, the acquisition unit can prioritize the acquisition of information about that landmark. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without a generating AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, which can then prioritize the acquisition of highly relevant information based on that data.

[0040] The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring location information. For example, the acquisition unit can acquire information about relevant tourist destinations based on information about places the user has shared on social media. It can also acquire information about relevant tourist destinations based on information about places the user follows on social media. Furthermore, the acquisition unit can acquire information about relevant tourist destinations based on information about places the user has checked into on social media. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the user's social media activity data into a generating AI, and the generating AI can acquire relevant information based on that data.

[0041] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information on important tourist destinations and concise information on minor tourist destinations. Alternatively, the provider can provide detailed information on topics of particular interest to the user and concise information on other topics. Furthermore, if the user is in a hurry, the provider can provide only the essential information concisely. This allows the provider to provide detailed information on topics important to the user by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input information importance data into a generative AI, which can then adjust the level of detail based on that data.

[0042] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, the provider can provide historical information in detail and basic information about tourist destinations concisely. Furthermore, depending on the user's interests, the provider can provide cultural information in detail and other information concisely. In addition, the provider can prioritize providing information of specific categories based on the user's areas of interest. This allows for more appropriate information provision by applying different information provision algorithms depending on the category of information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input information category data into a generative AI, which can then apply different information provision algorithms based on that data.

[0043] The information provider can determine the priority of information provision based on the timing of information submission. For example, the provider can prioritize providing information about a tourist destination immediately after the user arrives there. It can also provide information about a tourist destination before the user leaves it. Furthermore, it can prioritize providing information about a tourist destination when the user approaches it. This enables timely information provision by determining the priority of information provision based on the timing of information submission. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without a generating AI. For example, the provider can input information submission timing data into a generating AI, and the generating AI can determine the priority of information provision based on that data.

[0044] The information delivery unit can adjust the order of information delivery based on the relevance of the information. For example, the delivery unit can prioritize providing information that the user is interested in, and postpone other information. It can also prioritize providing information related to the user's current location, and postpone other information. Furthermore, it can prioritize providing information related to places the user has visited in the past, and postpone other information. In this way, by adjusting the order of information delivery based on the relevance of the information, it is possible to prioritize providing information that is important to the user. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the delivery unit can input information relevance data into a generative AI, and the generative AI can adjust the order of delivery based on that data.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The audio guide system can analyze a user's past travel history and predict the next tourist destination they should visit. For example, if a user has visited many temples in the past, it can predict which temple to visit next. Similarly, if a user has visited many museums, it can predict which museum to visit next. Furthermore, if a user has visited many natural landscapes in the past, it can predict which natural landscape to visit next. In this way, by analyzing a user's past travel history, the system can predict the next tourist destination they should visit.

[0047] Audio guide systems can provide information about tourist destinations based on the user's current activity. For example, if the user is sightseeing, it can provide information related to the tourist destination. If the user is shopping, it can provide information about the shopping area. Furthermore, if the user is eating, it can provide information about restaurants and cafes. This allows for the provision of highly relevant information based on the user's current activity.

[0048] Audio guide systems can provide information about tourist destinations while considering the user's geographical location. For example, when a user approaches a tourist destination, they can provide detailed information about that destination. They can also provide information related to a specific area when the user is in that area. Furthermore, when a user approaches a specific landmark, they can provide information about that landmark. This allows for the provision of highly relevant information by considering the user's geographical location.

[0049] The audio guide system can analyze users' social media activity and provide information on relevant tourist destinations. For example, it can provide information on relevant tourist destinations based on places users have shared on social media. It can also provide information on relevant tourist destinations based on places users follow on social media. Furthermore, it can provide information on relevant tourist destinations based on places users have checked in to on social media. In this way, by analyzing users' social media activity, it can provide relevant information.

