system

Abstract

The system according to the embodiment aims to facilitate communication with foreign tourists and provide high-quality tourist information. [Solution] A system according to an embodiment includes a language recognition unit, a question analysis unit, an answer provision unit, a plan proposal unit, and an information collection unit. The language recognition unit recognizes the language of the tourist. The question analysis unit analyzes the question based on the language recognized by the language recognition unit. The answer provision unit provides an answer based on the question analyzed by the question analysis unit. The plan proposal unit proposes a sightseeing plan tailored to the tourist's interests and concerns based on the information analyzed by the question analysis unit. The information collection unit collects information on tourist destinations.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult to communicate with foreign tourists, and there is room for improvement to improve the quality of tourist information.

[0005] The system according to the embodiment aims to facilitate communication with foreign tourists and provide high-quality tourist information. [Means for solving the problem]

[0006] The system according to the embodiment includes a language recognition unit, a question analysis unit, an answer provision unit, a plan proposal unit, and an information collection unit. The language recognition unit recognizes the language of the tourist. The question analysis unit analyzes the question based on the language recognized by the language recognition unit. The answer provision unit provides an answer based on the question analyzed by the question analysis unit. The plan proposal unit proposes a sightseeing plan that matches the tourist's interests and concerns based on the information analyzed by the question analysis unit. The information collection unit collects information on tourist destinations. [Effects of the Invention]

[0007] The system according to the embodiment facilitates communication with foreign tourists and can provide high-quality tourist information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A tourist information system according to an embodiment of the present invention utilizes AI to provide unmanned, high-quality tourist information. In this system, unmanned information terminals installed at tourist destinations automatically recognize the tourist's language and provide guidance in the appropriate language. Tourists simply ask questions to the terminal, and the AI ​​analyzes the questions and provides appropriate answers. For example, the AI ​​provides information on the tourist destination's history, highlights, transportation options, and nearby restaurants. Furthermore, the AI ​​proposes customized tour plans based on the tourist's interests. For example, if a tourist is interested in history, the AI ​​proposes a plan centered on the area's historical landmarks. If a tourist is interested in a particular food, the AI ​​introduces restaurants serving that food. This system compensates for the shortage of human resources in tourist destinations and aims to revitalize the local economy through inbound tourism by providing unmanned, high-quality tourist information. For example, a "language recognition unit" that recognizes the tourist's language and a "question analysis unit" that analyzes the question are interrelated. The question analysis unit analyzes the question based on the language recognized by the language recognition unit. The answer provision unit provides an appropriate answer based on the question analyzed by the question analysis unit. The plan proposal unit proposes sightseeing plans that match the tourist's interests and concerns based on the information analyzed by the question analysis unit. The information collection unit collects information on tourist destinations and provides it to the plan proposal unit. This enables the tourist information system to recognize the tourist's language, analyze the question, provide appropriate answers, and propose sightseeing plans, thereby realizing high-quality unmanned sightseeing guidance.

[0029] A tourist information system according to an embodiment includes a language recognition unit, a question analysis unit, an answer providing unit, a plan proposal unit, and an information collection unit. The language recognition unit recognizes the language of a tourist. Examples of tourist languages ​​include, but are not limited to, English, Japanese, and Chinese. The language recognition unit automatically recognizes the language of a tourist using, for example, speech recognition technology. The language recognition unit can also improve recognition accuracy by taking into account the tourist's pronunciation and accent. For example, the language recognition unit can learn the tourist's pronunciation characteristics and apply a recognition algorithm corresponding to the specific accent. The question analysis unit analyzes the tourist's question. The question analysis unit can analyze the tourist's question using, for example, natural language processing technology. The question analysis unit can also improve analysis accuracy by taking into account the context of the tourist's question. For example, the question analysis unit can analyze the context before and after the tourist's question to accurately understand the intent of the question. The answer providing unit provides an appropriate answer based on the question analyzed by the question analysis unit. The answer providing unit can provide, for example, information on the history and attractions of the tourist destination, transportation options, and nearby restaurants. The answer providing unit can also estimate the tourist's emotions and adjust the way the answer is expressed based on the estimated tourist's emotions. For example, if the tourist is nervous, the answer can be provided in gentle language to relax the tourist. The plan suggestion unit can propose a sightseeing plan based on the tourist's interests and concerns based on the information analyzed by the question analysis unit. For example, if the tourist is interested in history, the plan suggestion unit can propose a plan centered on historical landmarks in the area. The plan suggestion unit can also estimate the tourist's emotions and adjust the way the plan is proposed based on the estimated tourist's emotions. For example, if the tourist is nervous, the plan suggestion unit can propose a relaxed plan to relax the tourist. The information collecting unit collects information about tourist destinations. For example, the information collecting unit collects information by searching the Internet or collecting official information about tourist destinations. The information collecting unit can also improve the accuracy of the collection by taking into account past data and event information about tourist destinations. For example, the information collecting unit selects optimal information based on past data about tourist destinations. As a result, the tourist information system according to the embodiment can recognize the tourist's language, analyze the question, provide appropriate answers, and propose sightseeing plans, thereby achieving high-quality unmanned tourist guidance.

[0030] The language recognition unit can improve recognition accuracy based on the tourist's pronunciation and accent during language recognition. For example, the language recognition unit learns the tourist's pronunciation characteristics and applies a recognition algorithm corresponding to the specific accent. The language recognition unit can also analyze the tourist's pronunciation habits in real time and dynamically adjust recognition accuracy. The language recognition unit can also improve recognition accuracy by pre-registering the tourist's native language pronunciation patterns in a database. This improves language recognition accuracy by taking the tourist's pronunciation and accent into consideration. Some or all of the above-mentioned processing in the language recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the language recognition unit can input the tourist's pronunciation data into a generation AI and have the generation AI analyze the pronunciation and accent.

[0031] The language recognition unit can select a recognition method based on the tourist's past language usage history during language recognition. For example, the language recognition unit selects the optimal recognition algorithm based on data on the language used by the tourist in the past. The language recognition unit can also preferentially recognize specific phrases or words based on the tourist's past language usage history. The language recognition unit can also analyze the tourist's past language usage history and perform customization to improve recognition accuracy. This makes it possible to select the optimal recognition method by referring to the tourist's past language usage history. Some or all of the above-mentioned processing in the language recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the language recognition unit can input the tourist's past language usage history data into the generation AI and have the generation AI select the optimal recognition method.

[0032] The question analysis unit can select an analysis method based on the tourist's past question history when analyzing a question. For example, the question analysis unit selects the optimal analysis algorithm based on data on questions previously asked by the tourist. The question analysis unit can also prioritize analysis of questions related to specific topics from the tourist's past question history. The question analysis unit can also analyze the tourist's past question history and customize it to improve analysis accuracy. This makes it possible to select the optimal analysis method by referring to the tourist's past question history. Some or all of the above-mentioned processing in the question analysis unit may be performed using, or without, AI, for example. For example, the question analysis unit can input the tourist's past question history data into the generation AI and have the generation AI select the optimal analysis method.

[0033] The question analysis unit can improve the accuracy of analysis based on the context of the tourist's question when analyzing the question. The question analysis unit, for example, analyzes the context before and after the tourist's question to accurately understand the intent of the question. The question analysis unit can also prioritize analysis of related information based on the context of the tourist's question. The question analysis unit can also perform analysis to provide an appropriate answer by taking into account the context of the tourist's question. In this way, by taking into account the context of the tourist's question, the accuracy of analysis is improved. Some or all of the above-mentioned processing in the question analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the question analysis unit can input context data of the tourist's question into the generation AI and cause the generation AI to analyze the intent of the question.

[0034] When providing an answer, the answer providing unit can select an answer based on the tourist's past question history. The answer providing unit selects the optimal answer based on, for example, data on questions previously asked by the tourist. The answer providing unit can also preferentially provide answers related to specific topics from the tourist's past question history. The answer providing unit can also analyze the tourist's past question history and customize the answers to improve answer accuracy. This makes it possible to select the optimal answer by referring to the tourist's past question history. Some or all of the above-mentioned processing in the answer providing unit may be performed using, or without, AI, for example. For example, the answer providing unit can input the tourist's past question history data into the generation AI and have the generation AI select the optimal answer.

[0035] The answer providing unit can improve the accuracy of the answer based on the context of the tourist's question when providing the answer. The answer providing unit, for example, analyzes the context before and after the tourist's question to accurately understand the intent of the question. The answer providing unit can also prioritize providing related information based on the context of the tourist's question. The answer providing unit can also perform analysis to provide an appropriate answer, taking into account the context of the tourist's question. In this way, taking into account the context of the tourist's question improves the accuracy of the answer. Some or all of the above-mentioned processing in the answer providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer providing unit can input context data of the tourist's question to the generation AI and have the generation AI analyze the intent of the question.

[0036] When proposing a plan, the plan proposal unit can select a plan based on the tourist's past travel history. The plan proposal unit selects an optimal plan based on, for example, data on places the tourist has visited in the past. The plan proposal unit can also prioritize plans related to specific interests and concerns based on the tourist's past travel history. The plan proposal unit can also analyze the tourist's past travel history and customize the plan to improve its accuracy. This allows the optimal plan to be selected by referencing the tourist's past travel history. Some or all of the above-described processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can input the tourist's past travel history data into the generation AI and have the generation AI select an optimal plan.

[0037] The plan proposal unit can improve the accuracy of the plan based on the tourist's interests and concerns when proposing the plan. For example, the plan proposal unit can propose a plan related to a specific theme based on the tourist's interests and concerns. The plan proposal unit can also propose a plan that includes related tourist attractions and activities based on the tourist's interests and concerns. The plan proposal unit can also customize the details of the plan taking into account the tourist's interests and concerns. This improves the accuracy of the plan by taking into account the tourist's interests and concerns. Some or all of the above-mentioned processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can input tourist interest and concern data into the generation AI and have the generation AI improve the accuracy of the plan.