[0050] The audio guide system can determine which tourist destinations to recommend to the user based on the user's interests and travel history. For example, if the user has visited many temples in the past, it can recommend tourist destinations related to temples. Similarly, if the user has visited many museums in the past, it can recommend tourist destinations related to museums. Furthermore, if the user has visited many natural landscapes in the past, it can recommend tourist destinations related to natural landscapes. This allows for more appropriate recommendations of tourist destinations based on the user's interests and past travel history.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The acquisition unit obtains location information, which includes the user's current location and planned destinations. The acquisition unit can, for example, use GPS to determine the user's current location. The acquisition unit can also take input from the user about tourist destinations they plan to visit. For example, if the user enters their planned destinations into a travel app, the acquisition unit can obtain that information. Step 2: The providing unit provides historical information about the region corresponding to the location information acquired by the acquisition unit. For example, if the user is in Kyoto, the providing unit can provide information about Kyoto's history and famous places in audio format. The providing unit can also recommend tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it will recommend tourist destinations related to temples. Furthermore, the providing unit can also provide the optimal route for visiting the recommended tourist destinations. For example, it can suggest a route that efficiently visits multiple tourist destinations. Step 3: The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions. For example, if the user is tired, the emotion estimation unit can recommend relaxing tourist destinations and provide the information in a gentle tone.

[0053] (Example of form 2) An embodiment of the present invention provides an audio guide system that acquires information about the user's current location or planned location and provides historical information related to that location in audio. The audio guide system recommends tourist destinations based on the user's interests and past travel history and advises on the optimal sightseeing route and method of transportation. For example, the audio guide system acquires information about the user's current location or planned location. For example, the system can use GPS to identify the user's current location or the user can input the tourist destinations they plan to visit. This information is acquired by the acquisition unit. Next, the audio guide system provides historical information about the area based on the acquired location information. For example, if the user is in Kyoto, it provides information about Kyoto's history and famous places in audio. This information is provided by the provision unit. Furthermore, the audio guide system recommends tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it recommends tourist destinations related to temples. This recommendation is made by the provision unit. The audio guide system also provides the optimal route for visiting the recommended tourist destinations. For example, it proposes a route for efficiently visiting multiple tourist destinations. This route is provided by the service provider. Furthermore, the audio guide system provides the information in audio format. For example, users can listen to information about tourist spots while walking. This audio information is provided by the service provider. Finally, the audio guide system estimates the user's emotions and adjusts the content and tone of the information it provides. For example, if the user is tired, it will recommend relaxing tourist spots and provide the information in a gentle tone. This emotion estimation and information adjustment is performed by the service provider. In this way, the audio guide system provides information about the user's current location and planned destinations in audio format, recommends suitable tourist spots based on the user's interests and past travel history, and advises on the optimal sightseeing route and transportation method. In this way, the audio guide system can improve the user's sightseeing experience.

[0054] The voice guide system according to this embodiment comprises an acquisition unit, a provision unit, and an emotion estimation unit. The acquisition unit acquires location information, which is the user's current location and the locations the user plans to visit. The acquisition unit can, for example, use GPS to identify the user's current location. The acquisition unit can also take input of tourist destinations the user plans to visit. For example, the acquisition unit acquires the information when the user enters the planned destinations into a tourist app. The provision unit provides historical information about the region corresponding to the location information acquired by the acquisition unit. For example, if the user is in Kyoto, the provision unit can provide information about Kyoto's history and famous places in voice. The provision unit can also recommend tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it will recommend tourist destinations related to temples. Furthermore, the provision unit can also provide an optimal route for visiting the recommended tourist destinations. For example, it can suggest a route for efficiently visiting multiple tourist destinations. The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions of the user. The emotion estimation unit can, for example, recommend relaxing tourist destinations if the user is tired, and provide information in a gentle tone. As a result, the audio guide system according to this embodiment can provide information about the user's current location and planned destinations in audio, recommend tourist destinations based on the user's interests and past travel history, and advise on the optimal sightseeing route and transportation method.