[0038] The information collection unit can select information based on past data of tourist destinations when collecting information. For example, the information collection unit selects optimal information based on the past data of tourist destinations. The information collection unit can also prioritize collecting information related to a specific topic from the past data of tourist destinations. The information collection unit can also analyze the past data of tourist destinations and customize the information to improve its accuracy. This allows optimal information to be selected by referring to the past data of tourist destinations. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input past data of tourist destinations into the generation AI and have the generation AI select optimal information.

[0039] The information collection unit can improve the accuracy of information collection based on event information of tourist destinations when collecting information. For example, the information collection unit prioritizes collection of related information based on event information of tourist destinations. The information collection unit can also collect information related to specific events from the event information of tourist destinations. The information collection unit can also perform customization to improve the accuracy of information by taking into account the event information of tourist destinations. As a result, the accuracy of collection is improved by taking into account the event information of tourist destinations. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input event information of tourist destinations into the generation AI and cause the generation AI to collect information.

[0040] The information collection unit can prioritize collecting highly relevant information based on the geographical location information of tourist destinations when collecting information. The information collection unit, for example, prioritizes collecting relevant information based on the geographical location information of tourist destinations. The information collection unit can also collect information related to a specific area from the geographical location information of tourist destinations. The information collection unit can also customize the information to improve accuracy by taking into account the geographical location information of tourist destinations. This makes it possible to prioritize collecting highly relevant information by taking into account the geographical location information of tourist destinations. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the geographical location information of tourist destinations to the generation AI and cause the generation AI to collect highly relevant information.

[0041] The information collection unit can analyze the social media activities of tourist destinations when collecting information and collect relevant information. For example, the information collection unit can analyze the tourist destination's social media posts and identify and collect relevant information. The information collection unit can also prioritize collecting information related to interests and concerns from the tourist destination's social media activities. The information collection unit can also collect information related to specific topics based on the tourist destination's social media activities. In this way, relevant information can be collected by analyzing the tourist destination's social media activities. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input the tourist destination's social media data into the generation AI and cause the generation AI to collect relevant information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The tourist information system can further include an "image recognition unit." The image recognition unit analyzes photos and videos taken by tourists and provides tourist information based on their content. For example, it can analyze photos of buildings taken by tourists and provide information about the building's history and highlights. It can also analyze photos of food taken by tourists and provide recipes and recommended restaurants. The image recognition unit can also suggest related tourist spots and activities based on the tourists' interests. For example, if a tourist has taken many photos of natural landscapes, it can suggest natural parks and hiking trails in the area.

[0044] The tourist information system may further include a "weather forecast unit." The weather forecast unit provides weather information for tourist destinations that tourists plan to visit. For example, it provides a weather forecast for tourist spots that tourists plan to visit the next day and suggests appropriate clothing and items to bring. The weather forecast unit can also provide weather information for the tourist's current location based on the tourist's real-time location information. Furthermore, the weather forecast unit can also suggest sightseeing plans based on the tourist's past travel history according to the weather. For example, it can suggest indoor tourist spots on rainy days.

[0045] The tourist information system can further include a "cultural introduction unit." The cultural introduction unit provides information about the culture and traditions of the region the tourist is visiting. For example, it provides information about festivals and traditional events in the region the tourist is visiting and explains their background and meaning. The cultural introduction unit can also prioritize providing information related to specific cultures and traditions based on the tourist's interests. Furthermore, the cultural introduction unit can customize and provide information about related cultures and traditions based on the tourist's past travel history. For example, it can introduce the culture of a new region by comparing it with the cultures of regions the tourist has previously visited.

[0046] The tourist information system can further include an "entertainment information section." The entertainment information section provides entertainment information that tourists can enjoy. For example, it provides information on movie theaters, theaters, and live music venues in the area that tourists are visiting, and provides information on the schedules of movies and performances currently showing. The entertainment information section can also prioritize information related to specific entertainment based on the tourists' interests. Furthermore, the entertainment information section can customize and provide related information based on the tourists' past entertainment history. For example, it can suggest new movies and performances based on the movies and performances that the tourists have seen in the past.

[0047] The tourist information system can further include a "shopping information unit." The shopping information unit provides shopping information for areas visited by tourists. For example, it provides information on shopping malls and markets in areas visited by tourists and introduces recommended stores and products. The shopping information unit can also provide specific shopping information preferentially based on the tourists' interests. Furthermore, the shopping information unit can customize and provide related information based on the tourists' past shopping history. For example, it can suggest new products and stores based on products that the tourists have purchased in the past.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The language recognition unit recognizes the tourist's language. Examples of tourist languages ​​include, but are not limited to, English, Japanese, and Chinese. The language recognition unit automatically recognizes the tourist's language using, for example, voice recognition technology. The language recognition unit can also improve recognition accuracy by taking into account the tourist's pronunciation and accent. For example, the language recognition unit learns the tourist's pronunciation characteristics and applies a recognition algorithm that corresponds to the specific accent. Step 2: The question analysis unit analyzes the tourist's question. The question analysis unit may use natural language processing technology to analyze the tourist's question. The question analysis unit may also improve the accuracy of the analysis by taking into account the context of the tourist's question. For example, the unit may analyze the context before and after the tourist's question to accurately understand the intent of the question. Step 3: The answer providing unit provides an appropriate answer based on the question analyzed by the question analysis unit. The answer providing unit provides, for example, information about the history and attractions of the tourist spot, transportation options, and nearby restaurants. The answer providing unit can also estimate the tourist's emotions and adjust the way the answer is expressed based on the estimated tourist's emotions. For example, if the tourist is nervous, the answer can be provided in gentle language to help them relax. Step 4: The plan proposal unit proposes a sightseeing plan based on the tourist's interests and concerns based on the information analyzed by the question analysis unit. For example, if the tourist is interested in history, the plan proposal unit proposes a plan centered on the historical sites of the area. The plan proposal unit can also estimate the tourist's emotions and adjust the plan proposal method based on the estimated tourist's emotions. For example, if the tourist is nervous, the plan proposal unit proposes a relaxed plan to help them relax. Step 5: The information collection unit collects information about tourist destinations. For example, the information collection unit collects information from internet searches and official tourist destination information. The information collection unit can also improve the accuracy of collection by taking into account past data and event information about the tourist destination. For example, the information collection unit selects the most appropriate information based on past data about the tourist destination.

[0050] (Example 2) A tourist information system according to an embodiment of the present invention utilizes AI to provide unmanned, high-quality tourist information. In this system, unmanned information terminals installed at tourist destinations automatically recognize the tourist's language and provide guidance in the appropriate language. Tourists simply ask questions to the terminal, and the AI ​​analyzes the questions and provides appropriate answers. For example, the AI ​​provides information on the tourist destination's history, highlights, transportation options, and nearby restaurants. Furthermore, the AI ​​proposes customized tour plans based on the tourist's interests. For example, if a tourist is interested in history, the AI ​​proposes a plan centered on the area's historical landmarks. If a tourist is interested in a particular food, the AI ​​introduces restaurants serving that food. This system compensates for the shortage of human resources in tourist destinations and aims to revitalize the local economy through inbound tourism by providing unmanned, high-quality tourist information. For example, a "language recognition unit" that recognizes the tourist's language and a "question analysis unit" that analyzes the question are interrelated. The question analysis unit analyzes the question based on the language recognized by the language recognition unit. The answer provision unit provides an appropriate answer based on the question analyzed by the question analysis unit. The plan proposal unit proposes sightseeing plans that match the tourist's interests and concerns based on the information analyzed by the question analysis unit. The information collection unit collects information on tourist destinations and provides it to the plan proposal unit. This enables the tourist information system to recognize the tourist's language, analyze the question, provide appropriate answers, and propose sightseeing plans, thereby realizing high-quality unmanned sightseeing guidance.

[0051] A tourist information system according to an embodiment includes a language recognition unit, a question analysis unit, an answer providing unit, a plan proposal unit, and an information collection unit. The language recognition unit recognizes the language of a tourist. Examples of tourist languages ​​include, but are not limited to, English, Japanese, and Chinese. The language recognition unit automatically recognizes the language of a tourist using, for example, speech recognition technology. The language recognition unit can also improve recognition accuracy by taking into account the tourist's pronunciation and accent. For example, the language recognition unit can learn the tourist's pronunciation characteristics and apply a recognition algorithm corresponding to the specific accent. The question analysis unit analyzes the tourist's question. The question analysis unit can analyze the tourist's question using, for example, natural language processing technology. The question analysis unit can also improve analysis accuracy by taking into account the context of the tourist's question. For example, the question analysis unit can analyze the context before and after the tourist's question to accurately understand the intent of the question. The answer providing unit provides an appropriate answer based on the question analyzed by the question analysis unit. The answer providing unit can provide, for example, information on the history and attractions of the tourist destination, transportation options, and nearby restaurants. The answer providing unit can also estimate the tourist's emotions and adjust the way the answer is expressed based on the estimated tourist's emotions. For example, if the tourist is nervous, the answer can be provided in gentle language to relax the tourist. The plan suggestion unit can propose a sightseeing plan based on the tourist's interests and concerns based on the information analyzed by the question analysis unit. For example, if the tourist is interested in history, the plan suggestion unit can propose a plan centered on historical landmarks in the area. The plan suggestion unit can also estimate the tourist's emotions and adjust the way the plan is proposed based on the estimated tourist's emotions. For example, if the tourist is nervous, the plan suggestion unit can propose a relaxed plan to relax the tourist. The information collecting unit collects information about tourist destinations. For example, the information collecting unit collects information by searching the Internet or collecting official information about tourist destinations. The information collecting unit can also improve the accuracy of the collection by taking into account past data and event information about tourist destinations. For example, the information collecting unit selects optimal information based on past data about tourist destinations. As a result, the tourist information system according to the embodiment can recognize the tourist's language, analyze the question, provide appropriate answers, and propose sightseeing plans, thereby achieving high-quality unmanned tourist guidance.

[0052] The language recognition unit can estimate the tourist's emotions and adjust the accuracy of language recognition based on the estimated tourist's emotions. For example, if the tourist is nervous, the language recognition unit can perform language recognition at a slower pace to relax the tourist. Also, if the tourist is excited, the language recognition unit can perform language recognition quickly to promote smooth communication. Also, if the tourist is tired, the language recognition unit can perform concise and clear language recognition to reduce the tourist's burden. This enables more appropriate language recognition by adjusting the accuracy of language recognition according to the tourist's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the language recognition unit can be performed using AI, for example, or without AI. For example, the language recognition unit can input the tourist's facial expression data into the generation AI and have the generation AI estimate the tourist's emotions.