[0055] The acquisition unit obtains location information, including the user's current location and planned destinations. For example, the acquisition unit can use GPS to pinpoint the user's current location. Specifically, it utilizes the GPS function of smartphones and mobile devices to obtain the user's latitude and longitude information in real time. The acquisition unit can also input tourist destinations the user plans to visit. For example, the user can input their planned destinations into a travel app, and the acquisition unit will retrieve that information. The travel app stores the user's entered destinations in a database, and the acquisition unit retrieves the information from that database. Furthermore, the acquisition unit can collect the user's travel history and past visit information. This allows for understanding the user's behavior patterns and providing more accurate location information. For example, it can record previously visited tourist destinations and travel routes, which can be used as a reference for future visits. The acquisition unit centrally manages this location information and can collaborate with other departments and systems. For example, the acquired location information can be stored on a cloud server, allowing access by the provision unit and sentiment estimation unit. Furthermore, the acquisition unit can utilize auxiliary location information technologies such as Wi-Fi and Bluetooth beacons to improve the accuracy of location information. This allows the acquisition unit to accurately and quickly acquire information about the user's current location and planned destinations, thereby improving the overall system performance.

[0056] The information provider unit provides historical information about the region corresponding to the location information acquired by the information acquisition unit. For example, if the user is in Kyoto, the information provider unit can provide information about Kyoto's history and famous places in audio format. Specifically, based on the user's current location, the information provider unit retrieves information about historical events and famous places related to that region from its database and provides it as an audio guide. The audio guide is played through the user's smartphone or mobile device, allowing the user to listen to the information even while walking. The information provider unit can also recommend tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it will recommend tourist destinations related to temples. The information provider unit analyzes the user's travel history and selects tourist destinations that match the user's interests and preferences. Furthermore, the information provider unit can also provide the optimal route for visiting the recommended tourist destinations. For example, it can suggest a route to efficiently visit multiple tourist destinations. The information provider unit utilizes map information and traffic information to calculate a route that allows the user to visit multiple tourist destinations in the shortest possible time. This allows the service provider to provide users with voice-based information about their current location and planned destinations, recommend tourist spots based on their interests and past travel history, and advise on optimal travel routes and transportation methods. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and content of the information provided. For example, by having users provide ratings and comments on the information provided, the service provider can use that feedback to improve the quality of the information. This enables the service provider to provide users with a more satisfying service.

[0057] The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions. For example, if the user is tired, the emotion estimation unit can recommend relaxing tourist destinations and provide the information in a gentle tone. Specifically, the emotion estimation unit analyzes the user's voice, facial expressions, and behavioral patterns to estimate the user's emotional state. For example, it can determine if the user is tired from the tone and speed of the user's voice when speaking into the smartphone, and from facial expressions captured by the camera using facial recognition technology. It can also analyze behavioral data such as the user's walking speed and distance traveled to estimate the user's fatigue level. The emotion estimation unit comprehensively analyzes this data to grasp the user's emotional state in real time. Furthermore, the emotion estimation unit adjusts the content and tone of the information provided by the information provider based on the estimated emotional state. For example, if the user is tired, it can recommend relaxing tourist destinations and rest spots and provide the information in a gentle tone. Conversely, if the user is excited, it can provide information on active tourist destinations and events and convey the information in an energetic tone. This enables the emotion estimation unit to provide optimal information tailored to the user's emotional state, thereby improving user satisfaction. Furthermore, the emotion estimation unit can accumulate user emotional data and analyze long-term emotional trends. This allows for an understanding of user emotional patterns and the provision of more personalized services. For example, if a user consistently feels relaxed at a particular tourist destination, that destination can be recommended preferentially. In this way, the emotion estimation unit can provide personalized information based on the user's emotions, improving the overall user experience of the system.

[0058] The service provider can provide historical information about tourist destinations when recommending them to users. For example, if a user is in Kyoto, the service provider can provide audio information about Kyoto's history and famous places. Similarly, if a user is in Nara, the service provider can provide audio information about Nara's history and famous places. Furthermore, if a user is in Tokyo, the service provider can provide audio information about Tokyo's history and famous places. This enriches the user's travel experience by providing historical information about tourist destinations. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without one. For example, the service provider can input historical information about a tourist destination into a generative AI, which can then generate audio data based on that information.

[0059] The service provider can determine which tourist destinations to recommend to the user based on the user's interests and travel history. For example, if the user has visited many temples in the past, the service provider will recommend tourist destinations related to temples. Similarly, if the user has visited many museums in the past, the service provider can recommend tourist destinations related to museums. Furthermore, if the user has visited many natural landscapes in the past, the service provider can recommend tourist destinations related to natural landscapes. This allows for the recommendation of more appropriate tourist destinations based on the user's interests and past travel history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's interests and travel history into a generative AI, which can then recommend tourist destinations based on that information.