[0053] The language recognition unit can improve recognition accuracy based on the tourist's pronunciation and accent during language recognition. For example, the language recognition unit learns the tourist's pronunciation characteristics and applies a recognition algorithm corresponding to the specific accent. The language recognition unit can also analyze the tourist's pronunciation habits in real time and dynamically adjust recognition accuracy. The language recognition unit can also improve recognition accuracy by pre-registering the tourist's native language pronunciation patterns in a database. This improves language recognition accuracy by taking the tourist's pronunciation and accent into consideration. Some or all of the above-mentioned processing in the language recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the language recognition unit can input the tourist's pronunciation data into a generation AI and have the generation AI analyze the pronunciation and accent.

[0054] The language recognition unit can select a recognition method based on the tourist's past language usage history during language recognition. For example, the language recognition unit selects the optimal recognition algorithm based on data on the language used by the tourist in the past. The language recognition unit can also preferentially recognize specific phrases or words based on the tourist's past language usage history. The language recognition unit can also analyze the tourist's past language usage history and perform customization to improve recognition accuracy. This makes it possible to select the optimal recognition method by referring to the tourist's past language usage history. Some or all of the above-mentioned processing in the language recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the language recognition unit can input the tourist's past language usage history data into the generation AI and have the generation AI select the optimal recognition method.

[0055] The question analysis unit can estimate the tourist's emotions and adjust the question analysis method based on the estimated tourist's emotions. For example, if the tourist is nervous, the question analysis unit can start the analysis with simple questions to relax the tourist. Furthermore, if the tourist is excited, the question analysis unit can quickly analyze important questions to promote smooth communication. Furthermore, if the tourist is tired, the question analysis unit can prioritize simple and clear questions. This allows for more appropriate question analysis by adjusting the question analysis method according to the tourist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the question analysis unit can be performed using AI, for example, or without AI. For example, the question analysis unit can input the tourist's facial expression data into the generation AI and have the generation AI estimate the tourist's emotions.

[0056] The question analysis unit can select an analysis method based on the tourist's past question history when analyzing a question. For example, the question analysis unit selects the optimal analysis algorithm based on data on questions previously asked by the tourist. The question analysis unit can also prioritize analysis of questions related to specific topics from the tourist's past question history. The question analysis unit can also analyze the tourist's past question history and customize it to improve analysis accuracy. This makes it possible to select the optimal analysis method by referring to the tourist's past question history. Some or all of the above-mentioned processing in the question analysis unit may be performed using, or without, AI, for example. For example, the question analysis unit can input the tourist's past question history data into the generation AI and have the generation AI select the optimal analysis method.

[0057] The question analysis unit can improve the accuracy of analysis based on the context of the tourist's question when analyzing the question. The question analysis unit, for example, analyzes the context before and after the tourist's question to accurately understand the intent of the question. The question analysis unit can also prioritize analysis of related information based on the context of the tourist's question. The question analysis unit can also perform analysis to provide an appropriate answer by taking into account the context of the tourist's question. In this way, by taking into account the context of the tourist's question, the accuracy of analysis is improved. Some or all of the above-mentioned processing in the question analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the question analysis unit can input context data of the tourist's question into the generation AI and cause the generation AI to analyze the intent of the question.

[0058] The answer providing unit can estimate the tourist's emotions and adjust the way the answer is expressed based on the estimated tourist's emotions. For example, if the tourist is nervous, the answer providing unit can provide an answer using gentle language to relax the tourist. Furthermore, if the tourist is excited, the answer providing unit can provide a quick and concise answer. Furthermore, if the tourist is tired, the answer providing unit can provide a concise and clear answer. By adjusting the way the answer is expressed according to the tourist's emotions, a more appropriate answer can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the answer providing unit can be performed using, for example, AI, or without AI. For example, the answer providing unit can input the tourist's facial expression data into the generation AI and have the generation AI estimate the tourist's emotions.

[0059] When providing an answer, the answer providing unit can select an answer based on the tourist's past question history. The answer providing unit selects the optimal answer based on, for example, data on questions previously asked by the tourist. The answer providing unit can also preferentially provide answers related to specific topics from the tourist's past question history. The answer providing unit can also analyze the tourist's past question history and customize the answers to improve answer accuracy. This makes it possible to select the optimal answer by referring to the tourist's past question history. Some or all of the above-mentioned processing in the answer providing unit may be performed using, or without, AI, for example. For example, the answer providing unit can input the tourist's past question history data into the generation AI and have the generation AI select the optimal answer.

[0060] The answer providing unit can improve the accuracy of the answer based on the context of the tourist's question when providing the answer. The answer providing unit, for example, analyzes the context before and after the tourist's question to accurately understand the intent of the question. The answer providing unit can also prioritize providing related information based on the context of the tourist's question. The answer providing unit can also perform analysis to provide an appropriate answer, taking into account the context of the tourist's question. In this way, taking into account the context of the tourist's question improves the accuracy of the answer. Some or all of the above-mentioned processing in the answer providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the answer providing unit can input context data of the tourist's question to the generation AI and have the generation AI analyze the intent of the question.

[0061] The plan proposal unit can estimate the tourist's emotions and adjust the plan proposal method based on the estimated tourist's emotions. For example, if the tourist is nervous, the plan proposal unit can propose a relaxed plan to help the tourist relax. Furthermore, if the tourist is excited, the plan proposal unit can also propose an active plan. Furthermore, if the tourist is tired, the plan proposal unit can also propose a plan that includes many rest periods. By adjusting the plan proposal method according to the tourist's emotions, a more appropriate plan can be proposed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the plan proposal unit can be performed using, for example, AI, or without AI. For example, the plan proposal unit can input the tourist's facial expression data into the generation AI and have the generation AI estimate the tourist's emotions.

[0062] When proposing a plan, the plan proposal unit can select a plan based on the tourist's past travel history. The plan proposal unit selects an optimal plan based on, for example, data on places the tourist has visited in the past. The plan proposal unit can also prioritize plans related to specific interests and concerns based on the tourist's past travel history. The plan proposal unit can also analyze the tourist's past travel history and customize the plan to improve its accuracy. This allows the optimal plan to be selected by referencing the tourist's past travel history. Some or all of the above-described processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can input the tourist's past travel history data into the generation AI and have the generation AI select an optimal plan.

[0063] The plan proposal unit can improve the accuracy of the plan based on the tourist's interests and concerns when proposing the plan. For example, the plan proposal unit can propose a plan related to a specific theme based on the tourist's interests and concerns. The plan proposal unit can also propose a plan that includes related tourist attractions and activities based on the tourist's interests and concerns. The plan proposal unit can also customize the details of the plan taking into account the tourist's interests and concerns. This improves the accuracy of the plan by taking into account the tourist's interests and concerns. Some or all of the above-mentioned processing in the plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan proposal unit can input tourist interest and concern data into the generation AI and have the generation AI improve the accuracy of the plan.

[0064] The information collection unit can estimate the tourist's emotions and adjust the information collection method based on the estimated tourist's emotions. For example, if the tourist is nervous, the information collection unit can start collecting simple information to relax the tourist. Furthermore, if the tourist is excited, the information collection unit can quickly collect important information. Furthermore, if the tourist is tired, the information collection unit can prioritize collecting concise and clear information. By adjusting the information collection method according to the tourist's emotions, more appropriate information can be collected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information collection unit can be performed using, for example, AI, or without AI. For example, the information collection unit can input the tourist's facial expression data into the generation AI and cause the generation AI to estimate the tourist's emotions.

[0065] The information collection unit can select information based on past data of tourist destinations when collecting information. For example, the information collection unit selects optimal information based on the past data of tourist destinations. The information collection unit can also prioritize collecting information related to a specific topic from the past data of tourist destinations. The information collection unit can also analyze the past data of tourist destinations and customize the information to improve its accuracy. This allows optimal information to be selected by referring to the past data of tourist destinations. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input past data of tourist destinations into the generation AI and have the generation AI select optimal information.

[0066] The information collection unit can improve the accuracy of information collection based on event information of tourist destinations when collecting information. For example, the information collection unit prioritizes collection of related information based on event information of tourist destinations. The information collection unit can also collect information related to specific events from the event information of tourist destinations. The information collection unit can also perform customization to improve the accuracy of information by taking into account the event information of tourist destinations. As a result, the accuracy of collection is improved by taking into account the event information of tourist destinations. Some or all of the above-mentioned processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input event information of tourist destinations into the generation AI and cause the generation AI to collect information.

[0067] The information collection unit can prioritize collecting highly relevant information based on the geographical location information of tourist destinations when collecting information. The information collection unit, for example, prioritizes collecting relevant information based on the geographical location information of tourist destinations. The information collection unit can also collect information related to a specific area from the geographical location information of tourist destinations. The information collection unit can also customize the information to improve accuracy by taking into account the geographical location information of tourist destinations. This makes it possible to prioritize collecting highly relevant information by taking into account the geographical location information of tourist destinations. Some or all of the above-described processing in the information collection unit may be performed using AI, for example, or may be performed without using AI. For example, the information collection unit can input the geographical location information of tourist destinations to the generation AI and cause the generation AI to collect highly relevant information.