[0060] The service provider can provide users with an efficient route for visiting multiple recommended tourist destinations. For example, if a user is visiting multiple temples in Kyoto, the service provider can suggest an efficient route. Similarly, if a user is visiting multiple museums in Tokyo, the service provider can suggest an efficient route. Furthermore, if a user is visiting multiple natural landscapes in Nara, the service provider can suggest an efficient route. This allows the service provider to provide the optimal route for efficiently visiting multiple tourist destinations. Some or all of the processing described above in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input location information of multiple tourist destinations into a generative AI, which can then calculate the optimal route based on that information.

[0061] The information provider can provide historical information in audio format. For example, if the user is in Kyoto, the information provider can provide information about Kyoto's history and famous places in audio format. Similarly, if the user is in Nara, the information provider can provide information about Nara's history and famous places in audio format. Furthermore, if the user is in Tokyo, the information provider can provide information about Tokyo's history and famous places in audio format. This allows users to obtain information even while walking. Some or all of the processing described above in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input historical information into a generative AI, which can then generate audio data based on that information.

[0062] The emotion estimation unit can estimate the user's emotions and adjust the content or tone of the information provided based on the estimated emotions. For example, if the user is tired, the emotion estimation unit can recommend relaxing tourist destinations and provide the information in a gentle tone. If the user is excited, the emotion estimation unit can also recommend lively tourist destinations and provide the information in an energetic tone. Furthermore, if the user is relaxed, the emotion estimation unit can recommend calm tourist destinations and provide the information in a calm tone. This allows for more appropriate information provision by adjusting the content and tone of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the emotion estimation unit may be performed using a generative AI, or not. For example, the emotion estimation unit can input the user's facial expression data into a generative AI, which can then estimate emotions based on that data.

[0063] The acquisition unit can estimate the user's emotions and adjust the timing of location information acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can periodically acquire location information and provide information about tourist destinations. If the user is in a hurry, the acquisition unit can also frequently acquire location information and provide the optimal route in real time. Furthermore, if the user is tired, the acquisition unit can reduce the frequency of location information acquisition and suggest rest points. By adjusting the timing of location information acquisition according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or not using a generative AI. For example, the acquisition unit can input user emotion data into a generative AI, and the generative AI can adjust the timing of location information acquisition based on that data.

[0064] The acquisition unit can analyze the user's past location history and select an efficient acquisition method. For example, the acquisition unit can predict the next destination based on the user's past visits to tourist destinations and acquire location information. The acquisition unit can also analyze the user's past travel patterns and select an efficient method for acquiring location information. Furthermore, the acquisition unit can consider the frequency of places the user has visited in the past and prioritize the acquisition of important location information. This enables efficient acquisition of location information by analyzing past location history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without a generating AI. For example, the acquisition unit can input the user's past location history into a generating AI, and the generating AI can select an efficient acquisition method based on that data.

[0065] The acquisition unit can filter location information based on the user's current activities and areas of interest. For example, if the user is sightseeing, the acquisition unit can prioritize acquiring location information related to tourist destinations. Similarly, if the user is shopping, the acquisition unit can prioritize acquiring location information for shopping areas. Furthermore, if the user is eating, the acquisition unit can prioritize acquiring location information for restaurants and cafes. This allows the acquisition of highly relevant location information based on the user's current activities and areas of interest. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without one. For example, the acquisition unit can input data on the user's current activities and areas of interest into a generating AI, which can then filter the data.

[0066] The acquisition unit can estimate the user's emotions and determine the priority of location information to acquire based on the estimated emotions. For example, if the user is excited, the acquisition unit may prioritize acquiring location information of popular tourist destinations. Similarly, if the user is relaxed, the acquisition unit may prioritize acquiring location information of quiet places. Furthermore, if the user is tired, the acquisition unit may prioritize acquiring location information of places where the user can rest. This allows for the provision of more appropriate information by prioritizing location information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generative AI, or without one. For example, the acquisition unit can input user emotion data into a generative AI, which can then determine the priority of location information based on that data.