[0068] The information collection unit can analyze the social media activities of tourist destinations when collecting information and collect relevant information. For example, the information collection unit can analyze the tourist destination's social media posts and identify and collect relevant information. The information collection unit can also prioritize collecting information related to interests and concerns from the tourist destination's social media activities. The information collection unit can also collect information related to specific topics based on the tourist destination's social media activities. In this way, relevant information can be collected by analyzing the tourist destination's social media activities. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the information collection unit can input the tourist destination's social media data into the generation AI and cause the generation AI to collect relevant information. === Hard Collateral 1-1 === Each of the multiple elements, including the language recognition unit, question analysis unit, answer providing unit, plan proposal unit, and information collection unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the language recognition unit recognizes the tourist's language using the microphone 38B of the smart device 14, and the control unit 46A performs recognition processing. The question analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the tourist's question. The answer provision unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, provides an appropriate answer based on the analyzed question. The plan proposal unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, proposes a sightseeing plan based on the tourist's interests and concerns. The information collection unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, collects information on tourist destinations. === Hard Collateral 1-2 === Each of the multiple elements, including the language recognition unit, question analysis unit, answer providing unit, plan proposal unit, and information collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the language recognition unit recognizes the tourist's language using the microphone 238 of the smart glasses 214, and the control unit 46A performs recognition processing. The question analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tourist's question. The answer provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an appropriate answer based on the analyzed question. The plan proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a sightseeing plan based on the tourist's interests and concerns. The information collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects information on tourist destinations. === Hard Collateral 1-3 === Each of the multiple elements, including the language recognition unit, question analysis unit, answer providing unit, plan proposal unit, and information collection unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the language recognition unit recognizes the tourist's language using the microphone 238 of the headset terminal 314, and the control unit 46A performs recognition processing. The question analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tourist's question. The answer provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides an appropriate answer based on the analyzed question. The plan proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and proposes a sightseeing plan based on the tourist's interests and concerns. The information collection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects information on tourist destinations. === Hard Collateral 1-4 === Each of the multiple elements, including the language recognition unit, question analysis unit, answer providing unit, plan proposal unit, and information collection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the language recognition unit recognizes the tourist's language using the microphone 238 of the robot 414, and the control unit 46A performs recognition processing. The question analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the tourist's question. The answer provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides an appropriate answer based on the analyzed question. The plan proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a sightseeing plan based on the tourist's interests and concerns. The information collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects information on tourist destinations.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The tourist information system can further include a "real-time translation unit." The real-time translation unit translates the language spoken by tourists into other languages ​​in real time, enabling smooth communication with other tourists and local people. For example, if a tourist asks a question in Japanese, the real-time translation unit can translate the question into English and convey it to an English-speaking guide or other tourists. Real-time translation can also be performed when tourists communicate with local people, eliminating language barriers. Furthermore, the real-time translation unit can estimate the tourist's emotions and adjust the tone and expression of the translation based on the estimated emotions. For example, if the tourist is excited, the translation tone can be calmed down to promote smooth communication.

[0071] The tourist information system can further include an "image recognition unit." The image recognition unit analyzes photos and videos taken by tourists and provides tourist information based on their content. For example, it can analyze photos of buildings taken by tourists and provide information about the building's history and highlights. It can also analyze photos of food taken by tourists and provide recipes and recommended restaurants. The image recognition unit can also suggest related tourist spots and activities based on the tourists' interests. For example, if a tourist has taken many photos of natural landscapes, it can suggest natural parks and hiking trails in the area.

[0072] The tourist information system can further include an "audio guide unit." The audio guide unit allows tourists to receive audio guidance while walking around tourist spots. For example, when a tourist approaches a particular tourist spot, the audio guide unit automatically provides audio guidance about the spot's history and highlights. The audio guide unit can also estimate the tourist's emotions and adjust the tone and content of the audio guide based on the estimated emotions. For example, if the tourist is tired, the audio guide unit can provide guidance in a gentle tone to help the tourist relax. Furthermore, the audio guide unit can provide customized audio guidance based on the tourist's interests. For example, if the tourist is interested in history, the audio guide unit can provide detailed explanations about the historical background of the area.

[0073] The tourist information system can further include a "transportation information unit." The transportation information unit suggests optimal transportation means and routes to enable tourists to move efficiently within a tourist destination. For example, when a tourist moves to the next tourist spot, it provides the nearest bus stop and train timetable. The transportation information unit can also guide the tourist to the shortest route from their current location to their destination based on their real-time location information. Furthermore, the transportation information unit can estimate the tourist's emotions and adjust the method of transportation guidance based on the estimated emotions. For example, if the tourist is in a hurry, it can suggest the quickest means of transportation.

[0074] The tourist information system can further be equipped with an "emergency response department." The emergency response department provides information and support to quickly respond when a tourist encounters an emergency. For example, if a tourist falls ill, it can provide information on the nearest medical institution or pharmacy. If a tourist gets lost, it can also provide a route from the tourist's current location to their accommodation. Furthermore, the emergency response department can estimate the tourist's emotions and adjust its response method based on the estimated emotions. For example, if a tourist is panicking, it can respond using gentle language to calm them down.

[0075] The tourist information system may further include a "review collection unit." The review collection unit collects reviews of tourist spots and restaurants visited by tourists and provides them to other tourists. For example, it collects ratings and comments on restaurants visited by tourists and provides them as reference information for the next tourist. The review collection unit can also prioritize collecting reviews related to specific interests and concerns based on the tourist's past review history. Furthermore, the review collection unit can estimate the tourist's emotions and adjust the way reviews are displayed based on the estimated emotions. For example, if a tourist has positive emotions, it will prioritize displaying other positive reviews.