[0067] The acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring location information. For example, if the user approaches a tourist destination, the acquisition unit will prioritize the acquisition of detailed information about that tourist destination. Furthermore, if the user is in a specific area, the acquisition unit can prioritize the acquisition of information related to that area. Additionally, if the user approaches a specific landmark, the acquisition unit can prioritize the acquisition of information about that landmark. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without a generating AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, which can then prioritize the acquisition of highly relevant information based on that data.

[0068] The acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring location information. For example, the acquisition unit can acquire information about relevant tourist destinations based on information about places the user has shared on social media. It can also acquire information about relevant tourist destinations based on information about places the user follows on social media. Furthermore, the acquisition unit can acquire information about relevant tourist destinations based on information about places the user has checked into on social media. In this way, relevant information can be obtained by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the user's social media activity data into a generating AI, and the generating AI can acquire relevant information based on that data.

[0069] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is relaxed, the information provider will deliver the information in a calm tone. If the user is excited, the information provider can deliver the information in a lively tone. Furthermore, if the user is tired, the information provider can deliver the information in a gentle tone. By adjusting the way the information is presented according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using a generative AI, or not using a generative AI. For example, the information provider can input user emotion data into a generative AI, and the generative AI can adjust the way the information is presented based on that data.

[0070] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, the provider can provide detailed information on important tourist destinations and concise information on minor tourist destinations. Alternatively, the provider can provide detailed information on topics of particular interest to the user and concise information on other topics. Furthermore, if the user is in a hurry, the provider can provide only the essential information concisely. This allows the provider to provide detailed information on topics important to the user by adjusting the level of detail based on the importance of the information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input information importance data into a generative AI, which can then adjust the level of detail based on that data.

[0071] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, the provider can provide historical information in detail and basic information about tourist destinations concisely. Furthermore, depending on the user's interests, the provider can provide cultural information in detail and other information concisely. In addition, the provider can prioritize providing information of specific categories based on the user's areas of interest. This allows for more appropriate information provision by applying different information provision algorithms depending on the category of information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input information category data into a generative AI, which can then apply different information provision algorithms based on that data.

[0072] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is in a hurry, the provider can provide short, concise information. If the user is relaxed, the provider can also provide longer information with detailed explanations. Furthermore, if the user is excited, the provider can provide information with visually stimulating effects. By adjusting the length of information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using a generative AI, or not. For example, the information provider can input user emotion data into a generative AI, which can then adjust the length of the information based on that data.

[0073] The information provider can determine the priority of information provision based on the timing of information submission. For example, the provider can prioritize providing information about a tourist destination immediately after the user arrives there. It can also provide information about a tourist destination before the user leaves it. Furthermore, it can prioritize providing information about a tourist destination when the user approaches it. This enables timely information provision by determining the priority of information provision based on the timing of information submission. Some or all of the above processing in the information provider may be performed using, for example, a generating AI, or without a generating AI. For example, the provider can input information submission timing data into a generating AI, and the generating AI can determine the priority of information provision based on that data.

[0074] The information delivery unit can adjust the order of information delivery based on the relevance of the information. For example, the delivery unit can prioritize providing information that the user is interested in, and postpone other information. It can also prioritize providing information related to the user's current location, and postpone other information. Furthermore, it can prioritize providing information related to places the user has visited in the past, and postpone other information. In this way, by adjusting the order of information delivery based on the relevance of the information, it is possible to prioritize providing information that is important to the user. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the delivery unit can input information relevance data into a generative AI, and the generative AI can adjust the order of delivery based on that data.

[0075] The emotion estimation unit can estimate the user's emotions and adjust the emotion estimation method based on the estimated user emotions. For example, if the user is relaxed, the emotion estimation unit will estimate the emotions in a calm tone. If the user is excited, the emotion estimation unit can also estimate the emotions in a lively tone. Furthermore, if the user is tired, the emotion estimation unit can also estimate the emotions in a gentle tone. By adjusting the emotion estimation method according to the user's emotions, more accurate emotion estimation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion estimation unit may be performed using a generative AI, or not using a generative AI. For example, the emotion estimation unit can input user emotion data into a generative AI, and the generative AI can adjust the emotion estimation method based on that data.

[0076] The emotion estimation unit can analyze the user's past emotional history to select an efficient estimation method during emotion estimation. For example, if the user was relaxed in the past, the emotion estimation unit can estimate the emotion based on that history. It can also estimate the emotion based on the user's past excitement. Furthermore, it can estimate the emotion based on the user's past fatigue. This allows for more accurate emotion estimation by analyzing the user's past emotional history. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion estimation unit may be performed using a generative AI, or not. For example, the emotion estimation unit can input the user's past emotional history data into a generative AI, which can then select an efficient estimation method based on that data.

[0077] The emotion estimation unit can customize its estimation methods based on the user's current activity during emotion estimation. For example, if the user is sightseeing, the emotion estimation unit can estimate emotions based on information about the tourist destination. Similarly, if the user is shopping, the emotion estimation unit can estimate emotions based on information about the shopping area. Furthermore, if the user is eating, the emotion estimation unit can estimate emotions based on information about the restaurant or cafe. This allows for more accurate emotion estimation by customizing the estimation methods based on the user's current activity. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion estimation unit may be performed using a generative AI, or not. For example, the emotion estimation unit can input user's current activity data into a generative AI, which can then customize the estimation methods based on that data.

[0078] The emotion estimation unit can estimate the user's emotions and determine the priority of emotion estimation based on the estimated user emotions. For example, if the user is excited, the emotion estimation unit will prioritize estimating that emotion. It can also prioritize estimating the user's relaxed emotions. Furthermore, it can prioritize estimating the user's tired emotions. By determining the priority of emotion estimation according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion estimation unit may be performed using a generative AI, or not using a generative AI. For example, the emotion estimation unit can input user emotion data into a generative AI, and the generative AI can determine the priority of emotion estimation based on that data.

[0079] The emotion estimation unit can select the optimal estimation method by considering the user's geographical location information during emotion estimation. For example, if the user is in a tourist area, the emotion estimation unit can estimate the emotion based on information about that tourist area. Furthermore, if the user is in a shopping area, the emotion estimation unit can estimate the emotion based on information about that area. Additionally, if the user is in a restaurant, the emotion estimation unit can estimate the emotion based on information about that restaurant. This allows for more accurate emotion estimation by considering the user's geographical location information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion estimation unit may be performed using a generative AI, or not. For example, the emotion estimation unit can input the user's geographical location data into a generative AI, which can then select the optimal estimation method based on that data.

[0080] The sentiment estimation unit can analyze the user's social media activity and propose estimation methods during sentiment estimation. For example, the sentiment estimation unit can estimate the user's current sentiment based on the sentiments the user has shared on social media. It can also estimate the user's current sentiment based on information about accounts the user follows on social media. Furthermore, it can estimate the user's current sentiment based on information about locations the user has checked into on social media. This allows for more accurate sentiment estimation by analyzing the user's social media activity. Sentiment estimation is achieved using a sentiment estimation function, for example, using a sentiment engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the sentiment estimation unit may be performed using a generative AI, or not. For example, the sentiment estimation unit can input the user's social media activity data into a generative AI, which can then propose estimation methods based on that data.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The audio guide system can estimate the user's emotions and make recommendations that take into account the crowd levels of tourist destinations based on those estimated emotions. For example, if the user is relaxed, it can recommend quiet tourist destinations that are not crowded. If the user is excited, it can recommend lively tourist destinations. Furthermore, if the user is tired, it can recommend places where they can rest. This makes it possible to make recommendations that take into account the crowd levels of tourist destinations according to the user's emotions.

[0083] The audio guide system can estimate the user's emotions and provide safety information about tourist destinations based on those emotions. For example, if the user is feeling anxious, it can recommend safe tourist destinations and provide reassuring information. If the user is excited, it can provide safety information about active tourist destinations. Furthermore, if the user is relaxed, it can provide safety information about calm tourist destinations. In this way, by providing safety information about tourist destinations according to the user's emotions, they can enjoy sightseeing with peace of mind.