[0076] The tourist information system may further include a "weather forecast unit." The weather forecast unit provides weather information for tourist destinations that tourists plan to visit. For example, it provides a weather forecast for tourist spots that tourists plan to visit the next day and suggests appropriate clothing and items to bring. The weather forecast unit can also provide weather information for the tourist's current location based on the tourist's real-time location information. Furthermore, the weather forecast unit can also suggest sightseeing plans based on the tourist's past travel history according to the weather. For example, it can suggest indoor tourist spots on rainy days.

[0077] The tourist information system can further include a "cultural introduction unit." The cultural introduction unit provides information about the culture and traditions of the region the tourist is visiting. For example, it provides information about festivals and traditional events in the region the tourist is visiting and explains their background and meaning. The cultural introduction unit can also prioritize providing information related to specific cultures and traditions based on the tourist's interests. Furthermore, the cultural introduction unit can customize and provide information about related cultures and traditions based on the tourist's past travel history. For example, it can introduce the culture of a new region by comparing it with the cultures of regions the tourist has previously visited.

[0078] The tourist information system can further include an "entertainment information section." The entertainment information section provides entertainment information that tourists can enjoy. For example, it provides information on movie theaters, theaters, and live music venues in the area that tourists are visiting, and provides information on the schedules of movies and performances currently showing. The entertainment information section can also prioritize information related to specific entertainment based on the tourists' interests. Furthermore, the entertainment information section can customize and provide related information based on the tourists' past entertainment history. For example, it can suggest new movies and performances based on the movies and performances that the tourists have seen in the past.

[0079] The tourist information system can further include a "shopping information unit." The shopping information unit provides shopping information for areas visited by tourists. For example, it provides information on shopping malls and markets in areas visited by tourists and introduces recommended stores and products. The shopping information unit can also provide specific shopping information preferentially based on the tourists' interests. Furthermore, the shopping information unit can customize and provide related information based on the tourists' past shopping history. For example, it can suggest new products and stores based on products that the tourists have purchased in the past.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The language recognition unit recognizes the tourist's language. Examples of tourist languages ​​include, but are not limited to, English, Japanese, and Chinese. The language recognition unit automatically recognizes the tourist's language using, for example, voice recognition technology. The language recognition unit can also improve recognition accuracy by taking into account the tourist's pronunciation and accent. For example, the language recognition unit learns the tourist's pronunciation characteristics and applies a recognition algorithm that corresponds to the specific accent. Step 2: The question analysis unit analyzes the tourist's question. The question analysis unit may use natural language processing technology to analyze the tourist's question. The question analysis unit may also improve the accuracy of the analysis by taking into account the context of the tourist's question. For example, the unit may analyze the context before and after the tourist's question to accurately understand the intent of the question. Step 3: The answer providing unit provides an appropriate answer based on the question analyzed by the question analysis unit. The answer providing unit provides, for example, information about the history and attractions of the tourist spot, transportation options, and nearby restaurants. The answer providing unit can also estimate the tourist's emotions and adjust the way the answer is expressed based on the estimated tourist's emotions. For example, if the tourist is nervous, the answer can be provided in gentle language to help them relax. Step 4: The plan proposal unit proposes a sightseeing plan based on the tourist's interests and concerns based on the information analyzed by the question analysis unit. For example, if the tourist is interested in history, the plan proposal unit proposes a plan centered on the historical sites of the area. The plan proposal unit can also estimate the tourist's emotions and adjust the plan proposal method based on the estimated tourist's emotions. For example, if the tourist is nervous, the plan proposal unit proposes a relaxed plan to help them relax. Step 5: The information collection unit collects information about tourist destinations. For example, the information collection unit collects information from internet searches and official tourist destination information. The information collection unit can also improve the accuracy of collection by taking into account past data and event information about the tourist destination. For example, the information collection unit selects the most appropriate information based on past data about the tourist destination.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0136] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0137] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0138] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0140] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0141] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0142] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0143] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0144] 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.

[0145] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0146] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0147] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0148] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0149] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0150] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0153] [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a language recognition unit that recognizes the language of the tourist; a question analysis unit that analyzes a question based on the language recognized by the language recognition unit; an answer providing unit that provides an answer based on the question analyzed by the question analyzing unit; a plan proposal unit that proposes a sightseeing plan according to the tourist's interests and concerns based on the information analyzed by the question analysis unit; An information collection unit that collects information about tourist spots A system characterized by:

2. The language recognition unit Estimate the tourist's emotions and adjust the accuracy of language recognition based on the estimated tourist emotions. The system of claim 1 .

3. The language recognition unit Improve language recognition accuracy based on tourists' pronunciation and accents The system of claim 1 .

4. The language recognition unit When recognizing language, the recognition method is selected based on the tourist's past language usage history. The system of claim 1 .

5. The question analysis unit Estimate tourists' emotions and adjust the question analysis method based on the estimated tourists' emotions. The system of claim 1 .

6. The question analysis unit When analyzing questions, the analysis method is selected based on the tourist's past question history. The system of claim 1 .

7. The question analysis unit Improve accuracy of question analysis based on the context of tourists' questions The system of claim 1 .

8. The answer providing unit Estimate the tourist's emotions and adjust the way you express your answers based on the estimated tourist emotions. The system of claim 1 .

Images