[0084] Audio guide systems can estimate a user's emotions and provide event information at tourist destinations based on those emotions. For example, if a user is excited, they can provide information about lively events. If a user is relaxed, they can provide information about calming events. Furthermore, if a user is tired, they can provide information about relaxing events. By providing event information at tourist destinations according to the user's emotions, it is possible to provide a more enjoyable tourist experience.

[0085] The audio guide system can estimate the user's emotions and recommend photo spots at tourist destinations based on those emotions. For example, if the user is excited, it can recommend lively photo spots. If the user is relaxed, it can recommend calm photo spots. Furthermore, if the user is tired, it can recommend relaxing photo spots. By recommending photo spots at tourist destinations according to the user's emotions, it can help users take memorable photos.

[0086] The audio guide system can estimate the user's emotions and provide restaurant information in tourist areas based on those emotions. For example, if the user is relaxed, it can recommend quiet cafes and restaurants. If the user is excited, it can recommend lively restaurants. Furthermore, if the user is tired, it can recommend relaxing restaurants. By providing restaurant information in tourist areas according to the user's emotions, it can offer a more comfortable dining experience.

[0087] The audio guide system can analyze a user's past travel history and predict the next tourist destination they should visit. For example, if a user has visited many temples in the past, it can predict which temple to visit next. Similarly, if a user has visited many museums, it can predict which museum to visit next. Furthermore, if a user has visited many natural landscapes in the past, it can predict which natural landscape to visit next. In this way, by analyzing a user's past travel history, the system can predict the next tourist destination they should visit.

[0088] Audio guide systems can provide information about tourist destinations based on the user's current activity. For example, if the user is sightseeing, it can provide information related to the tourist destination. If the user is shopping, it can provide information about the shopping area. Furthermore, if the user is eating, it can provide information about restaurants and cafes. This allows for the provision of highly relevant information based on the user's current activity.

[0089] Audio guide systems can provide information about tourist destinations while considering the user's geographical location. For example, when a user approaches a tourist destination, they can provide detailed information about that destination. They can also provide information related to a specific area when the user is in that area. Furthermore, when a user approaches a specific landmark, they can provide information about that landmark. This allows for the provision of highly relevant information by considering the user's geographical location.

[0090] The audio guide system can analyze users' social media activity and provide information on relevant tourist destinations. For example, it can provide information on relevant tourist destinations based on places users have shared on social media. It can also provide information on relevant tourist destinations based on places users follow on social media. Furthermore, it can provide information on relevant tourist destinations based on places users have checked in to on social media. In this way, by analyzing users' social media activity, it can provide relevant information.

[0091] The audio guide system can determine which tourist destinations to recommend to the user based on the user's interests and travel history. For example, if the user has visited many temples in the past, it can recommend tourist destinations related to temples. Similarly, if the user has visited many museums in the past, it can recommend tourist destinations related to museums. Furthermore, if the user has visited many natural landscapes in the past, it can recommend tourist destinations related to natural landscapes. This allows for more appropriate recommendations of tourist destinations based on the user's interests and past travel history.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The acquisition unit obtains location information, which includes the user's current location and planned destinations. The acquisition unit can, for example, use GPS to determine the user's current location. The acquisition unit can also take input from the user about tourist destinations they plan to visit. For example, if the user enters their planned destinations into a travel app, the acquisition unit can obtain that information. Step 2: The providing unit provides historical information about the region corresponding to the location information acquired by the acquisition unit. For example, if the user is in Kyoto, the providing unit can provide information about Kyoto's history and famous places in audio format. The providing unit can also recommend tourist destinations based on the user's interests and past travel history. For example, if the user has visited many temples in the past, it will recommend tourist destinations related to temples. Furthermore, the providing unit can also provide the optimal route for visiting the recommended tourist destinations. For example, it can suggest a route that efficiently visits multiple tourist destinations. Step 3: The emotion estimation unit estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated emotions. For example, if the user is tired, the emotion estimation unit can recommend relaxing tourist destinations and provide the information in a gentle tone.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] For example, the acquisition unit can acquire information on the current location and planned destinations through the GPS function of the smart device 14 or user input. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides historical information and recommendations for tourist destinations in voice based on the acquired location information. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the content and tone of the information provided. The provision unit can also be implemented, for example, by the control unit 46A of the smart device 14, and recommends tourist destinations and provides the optimal sightseeing route based on the user's interests and past travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] For example, the acquisition unit can acquire information on the current location and planned destinations through the GPS function of the smart glasses 214 or user input. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides historical information and recommendations for tourist destinations in voice based on the acquired location information. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the content and tone of the information provided. The provision unit can also be implemented, for example, by the control unit 46A of the smart glasses 214, and recommends tourist destinations and provides the optimal sightseeing route based on the user's interests and past travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the acquisition unit can acquire information on the current location and planned destinations through the GPS function of the headset terminal 314 or user input. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides historical information and recommendations for tourist destinations in voice based on the acquired location information. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the content and tone of the information provided. The provision unit can also be implemented, for example, by the control unit 46A of the headset terminal 314, and recommends tourist destinations and provides the optimal sightseeing route based on the user's interests and past travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] For example, the acquisition unit can acquire information on the current location and planned destinations through the GPS function of the robot 414 or user input. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides historical information and recommendations for tourist destinations in voice based on the acquired location information. The emotion estimation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and estimates the user's emotions and adjusts the content and tone of the information provided. The provision unit can also be implemented, for example, by the control unit 46A of the robot 414, and recommends tourist destinations and provides the optimal sightseeing route based on the user's interests and past travel history. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] (Note 1) A location acquisition unit that acquires location information such as the user's current location and the locations the user plans to visit, A providing unit that provides historical information of the region corresponding to the location information acquired by the acquisition unit, It includes an emotion estimation unit that estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated user emotions. A system characterized by the following features. (Note 2) The aforementioned supply unit is, When recommending tourist destinations to the aforementioned user, provide historical information about those destinations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the user's interests and travel history, the recommended tourist destinations for the user are determined. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, To provide the aforementioned user with an efficient route that visits multiple recommended tourist destinations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The aforementioned historical information is provided in audio format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The emotion estimation unit, It estimates the user's emotions and adjusts the content or tone of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of location data acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past location history and select the most efficient method for acquiring it. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring location information, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and determines the priority of location information to acquire based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring location information, the system prioritizes acquiring highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring location information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing information, we will determine the priority of provision based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The emotion estimation unit, The system estimates the user's emotions and adjusts the emotion estimation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The emotion estimation unit, During emotion estimation, the system analyzes the user's past emotional history to select the most efficient estimation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The emotion estimation unit, When estimating emotions, customize the estimation method based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 22) The emotion estimation unit, The system estimates the user's emotions and determines the priority of emotion estimation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The emotion estimation unit, When estimating emotions, the optimal estimation method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The emotion estimation unit, When estimating sentiment, we analyze users' social media activity and propose estimation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A location acquisition unit that acquires location information such as the user's current location and the planned locations the user is scheduled to visit, A providing unit that provides historical information of the region corresponding to the location information acquired by the acquisition unit, The system includes an emotion estimation unit that estimates the user's emotions and adjusts the content and tone of the information provided based on the estimated user's emotions. A system characterized by the following features.

2. The aforementioned supply unit is, When recommending tourist destinations to the aforementioned user, provide historical information about those destinations. The system according to feature 1.

3. The aforementioned supply unit is, Based on the user's interests and travel history, the recommended tourist destinations for the user are determined. The system according to feature 1.

4. The aforementioned supply unit is, To provide the aforementioned user with an efficient route that visits multiple recommended tourist destinations. The system according to feature 1.

5. The aforementioned supply unit is, The aforementioned historical information is provided in audio format. The system according to feature 1.

6. The emotion estimation unit, The system estimates the user's emotions and adjusts the content or tone of the information provided based on the estimated emotions of the user. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of location information acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, The system analyzes the user's past location history and selects an efficient method for acquiring that information. The system according to feature 1.

9. The acquisition unit is, When acquiring location information, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

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